This kit contains a collection of styles, components, and building blocks to quickly create action-forward emails for Unemployment Insurance programs within the U.S.
This report shares the results of our comprehensive content audit and heuristic evaluation of eligibility pre-screeners, including ratings on security, mobile-friendly design, accessibility, and more.
This GitHub repository includes resources that users of the UI wage data toolkit may find helpful. It covers a variety of topics, including equity, data security, programming, and data QC tips. It also serves as a place for our team to continue to post information that the TANF Data Collaborative (TDC) pilot sites found useful during our partnerships with them.
This nine-minute video, produced after the completion of the TANF Data Collaborative (TDC) Pilot, features staff members from the California, Colorado, Minnesota, and Virginia TANF agencies reflecting on their challenges, accomplishments, and general experiences during the pilot. In particular, they describe their research questions and discuss building data capacity, integrating datasets, networking with other states, increasing collaboration between state and county agencies, learning new technical skills, and the benefits of being able to draw from diverse skillsets, all within the context of the TDC Pilot.
This toolkit is designed to assist state and local TANF agencies in accessing, linking, and analyzing employment data from unemployment insurance (UI) systems.
This page includes data and observations about authentication and identity proofing steps specifically for online applications that include child care applications.
On May 19, 2023, the Digital Benefits Network published a new, open dataset documenting authentication and identity proofing requirements across online SNAP, WIC, TANF, Medicaid, child care (CCAP) applications, and unemployment insurance applications.
Policymakers, program administrators, federal leaders, researchers, and advocates are increasingly focused on using administrative data to build evidence for improving government programs. Achieving this goal requires accessible data sources and the capacity to use them, yet stakeholders have little information about the baseline level of state capacity in these areas. How does one measure concepts such as “effective data use” and “analytic capacity?” This brief reports findings from a pioneering and comprehensive needs assessment that examined the capacity of Temporary Assistance for Needy Families (TANF) programs in 54 U.S. states and territories to analyze data used for program improvement, monitoring, and evidence-building. The needs assessment provides a foundation for technical assistance and continued improvement for the TANF program and may also provide valuable insights and frameworks for other state-administered human services programs.
Government agencies at all levels collect administrative data in the course of their day-to-day operations. While such information has been used to determine effectiveness through program evaluations for many years, program administrators view it increasingly as a valuable resource that can also be used to improve program performance. For example, administrative data from employment and public benefits programs such as Temporary Assistance for Needy Families (TANF) can offer insights into families’ unmet needs and ways to improve services.
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.
