Benefits Data Trust (BDT) is a nonprofit that connects people to public benefits through a streamlined, phone-based application system called Benefits Launch, which reduces redundant questions and speeds up the process for multiple programs. BDT's approach, supported by a custom-built rules engine, has facilitated over 800,000 benefit enrollments, helping secure over $9 billion for eligible households across seven states.
This resource allows policymakers, employers, benefits providers, and researchers assess benefits performance for constituents and identify opportunities in market and policy innovation to ensure equitable benefits distribution.
PolicyEngine is a nonprofit that provides a free, open-source web app enabling users in the US and UK to estimate taxes and benefits at the household level, while also simulating the effects of policy changes. By combining tax and benefits data, PolicyEngine helps individuals and policymakers better understand the impacts of existing policies and proposed reforms, using microsimulation models built from legislation and enhanced survey data.
The goal of the brief is to encourage policy makers and employers to consider benefits cliffs as they look to create mandatory wage increases, with a look at a legislative action in NYC.
Github repository for Policy Rules Database, which encodes up-to-date rules and provisions for all major federal and state public assistance programs, taxes, and tax credits.
This Issue Spotlight explores the challenges that recipients of public benefits programs offering cash assistance encounter in accessing funds through financial products or services, with a specific focus on assistance provided on prepaid cards.
ACCESS NYC is an online public screening tool that residents can use to determine the City, State, and Federal health and human service benefit programs for which they are eligible.
In this report, the U.S. Chamber of Commerce Foundation examines benefits cliffs – the loss of eligibility for public safety-net programs and benefits they provide as income rises above eligibility limits.
This report analyzes lawsuits that have been filed within the past 10 years arising from the use of algorithm-driven systems to assess people’s eligibility for, or the distribution of, public benefits. It identifies key insights from the various cases into what went wrong and analyzes the legal arguments that plaintiffs have used to challenge those systems in court.
The team explored using LLMs to interpret the Program Operations Manual System (POMS) into plain language logic models and flowcharts as educational resources for SSI and SSDI eligibility, benchmarking LLMs in RAG methods for reliability in answering queries and providing useful instructions to users.
