In response to exploding demand for social services during COVID-19, the Louisiana Department of children and Family services implemented text-message alerts and reminders for the state’s entire SNAP caseload, launched a text-based public campaign to help people understand and apply for SNAP benefits, and hired SNAP recipients to provide client feedback on communications and policy decisions.
Learn how to use generative AI to quickly create unemployment insurance translations that are accurate, easy to understand, and tailored to your state.
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.
This brief provides a summary of potential federal funding sources and programs that can be used to support programs specifically targeted towards young families. While this list is not exhaustive, it highlights major sources that can serve as a starting point for braiding and blending of funding to create comprehensive programming to serve young families.
American Public Human Services Association (APHSA)
This article highlights how state unemployment insurance (UI) agencies are leveraging data to modernize systems and enhance access for unemployed workers.
This simulator, created by the Joint Financial Management and Improvement Program (JFMIP), allows users to model program performance for a hypothetical government program, based on different identity verification decisions. The simulator illustrates concepts from the JFMIP report on payment integrity.
Joint Financial Management and Improvement Program (JFMIP)
Human-centered design is a problem-solving method that puts people at the center of the problem and aims to design solutions that address the needs of the people. Embracing such design methods in government is imperative to finding solutions that work for the people.
The Atlanta Fed’s CLIFF tools provide greater transparency to workers about potential public assistance losses when their earnings increase. We find three broad themes in organization-level implementation of the CLIFF tools: identifying the tar- get population of users; integrating the tool into existing operations; and integrating the tool into coaching sessions.
This academic paper examines how federal privacy laws restrict data collection needed for assessing racial disparities, creating a tradeoff between protecting individual privacy and enabling algorithmic fairness in government programs.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
