This report examines how the U.S. federal government can enhance the efficiency and equity of benefit delivery by simplifying eligibility rules and using a Rules as Code approach for digital systems.
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.
Executive Order 14058, issued by President Joe Biden on December 13, 2021, aims to enhance the federal customer experience and service delivery to rebuild trust in government.
In this webinar, a panel of experts discuss what states can do right now to improve EBT security, how to use data to analyze theft patterns, and how EBT payment technology needs to evolve to ensure efficiency, security, and dignity for beneficiaries.
This issue brief describes the Pennsylvania case study, outlines the historical context, and offers strategies and recommendations for successfully implementing Fast Track.
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.
There are frameworks available that could inform the standardization of communicating rules as code for U.S. public benefits programs. The Airtable communicates the differences between the frameworks and tools. Each entry is tagged with different categories that identify the type of framework or tool it is.
Applicants to federal aid programs face numerous barriers in accessing benefits they are eligible for. The Centers for Medicaid and Medicare conducted an extensive qualitative user research study to better understand applicant experience in enrolling in public assistance programs. Based on the results, the study emphasizes the need for simplified, streamlined and less burdensome application processes.
A guide from the General Service Administration to help government decision makers clearly see what AI means for their agencies and how to invest and build AI capabilities.
As a part of Benefit Data Trust (BDT)’s Medicaid Churn Learning Collaborative, BDT has created a memo describing policy options and state examples for Medicaid administrators to reduce churn for non-MAGI Medicaid enrollees when the federal public health emergency ends.
Hear perspectives on topics including centering beneficiaries and workers in new ways, digital service delivery, digital identity, and automation.This video was recorded at the Digital Benefits Conference (BenCon) on June 14, 2023.
