ArticlePDF Available

Why do persons from higher mean IQ populations do better even after selection?

  • Ulster Institute for Social Research


It has been found that workers who hail from higher socioeconomic classes have higher earnings even in the same profession. An environmental cause was offered as an explanation of this. I show that this effect is expected solely for statistical reasons.
Why do persons from higher mean IQ
populations do better even after selection?
12. May 2015
Emil O. W. Kirkegaard
Originally published in The Winnower.
It has been found that workers who hail from higher socioeconomic classes have higher earnings even
in the same profession. An environmental cause was offered as an explanation of this. I show that this
effect is expected solely for statistical reasons.
Friedman and Laurison (2015) offer data about the earnings of persons employed in the higher
professions by their social class of origin. They find that those who originate from a higher social class
earn more. I reproduce their figure below.
They posit an environmental explanation of this:
In doing so, we have purposively borrowed the ‘glass ceiling’ concept developed by
feminist scholars to explain the hidden barriers faced by women in the workplace. In a
working paper recently published by LSE Sociology, we argue that it is also possible to
identify a ‘class ceiling’ in Britain which is preventing the upwardly mobile from enjoying
equivalent earnings to those from upper middle-class backgrounds.
There is also a longer working paper by the same authors, but I did not read that. A link to it can be
found in the previously mentioned source.
A simplified model of the situation
How do persons advance to professions? Well, we know that the occupational hierarchy is basically a
(general) cognitive ability hierarchy (GCA; Gottfredson, 1997), as well as presumably also one of
various relevant non-cognitive traits such as being hard working/conscientiousness altho I am not
familiar with a study of this.
A simple way to model the situation is to think of it as a threshold system where no one below the
given threshold gets into the profession and everybody above gets into it. This is of course not like
reality. Reality does have a threshold which increases up the hierarchy. [Insert the figure from one of
Gottfredson’s paper that shows the minimum IQ by occupation, but I can’t seem to locate it. Help!] The
effect of GCA is probably more like a probabilistic function akin to the cumulative distribution
function such that below a certain cognitive level, virtually no one from below that level is found.
Simulating this is a bit complicated but we can approximate it reasonably by using a simple cut-off
value, such that everybody above gets in, everybody below does not, see Gordon (1997) for a similar
case with belief in conspiracy theories.
A simulation
One could perhaps solve this analytically, but it is easier to simulate it, so we do that. I used the
following procedure:
1. We make three groups of origin with 90, 100, and 110 IQ.
2. We simulate a large number (1e4) of random persons from these groups.
3. We plot these to get an overview of the data.
4. We find the subgroup of each group with IQ > 115, which we take as the minimum for some
high level profession.
5. We calculate the mean IQ of each subgroup.
The plot looks like this:
The vertical lines are the cut-off threshold (black) and the three means (in their corresponding colors).
As can be seen, the means in the subgroups are not the same despite the same threshold being applied.
The values are respectively: 121.74, 122.96, and 125.33. The differences between these are not large
for the present simulation, but they may be sufficient to bring about differences that are detectable in a
large dataset. The values depend on how far the population mean is from the threshold and the standard
deviation of the population (all 15 in the above simulation). The further away the threshold is from the
mean, the closer the mean of the subgroup above the threshold will be to the threshold value. For
subgroups far away, it will be nearly identical. For instance, the mean IQ of those with >=150 is about
153.94 (based on a sampling with 10e7 cases, mean 100, sd 15).
It should be noted that if one also considers measurement error, this effect will be stronger, since
persons from lower IQ groups regress further down. This is just to say that their initial IQs contained
more measurement error. One can correct for this bias, but it is not often done (Jensen, 1980).
Supplementary material
R source code is available at the Open Science Framework repository.
Friedman, S and Laurison, D. (2015). Introducing the ‘class’ ceiling. British Politics and Policy
Gordon, R. A. (1997). Everyday life as an intelligence test: Effects of intelligence and
intelligence context. Intelligence, 24(1), 203-320.
Gottfredson, L. S. (1997). Why g matters: The complexity of everyday life. Intelligence, 24(1),
Jensen, A. R. (1980). Bias in Mental Testing.
ResearchGate has not been able to resolve any citations for this publication.
To show why the importance of intelligence is often misperceived, an analogy between single test items and single nontest actions in everyday life is drawn. Three requirements of good test items are restated, and the analogy is employed to account for underrecognition of the importance of general intelligence in everyday actions, which often fail to meet the requirements and thus fail as intelligence measures for reasons that have little to do with their dependence on intelligence. A new perspective on the role of intelligence in nontest actions is introduced by considering its operation at three levels: that of the individual, that of the near context of the individual, and that of entire populations. Social scientists have misunderstood the operation and impact of IQ in populations by confining attention to the individual level. A population-IQ-outcome model is explained that tests for the pooled effects of intelligence at all three levels on differences between two populations in prevalences of certain outcomes. When the model fits, the difference between two populations in the outcome measured is found commensurate with the difference in their IQ or general intelligence distributions. The model is tested on and found to fit prevalences of juvenile delinquency, adult crime, single parenthood, HIV infection, poverty, belief in conspiracy rumors, and key opinions from polls about the O.J. Simpson trial and the earlier Tawana Brawley case. A deviance principle is extracted from empirical findings to indicate kinds of outcome the model will not fit. Implications for theories of practical and multiple intelligences are discussed. To understand the full policy implications of intelligence, such a fundamentally new perspective as that presented here will be needed.