The COVID Response Project was funded by the W.K. Kellogg Foundation to document the real-time impacts of the COVID-19 pandemic on state human services agencies and capture state perspectives on lessons learned to guide future federal policymaking and state implementation. The project was completed by the American Public Human Services Association (APHSA) in partnership with the U.S. Department of Health and Human Services, Administration for Children and Families (ACF), Office of Regional Operations. Insights from the report reflect information obtained through APHSA’s on-going support of state human services agencies’ COVID-19 response efforts as well as a series of in-depth interviews with executive leadership of the 14 state health and human services agencies in ACF’s Region 1 (New England) and Region 4 (Southeast) areas.
American Public Human Services Association (APHSA)
Our work with Pennsylvania to implement user experience and user interface changes shows that innovation can be easier to implement than it might seem.
MITRE developed the Comprehensive Careers and Supports for Households (CCASH™) tool to help individuals understand and manage federal benefits and employment services, transitioning from a consumer-focused tool to a policy analytics system. By integrating data from sources like the U.S. Census and the Policy Rules Database, MITRE created a model that allows users to analyze and compare benefits eligibility across states, supporting evidence-based policymaking.
In December 2024, the Digital Benefits Network released an updated open dataset on authentication and identity proofing requirements across various public benefits applications to highlight best practices and areas for improvement in identity management.
This GitHub repository includes resources that users of the UI wage data toolkit may find helpful. It covers a variety of topics, including equity, data security, programming, and data QC tips. It also serves as a place for our team to continue to post information that the TANF Data Collaborative (TDC) pilot sites found useful during our partnerships with them.
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 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.
The team developed an AI-powered explanation feature that effectively translates complex, multi-program policy calculations into clear and accessible explanations, enabling users to explore "what-if" scenarios and understand key factors influencing benefit amounts and eligibility thresholds.
This report explores policy options Utah and other states can adopt to mitigate benefit cliffs, which occur when small income increases lead to sudden loss of public assistance.
