As far as I know, all effective giving initiatives (EGIs) measure their cost-effectiveness in terms of multipliers representing how much they increase donations to the organisations and funds they deem cost-effective as a fraction of the EGI’s spending. The cost-effectiveness of EGIs would be proportional to those multipliers if all the organisations and funds they deem cost-effective were equally cost-effective, but I think this is far from true. I very much agree with what Giving What We Can (GWWC), arguably the most notable EGI, says in the section “What do we mean by “effective”?” of their recommendations page.
Not all charities are equal. Your choice of where to donate can lead to significant differences in impact.
Our research team estimates that you can often do 100x more good with your dollar by donating to the best charities, and sometimes this multiplier is even greater.
If this comes as a surprise, you’re not alone. Many donors vastly underestimate the difference between “good” and “great” charities, which explains why many of the best charities to donate to remain underfunded.
However, I believe the above holds not only in the broad charitable world, but also across EGIs’ recommendations if these cover animal and human welfare. I consider increasing donations to the best animal welfare interventions is way more valuable than to the best in human welfare (including not only global health and development, but also global catastrophic risk). I estimate:
Broiler welfare and cage-free campaigns are 168 and 462 times as cost-effective as GiveWell’s (GW’s) top charities, which are thought to be among the best human welfare interventions.
The Shrimp Welfare Project (SWP) has been 64.3 k times as cost-effective as GW’s top charities.
Paying farmers to use more humane pesticides would be 23.7 k times as cost-effective as GiveWell’s top charities, and guess that research on and advocacy for more humane pesticides would be way more cost-effective than paying farmers to use them more. The Wild Animal Initiative (WAI) does research on pesticides.
EGIs measure their impact in increased donations. For example, all the 5 EGIs incubated by Ambitious Impact (AIM) in 2024 set their targets this way. As a result, they risk focusing too much on what is popular, at least between cause areas. If the best animal welfare interventions are over 100 times as cost-effective as the best in human welfare, moving 1 $ to the former is more impactful than moving 100 $ to the latter, and therefore an EGI only has to increase donations to the former 1 % (= 1⁄100) as much as to the latter for animal welfare to be responsible for most of their impact.
It does not necessarily follow that EGIs should only recommend animal welfare interventions. There may be synergies in some cases. Broader recommendations will attract more donors, and some initially only interested in human welfare may end up donating to animal welfare. On the other hand, I think broader recommendations will disperse donors’ attention across many areas which are intuitively more appealing. All in all, I believe animal welfare should still be more prominently promoted by EGIs with recommendations in many areas.
In any case, it still makes sense to maximise impact instead of increased donations, even if the optimal offer covers lots of areas like that of Charity Navigator. So I encourage EGIs to be deliberate about how much they value increasing donations to their different recommendations, particularly across areas. I believe GWWC could lead by advocating for this in their impact evaluation toolkit[1]. In addition, I suggest EGIs not only track their multipliers, but also assess their cost-effectiveness in terms of DALYs averted per $, or another metric which facilitates comparisons with non-fundraising interventions.
I recommend people interested in donating to EGIs support FarmKind, which aims to increase donations to the best animal welfare interventions. I guess Animal Charity Evaluators (ACE) also increases donations to animal welfare by more than 1 $ per $ donated to them. However, I think their continuity is more secure, and that supporting FarmKind has a very high value of information.
I worry EGIs’ cause prioritisation is ultimately restricted by Dustin Moskovitz and Cari Tuna. These are the major funders of Open Philanthropy (OP), which in turn is the major funder of EGIs, and therefore sets their incentives. I encourage people at OP to be deliberate about differences in cost-effectiveness between cause areas instead of incentivising EGIs to simply increase donations to top interventions regardless of their areas.
Thanks to Lucas Moore for feedback on the draft. The views are my own.
Recurring issue I find with your posts is that you state expected values with huge uncertainty around them as if they’re certainties.
E.g. “The Shrimp Welfare Project (SWP) has been 64.3 k times as cost-effective as GW’s top charities”
These estimates are built on other estimates each having their own error bars, sometimes with some assumptions thrown in. These EVs are houses of cards that shouldn’t be taken very seriously at all.
While this is true, I also think it’s worth considering that this is often a criticism of any CEA, period. To the average person, the suggestion that a GiveWell top-recommended charity is more cost-effective than, say, a local food kitchen similarly requires estimates with error bars.
Yes, there are more assumptions when dealing with animals given welfare ranges, but I am reluctant to dismiss the analysis entirely because of that.
It is not immediately intuitive to me on what grounds one should value a human life more than that of a cow or pig. The moral weights project tries to put a number to something difficult to quantify. It’s not perfect, but it is better than nothing. (To be clear, I do value the human life more, but it seems unfair and speciest to do so.)
Jesse I don’t think your example is correct because did the GiveWell error bars don’t overlap with food kitchen ones. We can be 99+ percent sure malaria nets are more cost effective than soup kitchens. That just isn’t the case here. Comparing the certainly of human intervention effectiveness vs. animals is like chalk and cheese
I don’t agree with Henry that the huge error bars make RPs welfare ranges useless, probably because I value certainty a bit less than him. But I do think if we value certainly to any degree that can reasonably make us de-value animal welfare point estimates as RP demonstrate themselves in their moral parliament tool.
Thanks, Jesse.
I think it is fair to value human welfare more. Rethink priorities’ median welfare ranges are still lower than 1.
The Rethink Priorities Welfare Ranges have absurdly wide confidence intervals. So wide that I would argue they’re almost worthless.
I think it’s very relevant that animal welfare interventions look better than global health interventions almost everywhere within the RP intervals.
Alternative response: If someone told me that there was somewhere between a 0.00001 and 0.5 chance that I was to be struck by lightning tomorrow, it would not be reasonable for me to say “well almost everywhere within that confidence interval I have a >1% chance of being hit by lightning tomorrow”
Most of these CIs start at zero and they can’t go below zero so shouldn’t we consider these on a log scale? In which case the scale goes back to negative infinity and “almost everywhere within” is meaningless.
I don’t know of any reasonable justification for caring about expected log-welfare rather than expected welfare. For a welfare range estimate, the thing that matters is the expected value.
Agreed, Michael!
Right. I have been using Rethink Priorities’ (RP’s) median welfare ranges, but I care about expected welfare. RP thinks their median welfare ranges are a better proxy for the actual means than the means of the distributions they got, and I tend to agree.
I think this point is stronger than inferred from the graph because the 90 % confidence interval of the median is narrower than the range from the 5th to the 95th percentile.
Even if one takes the midpoint of the RP intervals as established fact, there are a lot of other assumptions Vasco’s arguments depend on, like the magnitude and duration of suffering a particular creature experiences with pain scales with thousands of points to cancel out the RP weights, and the cost-effectiveness of brand new charities in a field (campaigning) where marginal cost-effectiveness is relatively difficult to measure.
Unlike for RP we don’t have published estimates of distributions or confidence intervals for these, but if we did they’d also be extremely wide and I’m not sure that animal welfare interventions would look better across most of the distribution for them.
That argument is weak to me because you could take any intervention we are clueless about and it would look better than global health interventions within most of the interval. If our interval spans zero to close to infinity then global health interventions are going to be a speck near the bottom of that interval.
Hi Nick.
The overall effect of global health and development (GHD) interventions depends on effects of animals due to the meat-eating problem. So I think clueless about the benefits of helping animals implies cluelessness about whether GHD interventions are beneficial or harmful.
The cluelessness for GHD interventions would in that case be way more severe. It would go from minus to plus infinity instead of 0 to infinity. Less abstractly, one can be much more confident that humane slaughter interventions are beneficial than that saving human lives is beneficial. Humane slaughter interventions may have negligible benefits if the animals helped turn out to have a negligible welfare range, but it is very hard for them to be harmful in expectation, because they have minor effects on human- and animal-years. In contrast, saving human lives may well increase the animal-years with negative lives, thus potentially being harmful.
That might be true by your lights Vasco, but we are discussing a specific issue here (GiveWell vs. Animal Welfare confidence intervals) and I think its a bit disingenuous to bring adjacent arguments like the meat eating problem into this here.
If you’re clueless about an intervention and you use a fat-tailed prior, then the expected value might be very large but the median value will be very small, and most of the probability mass will be close to 0. For the RP welfare estimates, the median values make animal welfare interventions look highly effective.
Hi Henry! The reason why the intervals are so wide is because they’re mixing together several models. I’ve explained more about this modeling choice and result here: https://forum.effectivealtruism.org/posts/rLLRo9C4efeJMYWFM/welfare-ranges-per-calorie-consumption?commentId=Wc2xksAF3Ctmi4cXY
Hi Henry. That graph represents the welfare range distributions (minimum, 25th percentile, median, 75th percentile, and maximum), not the confidence intervals of the medians. I think these are what matters, and that they would be much narrower.
Hi Henry,
The evaluations of human welfare interventions neglect the uncertainty of welfare ranges too. By not considering effects on animals, they are implicitly assuming all non-human welfare ranges are equal to 0. For plausible welfare ranges, some grants from GiveWell (GW), and organisations incubated by Ambitious Impact (AIM) may be harmful. Lots of uncertainty about the benefits of helping animals translates into lots of uncertainty about whether saving human lives, which tends to increase the population of animals nearterm, is beneficial or harmful.
I strongly endorse expectational total hedonistic utilitarianism (increasing happiness, and decreasing suffering), but I think the takeaways are basically the same under desire theories, as beings want to be happy, and not suffer. For my conclusions to change significantly, I believe one has to strongly reject impartiality, or consider Rethink Priorities’ median welfare ranges dramatically overestimate animals’ capacity for welfare.
If these animal welfare analyses keep concluding that all human development has been net negative because of our terrible impact on animals, then, reductio ad absurdum, perhaps these analyses aren’t useful.
This reasoning should go in the basket of “hard to say it’s wrong but leads to impractical absurd conclusions” along with Ted Kaczynski’s manifesto and Antinatalism
I said “may be harmful”, but global health and development interventions may also be beneficial. My analysis looks into the nearterm effects, but one should also consider longterm effects, and even the nearterm ones are very uncertain.
Seems true assuming that your preferred conversion between human and animal lives/suffering is correct, but one can question those ranges. In particular, it seems likely to me that how much you should value animals is not an objective fact of life but a factor that varies across people.
Thanks, Nuño.
I think it is both an objective fact of life, and also a factor that varies across people. Similarly, for valuations of human welfare as a function of e.g. country of birth, there are objective biological differences between humans born in different countries (which I think have a negligible impact on their capacity for welfare), and also different levels of nationalism.
Agree. The factor on which we are weighting animals—their ability to feel—is objective, even if our assessment of it is uncertain (although it’s becoming increasingly certain).
One can disagree that this is the factor that we should be focusing on, but I’m yet to see such an argument which isn’t also speciesist.
While I generally share concerns about OP et al. having too much influence over the ecosystem, that doesn’t sound like what you’re describing here. Rather, it seems that you think OP et al. are implicitly deferring to judgments made of the end users of EGIs? It seems to me that would be the main practical effect of using donations to effective causes as a metric without scoring differently by cause area. And I think there are some pretty powerful structural reasons we would want OP et al. to broadly defer to EGI customers rather than attempt to use its influence over EGIs to shape the ecosystem in furtherance of any particular non-consensus cause prio.
More substantively: What is your model of potential EGI end users? My assumption is that most people are coming in with a cause area they are interested in, and need to feel that the EGI aligns fairly closely with their values in order to trust it. I am guessing the number of prospects who wish to offload their cause prio and moral judgment to an EGI is much lower. To the extent that an EGI was heavily promoting AW opportunities over others, I submit that it needs to be upfront about being an AW EGI. Anything else is going to come off as a bait-and-switch to most potential end users. The downside is that the catchment area (as it were) for AW EGIs may be much smaller than for multi-cause EGIs that take all comers and don’t clearly prefer some of their cause areas over others.
Thanks for the substantive answer, Jason.
Yes, I would agree OP and EGIs are effectively deferring too much to donors at the level of cause prioritisation. EGIs could have recommendations in many areas, but still present their views about which are more cost-effective, as 80,000 Hours does with respect to areas and careers.
There is significant agreement among people who have worked on cause prioritisation that the most cost-effective animal welfare organisations are more cost-effective at the margin than the most cost-effective human welfare organisations. I undersand this is far from consensual among the whole population, but the same applies to other considerations, although possibly less significantly. It is common for people to value local organisations way more than what would be justified to increase impact by tax benefits, because they value human welfare in their home country way more than in others. However, EGIs in high income countries mostly do not recommend organisations helping people there outside global catastrophic risk mitigation.
I agree that is a fair description of most people influenced by EGIs.
I agree, but I still think influencing that small fraction is relevant.
I agree EGIs should specialise more, and mostly across areas instead of countries. However, I would still like to see EGIs experimenting with ranking their areas, as 80,000 Hours does. As far as I know, none has tried, so there is high value of information. To minimise the risk of losing current donors, they could simply run surveys of potential future donors, or the general population, or just change the website for a small number of random visitors. Have you considered this, @David_Moss and @Luke Moore 🔸?
Animal welfare could be highlighted in subbtle ways. For example, GWWC lists recommendations in global health and development before ones in animal welfare, but these could appear 1st, which would be in agreement with ordering areas alphabetically.
To clarify the structural reasons to which I alluded—it’s already problematic for the community that OP et al. control so much of the available funding. Having mostly independent funding streams available is important, so having OP et al. putting a thumb on the scale to influence allocation of those independent streams poses significant costs in my book.
A necessary prerequisite for your proposal is that OP et al. move toward the cause prio you advocate. But such a movement would presumably cause OP’s own funding decisions to shift in a significant manner. OP et al. changing allocations of its own funds doesn’t pose the same problems to funding independence, and doesn’t require confronting the likelihood that EGI fundraising from outside sources is less cost-effective on a dollar basis in AW than in GHD.
It’s not clear to me whether the majority view that AW is currently underfunded would survive a significant reallocation in OP et al.’s allocation of its own funds. Even if it did, there would still be significant support in the community for the view that the marginal independent-funding dollar should go to GHD or GCR work (because there already is), and we’d still be facing the question of whether it was appropriate for OP et al. to be exercising pressure on this question.
Thanks Vasco! We’d be happy to run a survey or experiment on this topic if orgs think it would be useful to them.