A report that reviews what has been learned from guaranteed income pilot projects in Massachusetts and situates those findings within the broader national evidence base.
An overview of current technology systems used by WIC agencies nationwide, highlighting trends, challenges, and opportunities for modernization to improve program efficiency and participant experience.
This landscape analysis examines data, design, technology, and innovation-enabled approaches that make it easier for eligible people to enroll in, and receive, federally-funded social safety net benefits, with a focus on the earliest adaptations during the COVID-19 pandemic.
This report explores the financial challenges faced by U.S. workers, analyzing the roles of work arrangements and public and workplace benefits in achieving financial security, while highlighting the disparities in access and effectiveness for low- and moderate-income workers.
This toolkit provides guidance to protect participant confidentiality in human services research and evaluation, including legal frameworks, risk assessment strategies, and best practices.
U.S. Department of Health and Human Services (HHS)
This brief describes the TANF Data Collaborative (TDC), an innovative approach to increasing data analytics capacity at state Temporary Assistance for Needy Families (TANF) agencies.
This report poses the question of whether states are prepared to meet the new Medicaid work reporting and renewal mandates introduced by HR 1, given ongoing strain from the post-pandemic “unwinding.”
This is the summary version of a report that documents four experiments exploring if AI can be used to expedite the translation of SNAP and Medicaid policies into software code for implementation in public benefits eligibility and enrollment systems under a Rules as Code approach.
This provides a comprehensive look at child well-being across the U.S., ranking states and highlighting policy recommendations to improve outcomes for children.
Led by the Digital Benefits Network in partnership with Public Policy Lab, the Digital Doorways research project amplifies the lived experiences of beneficiaries to provide new insights into people’s experiences with digital identity processes and technology in public benefits. This report details the project’s findings, directly highlighting the voices of beneficiaries through videos and photos.
The article discusses the phenomenon of model multiplicity in machine learning, arguing that developers should be legally obligated to search for less discriminatory algorithms (LDAs) to reduce disparities in algorithmic decision-making.
