This document provides a template for SNAP agencies to use to communicate how they can meet able-bodied adults without dependents (ABAWD) work requirements.
American Public Human Services Association (APHSA)
A national survey of low-wage workers showing that administrative burdens in SNAP and Medicaid are common and strongly linked to food hardship, healthcare hardship, and chronic illness.
A blog post outlining key strategies states can use to lower SNAP payment error rates, a priority given new fiscal penalties tied to error rates under recent federal law.
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.
Louisiana issued an RFI to identify solutions that can provide a technology platform for determining eligibility and managing cases across multiple human services programs.
This publication explains current state integrated eligibility and enrollment (IEE) system implementation processes, approaches, and opportunities for future processes and technologies. It is a resource for state officials, advocates, funders, and tech partners working to implement these systems.
This resource provides state agencies and their implementation partners with context on how and why to conduct a Digital Identity Risk Management (DIRM) process, as well as a new spreadsheet-based tool to guide agency teams through the process.
An interactive dashboard that allows users to explore Supplemental Nutrition Assistance Program (SNAP) Quality Control data to better understand payment errors, eligibility issues, and administrative performance across states.
This workshop summary synthesizes key takeaways from a convening of nearly 40 research and data analytics staff from 15 states focused on SNAP Quality Control (QC) data modeling.
This report analyzes the critical role of SNAP’s "broad-based categorical eligibility" (BBCE) policy and the widespread consequences of its potential elimination by the Trump Administration.
This technical brief uses predictive analytics to identify the primary drivers of SNAP payment error rates (PER) following the implementation of the One Big Beautiful Bill (OBBB).
