Where research and policy join hands to drive social progress.
We believe policy should be shaped by rigorous, data-driven research that puts people first.
Our model is rooted in research-practice partnerships, working with partners around the globe to understand the challenges their communities face. We use data-driven research to co-develop workable solutions, to rigorously test them in programs and policies, and iterate as necessary.
Research Partnerships
We conduct collaborative, on-the-ground research across five continents.
Featured Projects
Our research supports real-world policy initiatives that address inequities and shed light on challenges faced by marginalized people.
Policy Partners
Our partners are governments at all levels, civil society groups, nonprofits, and anyone working to improve the lives of marginalized people.
Latest News
Visit our news page to see the latest Forward Society (FOS) Lab announcements and updates.
Examining Brazil’s Successful Efforts to Reduce Deforestation Through Satellite Imagery
FOS Affiliate Eduardo Souza-Rodrigues discusses the impact and future improvements of Brazil’s satellite-based deforestation policies.
Eva Vivalt Leads Landmark Basic Income Study
A new study reveals how large and temporary unconditional cash transfers can influence lives and preferences
Research-Practice Partnerships Explained ─ Financing Impact podcast with Raji Jayaraman
In this episode, FOS co-director Raji Jayaraman and practice partner at GIZ Rwanda Katia Halabi explain and reflect on research-practice partnerships (RPPs).
Events
We host seminars, workshops, and conferences.
Second International Symposium on Research and Impact in Education
A focused day of educational evidence at the Second International Symposium in San Juan, jointly organized by FOS and the Puerto Rico Department of Education
Nathan Kallus on Seminar Series: Causal Inference and Data Science for Improved Policy
Our third interdisciplinary series seminar featured Prof. Nathan Kallus from Cornell University discussing "Debiased Inference on Functionals of Inverse Problems and Applications to Long-Term Causal Inference"