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.
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.
This economic analysis article examines how state-level policy variations have created increasingly wide disparities in Unemployment Insurance (UI) benefit levels and access.
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.
This blog post serves as a guide for state agencies to develop flexible and actionable metrics systems for tracking the implementation and impact of new work requirements under H.R. 1.
An advisory playbook to help state and local government leaders improve SNAP payment accuracy and lower administrative burdens following the passage of H.R. 1.
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.
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).
An interactive dashboard that enables users to explore and monitor key metrics of the Supplemental Nutrition Assistance Program (SNAP) Quality Control (QC) system.
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.
