The default position is that while morally urgent, these donations may be 1-2 orders of magnitude less cost-effective (in $ per life saved) than top GiveWell picks.
For crises as severe and as underfunded as Sudan, I would question that (especially the ~2 OOM part). If I remember correctly, GiveDirectly-style cash is perhaps ~1 OOM off on cost-per-life-saved (without considering non-mortality benefits) even without a targeted population. It seems likely that an intervention like this would equal or likely beat GiveDirectly on that metric due to the particularly dire situation which people are facing.
That’s an excellent point, and you’re absolutely right to question that “1-2 OOM” heuristic. I agree with you, and I think your insight gets to the core flaw in that “default” assumption (which I was also challenging). That heuristic seems to originate from a (now quite old) 2010 GiveWell blog post, “Can choosing the right charity double your impact?” [1]. In it, they made the case that the total range of charity effectiveness “can easily vary by 2-3 orders of magnitude” and guessed that “disaster relief funds are closer to the less-cost-effective end of the range.” This appears to have hardened over time into a “default position.” This isn’t just because of that old post, but because GiveWell’s current research still reinforces this gap. Their top-tier picks are consistently modeled at ~$4,000-$5,500 per life saved [2], while their current minimum bar for new programs is “10x cash” (i.e., one order of magnitude) [3]. To my knowledge disaster response has never been recommended; their current page on the topic still states it “may not be the ideal cause,” effectively placing the entire category (by default) below the 10x cash threshold [4]. But as you’ve correctly pointed out, this heuristic is flawed because it lumps all disaster relief into one category. I agree that even basic cash transfers targeted to Sudan’s crisis-affected populations are likely much more cost-effective than the old heuristic suggests—your GiveDirectly comparison is well-taken. But my argument focuses on irreplaceable service delivery like MSF, where the calculation may be even more stark. In a total system collapse, the CE calculation changes. The “tourniquet” value of an org like MSF is not “Cost of MSF” vs. “Cost of Malaria Net.” It’s “Cost of MSF” vs. “very high preventable mortality” for that specific cohort (e.g., cholera patients, surgical trauma victims) because the service is the only thing preventing the next cascade. So, I fully agree. The 1-2 OOM gap is a flawed heuristic to apply here, and it’s highly plausible that this specific, targeted “tourniquet” intervention is dramatically more cost-effective than the old model suggests. The primary cost-effectiveness gap that remains is likely one of operational delivery in a warzone, not a lack of impact-per-dollar received by the beneficiary. The data would tell more of the story, but accessing high-quality data in a crisis is hugely challenging. Thanks for adding that critical distinction. It’s a more accurate way to frame the problem. [1] GiveWell. (2010). “Can choosing the right charity double your impact?” https://blog.givewell.org/2010/01/28/can-choosing-the-right-charity-double-your-impact/ [2] GiveWell. (2025). “Our Top Charities.” https://www.givewell.org/charities/top-charities [3] GiveWell. (2024). “GiveWell’s Cost-Effectiveness Analyses.” https://www.givewell.org/how-we-work/our-criteria/cost-effectiveness/cost-effectiveness-models [4] GiveWell. “Disaster relief charities.” https://www.givewell.org/international/disaster-relief
For crises as severe and as underfunded as Sudan, I would question that (especially the ~2 OOM part). If I remember correctly, GiveDirectly-style cash is perhaps ~1 OOM off on cost-per-life-saved (without considering non-mortality benefits) even without a targeted population. It seems likely that an intervention like this would equal or likely beat GiveDirectly on that metric due to the particularly dire situation which people are facing.
That’s an excellent point, and you’re absolutely right to question that “1-2 OOM” heuristic. I agree with you, and I think your insight gets to the core flaw in that “default” assumption (which I was also challenging).
That heuristic seems to originate from a (now quite old) 2010 GiveWell blog post, “Can choosing the right charity double your impact?” [1]. In it, they made the case that the total range of charity effectiveness “can easily vary by 2-3 orders of magnitude” and guessed that “disaster relief funds are closer to the less-cost-effective end of the range.”
This appears to have hardened over time into a “default position.” This isn’t just because of that old post, but because GiveWell’s current research still reinforces this gap. Their top-tier picks are consistently modeled at ~$4,000-$5,500 per life saved [2], while their current minimum bar for new programs is “10x cash” (i.e., one order of magnitude) [3]. To my knowledge disaster response has never been recommended; their current page on the topic still states it “may not be the ideal cause,” effectively placing the entire category (by default) below the 10x cash threshold [4].
But as you’ve correctly pointed out, this heuristic is flawed because it lumps all disaster relief into one category.
I agree that even basic cash transfers targeted to Sudan’s crisis-affected populations are likely much more cost-effective than the old heuristic suggests—your GiveDirectly comparison is well-taken. But my argument focuses on irreplaceable service delivery like MSF, where the calculation may be even more stark. In a total system collapse, the CE calculation changes. The “tourniquet” value of an org like MSF is not “Cost of MSF” vs. “Cost of Malaria Net.” It’s “Cost of MSF” vs. “very high preventable mortality” for that specific cohort (e.g., cholera patients, surgical trauma victims) because the service is the only thing preventing the next cascade.
So, I fully agree. The 1-2 OOM gap is a flawed heuristic to apply here, and it’s highly plausible that this specific, targeted “tourniquet” intervention is dramatically more cost-effective than the old model suggests. The primary cost-effectiveness gap that remains is likely one of operational delivery in a warzone, not a lack of impact-per-dollar received by the beneficiary. The data would tell more of the story, but accessing high-quality data in a crisis is hugely challenging.
Thanks for adding that critical distinction. It’s a more accurate way to frame the problem.
[1] GiveWell. (2010). “Can choosing the right charity double your impact?” https://blog.givewell.org/2010/01/28/can-choosing-the-right-charity-double-your-impact/
[2] GiveWell. (2025). “Our Top Charities.” https://www.givewell.org/charities/top-charities
[3] GiveWell. (2024). “GiveWell’s Cost-Effectiveness Analyses.” https://www.givewell.org/how-we-work/our-criteria/cost-effectiveness/cost-effectiveness-models
[4] GiveWell. “Disaster relief charities.” https://www.givewell.org/international/disaster-relief