Personality, competencies, and life outcomes: results from the German PIAAC longitudinal study
Rammstedt et al. Large-scale Assess Educ
Personality, competencies, and life outcomes: results from the German PIAAC longitudinal study
Beatrice Rammstedt beatrice.rammstedt@gesis
The present paper investigates the power of personality to predict important life outcomes in the context of the Programme for the International Assessment of Adult Competencies (PIAAC). On the most global level, personality can be described by the Big Five dimensions, extraversion, agreeableness, conscientiousness, emotional stability, and openness to experience. These five dimensions were assessed in the German PIAAC longitudinal study (N = 4122) and can thus be directly related to the central competence and outcome indicators measured in PIAAC. In a first step, we report the relationships between the Big Five dimensions and the basic competencies literacy and numeracy. In a second step, we investigate the extent to which the five personality dimensions can contribute to explaining six important life outcomes, above and beyond competencies and sociodemographic characteristics. Our results indicate that personality is substantially related to all six life outcomes. The portion of variance explained by personality was similar to, and sometimes larger than, that explained by competencies. After adjusting for competencies, personality was incrementally predictive of life satisfaction and health, in particular, and, to a lesser extent, of educational attainment, employment status, and income. The only outcome of which personality was not incrementally predictive over and above competencies was participation in continuing education. Overall, these findings highlight the merit of including measures for the Big Five personality domains in upcoming cycles of PIAAC.
PIAAC; Cognitive skills; Competencies; Personality; Health; Income; Wellbeing
Cognitive skills, such as literacy and numeracy, are undoubtedly powerful predictors of
important life outcomes such as educational attainment, income, and health
(Herrnstein and Murray 1994). However, during the last decade, other traits besides cognitive
skills have emerged as potent predictors of life outcomes. These traits—often collectively
referred to as “non-cognitive skills”—include personality, motivation, interests, and
The Nobel Prize-winning economist James Heckman was among the first to champion
the role of non-cognitive skills in shaping important life outcomes. In an influential
article (Borghans et al. 2008; see also Heckman et al. 2006), he and his co-authors urged that
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“new studies should incorporate validated personality, IQ, and preference measures, as
well as outcome measures” (p. 1037). In recent years, many researchers and studies have
heeded this call. Influential national surveys, such as the German Socio-Economic Panel
(SOEP), the German National Educational Panel Study (NEPS), Household, Income and
Labour Dynamics in Australia (HILDA), and the UK Household Longitudinal Study
(UKHLS), and international surveys, such as the World Values Survey (WVS) and the
International Social Survey Programme (ISSP), have included measures of personality
and other non-cognitive skills in their core questionnaires.
Results of these surveys and earlier landmark studies (e.g., Roberts et al. 2007; see
Ozer and Benet-Martínez 2006 for overviews) attest to the predictive power of
non-cognitive skills such as the Big Five personality traits, dispositional optimism, or locus of
control for a broad range of important life outcomes. For example, several studies have
shown that the personality dimensions conscientiousness and dispositional optimism
are related to a person’s health, including morbidity and even mortality (Allison et al.
2003; Arthur and Graziano 1996; Bogg and Roberts 2004; Rasmussen et al. 2009). Other
studies have found that individuals with a more external locus of control and low levels
of emotional stability report lower levels of life satisfaction (Rammstedt 2007) and that
the marriages of more conscientious individuals last longer (Roberts and Bogg 2004).
Personality also affects job performance (Hogan and Holland 2003) and income (Judge
et al. 2012).
Motivated by Heckman’s call and by these encouraging findings, large-scale
studies conducted under the auspices of the Organisation for Economic Co-operation and
Development (OECD) have become increasingly interested in including personality
and other non-cognitive skills in addition to the classical cognitive skill measures. For
example, the OECD is currently setting up a Longitudinal Study of Social and Emotional
Skills. The inclusion of non-cognitive skill measures is also progressing in the
well-established Programme for International Student Assessment (PISA). Finally, within the scope
of the Programme for the International Assessment of Adult Competencies (PIAAC), an
expert group has been established to identify the most central non-cognitive skills to be
included in the upcoming PIAAC cycle, which is scheduled to begin in 2018.
With this renewed interest in non-cognitive skills, the study of human abilities has
come full circle. Several early ability theorists prominently argued that non-cognitive
skills (or “non-ability traits”) should be studied alongside cognitive skills, and attempted
to integrate the former into their theoretical models of human abilities. For example,
Wechsler (1950) suggested the inclusion of what he called “non-intellective” traits into
IQ tests that were then already well-established; his view was that such traits might
offer added value in the prediction of real-life performance. His contemporary, Vernon
(1950, p. 47), included an “X” factor along with a cognitive Spearman-type g factor in
his model of the structure of “educational abilities”, under which, among other things,
he subsumed personality and interests (although he did not further specify which
noncognitive factors he deemed crucial). Yet the assessment and theory of non-cognitive
traits has long lagged behind that of achievement and intelligence, and even the
aforementioned early proponents did not follow up on their own arguments concerning
noncognitive skills, devoting little effort to studying them in their own research agenda. For
this reason, non-cognitive skills appear to somehow have fallen into oblivion among
ability researchers, especially in the wake of advances in cognitive testing and the
publication of influential studies on the potency of cognitive skills in predicting life outcomes,
such as Herrnstein and Murray’s (1994) famous “bell curve”. It so occurs that
surprisingly little theoretical work, let alone large-scale empirical efforts, have been devoted to
the interface of cognitive and non-cognitive skills—with some more recent exceptions,
most notably Ackerman’s (1996) PPIK theory (intelligence-as-process, personality,
interests, and knowledge) and a book-length treatment of the topic by Chamorro-Premuzic
and Furnham (2005).
Partly owing to this long-standing dearth of theoretical work, the definition of
“noncognitive skills”, as well as their relation to cognitive skills, remain rather vague. The
termis meant as a catchphrase describing a broad range of potentially relevant skills other
than the “cognitive skills” measured by standardized achievement and IQ tests. So far,
however, researchers have not reached any clear consensus as to which specific skills
should be included under this umbrella term. In the absence of a clear definition, most
extant surveys have included those non-cognitive skills that promised to be related to
the central outcomes of interest in these surveys. Thus, the set of constructs mostly
follows the specific intention of the study in question. Despite this definitional
uncertainty, nearly all of these surveys have included the major dimensions of personality, the
so-called Big Five. The Big Five aim to describe an individual’s personality on the most
global level with five largely independent dimensions (De Raad 2000; Goldberg 1990;
John et al. 2008). These dimensions are extraversion, agreeableness, conscientiousness,
emotional stability, and openness to experience. Also within the above-mentioned expert
group charged with identifying the most central non-cognitive skills to be included in
the PIAAC study, the only point on which a clear consensus has been reached is that the
Big Five personality dimensions should be included.
In summary, a growing body of evidence on personality and important life outcomes
has yielded a key insight that is of potentially high relevance to public policy and
interventions, namely that, even though the importance of cognitive skills in predicting key
indicators of life success is beyond doubt, non-cognitive skills such as personality are
often equally potent—and sometimes even more potent—predictors of these outcomes
(Roberts et al. 2007; Heckman and Kautz 2012). This view is further reinforced by the
finding that cognitive abilities and personality are substantially related (Rammstedt et al.
2016). This confounding of allegedly “pure” measures of cognitive skills with
non-cognitive skills has led some researchers (e.g., Heckman and Kautz 2012; Borghans et al. 2011)
to contend that many existing analyses of the purported effects of cognitive skills on life
outcomes may overestimate the effects of cognitive skills if these analyses do not control
for personality. Consequently, these researchers have called for further research that pits
the predictive power of cognitive and non-cognitive skills against each other.
The present study
Despite the important empirical advances made in research on the relationship between
non-cognitive skills, such as personality traits, and life outcomes during the past
decade, this body of evidence is still somewhat limited by the heavy reliance on largely
North American samples, and especially on samples that are often small and
non-representative. Furthermore, extant studies have used widely varying instruments to assess
cognitive and/or non-cognitive skills, many of which were not well validated. Finally,
most studies so far have not jointly considered non-cognitive and cognitive skills and
tested their predictive power against each other. In the present study, therefore, we aim
to replicate and extend previous findings on the linkages between non-cognitive skills,
cognitive skills, and life outcomes, drawing on the data from the German PIAAC and its
PIAAC aims to investigate the acquisition and loss of adult competencies, skill usage,
and the relations between these competencies and key indicators of life success. The
PIAAC data are particularly well suited for our present study, as they offer high-quality
measures of cognitive skills (competencies), non-cognitive skills (personality
dimensions), and a broad range of important life outcomes (e.g., labor market participation,
income, and health) in a large-scale representative sample of the adult population in
In line with the main research goals of PIAAC, the specific purpose of our present
study is twofold. First, we repeat and briefly report previous analyses (Rammstedt et al.
2016) of the relationships between the Big Five personality dimensions and two key
adult competencies assessed in PIAAC, namely literacy and numeracy. In so doing, we
aim to show that cognitive and non-cognitive skills are related (or, put differently,
confounded; Borghans et al. 2011). Second, and most importantly, we investigate the extent
to which the five personality dimensions can contribute—above and beyond basic
cognitive skills—to explaining central life outcome variables measured in PIAAC. To this end,
we compare the effects of separately analyzed non-cognitive skills and cognitive skills
assessed in PIAAC on six indicators of life success, namely health, life satisfaction,
educational attainment, labor force participation, participation in continuing education, and
income. In addition, we investigate whether personality can explain additional variance
in these life outcomes, even after adjusting for the effects of competencies (incremental
validity). As the present study is based on a cross-sectional approach no causal
interpretations regarding the direction of the associations between non-cognitive skills and the
investigated life success indicators can be drawn.
Sampling, method, and participants
Data for the present study come from the German PIAAC survey (Rammstedt et al.
2015) and the German PIAAC longitudinal study (PIAAC-L; see Rammstedt et al. 2017),
a follow-up survey of the same sample. The PIAAC program compares cognitive skills—
such as the two key adult competencies literacy and numeracy—across a large number
of (mainly OECD) countries. The target population in the German PIAAC study,
conducted in 2012, comprised adults (aged between 16 and 65 years) who were randomly
selected from local population registers in randomly selected municipalities across
Germany. Participation in PIAAC was voluntary; an incentive of 50 euros was offered upon
participation in the survey, which comprised a personal interview (average duration:
45 min) and a cognitive assessment lasting approximately 60 min. Zabal et al. (2014)
provide a detailed description of the sampling procedure and the technical implementation.
In PIAAC-L, a German follow-up study to PIAAC, 4122 of the original 5465 PIAAC
participants in Germany were re-interviewed in 2014. Participation was voluntary and
incentivized by a small amount of money (usually 25 euros). For a detailed description of
the study design and the technical implementation of PIAAC-L, see Zabal et al. (2016)
and Steinacker et al. (2016). For the present analyses, we combined data from the 2012
wave of the German PIAAC and the 2014 follow-up survey.1
Measures and procedure
For the present analysis, we investigated the following variables from PIAAC 2012 and
the 2014 PIAAC-L follow-up:
Big Five personality dimensions
In the 2014 PIAAC-L follow-up, respondents completed a 15-item short version of the
Big Five Inventory (BFI) originally developed for use in the German Socio-Economic
Panel (SOEP; Schupp and Gerlitz 2014). This questionnaire contains three items per Big
Five dimension, to be answered on a seven-point scale ranging from does not apply (1) to
applies fully (7).
Our cognitive skill measures in PIAAC comprised two key adult competencies: literacy
and numeracy. Literacy refers to the ability to understand and use information from
written texts in a variety of contexts to achieve goals and develop knowledge and
potential. Numeracy is defined as the ability to use, apply, interpret, and communicate
mathematical information and ideas (see OECD 2013). Both competencies represent acquired
skills, resembling the definition of Cattell’s “crystallized intelligence” (Gc) or Hebb’s
“Intelligence B” (see Ackerman 1996, for a Discussion of these models). were assessed
using a multistage adaptive testing design. For each participant, 10 plausible values were
estimated for each competency domain (for details of the design and the IRT scaling
process in PIAAC, see OECD 2013). All our models involving the competencies were
run separately for each of the ten plausible values per domain. For the correlational
analyses, we then averaged the results across these ten models per domain. For the
regression analyses, the first plausible value was used per domain. Deviating findings for the
other plausible values are reported where applicable.
Important life outcomes
Six central indicators of life success from the domains health and well-being, education,
and work were selected from the PIAAC and PIAAC-L variables.
Health In PIAAC, respondents rated their subjective general health with a single item
on a five-point scale ranging from very good to bad (I_Q08).
Life satisfaction In PIAAC-L, satisfaction with 11 different life domains (work, leisure,
dwelling, sleep, health, housework, childcare, family care, schooling, personal income,
household income; pzuf ) and with life in general (pzule1_14) was assessed. Respondents
answered these items on an 11-point rating scale ranging from 0 (totally unsatisfied) to
10 (totally satisfied). We computed an overall life satisfaction index by taking the mean
across all twelve items. Cronbach’s alpha for the twelve items was .81.
Educational attainment Respondents’ highest level of education was assessed with two
separate questions (highest general education and highest vocational education
qualification in the categories of the German education system), which were then mapped to the
levels 0–6 of the International Standard Classification of Education (ISCED) 1997 based
on the PIAAC variable PIAAC: B_Q01a.2
Labor force participation All respondents were asked in PIAAC, and again in PIAAC-L,
to report whether they were currently employed and, if not, what their current main
activity was. For the present analyses, we used the corresponding question of the
PIAACL assessment (perw). In 2014, 46% of the sample reported that they were employed
fulltime, 16% reported that they were employed part-time, 4% reported that they were
undergoing vocational training, 7% reported that they were marginally employed, and
27% reported that they were not employed. For the present analyses, we generated a
dichotomous variable indicating whether or not a respondent was employed full-time (1
in full-time employment, 0 not in full-time employment3).
Participation in continuing education All PIAAC respondents were asked whether
they had participated in any form of continuing education and training during the past
12 months, including distance learning (B_Q12a), on-the-job training (B_Q12c),
workshops or seminars (B_Q12e), or other courses or private tuition (B_Q12g). For our
analyses, we generated a dichotomous variable indicating whether a respondent participated
in any continuing education and training (53%) or not (47%) during the past 12 months.
Income For the subsample of full-time employed individuals, personal income was
estimated in PIAAC based on self-reported monthly income in euros (EARNMTHALL). We
logarithmized the income variable for the present analyses.
Sociodemographic control variables
In addition to these predictors and outcome variables, we included key
sociodemographic characteristics as statistical control variables. In particular, we controlled for the
respondent’s age in years (based on lgeb_14), gender (0 male; 1 female; lsex_14), their
highest level of education (ISCED 0-6; based on B_Q01a),4 and their migration status (0
born in Germany; 1 not born in Germany; lgebd_14).
For some of the analyses, it was appropriate to analyze only subsamples instead of the
full sample. Specifically, for educational attainment, employment status, and
participation in continuing education as outcomes, we focused on a subsample comprising only
those respondents who reported as their main activity (paus1_14) that they were not
2 Foreigners who obtained their qualifications in another country were asked to report the nearest German equivalent. If
this was not possible, they were excluded from analyses (n = 41).
3 Besides part-time employed respondents, this category includes those who were unemployed or not in the labor force.
4 The highest level of education was not controlled when education was the dependent variable.
Table 1 Associations between the Big Five scales and basic competencies (literacy
presently in education. For employment status and participation in continuing
education, we additionally excluded retired persons.5 For income as an outcome, our
subsample included only respondents working full-time (perw_14) and excluded respondents
who were employed part-time or retired. Descriptive statistics for these variables are
reported in Appendix Tables 4 and 5.
Personality and basic competencies
In a first step, we examined the degree to which the Big Five dimensions were related
to the basic competencies literacy and numeracy. As a recent study (Major et al. 2014)
demonstrated that there are not only linear but also quadratic associations between
personality and cognitive ability, we included both linear and quadratic terms in our models.
We will discuss effects that are statistically significant (α = .05) and practically
meaningful. There are no clear cut-off criteria for practical meaningfulness and we decided
discuss all standardized regression coefficients that exceed .05, thus explaining more
than .25% of the unique variance.67
As can be seen in Table 1, the Big Five domains conscientiousness, emotional stability,
extraversion, and openness showed substantial linear associations with both numeracy
and literacy. emotional stability and openness were positively related to both cognitive
domains, indicating that more emotionally stable and open respondents possess, on
average, slightly higher numeracy and literacy skills than their less emotionally stable
and open counterparts. By contrast, conscientiousness was negatively associated with
both competencies, indicating that more conscientious respondents have, on average,
lower numeracy and literacy skills than less conscientious respondents.
In addition to these linear effects, emotional stability and openness were also
quadratically related to numeracy and literacy (see Fig. 1a–h). As the figures show, for both
5 To differentiate between retired and non-retired persons, a variable was derived based on spelltype (=8 for the retired)
and begin (<2014 for the retired).
6 This cut-off criteria can be seen a compromise between Rosenthal (1990) who suggests that even .10% explained vari
ance can be meaningful and other (e.g. Rasch et al. 2010) who suggest that 1% explained variance is only a small effect.
7 Rammstedt et al. (2016) discuss these analyses in greater detail.
low medium high
low medium high
conscientiousness conscientiousness conscientiousness
260 low openness medium openness high openness low openness ompeendniuemss high openness
Fig. 1 a–h Associations between the Big Five personality domains and numeracy and literacy (means based
on tertile splits)
cognitive domains and for both personality domains the positive association between
emotional stability and openness with both numeracy and literacy was primarily caused
by low competence levels of persons low on emotional stability and openness,
respectively, whereas persons with intermediate and high levels of these personality domains
differed, on average, less in their competencies.
low emotional medium emotional high emotional
stability stability stability
Table 2 Relationships between personality, numeracy, and important life outcomes
of education (ISCED 1997)a
−.04* .01 .02 −.02 −.01
−.03 −.12*** −.12*** −.05* −.05
−.01 .17*** .18*** .03 .04
.05** .12*** .11*** .06* .04
.10*** −.02 −.02 .01 .01
−.01 .05 .05 −.04 −.04
−.04* −.02 −.01 .02 .02
−.03 .01 .02 −.03 −.02
−.02 −.01 −.01 −.02 −.02
.00 −.01 −.01 .01 .02
.52*** .50*** .15*** .16*** .18*** .18***
.23*** .22*** −.15*** −.10*** −.13*** −.07** −.03 −.04
.03* .04* −.53*** −.52*** −.52*** −.03 −.02 −.01
− .27*** .20*** .20*** .34*** .26*** .26***
.01 −.03 −.01 −.00 −.08*** −.06* −.06*
3174 2868 2868 2868 2800 2800 2800
.30 .31c .29c .32c .13c .14c .15c
d Using different plausible values did not change the pattern of results. The regression coefficients for numeracy maximally
changed by ∆ = .05. Detailed results for all ten plausible values are presented in Additional file 1: Table S1
* p < .05, ** p < .01, *** p < .001
Overall, the pattern of both the linear and the quadratic correlations between
personality and competencies was impressively homogeneous for the two cognitive domains.
The correlation between numeracy and literacy was r = .87 on average and ranged
between r = .86 and r = .87 across the ten plausible values.
Personality and important life outcomes
In a second step, we investigated the degree to which the Big Five domains are related to
important life outcomes measured in PIAAC. To investigate these relationships, we ran
linear and logistic regression analyses with three different models per outcome indicator.
In the first model, we estimated linear and quadratic associations of the Big Five domains
with the six life outcomes. In the second model, we estimated the associations between
the basic competencies (literacy and numeracy) and the six life outcomes. Finally, in our
third model, we jointly considered the effects of personality and competencies in order
to test the degree to which the personality dimensions explained variance over and
above the competencies (incremental validity). In all three models, we controlled for the
sociodemographic variables age, gender, educational attainment,8 and migration status.
The standardized regression coefficients (β) are shown in Tables 2 and 3. The
corresponding results for literacy are provided in Appendix Tables 6 and 7 because the
8 The highest level of education was not controlled when education was the dependent variable.
Table 3 Relationships between personality, numeracy, and important life outcomes
−.07** −.07** −.02 −.01 −.03 −.02
.14*** .14*** .12*** .10*** .13*** .11***
.22*** .25*** .25*** −.34*** −.30*** −.31*** −.12*** −.06*** −.09***
−.13*** −.13*** −.11*** .00 −.01 .01 .03* .01 .04**
.28*** .20*** .21*** .14*** .10*** .09*** .11*** .09*** .06**
−.01 .00 .01 −.02 −.01 .00 −.04** −.04* −.02
1626 1626 1626 3716 3716 3716 3717 3717 3717
.19 .18 .20 .16 .11 .17 .17 .04 .17
Values are standardized regression coefficients, * p < .05, ** p < .01, *** p < .001
a Only respondents who are not retired
b Only respondents who are employed full-time
c Using different plausible values did not change the pattern of results. The regression coefficients for numeracy maximally
changed by ∆ = .05. Detailed results for all ten plausible values are presented in Additional file 1: Table S1
resulting coefficients in the same models for literacy were highly similar to those for
One of the most central outcome variables of PIAAC is educational attainment. As
shown in the first model in Tables 2 and 3, the highest level of education was
substantially related to personality, which—together with the control variables—explained 8%
of the overall variance. Higher emotional stability and openness were related to higher
educational attainment. extraversion, by contrast, was negatively related to educational
attainment. conscientiousness showed a negative quadratic relationship with
educational attainment, which means that respondents with lower levels of conscientiousness
reported lower educational attainment. Respondents with average levels of
conscientiousness had the highest educational attainment, whereas an above-average level of
conscientiousness did not bring any additional advantage (see Fig. 2a).
Educational attainment is known and expected to be strongly related to the cognitive
abilities of a person. Therefore, it is not surprising that, as shown in our second model in
Tables 2 and 3, numeracy was highly related to the highest level of education, explaining
29% of the variance.
In our third model, we compared the relationship between personality and cognitive
competencies on the one hand and educational attainment on the other. Compared to
Highest level of education (ISCED)
low medium high
conscientiousness conscientiousness conscientiousness
low openness medium openness high openness
Model II (competencies only), jointly considering personality and competencies slightly
increased the explained variance to 30%. In addition, the linear negative associations
between extraversion and educational attainment, the positive effect of emotional
stability on educational attainment, and the quadratic association between
conscientiousness and educational attainment in Model I diminished when numeracy was taken into
account. Thus, after adjusting for numeracy, only the personality dimension openness
substantially contributed to explaining additional variance in educational attainment.
Our second outcome variable is labor force participation (full-time employment).
As can be seen from Model I, being employed full-time was positively related to
conscientiousness and emotional stability and negatively related to agreeableness.
Overall, Model I, which contained the personality dimensions and the control variables,
explained 31% of the variance in labor force participation. By contrast, the
competencies (Model II) explained only 29% of the variance. Taking both the non-cognitive and
the cognitive skills into account (Model III) did not substantially change the associations
observed in the preceding models, but it increased the variance explained to 32%.
In PIAAC, participation in continuing education is regarded as a central indicator for
the maintenance and broadening of skills over the life course. Therefore, a clear
positive association between the skills assessed in PIAAC and participation in continuing
education is hypothesized and supported by the PIAAC data (OECD 2013). Here, we
investigated the degree to which participation in continuing education was also related
to non-cognitive skills (i.e., personality) in addition to cognitive skills (i.e., the
competencies). Indeed, our analyses revealed a substantial positive association between
participation in continuing education and a person’s level of emotional stability (Model I),
indicating that emotionally more stable persons have a stronger tendency to
participate in continuing education. This model explains 13% of the variance. A quite similar
amount of variance could be explained when only numeracy skills rather than the
personality domains were taken into account (14%, Model II) or when both numeracy and
personality were taken into account (15%, Model III).
Income is another central outcome variable in PIAAC. Using Mincer regressions
(Mincer 1974), we were able to show in earlier studies (Klaukien et al. 2013) that
literacy and, in particular, numeracy skills substantially added to the prediction of a person’s
income. These results were largely replicated in our Model II.
Recent studies based on large Anglo–American and British samples have provided
initial evidence that income is related to an individual’s personality (e.g., Judge et al. 2012;
Heineck 2014; Mueller and Plug 2006). In particular, these studies have shown that low
agreeableness and high openness are associated with higher income. In addition,
previous studies have indicated that the negative association between agreeableness and
income, in particular, is curvilinear—that is, the agreeableness-income relationship is
steeper at lower levels of agreeableness (Judge et al. 2012). Gender also seems to play an
important role in the personality-income relationship. Previous results have indicated
that the negative effect of agreeableness on income holds especially for men but not for
women (Judge et al. 2012).
Results from our own analyses using the total sample comprising both genders did not
replicate previous findings on the positive association between openness and income
(e.g., Judge et al. 2012; Heineck 2014; Mueller and Plug 2006). On the contrary, we found
a significant negative association between openness and income, indicating that persons
who are higher on openness earn, on average, less than persons who are lower on
openness. In addition to this linear association, there was a quadratic relationship between
openness and income. Persons with higher levels of openness had the lowest average
income, while those with an intermediate level of openness had the highest average
income (see Fig. 2b). However, the negative quadratic association between agreeableness
and income reported by Judge et al. (2012) could be replicated in our data. As can be
seen in Fig. 2c, agreeableness showed a quadratic association with income,9 indicating
9 With a regression coefficient of .05, it is thus just below our self-imposed threshold of >.05.
that persons who are low in agreeableness have by far the highest average income, while
persons with intermediate levels of agreeableness earn the least. In additional analyses,
we were able to show that this curvilinear negative association between agreeableness
and income tended to be more pronounced in men (unstandardized b = .04, p = .031)
than in women (unstandardized b = .01, ns).
In our third model, we directly compared the strengths of the associations between
numeracy and personality and income when mutually controlling for each other. As
shown in Tables 2 and 3, the linear and quadratic effects of openness and
agreeableness and of numeracy on income are more or less additive, explaining a total of 20%
of the variance in income. Because income is usually predicted by means of a Mincer
regression (Mincer 1974) in economic research, we also replicated our analysis with this
method. Results from these alternative models (not shown) did not differ substantially.
Our final two outcomes were self-rated health status and life satisfaction. The
noncognitive skills investigated (Model I) clearly contributed to explaining self-rated health
(16%). emotional stability (β = .21) was substantially and strongly related to self-rated
health, with emotionally stable persons reporting better health, on average. Cognitive
skills (Model II) also contributed to explaining self-rated health. However, this model,
which included only numeracy and the control variables, explained markedly less
variance (11%) than Model I. As can be seen from Model III, the combined effects of
personality and numeracy remained more or less unchanged, and explained 17% of the overall
variance in self-rated health.
As one of the most crucial indicators of life success, we investigated overall life
satisfaction. An individual’s level of life satisfaction depends not only on external
circumstances but is also related to his or her personality. Specifically, individuals who are more
emotionally stable, extraverted, conscientious, and agreeable tend to experience greater
life satisfaction (e.g., Steel et al. 2008). The present results, where sociodemographic
effects were controlled for in parallel, largely confirmed these findings. As regards
overall life satisfaction, the more emotionally stable (β = .32), conscientious (β = .13), and
agreeable (β = .09) a person was, the higher he or she rated their life satisfaction. Overall
personality and the sociodemographic control variables together explained 17% of the
variance in life satisfaction (Model I). In contrast, the model that included only
numeracy and the control variables explained only 4% of this rating (Model II). Combining
non-cognitive and cognitive skills in Model III did not alter the amount of explained
variance compared to Model I, which included only the personality dimensions (17%).
Taken together, our results indicate that the Big Five personality dimensions
contribute substantially to explaining central indicators of life success in PIAAC. However, their
explanatory power differed markedly across the outcomes under investigation. Whereas
the competencies (i.e., numeracy and literacy) were much more potent than
non-cognitive skills (i.e., personality) in predicting educational attainment, for example, the
opposite was the case for life satisfaction and, to a lesser extent, for self-perceived health.
Here, the personality dimensions clearly outperformed the competencies in terms of
explanatory power. For other outcomes, such as participation in continuing education or
income, the non-cognitive and the cognitive skills were highly comparable with regard
to their predictive power.
The present study aimed to elucidate the impact of non-cognitive skills on central
outcomes measured in PIAAC. We therefore investigated the relations between the
personality domains of the Big Five approach to personality and (a) the cognitive skills assessed
in PIAAC, namely literacy and numeracy, and (b) central economic and social outcomes.
For the latter analyses, we compared the explanatory power of the non-cognitive skills to
that of the adult competencies assessed in PIAAC.
As previously discussed (Rammstedt et al. 2016), the PIAAC-L data revealed that
personality was substantially related to both numeracy and literacy. We were able to
replicate the typically found positive linear associations between emotional stability and
openness and cognitive skills and the negative association between conscientiousness
and cognitive skills (Ackerman and Heggestad 1997; DeYoung 2011;
Chamorro-Premuzic and Furnham 2005; Von Stumm and Ackerman 2013). Results of the quadratic
analyses demonstrated that the results for emotional stability and openness were
primarily triggered by comparatively low performance in the competency measures of
persons who were low on emotional stability or openness, respectively. Together with other
recent large-scale studies (e.g., Lechner et al. 2016), our results suggest that the
relationships between cognitive and non-cognitive skills—here in particular: personality and
crystallized intelligence, or Gc—may be stronger and more systematic than either
Ackerman (1996) or Chamorro-Premuzic and Furnham (2005) envisioned in their
important theoretical treatments. Whereas Ackerman (1996) looked at only a “small set of
personality factors” (p. 238) that appeared to be related to cognitive skills because there
was little evidence to suggest otherwise, we contend that it may be worth reconsidering
the personality–intelligence interface in the light of emerging large-scale findings. This
may lead to stronger theories explaining the acquisition of cognitive and non-cognitive
skills across the life span, as well as how they co-shape important life outcomes.
However, the focus of our study was on the relationships between the Big Five
personality domains and six important life outcomes measured in PIAAC. Most of the
associations between personality and important life outcomes in our analyses are largely in line
with previous findings reported in the literature, in which conscientiousness and
emotional stability, in particular, have emerged as powerful predictors of a broad range of
life outcomes (Roberts et al. 2007; Ozer and Benet-Martínez 2006). Only the repeatedly
reported positive association between openness and income (see Ng et al. 2005) did not
replicate in our analyses of the PIAAC data. On the contrary, our analyses revealed a
substantial negative association between openness and income. While the reasons for this
divergence are unclear, some findings have suggested that the openness–income
association is culture- or country-sensitive. For example, based on a large Dutch sample,
Gelissen and de Graaf (2006) also reported that openness was negatively related to earnings
among men (but unrelated among women). Similarly, based on the comprehensive data
of the German SOEP, Heineck and Anger (2010) found a negative association between
openness and hourly earnings among men (but a positive association among women).
Finally, Danner and Rammstedt (2015) compared the association between openness
and income in 19 countries worldwide based on the International Social Science Survey
Program (ISSP) data. Their results indicated that both the size and the direction of the
association differed markedly across the countries. In the US and Ireland, openness was
indeed positively related to income. By contrast, in Germany, as in the present study,
and in several other countries, for example Latvia, the openness-income association was
negative. Taken together, the associations between personality and life outcomes found
in the present study largely support the current state of research.
Previous research in the context of PIAAC has investigated the degree to which the
different life outcomes can be explained by cognitive skills. In the present study, we aimed
to investigate the extent to which these life outcomes can be incrementally—above and
beyond cognitive skills—explained by non-cognitive skills. Therefore, we examined the
effects of personality on these outcomes, controlling for sociodemographic
characteristics, and we compared these models to models that included the competencies assessed
in PIAAC, namely numeracy and literacy. Our results indicate that the Big Five
personality domains substantially contribute to explaining variance in all six life outcomes
investigated. After adjusting for the effects of the competencies, personality could, in all
cases, explain an additional proportion on the variance. However, the strength of these
contributions differed markedly across the different outcomes investigated. For the
economic outcome variables, income and employment status, the incremental validity of
personality above and beyond the competencies was comparatively low, ranging from 1
to 3%. Also in the case of educational attainment and participation in continuing
education, a greater portion of the variance was explained by the competencies than by the
personality dimensions. By contrast, life satisfaction and, to a lesser extent, self-rated
health were more strongly predicted by personality than by the competencies. The
reasons for these differences in the predictive power of personality across different types
of outcomes are currently unclear, but two possible explanations readily come to mind.
First, it might be argued that personality factors may simply have a stronger bearing on
subjective outcomes because they operate within the same domain of psychological
processes. Second, building on the idea of Brunswik’s construct symmetry (see Wittmann
1988), it may be argued that personality factors are measured on a high aggregation level
that corresponds more closely to that of life satisfaction self-rated health that to that
of more specific outcomes such as income; according to Brunswik symmetry, stronger
relationships can be expected between constructs that are measured on the same
aggregation level than among constructs measured at different aggregation levels. Future
research is needed to disentangle these explanations.
In sum, the present study showed that, for large-scale survey programs such as PIAAC,
non-cognitive skills—in this case, personality dimensions—are substantially related
to important life outcomes and can contribute to explaining these outcomes over and
above cognitive skills, although this contribution varies across outcomes. These results
attest to the usefulness of including measures of non-cognitive skills in future cycles of
these large-scale surveys. This is all the more true given the growing availability of
wellvalidated short-scale measures for these concepts.
Additional file 1. Additional tables.
Table 4 Descriptive statistics for continuous variables
Table 5 Descriptive Statistics for categorical variables
Employment status (full-time)
c Pseudo R2. The regression coefficients for literacy maximally changed by ∆ = .04
Table 7 Relationships between personality, literacy, and important life outcomes
Model Income (log)a,b Health
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