This brief describes TDI’s efforts to transform federal TANF and employment data into an integrated resource for program management and evidence building.
This proposal recommends a set of new federal performance standards that would measure and improve UI access. The proposal is intended to supplement existing federal UI standards, but all UI standards and metrics should be periodically reevaluated and updated as the conditions facing unemployed workers and benefit delivery change.
The Guaranteed Income Pilots Dashboard is designed to visualize data from 30+ guaranteed income pilots across the United States. This data and testimonies from participants shed light on ways in which unconditional cash is providing people the opportunity, freedom, and resilience to build financial security.
Github repository for Policy Rules Database, which encodes up-to-date rules and provisions for all major federal and state public assistance programs, taxes, and tax credits.
The CARES Act Stimulus Payments Report by New America analyzes the implementation and impact of the Economic Impact Payments (stimulus checks) distributed during the COVID-19 pandemic, highlighting accessibility challenges and policy recommendations for future direct payments.
The experience of the COVID-19 pandemic and its induced recession underscored the crucial importance of unemployment insurance (UI) to workers, and to the stability of the American economy. Temporary federal expansions of unemployment systems during the pandemic showed how they can quickly be scaled to increase benefit levels and to include categories of workers who were not previously eligible, such as the self-employed, caregivers, and low-wage workers. And, states showed that separate programs can be set up to provide similar benefits to workers who are explicitly excluded from unemployment insurance—in particular immigrants who do not have a documented immigration status.
Initially created to inform federal staff at the U.S. Department of Health and Human Services, this tip sheet provides key considerations for how organizations can identify potential diverse external partners, conduct outreach to them, and build and sustain productive relationships with them.
Office of the Assistant Secretary for Planning and Evaluation (ASPE)
This academic paper examines predictive optimization, a category of decision-making algorithms that use machine learning (ML) to predict future outcomes of interest about individuals. Through this examination, the authors explore how predictive optimization can raise concerns that make its use illegitimate and challenge claims about predictive optimization's accuracy, efficiency, and fairness.
This report explores how AI is currently used, and how it might be used in the future, to support administrative actions that agency staff complete when processing customers’ SNAP cases. In addition to desk and primary research, this brief was informed by input from APHSA’s wide network of state, county, and city members and national partners in the human services and related sectors.
American Public Human Services Association (APHSA)
Programs like Medicaid and SNAP are managed at the federal level, administered at the state level, and often executed at the local level. Because there are so many in-betweens, there is significant duplicated effort, demonstrating the need to simplify eligibility rules to facilitate easier implementation.
