Reducing SNAP Error Rates: Initial Observations from Modeling Public QC Data
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).
The analysis models Quality Control (QC) data from 2017 to 2023 to help states lower error rates while mitigating the disruption of food aid to eligible households.
Key observations include that model accuracy improves significantly when accounting for a household’s benefit amount as a share of the maximum allotment, noting that households at or near the maximum tend to have higher rates of counted errors. The report also finds that traditional employment is more strongly associated with over-threshold income errors than self-employment, and it suggests that states use decision trees to target high-risk cases without burdening households unlikely to have errors.Â
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