Disentangling nature from nurture

Take some trait on which people vary.  Personality characteristics, intelligence, educational attainment, political attitudes, etc.  Now consider all of the variation in that particular trait within a nation such as the United States.  Some of that variation may be due to variation in family upbringing.  (A man who has little confidence in himself attributes his insecurities to his critical mother.) Some of the variation is due to differences in non-familial environment.  (That same man’s insecurity might be an outgrowth of persistent putdowns by peers in primary school.)  And finally, some of the variation in that trait across the population will stem from variation in genetic propensities. (That unfortunate man carried genes that, however directly or indirectly, engendered a lack of confidence.

Can we divvy up the variation amongst these three sources of influence — family environment, non- family environment, and genes?

Yes, as it turns out, we (sort of) can.  Behavior geneticists have been doing this for a very long time.  According to the traditional behavior genetics model, variation in a trait is like a pie which has to be divided into three pieces.  One piece of the pie represents variation due to genetic influence.   Another piece of the pie represents the influence of family socialization.  This is called “shared environment.”  The third piece of the pie – the piece left over after you cut out the other two – includes environmental influences from outside the family.  This piece of the pie is called “non-shared environment.”

Now, the question is: how do researchers divide up the pie?  What percent goes for genes (BG’s call this piece A)?  What percent of the pie goes for shared environment (BG’s call this piece C), and what piece goes for non-shared environment, which BG’s call E?

It turns out you need some special kinds of data to decide how to slice the pie.  Let’s start with the easy part: separating family effects – both genes (A) and shared environment (C) from non-family effects (E).  Researchers can estimate the overall impact of family by looking at data on siblings.  Looking at the variation within and between siblings for, say, educational attainment, in a large, representative dataset will give you a good sense of how much family matters overall to someone’s educational attainment in this society.  The more similarity we observe between siblings in educational attainment, the more power we can attribute to families in influencing this trait.  The more that siblings are different in educational attainment, the less important that family influence would appear to be.

So data on “ordinary” (non-adopted, biological) siblings allows us to separate the impact of A & C on the one hand, from the effects of E on the other hand.  But such data does not allow us to distinguish between the effects of genes and the effects of family (mainly parental) socialization.  We often see (uncanny) similarities between parents and their children, but we can’t discern the origin of this similarity.  Say Johnny and his mother are both outgoing.  We don’t know how much the similarity between Johnny and his mom is due to their shared genes or due to the fact that Johnny was raised by his mother.

In the next post, I’ll talk about how BG’s have typically separated A from C.




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