This blog analyzes how the One Big Beautiful Bill Act (OBBBA) will dramatically shift SNAP costs onto state governments, projecting massive budget increases and fiscal strain.
The article analyzes the impacts of Arkansas's Medicaid work requirements, finding that while coverage losses were reversed after the policy was halted, it did not improve employment and led to negative consequences such as increased medical debt and delayed care.
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 economic analysis article examines how state-level policy variations have created increasingly wide disparities in Unemployment Insurance (UI) benefit levels and access.
A report examining how risk assessment tools are used to improve payment accuracy in nutrition assistance programs and identifying effective practices for their design and implementation.
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.
This project portfolio page details a human-centered service design partnership with the Michigan Unemployment Insurance Agency (UIA) to revitalize and streamline the state's unemployment benefits system following crisis-level strain.
A blog introducing an interactive viewer that helps users explore SNAP Quality Control error data to better understand payment accuracy trends and administrative challenges across states.
This guide outlines key strategies, definitions, and procedures for improving SNAP payment accuracy and reducing quality control (QC) error rates across states.
A report summarizing effective state practices, promising initiatives, and federal resources to improve payment accuracy in the Supplemental Nutrition Assistance Program (SNAP).
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 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).
