Let’s say we take 500 bright-eyed 6 year olds, just beginning the 1st grade, and give them a test of reading ability. Their scores are normally distributed, from those who still don’t know their letters to those who are already reading chapter books independently.
Then we find those same 500 kids, 12 years later, and give them another test of reading ability. Their scores are still normally distributed, but most likely over a wider range– some kids are reading Joyce and Tolstoy and physics textbooks at age 18 , some kids are still barely literate or essentially not literate at all.
We can look at each kid’s score over time: did they go up or down, relative to other kids their age, from age 6 to age 18?
We might be tempted to attribute their change in scores to their experiences in school, whether they had involved parents who encouraged them to learn, or lived in a supportive neighborhood.
In fact, maybe we could go find the kids who scored a standard deviation higher as 18-year-olds than as 6-year-olds: those kids must have had great teachers- give them a raise!
And then we could find the kids who scored a standard deviation lower as 18-year-olds than as 6-year olds: those kids must have had lousy teachers- better send ’em packing!
This is the basic intuition behind value-added modeling, although VAM is done over a shorter time period (usually from one school year to the next) and with more caveats and controls.
In fact, the basic intuition behind many quasi-experimental social science models is that, “if you can go back far enough,” you can control for the influence of genes, and that everything going forward can be attributed to environment, or policy, or parenting, or the quality of public services.
But genetics is a process, not an endowment. And the way in which it contributes to how much you know is dynamic, through preferences and habits as well as by abilities. For example, kids who have, genetically, a higher propensity at age 10 to be able to read have a largely heritable higher propensity to enjoy reading independently as well. Particularly in rich societies, kids seek out environments that suit their cognitive abilities and (largely genetically determined) interests.
As a result, perhaps the single most well-replicated finding in behavioral genetics is the linearly increasing heritability of cognitive abilities from infancy (20%) through adulthood (60%).
So, let’s take that finding seriously and go back to our group of 500 kids we were observing. What would the same group of kids’ scores look like if 35% of their scores at age 6 were due to some unobserved genetic factor, and that factor increased in influence linearly to age 18?
Oh. Kinda familiar.
And what would it look like if we were able to single out those with high values of that unobserved factor (call them “lucky genes”), 1 or more standard deviations above the mean, as well as low values (call them “unlucky”), 1 or more standard deviations below the mean?