The "Implementing Paid Family and Medical Leave" report examines New Jersey's experience with paid leave programs, offering insights and recommendations for effective policy design and implementation.
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.
An online hub that connects WIC agencies and their partners through a national Data Matching Community of Practice, offering quarterly virtual convenings to share best practices, case studies, and peer learning on strategies to improve WIC outreach and enrollment.
A detailed guide outlining how states can minimize coverage losses and administrative burden while implementing new Medicaid work requirements established under the 2025 federal reconciliation law.
This case study documents how Civilla partnered with the Michigan Department of Health and Human Services (MDHHS) to redesign and modernize online enrollment for the state’s largest benefit programs.
This toolkit is designed to assist state and local TANF agencies in accessing, linking, and analyzing employment data from unemployment insurance (UI) systems.
This guide consolidates learning and spotlights principles, insights, and emerging practices to guide municipal leaders and public-private partnerships interested in designing basic income programs that are ethical, equitable, rigorous, informative, and consequential for local, state and national policymaking.
This paper introduces the problem of semi-automatically building decision models from eligibility policies for social services, and presents an initial emerging approach to shorten the route from policy documents to executable, interpretable and standardised decision models using AI, NLP and Knowledge Graphs. There is enormous potential of AI to assist government agencies and policy experts in scaling the production of both human-readable and machine executable policy rules, while improving transparency, interpretability, traceability and accountability of the decision making.
This report contributes to the quantitative measurement of psychological burdens by examining a case study of a single social program: the Supplemental Nutrition Assistance Program, by considering new quantitative measures of the psychological burdens faced by SNAP applicants.
