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IES Grant

Title: Student Supports: The Role of Social Safety Net Programs in Community College Student Success
Center: NCER Year: 2022
Principal Investigator: Rothstein, Jesse Awardee: University of California, Berkeley
Program: Postsecondary and Adult Education      [Program Details]
Award Period: 2 years (07/01/2022 – 06/30/2024) Award Amount: $707,623
Type: Exploration Award Number: R305A220451

Co-Principal Investigators: Lacoe, Johanna; Dizon-Ross, Elise

Purpose: This project will examine the extent to which college students access existing basic needs safety net benefits they are eligible for, and how those benefits affect their educational outcomes. Across the country, many college students struggle to meet their basic needs while attending school. Public higher education institutions are well-situated to facilitate student access to safety net benefits, but data limitations have hampered previous efforts to assess eligibility and encourage take-up. To overcome this, the researchers will link data from three agencies: the California Student Aid Commission (CSAC), the California Community Colleges Chancellor's Office (CCCCO) and the California Department of Social Services (CDSS), to create the Student Supports database. Researchers will focus on uptake of benefits through the Supplemental Nutrition Assistance Program (SNAP, referred to as CalFresh in California), and may also explore uptake of tuition supports such as Cal Grants, Pell Grants, and Promise Grants. The cross-institution dataset will enable researchers to estimate safety net and financial aid program eligibility and take-up among the community college student population in California, examine whether participation in social safety net programs promotes post-secondary educational success, and understand whether administrative data might provide a resource for future research to increase the number of students receiving benefits.

Project Activities: The project will involve three phases of work. In Phase 1 researchers will use a hashed linkage methodology to link de-identified data from three agencies to establish the Student Supports database and conduct a landscape scan to document existing outreach efforts at community colleges throughout the state. In Phase 2 researchers will use this new data resource to simulate eligibility measures for SNAP and analyze students' take-up rates. In Phase 3, researchers will examine the relationship between SNAP participation and learner outcomes (persistence in college, progress toward a certificate or degree, academic achievement, and credential completion).

Products: The expected products of the project are as follows:

  • Two concise policy briefs using non-technical language and summarizing the results of the analyses conducted, for policymakers, educators, parents, and students;
  • A white paper on data linkage, documenting the methods and approach used to conduct secure linkages between databases of sensitive information without the researchers having access to any PII; and
  • A journal article presenting the full results of the study and all relevant technical details to an academic and research audience.

Findings from the study will be shared via multiple dissemination outlets, including the California Higher Education Basic Needs Alliance (CHEBNA) Webinars and a convening of California program administrators and policymakers from partner agencies with academic experts on community college student success, in addition to traditional media outreach and academic conference presentations.

Structured Abstract

Setting: The setting will be the state of California, with a focus on the California Community College system - 115 colleges across the state serving over 2 million students each year. The study will analyze up to 15 years of historical administrative data provided to the researchers by the California Community Colleges Chancellor's Office (CCCCO), the California Student Aid Commission (CSAC), and the California Department of Social Services (CDSS).

Sample: The study sample will comprise over 9 million students who enrolled in a California Community College in California between 2010 and 2020, and additional students who enroll between 2020 and 2025. Data on this population from California Community College Chancellor's Office (CCCCO) records will be linked to universe data from students' Free Applications for Federal Student Aid (FAFSAs) from the California Student Aid Commission (CSAC), and to CalFresh (SNAP) participation records from the California Department of Social Services (CDSS). Each agency will provide census data on the universe of relevant individuals in California from 2010 to 2025.

Factors: This study will examine the relationship between safety net program participation and learner outcomes (measured as persistence in college, progress toward a certificate or degree, academic achievement, and credential completion).

Research Design and Methods: First, researchers will conduct descriptive analyses to estimate SNAP eligibility and rates of participation. Researchers will use data from California community college records and from FAFSAs held by CSAC to construct measures of student eligibility. Rules will be created for the analysis based on unique eligibility criteria. Estimated SNAP eligibility and participation rates will be presented by student group, including by race, ethnicity, gender, financial aid status, county, and community college region.

Next, researchers will examine the relationship between receiving benefits and student success. Propensity score weighting methods will provide estimates of the effect of participation in SNAP on learner outcomes for those receiving and not receiving SNAP benefits.

Control Condition: In the propensity score weighting analysis, California community college students who do not receive SNAP benefits will serve as the control group. These students will be observably similar (on demographics, region, college attended, and financial circumstances) to those who receive SNAP benefits.

Key Measures: Researchers will use measures of students' income and financial resources to construct measures of eligibility for/enrollment in SNAP. They will analyze a host of postsecondary learner outcomes: persistence in college (continued enrollment); progress through college (number of credits earned, time between enrollment and completion); academic achievement (grade point average); and completion of degree, credential, or transfer to a four-year institution. They will employ measures of student demographics (e.g., race, ethnicity, age) to understand subgroup differences in eligibility and uptake of benefits, and to assess differences in their effects on learner outcomes.

Data Analytic Strategy: Phase 1 will employ a hashed linkage methodology to link de-identified data from three agencies to establish the Student Supports database which for use in subsequent phases of the project. Phase 2 of the study will employ descriptive analytic tools including t-tests and chi-square tests to assess the significance of differences in program participation and take up rates. The researchers will explore heterogeneity in participation and take-up rates across a range of different student characteristics, including demographic characteristics, previous enrollment in any services (and/or family enrollment), student type (e.g., new or returning, enrolled in one or multiple campuses), and college and program type. The researchers will also examine geographic variation in participation and take-up rates by county and community college region.

In Phase 3 of the study, the researchers will employ multivariate regression methods to compare students receiving SNAP benefits to other students who appear eligible but are not receiving benefits, using inverse propensity score weighting to ensure comparability between the groups. First, researchers will estimate a propensity score model via a logistic regression of an indicator of SNAP participation on students' observable characteristics and year and term fixed effects. Using the estimated propensity scores, the researchers will form new weights for the control observations, to generate a weighted control sample that will have approximately the same distribution of propensity scores as the treated sample. Then, the researchers will estimate weighted regression models of the outcomes on the treatment status that include covariates, campus fixed effects, and year fixed effects.