The Schistosomiasis Control Initiative (SCI) told us earlier this year that it had received a donation of praziquantel (the more expensive of the drugs it uses for deworming) from World Vision. Since it already has the drugs, donations can pay just for delivery costs. Thus, SCI observed, your gift is “leveraged” – $1 in donations buys more than $1 in health programming, since the drugs are essentially “free.”

The Against Malaria Foundation (AMF) in some ways achieves leverage through the opposite strategy. It doesn’t pay for the costs of distributing nets or even shipping nets – it only pays for the nets themselves. Partners are charged with raising funding elsewhere for shipping and distribution (though they are required to do these things; nets aren’t granted without a distribution plan in place). By adhering to this policy, AMF puts other funders – including some government agencies – in a similar situation to SCI’s donors: the nets are essentially “free,” so $1 from these agencies buys far more than $1 in health programming. Because of this dynamic, these other funders (AMF argues, correctly from what we can see) are happy to provide this funding – so the original donor’s gift is “leveraged” too (by buying just a net, the donor gets “free” shipping/distribution costs).

How far could this logic be extended? What if a charity first raised money for travel expenses, then went to another donor and raised money for field staff arguing that “travel expenses are essentially free,” then went to another donor and raised money for supplies arguing that “field staff and travel expenses are essentially free,” etc.? What if a charity went to 50 million separate donors, asking each to give $1 contingent on each of the other 49,999,999 giving as well, arguing that this created 50,000,000:1 leverage for each of the $1 donors?

We think that much of our discussion of donation matching applies here. If you take claims of “leverage” literally, you’re allowing other funders – the ones who gave pills, or nets, or covered distribution costs – to influence your giving merely through the structure of their gift. That in turn creates incentives for them to take gifts they would have made anyway, and structure them in a way that gets you to give more to the program of their choice. In fact, these other donors may even be taking advantage of you to pay for costs they would have been happy to cover themselves. Perhaps World Vision would pay for distribution, but knows that by giving “only pills” it can get others to pay for distribution. Perhaps AMF would pay for distribution if it had to, and doesn’t only because it believes it can get others to.

Understanding the true nature of leverage – who is the “leverager” and who is the “leveragee” – is difficult. When someone else is giving contingent on your giving, and this fact in turn influences your giving … sometimes this means that you’re getting them to give more (or differently) than they would have otherwise (in which case you’re the “leverager”) and sometimes it means the reverse (in which case you’re the “leveragee”). We don’t know of any easy/reliable way to tell which situation you’re looking at, and when.

With this in mind, our general principles for considering leverage are:

• You can’t take “leverage factors” at face value, for largely the same reasons that you can’t take donation matches at face value.
• It’s probably a good thing when the charity you support is engaging in some sort of “leveraging.” This means it’s putting thought into getting your funds to influence others’ funds. If you believe that you’re supporting the most impactful charity possible, this means that others’ funds may be moving from less impactful activities to more impactful activities. (Though the situation isn’t this simple: we believe that governments and other large funders often have a much better array of options for impactful giving than the individual donors we serve.)
• The exact relationship between the claimed “leverage” and your actual impact is very non-straightforward. It may seem that you have more “leverage” when your $1 causes someone else to spend $9, as opposed to when it gets someone else to spend 10c. However, in the former situation, there’s a much higher probability that you are in fact the “leveragee” – that the other funder would cover the other 10% themselves if it weren’t for you, and you’re just saving them money. In general, it seems to us that the higher your claimed “leverage,” the greater the probability that someone else is in fact leveraging you.

  If anything, we would guess that your true impact is higher when the “leverage” is well under 100% (i.e., the other funder is giving less than $1 for each $1 you give), implying that the other party is likely providing funding because of the “leverage” they attain rather than because they would want to fund the project on their own. Once you have another major funder covering a major chunk of the costs, it becomes more important to ask whether they would be willing to cover all of them if the “leverage” from you and similar donors weren’t available.

When we do cost-effectiveness estimates (e.g., “cost per life saved”) we consider all expenses from all sources, not just funding provided by GiveWell donors. For SCI, we count both drug and delivery costs, even when drugs are donated. (Generally, we try to count all donated goods and services at market value, i.e., the price the donor could have sold them for instead of donating them.) For AMF, we count net costs and distribution costs, even though AMF pays only for the former. In the case of VillageReach, we even count government costs of delivering vaccines, even though VillageReach works exclusively to improve the efficiency of the delivery system.

We consider this approach the simplest approach to dealing with the issues discussed here, and given our limited understanding of how “leverage” works, we believe that this approach minimizes the error in our estimates that might come from misreading the “leverage” situation. As our understanding of “leverage” improves, we may approach our cost-effectiveness estimates differently.