Many thanks for the blog post, James. I completely agree with you that here intelligence is the engine and adult SES is the conveyor belt that takes things forward, as you aptly put it. I included SES as a covariate to assess how much of the migration patterns associated with IQ were due to the fact that people with higher IQ pursue and achieve higher education, which is linked to moving to more urban areas.

I used the word "attenuate" only in a statistical sense—the B coefficients get smaller—and I was only interested in mediation effects of SES, not any “confounding” effects of SES. At least for social scientists reading the paper, I thought it would be necessary to show how SES comes into play in the association, even though IQ was assessed in young adulthood. So adjusting for SES does not take away any explanatory power from IQ that was measured many years before SES.

But I do see that the adjusted models may not come across the way I intended. Some readers might erroneously conclude that only SES matters. I hope most the readers don’t interpret the results this way! The unadjusted models are the more interesting ones, the adjusted models only elaborate the social processes by which IQ becomes associated with migration patterns.

It’s true that the analysis only looked at average IQ levels of migrants and non-migrants (instead of the migration probabilities of individuals with different IQ levels). I thought presenting the average IQ of these groups would be the most intuitive way to present the data, especially when you consider the analysis from a demographic point of view of population dynamics. The data could have been presented the other way around, but this would have complicated the analysis of the interaction effects between origin and destination. I kind of had a similar reaction to the differences as Steve Sailer in his comment—not as large as you might have expected. But the population-level effects might still be non-trivial.

I had a quick look at the probabilities of moving by IQ deciles by first categorizing IQ into 9 groups in the total population (1= below 20 percentile, 9=above 90 percentile) and using this grouping as a categorical variable in a multinomial logistic regression predicting future residence. I included only participants who lived in a rural area at baseline. The probabilities of moving to a central city for these rural residents were:

6%, 7%, 9%, 9%, 10%, 12%, 11%, 14%, and 16%

in increasing order of IQ decile groups (i.e., 6% for those below 20 percentile, 16% for those above 90 percentile, allowing non-linear associations).

The same analysis showed that the probabilities of staying in a rural area were:

54%, 53%, 52%, 46%, 45%, 44%, 42%, 36%, and 35%

in increasing order of IQ decile groups. So about one-third of the people scoring in the top 10% of IQ distribution did not leave rural areas if they were living there as adolescents/young adults.

But migration is a complex outcome to study, because there are so many ways to operationalize it, and people move around back and forth. Not to mention differences between different subgroups. As always, more studies are needed!

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