Here’s how the above “risk aversion” would translate to charity: the risk is not financial loss, but donating to a charity that uses that donation in an inefficient way. So risk aversion in the above described context is not avoiding financial loss, but avoiding utility loss.
The problem with mutual funds in the for-profit domain is that those mostly underperform index funds. General market indexes select the N best-performing companies—like 500 out of tens of thousands. This would translate to charity as selecting the N best-performing” charities, measured in something like “delivered utility per dollar”. So let’s select the 500 most cost-efficient charities, out of tens of thousands.
The selection process is still the hand-picking part, and that’s an unavoidable part of donating, as we need to find out which charities are cost-efficient (we don’t have the market efficiency clue as you pointed out). What I’m arguing for is then to have a broad selection of the hand-picked ones, and to construct a weighting among them, based on their properties. There exists an optimal distribution of donations, and we’re very unlikely to find it by hand-picking alone. The for-profit market analogy shows that using the right statistical methods for finding the distribution (among the selected) is very likely superior, and the broader the sample (again, among the selected), the better the results will be.
Here’s how the above “risk aversion” would translate to charity: the risk is not financial loss, but donating to a charity that uses that donation in an inefficient way. So risk aversion in the above described context is not avoiding financial loss, but avoiding utility loss.
The problem with mutual funds in the for-profit domain is that those mostly underperform index funds. General market indexes select the N best-performing companies—like 500 out of tens of thousands. This would translate to charity as selecting the N best-performing” charities, measured in something like “delivered utility per dollar”. So let’s select the 500 most cost-efficient charities, out of tens of thousands.
The selection process is still the hand-picking part, and that’s an unavoidable part of donating, as we need to find out which charities are cost-efficient (we don’t have the market efficiency clue as you pointed out). What I’m arguing for is then to have a broad selection of the hand-picked ones, and to construct a weighting among them, based on their properties. There exists an optimal distribution of donations, and we’re very unlikely to find it by hand-picking alone. The for-profit market analogy shows that using the right statistical methods for finding the distribution (among the selected) is very likely superior, and the broader the sample (again, among the selected), the better the results will be.
Mutual funds underperform in an environment where arbitrage exists and prices are at least close to efficient.
Indices don’t select the “best performing” companies, they usually select the “biggest” companies. Here the analogy to the charity world breaks.