PolicyEngine US is a Python-based microsimulation model of the US tax and benefit system. It models federal individual income taxes (including credits), major benefit programs, and state income taxes (currently in six states). The PolicyEngine US package can be used as a Python package, via the PolicyEngine API, or via the policyengine.org web app.
A guide to navigating New York City’s public services. It was made with and for families of students living in temporary housing or experiencing homelessness and the NYC Department of Education’s Office of Students in Temporary Housing (STH).
mRelief launches Johnnie, a platform that centers client dignity and enables client management from anywhere. Features include client communication mechanisms, assistance for document submission, keeping track of enrollment process, and tracking enrollment metrics.
My File NYC is a document storage and sharing website that provides New York City residents a safe place to store and share vital documents when applying for City services.
While millions of workers have gained access to PFML, the lack of uniformity in mandatory PFML programs created a growing patchwork of state laws, differing on nearly 30 policy components across four key areas: substantive benefits, financing, eligibility, and administrative requirements.
This OPRE brief provides strategies for enhancing cultural responsiveness in social service agencies, focusing on improving services for diverse communities through organizational change, staff development, and culturally informed program design.
U.S. Department of Health and Human Services (HHS)
The New York State WIC program website provides access to nutritious foods, nutrition education, breastfeeding/chestfeeding support, and referrals to eligible pregnant, breastfeeding, and postpartum individuals, infants, and children up to age five.
This brief examines the treatment of PFML for purposes of state and federal taxation, as well as determining income and eligibility in five means-tested programs.
This report 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.
