Many low-income individuals face a hidden economic trap known as the "benefits cliff," where earning slightly more income causes them to lose eligibility for crucial public assistance programs (like housing subsidies or Medicaid) while also facing higher taxes. This paradox can create a net loss in resources, discouraging upward mobility and trapping people in poverty. Despite its severe impact, the issue is poorly understood due to fragmented data across federal, state, and local programs. Without a clear analysis of how these cliffs work, policymakers struggle to address them, and beneficiaries lack the information to navigate these risks.

Mapping the Problem

One way to tackle this issue could be to systematically analyze the interplay between income levels and assistance programs. This could involve:

• Compiling eligibility rules and phase-out structures for major programs like SNAP, TANF, and Medicaid at all government levels.
• Modeling how these rules affect net income for different household types to estimate how many people face marginal tax rates exceeding 100%.
• Surveying beneficiaries to understand their awareness of these cliffs and how they influence decisions about work or income.

Tools for Change

A key output could be an open-access digital tool—such as an interactive calculator or map—to help users input their location and income and visualize how earning more might impact their net benefits. For policymakers, the research could highlight where reforms (like smoother phase-outs or "bridge benefits") would have the most impact. Early versions might focus on a single state with complex welfare systems (e.g., California) to refine the methodology before expanding nationally.

Building on Existing Work

While tools like the Urban Institute’s Safety Net Calculator or PolicyEngine provide broad simulations of benefits, they often miss local program details or don’t emphasize marginal rates. This project could differentiate itself by focusing on hyper-local data and translating findings into actionable insights for both individuals and reform advocates. Potential funding might come from mobility-focused foundations or partnerships with state agencies seeking data-driven policy improvements.

By making benefits cliffs visible and quantifiable, this effort could empower households to make informed choices while giving policymakers the evidence needed to redesign outdated systems.