This explores how tax credit systems can be redesigned to better meet the needs of families, especially those facing systemic barriers to filing and receiving benefits.
Closing the Medicaid coverage gap could significantly reduce healthcare disparities as 65% of those affected are people of color, specifically impacting low-wage workers and caregivers who often experience economic and health vulnerabilities.
Learn how to use generative AI to quickly create unemployment insurance translations that are accurate, easy to understand, and tailored to your state.
This session from FormFest 2024 focused on how governments are scaling their SNAP benefits programs, with Maryland’s improved integrated benefits application and the Office of Evaluation Sciences’ changes to questions on the SNAP application.
The team developed an application to simplify Medicaid and CHIP applications through LLM APIs while addressing limitations such as hallucinations and outdated information by implementing a selective input process for clean and current data.
Many low-income households lack the savings to weather financial shocks like layoffs, and SNAP plays a crucial role in helping them manage essential expenses during difficult times.
This Urban Institute report identifies strategies to improve young people’s access to public benefits through targeted outreach, benefit navigation, cross-organizational partnerships, and streamlined eligibility processes.
Our existing maze of family tax benefits — including the CTC, Earned Income Tax Credit (EITC), Child and Dependent Care Tax Credit (CDCTC), and head of household (HoH) filing status — has several structural deficiencies that make overhauling the system a prerequisite for any effort to boost support for families with children. The report offers several options for expanding and streamlining family tax benefits to address these issues.
This paper introduces a method for auditing benefits eligibility screening tools in four steps: 1) generate test households, 2) automatically populate screening questions with household information and retrieve determinations, 3) translate eligibility guidelines into computer code to generate ground truth determinations, and 4) identify conflicting determinations to detect errors.
This brief describes the TANF Data Collaborative (TDC), an innovative approach to increasing data analytics capacity at state Temporary Assistance for Needy Families (TANF) agencies.
This webpage links to materials ASPE has prepared as it leads work on how federal agencies and programs can meaningfully and effectively engage people with lived experience.
Office of the Assistant Secretary for Planning and Evaluation (ASPE)
