The Michigan Department of Health and Human Services, together with the Food Bank Council of Michigan and the Michigan Department of Education developed a comprehensive Food Insecurity Map and a closed-loop referral system for nutrition and economic supports. The goal of these initiatives was to leverage cross-sector data to inform policy decisions, streamline access to food assistance, and reduce administrative burden. This report documents lessons learned and outcomes of their project.
American Public Human Services Association (APHSA)
APHSA established a working group to identify strengths, barriers, and opportunities for better system alignment in human services for young parents and children, leading to the development of a roadmap to support meaningful systems-level changes.
American Public Human Services Association (APHSA)
This brief shares findings from a November 2021 survey of state SNAP agencies about their use of the SNAP ARPA funds in fiscal year 2021, and their initial planned activities for fiscal year 2022 and 2023. This brief also draws from learnings from work groups conducted with states in early 2022. The brief looks to explore the following questions: 1. How did state SNAP agencies use 2021 SNAP ARPA administrative funding? 2. What factors influenced how states used 2021 SNAP ARPA administrative funding? 3. What are states aiming to prioritize in 2022 and 2023? 4. What do early insights on implementation convey about national SNAP priorities?
American Public Human Services Association (APHSA)
It is necessary give the public servants who manage safety-net systems the technology tools and incentives to track critical outcomes and meet people where they are.
The Better Government Lab at the McCourt School of Public Policy at Georgetown University has developed a new scale for measuring the experience of burden when accessing public benefits. They offer both a three-item scale and a single-item scale, which can be utilized for any public benefit program. The shorter scales provide a less burdensome way to measure by requiring less information from users.
A modernized public benefits system would better serve program participants, administrators, policy makers, and taxpayers. This paper proposes a set of principles both define the desired future state and outline the values that shape decision making along the way. Practices describe the processes needed to achieve modernization.
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)
This study investigates how administrative burdens influence differential receipt of income transfers after a family member loses a job, looking at Unemployment Insurance, Temporary Assistance for Needy Families, and the Supplemental Nutrition Assistance Program.
This article explores how although SNAP and Medicaid programs have succeeded in narrowing health and nutrition disparities, certain policies hinder goals of racial and ethnic equity, even though they do not explicitly mention race or ethnicity.
This study examines the use of robotic process automation (RPA) technologies by three state agencies—Georgia, New Mexico, and Connecticut—to administer the Supplemental Nutrition Assistance Program (SNAP). RPA is software that integrates with other programs to automatically complete repetitive processes that normally are performed by humans.
