Heritabilities Are Meaningful and Important By Bryan Caplan

The punchline of most twin and adoption studies is an estimate of a trait’s “heritability.” Heritability, usually written h2, is the fraction of variance explained by heredity. Twin and adoption studies also estimate the fraction of a trait’s variance explained by shared family environment, usually written c2. Literally hundreds of published papers go to great lengths to estimate h2 and c2.

Given the effort researchers apply to measure these variances, you’d think they were measuring something worth knowing. But the more technically proficient critics of behavioral genetics often say otherwise. Latest example: In the Huffington Post, Scott Barry Kaufman tells us that “The Actual Heritability Value Simply Does Not Matter.” Kaufman’s main argument:

[O]ur 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.

If we took Kaufman’s argument seriously, it wouldn’t just discredit twin and adoption research; it would discredit all of social science. Given any social science result – or experiment! – you can always say, “Maybe the world changes so chaotically that your results are no longer valid.” The only reply to such sweeping agnosticism is to stop talking and bet. My money says, for example, that the average adult IQ heritability estimate published in 2020 will exceed .5. If Kaufman’s isn’t convinced, I’m happy to take his money.

To be fair, though, there’s a narrower and more plausible complaint about heritability estimates: They’re an answer to a question that no one asked. Who cares if “heredity explains 80% of the variance of adult IQ”? What does that mean in English? I doubt even most practicing researchers have a compelling answer to this challenge.

Nevertheless, a compelling answer does exist. If you delve deeply into the math of nature and nurture, you’ll learn two striking results:

1. The expected correlation between identical twins raised apart equals h2.

2. The expected correlation between unrelated individuals raised together equals c2.

If you set up your study correctly, these are causal estimates that speak directly to the nature/nurture debate. Suppose h2=.4. Then if you were separated at birth from an identical twin, and you’re 1 SD above average in a trait, you should still expect your twin to be .4 SDs above average in that trait. Why? Because of your shared genes. Or suppose c2=.1. Then if you were raised with a random baby, and you’re 1 SD above average in a trait, you should expect him to be only .1 SDs above average in that trait. Why? Because of your common upbringing.

Admittedly, these conclusions don’t tell you which genes or which aspects of upbringing are responsible for these correlations. But they do measure something both meaningful and important: the overall causal effects of genes and upbringing. h2 and c2 aren’t everything we’d like to know about nature and nurture. Yet they’re a huge advance over the preceding three thousand years of fruitless debate.