The heritability of a trait can vary from 0.00 to 1.00, depending on the environments from which research participants are sampled. Because we know that genes play some role in the development of any trait, the precise heritability estimate doesn’t matter in a practical sense.

Heritability depends on the amount of variability in the environmental factors that contribute to a trait. The problem is that our understanding of the factors that contribute to the development of human traits in general — and to IQ in particular — is currently so deficient that we typically do not know if the environmental factors important in the development of a particular trait are stable across testing situations, vary somewhat across those situations, or vary wildly across those situations.

In his conclusion he states:

At the very least, heritability tells us how much of the variation in IQ can be accounted for by variation in genetic factors when development occurs in an exquisitely specific range of environments. However, David S. Moore has argued that even this is not significant when we realize that the magnitude of any heritability statistic reflects the extent of variation in unidentified non-genetic factors that contribute to the development of the trait in question.

(HT: Bryan Caplan)

Through his post, Kaufman constructs a series of paper tigers, tears them down and implies that because the extreme case does not hold, we should be wary of heritability estimates. I did not find much to disagree with in his examples, but the I differed on the conclusions we should draw.

So, where I do not agree – first, the heritability estimate does matter. While I don’t think it is hugely important whether the heritability of IQ in a specific sample is 0.5 or 0.6, it is important whether the measured heritability is 0 or 0.6. As Caplan notes in his post:

My money says, for example, that the average adult IQ heritability estimate published in 2020 will exceed .5.

I think that Caplan is right (although I might have stated some conditions about the relevant sample), and Kaufman’s argument overstates how finely tuned the environment needs to be to get a meaningful heritability estimate. Heritability estimates of a sample of children growing up in extreme poverty might be much lower (or zero) but as is found again and again, once the basic requirements of a child are met, heritability estimates for IQ are consistently above 0.4. We can construct arguments that in each study there are different gene-environment interactions and so on, but if genes weren’t important in variation in IQ and the gene-environment interactions weren’t consistent to some degree, why would such consistent heritability results (and correlation between parent and child IQ) be found?

Further, these results matter. They suggest that poverty is affecting the IQ of some children, and policies could be tailored to cut this disadvantage. For children not subject to deficient environments, the high heritability of IQ should influence policies such as those for education. Children are different and the education system should take this into account.

Implicit in Kaufman’s post was the “its all too complex” argument.  Social and biological sciences are complex (which is why I find them interesting). However, if we fully accepted Kaufman’s argument that “our understanding of the factors that contribute to the development of human traits … is currently so deficient that we typically do not know if the environmental factors important in the development of a particular trait are stable across testing situations”, it would put into question most of the data analysis in economics, sociology and biology. Econometrics operates on the idea of all other things being equal.

Fortunately, Kaufman has not taken the Gladwell-esque approach of suggesting that we forget about genetic factors. Kaufman suggests further research into how nature and nurture are intertwined. If it is all too complex, we should start unwinding the complexity. However, I believe that, in the meantime, this complexity does not mean that we should throw out all the results that have previously been obtained.