This template can be used when estimating expenditures. It offers guidance on key components and proposed questions to answer.

This template can be used for Other Expenditures (Line 23 - includes Tribal TANF Programs and unique programs). It offers guidance on key components and proposed questions to answer.

This template can be used for Non-Assistance Authorized Solely Under Prior Law (Lines 8a-8c). It offers guidance on key components and proposed questions to answer.

This template can be used for Assistance Authorized Solely Under Prior Law (Lines 7a-7c). It offers guidance on key components and proposed questions to answer.

This guide addresses two significant fraud vulnerabilities the GAO identified: beneficiary misuse of Electronic Benefit Transfer (EBT) cards (including selling cards on e-commerce platforms) and EBT card fraud perpetrated by agency employees. The checklist provides concrete security features, monitoring protocols, and response procedures to safeguard TANF benefits delivered through EBT cards.

The entirety of the TANF Fraud Risk Management: Visual Resources for TANF Agencies can be accessed here: https://peerta.acf.hhs.gov/content/tanf-fraud-risk-management-visual-resources-tanf-agencies.

This visual guide, developed in alignment with GAO recommendations, focuses on key TANF fraud risks organized by the type of individual potentially involved in committing fraud (e.g., beneficiary, agency employee, subrecipient, contractor) and outlines clear risk indicators, prevention strategies, and detection methods. The guide is designed to support TANF agencies in strengthening program integrity through a user-centered, accessible format that promotes transparency and accountability.

The entirety of the TANF Fraud Risk Management: Visual Resources for TANF Agencies can be accessed here: https://peerta.acf.hhs.gov/content/tanf-fraud-risk-management-visual-resources-tanf-agencies.

These visual guides support TANF agencies in proactively identifying, preventing, and mitigating fraud risks before they escalate to federal oversight. Grounded in the findings of GAO Report 25-107290, these resources address specific vulnerabilities in TANF fraud risk management and translate those insights into practical tools. By implementing these strategies, TANF agencies can strengthen program integrity, reduce inefficient spending, enhance accountability, and prevent the diversion of funds from their intended purpose, thereby upholding TANF’s core mission of assisting families in need.

This op-ed written by Acting Assistant Secretary Andrew Gradison outlines that current welfare policies focused on unconditional cash transfers are failing to lift low-income families out of poverty. Instead, he advocates for a welfare model that emphasizes work incentives, family stability, personal responsibility, and reduced dependency on government aid.

The pilot is authorized under the Fiscal Responsibility Act of 2023 and reflects the Administration’s commitment to reshaping welfare programs to encourage employment, personal responsibility, and strong, stable two-parent families. States were encouraged to propose alternative performance measures to the work participation rate that prioritize rapid employment outcomes, earnings progression, and reduced dependency on Temporary Assistance for Needy Families (TANF), Medicaid, Supplemental Nutrition Assistance Program (SNAP) benefits, and other welfare. 

The Administration for Children and Families (ACF) at the U.S. Department of Health and Human Services selected Arizona, Iowa, Nebraska, Ohio, and Virginia to participate in the redesigned TANF pilot. These states were chosen from a strong applicant pool to develop models and metrics other states can replicate to help families achieve self-sufficiency. In addition to concretely tracking employment and earnings, pilot states will pursue the following strategies to reduce dependency:

• Arizona will engage directly with employers that have vacancies for in-demand, well-paying positions to directly connect TANF participants with quality, sustainable employment.
• Iowa will improve referral coordination across services, enhance the quality of information available to TANF participants through financial literacy and decision-making tools, and build partnerships with employers to create employment and matched savings opportunities.
• Nebraska, in partnership with community organizations, colleges, and businesses, will develop personalized pathways for TANF participants to strengthen connections to local jobs. Pathways will include referrals to Nebraska’s TANF-funded Fatherhood and Healthy Marriage Initiative.
• Ohio will implement a personalized “well-being assessment” for TANF participants, which will include intensive case management services, financial literacy training, and support for counties to build community capacity.
• Virginia will establish Personalized Results Plans for TANF participants to build upon the success of the Career Pathway Pilot, which blends sector-based training, intensive case management, and employment engagement to support participants as they gain credentials in fields like health care and skilled trades.

The TANF pilot program officially launches on October 1, 2025.

As part of the Fiscal Responsibility Act (FRA) of 2023, all states are required to submit the Secondary School Diploma or its Recognized Equivalent Attainment Rate report annually, with the first report due on November 14, 2027. This outcome measure is intended to assist federal and state policymakers to better understand the effectiveness of TANF programs in promoting successful education credential attainment. The outcome is defined as the percentage of applicable TANF recipients and exiters who, during the reporting period, attained a secondary school diploma or a recognized equivalent credential.

TANF agencies may opt to collect the data necessary for this report from their state education agency or longitudinal data system. This document contains a series of questions to begin a conversation with these data providers about the possible use of their data.