The Office of Family Assistance has released tables that provide demographic data on the age, gender, and race/ethnicity of adults and children in TANF and Separate State Program-Maintenance-of-Effort active families and closed cases, as well as data on the financial circumstances of TANF cash assistance recipients for Federal fiscal year 2021. The data is presented for all 50 states, the District of Columbia, Puerto Rico, the Virgin Islands, and Guam. Data is available in Excel and PDF formats.

The Temporary Assistance for Needy Families (TANF) Data Collaborative Pilot Initiative is a component of the TANF Data Innovation project. The 30-month pilot offered technical assistance and training to support cross-disciplinary teams of staff at eight state and county TANF programs in the routine use of TANF and other administrative data to inform policy and practice. This collection of eight profiles summarizes the data analytics projects undertaken by teams from California, Colorado, Michigan, Minnesota, New Jersey, New York, Utah, and Virginia. Each profile provides project-specific details for each pilot, including the research questions, data landscape, approach and research methods, and initial findings and next steps as well as an overview of the state TANF program.

The Temporary Assistance for Needy Families (TANF) Data Innovation project and the TANF Data Collaborative (TDC) Pilot were established to expand the routine use, integration, and analysis of TANF and employment data by agency staff to improve program services and outcomes for families with low incomes. The 30-month TDC Pilot offered technical assistance and training to support cross-disciplinary teams of staff at eight state and county TANF programs. This nine-minute video, produced after the completion of the TDC Pilot, features staff members from the California, Colorado, Minnesota, and Virginia TANF agencies reflecting on their challenges, accomplishments, and general experiences during the pilot. Staff describe their research questions and discuss building data capacity, integrating datasets, networking with other states, increasing collaboration between state and county agencies, learning new technical skills, and the benefits of being able to draw from diverse skillsets.

The tables in this resource are a compilation of Federal TANF and State MOE expenditures for FY 2021 as they are reported by states. As a condition of receiving Federal TANF funds, states are required to spend a certain amount of their own funds (MOE) on TANF-allowable categories. Expenditures are grouped into 20 main categories in the accompanying tables. Data is available in PDF and Excel formats.

State and local leaders at TANF agencies have been increasingly focused on using administrative data from TANF and other state agencies to better assess how well programs are working, inform policies and practices, and improve the lives of families with low incomes. Economic mobility through employment retention and advancement is of particular interest to TANF leaders, but administrative data on TANF recipients’ earnings are often difficult to access except for the purpose of investigating noncompliance. This toolkit was created to help TANF professionals develop more robust, data-driven practices using administrative data on earnings. It offers practical guidance to state and local TANF agencies on how to access, link, and analyze employment data from unemployment insurance systems for program monitoring, reporting, and evaluation. The toolkit may also be useful to other state human services agencies (for example, SNAP and Child Support) that want to expand their data use, as well as policymakers interested in supporting improved workforce outcomes.

Social services programs are increasingly looking for ways to forecast which participants are likely to reach major milestones so they can tailor services and allocate resources. In recent years, some programs have explored advanced predictive modeling approaches that harness potentially millions of data points and may incorporate machine learning: a variety of algorithms that determine relationships between prediction measures and the outcome. While there is potential for social service programs to use advanced models, MDRC’s Center for Data Insights (CDI) has found that such methods are not always better at making reliable predictions and come with trade-offs. This post outlines CDI’s approach to predictive analytics, using illustrations from two studies: Career Pathways, a workforce training program, and Child First, a home visiting program.

This brief describes the TANF Data Collaborative (TDC), an innovative approach to increasing data analytics capacity at state Temporary Assistance for Needy Families (TANF) agencies. TDC was part of the TANF Data Innovation project, launched by the Administration for Children and Families within the U.S. Department of Health and Human Services to strengthen agencies’ use of TANF, employment, and other administrative data. The brief shares the TDC framework and guiding principles, as well as the components of a pilot initiative, to inform similar and future efforts.

The Office of Family Assistance published data tables which show TANF and Separate State Programs-Maintenance of Effort (SSP-MOE) caseloads separately, as well as combined by state and at the national level from October 2021 through March 2022. The tables are cross-tabbed to include total numbers of recipients, adult recipients, child recipients, families, one-parent families, two-parent families, no parent families, and monthly caseload data.

This brief outlines how New York City’s Human Resources Administration (HRA) successfully restructured its upfront assessment process for individuals receiving Cash Assistance by utilizing data-driven reflection. In particular, HRA used a method called Learn, Innovate, Improve, an approach that supports rapid agency changes and ongoing improvement through the incorporation of data-informed reflection into the process of evaluating and refining solutions. HRA found that implementing this collaborative, evidence-based decision-making technique is both feasible and effective.

Individuals who receive human services and other assistance often receive those services from several independent programs. Analyzing data from multiple programs can improve leadership’s understanding of the overall system and how to improve services for all recipients. However, sharing data with other offices and agencies often raises legitimate concerns, such as whether it complies with applicable law, meets individuals’ privacy expectations, or creates a security risk. This case study reviews the data privacy and confidentiality challenges that Iowa encountered and resolved, and also discusses how Iowa’s Integrated Data System for Decision-Making (I2D2) worked with its stakeholders to develop a solution that met their needs and expectations.