Resources and Toolkits

Common policy missteps states may take in efforts to reduce SNAP payment error rates (PERs), such as adopting change reporting, imposing asset tests, or limiting discretionary exemptions, can have unintended effects of increasing error rates. Instead, states should make informed policy choices, such as adopting simplified reporting, waiving asset tests, allowing discretionary exemptions, and implementing…

Paper exploring how uncapped benefit calculations in payment error predictive models can help create space for more “buffer” and create inclusion or exclusion criteria for better targeted reviews and greater predictive power.

An interactive cross-state analysis tool demonstrating how removing the $58 SNAP QC tolerance threshold could increase payment error rates and related state costs.

An interactive dashboard that enables users to explore and monitor key metrics of the Supplemental Nutrition Assistance Program (SNAP) Quality Control (QC) system.

Guidance on state policy chocies that tend to increase SNAP payment error rates include requiring change reporting or monthly reporting, imposing asset tests, and limiting discretionary exemptions. In contrast, states can reduce errors by simplifying reporting requirements, using BBCE, applying exemptions, and using one-month lookbacks to minimize reassessments and ensure coverage for eligible populations.

An interactive dashboard that enables users to explore and monitor key metrics of the Supplemental Nutrition Assistance Program (SNAP) Quality Control (QC) system.

Resource Hub including data visualization tools, modeling tips, and lessons gathered from States to support the work of QC data modeling nationwide.

Workshop to foster collaboration and share modeling practices among state agencies in response to HR1’s new SNAP PER cost share requirements. Gathering encompassed convened a group of nearly 40 research and data analytics staff from across 15 states involved in SNAP QC data modeling.

Key strategies to improve accuracy and efficiency of SNAP program delivery, including evaluating intervention impacts independently, developing strong proxy metrics, watching for unintended consequences, strategically reviewing most error-prone case elements and actions, leveraging new technology for caseworkers, and improving client communication and outreach.