The report highlights that many eligible low-income children are not receiving WIC benefits during the COVID-19 pandemic, with participation rates varying significantly by state and lagging behind programs like Medicaid and SNAP.
This policy brief outlines how improved data sharing between federal agencies, state and local governments, and institutions can leverage existing data from other benefits programs to streamline eligibility processes and benefits uptake for the Affordable Connectivity Program (ACP) and other programs.
Building on our February 2022 report Benefit Eligibility Rules as Code: Reducing the Gap Between Policy and Service Delivery for the Safety Net, the Beeck Center’s Digital Benefits Network (DBN) recently held a convening to share progress and potential in digitizing benefits eligibility and to begin addressing how a national approach could be started.
The New Mexico Human Services Department and Department of Health, as part of the Coordinating SNAP & Nutrition Supports program, leveraged data sharing to align SNAP, Medicaid, TANF, and WIC.
American Public Human Services Association (APHSA)
The DBN’s Rules as Code Community of Practice (RaC CoP) creates a shared learning and exchange space for people working on public benefits eligibility and enrollment systems — and specifically people tackling the issue of how policy becomes software code.
This plan promotes responsible AI use in public benefits administration by state, local, tribal, and territorial governments, aiming to enhance program effectiveness and efficiency while meeting recipient needs.
U.S. Department of Health and Human Services (HHS)
The National Health Law Program released an updated Guide to Modified Adjusted Gross Income, including sections on ACA tax filing and reporting, clarification on commonly asked questions about Social Security Income, and updated IRS tax filing thresholds.
This interactive dashboard allows users to explore Supplemental Nutrition Assistance Program (SNAP) participation and household characteristics by U.S. congressional district using American Community Survey data.
