The idea that iq predicts income, life expectancy, criminal justice record, etc. depends on what you mean by ‘predicts’ (eg conjunction fallacy). I and many others suggest these are correlations, and many argue instead things like income (of parents), social environment, etc predict iq, crime, health, etc. (of children, via a kind of markov process). (Also, if you look at income/iq correlations, I wouldn’t be surprised that it is quite different for different kinds of income—those who made money via IT or genomics, versus those who made it via Walmart, or sports. One may actually have a mixture distribution which only appears ‘normal’ because of sufficiently large size. )
The scatter plots are interesting, and remind me of S J Gould’s (widely criticized ) discussion of attempts to define G, a measure of general intelligence, using factor analyses. I think the general conclusion before the analyses is the right one—there are multiple factors. I would say many of the ‘smartest’ people (as measured by say, iq) end up in academic fields in math/science/technology rather than in business with the aim of making money. There are so many factors. Some academics later on do go into business, either working in finance or genomics industries, but many don’t.
One reason academic economics is criticized is because it follows the pattern of this post—it starts with general observations, comes up with tentative conclusions, and then goes into highly detailed, mathematical analyses which doesn’t really add much more insight, though its an interesting excercize.
The upvoters have spoken. Moving to Main and promoting.
Comments---
The idea that iq predicts income, life expectancy, criminal justice record, etc. depends on what you mean by ‘predicts’ (eg conjunction fallacy). I and many others suggest these are correlations, and many argue instead things like income (of parents), social environment, etc predict iq, crime, health, etc. (of children, via a kind of markov process). (Also, if you look at income/iq correlations, I wouldn’t be surprised that it is quite different for different kinds of income—those who made money via IT or genomics, versus those who made it via Walmart, or sports. One may actually have a mixture distribution which only appears ‘normal’ because of sufficiently large size. )
The scatter plots are interesting, and remind me of S J Gould’s (widely criticized ) discussion of attempts to define G, a measure of general intelligence, using factor analyses.
I think the general conclusion before the analyses is the right one—there are multiple factors. I would say many of the ‘smartest’ people (as measured by say, iq) end up in academic fields in math/science/technology rather than in business with the aim of making money. There are so many factors. Some academics later on do go into business, either working in finance or genomics industries, but many don’t. One reason academic economics is criticized is because it follows the pattern of this post—it starts with general observations, comes up with tentative conclusions, and then goes into highly detailed, mathematical analyses which doesn’t really add much more insight, though its an interesting excercize.