Wow, I’m surprised at how off the numbers are for the charity multipliers and the cost to save lives. Do you have any explanations for how they could be so divergent from EA views? You mention they’re highly specialized, but I’m still surprised if their answers mirror the public’s guesses more than EA’s
If I wanted to be charitable to their answer of the cost of saving a life I’d point out that $5000 is roughly the cost of saving a life reliably and at scale. If you relax any of those conditions, saving a life might be cheaper (e.g. Givewell sometimes finances opportunities more cost-effective than AMF, or perhaps you’re optimistic about some highly leveraged interventions like political advocacy). However, I wouldn’t bet that this phenomenon would be behind a significant fraction of the divergence of their answers.
I think that’s fair (see also, footnote 2). Fwiw this was the actual question: “Consider a charity whose programs are among the most cost-effective ways of saving the lives of children. In other words, thinking across all charities that currently exist, this one can save a child’s life for the smallest amount of money.
Roughly what do you think is the minimum amount of money that you would have to donate to this charity in order to expect that your money has saved the life of one child?”
Although many IDev professors (estimate: ~70%) are likely just poorly calibrated, and have no incentives to look into the cost-effectiveness of interventions, many who do know about CEAs might underestimate.
For “the cost to save the life of a child” question, an IDev policy expert might take a different perspective. In my IDev masters, one prof in his 70s explained that, if you’ve already paid the fixed costs of getting into the decision making process, it’s very often possible to find low-hanging fruit policy changes that save more lives and cost less money (bottom right quadrant in the picture below, taken from one of his classes).
Wow, I’m surprised at how off the numbers are for the charity multipliers and the cost to save lives. Do you have any explanations for how they could be so divergent from EA views? You mention they’re highly specialized, but I’m still surprised if their answers mirror the public’s guesses more than EA’s
If I wanted to be charitable to their answer of the cost of saving a life I’d point out that $5000 is roughly the cost of saving a life reliably and at scale. If you relax any of those conditions, saving a life might be cheaper (e.g. Givewell sometimes finances opportunities more cost-effective than AMF, or perhaps you’re optimistic about some highly leveraged interventions like political advocacy). However, I wouldn’t bet that this phenomenon would be behind a significant fraction of the divergence of their answers.
I think that’s fair (see also, footnote 2). Fwiw this was the actual question: “Consider a charity whose programs are among the most cost-effective ways of saving the lives of children. In other words, thinking across all charities that currently exist, this one can save a child’s life for the smallest amount of money.
Roughly what do you think is the minimum amount of money that you would have to donate to this charity in order to expect that your money has saved the life of one child?”
Although many IDev professors (estimate: ~70%) are likely just poorly calibrated, and have no incentives to look into the cost-effectiveness of interventions, many who do know about CEAs might underestimate.
For “the cost to save the life of a child” question, an IDev policy expert might take a different perspective. In my IDev masters, one prof in his 70s explained that, if you’ve already paid the fixed costs of getting into the decision making process, it’s very often possible to find low-hanging fruit policy changes that save more lives and cost less money (bottom right quadrant in the picture below, taken from one of his classes).