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Top areas to work on
* Preventing an AI-related catastrophe
* Catastrophic pandemics
* Nuclear war
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* Climate change
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* Improving decision making
Other promising areas
* Civilisation resilience
* The moral status of digital minds
* Promoting positive values
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* Global health
* Wild animal suffering
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* Policy and politics
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* AI safety technical research
* Biorisk research, strategy, and policy
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* Expert in AI hardware
* China-related AI safety and governance
* Grantmaker
* Helping build the effective altruism community
* Nuclear weapons safety and security
* Operations in high-impact organisations
* Research into global priorities
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* Be a founder
* Software engineering
* Journalism
* See more →
How to build great career capital
* See our top recommendations →
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* How to think about your contribution
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* Gut instinct
* Differences in productivity
* Comparative advantage
Strategy
* Ambition
* Coordination
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* How to make your career plan
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* Anonymous advice
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INTELLIGENCE MATTERS MORE THAN YOU THINK FOR CAREER SUCCESS
By Richard Batty · Published May 1st, 2013 ·
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When you’re trying to have an impact, it’s useful to know how successful you’ll
be in different careers so you can pick the right one. But how can you do this?
There are a few predictors of success that have been studied by psychologists,
but the results aren’t widely known. The scientific consensus is that the best
way to predict someone’s career success is to assess their general mental
ability (GMA), which is similar to what most people mean by “intelligence”. You
might find this surprising, so I’m going to summarise the evidence backing it
up. Then I’ll talk about:
* Why GMA is so important in work – mainly because people with higher GMA learn
faster.
* Which other factors affect success – job complexity, personality, and
experience.
* What this all means for your career – choose jobs that fit your GMA and find
the best ways to increase your chances of success.
WHAT IS GENERAL MENTAL ABILITY (GMA)?
In 1904, psychologist and statistician Charles Spearman noticed that children
tend to get similar marks in unrelated school subjects and he thought that there
might be an underlying factor affecting their performance. He developed the
statistical method of factor analysis to find how many underlying factors there
were.1 Since then, many psychologists psychologists have used factor analysis to
conclude that there is a single underlying general (or g) factor that explains
people’s performance on different cognitive tests, although there is some
disagreement about this.2 No single test measures general mental ability, but it
can be measured from the results of tests of more specific cognitive abilities.
But what is it? One description is “the ability to deal with cognitive
complexity.” “[M]ore complex tasks require more mental manipulation, and this
manipulation of information – discerning similarities and inconsistencies,
drawing inferences, grasping new concepts and so on – constitutes intelligence
in action.”3
GENERAL MENTAL ABILITY PREDICTS CAREER SUCCESS: THE EVIDENCE
Systematic analyses of thousands of studies support the idea that GMA is a good
predictor of career success. Depending type of job and how performance is
measured GMA explains between 30% and 70% of the variation in people’s work
performance (i.e. correlations of between .56 and .84), which is larger than any
other known predictor.4
Before we look at this evidence in more detail, we need to know what we mean by
success. There are 3 main measures of work success:
* Evaluation of performance on tasks similar to those encountered on the job
(work-sample tests)
* Performance ratings by supervisors
* Position in the occupational hierarchy
All three are predicted by GMA.
First, GMA predicts performance on work-sample tasks and ratings of supervisors.
Evidence from several meta-studies shows that when performance is measured using
work-sample tests, the correlation between GMA and performance is 0.84. When
supervisor ratings are used, the correlation is lower, at 0.74 for
high-complexity jobs.5
GMA also predicts how high up you get in the job hierarchy – i.e. your
occupational level.6 US Employment Service data shows a strong correlation
(0.72) between GMA and occupational level and US military data shows that mean
GMA scores are higher at higher occupational levels. Also, there is a wider
variety of GMA scores at lower occupational levels than at higher ones. It seems
that there are high-scoring people in low-level occupations, but low-scoring
people are unlikely to get promoted to higher levels.7
But to fully show the link we need to track people with known GMA over time to
see if high GMA individuals end up being more successful. This has been done.8
In a longitudinal study of 3,887 young adults, GMA predicted movement in the job
hierarchy 5 years later. Another study found that if people were in a job that
was less complex than their GMA would predict, they moved up to a more complex
job and vice versa. The predictivity of GMA even holds when controlling for
socioeconomic status by comparing biological siblings. “When the siblings were
in their late 20s (in 1993), a person with average GMA was earning on average
almost $18,000 less per year than his brighter sibling who had an IQ of 120 or
higher and was earning more than $9,000 more than his duller sibling who had an
IQ of less than 80.”9
The link has also been confirmed by two natural experiments.10 In 1978 a US
Steel plant in Pennsylvania changed the way they chose people for their skilled
trades apprentice programs. Before the change they filled their positions with
people who got the highest results on a GMA test. The plant replaced this system
with a test that most people passed followed by allocation of jobs by seniority.
The plant’s performance records show that after the new system was introduced
trainees learned less and dropped out more. And because apprentices took longer
to get through the course, training got more expensive.
The second natural experiment happened in the 1980s in Washington DC. The police
force changed their selection procedures for new applicants from a GMA to a
non-GMA based selection. After the change, it became more difficult to train new
officers. 80% of new hires couldn’t complete the required training, so the
content of the training had to be made easier. But the police officers produced
from this training were often incompetent, which caused lots of problems. For
example, the solution rate for murder cases declined from one of the highest to
one of the lowest in the US.
WHY DOES GENERAL MENTAL ABILITY PREDICT SUCCESS?
The evidence for the link between GMA and performance is strong, but it’s made
more plausible by if we can understand why GMA predicts performance. GMA mainly
influences performance through the rate at which people learn knowledge relevant
to the job – people with higher GMA learn faster.11 But GMA predicts success
even when you take account of job knowledge. With high GMA, people are more able
to go beyond existing job knowledge and make judgements in unfamiliar
situations.12
OTHER RELEVANT FACTORS
Job complexity
Although GMA predicts performance in all jobs the more complex the job is13, the
stronger the relationship between GMA and performance.14 And the more complex
the job, the more variation there is between top performers and bottom
performers.15 So if you have one of the highest levels of GMA in a highly
complex job, you’ll have a high output compared to the average performer.
Personality
You might think that personality matters more than GMA – some people seem to
have the kind of drive that leads to success. And this is true – personality
does matter, but less than general mental ability.16 The consensus model of
personality suggests that there are five fairly independent dimensions of
personality. These are conscientiousness, emotional stability, agreeableness,
extraversion, and openness to experience. Of these, the most valid predictor of
success is conscientiousness with a correlation between conscientiousness and
job performance of 0.31.17 So personality is predictive of success, but not as
much as general mental ability.
One possibility is that success is related to personality characteristics
multiplied by GMA. So maybe someone with low conscientiousness wouldn’t be
successful, however high their GMA was. One study suggests that this may be
true: “the combination of highly general cognitive ability and motivation is
significantly associated with more early career success.”18
Experience
The importance of experience is unclear. Some studies suggest that experience is
less important than mental ability.19 Even more surprisingly, the more
experience you have, the worse experience is as a predictor of success.20 For
people with 0-3 years experience, the correlation between experience and
performance is .49 but this drops to .15 for people with 12+ years of
experience. Conversely, the strength of GMA as a predictor increases the more
experience you have. With 0-3 years of experience, GMA correlates with
performance at .35 but this rises to .59 for people with 12+ years of
experience. One source suggests that after a few years, additional experience
doesn’t lead to more job knowledge and therefore doesn’t increase performance.21
However, this is highly unintuitive – some people spend decades developing
expertise and don’t reach a plateau after 5 years. Top performers in most fields
tend to be middle-aged (think of scientists, CEOs, chess-players). If experience
didn’t matter that much after about 5 years then we’d expect to see a wider
spread of ages at the top.22 Since fluid intelligence (the ability to reason
using novel information as opposed to existing knowledge) declines as people get
older,23 we’d expect the top performers in many fields to be young.
We can try and resolve this apparent contradiction as follows. Perhaps the
research showing that the importance of experience declines is correct for many
jobs because these jobs have a relatively small amount of knowledge that needs
to be learnt. Once this is learnt, the main determinant of success is GMA
because it predicts people’s ability to reason in novel situations. However,
some jobs have much more knowledge and experience required, so it takes decades
to become an expert.
Even further than this, we could divide jobs into those that require a great
deal of knowledge and those that don’t require so much knowledge but do require
a strong ability to reason in novel ways. Then we’d expect top performers in the
knowledge-dependent jobs to be older and those in the novel reasoning jobs to be
younger. This is what we observe:24 people in pure maths, theoretical physics,
and lyric poetry tend to peak in their 20s or 30s, whereas people other fields
such as history, novel writing, philosophy, and medicine tend to peak in their
40s or 50s.
COMMON OBJECTIONS TO THIS RESEARCH
I have covered the importance of personality and experience above, but there are
other possible objections:
Aren’t specific aptitudes more important?
You might think that specific aptitudes (such as verbal, spatial, or numerical
ability) would predict job performance better than GMA, since different jobs
seem to need different specific aptitudes. Because of this, you’d expect that
you could predict success better if you gave people specific aptitude tests and
put more emphasis on the aptitudes that were most relevant to the job. It’s not
clear whether this is true. One review paper25 suggests that if you make a test
that weights certain specific aptitudes more than others, it doesn’t have much
more predictive power than a general mental ability test. This paper suggests
that specific aptitude tests are predictive, but they are predictive because
they measure g as well as measuring the specific aptitude. However, there is a
theory of cognitive abilities that says that there are approximately 8 broad
abilities and there is some debate as to whether they can be summarised into a
single general factor.26 Even more importantly, the correlations between these
specific abilities is lower for people of high ability.27 So if you’re
intelligent you should consider whether you have one type of ability which you
are much stronger in than others.
Doesn’t GMA only matter up to a certain level?
It’s possible that for each job there is a certain level of general mental
ability required, and after that level is reached there is not much benefit from
being more able. If this was true, we’d expect to see a non-linear relationship
between GMA and performance. But overall,
the relationship between GMA and performance seems to be linear. One
meta-study28 analysed 174 studies involving a total of 36,614 people and tested
for linearity. They found that nonlinear relationships were not found at levels
greater than expected by chance. However, this study did not cover the highest
and lowest ends of the occupational spectrum very well.29 Similarly, most of the
studies I referenced earlier have been done on large populations of people with
fairly normal jobs and so unusual patterns at the highest levels could have been
missed. It’s still possible that the very highest jobs and achievements need
only a certain level of GMA, and other factors are more important beyond that
level. Malcolm Gladwell claims this in his book Outliers.30 He suggests that
while Nobel prize winners are intelligent, they aren’t necessarily of the very
highest intelligence. Gladwell’s evidence is quite weak – he supports his claims
with single studies, anecdote, and quotes from experts. This kind of evidence is
not strong in psychology, where results often turn out to not be repeatable.
Overall, I haven’t seen strong evidence either way on whether GMA is predictive
at the very highest levels. However, given that GMA is so strongly predictive
for most jobs, I suspect that the pattern will hold even at high levels.
If GMA predicts success, why is it not more commonly known?
Many companies do not use tests of GMA in their hiring. If GMA is so predictive
of work success, why would this be? Here are some possibilities, although I
don’t have evidence on this:
* It may simply be that hiring managers don’t know about the evidence. The first
meta-studies on the topic were done in the 1980s and so perhaps this knowledge
hasn’t filtered through to hiring managers yet.
* It may be that employers are worried about legal or worker morale issues from
using ability tests in job interviews.
* Some of the conclusions of this research are unintuitive – e.g. that
experience often doesn’t matter as much as GMA for job performance.
* Perhaps they do select for GMA, but indirectly, through interviews and
assessments of past performance.
WHAT THIS MEANS FOR YOUR CAREER
Some of your career options will be more of a gamble than others. Most
accountants, for example, will earn a good salary and be able to donate a lot of
money to charity. But in some careers only a few people can have an impact and
they have a disproportionate amount of impact. Research is one example – only a
few researchers make the most important breakthroughs. Entrepreneurship is
another example – most new businesses fail but a few become so successful that
their owners can donate hundreds of millions to charity. Because there’s more
variation between people’s performance in highly complex jobs31 and because
these jobs require higher GMA we’d expect that people with higher GMA will have
a better chance of succeeding at highly variable careers like research and
entrepreneurship. People with lower GMA might want to go into lower-variance
jobs with more sure outcomes.
From personal experience, I’ve also found it useful to know that job knowledge
is a strong predictor of performance. Since reading the evidence about GMA and
success, I have concentrated more on effective learning and choosing the most
useful job-related knowledge to learn.
DOES THIS MEAN THAT YOU CAN’T DO MUCH TO IMPROVE YOUR CHANCES OF SUCCESS?
On first reading this, you might be worried that there is very little you can do
about your chances of success. But these results shouldn’t cause you to give up:
First, it may be possible to increase your GMA. I haven’t seen evidence on
whether or not this is possible, but it’s worth investigating.
Second, impact isn’t the same as what most people call success. You could have
far more impact than someone with much higher GMA just by taking opportunities
for impact that other people don’t know about.
Third, the studies I’ve quoted didn’t look at whether some people achieved more
success than you’d expect from their GMA. Perhaps some people used better
methods of working. We know from the GMA and success research that job knowledge
is an important predictor of success. So how can you learn more effectively?
This paper32 (summarised here) shows that the methods most commonly used by
students are not the most effective and it shows which methods are the most
useful. And few people know that using a spaced repetition system can improve
your learning. Apart from learning, there are probably other areas ways you can
improve your chances of success such as becoming more conscientious33 or by
building a network.
So don’t see this evidence on the link between GMA and success as a reason to
give up – instead use it to understand that a major cause of success is a
mixture of knowledge and the ability to think, and find ways to improve your
ability at those things. And then look for other major determinants of success
and improve on those. In the future, we’ll write more on other determinants of
success and how to improve your chances.
--------------------------------------------------------------------------------
You may also enjoy
* Biases: how they affect your career decisions, and what to do about them
* Want to be successful? Know your odds.
* How to judge your chances of success
--------------------------------------------------------------------------------
Highlights from the comments:
We previously had a comment section on the blog. Here are some of the highlights
from the old section:
Eric Gastfriend:
It would be great to update this article to incorporate or address this
criticism of IQ testing that was published in 2015.
Richardson, Ken, and Sarah H. Norgate. “Does IQ really predict job
performance?.” Applied Developmental Science 19.3 (2015): 153-169.
Pablo Stafforini:
The thesis that, beyond a certain threshold (~120), IQ confers no additional
benefits has been the subject of many popular books, most notably Malcolm
Gladwell’s Outliers. However, the thesis seems to be contradicted by the
available evidence:
* Randomly selected eminent scientists have IQs far above the population mean
(which we can approximate as the mean of the population of PhD science
students: 130).
* SAT-M scores within the top 1% of the population predict future scientific
success.
* The IQ scores of participants in the Terman study of gifted individuals (who
had minimum IQ of 134) were positively correlated with lifetime earnings.
We also linked to this article from Slate Star Codex arguing that individuals
shouldn’t worry too much about their scores.
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* Ability
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NOTES AND REFERENCES
1. : Gottfredson, Linda S. The general intelligence factor. Scientific
American, Incorporated, 1998.↩
2. : There is a theory of cognitive ability called extended Gf-Gc theory which
uses factor analysis to conclude that there are approximately 8 major
cognitive abilities that can’t be summarised into one general factor. See
Horn, John, and Hiromi Masunaga. “A Merging Theory of Expertise and
Intelligence.” (2006). in Charness, Neil, Paul J Feltovich, and Robert R
Hoffman. The Cambridge handbook of expertise and expert performance.
Cambridge University Press, 2006.↩
3. : Gottfredson, Linda S. The general intelligence factor. Scientific
American, Incorporated, 1998.↩
4. : When performance is measured objectively using carefully constructed work
sample tests (samples of actual job tasks), the correlation (validity) with
intelligence measures is about .84 – 84% as large as the maximum possible
value of 1.00, which represents perfect prediction. When performance is
measured using ratings of job performance by supervisors, the correlation
with intelligence measures is .66 for medium complexity jobs (over 60% of
all jobs). For more complex jobs, this value is larger (e.g. .74 for
professional and managerial jobs), and for simpler jobs this value is not
as high (e.g. .56 for semi-skilled jobs). Another performance measure that
is important is the amount learned in job training programs (Hunter et al.,
2006). Regardless of job level, intelligence measures predict amount
learned in training with validity of about .74 (Schmidt, Shaffer, and Oh,
2008). From: Schmidt, Frank L, and John E Hunter. “Select on intelligence.”
Handbook of principles of organizational behavior(2000): 3-14.↩
5. : Schmidt, Frank L, and John E Hunter. “Select on intelligence.” Handbook
of principles of organizational behavior(2000): 3-14.↩
6. : Occupational level is a reasonable measure of success: “People’s rankings
or ratings of the occupational level or prestige of different occupations
are very reliable; correlations between mean ratings across studies are in
the .95 to .98 range, regardless of the social class, occupation, age, or
country of the raters (Dawis, 1994; Jensen, 1980, pp. 339347)” From:
Schmidt, Frank L, and John Hunter. “General mental ability in the world of
work: occupational attainment and job performance.” Journal of personality
and social psychology 86.1 (2004): 162.↩
7. : Schmidt, Frank L, and John Hunter. “General mental ability in the world
of work: occupational attainment and job performance.” Journal of
personality and social psychology 86.1 (2004): 162.↩
8. : Schmidt, Frank L, and John Hunter. “General mental ability in the world
of work: occupational attainment and job performance.” Journal of
personality and social psychology 86.1 (2004): 162.↩
9. : Schmidt, Frank L, and John Hunter. “General mental ability in the world
of work: occupational attainment and job performance.” Journal of
personality and social psychology 86.1 (2004): 162.↩
10. : Schmidt, Frank L, and John E Hunter. “Select on intelligence.” Handbook
of principles of organizational behavior(2000): 3-14.↩
11. : Schmidt, Frank L, and John E Hunter. “Select on intelligence.” Handbook
of principles of organizational behavior(2000): 3-14.↩
12. : Schmidt, Frank L, and John E Hunter. “Select on intelligence.” Handbook
of principles of organizational behavior(2000): 3-14.↩
13. : I haven’t seen a definition of job complexity. In these studies, jobs are
divided into several broad categories of complexity based on a judgement of
their complexity.↩
14. : Hunter, John E. “Cognitive ability, cognitive aptitudes, job knowledge,
and job performance.” Journal of vocational behavior 29.3 (1986): 340-362.↩
15. : Hunter, John E, Frank L Schmidt, and Michael K Judiesch. “Individual
differences in output variability as a function of job complexity.” Journal
of Applied Psychology 75.1 (1990): 28.↩
16. : Schmidt, Frank L, and John Hunter. “General mental ability in the world
of work: occupational attainment and job performance.” Journal of
personality and social psychology 86.1 (2004): 162.↩
17. : Schmidt, Frank L, and John Hunter. “General mental ability in the world
of work: occupational attainment and job performance.” Journal of
personality and social psychology 86.1 (2004): 162.↩
18. : O’Reilly III, Charles A, and Jennifer A Chatman. “Working smarter and
harder: A longitudinal study of managerial success.” Administrative Science
Quarterly (1994): 603-627.↩
19. : Schmidt, Frank L, and John Hunter. “General mental ability in the world
of work: occupational attainment and job performance.” Journal of
personality and social psychology 86.1 (2004): 162.↩
20. : Schmidt, Frank L, and John Hunter. “General mental ability in the world
of work: occupational attainment and job performance.” Journal of
personality and social psychology 86.1 (2004): 162.↩
21. : Page 11 of Schmidt, Frank L, and John E Hunter. “Select on intelligence.”
Handbook of principles of organizational behavior(2000): 3-14.↩
22. : Horn, John, and Hiromi Masunaga. “A Merging Theory of Expertise and
Intelligence.” (2006). in Charness, Neil, Paul J Feltovich, and Robert R
Hoffman. The Cambridge handbook of expertise and expert performance.
Cambridge University Press, 2006.↩
23. : Horn, John, and Hiromi Masunaga. “A Merging Theory of Expertise and
Intelligence.” (2006). in Charness, Neil, Paul J Feltovich, and Robert R
Hoffman. The Cambridge handbook of expertise and expert performance.
Cambridge University Press, 2006.↩
24. : “At one extreme, some fields are characterized by relatively early peaks,
usually around the early 30s or even late 20s in chronological units, with
somewhat steep descents thereafter, so that the output rate becomes less
than one quarter the maximum. This agewise pattern apparently holds for
such endeavors as lyric poetry, pure mathematics, and theoretical physics,
forexample (Adams, 1946; Dennis, 1966; Lehman, 1953a; Moulin, 1955; Roe,
1972b; Simonton, 1975a; Van Heeringen & Dijkwel, 1987). At
the contrary extreme, the typical trends in other endeavors may display a
leisurely rise to a comparatively late peak, in the late 40s or even 50s
chronologically, with a minimal if not largely absent drop-off afterward.
This more elongated curve holds for such domains as novel writing, history,
philosophy, medicine, and general scholarship, for instance (Adams, 1946;
Richard A. Davis, 1987; Dennis, 1966; Lehman, 1953a; Simonton, 1975a). Of
course, many disciplines exhibit age curves somewhat between these two
outer limits, with a maximum output rate around chronological age 40 and a
notable yet moderate decline thereafter (see, e.g., Fulton & Trow, 1974;
Hermann, 1988; McDowell, 1982; Zhao & Jiang, 1986).” From Simonton, Dean K.
“Age and outstanding achievement: What do we know after a century of
research?.” Psychological Bulletin 104.2 (1988): 251.↩
25. : Schmidt, Frank L. “The role of general cognitive ability and job
performance: Why there cannot be a debate.” Human performance 15.1-2
(2002): 187-210.↩
26. : Horn, John, and Hiromi Masunaga. “A Merging Theory of Expertise and
Intelligence.” (2006). in Charness, Neil, Paul J Feltovich, and Robert R
Hoffman. The Cambridge handbook of expertise and expert performance.
Cambridge University Press, 2006.↩
27. : Hunt, Earl. “Expertise, talent, and social encouragement.” The Cambridge
handbook of expertise and expert performance (2006): 31-38.↩
28. : Coward, W Mark, and Paul R Sackett. “Linearity of ability-performance
relationships: A reconfirmation.” Journal of Applied Psychology 75.3
(1990): 297.↩
29. : “One can also question the range of jobs across which these findings can
be generalized. As noted earlier, neither the high (professional and
managerial) nor the low (simple, repetitive jobs) ends of the occupational
spectrum are represented in the present study” From: Coward, W Mark, and
Paul R Sackett. “Linearity of ability-performance relationships: A
reconfirmation.” Journal of Applied Psychology 75.3 (1990): 297.↩
30. : Gladwell, Malcolm. Outliers: The story of success. Little, Brown, 2008.↩
31. : Hunter, John E, Frank L Schmidt, and Michael K Judiesch. “Individual
differences in output variability as a function of job complexity.” Journal
of Applied Psychology 75.1 (1990): 28.↩
32. : Dunlosky, John et al. “Improving Students’ Learning With Effective
Learning Techniques Promising Directions From Cognitive and Educational
Psychology.” Psychological Science in the Public Interest 14.1 (2013):
4-58.↩
33. : Barrick, Murray R, and Michael K Mount. “Select on conscientiousness and
emotional stability.” Handbook of principles of organizational behavior 15
(2000): 28.↩
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