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.
This discussion paper advocates for states to use the implementation of OBBBA (One Big Beautiful Bill Act) as a catalyst to build integrated, cross-agency data systems.
This brief provides a summary of potential federal funding sources and programs that can be used to support programs specifically targeted towards young families. While this list is not exhaustive, it highlights major sources that can serve as a starting point for braiding and blending of funding to create comprehensive programming to serve young families.
American Public Human Services Association (APHSA)
The primer–originally prepared for the Progressive Congressional Caucus’ Tech Algorithm Briefing–explores the trade-offs and debates about algorithms and accountability across several key ethical dimensions, including fairness and bias; opacity and transparency; and lack of standards for auditing.
In recent years, there has been a deliberate shift to move our public systems that support child and family well-being upstream. These efforts reflect the growing consensus that true and lasting progress toward a nation where everyone can thrive requires we get to the root of the barriers that keep people and communities from achieving their potential. A foundational building block of this effort is the work happening to advance prevention strategies within child welfare agencies. In this brief, we focus on the challenges and opportunities that the Family First Prevention Services Act (Family First) offers to accelerate the shift toward a prevention-oriented child well-being system.
American Public Human Services Association (APHSA)
18F, a consultancy within the U.S. General Services Administration, developed a prototype API and pre-screener to model federal SNAP eligibility rules, aiming to simplify benefits access through open-source technology.
This study explores the causal impacts of income on a rich array of employment outcomes, leveraging an experiment in which 1,000 low-income individuals were randomized into receiving $1,000 per month unconditionally for three years, with a control group of 2,000 participants receiving $50/month.
Closing the Medicaid coverage gap could significantly reduce healthcare disparities as 65% of those affected are people of color, specifically impacting low-wage workers and caregivers who often experience economic and health vulnerabilities.
The Policy2Code Prototyping Challenge explored utilizing generative AI technology to translate U.S. government policies for public benefits into plain language and code, culminating in a Demo Day where twelve teams showcased their projects for feedback and evaluation.
