I’m afraid I don’t know of great sources for the numbers you list. They may also only exist for the distribution of compute. Perhaps the numbers on the EA community are too uncertain and dynamic to be a good fit for Anki anyway. On the other hand, it may be mainly the order of magnitude that is interesting, and it should be possible to get this right using crude proxies.
One proxy for the size of the EA community could be the number of EA survey respondents (or perhaps one above a certain engagement level).
On the other points:
For the Great Decoupling you could use “total growth of US labor productivity since 1980” together with “total growth of median household income since 1980″ (or both up to some recent year for which data is available). And the same for labor productivity vs. number of jobs since 2000. See, for instance, the graph here. You could also use the graph itself as an answer.
For changes in the distribution of world income, you could just use the two graphs in this article as answers (the ‘elephant graph’ is the one for 1988-2008, and there is also a newer one for 2008-2013/14). You could also extract some key numbers from these graphs, or some other statistics. E.g., the article provides the change of the Gini coefficient of the world income distribution, but this may have the downside that it’s hard to interpret:
As measured by the Gini coefficient, which ranges from zero (a hypothetical situation in which every person has the same income) to one (a hypothetical situation in which one person receives all income), global inequality fell from 0.70 in 1988 to 0.67 in 2008 and then further to 0.62 in 2013. There has probably never been an individual country with a Gini coefficient as high as 0.70, while a Gini coefficient of around 0.62 is akin to the inequality levels that are found today in Honduras, Namibia, and South Africa. (Loosely speaking, South Africa represents the best proxy for the inequality of the entire world.)
For the heavy-tailedness of various distributions I’d use the share of, e.g., the top 10% and 1% in the total.
Thanks! It hadn’t occurred to me to use the graph as the figure, but that’s a good idea. On reflection, we could perhaps use “image occlusion” for this or other questions.
We’ve now turned most of these into Anki cards, but I’d appreciate pointers to reliable sources or estimates for the following:
Net present value of expected total EA-aligned capital by cause area/worldview
Number of people working on certain cause areas such as AI safety, GCBR reduction, nuclear security, …
How much total compute there is, and how it’s distributed (e.g. supercomputers vs. gaming consoles vs. personal computers vs. …)
How much EAs should discount future financial resources
Size of the EA community
For others, I have the relevant information (or know where to find it), but am not sure what numbers should be used to express it:
The ‘Great Decoupling’ of labor productivity from jobs + wages in the US
Some key stats about the distribution of world income and how it has changed, e.g., Milanovic’s “elephant graph” and follow-ups
Some key stats about impact distributions where we have them, e.g., on how heavy-tailed the DCP2 global health cost-effectiveness numbers are
(This is addressed to anyone in a position to help, not just to Max. Thanks.)
I’m afraid I don’t know of great sources for the numbers you list. They may also only exist for the distribution of compute. Perhaps the numbers on the EA community are too uncertain and dynamic to be a good fit for Anki anyway. On the other hand, it may be mainly the order of magnitude that is interesting, and it should be possible to get this right using crude proxies.
One proxy for the size of the EA community could be the number of EA survey respondents (or perhaps one above a certain engagement level).
On the other points:
For the Great Decoupling you could use “total growth of US labor productivity since 1980” together with “total growth of median household income since 1980″ (or both up to some recent year for which data is available). And the same for labor productivity vs. number of jobs since 2000. See, for instance, the graph here. You could also use the graph itself as an answer.
For changes in the distribution of world income, you could just use the two graphs in this article as answers (the ‘elephant graph’ is the one for 1988-2008, and there is also a newer one for 2008-2013/14). You could also extract some key numbers from these graphs, or some other statistics. E.g., the article provides the change of the Gini coefficient of the world income distribution, but this may have the downside that it’s hard to interpret:
For the heavy-tailedness of various distributions I’d use the share of, e.g., the top 10% and 1% in the total.
Thanks! It hadn’t occurred to me to use the graph as the figure, but that’s a good idea. On reflection, we could perhaps use “image occlusion” for this or other questions.
Amazing, thank you so much!