It all depends what numbers you use, if you take the more speculative numbers for our counterfactual cost but the harder numbers for our money moved than you can get a negative ratio. Although this way of breaking it down is unintuitive to me. As I mentioned depending on what numbers you chose to use our ratios can change âbetween 1:11 Charity Science lifetime returns to 1:0.5â.
Although this way of breaking it down is unintuitive to me.
I disagree with this actually.
If youâre relatively skeptical, then you should include all the âsoftâ estimates of costs, but only include the âhardâ estimates of benefits. Thatâs the most skeptical treatment.
You seem to be saying that you should only compare hard numbers to hard numbers, or soft numbers to soft numbers. Only including hard estimates of costs underestimates your costs, which is exactly what you want to avoid if youâre trying to make a solid estimate of cost-effectiveness.
Concrete example: Bill Gates goes to volunteer at a soup kitchen. The hard estimate of costs is zero, because Gates wasnât paid anything. Thereâs a small hard benefit though, so if you only compare hard to hard, this looks like a good thing to do.
But thatâs wrong. Thereâs a huge âsoftâ cost of Gates working at the kitchenâthe opportunity cost of his time which could be used doing more research on where the foundation spends its money or convincing another billionaire to take the pledge.
Interestingly, if you do the same pessimistic calculation for GWWC, youâll still get a ratio of something like 6:1 or 4:1.
I donât think GWWCâs staff opportunity costs are more than 50% of their financial costs, and very unlikely more than 100%, at least if you measure them in the same way: money the staff would have donated otherwise if theyâd not worked at GWWC.
Or if you apply a harsher counterfactual adjustment to GWWC, you might drop to 3:1 or 2:1. But I think itâs pretty hard to go negative. (And thatâs ignoring the future value of pledges, which seems very pessimistic, given that itâs a lifetime public pledge).
It all depends what numbers you use, if you take the more speculative numbers for our counterfactual cost but the harder numbers for our money moved than you can get a negative ratio. Although this way of breaking it down is unintuitive to me. As I mentioned depending on what numbers you chose to use our ratios can change âbetween 1:11 Charity Science lifetime returns to 1:0.5â.
I disagree with this actually.
If youâre relatively skeptical, then you should include all the âsoftâ estimates of costs, but only include the âhardâ estimates of benefits. Thatâs the most skeptical treatment.
You seem to be saying that you should only compare hard numbers to hard numbers, or soft numbers to soft numbers. Only including hard estimates of costs underestimates your costs, which is exactly what you want to avoid if youâre trying to make a solid estimate of cost-effectiveness.
Concrete example: Bill Gates goes to volunteer at a soup kitchen. The hard estimate of costs is zero, because Gates wasnât paid anything. Thereâs a small hard benefit though, so if you only compare hard to hard, this looks like a good thing to do. But thatâs wrong. Thereâs a huge âsoftâ cost of Gates working at the kitchenâthe opportunity cost of his time which could be used doing more research on where the foundation spends its money or convincing another billionaire to take the pledge.
Interestingly, if you do the same pessimistic calculation for GWWC, youâll still get a ratio of something like 6:1 or 4:1.
I donât think GWWCâs staff opportunity costs are more than 50% of their financial costs, and very unlikely more than 100%, at least if you measure them in the same way: money the staff would have donated otherwise if theyâd not worked at GWWC.
Or if you apply a harsher counterfactual adjustment to GWWC, you might drop to 3:1 or 2:1. But I think itâs pretty hard to go negative. (And thatâs ignoring the future value of pledges, which seems very pessimistic, given that itâs a lifetime public pledge).