Comments
My major concern: Making this less prominent further increases the 'gap in the market' for people who could be convinced to care about effectiveness, but are not eager to give in these particular categories. The GiveWell Top 9 charities do 6 things, and other than GiveDirectly all of these are health interventions.
Which is great.
But we all know many people and orgs that say
- 'we want to give to something education (or female, or food...) related'
- 'we already supported mosquito nets and vitamins, what else can we do' or
- 'we need to support an organization that has been around a long time (or is top-rated on Charity Navigator, or is associated with some country or religion)
To some extent, I agree this is misguided.
But at the moment these people often end up giving to something we know to much less effective (like giving science equipment to schools in New York, or supporting food banks in London).
I think we need a credible rating to give to people, to be able to discern between, e.g., the Fistula Foundation and St. Jude's, or between Donors Choose and Development Media International ... rather have these people donate to something we have a good reason to think is many orders of magnitude less efficient.
There is no place these people can go to. Impact Matters offered something in this direction, but they were taken over by Charity Navigator, and the integration does not look promising (at least not yet).
SoGive does a lot of things right, and Sanjay is EA-aligned, but it's reach is limited (UK focused) and it concentrates on outputs rather than impact, in a sense.
So I think we are missing the chance to positively influence a huge amount of funds if we don't have a 'real impact based charity rating' that
- includes a somewhat more diverse and mainstream list of charities,
- compares among charities that are not necessarily at the very top
- where the nature of the intervention is somewhat harder to asses (or the charity does more than one intervention, some of which may even be in the top categories)
While well-intended, I fear that de-emphasizing standout charities is a step away from this, in the wrong direction.
This line of reasoning seems sensible to me. However, it does raise the following question: will GiveWell also stop recommending GiveDirectly, given that, by your own cost-effectiveness numbers, it's 10-20x less cost-effective than basically all your other recommendations? And, if not, why not?
I can understand the importance of having some variety of options to recommend donors, which necessitates recommending some things that are worse than others, but 10x worse seems to leave quite a lot of value on the table. Hence, I'd be curious to hear the rationale.
They answered this in their own comments section.
I'll post Catherine's reply and then raise a couple of issues:
I don't see a justification here for keeping GiveDirectly in the list. Okay, there are charities GiveWell is 'confident' in, and those that they aren't, and GiveDirectly, like the other top picks, is in the first category. But this still raises the question of why to recommend GiveDirectly at all. Indeed, it's arguably more puzzling: if you think there's basically no chance A is better than B, why advocate for A? At least if you think A might be better than B, then you might defend recommending A on the grounds there's a chance, that is, if someone believes X, Y, Z they might sensibly believe it's better.
The other thing that puzzles me about this response is its seemingly non-standard approach to expected value reasoning. Suppose you can do G, which has a 100% chance of doing one 'unit' of good, or H, which has a 50% chance of doing 3 'units' of good. I say you should pick H because, in expectation, it's better, even though you're not sure it will be better.
Where might having less evidence fit into this?
One approach to dealing with different levels of evidence is to discount the 'naive' expected value of the intervention, that is, the one you get from taking the evidence at face value. Why and by how much should you discount your 'naive' estimate? Well, you reduce it to what you expect you would conclude its actual expected value was if you had better information. For instance, suppose one intervention has RCTs with much smaller samples, and you know that effect sizes tend to go down when interventions use larger samples (they are harder to implement at scale, etc.). Hence, you're justified in discounting it because and to that extent. Once you've done this, you have the 'sophisticated' expected values. Then you do the thing with the higher 'sophisticated' expected value.
Hence, I don't see why lower ('naive') cost-effectiveness should stop someone from recommending something.