According to the Fed study, about 60 percent of black children whose parents had income that fell into the top 50 percent of the distribution saw their own income fall into the bottom half during adulthood. This type of downward slide was common for only 36 percent of white children.
…Still, most economists lack a clear, definitive explanation for why, after reaching the middle class, many black American families quickly lose that status as their children fall behind.
Pointer from Mark Thoma.
Obviously, she did not read my review of Gregory Clark’s latest book.
Clark suggests that this may reflect that the underlying mean for these ethnic groups may differ, and the higher propensity of middle-income blacks and Hispanics to have their children’s income fall to the bottom third might be due to regression toward a lower mean.
Suppose that you have two populations of men with different height-producing genetic characteristics. The mean height in group A is 5 feet, 9 inches, and the mean height in group B is 5 feet, 7 inches. There is substantial variation within each group.
Now, out of the current generation of men, you select men from each group who happen to be 5 feet, 8 inches. Track the height of their sons. It seems reasonable to predict that, starting with men who are 5 feet 8 inches, the sons of men from group A are likely to be taller than the sons of men from group B. This does not result from social prejudice against men from group B. It is the result of laws of probability.
Someone has to be in the bottom half. It probably doesn’t take much. You’d roughly expect half of kids to end up in the bottom half. It may not even require any mean difference. Maybe just starting with a slightly lesser endowment means that part of your adulthood is spent attaining your expected mean level. What is the marital status of the 60% of blacks and 36% of whites? Fallacy of composition warning level orange.
Saying “it’s reversion to the mean” doesn’t tell us why populations have different means. In your example the different means are the result of underlying genetic characteristics. Maybe for racial groups the different means are the result of social prejudice.
You are assuming “black” and “white” are the correct population labels. Just sayin’. It is weird how we do this- use these labels as instrumental variables while we do so precisely in hopes to show (or pretend) they aren’t.
There are existing ways to test for this to make sure, but good luck getting a grant or a journal that will publish the result.
Let’s use height as an example. There is a West-African (WA) mean, an Anglo-American mean (AnA), and an African-American (AfA) mean.
AfA is probably somewhere between WA and AnA, because the average African American has about 20% Anglo-American genetic admixture, though there is plenty of variance, and some are almost 100% WA, and some are less than 50% WA even if they are not certain of a European ancestor (Henry Louis Gates Jr. is an example).
But we can quickly and cheaply test precisely for admixture – 23andMe will do it for $99. And what you would expect to see is that the mean height for anyone with X% European admixture = X*AnA + (1-X)*WA.
Now, when it comes to comparing life outcomes to social discrimination and testing whether there is a causality, the null hypothesis would be that the same result above would apply. Test a lot of African Americans for European genetic admixture, and compare to life outcomes.
The null hypothesis (social prejudice is not causal) is that they are tightly correlated, and the graph should be smooth, linear and continuous.
The social prejudice hypothesis is that, unless prejudice is somehow exactly proportional to admixture (which is unlikely) then they are not correlated, and the best-fit line you would plot on the data would not extend to the AnA mean.
In fact, the usual academic theory of American social prejudice against blacks is that it follows the ‘one-drop rule’ of hypodescent, and so variance in small amount of admixture shouldn’t matter much at all to that.
The point is that if anything were genuinely interested in finding out the answer and settling this question definitively once and for all, it would be entirely feasible, and they could do so easily, cheaply, and quickly today, with existing capabilities, and using less resources than a typical social science study.
But a definitive result – especially a taboo one – is never a good thing for people spread narratives which rely on assumptions which may be falsified thereby.
Arnold: 100% correct. I was stumped until I read your explanation, but now it seems so obvious. 🙂
The Chicago Fed study is gated, so I will have to wait to read it.
Presumably it covers a specific set of periods (at least two generations) so the time-spans and related economic conditions are probably relevant (and revealant) to the respective generational data.
We have also to consider the factors that contributed to the shaping of individual motivations of the differing generations. Those motivations then “collided” with changes in the economic conditions (reductions of opportunities and less evaluation of “equalities of opportunities”).
A sample might be found in Washington, D.C. and the related Prince Georges County, MD.
I have an even simpler question. Assume that blacks in the top 50% are centered somewhere in the 3rd ventile. I just wanted to use the word ventile. Assume blacks in the top 50% are 60% whereas whites are spread throughout the top 50% so their average is 75% (halfway between 50% and 100%).
What is the probability of the generational coin flip taking someone at 60% below 50% relative to taking someone at 75% below 50%?
These studies are done using tax figues on household incomes, not individual incomes. That is the available data. Changes in household structure invalidate any conclusions about individuals. If the child of a two-parent family goes on to be a one-parent family, his/her family income will fall in the statistics.
It is hard to do good studies with valid statistics. Most studies are worthless.
I was expecting to see a bevy of comments from the PC police… I guess they haven’t arrived yet.
The explanations for this phenomenon are varied, but largely hinge on many of the criticisms that already exist in regard to socioeconomics and race in the U.S. Economists cite lower educational attainment, higher rates of single-parent households, and geographic segregation as potential explanations for these trends. The latter determines not only what neighborhoods people live in, but often what types of schools children attend, which could play a role in hindering their educational and professional attainment later on. According to Reeves, “In terms of opportunity, there are still two Americas, divided by race.”
The last paragraph of the article (that I quote above) implies that blacks are bad patents and that black majority areas are bad environments. So how is it that despite the above that black Americans dominate the most desired jobs in the USA(those being NCAA and Professional basketball and football players)? Black’s domination of NCAA and professional basketball and football say that they are great parents and provide nurturing environments but have slightly different values from what elites want them to have.
I know guys in their 60’s who still love to talk about playing NCAA and professional sports and they get a lot of status from it. Sport is important to regular people so do not minimize it. I think blacks do great in the USA.
One persons nature is anothers nuture and others environment and yet others culture and much is handed down.
Reversion to the mean is just a mathematical concept, a mental device, which we use to describe observations. Yours is the description of the process, but where is the explanation?
And, by the way, social prejudice is compatible with laws of probability, no?