Benefits Data Trust (BDT), in collaboration with the Center for Health Care Strategies (CHCS), conducted a nationwide analysis of how states coordinate across Medicaid and SNAP programs to streamline access to benefits.
This Urban Institute article argues that poverty is driven by structural barriers rather than individual choices and advocates for safety net programs that address systemic inequities.
We kicked off Rules as Code Demo Day with Alex Soble of 18F and Mike Gintz of 10x presenting their Eligibility APIs Initiative that explores whether APIs and rules as code might improve the efficiency and effectiveness with which federal public benefits programs communicate their policy to states. They demonstrated their original prototype, and how the open source code has now been extended into several initiatives.
Article describing the “time tax,” the costs to people applying or benefits in terms of spending substantial amounts of time navigating user-unfriendly interfaces. The article describes the necessity of simplifying safety-net programs and cross-coordinating across various social service programs.
This case study highlights how states used data sharing and targeted outreach to boost WIC enrollment among Medicaid and SNAP participants, improving program reach and reducing disparities.
The existing system for evaluating state safety net programs does not adequately capture the human experience of accessing services. This new National Safety Net Scorecard is a more meaningful set of metrics that can effectively asses the true state of the current program delivery landscape and measure progress over time, creating a more human-centered safety net.
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.
