The article analyzes the impacts of Arkansas's Medicaid work requirements, finding that while coverage losses were reversed after the policy was halted, it did not improve employment and led to negative consequences such as increased medical debt and delayed care.
This paper examines the challenges U.S. state and local digital service teams face in retaining talent and offers strategies to improve retention and team stability.
This report presents new national survey data showing how benefits cliffs and asset limits negatively affect the economic mobility of low-wage workers in the U.S.
A national survey of low-wage workers showing that administrative burdens in SNAP and Medicaid are common and strongly linked to food hardship, healthcare hardship, and chronic illness.
This article examines the matrix of vulnerabilities that low-income populations face from the widespread collection of big data and predictive analytics.
A tool for CDOs advocating for funding, authority, and expansion—and a primer for government leaders unfamiliar with the role. The report establishes shared vocabulary, identifies six CDO office archetypes, and offers cross-state insights on structures, priorities, and challenges.
This research article explores how framing income eligibility guidelines in either dollar amounts or as a percentage of the Federal Poverty Line (FPL) affects public attitudes toward program access and administrative burdens in Medicaid and SNAP.
This guide is a practical introduction to Digital Service Teams (DSTs) for state and local governments. It is designed to help leaders interested in standing up new government DSTs understand what they are, why they exist, and how they are structured, staffed, funded, and more.
This resource is a research paper examining the role of the public safety net in insuring job losers against income loss, analyzing which government programs provide financial support and how benefits vary based on pre-job loss income levels.
The article discusses the phenomenon of model multiplicity in machine learning, arguing that developers should be legally obligated to search for less discriminatory algorithms (LDAs) to reduce disparities in algorithmic decision-making.
