This site contains resources explaining the 2025 Working Families Tax Cut Act (WFTC) — formally Public Law 119-21, which changes eligibility, financing, and community-engagement requirements for Medicaid and Children’s Health Insurance Program (CHIP).
Provides state and local Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) agencies with a practical guide for developing or improving online WIC application systems.
An online hub that connects WIC agencies and their partners through a national Data Matching Community of Practice, offering quarterly virtual convenings to share best practices, case studies, and peer learning on strategies to improve WIC outreach and enrollment.
Led by the Digital Benefits Network in partnership with Public Policy Lab, the Digital Doorways research project amplifies the lived experiences of beneficiaries to provide new insights into people’s experiences with digital identity processes and technology in public benefits. This report details the project’s findings, directly highlighting the voices of beneficiaries through videos and photos.
This Urban Institute report identifies strategies to improve young people’s access to public benefits through targeted outreach, benefit navigation, cross-organizational partnerships, and streamlined eligibility processes.
This report contributes to the quantitative measurement of psychological burdens by examining a case study of a single social program: the Supplemental Nutrition Assistance Program, by considering new quantitative measures of the psychological burdens faced by SNAP applicants.
This report recommends updating the methodology used by the Census Bureau to calculate the Supplemental Poverty Measure (SPM) to reflect household basic needs and replace the current Official Poverty Measure as the primary statistical measure of poverty. The report assesses the strengths and weaknesses of the SPM and provides recommendations for updating its methodology and expanding its use in recognition of the needs of most American families such as medical care, childcare, and housing costs.
National Academies of Sciences, Engineering, and Medicine
This paper introduces the problem of semi-automatically building decision models from eligibility policies for social services, and presents an initial emerging approach to shorten the route from policy documents to executable, interpretable and standardised decision models using AI, NLP and Knowledge Graphs. There is enormous potential of AI to assist government agencies and policy experts in scaling the production of both human-readable and machine executable policy rules, while improving transparency, interpretability, traceability and accountability of the decision making.
This presentation from Steph White, Cross Enrollment Coordinator at the Michigan Department of Health and Human Services offers an in-depth example on implementing cross enrollment with WIC and general tools for cross enrollment.
