The NYC Mayor’s Office for Economic Opportunity (NYC Opportunity) developed the NYC Benefits Platform, including ACCESS NYC, to help residents easily discover and check eligibility for over 80 social programs.
Oklahoma Human Services (OKDHS) modernized their service delivery by reducing their real estate footprint, designing trauma-informed and user-friendly spaces, and expanding an embedded worker program to improve accessibility and client experience. Through their "Service First" strategy, OKDHS aims to create more equitable and compassionate interactions, reaching vulnerable populations while addressing high occupancy costs.
This guide outlines how states can use TANF funds to provide direct cash assistance to families, particularly through flexible mechanisms like nonrecurrent short-term benefits (NRSTs).
Article describing the “time tax,” the costs to people applying or benefits in terms of spending substantial amounts of time navigating user-unfriendly interfaces. The article describes the necessity of simplifying safety-net programs and cross-coordinating across various social service programs.
This study investigates how administrative burdens influence differential receipt of income transfers after a family member loses a job, looking at Unemployment Insurance, Temporary Assistance for Needy Families, and the Supplemental Nutrition Assistance Program.
This Urban Institute report explores the impact of benefit cliffs, plateaus, and trade-offs on families receiving public assistance, examining how changes in earnings affect access to essential benefits like SNAP, Medicaid, and housing subsidies.
In this webinar, a panel of experts discuss what states can do right now to improve EBT security, how to use data to analyze theft patterns, and how EBT payment technology needs to evolve to ensure efficiency, security, and dignity for beneficiaries.
Temporary Assistance for Needy Families (TANF) leaders, policymakers, and researchers all recognize the need for TANF agencies to use the data they collect to better understand how well their programs are working and how to improve them, given the impact on the families they serve. It is often difficult, however, for agencies already stretched to capacity to prioritize and execute data use and analytics. State TANF leaders are seeking roadmaps for how to transform their organizations and become data-driven.
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.
