This report calculates the cumulative impact of major benefit programs on two types of families and how their benefits change as they move into the labor market and climb the ladder of upward mobility.
Medicaid and SNAP have reduced racial and ethnic disparities in healthcare access and food security, but some administrative and eligibility policies continue to create inequitable barriers.
This resource examines the role of Medicaid in West Virginia and documents how the post-pandemic Medicaid “unwinding” process affected residents, highlighting participant experiences and the program’s importance for health and economic stability.
This GitHub repository includes resources that users of the UI wage data toolkit may find helpful. It covers a variety of topics, including equity, data security, programming, and data QC tips. It also serves as a place for our team to continue to post information that the TANF Data Collaborative (TDC) pilot sites found useful during our partnerships with them.
A report outlining human-centered design strategies to help states implement new federal Medicaid work requirements in ways that minimize coverage loss and administrative burden
This brief describes TDI’s efforts to transform federal TANF and employment data into an integrated resource for program management and evidence building.
This research paper examines how stigma shapes participation in U.S. social safety net programs and influences public support for benefit design and access.
This case study highlights how Illinois is modernizing its student data infrastructure and interagency data sharing to increase access to SNAP and Summer EBT benefits for eligible children and families, particularly those facing systemic barriers.
American Public Human Services Association (APHSA)
Post-Medicaid continuous enrollment's end in March 2023, states faced renewal challenges through August 2024, seeing improved auto-renewals but persistent procedural disenrollments despite outreach and intervention.
This quarterly research update aims to highlight key learnings related to improving unemployment insurance (UI) systems in the areas of equity, timeliness, and fraud, and monitor for model UI legislation and policy related specifically to technology. Subscribe to receive future editions.
This study explores the causal impacts of income on a rich array of employment outcomes, leveraging an experiment in which 1,000 low-income individuals were randomized into receiving $1,000 per month unconditionally for three years, with a control group of 2,000 participants receiving $50/month.
