This blog post discusses strategies that states can implement to make public assistance applications more accessible during the COVID-19 crisis, emphasizing the importance of flexibility in application processes to accommodate increased demand and social distancing measures.
This case study documents how Civilla partnered with the Michigan Department of Health and Human Services (MDHHS) to redesign and modernize online enrollment for the state’s largest benefit programs.
This webpage provides state agency resources and policy memos detailing how the One Big Beautiful Bill Act (H.R. 1) of 2025 affects SNAP implementation.
This report provides an initial fiscal analysis of how H.R. 1 (the “One Big Beautiful Bill”) will affect the state’s federally funded programs across agencies, estimating multi-billion-dollar reductions in SNAP, Medicaid, education, and infrastructure revenues.
The report reviews the scope and methods of SNAP benefit theft—including card skimming, cloning, phishing, and algorithmic attacks—and examines the effectiveness of state and federal countermeasures.
An interactive dashboard that enables users to explore and monitor key metrics of the Supplemental Nutrition Assistance Program (SNAP) Quality Control (QC) system.
This report analyzes how administrative burdens in SNAP caused one in eight working-age adults to lose benefits in 2024, with future federal policy changes expected to worsen disruptions
A directive issued by the Commonwealth of Virginia to materially reduce the error rate in Supplemental Nutrition Assistance Program (SNAP) benefit processing among local social services offices.
This document is a caseworker-facing flowchart for use in screening SNAP applicants and participants to determine if they are subject to work requirements.
American Public Human Services Association (APHSA)
An interactive chatbot that helps SNAP participants and the public ask questions and receive guidance about SNAP work and community engagement requirements in conversational form.
A case study explaining how a predictive, data-driven machine-learning model was developed to detect unauthorized cash benefit withdrawals more quickly and accurately in California.
This report examines federal efforts to connect eligible college students with Supplemental Nutrition Assistance Program (SNAP) benefits and identifies actions needed to improve outreach and program access.
