J-PAL North America, based at MIT, leads J-PAL’s work in the North America region. J-PAL North America conducts randomized evaluations, builds partnerships for evidence-informed policymaking, and helps partners scale up effective programs.
Our work spans a wide range of sectors including health care, housing, criminal justice, education, and economic mobility. We leverage research by affiliated professors from universities across the continent and a full-time staff of researchers, policy experts, and administrative professionals to generate and disseminate rigorous evidence about which anti-poverty social policies work and why.
More about J-PAL North America
To stay up to date on J-PAL North America's most exciting announcements, stories, and successes, subscribe to our monthly newsletter.
Considering a career at J-PAL North America? Review our Join Our Team brochure to learn more about our work and team culture.
J-PAL updates
March 2024 North America Newsletter
J-PAL North America's March Newsletter features reflections on our recent work and upcoming plans to advance rigorous research on racial equity, including our seven-part blog series on how research plays a critical role in identifying systems and policies that further racial equity.
Blog
The LA Homelessness Evaluation Network: Lessons learned from supporting organizations build evidence and evaluation capacity
In this post, J-PAL North America shares lessons learned from homeless service providers who have been participating in our Los Angeles Homelessness Evaluation Network.
Blog
Centering parents and parenting in randomized evaluations of cash transfers to families
The Baby’s First Years evaluation is a J-PAL-supported study evaluating the impact of poverty alleviation on child development and families. Two researchers involved in Baby’s First Years discuss the importance of centering parents and their experiences to better understand the impact cash payments...
Blog
Regression to the mean: What it is and why it matters for impact evaluations
Regression to the mean is a statistical phenomenon where extreme outcomes tend to be followed by more moderate outcomes—closer to the mean. In the field of social policy, this could mean that individuals selected to participate in a program because of an extreme signal will naturally return back...