The Atlanta Fed’s CLIFF tools provide greater transparency to workers about potential public assistance losses when their earnings increase. We find three broad themes in organization-level implementation of the CLIFF tools: identifying the tar- get population of users; integrating the tool into existing operations; and integrating the tool into coaching sessions.
This brief analyzes the current state of federal and state government communication around benefits eligibility rules and policy and how these documents are being tracked and adapted into code by external organizations. This work includes comparisons between coded examples of policy and potential options for standardizing code based on established and emerging data standards, tools, and frameworks.
The OECD report explores the concept of "Rules as Code" (RaC), proposing a transformation in government rulemaking by developing machine-consumable regulations alongside human-readable versions.
Organisation for Economic Co-operation and Development (OECD)
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.
MITRE developed the Comprehensive Careers and Supports for Households (CCASH™) tool to help individuals understand and manage federal benefits and employment services, transitioning from a consumer-focused tool to a policy analytics system. By integrating data from sources like the U.S. Census and the Policy Rules Database, MITRE created a model that allows users to analyze and compare benefits eligibility across states, supporting evidence-based policymaking.
mRelief is a nonprofit that helps individuals in all 53 U.S. states and territories determine SNAP eligibility and apply using easy-to-use web and text tools. Their simplified, inclusive approach has supported over 2.7 million people and unlocked over $1 billion in benefits, focusing on minimizing barriers and adapting eligibility rules across states.
The team aimed to automate applying rules efficiently by creating computable policies, recognizing the need for AI tools to convert legacy policy content into automated business rules using Decision Model Notation (DMN) for effective processing and monitoring.
