Show Notes
This episode follows up on the November 26, 2025 episode of Planet Money, “Saving lives with fewer dollars,” which covered GiveWell’s evaluation of a grant to the Alliance for International Medical Action (ALIMA) to maintain primary healthcare, hospital services, and malnutrition treatment in two subdistricts of North Cameroon following unexpected aid cuts earlier this year. We recommend listening to the Planet Money episode first, as it provides important context.
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Significant changes to foreign aid this year created challenges for implementing organizations—and for funders evaluating which programs to support with limited resources. The Planet Money team followed along as we assessed the effects of the cuts in real time, focusing on our evaluation of a potential grant to ALIMA to maintain nutrition and primary healthcare services in Cameroon.
Following the announcement of the US government’s stop-work order and funding freeze in January, we created a rapid response research team and began assessing opportunities we thought were potentially highly cost effective. In March, we launched an investigation of the $1.9 million ALIMA grant, which we funded in June based on the team’s findings.
In this episode, GiveWell CEO and co-founder Elie Hassenfeld dives deeper into the grant investigation with Program Officers Rosie Bettle and Alice Redfern, discussing the timeline, modeling approach, and what ultimately led us to make the grant.
Elie, Rosie, and Alice discuss: The grant investigation timeline: GiveWell completed the investigation in about six weeks from start to finish. Typically, GiveWell grant investigations build on months or years of prior research. While we’ve researched and funded malnutrition programs in the past (including ALIMA’s programs), this program’s scope was wider—covering primary healthcare, disease surveillance, and hospital logistics. How we adapted our modeling: As part of evaluating this grant, GiveWell attempted to estimate several parameters related to mortality, then used a series of simple models—rather than one comprehensive model—to estimate cost-effectiveness based on those parameters. These models, along with conversations with experts and other inputs, allowed the team to move quickly and respond to the urgent need. An update on grant progress: With GiveWell’s funding, ALIMA’s program is now up and running again. The program has been adapted to incorporate mobile clinics, and ALIMA is on track to treat the number of children GiveWell expected. Based on a number of conversations, we believe that ALIMA’s programs are leading to increased care-seeking behavior.
As GiveWell’s research team grows, that increased capacity and expertise allows us to evaluate a wider range of programs and adapt our approaches to better find the most cost-effective opportunities to help people. In this case, that growth enabled us to move quickly and navigate uncertainty to evaluate and fund ALIMA’s program.
Visit our Foreign Aid Funding Cuts page to learn more about our response to this year’s aid cuts, visit the All Grants Fund page to learn more about how you can support this work, and listen or subscribe to our podcast for our latest updates.
This episode was recorded on December 3, 2025 and represents our best understanding at that time.
More from GiveWell Conversations
In an effort to direct substantially more funding to the most cost-effective opportunities we can find to help people, GiveWell’s research is expanding in two directions at once: going broader, to cover more areas of global health and development, and going deeper, to improve the inputs that inform our decision-making.
GiveWell works to continually improve our research so we can make better grant decisions and direct donations where we believe they can do the most good. GiveWell’s Cross-Cutting research subteam is an important part of that effort—it investigates the impact of the programs we fund, works to resolve our largest uncertainties, and aims to improve our research. As GiveWell’s grantmaking grows, the Cross-Cutting subteam is strengthening our data collection and analysis to support that growth. In this episode, GiveWell co-founder and CEO Elie Hassenfeld speaks with Program Director Alex Cohen, who leads the Cross-Cutting subteam, about how better data can improve our decision-making, our work to gather stronger evidence, and the ways we’ve tested AI as a tool to increase our research capacity and quality.
GiveWell, with co-funding from Coefficient Giving, recently made our largest grant to date: $276 million to the Against Malaria Foundation (AMF) to support the procurement and distribution of 92 million nets in the Democratic Republic of the Congo (DRC) from 2027 through 2029. We estimate that the nets, 46 million of which we think would not otherwise be distributed, will avert roughly 67,000 deaths. We have supported AMF’s work for more than 15 years, and the insecticide-treated nets it procures and distributes remain one of the most cost-effective ways we've found to save lives.
When GiveWell investigates a new cause area, we’re often focused on two priorities: directing funding where we think it can help people most and generating evidence to inform larger investments later. Family planning is a clear example. Recent foreign aid cuts—including reductions from the US, UK, and Dutch governments—deepened an already substantial need and opened up new cost-effective funding opportunities.
Diarrhea is one of the leading causes of death for children under five, killing more than 400,000 children every year. GiveWell has funded several programs to prevent and treat diarrhea, and we recently expanded our work by making a grant to support the implementation and evaluation of WASHmobile in the Democratic Republic of the Congo (DRC) and South Sudan. The program, which was developed by Johns Hopkins University, delivers water treatment supplies and hygiene messaging to households at the highest risk of diarrheal disease. It has shown promising results in randomized controlled trials (RCTs) but hasn’t yet been implemented at scale in the challenging settings where the need is greatest.