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State and local agencies that provide services to low income families to achieve self-sufficiency are increasingly looking to incorporate better data into decision-making around policies, programs, and practices in an effort to improve outcomes and make efficient use of scarce taxpayer dollars. High quality data will support research to build evidence around what works to improve self-sufficiency. The Family Self-Sufficiency Data Center (FSSDC) was established to support states to better use data and to increase the amount and quality of data available for research related to family self-sufficiency. The relevant programs include, but are not limited to, Temporary Assistance to Need Families, Supplemental Nutritional Assistance Program and other food assistance programs, workforce development and training programs, early care and education, and health insurance.
The FSSDC was a partnership with Harris Public Policy, Chapin Hall at the University of Chicago, NORC at the University of Chicago, and Orlin Research. The principal investigator was Robert Goerge and the co-PI was Scott Allard, both recognized experts in family self-sufficiency research. Collectively, the team brought expertise in executing data sharing agreements, data management (including cleaning, linking, and visualizing data), data analysis, and communicating and applying findings from data. Together with funding and leadership from the Office of Planning Research and Evaluation (OPRE) and input from Administration for Children and Family (ACF) at the US Department of Health and Human Services (DHHS), we created a Data Center to become a hub for research and resources around the promotion, use, and dissemination of family self-sufficiency data in a six-year project, funded from 2013–19.
The FSSDC partnered with researchers, policymakers and administrators to answer fundamental policy and program questions and build knowledge that will ultimately be translated into better policy and practice. The FSSDC achieved this mission through a number of core projects and functions, including data support and technical assistance, outreach and collaboration, research, and dissemination of Data Center work through written products.
Data support and technical assistance: The Data Center provided modeling, analytic resources and technical support to both data providers and users, targeted at issues of family self-sufficiency. We partnered directly with a set of pilot states to assess data quality, restructure data, examine caseload dynamics and churn, and explore specific policy questions around issues such as employment mandates for food stamp recipients. We also generated publicly available resources including cross-state comparative analyses and data models and code to build the capacity to use data at the state level.
Outreach and collaboration: The Data Center sought to engage a broad community of state partners and researchers, fostering connections and spurring innovation in self-sufficiency data analysis and research. We worked directly with states hosting workshops that bring together state agency staff from around the country for sessions focused on administrative data usage and sample analyses with TANF, SNAP, employment, and child care data sources. We supported a network of state agency staff using social media to build a community of practice in the family self-sufficiency data space.
The Data Center also disseminated work through conference presentations and collaborates with federal agencies and other stakeholders nationally via participation in OPRE’s Family Self-Sufficiency and Stability Research Consortium, which includes the Scholars Network and the Advancing Welfare and Self-Sufficiency Research Project (Project AWESOME).
Research: We developed new research products that are intended to be resources for a broad community of state partners and researchers with regards to data models, structuring data, analyses, tools, and data sharing. Areas of research included: caseload size and characteristics, including spell length and churn; common metrics used by states to consider program performance and outcomes and ways to accurately compare populations and policies across states; cross-state analyses with TANF data regarding caseload dynamics and employment trajectories; and specific policy questions around issues such as criminal history as a barrier to employment for benefit recipients.
Although this project has ended, we invite you to keep in touch with our team. Principal Investigator Robert George and other team members continue to support improvements in data use in a variety of ways, including through the TANF Data Collaborative (TDC).