ThesisPDF Available

Intelligence and socioeconomic success: A study of correlations, causes and consequences

Intelligence and socioeconomic success
A study of correlations, causes and consequences
Institute of Social Studies, University of Tartu, Estonia
The dissertation is accepted for commencement of the degree of Doctor of
Philosophy (in Sociology) on 28.05.2015, by the council of the Faculty of
Social Sciences and Education, University of Tartu.
Supervisor: Henn Käärik, Research fellow of sociology, Institute of
Social Studies, University of Tartu
Opponent: Robert Erikson, Professor of sociology, Swedish Institute for
Social Research, University of Stockholm
Commencement: Date: September 9, 2015. Time: 12:15. Place: Lossi 36-214,
The publication of this dissertation is granted by the Institute of Social Studies,
University of Tartu.
ISSN 1736-0307
ISBN 978-9949-32-851-2 (print)
ISBN 978-9949-32-852-9 (pdf)
Copyright: Tarmo Strenze, 2015
University of Tartu
LIST OF ORIGINAL PUBLICATIONS ....................................................... 6
ACKNOWLEDGEMENTS ........................................................................... 7
1. INTRODUCTION ...................................................................................... 8
OF RESEARCH ........................................................................................ 10
2.1. What is intelligence? .......................................................................... 10
2.2. What is success? ................................................................................. 13
2.3. Intelligence and various forms of success .......................................... 14
2.4. Intelligence and socioeconomic success ............................................ 16
2.5. Why intelligence predicts socioeconomic success? ........................... 18
2.6. Intelligence and socioeconomic success in different societies .......... 24
2.7. Allocation of talent in different societies ........................................... 27
3. OVERVIEW OF THE ORIGINAL STUDIES ......................................... 31
3.1. Aims of the original studies ............................................................... 31
3.2. Data and methods of the original studies ........................................... 32
3.3. Results of the original studies ............................................................ 33
3.4. Original contributions of the original studies ..................................... 34
4. CONCLUSIONS ....................................................................................... 36
4.1. Relationship between intelligence and socioeconomic success ........ 36
4.2. Causes of the relationship .................................................................. 37
4.3. Social consequences of the relationship ............................................. 37
REFERENCES ............................................................................................... 39
SUMMARY IN ESTONIAN ......................................................................... 46
PUBLICATIONS ........................................................................................... 49
CURRICULUM VITAE ................................................................................ 1
1 7
The dissertation in based on the following original publications which will be
marked in the text by the Roman numerals.
I Strenze, T. (2006). Who gets ahead in Estonia and America? A com-
parative analysis of mental ability and social origin as determinants of
success. Trames: A Journal of the Humanities & Social Sciences, 10, 232–
II Strenze, T. (2007). Intelligence and socioeconomic success: A meta-
analytic review of longitudinal research. Intelligence, 35, 401–426.
III Strenze, T. (2013). Allocation of talent in society and its effect on eco-
nomic development. Intelligence, 41, 193–202.
The articles are reproduced with permission from the publishers Elsevier and
Estonian Academy Publishers.
Author’s contribution
The author of this dissertation is the sole author of all the above publications.
For about ten years I have been “bugged” about my dissertation. And this has
been a great motivation. I thank people for doing it and thereby helping me to
get the dissertation done. These people include Henn Käärik, Liina-Mai
Tooding, Dagmar Kutsar, Avo Trumm, Mare Ainsaar, Kairi Kasearu, Jüri Allik
and several other colleagues. I have received the same treatment from my
friends Katri Lamesoo and Evelin Espenberg. And also from my mother and
sisters. Even my grandmother asked if I have already got my “candidate”.
In the final stages of writing I received valuable comments from Veronika
Kalmus, Olev Must, Pille Pruulmann-Vengerfeldt and Ellu Saar.
The original topic of my dissertation was “theory of action”. Over time it
was narrowed down to “intelligence and socioeconomic success”. Looking at
this thematic evolution (and the time it took), I am grateful to the Institute of
Sociology and Social Policy for providing me with an open intellectual
environment that has allowed my thoughts to evolve.
To some people the English word “intelligence” brings to mind espionage,
secret agents, counter intelligence and CIA. The present dissertation is not about
that kind of intelligence. In this dissertation the word “intelligence” refers to
mental abilities, intellectual achievements, IQ tests, and the like. But no need to
be disappointed. Intelligence (of the second kind) is one of the most
controversial topics in the social sciences. Many writings on intelligence start
with observations like “Intelligence has been a much debated construct in all of
its history. Some swear by it, others swear at it.” (Viswesvaran & Ones, 2002:
211), “Few topics have sparked such heated debate within the academic
community and society at large as that of intelligence and intelligence testing.
(Schlinger, 2003: 15), “Few debates in the history of science have been
conducted with such stupidity as the one about intelligence.” (Ridley, 1999: 77).
The IQ debate (or “IQ war” as some have called it) started at the beginning of
the 20th century and continues to this day over questions like: do IQ tests
measure intelligence, is intelligence genetically determined, can intelligence be
changed, are whites more intelligent than blacks?
The present dissertation focuses on the following question: are intelligent
people more successful than less intelligent people? A lot of scientific research
has addressed this question and the simple answer to the question is a firm
“yes”: intelligent people are indeed more successful than less intelligent people.
In other words, there is a positive relationship (correlation) between intelligence
and success. However, that simple fact is actually not that simple, there are
many details about this fact that need to be discussed. The causes of the positive
relationship between intelligence and success are not entirely understood,
despite many decades of research, and the consequences of that relationship for
society are just beginning to be studied.
“Success” can be defined in various ways. The present dissertation is
devoted mainly to one form of success: the so called “socioeconomic success”.
That is success in educational and occupational world – receiving a good
education, getting a decent job and making enough money. The general aim of
this dissertation is to contribute to the scientific knowledge on the relationship
between intelligence and socioeconomic success.
To attain systematic knowledge about something, one has to pursue three
goals: describe the thing of interest, analyze the causes of the thing, and analyze
the consequences of the thing. Following this simple logical schema, we can set
up three specific goals for the present dissertation:
First, to describe the relationship between intelligence and socioeconomic
success – how strong is the relationship, how it compares to the relationship
with other measures of success and other determinants of success?
Second, to analyze the causes of the relationship – what mechanisms explain
the relationship between intelligence and socioeconomic success?
Third, to analyze the social consequences of the relationship – how the
existence (or absence) of the relationship between intelligence and
socioeconomic success influences society?
The dissertation is based on three original studies (Strenze, 2006, 2007, 2013, or
studies I, II, III, respectively). These studies are rather different from one
another but they all deal with the relationship between intelligence and
socioeconomic success in some way. The original studies contribute to the three
goals of the dissertation in the following manner: the first goal (description) is
achieved through studies I and II, the second goal (causes) through study II,
and the third goal (consequences) through study III.
The topic of the relationship between intelligence and socioeconomic
success is a multidisciplinary topic that joins the psychological study of human
mind to the sociological study of human behavior in society. The present
dissertation has to find an appropriate balance between psychology and
sociology. That is why the dissertation will not go very deeply into the
psychological mechanisms underlying intelligence (although, a short review
will be given in chapter 2.1). Likewise, the dissertation will not delve into the
sociological meaning of success (aside from brief remarks in chapter 2.2). The
main concern of the dissertation is the relationship between intelligence and
success, not either of them separately.
The introductory chapters of the dissertation are structured as follows.
Chapter 2 will elaborate on the theoretical and empirical context of the original
studies. More specifically, it will discuss the meaning of intelligence and
success (chapters 2.1 and 2.2), describe the relationship between intelligence
and socioeconomic success (2.3 and 2.4), analyze the causes of the relationship
(2.5 and 2.6) and its consequences (2.7). Chapter 3 will review the aims,
methods and results of the original studies. Chapter 4 will present conclusions.
This chapter of the dissertation serves as a general review and discussion of the
research on intelligence and socioeconomic success. The results from the
original studies of the dissertation are included in this review to allow the reader
to see what the original studies contribute to this research.
2.1. What is intelligence?
In order to provide some background it is necessary to start by discussing the
nature and definition of intelligence. My treatment of these topics will naturally
be brief, for more detailed reviews see Jensen (1998) or Sternberg & Kaufman
(2011). Scientists from different fields and of different persuasion have given
various definitions to intelligence (see Legg & Hutter, 2007). A good
description of what is generally meant by intelligence is offered by Gottfredson:
Intelligence is a very general mental capability that, among other things, in-
volves the ability to reason, plan, solve problems, think abstractly, comprehend
complex ideas, learn quickly and learn from experience. It is not merely book
learning, a narrow academic skill, or test-taking smarts.” (Gottfredson, 1997:
13). Some prominent researchers would probably not agree with this definition
(e.g., Flynn, 2007), but I find the definition useful as a starting point because it
includes the basic attributes that are necessary for the concept of intelligence to
be meaningful.
First, as the above definition states, intelligence is capability or ability, not
book learning or academic skill. Ability is the potential to do something in case
of sufficient motivation and opportunity (Carroll, 1993). Intelligence is the
potential to think, comprehend, learn and perform other mental operations. It
must be distinguished from knowledge and skill, which refer to the specific
information the person has already learned, while intelligence is the potential to
learn any information (see Furnham & Chamorro-Premuzic, 2006). Second,
intelligence is general ability, not specific ability that is related only to a
particular task or field. Each person has one overall level of mental ability and
that ability is not specialized to any particular activity. In addition to that
general ability, people also have more specific abilities, such as verbal or
numerical ability (see Willis et al., 2011, for a review). This dissertation will be
limited to general ability.
Another important attribute of intelligence is that it differentiates people, it is
not the same for all people, some have more intelligence than others. In other
words, intelligence is an “individual difference variable”, a variable that has
been invented mainly to characterize how people differ from one another (see
Maltby et al., 2007). To measure individual differences in terms of this variable,
psychologists have constructed IQ tests. Much of what will be said in the
dissertation is dependent on the assumption that IQ tests are more or less
adequate measures of intelligence. Not everyone agrees with that assumption,
however, there has been a lot of dispute if IQ tests really measure general
ability, like they are meant to, or do they measure specific knowledge and skills
(see Urbina, 2011, for a short review). This dissertation cannot resolve this
dispute or discuss the nature and construction of IQ tests at any length. Let us
proceed on the assumption that testing intelligence is possible, even if some IQ
tests do not live up to the expectation. Perhaps the problems with IQ testing can
be alleviated if traditional IQ tests are replaced by more objective biological
measures of intelligence (Matarazzo, 1992).
Now, having defined intelligence in such a manner, an important question
presents itself – does intelligence like that really exist, what is the basis for
saying that each person can be characterized by a single level of general mental
ability? That is a critical question and, indeed, some authors have said that
intelligence (defined in the above manner) does not exist (e.g., Gould, 1981;
Schlinger, 2003). So let me present what many believe is the main argument for
the existence of intelligence. When a group of people is given a number of
mental tasks to solve, then what usually happens is that some people do it better
than others and those people who are better in one task are also better in the
second task and the third task and so on. In other words, there is a positive
correlation between the scores of those different tasks. This phenomenon was
first studied by Spearman (1904) and has since become one of the major
findings of test research. In a huge meta-analysis of 460 data sets from previous
studies, Carroll found that there is a uniform tendency for different ability tests
to correlate positively with one another (Carroll, 1993). Other meta-analyses
that have obtained the same result include Kuncel et al. (2001) and Ackerman et
al. (2005). Similar positive intercorrelations have been found in education –
students’ results in different school subjects tend to correlate positively with one
another (Deary et al., 2007). The tendency for positive correlations can also be
observed over time – when the same individuals are given the same or similar
test after some time, then those who got better results the first time, will also get
better results the second time, even if the time interval between the first and
second testing is several decades (Deary et al., 2000).
Where do these correlations come from? Why do some people get
consistently better results than others? To answer that question, Spearman
(1904) came up with the concept of general intelligence, or g-factor as it is often
called. General intelligence is the “mental energy” within people that fuels the
solving of all intellectual tasks and people who have more of this energy get
better results in most tasks. In factor-analytic terms, it is the unobserved
hypothetical construct that explains the positive correlations among tasks
(Jensen, 1998). That is one explanation for the positive correlations. An
alternative explanation would be social environment – people who live in safe,
healthy and culturally stimulating environment are better prepared to solve any
kind of intellectual tasks. Thus, in this case the source of the correlations is not
within the person but outside of him or her; and IQ tests are really measures of
social advantages and disadvantages, not of some ability inside the person
(Block & Dworkin, 1976; Richardson, 2002). The present dissertation is not in a
position to decide conclusively which of these explanations is correct. But the
opposition between these two views is of central importance for this dissertation
because it has been the main source of dispute throughout the IQ debate.
Probably the best known opposition of the IQ debate is the nature-nurture
question – what is the ultimate source of intelligence, genes or environment?
This question has been on the forefront of intelligence research since the first
half of the 20th century and continues to attract attention. Intelligence certainly
would not be such a controversial subject if there was no reason to believe that
it is, to a considerable degree, determined by genetic factors. Dozens of
behavior genetic studies have tried to determine the heritability of intelligence
(see meta-analyses in Bouchard & McGue, 1981; Devlin et al., 1997). The
estimates of heritability (percentage of the variation in IQ scores that is
explained by variation in genes) vary considerably. Some researchers have
suggested that it could be as high as 0.80 (Jensen, 1969), but most have come
up with lower estimates around 0.50 (Devlin et al., 1997). The consensus seems
to be that about half of the variation in intelligence comes from genes. The other
half is left for the environmental influences, such as parental wealth, home
atmosphere, and the like (see meta-analyses in White, 1982; Kall, 2010).
Another, less controversial, topic in the IQ debate is the consequences of
intelligence. By that I mean the consequences of individual differences in
intelligence – what differences between people are caused by the fact that
people do not have the same level of intelligence? This is, of course, the central
topic of the present dissertation and will be covered in the following chapters.
Here, let me just state the two main views. One view is that intelligence is
highly consequential for people in their everyday lives, those with higher
intelligence achieve all sorts of desirable outcomes thanks to their ability to
overcome the hardships that life might set up for them. The other view is that
intelligence is really not that important; intelligent people may usually achieve
more desirable outcomes than less intelligent people, but that is not because of
their superior intelligence, but for some other reason, such as rich parents.
These two views will be discussed later in relation to socioeconomic success
(see chapter 2.5).
To end this chapter, take a look at Table 1 which presents some of the
central topics of the IQ debate from the two opposing points of view. The
statements in both columns of Table 1 usually come in packages, such that a
researcher who supports one of the statements in the column is likely to support
the other statements in the same column. I am myself not committing to either
one of these extreme views on intelligence, rather, these views are presented
here to provide a general background for the results that will be discussed later.
One thing we should remember from this table is that the question about the
relationship between intelligence and success – the topic of the present
dissertation – is closely connected to other questions about intelligence; our
interpretation of the relationship between intelligence and success is dependent
on our beliefs regarding the other topics of the IQ debate.
Table 1. Overview of the opposing views on some of the central problems regarding
Each person can be characterized by
general mental ability (Jensen, 1998). There is no such thing as general
mental ability (Gould, 1981).
IQ tests are reasonably good measures
of that ability (Eysenck, 1979). IQ tests are really measures of social
environment (Block & Dworkin, 1976).
Correlations among tests are proof of
the existence of general ability (Carroll,
Correlations among tests are the result
of environmental influence
(Richardson, 2002).
Genetic effects on IQ scores are large
(Jensen, 1969). Genetic effects on IQ scores are not
that large (Devlin et al., 1997).
Intelligence has a causal effect on
success in many areas of life
(Gottfredson, 2003).
The correlation between IQ scores and
success does not represent a causal
effect (McClelland, 1973).
2.2. What is success?
Before going on to the relationship between intelligence and success, I should
say a few words about the concept of “success”. Contemporary western society
is often said to be highly success-centered, there is even talk about the “cult of
success” (Sutrop, 2004). In a wider sense of the term, success is present in every
society. Success can be defined as doing or achieving something that is
generally considered desirable in the society. Naturally, there are many ways to
be successful. This dissertation is mostly devoted to socioeconomic success –
success in the field of education and work – but other forms are also discussed.
Some readers may be tempted to say that success is a purely subjective
phenomenon, which each individual defines for oneself. There is certainly some
truth to this statement, but it seems that there is usually a high degree of
consensus in society as to what is desirable and what is not. This consensus
provides individuals with socially accepted goals to strive for (Merton, 1938).
Even if there are individuals who reject some form of success (for instance,
claim that they do not care about money), that form of success still remains
socially relevant and worthy of study.
The present dissertation focuses mostly on socioeconomic success. “Socio-
economic success” is a vague term that usually refers to success in the edu-
cational and occupational sphere. It can also be termed “career success” if by
career we mean occupational as well as educational career. Another related term
is “status attainment” – attaining social status. The main indicators of socio-
economic success are education, occupation and income attained by the person
in adulthood. In addition to these, socioeconomic success could also be defined
as promotions received at work, upward social mobility, being employed (as
opposed to unemployed), etc.
It should also be noted that “success” is closely related to social inequality: if
some people are more successful than others, then there is inequality between
them. That is especially true of socioeconomic success because differences
between people in terms of education, occupation and income are at the very
heart of the study of social inequality and stratification. Therefore, a study of
success is a study of social inequality; if intelligence contributes to people’s
success, then it means that intelligence creates inequality between people.
2.3. Intelligence and various forms of success
So what is the evidence for the relationship between intelligence and success?
Hundreds of studies have examined the relationship between intelligence and
some form of success; it is obviously impossible to review all of these studies
here. I will concentrate on meta-analyses (quantitative reviews of previous
research) because results from meta-analyses are more reliable than results from
single studies. Table 2 presents a list of meta-analytic correlations between IQ
scores and various outcomes that can reasonably be designated as “success” or
lack of success. Of course, several important forms of success have never been
subjected to meta-analysis and are, consequently, absent from Table 2. On the
other hand, some forms of success have been meta-analyzed more than once, in
which case I chose the largest meta-analysis. What interests us most in Table 2
is the comparison of correlations with socioeconomic success to correlations
with other forms of success.
Overall, it is evident from Table 2 that intelligence tends to be positively
correlated with desirable outcomes and negatively correlated with undesirable
outcomes. This means that intelligent people generally manage to achieve good
things and keep away from bad things. The size of the correlations varies a lot,
however. Some correlations are around .50, while others are close to zero.
These differences are quite natural given that the forms of success depicted in
the table are rather different from one another. In a review of meta-analyses in
psychology, Hemphill (2003) found that meta-analytic correlations tend to be
somewhere between .20 to .30. Richard et al. (2003) found in a similar review
that the average meta-analytic correlation in social psychology is .21. Some of
the correlations with intelligence are clearly stronger than that. In particular, the
correlations with education- and work-related success tend to be the stronger
ones. Socioeconomic success, as measured by educational and occupational
attainment, is among the strongest correlates of intelligence (see Strenze 2011,
2015, for further discussion of intelligence and various forms of success).
Table 2. Correlations between intelligence and success (results from meta-analyses).
Measure of success (or lack of success) r k N Source
Academic performance in primary education .58 4 1791 Poropat (2009)
Educational attainment .56 59 84828 Strenze (2007)
Job performance (supervisory rating) .53 425 32124 Hunter & Hunter (1984)
Occupational attainment .43 45 72290 Strenze (2007)
Job performance (work sample) .38 36 16480 Roth et al. (2005)
Skill acquisition in work training .38 17 6713 Colquitt et al. (2000)
Degree attainment speed in graduate school .35 5 1700 Kuncel et al. (2004)
Group leadership success (group productivity) .33 14 Judge et al. (2004)
Promotions received at work .28 9 21290 Schmitt et al. (1984)
Interview success (interviewer rating) .27 40 11317 Berry et al. (2007)
Becoming a leader in group .25 65 Judge et al. (2004)
Academic performance in secondary education .24 17 12606 Poropat (2009)
Academic performance in tertiary education .23 26 17588 Poropat (2009)
Voluntary activism at workplace .23 43 12507 Gonzales-Mule (2014)
Income .20 31 58758 Strenze (2007)
Having anorexia nervosa .20 16 484 Lopez et al. (2010)
Research productivity in graduate school .19 4 314 Kuncel et al. (2004)
Participation in group activities .18 36 Mann (1959)
Group leadership success (peer rating) .17 64 Judge et al. (2004)
Creativity .17 447 Kim (2005)
Self-confidence .12 8 2219 Chang et al. (2012)
Class attendance in college .11 4 1047 Crede et al. (2010)
Popularity among group members .10 38 Mann (1959)
Negotiation success .07 5 862 Sharma et al. (2013)
Happiness .05 19 2546 DeNeve & Cooper (1998)
Procrastination (needless delay of action) .03 14 2151 Steel (2007)
Changing jobs .01 7 6062 Griffeth et al. (2000)
Counterproductive behavior at workplace –.02 35 12074 Gonzales-Mule (2014)
Physical attractiveness –.04 31 3497 Feingold (1992)
Recidivism (repeated criminal behavior) –.07 32 21369 Gendreau et al. (1996)
Number of children –.11 3 Lynn (1996)
Traffic accident involvement –.12 10 1020 Arthur et al. (1991)
Conformity to persuasion –.12 7 Rhodes & Wood (1992)
Communication anxiety –.13 8 2548 Bourhis & Allen (1992)
Having schizophrenia –.26 18 Woodberry et al. (2008)
r – correlation between intelligence and the measure of success, k – number of studies included in the meta-
analysis, N – number of individuals included in the meta-analysis.
To make sense of the correlations in Table 2, it would be useful to have a theory
that does not concentrate on just one specific form of success, but strives to
explain the whole pattern of correlations. Such theories are not very abundant,
but one that is quite suitable is the evolutionary theory of intelligence developed
by Kanazawa (2004). According to this theory, general intelligence is a brain
function that has evolved in human evolution to deal with evolutionarily novel
tasks. Take, for instance, activities like finding food, having children,
collaborating with other humans – these are all tasks that our ancestors have
been solving for millions of years and for these tasks, it is likely, specific
hereditary brain mechanisms have developed that promote the successful
performance of that task. But activities like getting good grades at school,
making a lot of money or being thin have just recently been invented by our
society and they do not have their own brain mechanisms. For these novel tasks,
people use intelligence, which is a generic ability to solve any type of
(unexpected) problems. Kanazawa notes that intelligence correlates positively
with evolutionarily novel activities, but the correlation with ancient activities is
zero or even negative. This is also evident in Table 2, which mostly lists novel
school- or job-related forms of success that have the expected positive
correlation with intelligence; but one of the most ancient forms of success,
number of children, has a negative correlation (–.11). A detailed discussion of
Kanazawa’s theory is beyond the scope of this dissertation (see Dutton, 2013,
for criticism), but this theory deserves to be noted as one that tries to explain
why different forms of success have different correlations with intelligence.
2.4. Intelligence and socioeconomic success
The main correlations between intelligence and socioeconomic success
(education, occupation and income) were already reported in Table 2. But given
that it is difficult to evaluate the importance of a predictor in isolation, let us
compare the predictive power of intelligence to the predictive power of other
relevant variables. Table 3 presents a selection of meta-analytic correlations
between income and some of its predictors. I concentrate on income because,
among the typical measures of socioeconomic success, income is arguably the
most important one, and also the most thoroughly studied in meta-analyses.
Intelligence is represented twice in Table 3, one correlation from the meta-
analysis by Ng et al. (2005) and the other from the meta-analysis by Strenze
(2007) [II]. The latter meta-analysis was based on general population samples,
the former leaned more towards specific samples of workers from single
organizations or occupational groups. The other predictors in Table 3 include
parental socioeconomic status or SES (parental income, father’s occupation),
personality traits (extroversion, conscientiousness), educational variables
(educational level, grades), and some demographic characteristics (age, gender).
These are the typical “competitors” in the prediction of success. Of course,
there may be other important determinants of income but these have not been
subjected to meta-analysis.
Table 3. Correlations with income (results from meta-analyses).
Predictor of income r k N Source
Educational level .29 45 45293 Ng et al. (2005)
College grades .28 48 9759 Roth & Clarke (1998)
Intelligence (specific samples) .27 8 9560 Ng et al. (2005)
Age .26 52 40197 Ng et al. (2005)
Intelligence (general samples) .20 31 58758 Strenze (2007)
Parental income .20 17 395562 Strenze (2007)
Father’s occupation .19 31 98812 Strenze (2007)
Gender (male vs. female) .18 51 33211 Ng et al. (2005)
Parental SES index .18 14 64711 Strenze (2007)
Father’s education .17 45 107312 Strenze (2007)
Mother’s education .13 37 93616 Strenze (2007)
Race (white vs. non-white) .11 13 6443 Ng et al. (2005)
Extroversion .10 7 6610 Ng et al. (2005)
High school grades .09 14 41937 Strenze (2007)
Conscientiousness .07 6 6286 Ng et al. (2005)
Locus of control .06 7 2495 Ng et al. (2005)
Neuroticism –.12 7 6433 Ng et al. (2005)
r – correlation between the predictor and income, k – number of studies included in the meta-
analysis, N – number of individuals included in the meta-analysis.
All the correlations in Table 3 are relatively weak, the strongest one is just .29,
suggesting that the financial success of people is rather difficult to predict.
Intelligence is firmly among the stronger predictors of income, although the
differences between some of the correlations are too small to be of much
consequence. The correlations with intelligence are a bit stronger than the corre-
lations with parental variables and significantly stronger than correlations with
personality traits. Educational level is, not surprisingly, the strongest predictor.
College grades are, somewhat surprisingly, much better predictors than high
school grades. Overall, we can conclude that, as much as income is predictable,
it can be predicted from intelligence, a bit less from parental SES and
noticeably less from personality. It pays to have a good education and study
well in college, but there is not much monetary incentive to doing well in high
As for the other measures of socioeconomic success, the meta-analysis by
Strenze (2007) [II] also analyzed the determinants of education and occupation,
and found that these two are easier to predict than income. Correlations with
intelligence are .56 and .43, respectively. Parental SES and high school grades
have more or less similar correlations with education and occupation. The meta-
analysis by Ng et al. (2005) also analyzed the determinants of promotions, but
did not include intelligence among the determinants. All in all, it can be con-
cluded that among the various predictors of socioeconomic success, intelligence
stands out as one of the better ones.
The research on intelligence and socioeconomic success shows us that
intelligent people generally occupy higher positions in society. A society with
such ability-based stratification is called meritocracy (Young, 1958) and is often
considered to be a desirable form of society, because people are allowed to
achieve positions that corresponds to their abilities, as opposed to being
allocated to positions according to their social origin (parental SES). There has
been quite a lot of dispute on how meritocratic contemporary western society
really is (see Kingston, 2006). In 1994 Herrnstein and Murray published a book
called The Bell Curve that became notorious for claiming that, in the United
States, intelligence has a considerably stronger effect on various forms of
success than parental SES and that American society is moving towards IQ-
based class system. Saunders (1997) found that the same might be true for Great
Britain. Such results imply that society is rather meritocratic. However, critics
have argued that these studies overestimated the importance of intelligence and
underestimated the importance of parental SES (Fisher et al., 1996; Breen &
Goldthorpe, 1999).
Meritocracy is an important topic for theoretical as well as practical reasons.
In chapter 2.7 of this dissertation I will suggest that the level of meritocracy in
society shows how efficiently the society uses the talents of its people; a more
efficient allocation of talent (more meritocracy) should lead to faster economic
2.5. Why intelligence predicts socioeconomic success?
Science should not stay content with just establishing a relationship between
two phenomena, it should also try to explain this relationship. Therefore, having
seen that there is a reasonably strong positive relationship between intelligence
and socioeconomic success, we should now ask: where does this relationship
come from, why intelligence predicts socioeconomic success, what is the
mechanism? My experience with the literature has led me to conclude that there
are three distinct explanations for the relationship between intelligence and
socioeconomic success. Figure 1 presents a simple visual overview of all the
three explanations.
The first one is “psychometric” explanation, which states that intelligence is
a general ability to solve all sorts of problems and people who have more of this
ability are more successful in their lives because they are better at solving their
everyday problems, these same people are also better at solving the tasks of an
IQ test, hence the positive observed correlation between IQ scores and success –
both are consequences of the underlying intelligence. There is no generally
accepted word to describe this explanation, I have labeled it “psychometric”
following Neisser et al. (1996). The psychometric explanation represents the
original, classical view of what intelligence is and what IQ tests are supposed to
measure. The supporters of this explanation are mostly psychologists and
psychometricians, some of them are involved in the construction of tests (e.g.,
Herrnstein & Murray, 1994; Jensen, 1998; Gottfredson, 2003; Schmidt &
Hunter, 2004).
Figure 1. Three explanations of the relationship between IQ scores and success.
The second explanation is called “environmental” and according to that, social
environment is the real cause of success, people who come from good environ-
ment are more successful because they have all sorts of social advantages, these
same people are also better at solving the tasks of an IQ test, hence the positive
observed correlation between IQ scores and success – both are consequences of
the social environment. Environment is a vague concept, of course, but mostly it
is specified as social origin or parental socioeconomic status (SES); the idea
being that children of wealthy and educated parents have the necessary
resources to be successful in life as well as in IQ tests. Intelligence as a stable
characteristic of people has no role in this explanation or only a marginal role.
The supporters of this explanation tend to be sociologists and sociologically
minded psychologists (e.g., McClelland, 1973; Bowles & Gintis, 1976; Fischer
et al., 1996).
The third explanation is a bit more specific and less known, I call it
“credentialist” because of its affinity to the theory of educational credentials
(see Brown, 2001). In this explanation, it is the IQ score itself that directly
causes success; it does not matter much if IQ score measures intelligence or
social environment, what matters is that people are given IQ tests and are
assigned to social positions (admitted to colleges, hired to jobs) on the basis of
their IQ scores; people with higher scores, of course, get better positions. If
such IQ-based assignment takes place in a large enough scale, then it could
shape the social structure and create a society-wide positive correlation between
IQ scores and success. Several authors believe that this is what is happening in
the United States and, possibly, in other western societies (see Block &
Dworkin, 1976; Lemann, 1997; Byington & Phelps, 2010).
As we evaluate the mechanisms presented in the three explanations of
Figure 1, it is evident that the first mechanism (psychometric) is the only one
that presents intelligence as the real cause of socioeconomic success – this is the
only one where people with high IQ scores achieve success because of their
superior mental abilities. In the environmental mechanism, the correlation
between IQ scores and success is spurious, social environment is the real cause;
the researchers who lean towards this explanation often doubt the existence of
intelligence as a stable mental characteristic of people (see Table 1). The
credentialist mechanism does not necessarily deny that intelligence causes
success, but the causation takes place in a “wrong manner”. A lot depends on
whether IQ scores represent real intelligence or social environment – if IQ
scores represent intelligence, then the IQ score-based assignment of people
would simply accelerate the natural process of intelligent people ending up in
superior positions; if however, IQ scores represent environment, then it would
mean that any IQ score-based assignment is arbitrary and does not have the
alleged effect of sorting people according to their real ability.
So what evidence would allow us to say which explanation is the best one?
The most general kind of evidence can be obtained by just comparing the
correlations of intelligence and parental SES with socioeconomic success. We
saw in Table 3 that both intelligence and parental SES have positive
correlations with income; Strenze (2007) [II] showed that both have positive
correlations with education and occupation, as well. Neither intelligence nor
parental SES seems to be an overwhelmingly stronger predictor of socio-
economic success, although there is a slight tendency for intelligence to be a
better predictor in several instances (see Strenze, 2007 [II], for further
discussion). This finding can be interpreted as showing that the environmental
explanation cannot be hundred percent correct, the correlation between IQ
scores and socioeconomic success cannot be completely explained by parental
SES – if this were the case, then parental SES should have stronger correlations
with success but, as we saw, this is not the case. Therefore, the effect of
intelligence must be, to some degree, independent from the effect of parental
That last conclusion has been confirmed in much greater detail by the studies
of status attainment. These studies have used sophisticated regression and path
models to analyze the determinants educational, occupational and income
attainment. Many of these studies have included intelligence among the
determinants, making it possible to see if intelligence affects attainment after
controlling for a number of alternative determinants. The typical result has been
that intelligence remains a significant predictor even after controlling for
parental education, occupation and income (Jencks, 1979; Sewell et al., 1980,
Herrnstein & Murray, 1994; Fischer et al., 1996; Saunders, 1997), absence of
one parent (Jencks, 1979; Sewell et al. 1980), number of siblings (Jencks, 1979;
Blake, 1989), parents’ educational support for the child (Jencks, 1979; Otto &
Haller, 1979; Sewell et al., 1980; Bond & Saunders, 1999), ethnicity or race
(Jencks, 1979; Strenze, 2006 [I]). Thus, the influence of intelligence cannot be
explained away by many of the important environmental variables. On the other
hand, most studies show that the effect of environmental variables on success
also remains statistically significant after controlling for intelligence. A rea-
sonable conclusion is that both intelligence and environment have independent
effects on socioeconomic success. In terms of theoretical mechanisms (of
Figure 1) it means that both psychometric and environmental mechanisms are at
Such “battle of coefficients” supplies us with important information but it
cannot tell us, what is the ultimate source of the intelligence-success relation-
ship – genes or environment. The intersection of genetics, intelligence and
success is a complicated subject full of different methods and contradictory
findings (see Gottfredson, 2011, for a review). On the one hand, it is long
known that intelligence is substantially heritable (determined by genes; Devlin
et al., 1997), more recently it has become known that socioeconomic success is
also heritable (Plomin & Bergeman, 1991; Rowe et al., 1999). These results
seem to imply that the “genetic intelligence” is an important cause of socio-
economic success. Indeed, Rowe et al. were able to determine that both intelli-
gence and socioeconomic success are influenced by the same genes. Thus, the
“genes for intelligence” are also the “genes for success”. On the other hand,
using different methods, Bowles and Gintis (2002) have shown that the role of
“genetic intelligence” in the status attainment process has been greatly overes-
timated. They do not deny that socioeconomic success is heritable, but they
claim that it is so mostly due to other genetic characteristics, like race, health or
personality. The present dissertation cannot pass a final judgement on this topic
but it must be remembered that the question of genetics looms behind every
study of intelligence, even if the question is not addressed explicitly.
What about the credentialist explanation to the relationship between intelli-
gence and socioeconomic success? There has been a lot of dispute in the United
States over the use of IQ tests in educational and employment setting. In edu-
cational setting, psychological tests (including IQ tests) are often used for
admitting students into schools and placing them into tracks within schools
(Byington & Phelps, 2010). The use of IQ tests for track placement has actually
diminished since the 1970s (Loveless, 1998), but admission into colleges is still
largely based on SAT and ACT tests, which are both strongly correlated with
traditional IQ tests (Frey & Detterman, 2004; Koenig et al., 2008). In employ-
ment setting, psychological tests (including IQ tests) are mostly used for
selecting new employees, but sometimes also for promoting existing employees
(Wigdor & Garner, 1982, chapter 4). IQ-based personnel selection has been
under heavy criticism in the United States since the 1960s and has, con-
sequently, declined (Hunter & Schmidt, 1996). In some other countries,
however, ability tests are used quite frequently (Ryan, et al., 1999). Based on
these facts, it seems only natural to assume that IQ testing could have a
considerable effect on what happens to people during their educational and
occupational career.
Direct empirical research on this assumption is unfortunately very scarce.
Only one published study has conducted an explicit statistical analysis of the
idea that the use of IQ tests has an effect on the relationship between intelli-
gence and success. This is the study by Tittle and Rotolo (2000) that attempted
to find out if the correlation between IQ scores and income (or occupation) in
U.S. states depends on the amount of standardized personnel testing that goes
on in the states. And indeed, they found that the correlation is stronger in the
states where personnel testing is more prevalent. Another, unpublished, study
asked if the correlation between IQ scores and income is stronger among
individuals who have been tested for IQ in their current occupation? The
correlation was slightly stronger among tested individuals but not significantly
so (Strenze, n.d.). Both of these studies, thus, found that the relationship
between intelligence and socioeconomic success is somewhat stronger among
individuals who have been tested for IQ as part of their employment, suggesting
that IQ testing has boosted the positive relationship between IQ scores and
socioeconomic success. However, both studies had several problems with the
data, so it is too early to say how much personnel testing explains the corre-
lation between intelligence and socioeconomic success.
Based on this review, we can conclude that all the three explanations have
some supporting empirical evidence under their belt. In other words, the overall
positive relationship between intelligence and socioeconomic success probably
owes something to all three mechanisms. But that does not mean that all three
mechanisms are equally important all the time. It is possible that different
mechanisms “dominate” different parts of the status attainment process.
Take, for instance, the relationship between intelligence and college edu-
cation. Majority of college students are accepted into college (at least partly) on
the basis of various college admission tests (SAT and ACT tests in USA, state
examinations in Estonia); these admission tests are not officially labeled as IQ
tests but they are known to be positively correlated to traditional IQ tests (Tina,
2002; Frey & Detterman, 2004); the usage of such admission tests is bound to
create a positive correlation between intelligence and getting into college – this
correlation is the work of the credentialist mechanism. Of course, achieving a
college education entails more than just getting into a college – you also have to
study in college – but at the specific moment of getting admission into college,
the credentialist mechanism takes precedence over other mechanisms.
As another example, let us consider the role of intelligence during different
life periods of people. There has been a lot of dispute over what IQ tests really
measure – is it the stable mental ability inside a person or the social environ-
ment around a person (see chapter 2.1)? An interesting possibility is that IQ
tests actually measure somewhat different things for people of different ages.
Studies have shown that the heritability of IQ scores gets stronger as people
grow older (Briley & Tucker-Drob, 2013); this means that IQ tests measure
mostly genetically determined ability among older people and mostly environ-
mental influences among younger people. Studies have also shown that IQ
scores become more stable as people grow older (Schuerger & Witt, 1989); this
means that IQ tests measure a rather stable ability among older people and a
more fluctuating ability among younger people. In addition to that, studies have
shown that the relationship between children’s IQ scores and parental SES
grows weaker as children grow older (Kall, 2010); this means that IQ scores are
more dependent on social influences among younger people than among older
people. To these results, let us add the finding that the effect of intelligence on
career success (occupation and income) gets stronger as people grow older
(Strenze, 2007) [II], the effect of parental SES on career success, however, gets
weaker as people grow older (Ganzach, 2011).
A possible interpretation of all these results is that the relationship between
intelligence and socioeconomic success is better explained by the environmental
mechanism among younger people – because their IQ scores are mostly a
reflection of the environment and the relationship between their IQ scores and
success is not that strong. Among older people, however, the relationship is
better explained by the psychometric mechanism – because their IQ scores are
mostly a reflection of stable ability and the relationship between their IQ scores
and success is quite strong. In very simple terms, young intelligent people owe
their success mostly to their privileged social background, older intelligent
people owe their success mostly to their superior mental capacities. This
statement is a simplification, of course, and it should be taken as a hypothesis,
not a final conclusion.
Such age related changes provide interesting examples of how different
mechanisms can switch on and off during life course. That is why it is unlikely
that any of the three mechanisms in Figure 1 can provide a total explanation for
the relationship between intelligence and socioeconomic success – that relation-
ship is the result of all three mechanisms working at different times and in
different situations.
2.6. Intelligence and socioeconomic
success in different societies
The evidence for the relationship between intelligence and socioeconomic success
comes almost exclusively from contemporary western societies (Strenze, 2007)
[II]. But what about earlier historical periods and less developed societies? Do
these societies also have intelligent people on top? This question is important
for explaining the relationship between intelligence and socioeconomic success –
if there are systematic differences between societies in terms of the relationship,
then it would mean that societal context has to be taken into account to provide
a full explanation of the relationship.
There is, of course, no direct evidence from earlier than the 20th century
because IQ tests had not been invented yet. But the general opinion seems to be
that earlier historical periods mostly did not allow intelligent people to get
ahead in society. These societies presumably had rigid class systems and a
person born to lower ranks had no opportunity to rise to upper ranks, no matter
how intelligent he or she was. According to The Bell Curve (Herrnstein &
Murray, 1994), western societies started to become more meritocratic only in
the middle of the 20th century. Around that time, the educational system
became more democratic and universities were opened up to intelligent youth
from all social backgrounds. At the same time, the occupational system became
more complex with a lot of new cognitively demanding jobs requiring
intelligent workers. These two historical developments – increasing openness
and complexity – are the main social factors that created the positive correlation
between intelligence and socioeconomic success, according to Herrnstein and
Murray (1994).
This scenario sounds convincing but it has been criticized on several grounds.
First, there is reason to believe that intelligent people were, in fact, able to
achieve some success in earlier historical periods. Such as the 19th century
French army officers who were recruited and promoted on the basis of their
talent, rather than social background (Botton, 2004). Or the young men from
modest social background who were able to work themselves into higher
positions in the 16–17th century Germany (Weiss, 1995). These are probable
examples of the positive correlation between intelligence and socioeconomic
success in earlier historical periods (see Strenze, 2015, for a longer discussion).
Second, the supposed strengthening of the intelligence-success relationship
during the 20th century has been questioned. A number of studies have tried to
test this claim and most have failed to find the strengthening of the IQ-success
correlation, predicted by The Bell Curve (Hauser & Huang, 1997; Bowles et al.,
2001; Strenze, 2007 [II]). All these studies have used data collected over
several decades (mostly starting with the 1960s) and they have not found any
signs of the IQ-success relationship getting stronger during that time.
An alternative way to address the same issue is to compare data from
different countries to see if less developed countries have a weaker relationship
between intelligence and socioeconomic success – that would support the idea
that societies become more meritocratic as they evolve from traditional into
industrial and postindustrial. Research on intelligence and success in the
developing world is not very abundant. In their review, Hanushek and Woess-
mann (2008) found about 10 studies on the relationship between cognitive skills
and wages, conducted mostly in African countries. They concluded that “the
returns to cognitive skills may be even larger in developing countries than in
developed countries” (p. 621). However, the results of these studies are
somewhat difficult to compare to each other and to the results from developed
countries, because each study used its own analytical tools. Also, it is not clear
how much the measures of “cognitive skill” in these studies correspond to
standard measures of intelligence.
A better way to compare societies is to use a single cross-national data set
that includes the same measures for all participating countries. A conclusive
cross-national analysis of the relationship between intelligence and socio-
economic success is yet to be conducted. But as a preliminary gauge, take a
look at Figure 2 that presents a simple scatterplot based on data from
Programme for the International Assessment of Adult Competencies (PIAAC).
PIAAC is a cross-national survey, conducted in 2012, that measured the
numeracy and literacy ability of adults in 22 countries; it also included data on
the career success of these adults. Hanushek et al. (2013) calculated for each
country the effect (regression coefficient) of numeracy ability on income,
controlling for gender and work experience – that effect is presented on the
vertical axis of Figure 2. The horizontal axis of Figure 2 is the 2011 per capita
Gross National Income (GNI), a measure of economic development taken from
the World Bank database. Based on the reasoning offered above, one would
expect to find a positive relationship between GNI and ability-income corre-
lation, but in fact the relationship in Figure 2 is not that clear. Some of the more
developed countries with higher GNI, like Norway or Sweden, tend to exhibit
the lowest correlations between people’s ability and income, while the less
developed countries like Poland and Spain have stronger correlations. A
remarkable exception is USA that has one of the highest GNI and also the
strongest relationship between ability and income – this suggests that USA
might be the model case of a society where economic development has resulted
in strong meritocracy.
Of course, the data used in Figure 2 is far from perfect as the number of
countries is too small to draw any ironclad conclusions and the sample of
countries is not representative of the entire spectrum of economic development.
Also, the ability tests of PIAAC are not really tests of “intelligence” in the strict
sense. All these considerations force us to be careful when interpreting Figure 2.
However, a more representative cross-national analysis was conducted by
Psacharopoulos and Patrinos (2004) as they compared the relationship between
education and income in nearly 100 countries and found that the relationship is
stronger in less developed countries. That supports the impression that, among
the societies that exist today, less developed societies tend to be the ones where
people with higher ability (and education) get better financial rewards.
Figure 2. Intelligence-success relationship in the country (vertical axis) and economic
development of the country (horizontal axis).
Based on the evidence presented in this chapter, we can conclude that the
relationship between intelligence and socioeconomic success is indeed
dependent on the societal context. But that dependence might not be quite the
way it was imagined by the authors of The Bell Curve. It is difficult to say
anything conclusive about earlier centuries, but in the 20th and 21st century
there seems to be no clear trend of the intelligence-success relationship getting
stronger as societies become more developed. Indeed it seems that the opposite
is true: the relationship is weaker in more developed societies and stronger in
less developed societies. If asked for an explanation, one could speculate that
there is an intense competition for scarce resources in the less developed
societies, which gives rise to a “survival of the intelligent” effect, while in the
more developed societies most people have access to resources.
2.7. Allocation of talent in different societies
The present discussion on intelligence and success has so far stayed on the
individual level; it has been about the intelligence of individuals and the
consequences of that intelligence for these individuals. In recent decades,
however, a new research tradition has emerged that studies intelligence on the
macro or collective level. This research typically takes the results of individual-
level ability testing and uses the average test score of individuals as a macro-
level variable. This average IQ is then used in macro-level analysis along with
other macro-level variables. Among psychologists this approach has recently
become known through the work of Lynn and Vanhanen (2002, 2006). They
have found that the average IQ scores of people living in different countries
(national IQs) are strongly and positively correlated with the level of economic
development among these countries. In economics, a similar result has been
obtained in the studies of economic growth: the average scores on tests of
academic achievement (such as PISA or TIMSS test) are strongly and positively
related to the rate of economic growth of the countries (Hanushek & Kimko,
2000; Hanushek & Woessmann, 2008). Studies of smaller collectives have
shown that work teams that are composed of more intelligent members are
better at performing the work tasks (Devine & Philips, 2001). This research is
not without critics (e.g., Volken, 2003) but, nevertheless, it complements the
individual-level research by showing that intelligence can create inequality not
just between people but also between societies or groups.
This macro-level paradigm has mostly focused on the average ability of
people as a determinant of macro-level success. But there is another way to
approach the issue: instead of the average level of ability, one can look at the
allocation of ability in society. Given that people are not all equal in their
abilities and talents, one can ask: how do societies allocate (distribute) people
with different abilities? The central hypothesis would be: societies that allocate
people more efficiently are more successful. Thus, even if two societies are
equal in terms of the average level of ability, one can still outdo the other if it
has a better allocation of talent (Murphy et al., 1991).
The basic idea behind the problem of allocation of talent is that society is
composed of people who differ in terms of their abilities (talents) and other
characteristics (such as personality traits). These people have to be allocated in
society between different tasks (by “task” we mostly mean job or occupation).
The allocation is efficient or beneficial if there is a good match between people
and jobs. The good match can be understood in various ways depending on
which characteristics of people and jobs we have in mind. Table 4 provides a
preliminary framework for thinking about matching people to jobs. The table
lists some important variables that differentiate people (intelligence, perso-
nality) and jobs (complexity, productivity). Within the cells of the table, I have
written simple suggestions as to what would constitute a good match of people
and jobs in terms of these variables. The suggestions are derived from the
research discussed below.
Table 4. Some examples of the ideal allocation of people to jobs.
Intelligence of people Personality of people
of jobs Complex jobs should be filled
with intelligent workers, because
they are less likely to fail when
performing these complex tasks.
The less job-related failure there
is, the better for the society.
Complex jobs should be filled with
mentally stable workers, because
they are less likely to fail under the
pressure of these jobs. The less job-
related failure there is, the better
for the society.
of jobs Productive jobs should be filled
with intelligent workers, because
they are more likely to perform
well. It is important to perform
well in productive jobs, because
these jobs contribute more to
Productive jobs should be filled
with hard working workers,
because they are likely to work
harder. It is important to work hard
in productive jobs, because these
jobs contribute more to society.
The idea that a good fit between people and tasks is beneficial for society dates
back to Ancient Greek (see Plato, 2000: 127). In more recent times, the idea
was picked up by the sociologists of the functionalist tradition who theorized
that a match between people’s talents and their social positions is necessary for
the effective functioning of society (see Davis & Moore, 1945). Currently, the
idea lives on in psychology and economics. But unfortunately, most of the
discussion is theoretical and very little empirical research has been conducted
on this subject.
In psychology, there has been some interest in the correspondence between
people’s intelligence and cognitive complexity of their jobs. There is substantial
evidence that different jobs and occupations can be reliably distinguished in
terms of the type and level of ability required to perform the job tasks (Camp-
bell, 1988; Gottfredson, 1986a, 1986b). For instance, engineer and dentist are
cognitively complex jobs, dishwasher and weaver are not so complex (Roos &
Treiman, 1980). The idea is that more intelligent people should be allocated to
more complex jobs and less intelligent people to less complex jobs. That should
improve the overall output of the national economy (Gottfredson, 1986a). How
to allocate people to jobs that suit their abilities? Hunter and Schmidt have
argued that it can be achieved through mental testing – if most organizations in
the society used IQ tests for selecting their employees, the overall allocation of
talent in the society would increase dramatically and that could, in turn, boost
the national economy (Hunter, 1983; Hunter & Schmidt, 1982, 1996). The only
empirical test of this theory is the study by Strenze (2013) [III]. It found that
economic growth is indeed a little faster in the countries with a stronger
association between intelligence and job complexity, and with more frequent
ability testing in personnel selection. However, the data used in this study were
far from ideal and the effects were rather weak. A proper confirmation of this
theory awaits future studies.
In economics, the approach to allocation of talent has been somewhat
different as economists have been interested in the productivity of occupations
(rather than complexity). At heart of the economic approach is the idea that
some occupations are, by their very nature, more productive (useful) than others;
for instance, engineering and entrepreneurship have been described as produc-
tive activities, law and religion as unproductive ones. Talented people should be
directed to productive occupations; the more society is able to do that, the better
off it will be in economic terms (Murphy et al., 1991; Galor & Tsiddon, 1997;
Hassler & Mora, 2000). The major problem with the economic approach is the
lack of clarity about the measurement of the productivity of occupations. There
is no numerical scale of occupational productivity (as there is for occupational
complexity, see Roos & Treiman, 1980), so there is no objective basis for
telling which occupations are the most productive. Allocation of talent in
relation to productivity cannot be systematically analyzed until this problem has
been overcome.
Now it is time to ask: how is the topic of allocation of talent related to the
topic of intelligence and socioeconomic success? I hope the reader has already
guessed the answer – the relationship between intelligence and socioeconomic
success is a measure of allocation of talent; the stronger the positive relationship
between intelligence and socioeconomic success in society, the better the
allocation of talent. It is possible to claim so because of the strong correlation
between socioeconomic success and occupational complexity. Studies of
occupational complexity have found that the cognitive complexity of jobs is
positively correlated with the variables that are typically used to measure socio-
economic success. Spaeth (1979) reported a correlation of .74 between occu-
pational complexity and occupational prestige, and a correlation of .81 between
occupational complexity and occupational authority in the United States. Menes
(2008) reported correlations around .80 between the technological complexity
of occupations and prestige or typical wages of occupations. Wilk and Sackett
(1996) reported a correlation of .70 between the cognitive complexity and
typical wages of occupations. Using people (not occupations) as units of
analysis, Ganzach (2003) found that the complexity of people’s occupation
correlates around .50 with their education and around .30 with their salary.
Therefore, if intelligent people have achieved more success in terms of edu-
cation, occupation or salary, then they are likely to be working in more complex
jobs, which means that their superior intelligence is put to good use. A strong
and positive correlation between intelligence and socioeconomic success is,
thus, good for the society because it should foster economic growth.
That puts the relationship between intelligence and socioeconomic success
into a new light. That relationship is not just a “thing in itself”, a result of past
societal processes that has no further implications. On the contrary, that
relationship is possibly an important influence on future societal development.
The size of that relationship could be used as an indicator of the economic
potential of society. If so, governments should be interested in measuring that
relationship in their country and take steps to increase the relationship if
necessary. In chapter 2.6 we saw that contemporary societies differ in terms of
the strength of the relationship between ability and income, the relationship
tends to be stronger in less developed societies (see also Strenze, 2015). This
could be taken as a warning sign for some the most affluent societies implying
that these societies have perhaps become complacent with the achieved level of
well-being and are not using their intellectual resources to the fullest extent. For
some of the less developed societies this could be taken as a promise of future
This chapter of the dissertation provides an overview of the original studies that
form the basis of the dissertation. In fact, results from the original studies have
already been cited numerous times in the preceding text but now the studies will
be described in more detail.
3.1. Aims of the original studies
Study I (Strenze, 2006) is a rather simple and straightforward study that follows
the tradition of sociological status attainment research (e.g., Jencks, 1979). It
analyzed intelligence and parental socioeconomic status (SES) as predictors of
education, occupation and income in Estonia and the United States. The main
reason for conducting this study was to investigate the relationship between
intelligence and socioeconomic success in Estonia – no study had done that
before. The study set out to show that intelligence is a significant predictor of
socioeconomic success in Estonia, as it is known to be in other western
societies. The predictive power of intelligence was compared to that of parental
SES to determine which one has a stronger effect on success. To provide inter-
national context, the study included the analysis of the same relationships in
Study II (Strenze, 2007) is a meta-analysis of the relationship between
intelligence and socioeconomic success. The starting point for the study was the
observation that quite a lot of research had investigated intelligence as a
predictor of education, occupation or income, but so far very few attempts had
been made to systematically review that research. That seemed like a good
reason to apply the method of meta-analysis, which means collecting the results
of the original studies and providing a quantitative summary of these results
(see Hunter & Schmidt, 2004). Thus, the study set out to provide a comprehen-
sive meta-analysis of the longitudinal research on the relationship between
intelligence and socioeconomic success. The meta-analysis was limited to
longitudinal research (where intelligence of people is measured before their
success) because only longitudinal design allows one to make conclusions about
the possible causal effect of intelligence on success. Given that is difficult to
evaluate the importance of a predictor in isolation, the study also included the
meta-analysis of parental SES and academic performance (school grades) as
predictors of socioeconomic success to see if intelligence is a better predictor of
success than the other variables.
Study III (Strenze, 2013) is a cross-national analysis of the economic growth
of countries. The study was based on the idea that the economic success
(growth) of a society should depend on how well it utilizes the mental abilities
of its people. This is what economists call the “allocation of talent”. There has
been quite a lot of theoretical discussion about this idea but virtually no
empirical research. This study set out to clarify the concept of allocation of
talent, construct some indicators of allocation of talent for countries and analyze
the relationship between these indicators and the economic growth of countries.
Because there was not much data available for countries, the study also included
the analysis of the economic growth of U.S. states to see if the same relation-
ships exist both at the country and state level.
3.2. Data and methods of the original studies
Study I used longitudinal data from Estonia and the United States. The Estonian
data set is called Paths of a Generation, which is a longitudinal survey started in
1983 with a sample of young people aged about 17 (see Titma, 1999). As part
of the first round of data collection, the respondents were given an IQ test. That
makes it the only data set in Estonia that offers an opportunity to study the long
term effects of intelligence on later life course. In USA there are several
longitudinal data sets to choose from, I used the National Longitudinal Survey
of Youth because of its similarity to the Estonian data set in terms of age of the
sample and timing of the first round. For data analysis I used simple descriptive
statistics and regression analysis.
Study II used common meta-analytic methods (see Hunter & Schmidt, 2004).
In order to conduct a meta-analysis of the relationship between intelligence and
socioeconomic success, the first step was to assemble a database of the results
from original studies. I used correlation coefficient as the measure of the
relationship, therefore, I collected as many correlations as possible from various
articles and books. The correlations I looked for were between intelligence and
measures of socioeconomic success (education, occupation and income),
between measures of parental SES (father’s and mother’s education, father’s
occupation, parental income, SES index) and socioeconomic success, and
between academic performance and socioeconomic success. In some cases I
obtained the raw data and calculated the necessary correlations myself, if the
data had not been used in any publication. The correlations were weighted with
sample size and corrected for unreliability. The analysis of the correlations
proceeded in two steps. First, the overall summary of the strength of the
relationship between predictors and socioeconomic success. Second, a
moderator analysis of the correlations between intelligence and socioeconomic
success to determine if the strength of the correlations depended on sample
characteristics (e.g., age of the sample or year of data collection).
Study III used macro-level data to analyze allocation of talent as a
determinant of economic growth of countries and U.S. states. The first step of
the analysis was constructing the indicators of allocation of talent for countries
and states. The different indicators were based on ideas developed in psycho-
logy and economics; and various sources of data were used for their const-
ruction. Some of the indicators were calculated from individual-level data for
countries or states (the International Adult Literacy Survey data set or the U.S.
census public use data), some were obtained from international data sets (the
Occupational Wages Around the World data set), some were obtained from
published sources. Data for the economic growth came from Penn World Tables
and U.S. Bureau of Economic Analysis. The statistical analysis of the determi-
nants of economic growth was done in the tradition of “growth regression”,
which is a regression analysis that attempts to predict the economic growth rate
(see Barro & Sala-I-Martin, 1995).
3.3. Results of the original studies
Study I found that intelligence has a positive effect on educational, occupational
and income attainment in Estonia. However, comparison with the United States
showed that the effect of intelligence is somewhat weaker in Estonia (compared
to USA); parental SES has a more or less equal effect in both countries. From
that the study concluded that “Estonian society is less open and meritocratic
than American society” (p. 232). A possible reason for this could be the relative
instability of the Estonian society in the 1990s (the time the data on socio-
economic success were collected). It was initially hypothesized that the harsh
and unstable social environment of Estonia could increase the importance of
intelligence, but actually the opposite seemed to be the case – the stable and
open American society apparently creates better conditions for intelligent people
to realize their potential in the labor market. This interpretation contradicts the
observations presented in chapter 2.6 about intelligence being less important for
success in the most developed societies. But let us remember that the analysis in
study I compared just two societies, it is difficult to draw firm conclusions
about why the societies differ from so few societies.
The meta-analysis in study II found that intelligence is positively correlated
with later education, occupation and income; the average corrected correlations
are .56, .43 and .20, respectively. The existence of the positive correlation
between intelligence and socioeconomic success is in no way surprising, but
things get more interesting if we compare these correlations with other
correlations and do moderator analysis. The meta-analysis also found positive
correlations between parental SES and academic performance with socio-
economic success; these correlations range from .09 to .50. Thus, the study
showed that intelligence is at least as good a predictor of success as are parental
SES and academic performance, and perhaps even a bit better. The theoretical
significance of this result was already explained in chapter 2.5 – it shows that
the correlation between intelligence and socioeconomic success cannot be
completely explained by parental SES or academic performance; therefore, the
effect of intelligence on success must be, to some degree, independent from the
effect of social environment.
Moderator analysis in study II also uncovered some interesting patterns. It
found that the correlation of intelligence with occupation and income becomes
stronger as people get older. This result supports the so called “gravitational
hypothesis”, which states that the impact of intelligence on people’s career
becomes stronger with aging as people “gravitate” to social positions that
correspond to their intelligence. However, the gravitational hypothesis does not
work in educational attainment – the effect of intelligence on education grows a
bit weaker after early twenties, indicating that most people achieve their “right”
level of education rather quickly and later there is some readjustment as less
intelligent people catch up in terms of educational qualifications. The meta-
analysis also investigated the historical changes in the correlation between
intelligence and socioeconomic success, but no clear pattern was found. Thus,
the study offered no support for the claim that intelligence has recently become
more important as a determinant of status attainment.
Study III found that the countries and states that have a better allocation of
talent exhibit somewhat faster rates of economic growth. This result supports
the idea that allocation of talent is one of the determinants of the wealth of
nations. However, it must be noted that the study had several methodological
problems, the most noticeable of them being small sample size in some of the
analyses. Thus, the empirical results of the study can, at best, be taken as a first
indication that allocation of talent could be important for the economy, no firm
conclusions can be drawn about it right now. Another empirical matter that the
study dealt with was the measurement of allocation of talent. Four distinct
indicators of allocation were constructed for the study – relationship between
ability and job complexity in a country, prevalence of ability testing in a
country, monetary returns to education in a country, and monetary rewards in
complex occupations in a country. It is of some interest that all the indicators
were positively correlated with one another, indicating that a common underlying
construct was being measured. This result offers some cause for optimism about
the construct of allocation of talent as a “real” social phenomenon.
3.4. Original contributions of the original studies
In this section I will briefly describe what I believe to be the original cont-
ributions of the original studies to scientific progress. Study I was rather modest
in this regard, it did not offer much originality in terms of theory development
or novel research questions. The most original thing in this study was the
analysis of Estonian data – no previous study had analyzed the relationship
between intelligence and socioeconomic success in Estonia – the theoretical and
methodological background of the analysis was the same as in numerous
previous studies (e.g., Jencks, 1979). Still, such low key research should not be
underestimated as it is the foundation of scientific knowledge.
Study II aimed at covering more ground by offering a systematic review of
the research on intelligence and socioeconomic success. This study is arguably
the most extensive analysis that has ever been conducted on this topic. The
empirical results of the study were presented as conclusive answers to questions
that had been studied for decades by social scientists in various countries (e.g.,
which is a better predictor of socioeconomic success, intelligence or parental
SES; does the relationship between intelligence and socioeconomic success
change with age and historical time?). Whether or not the answers remain
“final” is another matter – it is entirely possible that future empirical studies
might challenge the conclusions of the study.
Study III was, perhaps, the most original of the three in terms of offering
novel ideas. Through the analysis of theories from various fields, the study
developed an approach to thinking about the relationship between individual
talents and societal development; at the center of the approach is the idea that
societal development depends on how individuals with different talents are
allocated (distributed) in society. The idea itself has been discussed by other
authors, but this study brought the idea closer to empirical investigation than
most previous studies. The empirical section of the study was somewhat
lagging, however, because of lack of suitable data, so the main contribution of
the study was asking new questions, rather than offering answers.
The general aim of this dissertation is to contribute to the scientific knowledge
on the relationship between intelligence and socioeconomic success. More
specifically, the dissertation had three goals: describe the relationship, analyze
its causes and its social consequences. Now it is time to present conclusions
about these three goals. The short version of the conclusions is the following:
the relationship between intelligence and socioeconomic success is strong, it has
multiple underlying causes and it affects the economic growth of society. But
let us take a closer look at the conclusions one by one.
4.1. Relationship between intelligence and
socioeconomic success
The first goal of the dissertation was to describe the relationship between intelli-
gence and socioeconomic success. It is clear that intelligence is positively
related to socioeconomic success, as well as to various other forms of success
(see chapters 2.3 and 2.4). The correlation between intelligence and socio-
economic success is strong, when compared to correlations with other forms of
success (see Table 2) and to correlations with other predictors of socioeconomic
success (see Table 3). In other words, intelligence predicts socioeconomic
success better than most other forms of success and among the known pre-
dictors of socioeconomic success intelligence is one of the strongest.
The existence of a positive correlation between intelligence and success is
hardly surprising. What the present dissertation adds to this knowledge is the
comparison of various forms of success and predictors. Such comparisons (as in
Table 2 and 3) allow us to get a general understanding of the pattern of
relationships between variables of interest. This understanding could come in
handy when developing a theory of intelligence (e.g., Kanazawa, 2004). Indeed,
one useful avenue of future research is to extend the review of the correlates of
intelligence. That is, to assemble and compare correlations between intelligence
and relevant variables. The relevant variables might include psychological
characteristics (e.g., personality traits), possible determinants of intelligence
(e.g., parental SES), possible behavioral outcomes (e.g., religiosity).
Of course, the mere knowledge of an empirical relationship is not enough.
We have to put it into a wider theoretical context. In this regard, it is important
to realize that the question about the relationship between intelligence and
socioeconomic success is intimately tied to other scientific questions about
intelligence (see Table 1). Questions like: does intelligence really exist, what IQ
tests really measure, etc.? A researcher who does not believe that IQ tests
measure general mental ability would have a very different interpretation of the
correlation between IQ scores and success, compared to the researcher who
does believe in the validity of IQ tests. This is why the study of the relationship of
intelligence and success cannot be isolated from other topics of the IQ debate.
4.2. Causes of the relationship
The second goal was to analyze the causes of the relationship between
intelligence and socioeconomic success. While the existence of the relationship
between intelligence and socioeconomic success has rarely been questioned, the
mechanism of that relationship remains a contested issue. Chapter 2.5 discussed
three possible explanations for the relationship: the first explanation states that
intelligent people are successful thanks to their intelligence, the second
explanation states that intelligent people typically come from privileged social
background and this is the reason for their success, the third explanation states
that intelligent people are rewarded for their IQ test scores. No firm conclusion
could be drawn as to which explanation is the correct one. Indeed, such a
conclusion will likely never be drawn because there is empirical evidence to
support all the three explanations. A possible solution to this situation is the
hypothesis that the three explanations apply to different parts of the status
attainment process (see chapter 2.5).
Only one of the explanations (the psychometric explanation, see Figure 1)
views intelligence as the actual cause of socioeconomic success; in other
explanations, intelligence is not the actual cause, but merely correlated to the
actual cause. It is of some interest that the causal explanation is quite successful in
accounting for the correlation between intelligence and success in work and
educational contexts. This means that the effect of intelligence on socio-
economic success is, to a considerable extent, causal (non-spurious, not
explained by third variables). Therefore, even if intelligence is not the
underlying cause of all the success and failure that people experience, quite a
large part of it can still be attributed to intelligence.
An additional conclusion about the causes of the relationship between
intelligence and socioeconomic success is that the analysis of these causes
should also take account of the societal context (see chapter 2.6) – the relation-
ship between intelligence and socioeconomic success is somewhat different in
different societies because society can either facilitate or hinder the relationship.
Further cross-national studies of the relationship between intelligence and
socioeconomic success are needed to fully understand how societal context
affects the relationship. Studies from developing, non-western countries would
be especially valuable because most of the research has so far been conducted in
rich western countries.
4.3. Social consequences of the relationship
The third goal was to analyze the social consequences of the relationship
between intelligence and socioeconomic success. The positive individual-level
relationship between intelligence and socioeconomic success has important
consequences for the economic growth of society; societies with a stronger
relationship grow faster because of better allocation of talent (see chapter 2.7).
Societies with good allocation of talent have assigned intelligent people to
cognitively complex jobs and less intelligent people to simple jobs. Such
division of labor assures that the talent of intelligent people does not go
“wasted” in simple jobs and the lower ability of less intelligent people does not
jeopardize the execution of complex jobs. These are, however, preliminary
ideas and more research is needed to confirm these findings and understand
their theoretical and practical implications.
Allocation of talent is still a relatively new research topic. Sociological
theory could use it as a mechanism to connect the characteristics of people to
the functioning of society, a topic that has fascinated social theorists for a long
time (see Alexander et al., 1987). This dissertation was more interested in the
empirical analysis of allocation of talent. However, the empirical research is
hindered by lack of suitable data. Hopefully, this situation improves in the
future as better data become available.
The conclusion about the economic benefits of allocation of talent is the
most practical conclusion of this dissertation. If confirmed, it would mean that
the relationship between intelligence and socioeconomic success could be used
as a social indicator of the allocation of talent. Governments might want to
measure this relationship regularly and take steps to increase it. Also, it should
make it quite rewarding for social scientists to study the relationship between
intelligence and success, because confirming that there is a relationship is not
the “end of the road” for the researcher – the relationship has further practical
implications that need to be studied.
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Intelligentsus ja sotsiaalmajanduslik edukus:
Seoste, põhjuste ja tagajärgede analüüs
Juba ammu on teada, et intelligentsed inimesed on edukamad kui vähem intelli-
gentsed inimesed. Siiski on jätkuvalt põhjust seda teemat uurida, sest intelli-
gentsuse ja edukuse vahelise seose põhjuste osas puudub siiamaani selge kon-
sensus, selle seose tagajärgede uurimine on aga alles lapsekingades. Käesoleval
doktoritööl on kolm eesmärki. Esiteks, kirjeldada intelligentsuse ja sotsiaal-
majandusliku edukuse vahelist seost – uurida kui tugev see seos on, kas intelli-
gentsus on sotsiaalmajandusliku edukusega tugevamalt seotud kui teiste edu-
kuse vormidega ja kas intelligentsus mõjutab sotsiaalmajanduslikku edukust
tugevamini kui teised edukuse mõjutegurid? Teiseks, analüüsida intelligentsuse
ja sotsiaalmajandusliku edukuse vahelise seose põhjusi – miks see seos eksis-
teerib, milline on selle seose mehhanism, tänu millele saavutavad intelligentsed
inimesed suuremat edu? Kolmandaks, analüüsida intelligentsuse ja sotsiaal-
majandusliku edukuse vahelise seose sotsiaalseid tagajärgi – millist mõju
avaldab ühiskonnale selle seose olemasolu või puudumine?
Intelligentsus on üldine vaimne võimekus, inimese võime lahendada prob-
leeme erinevates eluvaldkondades. Edukus tähendab millegi sellise tegemist,
mida peetakse ühiskonnas õigeks ja ihaldusväärseks. Sotsiaalmajanduslik edukus
on edukuse vorm, mis on hõlmab hariduse, töökoha ja sissetuleku omandamist.
Doktoritöö koosneb katustekstist ja kolmest artiklist, mis on avaldatud
rahvusvahelise levikuga teadusajakirjades (Strenze, 2006, 2007 ja 2013). Kõik
kolm artiklit annavad omal viisil panuse intelligentsuse ja sotsiaalmajandusliku
edukuse seose uurimisse. Doktoritöö katustekst paigutab artiklid üldisemasse
sotsiaalteaduslikku konteksti.
Artiklite ja katusteksti baasil võib teha järgmised järeldused.
Esimene järeldus puudutab intelligentsuse ja sotsiaalmajandusliku edukuse
vahelise seose üldist iseloomu. Doktoritöös antakse ülevaade paljudest teadus-
likest uurimustest, kus on analüüsitud intelligentsuse ja erinevate edukuse vor-
mide vahelist seost; samuti antakse ülevaade uurimustest, kus on analüüsitud
sotsiaalmajandusliku edukuse mõjutegureid. Ülevaade näitab, et intelligentsuse
ja sotsiaalmajandusliku edukuse vahel on tugev seos. Kui vaadelda intelligent-
suse seost erinevate edukuse vormidega, siis torkab sotsiaalmajanduslik edukus
silma, kui üks tugevamalt intelligentsusega seotud edukuse vorme. Kui vaa-
delda erinevate mõjutegurite seost sotsiaalmajandusliku edukusega, siis paistab
intelligentsus silma, kui üks tugevamaid sotsiaalmajandusliku edukuse mõju-
tegureid. Seega, intelligentsus mängib tänapäeva inimeste elus olulist rolli, olles
üheks tähtsaimaks eluteed kujundavaks teguriks.
Teine järeldus puudutab intelligentsuse ja sotsiaalmajandusliku edukuse
vahelise seose põhjusi. Intelligentsuse ja sotsiaalmajandusliku edukuse vahelisele
seosele on sotsiaalteadustes aegade jooksul pakutud kolm erinevat seletust,
millest igaüks esitab erineva nägemuse sellest, mis on intelligentsete inimeste
edu aluseks. Esimese seletuse järgi saavutavad intelligentsed inimesed edu tänu
oma intelligentsusele, teise seletuse järgi on intelligentsed inimesed enamasti
pärit parematest sotsiaalsetest oludest ja see on nende edu aluseks, kolmanda
seletuse järgi on edu aluseks see, et ühiskond usub IQ testidesse ja kasutab testide
tulemusi hüvede jagamisel inimestele. Empiiriliste uuringute ja teoreetilise
analüüsi baasil võib öelda, et kõik kolm mehhanismi on mingil määral tõesed,
st. kõik seletavad mingi osa intelligentsuse ja sotsiaalmajandusliku edukuse seo-
sest. Sealjuures on alust arvata, et erinevad mehhanismid on aktiivsed erinevatel
aegadel ja erinevates situatsioonides. Intelligentsuse ja sotsiaalmajandusliku
edukuse seose seletamisel on vaja arvestada ka ühiskondlikku konteksti – osad
ühiskonnad soodustavad seda seost rohkem kui teised, seniste uuringute baasil
võib öelda, et rikkamates riikides on seos nõrgem kui vaesemates riikides.
Kolmas järeldus puudutab intelligentsuse ja sotsiaalmajandusliku edukuse
vahelise seose sotsiaalseid tagajärgi. On alust arvata, et intelligentsuse ja sotsiaal-
majandusliku edukuse vaheline seos ühiskonnas avaldab mõju ühiskonna
majanduslikule arengule. Kusjuures, mida tugevam on intelligentsuse ja
sotsiaalmajandusliku edukuse vaheline positiivne seos riigis, seda kiiremini
kasvab riigi majandus. Sellise mõju põhjuseks on ilmselt parem talentide
kasutamine ühiskonnas – kui intelligentsed inimesed on edukad, siis tähendab
see, et nende inimeste võimed leiavad ühiskonnas head rakendust ja nad panus-
tavad seetõttu rohkem riigi majandusse. Seega ühiskond mis soodustab intelli-
gentsete inimeste sotsiaalmajanduslikku edukust loob sellega tingimused
iseenda majanduslikuks arenguks.
Järgneb lühike ülevaade doktoritöö aluseks olevatest artiklitest.
Esimene artikkel (Strenze, 2006) uuris intelligentsuse ja sotsiaalmajandusliku
edukuse seost Eestis. Selleks kasutati longituudandmestikku Ühe Põlvkonna
Eluteed, mille raames testiti Eesti keskkooliõpilaste intelligentsust 1983. aastal
ja tehti kindlaks nende haridus, töökoht ja sissetulek 1997. aastal. Nende and-
mete analüüs näitas ootuspäraselt, et kõrgema intelligentsusega inimesed saavu-
tasid hilisemas elus suuremat edu. Pakkumaks võrdlusmomenti Eesti tule-
mustele analüüsiti samas artiklis ka USA andmeid. Võrreldes kahe riigi tule-
musi selgus, et Eestis on intelligentsuse ja sotsiaalmajandusliku edukuse seos
natuke nõrgem kui USA-s.
Teise artikli (Strenze, 2007) raames teostati intelligentsuse ja sotsiaal-
majandusliku edukuse vahelise seose meta-analüüs. Selleks et nimetatud seose
tugevust paremini hinnata, tehti võrdluse jaoks meta-analüüs ka sotsiaalse
päritolu ja sotsiaalmajandusliku edukuse vahelisest seosest. Tulemused näitasid,
et nii intelligentsus kui sotsiaalne päritolu on sotsiaalmajandusliku edukusega
positiivselt seotud. Intelligentsuse mõju edukusele on natuke tugevam kui
sotsiaalse päritolu oma, kuid see erinevus pole suur. Veel selgus meta-
analüüsist, et intelligentsuse ja sotsiaalmajandusliku edukuse seos kasvab koos
inimeste vanusega – ilmselt seetõttu, et inimesed leiavad vanemaks saades oma
intelligentsile vastava positsiooni ühiskonnas. Meta-analüüs ei leidnud kinnitust
populaarsele arvamusele nagu oleks intelligentsuse ja sotsiaalmajandusliku
edukuse seos 20. sajandi jooksul tugevnenud.
Kolmas artikkel (Strenze, 2013) uuris riikide majandusliku arengu sõltuvust
intelligentsuse ja sotsiaalmajandusliku edukuse vahelise seose tugevusest riigis.
Sellist teemat on vaja uurida, sest intelligentsuse ja sotsiaalmajandusliku edu-
kuse seose tugevus ühiskonnas näitab talentide kasutamise efektiivsust ühis-
konnas – mida rohkem ühiskond võimaldab intelligentsetel inimestel hariduse ja
töö valdkonnas edu saavutada, seda paremini ühiskond rakendab nende inimeste
vaimseid võimeid tööturul ja seda rohkem need inimesed panustavad riigi
majandusse. Riikide analüüs näitas, et mida tugevam on intelligentsuse ja
sotsiaalmajandusliku edukuse seos riigis, seda kiiremini kasvab riigi majandus.
Selline tulemus lubab oletada, et talentide kasutamine on riigi majandusarengut
soodustav tegur.
... In this section, the central question is to what extent the initiators of draft acts follow the rules of law-making (1996) 10 in the mandatory categories of impact assessment, research references and civic engagement? I will compare the periods before and after the adoption of the "Development Plan for Legislative Policy until 2018" (2011), and I argue that without specific impact assessment and civic engagement knowledge it is hard to seriously claim that the provisions of draft acts are in accordance with the constitution, stakeholders' rights, or such good governance principles as subsidiarity, transparency, cost-efficiency and simplicity. ...
... According to many comparative studies, the institutionalisation of OECD and EU good governance and better regulation concepts in the relatively small Estonian governance system has been quite successful and, as of 2010, the 9 Reflexivity has different meanings and levels in the social sciences and humanities: reflexivity as self-awareness, as philosophical critique, as a critical comparison of empirical findings and theoretical structure, etc. See 10 The norms of the Rules for Good Legislative Practice and Legislative Drafting (2011) have remained the same since 1996. transition period from a totalitarian Soviet regime to a stable market economy and open access democracy should be considered finished (Chap. ...
... law-making) routines. In my opinion, this tension between institutional carriers and noticeable dissatisfaction among officials shows readiness for institutional changes and explains some specific requirements for regulatory governance institution building (Studies I-III; also Scott 2001; Table 4; Figures 5,6,10) In general, by analysing the role of normative influences in organisational decision-making processes of ministries (e.g. the constitution), institutional theory offers many important analytical approaches to explain the changes in organisational structures, procedures, processes and the actions of decision-makers. ...
... This is relevant as it could further specify for which group of learners tests are beneficial and for which they are not. We thereby choose intelligence as an individual difference because it was often cited as one of the strongest predictors for academic achievement and is generally strongly associated with varying operationalizations of successful human behavior (see, e.g., Bornstein et al., 2013;Strenze, 2015). Surprisingly, we could not find much research concerning potential moderating effects of intelligence on the effectiveness of tests for long-term learning outcomes. ...
... Intelligence has often been shown to be one of the strongest predictors for long-term learning, information retrieval, or academic achievement, and it is also argued to be especially valuable and predictive for difficult and stimulating learning environments and complex materials (e.g., Gottfredson, 1997;Kuncel et al., 2004;Fergusson et al., 2005;Bornstein et al., 2013;Roth et al., 2015;Stadler et al., 2015;Stern, 2015Stern, , 2017Strenze, 2015). Moreover, intelligence is even defined as the ability to learn, to reason, and to solve problems and has also often been found to be associated with successful information processing, successful retrieval from long-term memory, and higher working memory capacities (see, e.g., Gottfredson, 1997;Sternberg, 1997;Oberauer et al., 2005;Bornstein et al., 2013;Stern, 2015Stern, , 2017Wang et al., 2017). ...
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It has often been shown that tests as intentionally hindered and difficult learning tasks increase long-term learning compared to easier tasks. Previous work additionally indicated that higher intelligence might serve as a prerequisite for such beneficial effects of tests. Nevertheless, despite their long-term learning effects, tests were also found to be evaluated as more negative and to lead to more stress and anxiety compared to easier control tasks. Stress and anxiety, in turn, often yield detrimental effects on learning outcomes. Hence, we hypothesized that tests increase later learning outcomes but simultaneously also lead to more stress perceptions. Such increased stress was, in turn, hypothesized to reduce later learning outcomes (thus, stress might serve as a mediator of the beneficial effects of tests on learning). All these assumed effects should further be moderated by intelligence, insofar as that higher intelligence should increase beneficial effects of tests on learning, should decrease stress perceptions caused by tests, and should reduce detrimental effects of stress on learning outcomes. Higher intelligence was also assumed to be generally associated with higher learning. We conducted a laboratory study ( N =89) to test these hypotheses: Participants underwent an intelligence screening, then worked on either a test or a re-reading control task, and reported their immediate stress perceptions. Later learning outcomes were assessed after 1week. The results supported all assumed main effects but none of the assumed interactions. Thus, participants using tests had higher long-term learning outcomes compared to participants using re-reading tasks. However, participants using tests also perceived more immediate stress compared to participants that only re-read the materials. These stress perceptions in turn diminished the beneficial effects of tests. Stress was also generally related to lower learning, whereas higher intelligence was linked to higher learning and also to lower stress. Hence, our findings again support the often assumed benefits of tests—even when simultaneously considering learners’ intelligence and and when considering the by tests caused stress perceptions. Notably, controlling for stress further increases these long-term learning benefits. We then discuss some limitations and boundaries of our work as well as ideas for future studies.
... Furthermore, many studies found intelligence to be strongly and positively correlated to long-term learning and academic achievement, and it is often cited as one of the strongest predictors of long-term learning and academic achievement. This includes varying measures of learning outcomes in laboratories and classrooms, like final test performance or memory outcomes, as well as varying measures of academic success like grades, gained school qualifications, or probabilities of gaining university degrees (e.g., Bornstein, Hahn, & Wolke, 2013;Fergusson et al., 2005;Kuncel et al., 2004;Stern, 2015;Strenze, 2007;Roth et al., 2015; for an overview of multiple meta-analyses regarding intelligence and different operationalizations of success, including long-term learning and academic achievement, see Strenze, 2015). ...
... Although we did infer causal effects due to the different times of measurements of intelligence and long-term learning, further causal analyses are still advantageous. Future studies should implement longitudinal designs because these are supposed to serve as a basis for causal effects (cf., Strenze, 2007Strenze, , 2015. ...
Intelligence is an important predictor of long-term learning and academic achievement. In two studies we focused on the relation among intelligence, desirable difficulties–active generation/production of information and taking tests–, and long-term learning. We hypothesized that intelligence is positively correlated to long-term learning and that difficult learning situations, as opposed to easier reading, increase later long-term learning. We further assumed that the beneficial effects of difficult learning would be moderated by intelligence, thus, we supposed the positive effects to be stronger for learners with higher intelligence and weaker for learners with lower intelligence. We in turn conducted two experiments (N1 = 149, N2 = 176, respectively), measured participants' intelligence, applied desirable difficulties–generation/testing–in contrast to control tasks, and later assessed long-term learning indicated by delayed final test performance. Both studies showed positive correlations between intelligence and later long-term learning. Study 2 further found the expected beneficial effect of difficult learning, which was also moderated by intelligence. There was no difference between difficult tasks and control tasks for participants with relatively low intelligence. Retrieving answers in learning tests was, however, beneficial for participants with average intelligence and even more beneficial for participants with higher intelligence. In general, our two experiments highlight the importance of intelligence for complex and challenging learning tasks that are supposed to stimulate deeper encoding and more cognitive processing. Thus, specifically learners with higher, or at least average, intelligence should be confronted with difficulties to increase long-term learning and test performance.
... Cognitive ability in our operationalization refers to a general knowledge acquisition/retention, problem solving and pattern recognition capacity which is most frequently measured by IQ tests. Cognitive ability is a key psychological phenotype which is associated with career success, income and education (Strenze, 2007) as well as social success (Hegelund et al., 2018;Hegelund et al., 2019;Strenze, 2015aStrenze, , 2015b and health (Calvin et al., 2011;Calvin et al., 2017;Gale et al., 2010). The majority of the variance in adult cognitive ability is accounted for by genetic factors (Bouchard, 2013;Plomin & Deary, 2015), but selective migration creates notable geographic differences, with higher average cognitive ability in urban areas (Alexopoulos, 1997;Gist & Clark, 1938;Lehmann, 1959;Teasdale et al., 1988). ...
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Chronotype and cognitive ability are two human phenotypes with an uneven geographic distribution due to both selective migration and causal environmental effects. In our study, we aimed to examine the relationship between geographic variables, cognitive ability and chronotype. We used a large anonymized sample (n = 25,700, mostly from the USA, UK, Canada and Australia) of dating site users to estimate chronotype and cognitive ability from questionnaire responses using item response theory. We matched each user to geographic coordinates and city size using the reported locations and geographic databases. In line with previous research we found that male sex, younger age, residence in a more populous locale, higher cognitive ability and more westward position within the same time zone were associated with later chronotype. Male sex, younger age, residence in a more populous locale, later chronotype and higher latitude were associated with higher cognitive ability, but the effect of population on chronotype and latitude on cognitive ability was only present in the USA. The relationship between age and chronotype was stronger in males, and the relationship between chronotype and cognitive ability was stronger in males and in older participants. Population density had an independent association with cognitive ability, but not chronotype. Our results confirm the uneven geographic distribution of chronotype and cognitive ability. These findings generalize across countries, but they are moderated by age and sex, suggesting both biological and cultural effects.
Full-text available
Chronotype and cognitive ability are two psychological phenotypes with an uneven geographical distribution due to both selective migration and causal environmental effects. In our study we aimed to unravel the relationship between geographical variables, cognitive ability and chronotype. We used a large anonymized sample (N=25700) of dating site users to estimate chronotype and cognitive ability from questionnaire responses using item response theory. We matched each user to geographical coordinates and city size using the reported locations and geographical databases. In line with previous research we found that male sex (β=0.029), younger age (β=-0.178), residence in a more populous locale (β=0.02), higher cognitive ability (β=0.05) and more westward position within the same time zone (β=-0.04) was associated with later chronotype. Male sex (β=0.065), younger age (β=-0.04), residence in a more populous locale (β=0.149), later chronotype (β=0.051) and higher latitude (β=0.03) was associated with higher cognitive ability, but the effect of population on chronotype and latitude on cognitive ability was only present in the United States. The relationship between age and chronotype was stronger in males, and the relationship between chronotype and cognitive ability was stronger in males and in older participants. Population density had an independent association with cognitive ability, but not chronotype. Our results confirm the uneven geographical distribution of chronotype and cognitive ability. Country-wise analyses distinguish universal cultural/biological and country-specific effects. The moderating effect of age on the cognitive ability-chronotype relationship suggests that cultural rather than biological effects underlie this relationship.
Intelligence is a precious asset, highly valued in society. It is not surprising that bold claims, which on the one side, stress the genetic influences on intelligence and, on the other side, state that it can be raised via cognitive training, cause fierce discussions among scientists and catch the attention of media and the general public. Several hypotheses have tried to explain why, in the history of human evolution, no organ has grown faster than the brain. It is likely that a combination of social and ecological factors promoted the proliferation of neural precursor cells, making our brain the most complex organ. Intelligence has been linked to academic performance and career success, therefore in the 20th century much energy has been devoted to the design of preschool and early school interventions that would raise intelligence. In contrast, new age approaches are mainly conducted in controlled laboratory experiments showing promising results, although a definite conclusion is still out of reach.
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Although there is general agreement that people differ in how easily they can be influenced, little evidence is available concerning the source of these individual differences. A meta-analytic review was conducted to determine whether message recipients' self-esteem or intelligence predicts influenceability. Recipients of moderate self-esteem proved to be more influenceable than those of low or high esteem. According to the Yale-McGuire model, this curvilinear pattern stems from individual differences in reception of as well as yielding to the influence appeal. Recipients low in self-esteem have difficulty receiving the message; those high in self-esteem tend not to yield. Adequate data were not available to examine curvilinear effects of intelligence. Instead, low intelligence recipients were more influenceable than highly intelligent ones. In general, the findings highlight the importance of message reception in understanding the processes of opinion change.
In this famous excerpt from The Republic, Plato develops and explains the allegory of the cave. In the cave are people who have lived their entire lives chained to the cave, and they have only been able to watch the shadows that are projected onto the walls in front of them. Plato surmises that the people in the cave would assume that the shadows on the wall constitute reality. Plato then supposes that a person leaves the cave and steps out into the sunshine. Once his/her eyes adjusted, s/he would see that the things around him/her were real, while the shadows would appear fake. Plato likens this to the search for Truth that he advocates. He argues that once one sees the Truth, all other ideas will be no different than shadows on a cave wall.
Core self-evaluation (CSE) represents the fundamental appraisals individuals make about their self-worth and capabilities. CSE is conceptualized as a higher order construct composed of broad and evaluative traits (e.g., self-esteem and generalized self-efficacy). The authors review 15 years of CSE theory and research, focusing in particular on the outcomes, mediators, and moderators of CSE via qualitative and quantitative literature reviews. Meta-analytic results support the relation of CSE with various outcomes, including job and life satisfaction, in-role and extra-role job performance, and perceptions of the work environment (e.g., job characteristics and fairness). The authors conclude with a critical evaluation of CSE theory, measurement, and construct validity, highlighting areas of promise and concern for future CSE research. Key topics requiring further research include integrating CSE within an approach/avoidance framework, ruling out alternative explanations for the emergence of the higher order construct, testing the possibility of intraindividual change in CSE, evaluating the usefulness of CSE for staffing and performance management, and moving beyond CSE to also consider core external evaluations.
There has been a longstanding consensus among researchers that individual differences play a limited role in predicting negotiation outcomes. However, this consensus results historically from early reviews that relied on limited data and problematic research designs. Questioning this consensus, a meta-analysis of negotiation studies revealed a significant role for individual difference variables. The analysis demonstrated predictive validity for numerous personality traits, cognitive ability, and emotional intelligence. Multiple outcome measures were examined, namely economic individual value, economic joint value, and psychological subjective value for both the negotiator and counterpart. Each individual difference measure had predictive validity for at least one outcome measure, with the exception of conscientiousness. Characteristics of research design moderated some associations. Field data showed stronger effects than did laboratory studies. Implications for theory and practice are considered.
Test‐retest reliability data gathered from 79 sources (34 separate studies) were analyzed by a multiple‐regression method in an attempt to estimate the effects of several factors on the temporal stability of individually tested intelligence. Five intelligence tests were examined: the Standford‐Binet (except the fourth edition), the WISC, the WISC‐R, the WAIS, and the WAIS‐R. Samples encompassed a wide range of subjects divergent on status, age, and sample size. Subject age and status, gender, and test‐retest interval were evaluated, and age and interval were found to be significant predictors of reliability. Subject sex and specific instrument were not found to have a significant effect on reliability. A summary table provides expected reliability coefficients, standard error, and percent of persons with IQ change in excess of 15 points, tabulated for combinations of each of the two predictors.
I History and Definition of the Concept.- The Evidence for the Concept of Intelligence.- On Defining Intelligence.- II Measurement and the Problem of Units.- The Absolute Zero in Intelligence Testing.- Is Intelligence Distributed Normally?.- III Development and Constancy of the IQ.- The Effect of the Interval between Test and Retest on the Constancy of the IQ.- The Limitations of Infant and Preschool Tests in the Measurement of Intelligence.- Intellectual Status and Intellectual Growth.- IV Types of Intelligence.- Primary Mental Abilities.- Organization of Abilities and the Development of Intelligence.- A Culture-Free Intelligence Test.- Ability Factors and Environmental Influences.- Personality and Measurement of Intelligence.- V Analysis of IQ Performance.- Intelligence Assessment: a Theoretical and Experimental Approach.- Intellectual Abilities and Problem-Solving Behaviour.- The Speed and Accuracy Characteristics of Neurotics.- Individual Differences in Speed, Accuracy, and Persistence: a Mathematical Model for Problem Solving.- VI Heredity and Environment: I. Twin and Familial Studies.- Genetics and Intelligence: a Review.- Twins: Early Mental Development.- IQs of Identical Twins Reared Apart.- VII Heredity and Environment: II. Foster and Orphanage Children.- A Critical Examination of the University of Iowa Studies of Environmental Influences upon the IQ.- The Relative Influence of Nature and Nurture upon Mental Development: a Comparative Study of Foster Parent-Foster Child Resemblance and True Parent-True Child Resemblance.- VIII Intelligence and Social Class.- Intelligence and Social Mobility.- Achievement and Social Mobility: Relationships among IQ Score, Education, and Occupation in Two Generations.- Differential Fertility and Intelligence: Current Status of the Problem.- Does Intelligence cause Achievement? A Cross-Legged Panel Analysis.- Ability and Income.- IX The Biological Basis of Intelligence.- Evoked Cortical Potentials and Measurement of Human Abilities.- Effects of Glutamic Acid on the Learning Ability of Bright and Dull Rats.- Effects of Heredity and Environment on Brain Chemistry, Brain Anatomy and Learning Ability in the Rat.- X The Paradigm and Its Critics.
Even before The Bell Curve was published, the idea of what Richard Herrnstein and Charles Murray call “the cognitive elite”—a dominant and rich group of well-educated smart people—was taking form.1 This elite has enemies across the political spectrum, but usually it is described as liberal and most criticism of it comes from conservatives. This chapter argues that the dimensions of the cognitive elite have been wildly exaggerated. After examining the original historical documents from which The Bell Curve makes its case for the rise of the cognitive elite, it finds that the case is much flimsier than Herrnstein and Murray let on. The chapter also points out that people with elite educational backgrounds control some but by no means all of the turf in the United States; they don’t control the central American institution, business, contra The Bell Curve. An elite education neither guarantees money and power, nor provides the only route to them. Therefore the cognitive elite should be understood as a sociological cartoon with political uses, not a phenomenon to be accepted at face value.