POSE: Phase I: PolicyEngine - Advancing Public Policy Analysis

Award ID: 2518372

This Pathways to Enable Open-Source Ecosystems (POSE) project increases access to sophisticated economic policy analysis tools, enabling evidence-based decision-making across all levels of American society. The project transforms tax-benefit microsimulations into open-source infrastructure accessible to local governments, nonprofits, and citizens. This innovation enhances scientific understanding by making policy models transparent and reproducible, allowing researchers to validate assumptions and build upon existing work. The OSE provides durable competitive advantage by establishing the United States as the global leader in open-source policy modeling. The model leverages distributed open-source development, where commercial users fund expansions that benefit the entire ecosystem. Primary beneficiaries include households accessing benefits, local governments analyzing policy impacts, and researchers advancing economic science. This POSE project addresses the critical gap between sophisticated policy analysis capabilities and organizational infrastructure needed for sustainable open-source development. While the team has built comprehensive tax-benefit microsimulation tools with a strong user base, transitioning to a self-sustaining ecosystem requires systematic organizational development. The project's objectives include: 1) conducting ecosystem discovery to map stakeholder needs; 2) establishing a new governance structure; and 3) building contributor infrastructure including documentation, mentorship programs, and community engagement frameworks. The effort involves surveying stakeholders, conducting focus groups, developing governance bylaws, creating contributor onboarding systems, and launching a dashboard tracking ecosystem health. Technical goals include a comprehensive ecosystem landscape report, formalized intellectual property frameworks (e.g., AGPL-3.0 code, trademark protection, contributor agreements), documented partnership models, and quantitative metrics showing increasing contribution rates and growth in monthly users. This organizational infrastructure ensures PolicyEngine's technical capabilities, including state and congressional district-level analysis powered by machine learning calibration, can sustainably serve community interests and drive development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Florence Rabanal