The plateauing of cognitive ability among top earners

Abstract

Are the best-paying jobs with the highest prestige done by individuals of great intelligence? Past studies find job success to increase with cognitive ability, but do not examine how, conversely, ability varies with job success. Stratification theories suggest that social background and cumulative advantage dominate cognitive ability as determinants of high occupational success. This leads us to hypothesize that among the relatively successful, average ability is concave in income and prestige. We draw on Swedish register data containing measures of cognitive ability and labour-market success for 59,000 men who took a compulsory military conscription test. Strikingly, we find that the relationship between ability and wage is strong overall, yet above €60,000 per year ability plateaus at a modest level of +1 standard deviation. The top 1 per cent even score slightly worse on cognitive ability than those in the income strata right below them. We observe a similar but less pronounced plateauing of ability at high occupational prestige.

Full text

European Sociological Review, 2023, XX, 1–14
https://doi.org/10.1093/esr/jcac076
Advance access publication 28 January 2023
Original Article
Received: May 2020; revised: December 2022; accepted: December 2022
© The Author(s) 2023. Published by Oxford University Press.
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The plateauing of cognitive ability among top
earners
MarcKeuschnigg1,2,*,, Arnoutvan de Rijt3,4, and ThijsBol5,
1Institute for Analytical Sociology, Linköping University, Sweden
2Institute of Sociology, Leipzig University, Germany
3Department of Political and Social Sciences, European University Institute, Italy
4Department of Sociology, Utrecht University, the Netherlands
5Department of Sociology, University of Amsterdam, the Netherlands
*Corresponding author. Email: marc.keuschnigg@liu.se
Are the best-paying jobs with the highest prestige done by individuals of great intelligence? Past studies find job success to increase
with cognitive ability, but do not examine how, conversely, ability varies with job success. Stratification theories suggest that social
background and cumulative advantage dominate cognitive ability as determinants of high occupational success. This leads us to hypoth-
esize that among the relatively successful, average ability is concave in income and prestige. We draw on Swedish register data
containing measures of cognitive ability and labour-market success for 59,000 men who took a compulsory militar y conscription test.
Strikingly, we find that the relationship between ability and wage is strong overall, yet above €60,000 per year ability plateaus at a mod-
est level of +1 standard deviation. The top 1 per cent even score slightly worse on cognitive ability than those in the income strata right
below them. We observe a similar but less pronounced plateauing of ability at high occupational prestige.
Introduction
Scholars of cultural consecration and commemoration
document how the most successful members of our
society are commonly attributed exceptional talents
in documentaries, biographies, and hall-of-fame elec-
tions (Bourdieu, 1984; Lang and Lang, 1988; Allen
and Lincoln, 2004; Allen and Parsons, 2006; Watts,
2011; van de Rijt et al., 2013). Experiments show that
people readily infer the possession of extreme skill
from the achievement of extreme success (Baron and
Hershey, 1988; Gilbert and Malone, 1995; Denrell and
Liu, 2011). Academics themselves have also explained
extremely successful careers in terms of the superior
intelligence of the individuals involved (Pareto, 1916;
Domhoff, 1967; Rosen, 1981; Neal and Rosen, 2000;
Murray, 2003; Rahman Khan, 2012; Mankiw, 2013).
But do the highest earners and those with the most
prestigious jobs indeed have the greatest minds?
Elite jobs are of special interest, for two reasons.
First, income distributions have strong right skew. In
all Western countries, top income shares have been
steadily rising since the 1980s, with the 1 per cent
highest earners receiving 9 per cent of national income
in Sweden and even 20 per cent in the United States—
excluding capital gains (Piketty, 2014; Alvaredo et al.,
2017; Statistics Sweden, 2020). This extremity of top
incomes as well as their public salience render it cru-
cial that they be earned by very capable individuals.
Second, those with the most prestigious jobs wield the
greatest economic and political power, and the intelli-
gence of their decisions is consequential.
While scholars debate the origins and measurement
of cognitive ability as well as the causal mechanisms
linking it to labour-market success, there is a broad the-
oretical and empirical consensus that expected wages
and occupational prestige monotonically increase in
cognitive ability. But how, conversely, average ability
varies with job success has not been systematically
investigated. The present paper departs from prior
work by swapping the axes, focusing on the relative
intelligence of those with better jobs.
Drawing on arguments from the sociological liter-
atures on stratication and cumulative advantage we
propose that the ability—success relationship atten-
uates at high levels of success. In other words, we
hypothesize that there are starker ability differences
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2KEUSCHNIGG, VAN DE RIJT AND BOL
between adjacent ranks at moderate levels of income
and prestige than at the highest levels. Our argument
draws on the role of two key non-meritorious deter-
minants of occupational success: Family resources
and luck. The class- and network-advantages of
those with elite family backgrounds are assumed
instrumental for gaining access to the most privi-
leged and best-paying jobs (Bourdieu, 1984; Lamont,
1994; Rivera, 2015; Friedman and Laurison, 2020).
Second, rich-get-richer processes are assumed to
allow inequalities in job success to grow between
those who got a lucky break early in the career
and those who did not (Merton, 1968; DiPrete and
Eirich, 2006; Salganik, Dodds and Watts, 2006; Bol,
de Vaan and van de Rijt, 2018). Our argument relies
on these two determinants of occupational success,
family resources and luck, having distributions in
which extremely high values are common (Denrell
and Liu, 2011; Frank, 2016). Because extreme ability
is rare, extreme occupational success is more likely
driven by family resources or luck than by ability.
Hence, at higher levels of occupational success addi-
tional degrees of success will be less and less associ-
ated with greater ability. We illustrate this argument
using a model by Denrell and Liu (2011).
While past studies have shown positive direct and
indirect effects of cognitive ability on wage earnings
(Coward and Sackett, 1990; Herrnstein and Murray,
1994; Ng et al., 2005; Strenze, 2007; Ganzach
et al., 2013; Lubinski, 2016; Jokela et al., 2017;
Gensowski, 2018), they have not systematically
studied the relative ability of top earners. This gap
in the literature is mostly driven by data restrictions:
examining the precise cognitive ability levels at all
levels of labour-market success requires a represent-
ative and comprehensive dataset that has good cov-
erage also of very successful individuals—especially
top income data is often missing in survey data.
In this article, we analyse Swedish register data on
59,000 men who took a mandatory cognitive-abil-
ity test at age 18–19, allowing detection of minute
average ability differences between adjacent levels of
occupational success with representative data. Our
focus on men is an issue of data availability because
only men took the conscription-related ability test.
We discuss this limitation in the concluding section
and invite further research that includes all members
of society.
Literature
The empirical relationship between cognitive
ability and job success
While ‘cognitive ability’ lacks a generally agreed upon
denition, it is broadly used to indicate the capacity
of the brain to perform a variety of cognitive tasks,
including verbal understanding, technical comprehen-
sion, spatial ability, and logic (Borghans et al., 2016).
Such skills are thought to be partly learned, partly
genetically determined, and partly acquired through
interaction between genes and social environments. A
sizeable literature has explored how wages are corre-
lated with measures of cognitive ability (for overviews,
see Ng et al., 2005; Strenze, 2007). Among the relatively
few studies that use direct measures of cognitive abil-
ity, the consistent result is that individuals with greater
cognitive ability earn on average higher wages. In their
book The Bell Curve, Herrnstein and Murray (1994)
nd that general cognitive ability is positively corre-
lated with various indicators of labour-market success.
Jensen (1998) also nds a positive correlation between
test scores on a cognitive aptitude test and wage earn-
ings. A meta-study by Strenze (2007) nds an average
bivariate correlation of 0.23 on the basis of a large
number of datasets containing labour-market earn-
ings and cognitive ability measures. Achievement tests
such as the Armed Forces Qualications Test (AFQT)
have been found to correlate more strongly with wages
than tests of general intelligence (IQ) as the former
capture closely related personality characteristics also
relevant for job-market success (Fischer et al., 1996;
Borghans et al., 2016). Several studies (Lubinski, 2016;
Gensowski, 2018) have examined the popular claim
by Gladwell (2008) that above some threshold level of
cognitive ability further increases in ability would not
matter for job success. Predicting occupational success
from cognitive ability in multivariate regression they
reject this thesis, nding a strictly monotonic relation-
ship, with the very smartest being the most successful.
To the best of our knowledge, there are no empirical
studies that systematically probe cognitive ability at
different levels of occupational success. Several studies
have looked at traits of highly successful people. Wai
(2013) uses elite US college attendance as a proxy for
extraordinary intelligence based on the logic of very
high SAT score requirements for elite college entry. He
nds that roughly 40 per cent of Fortune 500 CEOs,
federal judges, billionaires, and Senators have elite col-
lege degrees. However, high school grades and achieve-
ment tests have been found to be signicantly impacted
by other factors besides cognitive ability such as family
background and personality traits (e.g. Borghans et
al., 2016). Several studies nd that top jobs in the pri-
vate sector are not characterized by excessive cognitive
ability. Bihagen, Nermo and Stern (2013), using direct
measures of cognitive ability provided by Swedish
register data, nd a relatively minor role of cognitive
ability in the explanation of elite-position occupancy in
business rms. Adams, Keloharju and Knüpfer (2018),
also using Swedish register data, nd that the median
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3THE PLATEAUING OF COGNITIVE ABILITY AMONG TOP EARNERS
CEO of a large company ranks in the 83rd percentile
of cognitive ability. Antonakis, House and Simonton
(2017) nd a mean IQ of 111 (less than a standard
deviation above average) among mid-level executives
from various Western countries.
To sum up, existing empirical studies nd that
labour-market success monotonically increases with
cognitive ability. As far as we know, no prior study has
systematically evaluated how cognitive ability expecta-
tions vary with labour-market success.
Theoretical mechanisms linking wage to
family background
A vast body of work in the sociology of stratication
nds that individuals in the right tail of the wage distribu-
tion often come from advantageous social backgrounds
(Breen and Jonsson, 2007; Grusky, 2019). Sociologists
have emphasized the relatively important role of paren-
tal socio-economic background vis-à-vis cognitive abil-
ity as wage determinant. Most prominently, in a reaction
to The Bell Curve, Fischer et al. rejected the unidimen-
sional focus on intelligence for explaining inequality in
society, and argued that Herrnstein and Murray ‘err in
asserting that [intelligence] largely determines how peo-
ple end up in life’ (Fischer et al. 1996: p. 10). In contrast,
the authors argue that, while genetic traits are impor-
tant, the most important factor is the ‘social milieux in
which people grow up and live’ (Fischer et al., 1996: p.
9). More recent research has theorized and sought to
evidence a variety of pathways through which social
background and cognitive ability may co-determine
occupational success.
A primary pathway through which socio-economic
background impacts occupational success is educa-
tion. In the human capital literature, wage differences
are assumed to be driven by variation in human cap-
ital (Mincer, 1958). Wage is typically assumed to be
a log-linear function of education and experience
(Mincer, 1958; Heckman, Stixrud and Urzua, 2006;
Jones and Schneider, 2006; Lemieux, 2006; Gensowski,
2018). Also in studies on social reproduction, education
is a key mechanism. Educational credentials are more
easily observed than ability, and several studies show
that employers select strongly on the former (Collins,
1979; Barone and Van de Werfhorst, 2011; Di Stasio
and Van de Werfhorst, 2016). It is well-known that the
educational system does not grant all children equal
opportunities for attaining high qualications (Breen
and Jonsson, 2007; Brand and Xie, 2010; Torche,
2011; Jackson, 2013; Bernardi, 2014). In a conict-so-
ciological perspective, educational degrees serve as cru-
cial instruments in maintaining class barriers (Collins,
1979). Often these barriers become institutionalized,
and educational degrees are formally required to
enter occupations (Weeden, 2002). Studying Sweden,
Erikson (2016) nds that cognitive ability accounts for
about one third of the variance in educational attain-
ment, whereas parental background explains about 20
per cent. Social background plays an important role in
determining who ends up where in the education sys-
tem (Breen and Jonsson, 2007), and thereby also where
in the wage distribution. In a similar vein, Bowles and
Gintis (2002) argue that cognitive skills only play a
minor role in explaining the relation between educa-
tion and earnings (about 20 per cent). In their cross-na-
tional study, Barone and Van de Werfhorst (2011) nd
that this portion is larger (32–60 per cent), but never-
theless argue that income differences are driven by fac-
tors unrelated to ability. In a eld experiment, Gaddis
(2015) nds that students from elite universities obtain
a labour market premium for their educational degree.
Wealthy families may thus be able to reproduce their
advantage across generations by being able to pay for
enrolment in elite institutions.
Wage may also be impacted by socio-economic back-
ground through an increase in cognitive ability, which in
turn impacts education. Some have found a heritability
factor in intelligence (Bowles and Gintis, 2002; Nisbett
et al., 2012), with part of the socio-economic inequal-
ity in educational and labour-market outcomes driven
by inequality in genetic factors (Conley and Fletcher,
2017; Belsky et al., 2018). However, only a small por-
tion of parent-child correlations in education may be
due to genetic transmission (Conley et al., 2015). It
has also been suggested that genetic endowments have
larger effects on cognition for children born into higher
socio-economic classes (Scarr-Salapatek, 1971; Rowe et
al., 1999), but evidence is equivocal (Fischbein, 1980;
Guo and Stearns, 2002; Figlio et al., 2017; Baier and
Lang, 2019; Gottschling et al., 2019). Yet differently,
being raised in a poor neighbourhood has been found
to impact one’s cognitive ability (McCulloch and Joshi,
2001) and that of one’s children (Sharkey and Elwert,
2011). Wealthy parents prevent such adverse effects on
their children by living in better neighbourhoods.
Family background may also impact occupational
success net of education (Torche, 2011, 2018; Falcon
and Bataille, 2018; Oh and Kim, 2020). Laurison and
Friedman (2016) and Friedman and Laurison (2020)
show that individuals with a higher class background
obtain higher wages than those from a lower class
background, keeping their educational level constant.
Bernardi and Gil-Hernández (2021) nd that this direct
effect of social origin is stronger among those with
higher levels of education. One mechanism through
which socio-economic background may impact occu-
pational success net of education is the cultural capi-
tal that individuals gain from home (Bourdieu, 1984).
Those from privileged backgrounds are thought to be
more likely to occupy privileged positions themselves
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4KEUSCHNIGG, VAN DE RIJT AND BOL
because their cultural backgrounds provide a leg up
in the educational system and subsequently the labour
market (Bourdieu and Passeron, 1979; Lareau, 2011).
The acquisition of high-status positions would require
mastering the right tastes, behaviours, and customs.
Such subtle and elaborate displays of sophistication are
naturally taught during upbringing in some families.
Cultural capital might thus push children from higher
social classes into steeper career trajectories (Lamont,
1994). Cultural tastes may also indirectly affect careers
through the ability to build more extensive networks to
important others (Lizardo, 2006).
An accelerator of social-capital effects on job suc-
cess is the fact that skill requirements and recruitment
processes for top jobs are less transparent and objec-
tive than those for intermediate-level jobs. Some note
that in top management, hiring, and wage setting is
often done by peers (Bebchuk, Fried and Walker, 2002;
DiPrete, Eirich and Pittinsky, 2010; Piketty, 2014: pp.
331–332). Such lack of transparency and objectiv-
ity provides room for factors unrelated to cognitive
ability to determine appointment decisions (Clauset,
Arbesman and Larremore, 2015; Rivera, 2015;
Laurison and Friedman, 2016). In a case study of hir-
ing practices for elite positions, Rivera (2012) nds
that decisions are only rarely based on competence
assessments but largely on cultural tastes. Similarly,
Friedman and Laurison (2020) argue that access to
elite positions in the United Kingdom is to a large
extent explained by having the right tastes and right
connections.
In sum, then, while the causal pathways through
which family background and cognitive ability may
co-determine career success are varied, the literature
suggests that net of cognitive ability, the resources elite
families provide play an important role in the attain-
ment of top jobs.
Theoretical mechanisms linking wage to
cumulative advantage
Earning an extremely high wage may also be a tell-
tale sign of luck. Frank (2016) illustrates the relative
importance of luck in competitive labour markets
using a simulated hiring tournament in which indi-
viduals’ ability and luck are independently drawn
numbers (from the same 0 to 100-interval), and their
weighted sum determines a contestant’s chance to land
a scarce high-income job. Even under a strongly perfor-
mance-based calibration of the model in which ability
accounts for 98 per cent of individuals’ achievement
and luck accounts for the remaining 2 per cent, most
winners (78 per cent) do not have the highest ability
scores.
The role of luck may be enhanced through a pro-
cess of cumulative advantage (Coleman, 1964; Allison,
1980; DiPrete and Eirich, 2006), whereby an initial
resource advantage of one individual over another
skews also the subsequent allocation of successes in
favour of that individual. As a result, small initial suc-
cess differences between individuals are not cancelled
out over time but instead grow into winner-take-all dis-
tributions characterized by extreme inequalities. When
individuals with great talent compete in a market that
can only sustain a few winners, cumulative advantage
processes following chance events may determine the
difference between moderate and great success (Adler,
1985; Salganik, Dodds and Watts, 2006; Keuschnigg,
2015). In its most problematic form, cumulative
advantage is capable of perpetuating a random advan-
tage of a less able over a more able individual (Arthur,
1994; Lynn, Podolny and Tao, 2009). This may for
example happen when labour market contexts vary in
the degree to which they enable chance and cumulative
advantage to determine outcomes, so that extreme suc-
cess becomes a marker of luck (Denrell and Liu, 2011).
Empirically, cumulative (dis-)advantage has been
investigated as temporal relationships between various
career and health outcomes (e.g. Heckman and Borjas,
1980; O'Rand, 1996; Dannefer, 2003; Gangl, 2004;
Willson et al., 2007; Mazoni et al., 2014; Birkelund
et al., 2017; Huckfeldt, 2022). Studies generally nd
a positive effect of past on future unemployment and
a negative effect on future wages. Cumulative advan-
tage has also been investigated in specic sectors such
as academic careers. Early work looked for growing
cohort inequality in citations and publications, with
mixed results (Allison et al., 1982). More recent studies
have employed quasi-causal designs to identify cumu-
lative advantage effects (Azoulay et al., 2014; Bol, de
Vaan and van de Rijt, 2018; Wang et al., 2019).
Theoretical expectations
The model proposed by Denrell and Liu (2011) from
which we derive our main hypothesis linearly relates
generic notions of performance Pi, skill ui and noise ei
for individuals i:
Pi=ui+ei
Of interest is how expected skill changes with perfor-
mance. Denrell and Liu vary how common extremely
high and extremely low values of skill and noise are (i.e.
the kurtosis and thus fat-tailedness of the respective dis-
tributions). They do so by assuming that an individual’s
skill ui and noise ei are drawn from normal distributions
with zero mean and individual-specic standard devi-
ations. The more varied these standard deviations are
across individuals, the more common extreme values
are. Specically, an individual’s standard deviation of
the skill distribution is drawn from a gamma distribu-
tion with a shape parameter s > 0 and scale parameter
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5THE PLATEAUING OF COGNITIVE ABILITY AMONG TOP EARNERS
1/s. Similarly, an individual’s standard deviation of the
noise distribution is drawn from a gamma distribution
with a shape parameter n > 0 and scale parameter 1/n.
Lower values of s and n correspond to greater heter-
ogeneity in respectively skill and noise. This method
for varying the prevalence of extreme values through
individual heterogeneity permits the natural interpreta-
tion that the importance of skill and noise depend on
contextual factors specic to individuals’ environments.
For example, skill may play a greater role in sports and
research, while inherited wealth and luck may play a
larger role in the determination of success in business
and investment. Alternatively, we can interpret the
model to implement the sharing of a single environment
by all individuals in which s and n determine how com-
mon outliers in skill and noise are.
Denrell and Liu nd that the more outliers the noise
distribution has relative to the skill distribution, the less
top performance is indicative of extraordinary skill. In
Figure 1 we x s at 100, so that outliers are rare. The
distribution of skill is then approximately standard
normal, and skill is conveniently measured in z-scores.
Across panels A, B, C we reduce n, thereby increas-
ing the role of noise in the production of extreme per-
formance values. In panel A, n equals 100 so that the
distribution of noise is also approximately normal and
outliers in noise are as rare as outliers in skill. In this
scenario, skill monotonically increases with perfor-
mance. The 1 per cent best performers, denoted by the
right-most blue dot, are superstars who are on aver-
age nearly two standard deviations more skilled than
the average person (Rosen, 1981). The inset of panel
A shows how, when the horizontal axis is rescaled to
represent performance percentiles, the relationship is
convex on the right: The difference in average skill
between adjacent percentiles increases at the top.
As n decreases, top performance is increasingly the
product of extreme luck rather than extreme skill. In
panel B, for example, where n equals 5, so that outli-
ers in noise are more common, at intermediate levels,
performance is as indicative of skill as it was in panel
A. However, there is now a point beyond which further
increases in performance no longer indicate noticea-
bly greater skill. (And similarly, there is a point below
which further decreases in performance do not indicate
a further lack of skill.) Skill peaks at somewhat over
one standard deviation above the mean.
In panel C, where n equals 2, and outliers in noise
are even more common, the correspondence between
performance and skill at intermediate levels of perfor-
mance is again as strong as before, but at high levels of
performance skill now declines as performance rises.
The 1 per cent best performers have a moderate skill
level that falls short of a standard deviation above the
mean.
We then arrive at our hypothesis by considering
income and job prestige to represent performance, cog-
nitive ability to represent our key skill variable, and
privilege and luck together to constitute the noise var-
iable. We take the literature reviewed in the previous
sections to indicate that cognitive ability is approxi-
mately normally distributed, suggesting a high value of
s, while privilege and luck are characterized by common
outliers, implying lower n: Family resources are con-
centrated in high quantities among a small fraction of
elite families (e.g. McDonald and Ransom, 2008) and
models of cumulative (dis-)advantage render extreme
forms of success and failure common (Coleman, 1964;
Allison, 1980; Adler, 1985; DiPrete and Eirich, 2006).
Assuming a sufciently high level of s relative to n it
then follows that average cognitive ability ceases to
increase beyond a certain job success level.
-2 -1 0 12
Skill
-5 -3 -1 1 3 5
Performance
A
-2 -1 0 1 2
1 20 40 60 80 100
Skill
Performance
p
ercentile
-2 -1 0 1 2
Skill
-5 -3 -1 1 3 5
Performance
B
-2 -1 0 1 2
1 20 40 60 80 100
Performance
p
ercentile
-2 -1 0 1 2
Skill
-5 -3 -1 1 3 5
Performance
C
-2 -1 0 1 2
1 20 40 60 80 100
Performance
p
ercentile
Skill
Skill
Figure 1 Expected skill by performance. One hundred blue dots represent the mean skill and mean performance of each performance
percentile of a population of 59,387 agents, averaged across 100 simulation runs of the Denrell and Liu model with s = 100. The insets
show the same results but with the horizontal axis rescaled to measure performance percentiles. (A) Results when extreme values of
noise happen rarely (n = 100). (B) Results when extreme values of noise happen occasionally (n = 5). (C) Results when extreme values
of noise happen often (n = 2).
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6KEUSCHNIGG, VAN DE RIJT AND BOL
The Denrell and Liu model was not formulated with
our specic application in mind. It assumes that skill
and noise are uncorrelated. When applied to our con-
text of interest this is untenable as this, for example,
implies that family background and cognitive ability
are uncorrelated, which the past evidence we have
reviewed before suggests is false. It is to be seen in the
empirical analysis whether this simplication is conse-
quential. Another discrepancy is that the performance
variable takes on negative values, creating a mismatch
with income and prestige which are always positive.
We address this by considering performance ranks.
Performance ranks are invariant under any monotonic
transformation of the performance variable. The insets
of Figure 1 show that also the relationship between
expected skill and performance rank becomes concave
as n decreases.
In sum, we have taken a generic model of perfor-
mance and used theoretical arguments about outliers
in the distributions of family resources and luck to
derive the result that above some level of occupational
success cognitive ability ceases to detectibly increase,
and perhaps even decreases. This yields the following
hypothesis:
Hypothesis: At high levels of occupational success,
cognitive ability is concave in occupational success.
We test this hypothesis both for the wage–ability and
the prestige–ability relationship.
Data and measures
Statistics Sweden, the country’s central statistical ofce,
assembled micro-data on cognitive ability, socio-eco-
nomic background, education, wage, and occupational
prestige for us by merging administrative population
registers. The data are collected and directly reported
by government agencies, including tax authorities, edu-
cational institutions, and the military. Missing data are
virtually non-existent and—unlike in population sur-
veys in which most captured households locate in the
middle of the wage distribution—the register data are
not truncated but cover the entire wage distribution.
Our analysis includes men who joined the labour
force between 1991 and 2003 (median 1993). In
total, 670,203 men, aged 18–60, entered the labour
market in this period to be fully employed for at least
1 year. Cognitive-ability scores are available only
for Swedish men who had the obligation to undergo
military conscription. We subset on men who took
a compulsory conscription test at age 18–19 during
1971–1977 or 1980–1999, years in which ≥90 per
cent of each cohort enlisted (94 per cent on aver-
age). Enlistment became less comprehensive after
1999 and dropped substantially until its abolition
in 2010. We focus on multi-year career success for
those men for which we observe 11 years of labour
market participation centred around the age of 40
(see below). This leaves us with a full census of
59,387 Swedish-born men for whom we observe
a balanced 11-year panel of annual labour-market
success.
Cognitive ability
We draw on standardized test results of cognitive abil-
ity among male conscripts (see Figure 2A and Table
1 for a description). The Swedish military enlistment
procedure consisted of a series of physical, psycho-
logical, and intellectual tests all men had to take at
age 18–19. Motivation for participation in military
service was not a factor for evaluation as avoiding
enlistment by obtaining low-ability scores was not
possible (Lindqvist and Vestman, 2009). Early-life
measurement of cognitive ability circumvents endo-
geneity problems which may arise when using ability
scores measured after job market entry (e.g. Aldén,
Hammarstedt and Neuman, 2017): One can plau-
sibly assume that learning on the job is steeper in
highly paid jobs and that these learning effects may
reduce the role of innate ability in later-life cognitive
skills. The enlistment procedure included an assess-
ment of cognitive ability similar to the AFQT used in
the United States (Carlsson et al., 2015), which was
found a better predictor of wages than IQ test scores
(Borghans et al., 2016). There were separate paper
and pencil tests for verbal understanding, technical
comprehension, spatial ability, and logic. Each test
consisted of 40 items presented in order of increasing
difculty and speed (Carlstedt and Mårdberg, 1993)
and grades from each dimension were combined into
a normally distributed stanine scale ranging from 1
to 9 (Lindqvist and Vestman, 2009). This measure has
been frequently used in medical studies (e.g. Åberg et
al., 2009) and in the social sciences, for example for
assessing managers’ intelligence (Adams, Keloharju
and Knüpfer, 2018), entrepreneurs’ balance in skill
sets (Aldén, Hammarstedt and Neuman, 2017), the
importance of social origin in achieving promotions
to managerial positions (Bihagen, Nermo and Stern,
2013), and the effect of smart teachers on student
performance (Grönqvist and Vlachos, 2016). With
only minor revisions implemented over the years,
this procedure evaluated the same four underlying
dimensions of ability throughout the full observation
period.
Labour-market success
We consider two success measures, individuals’ aver-
age annual wage and their average occupational
prestige during a 11-year career window (age 35–45)
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7THE PLATEAUING OF COGNITIVE ABILITY AMONG TOP EARNERS
centred on the age of 40 (Haider and Solon, 2006).
We measure annual gross wage—directly reported by
employers to the Swedish tax authorities—in hundreds
of thousands of Swedish krona (roughly equivalent to
units of 10,000 present-day Euros). We include years
of part-time employment and of zero-income, and
our measure includes bonuses paid out as additional,
directly taxable wage income. We adjust annual wages
for ination (using an OECD consumer price index for
Sweden, with base year 2012) to make wages compa-
rable between individuals who entered the labour mar-
ket in different calendar years where the same nominal
incomes represent different levels of purchasing power.
See Figure 2B and Table 1 for descriptives of the wage
variable. To demonstrate the robustness of our ndings,
we change to snapshot annual wages in individuals’
2nd, 10th, and 20th year of labour-market participa-
tion in Appendix A. Because these snapshots do not
require a balanced panel of individuals with 11 years
of labour-market participation, we can include up to
238,000 earners in these analyses. Our second meas-
ure of labour-market success is occupational prestige,
measured on the International Socio-Economic Index
(ISEI) scale, where higher values indicate occupations
with greater social status (Ganzeboom, De Graaf and
Treiman, 1992). We obtain ISEI scores from employees’
registered occupation which are available for 49,022
employees. Following the above operationalization, we
measure individuals’ multi-year average occupational
prestige (see Appendix A for prestige in the 2nd, 10th,
and 20th career year). Ranging from 16 (farm workers)
to 90 (judges), ISEI averages 48.9 and density peaks
at low (e.g. industrial workers, construction workers),
intermediate (e.g. ofce clerks, policemen), and sub-top
levels (e.g. accountants, engineers; see Figure 2C).
Analysis strategy
Our theoretical analysis yielded a hypothesis about the
bivariate relationship between labour-market success
and expected ability. Accordingly, our analysis strategy
is to evaluate the bivariate relationships between wage
and occupational prestige on the one hand and aver-
age ability on the other. We examine the variables of
labour-market success both as absolute scores and, to
avoid the problem of different results for different dis-
tributional transformations, also as ranks. We exploit
the size of our dataset and partition the income distri-
bution into percentile bins, each containing 594 (wage)
respectively 490 (prestige) cases.
Results
Figure 3A shows the conditional mean, geometric mean,
and median wage for each cognitive-ability score. The
gure reproduces the positive effect of ability on wage
identied in earlier work (Coward and Sackett, 1990;
05 10 15 20
Percent
1 2 3 4 5 6 7 8 9
Cognitive ability
A
0.05 .1 .15 .2 .25
Density
1 20 40 60 80
B
farm workers
industrial workers
office clerks
accountants
professors
judges
0.005 .01 .015 .02 .025
Density
1620 25 30 35 40 45 50 55 60 65 70 75 80 85 90
Occupational prestige (ISEI)
C
Wage (SEK100k)
Figure 2 Distributions of cognitive ability, wage, and occupational prestige. (A) Relative frequencies of cognitive-ability scores (1–9)
measured at age 18–19 in our target population of 59,387 Swedish men. (B) Kernel density distribution of multi-year average gross
annual wage in 100k Swedish krona (SEK). (C) Kernel density distribution of occupational prestige (ISEI); exemplary occupations
highlighted for orientation.
Table 1 Description of variables
Mean SD Median Min Max N
Cognitive ability (1–9) 5.45 1.98 5 1 9 59,387
Annual wage (SEK100k) 3.33 2.66 2.89 0 84.40 59,387
Occupational prestige (ISEI) 48.88 17.94 49 16 88 49,022
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8KEUSCHNIGG, VAN DE RIJT AND BOL
Herrnstein and Murray, 1994; Ng et al., 2005; Strenze,
2007; Jokela et al., 2017). The rank correlation
between ability and wage equals ρ = 0.400 (P < 0.001;
N = 59,387). The monotonicity of the relationship in
Figure 3A is consistent with previous studies (Lubinski,
2016; Gensowski, 2018) rejecting the claim that past
a certain threshold having even higher cognitive abil-
ity does not matter (Gladwell, 2008). The most able
Swedish men can expect to make more money than less
able others. The substantive magnitude of the relation-
ship is, however, modest, as the worst scoring men on
average still earn more than a third of the salary of the
best scoring men. The most cognitively able individuals
clearly cannot expect outlandish labour-market returns
(Rosen, 1981; Frank and Cook, 1995; Borghans and
Groot, 1998; Neal and Rosen, 2000; Brynjolfsson and
McAfee, 2011; Mankiw, 2013).
Figure 3B ips the axes, conditioning ability on wage
to allow an evaluation of the comparative intelligence
of top earners. Each dot represents the average ability
level for a wage percentile. The gure reveals a marked
contrast between the body and the tail of the wage
distribution, with a strong correspondence at inter-
mediate wage levels, while above a certain wage level
average ability plateaus at an average of around 7.25.
This plateauing of the wage–ability relation occurs
at approximately SEK600,000 annual wage (about
€60,000). In the three top wage percentiles, that earn
between SEK800,000 and SEK8,400,000 annually, the
relationship even slightly reverses. As in our model
simulations (Figure 1C), there are no signicant differ-
ences in ability between the three top income percen-
tiles, despite there being 594 cases in each percentile
bin and despite those in the 100th percentile earning
more than double the wage of those in the 98th per-
centile. This result supports our hypothesis, and it sug-
gests that the inverse of Gladwell’s (2008) claim does
hold: Past a certain wage threshold, having a higher
wage is no longer telling of cognitive ability. The aver-
age score individuals in the top percentile achieved as
adolescents on the cognitive-ability test is 7.15±0.11
(95 per cent condence interval). On a stanine scale
this amounts to less than a standard deviation (+0.86)
above average.
Figure 3C shows the same 100 cognitive ability lev-
els but this time expressed with wage percentiles on
the horizontal axis. The upper part of the distribution
exhibits the predicted concave pattern, just as it does in
Figure 3B. This lends further support to our hypothesis.
Figure 4 shows results when using occupational
prestige instead of wage as measure of career success.
Results are similar as for wage: Figure 4A shows that
expected prestige monotonically increases in cognitive
ability, with extremely intelligent people having the
best job-market prospects. Yet when ability is condi-
tioned on occupational prestige in Figure 4B, we nd
no systematic pattern in how average cognitive ability
varies between those with ISEI scores 70 and above.
Individuals in these professions (judges, lawyers,
professors, and doctors) achieved an average cogni-
tive-ability test score of 7.13±0.04, which is less than
a standard deviation (+0.80) above the mean.
Figure 4C, however, shows that when average cogni-
tive ability levels are expressed with prestige percentiles
on the horizontal axis, the upper end of the relation-
ship is neither clearly convex nor clearly concave. The
evidence for our hypothesis in the case of occupational
prestige is thus weaker than for income.
Appendix A shows that the pattern of plateauing
skill at high job success displayed in Figures 3 and 4 is
robust when wage and prestige are measured in specic
years after entering the job market (2, 10, 20 years)
rather than as multi-year averages. An additional anal-
ysis in Appendix B shows that the share of men with
a maximum ability score of 9 is strictly lower among
the 1 per cent than among the percentiles below them,
234 5
Wage (SEK100k)
Mean
Geometric mean
Median
A
123456789
Cognitive ability
-2.2 -1.7 -1.1 -0.6 -0.10.4 0.91.5 2
units
z
-scores
4.5 5 5.5 66.5 7 7.5
Cognitive ability
0 2 4 6 8 10 12 14 16 18 20
Wage (SEK100k)
B
4.5 5 5.5 6 6.5 7 7.5
Cognitive ability
1 10 20 30 40 50 60 70 80 90 10
0
Wage percentile
C
Figure 3 Ability and wage. (A) Mean wage (and 95 per cent confidence intervals) by ability units 1–9 and corresponding z-scores. (B)
Mean ability (and 95 per cent confidence intervals) by mean wage per wage percentile. (C) Mean ability by wage percentile.
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9THE PLATEAUING OF COGNITIVE ABILITY AMONG TOP EARNERS
rendering it implausible that a ceiling effect is prevent-
ing cognitive ability levels that are off-the-charts from
showing up at high levels of job success.
Discussion
The empirical results lend support to our argument
that cognitive ability plateaus at high levels of occu-
pational success. Precisely in the part of the wage dis-
tribution where cognitive ability can make the biggest
difference, its right tail, cognitive ability ceases to play
any role. Cognitive ability plateaus around €60,000 at
under a standard deviation above the mean. In terms
of occupational prestige, it plateaus at a similar level
above a job prestige of 70: The differences in the pres-
tige between accountants, doctors, lawyers, professors,
judges, and members of parliament are unrelated to
their cognitive abilities.
A limitation of our study is that we do not account
for effort or non-cognitive capacities—motivation,
social skills, creativity, mental stability, and physical
ability (Borghans et al., 2016). Cognitive ability is
more relevant for some occupations than for others,
and academia, for which it is arguably most relevant,
is neither the best-paid nor the most prestigious profes-
sional eld. Our results thus raise the question to what
degree top wages are indicative of other, unobserved
dimensions of ability. However, omission of effort and
non-cognitive ability from the analysis is only problem-
atic for our conclusions about the relationship between
ability and success if there are theoretical arguments to
be made that their effects dominate luck in the produc-
tion of top income and prestige, either because their
distributions have many extreme values or if there are
strongly increasing returns.
Our analysis, further, is limited to a single country.
Sweden may be seen as a conservative testing ground.
In countries where higher education is less inclusive,
one would expect an overall weaker relationship
between labour-market success and ability (Breen and
Jonsson, 2007). Namely, less income redistribution and
steep tuition barriers to elite colleges may impede the
ow of gifted individuals from lower classes into top
jobs. On the other hand, higher net wages and greater
social status at the top may attract more talent, and
greater differentiation in college prestige elsewhere
may allow rms to select on cognitive skills among
those with a college degree by using elite afliations
as a proxy. Future research on different countries may
seek to evaluate to what extent our ndings generalize.
Third, we limit our analyses to native-born men.
This is an unavoidable restriction of the data (women
and immigrants were not enrolled in the military), and
it is important to learn whether our ndings general-
ize to the full working population. We invite further
research that includes women and citizens from differ-
ent ethnic backgrounds, and we call for careful adjust-
ments in measuring occupational success for different
cohorts in light of marked increases in female labour-
force participation over time as well as in the share
of the immigrant workforce and the varying disadvan-
tages they face along different career paths in many
countries. Such research could also explore potential
variation in meritocracy regimes across social groups,
connecting debates on gender equality and integration
to quantitative studies of the relationship between suc-
cess and ability.
Finally, our analysis was descriptive in nature and
did not assess the proposed theoretical mechanism.
An additional mechanism that may drive the pla-
teauing of the success–ability relation at high wages
is that brighter individuals select into more poorly
remunerated occupational groups, even if within these
groups the brightest are rewarded the highest wages.
If these worse-paying jobs are of higher prestige, this
could explain the weaker patterns we observed for the
30 40 50 60 70
Occupational prestige (ISEI)
Mean
Median
Geometric mean
A
123456789
Cognitive ability
-2.2 -1.7 -1.1 -0.6 -0.1 0.40.9 1.5 2
units
z
-scores
3 3.5 4 4.5 5 5.5 6 6.5 77.5 88.5
Cognitive ability
1620 25 30 35 40 45 50 55 60 65 70 75 80 85 90
Occupational prestige (ISEI)
B
3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5
Cognitive ability
1 10 20 30 40 50 60 70 80 90 10
0
Occupational prestige percentile
C
Figure 4 Ability and occupational prestige. (A) Mean prestige (and 95 per cent confidence intervals) by ability units 1–9 and
corresponding z-scores. (B) Mean ability (and 95 per cent confidence intervals) by prestige score. (C) Mean ability by prestige percentile.
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10 KEUSCHNIGG, VAN DE RIJT AND BOL
relationship between wage and occupational prestige.
While we could not effectively explore the operation
of this possible mechanism, future studies may be able
to disentangle competing mechanisms through lon-
gitudinal analysis of educational and labour market
trajectories.
Recent years have seen much academic and public
discussion of rising inequality (e.g. Mankiw, 2013;
Piketty, 2014; Alvaredo et al., 2017). In debates about
interventions against large wage discrepancies, a
common defence of top earners is the superior merit
inferred from their job-market success using human
capital arguments (Murray, 2003; Mankiw, 2013).
However, along an important dimension of merit—cog-
nitive ability—we nd no evidence that those with top
jobs that pay extraordinary wages are more deserving
than those who earn only half those wages. The main
takeaway of our analysis is thus the identication, both
theoretically and empirically, of two regimes of strati-
cation in the labour market. The bulk of citizens earn
normal salaries that are clearly responsive to individual
cognitive capabilities. Above a threshold level of wage,
cognitive-ability levels are above average but play no
role in differentiating wages. With relative incomes
of top earners steadily growing in Western countries
(Alvaredo et al., 2017), an increasing share of aggregate
earnings may be allocated under the latter regime.
Acknowledgements
We thank Selcan Mutgan for compiling the register
data, and Elias Dinas, Vincenz Frey, Juho Härkönen,
Karl Wennberg, attendants of the 2018 Future of the
Social Sciences Symposium, the Nufeld Sociology
Seminar, and the 2019 Conference of the International
Network of Analytical Sociology in St. Petersburg, as
well as three anonymous reviewers for helpful com-
ments. M.K. acknowledges funding from the Swedish
Research Council (2018-05170). T.B. acknowledges
funding from the Netherlands Organization for
Scientic Research (Veni grant, 451-15-001).
Data Availability
The Swedish register data come from administrative and
tax records and can therefore not be shared; access may
be requested from Statistics Sweden. The code used for
the empirical analysis as well as for the simulation anal-
ysis is available from the authors upon request.
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Appendix
A. Snapshots: 2nd, 10th, and 20th year of
employment
In the main text, we report results for multi-year aver-
ages of labour-market success. Here, we look at snap-
shots of workers’ 2nd, 10th, and 20th career year. We
focus on those who were fully employed in the respec-
tive year. We chose year 2 to capture entry-level wages,
because annual wage data is truncated in year 1 for
all those who did not start their rst employment in
the month of January. Figure A1 shows the density dis-
tribution of log(wage) at tenure length t = 2, t = 10,
and t = 20 with average ination-adjusted wages rising
from SEK2.89k in the 2nd career year to SEK4.28k in
the 20th career year (panel A). Panels B and C demon-
strate the robustness of our results based on early, mid,
and late career wages. At each career stage there is a
positive wage–ability relation for the vast majority of
earners. Among the top earners, however, this posi-
tive association levels off. Panels D–F repeat a similar
robustness analysis for occupational prestige.
B. A ceiling effect?
An alternative interpretation of the plateauing of cog-
nitive ability at high job success levels is that a ceil-
ing effect in measured ability prevents higher averages
from showing up at the highest percentiles. To further
explore this alternative explanation for the observed
pattern, we compute—within each wage percentile—
the share of those who reached a maximum ability
score of 9 on the 1–9 stanine scale of cognitive ability.
A large and increasing number of top smart individuals
among the top percentiles would be particularly con-
cerning. Figure A2 shows that the percentage of those
scoring 9 in cognitive ability is highest among earn-
ers of relatively high wages (between SEK500,000 and
SEK800,000). With 23.2 per cent it is highest in the
97th wage percentile. But among the top 3 percentiles,
the percentage of top scorers declines to 18.4 per cent
on average, for the top percentile it is 17.0 per cent.
This suggests that our result is not driven by a ceiling
effect.
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14 KEUSCHNIGG, VAN DE RIJT AND BOL
Figure A1. Labour market success in the 2nd, 10th, and 20th year of employment. (A) Kernel density distribution of log(annual wage) in
SEK100k. Average inflation-adjusted wages rise from SEK2.89k (orange) to SEK3.73k (blue) and SEK4.28k (red). (B) Mean ability (and 95
per cent confidence intervals) by mean wage per wage percentile. (C) Mean ability by wage percentile. (D) Kernel density distribution of
ISEI scores. (E) Mean ability (and 95 per cent confidence intervals) by prestige score. (F) Mean ability by prestige percentile.
Figure A2. (A) Percentage of earners who received a maximum score of 9 in ability (and 95 per cent confidence intervals) by mean
wage per wage percentile. (B) The percentage of top scorers by wage percentile.
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