If you believe donating to the most cost-effective organisation (at the margin) is over 100 times as cost-effective as donating to the one you work for, and that you are 10 % more cost-effective than the 2nd best candidate for your job, you can have more impact through donations than through work donating 0.1 % (= 0.1/100) more of your gross income to the most cost-effective organisation, and more than 100 (= 0.1*100/0.1) times as much impact through donations as through work donating 10 %. In this case, increasing your donations to the most cost-effectiveness organisation by x % would be 100 times as impactful as increasing the cost-effectiveness of your work by x %, which suggests focussing on donating more and better.
I think the most cost-effective organisation is over 100 times as cost-effective as the vast majority of impact-focussed organisations. My top candidates for the most cost-effective organisation are, ordered alphabetically, the Arthropoda Foundation, Shrimp Welfare Project (SWP), and Wild Animal Initiative (WAI). I believe any of these is:
- More than 100 times as cost-effective as the most cost-effective organisations in human welfare, including not only global health and development, but also global catastrophic risk. I estimate:
- The Shrimp Welfare Project (SWP) has been:
- 64.3 k times as cost-effective as GiveWell’s top charities (neglecting the effects of these on animals), which are often considered the best helping humans.
- 19.6 k (= 64.3*10^3/3.28) times as cost-effective as Founders Pledge’s Climate Fund, which is often considered the best option to decrease the harm caused by global warming to humans.
- 265 k (= 64.3*10^3*4.12) times as cost-effective as epidemic/pandemic preparedness.
- 1.43 M times (= 64.3*10^3/0.045) as cost-effective as research and development (R&D) if this did not increase SWP’s funding, since Open Philanthropy (OP) estimated it is 4.5 % as cost-effective as GiveWell’s top charities. SWP received 1.82 M 2023-$ (= 1.47*10^6*1.24) during the year ended on 31 March 2024, which is 1.72*10^-8 (= 1.82*10^6/(106*10^12)) of the gross world product (GWP) in 2023, and OP estimated R&D has a benefit-to-cost ratio of 45. So I estimate SWP is 1.29 M (= 1/(1.72*10^-8)/45) times as cost-effective as R&D due to this increasing SWP’s funding.
- Paying farmers to use more humane pesticides would be 23.7 k times as cost-effective as GiveWell’s top charities.
- I guess that research on and advocacy for more humane pesticides would be way more cost-effective than paying farmers to use them more.
- WAI has supported research on pesticides.
- Possibly more than 100 times as cost-effective as ones in animal welfare which are often considered to be among the most cost-effective. I estimate:
- SWP has been 139 times as cost-effective as cage-free campaigns.
- Paying farmers to use more humane pesticides would be 51.4 times as cost-effective as cage-free corporate campaigns helping hens, and guess that research on and advocacy for more humane pesticides would be way more cost-effective than paying farmers to use them more.
So I conclude increasing the impact of donations via donating more and better is the best strategy to maximise impact for the vast majority of people working in impact-focussed organisations.
You may disagree with my estimates which suggest prioritising animal welfare. However, I would say increasing the impact of donations is also the best strategy to maximise impact for (random) people working in the area they consider most cost-effective. Benjamin Todd thinks “it’s defensible to say that the best of all interventions in an area are about 10 times more effective than [as effective as] the mean, and perhaps as much as 100 times”, which is in agreement with the cost-effectiveness of interventions following a heavy-tailed distribution. If so, jobs were uniformly distributed across interventions, and a person in a random job within an area were 10 % more cost-effective than the 2nd best candidate for their job, them donating 10 % more of their gross salary to the best interventions in the area could have 10 (= 0.1*10/0.1) to 100 (= 0.1*100/0.1) times as much impact through donations as through work. In reality, I assume there are more jobs in less cost-effective interventions, as the best interventions only account for a small fraction of the overall funding. Based on Ben’s numbers, if there are 10 times as many people in jobs as cost-effective as a random one as in the most-effective jobs, a person in a random job within an area who is 10 % more cost-effective than the 2nd best candidate for their job, and donates 10 % more of their gross salary to the best interventions in the area is 100 (= 10*10) to 1.00 k (= 100*10) times as impactful as a person in the same job not donating.
I think you are conflating your specific cause prioritisation and a general question of how people who care about impact should think. If someone held your course prioritisation then they should clearly work at one of those top organisations, otherwise help with the issues, or earn the highest salary they can and donate that. I.E earning to give. Working at other impact-focused organisations not focused on those top causes wouldn't make sense. I think that generally you should optimise for one thing rather than half-hardly optimising for several.
However, many people do not share your cause participation which leads to quite different conclusions. I have no regrets about doing direct work myself
Thanks for the comment, Neel! I would say increasing the impact of donations is also the best strategy to maximise impact for (random) people working in the area they consider most cost-effective:
Your maths needs a term for the conversion between money and labour for the company. I think your current equation assumes 1 to 1 which seems patently false
Thanks, Neel. I am assuming the marginal cost-effectiveness of spending on capital and labour is the same. Organisations should move money from the least to the most cost-effective activities until the marginal cost-effectiveness of all activities is equalised. I understand organisations do not manage their resources perfectly. However, for one to argue against my assumption, one would need specific arguments about why, for example, AI safety organisations are under or overspending on compute, or are under or overpaying their employees.