Greg Clark: Genetics and Social Mobility — Manifold Episode #14

 

Gregory Clark is Distinguished Professor of Economics at UC-Davis. He is an editor of the European Review of Economic History, chair of the steering committee of the All-UC Group in Economic History, and a Research Associate of the Center for Poverty Research at Davis. He was educated at Cambridge University and received a PhD from Harvard University. His areas of research are long-term economic growth, the wealth of nations, economic history, and social mobility. 

Steve and Greg discuss: 

0:00 Introduction 

2:31 Background in economics and genetics 

10:25 The role of genetics in determining social outcomes 

16:27 Measuring social status through marriage and occupation 

36:15 Assortative mating and the industrial revolution 

49:38 Criticisms of empirical data, engagement on genetics and economic history 

1:12:12 Heckman and Landerso study of social mobility in US vs Denmark 

1:24:32 Predicting cognitive traits 

1:33:26 Assortative mating and increase in population variance 

Links: 

For Whom the Bell Curve Tolls: A Lineage of 400,000 English Individuals 1750-2020 shows Genetics Determines most Social Outcomes http://faculty.econ.ucdavis.edu/faculty/gclark/ClarkGlasgow2021.pdf 

Further discussion: https://infoproc.blogspot.com/2021/03/genetic-correlation-of-social-outcomes.html 

A Farewell to Alms: A Brief Economic History of the World https://en.wikipedia.org/wiki/A_Farewell_to_Alms 

The Son Also Rises https://en.wikipedia.org/wiki/The_Son_Also_Rises_(book)

Transcript.

Genetic correlation of social outcomes between relatives (Fisher 1918) tested using lineage of 400k English individuals

Greg Clark (UC Davis and London School of Economics) deserves enormous credit for producing a large multi-generational dataset which is relevant to some of the most fundamental issues in social science: inequality, economic development, social policy, wealth formation, meritocracy, and recent human evolution. If you have even a casual interest in the dynamics of human society you should study these results carefully...

See previous discussion on this blog. 

Clark recently posted this preprint on his web page. A book covering similar topics is forthcoming.

For Whom the Bell Curve Tolls: A Lineage of 400,000 English Individuals 1750-2020 shows Genetics Determines most Social Outcomes 

Gregory Clark, University of California, Davis and LSE (March 1, 2021) 

Economics, Sociology, and Anthropology are dominated by the belief that social outcomes depend mainly on parental investment and community socialization. Using a lineage of 402,000 English people 1750-2020 we test whether such mechanisms better predict outcomes than a simple additive genetics model. The genetics model predicts better in all cases except for the transmission of wealth. The high persistence of status over multiple generations, however, would require in a genetic mechanism strong genetic assortative in mating. This has been until recently believed impossible. There is however, also strong evidence consistent with just such sorting, all the way from 1837 to 2020. Thus the outcomes here are actually the product of an interesting genetics-culture combination.

The correlational results in the table below were originally deduced by Fisher under the assumption of additive genetic inheritance: h2 is heritability, m is assortativity by genotype, r assortativity by phenotype. (Assortative mating describes the tendency of husband and wife to resemble each other more than randomly chosen M-F pairs in the general population.)

Fisher, R. A. 1918. “The Correlation between Relatives on the Supposition of Mendelian Inheritance.” Transactions of the Royal Society of Edinburgh, 52: 399-433

Thanks to Clark the predictions of Fisher's models, applied to social outcomes, can now be compared directly to data through many generations and across many branches of English family trees. (Figures below from the paper.)

The additive model fits the data well, but requires high heritabilities h2 and a high level m of assortative mating. Most analysts, including myself, thought that the required values of m were implausibly large. However, using modern genomic datasets one can estimate the level of assortative mating by simply looking at the genotypes of married couples. 

From the paper:

(p.26) a recent study from the UK Biobank, which has a collection of genotypes of individuals together with measures of their social characteristics, supports the idea that there is strong genetic assortment in mating. Robinson et al. (2017) look at the phenotype and genotype correlations for a variety of traits – height, BMI, blood pressure, years of education - using data from the biobank. For most traits they find as expected that the genotype correlation between the parties is less than the phenotype correlation. But there is one notable exception. For years of education, the phenotype correlation across spouses is 0.41 (0.011 SE). However, the correlation across the same couples for the genetic predictor of educational attainment is significantly higher at 0.654 (0.014 SE) (Robinson et al., 2017, 4). Thus couples in marriage in recent years in England were sorting on the genotype as opposed to the phenotype when it comes to educational status. 

It is not mysterious how this happens. The phenotype measure here is just the number of years of education. But when couples interact they will have a much more refined sense of what the intellectual abilities of their partner are: what is their general knowledge, ability to reason about the world, and general intellectual ability. Somehow in the process of matching modern couples in England are combining based on the weighted sum of a set of variations at several hundred locations on the genome, to the point where their correlation on this measure is 0.65.

Correction: Height, Educational Attainment (EA), and cognitive ability predictors are controlled by many thousands of genetic loci, not hundreds! 

This is a 2018 talk by Clark which covers most of what is in the paper.

For out of sample validation of the Educational Attainment (EA) polygenic score, see Game Over: Genomic Prediction of Social Mobility.

 

Assortative mating, regression and all that: offspring IQ vs parental midpoint

In an earlier post I did a lousy job of trying to estimate the effect of assortative mating on the far tail of intelligence.

Thankfully, James Lee, a real expert in the field, sent me a current best estimate for the probability distribution of offspring IQ as a function of parental midpoint (average between the parents' IQs). James is finishing his Ph.D. at Harvard under Steve Pinker -- you might have seen his review of R. Nesbitt's book Intelligence and how to get it: Why schools and cultures count.

The results are stated further below. Once you plug in the numbers, you get (roughly) the following:

Assuming parental midpoint of n SD above the population average, the kids' IQ will be normally distributed about a mean which is around +.6n with residual SD of about 12 points. (The .6 could actually be anywhere in the range (.5, .7), but the SD doesn't vary much from choice of empirical inputs.)

So, e.g., for n = 4 (parental midpoint of 160 -- very smart parents!), the mean for the kids would be 136 with only a few percent chance of any kid to surpass 160 (requires +2 SD fluctuation). For n = 3 (parental midpoint of 145) the mean for the kids would be 127 and the probability of exceeding 145 less than 10 percent.

No wonder so many physicist's kids end up as doctors and lawyers. Regression indeed! ;-)

Below are some more details; see here for calculations. In my earlier post I arrived at the same formulae as below, but I had rho = 0.

Assuming bivariate normality (and it appears that IQ has been successfully scaled to produce this), the offspring density function is normal with mean n*h^2 and variance 1-(1/2)(1+rho)h^2, where rho is the correlation between mates attributable to assortative mating and h^2 is the narrow-sense heritability.

I put h^2 between .5 and .7. Bouchard and McGue found a median correlation between husband and wife of .33 in their review many years back, but not all of that may be attributable to assortative mating. So anything in (.20, .25) may be a reasonable guesstimate for rho.

In discussing this topic with smart and accomplished parents (e.g., at foo camp, in academic science, or on Wall Street), I've noticed very strong interest in the results ...

See related posts mystery of non-shared environment , regression to the mean

Note: Some people are confused that the value of h^2 = narrow sense (additive) heritability is not higher than (.5 - .7). You may have seen *broad sense* heritability H^2 estimated at values as large as .8 or .9 (e.g., from twin studies). But H^2 includes genetic sources of variation such as dominance and epistasis (interactions between genes, which violate additivity). Because children are not clones of their parents (they only get half of their genes from each parent, and in a random fashion), the correlation between midparent IQ and offspring IQ is not as large as the correlation between the IQs of identical twins. See here and here for more.

Regression to the mean

Consider a trait like height or intelligence that is at least partially heritable. For simplicity, suppose the adult value of the trait X is equally affected by genes G and environment E, so

X = G + E

where G and E are, again for simplicity, independent Gaussian random variables (normally distributed) with similar standard deviations (SDs).

Suppose that you meet someone with, say X = +4 SD (i.e., someone with an IQ of 160 or a (male) height of roughly 6 ft 9). What are the likely values of G and E? It's more likely that the +4 SD is obtained from two +2 SD draws from the G and E distributions than, say, a +3 SD and +1 SD draw. That is, someone who was lucky(?) enough to grow to seven feet tall probably benefited both from good genes and a good environment (e.g., access to good nutrition, plenty of sleep, exercise, low stress).

Now consider a population of +4 SD men married to +4 SD women. (More generally, we can consider a parental midpoint value of X which is simply the parental average in units of SD.) Suppose they have a large number of children. What will the average X be for those children?

If we treat the environment E as a truly independent variable (i.e., it is allowed to fluctuate randomly for each child or family), then the children will form a normal distribution peaked at only +2 SD even though the parental midpoint was +4 SD. In other words, given the random E assumption the kids are not guaranteed to get the environmental boost that the parents likely had. Most of the parents benefited from above average E as well as G. This is called regression to the mean, a well documented phenomenon in population genetics that was originally discovered by Galton.

Regression to the mean implies that even if two giants or two geniuses were to marry, the children would not, on average, be giants or geniuses. On the positive side, it means that below average parents typically produce offspring that are closer to average.

How reasonable is this model for the real world? I already mentioned that regression is confirmed by data. (In fact, one uses this kind of data to deduce the heritability of a trait - the relative contributions of G and E need not be equal.) An interesting possibility in the context of intelligence is that, perhaps due to the modern phenomena of assortative mating and obsession with elite education, E and G can no longer be treated within the population as indpendent variables. In this case we should see a reduction in the level of regression to the mean among the intellectual elite, and further separation in cognitive abilities within the population.

Another possibility is that the original model is too simplistic, and that there are intricate and important interactions between G and E. It may not be very easy for a parent to ensure a positive environment tailored to their particular child's G. That is, buying lots of books and sending the child to good schools may not be enough. It may be that development is nonlinear and chaotic -- not determined by coarse average characteristics of environment. For example, the parent may have benefited from a special interaction with a mentor that cannot be reproduced for the child. Or perhaps different children respond very differently to peer competition. Although I find this picture plausible at the individual level, it seems likely that, averaged over a population, obvious enrichment strategies have a positive effect.