A report that reviews what has been learned from guaranteed income pilot projects in Massachusetts and situates those findings within the broader national evidence base.
A comprehensive human services policy that strengthens due process, transparency, and administrative fairness across housing assistance, CalWORKs, and CalFresh programs while funding system changes to reduce payment errors and administrative burden.
This document provides a template for SNAP agencies to use to communicate how they can meet able-bodied adults without dependents (ABAWD) work requirements.
American Public Human Services Association (APHSA)
A strategy outlining a new, outcome-driven, slice-based approach to modernizing Medicaid Enterprise Systems, prioritizing experimentation, measurable outcomes, and cultural transformation over traditional large-scale system replacements.
Practitioner Picks is a quarterly series designed to add fresh resources to the Digital Government Hub’s library, helping people improve government digital service delivery. Each issue spotlights resources chosen by practitioners in a specific service delivery area along with their insights on why these picks are valuable additions to the Hub.
This publication explains the fundamentals of state IEE systems—including the technology, opportunities, risks, and stakeholders involved. It is a resource for state officials, advocates, funders, and tech partners working to implement these systems.
A nationwide survey capturing how state chief data officers are structured, resourced, and operating, and how the role is evolving to support data-driven government.
National Association of State Chief Information Officers (NASCIO)
A practical guidance document that explains how to design, code, and test HTML web forms so they are accessible to all users, including people with disabilities.
Recapping the work and achievements of the Digital Benefits Network (DBN), Digital Service Network (DSN), and the State Chief Data Officers Network (CDO) in 2025.
A case study explaining how a predictive, data-driven machine-learning model was developed to detect unauthorized cash benefit withdrawals more quickly and accurately in California.
