Policy & Practice | Winter 2025

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By Paul Lefkowitz

SNAP Payment Error Rate Improvement: Critical Elements for Success

F or decades, the Supplemental Nutrition Assistance Program (SNAP)—formerly known as food stamps—has been fully funded by the federal government with no con tribution required from states. H.R. 1 introduced a cost-sharing require ment for states that exceed a SNAP Payment Error Rate (PER) of 6 percent. The potential budget impact—in the hundreds of millions of dollars for most states—has been well documented. Effective in FFY 28, the cost share will be based on FFY 25 or FFY 26 error rates (whichever is more advantageous to the state), with a potential up-to two-year delay in implementation for states if their error rate multiplied by 1.5 is greater than 20 percent. Public Consulting Group (PCG) has been working with states to improve SNAP accuracy long before the recent sea change in the program’s financing. Here are a few things that we’ve found are critical to success: n It Takes Teamwork: Organizations that are most effective at improving their error rate have a shared under standing of the problem at every level, effectively communicate goals, and closely manage the work. n It Takes Focus: There’s a lot of talk about “deep dives” into SNAP data to identify the root causes of errors. While this is vital, it must be followed by interventions that logically and directly respond to the findings of those analyses. States need to be clear-eyed about proposed solutions and cannot afford to just pay lip service with their corrective action. n It Takes Time: Improving the error rate is not a quick fix, and

improvements may not show up in PER data for, at a minimum, several months. It requires a plan for mea suring improvement outside the formal Quality Control (QC) process and “urgent patience,” acting each day with purpose and the under standing that seeing results where they count will take time. What Else Have We Learned? Quality Assurance (QA) teams are expanding. States are re-thinking, growing, or creating QA teams. The reviews they conduct may be pre- or post-authorization; either way, they are intended to identify and correct

errors before a case is sampled by SNAP Quality Control. Beyond that: n QA data allow states to track the effectiveness of interventions in near real time, providing state leaders and management indicators of improve ment that may not be evident in QC data for several months; n With review samples much larger than SNAP QC, QA data can identify trends and support performance man agement of individual workers; and n Reporting tools help identify trends at statewide, regional, office, unit, and individual levels.

Old assumptions are being tested. Some anecdotes have stood the test

Illustration by Chris Campbell

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Policy & Practice Winter 2025