PolicyEngine US is a Python-based microsimulation model of the US tax and benefit system. It models federal individual income taxes (including credits), major benefit programs, and state income taxes (currently in six states). The PolicyEngine US package can be used as a Python package, via the PolicyEngine API, or via the policyengine.org web app.
An interview with Wendy De La Rosa, assistant professor at the Wharton School at the University of Pennsylvania. De La Rosa discusses how the concept of “psychological ownership” can encourage people to take up benefits they are eligible for.
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.
This resource provides guidance on streamlining enrollment across public benefit programs to improve efficiency, reduce administrative burdens, and enhance access for eligible individuals and families.
The IRS is arguably the single most critical benefits administrator in the country, given its responsibility for tax credit-based relief programs, and COVID-19 relief payments. Despite these programs’ incredible progress in reducing poverty, and despite great strides by the IRS to implement them successfully, accessing IRS benefits remains too difficult for many low-income families. This report presents a comprehensive agenda to increase benefit coverage rates, simplify Americans’ interactions with the IRS, and decrease the portion of IRS benefits diverted to third parties.
The NYC Benefits Screening API provides machine-readable calculations and criteria for benefits screening that power the ACCESS NYC screening questionnaire.
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.
The team introduced an AI assistant for benefits navigators to streamline the process and improve outcomes by quickly assessing client eligibility for benefits programs.
The Improving Service Delivery in EITC for New Yorkers initiative explores ways to enhance access to the Earned Income Tax Credit (EITC) through improved outreach, application processes, and service delivery.
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 article examines how administrative burdens in U.S. social safety net programs have changed over the past 30 years, showing that while average burdens have declined, inequality in who faces these burdens has grown.
The ANNALS of the American Academy of Political and Social Science
A brief report on our quantitative research about messages that increase people's take-up of government benefits by making them feel like those benefits belong to them.
