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.
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.
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 innovative solutions and insights from CMS Innovation Center's Hackathon series to address the unique healthcare challenges faced by rural, Tribal, and geographically isolated communities.
In this updated primer, the DBN describes how identity proofing and authentication show up in public benefits applications and outlines equity and security concerns raised by common identity proofing and authentication methods.
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.
Drawing on the Beeck Center’s research on government, nonprofit, academic, and private sector organizations that are working to improve access to safety net benefits, this report highlights best practices for creating accessible benefits content.
In this updated primer, the DBN introduces the concept of digital identity, and provides brief snapshots of digital identity-related developments internationally and in the U.S.
This Urban Institute report highlights how immigrant and mixed-status families continued to avoid safety net programs in 2023 due to lingering fears around the public charge rule.
This study explores the causal impacts of income on a rich array of employment outcomes, leveraging an experiment in which 1,000 low-income individuals were randomized into receiving $1,000 per month unconditionally for three years, with a control group of 2,000 participants receiving $50/month.
