Great Minds and Hive Minds

Scott Alexander on Garett Jones’ book:

Hive Mind‘s “central paradox” is why IQ has very little predictive power among individuals, but very high predictive power among nations. Jones’ answer is [long complicated theory of social cooperation]. Why not just “signal-to-noise ratio gets higher as sample size increases”?

Me:

Can we rule out statistical artifact? Put it this way. Suppose we chose 1000 people at random. Then we create 50 groups of them. Group 1 has the 20 lowest IQ scores. Group 2 had the next 20 lowest IQ scores, etc. Then we run a regression of group average income on group average IQ for this sample of 50 groups. My prediction is that the correlation would be much higher than you would get if you just took the original sample of 1000 and did a correlation of IQ and income. I think that this is because grouped data will filter out noise well. Perhaps the stronger correlation among national averages is just a result of using (crudely) grouped data.

Questions for Garett Jones

After a quick reading of Hive Mind. The core issue is what he calls the paradox of IQ. That is, among individuals, the correlation between IQ and income is modest. However, among nations, the correlation between average IQ and average income is strong.

How does your high IQ raise my income? Think of four possible explanations for this paradox.

1. Statistical artifact.
2. Proximity effect–I earn more income by living close to people with high IQ’s.
3. Cultural effect–people with high IQ’s transmit good cultural traits to me.
4. Political effect–having people with high IQ in my jurisdiction leads to me enjoying better government.

Can we rule out statistical artifact? Put it this way. Suppose we chose 1000 people at random. Then we create 50 groups of them. Group 1 has the 20 lowest IQ scores. Group 2 had the next 20 lowest IQ scores, etc. Then we run a regression of group average income on group average IQ for this sample of 50 groups. My prediction is that the correlation would be much higher than you would get if you just took the original sample of 1000 and did a correlation of IQ and income. I think that this is because grouped data will filter out noise well. Perhaps the stronger correlation among national averages is just a result of using (crudely) grouped data.

Can we sort out between proximity effects, cultural effects, and political effects? Perhaps a natural experiment involving people from different cultures living moving to different jurisdictions, or people living close to one another but having different cultures?

The most parsimonious proximity effect could be capital per worker. Assume that people tend to invest close to home (Jones calls this the Feldstein-Horioka effect when it applies across countries). Then if high-IQ people invest more wisely, then I will have better capital to work with if I live close to high-IQ people. Or if high-IQ people invest more (because, as Jones points out, they are more patient), then I will have more capital to work with if I live close to high-IQ people. How well does capital per worker serve as a channel for transmitting someone else’s IQ to my income?

Another proximity effect would be strong complementarity in team production (what Jones, following Kremer, calls the O-Ring effect). If the value of my output depends on the value of others in a team, then I will be better off living close to people with high IQ’s.

What happens when you divide the U.S. into fifty states and put teach state into the database with other countries? My guess is that Mississippi will look really good on average income relative to average IQ when you compare it with Denmark. If so, is that because of high capital per worker in Mississippi? A higher trust culture? Or better overall governance than Denmark?