Resources and Toolkits

Guide about the member-facing side of implementation, and why user experience is just as important as what happens on the backend

Conversation sharing drivers and targeted interventions to reduce SNAP Payment Error Rates, drawing on six months of hands-on research with Arizona, North Carolina, and Maryland

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.

A free resource that focuses on strategies that states can test immediately to reduce PER and lessen the burden on frontline eligibility workers