Psychological traits and wages in the Czech Republic

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Prague Economic Papers
2024, Volume 33, Issue 1
https://doi.org/10.18267/j.pep.853
Psychological traits and wages intheCzech
Republic*
Pavlína Vydrželová , Jiří Balcar , Lenka Johnson Filipová
VSB – Technical University ofOstrava, Ostrava, Czech Republic, email: pavlina.vydrzelova@vsb.cz
Abstract1
Psychological traits have received significant attention in labour market research in recent
decades. Unfortunately, empirical evidence remains limited for some psychological traits and
their interactions. To address this gap, we conduct a representative survey of employees, assessing
competitiveness, persistence, and risk tolerance using single-item scales. This comprehensive
study sheds light on the connection between these traits and wages. Our results confirm that
individuals possessing these traits tend to earn higher wages, even when we account for indirect
factors, such as higher educational attainment and better job positions. It also suggests that
competitiveness and risk tolerance are particularly valuable for individuals with middle and
high incomes, while persistence is valuable for those with low and middle incomes. These
findings support the systematic development of competitiveness, persistence, and risk tolerance
through education and sporting activities.
Keywords: competitiveness, persistence, risk tolerance, wage, psychological traits
JEL Codes: J24, J31, J71, C21, C26
1. Introduction
Psychological traits, preferences, and motivation, often referred to as “non-cognitive skills”
(Heckman et al., 2006), represent an important determinant of worker productivity and labour
market success. As their measurement is far from straightforward, as Heckman and Rubinstein
* This research was supported by SGS grant from the VSB – Technical University of Ostrava
(grant number SP2022/54).
The authors would like to express their gratitude to the participants of the EALE 2022
conference and the ICEA’s Future of Work conference (Trchalíková et al, 2022) for numerous
valuable comments and suggestions pertaining to early versions of this article.

Prague Economic Papers, 2024, 33 (1), 79–102, https://doi.org/10.18267/j.pep.853
Pavlína Vydrželová, Jiří Balcar, Lenka Johnson Filipová
80
(2001) noted, many empirical studies employ similar measurement tools such as the Big Five
personality test (e.g., Laible and Brenzel, 2021; Collischon, 2020; Lee and Ohtake, 2018; Mac-
zulskij and Viinikainen, 2018), Rotter’s locus of control scale (e.g., Bühler et al., 2020; Cobb-Clark,
2015; Girtz, 2015), or Rosenberg’s self-esteem scale (e.g., Beck, 2021; Botea et al., 2021; DeBeau-
mont and Girtz, 2020). This makes it possible to provide robust evidence on several psychological
traits but inevitably leads to an understudying of a number of other, usually more work-oriented
psychological traits. Among these qualities, employers value competitiveness, persistence, and risk
tolerance the most often.
Previous empirical studies, although limited in number, examining the impact of competitive-
ness, persistence, and risk tolerance on productivity and other labour market outcomes consistently
demonstrate their positive relationships. For instance, Collischon (2020) and Braakmann (2009)
have both revealed a positive correlation between risk tolerance and wages in Germany. Several
studies have emphasized the signicance of competitiveness for employee performance, meas-
ured not only by wages (Karatepe et al., 2006; Karatepe and Olugbade, 2009; Schrock et al.,
2014). Díaz et al. (2013), Feng and Papi (2020), and Buechel et al. (2018) have concentrated
on persistence and have found a positive relationship between this personality trait and the per-
formance of students and employees. It is worth noting that, with the exception of Collischon
(2020), Braakmann (2009), and Feng and Papi (2020), who employed representative samples
of employees in Germany or Peru, all the other articles have analysed specic samples of work-
ers or university students. Furthermore, to the best of our knowledge, no study has explored
these psychological traits in relation to wages in the Czech Republic.
To address this gap in the literature, our article aims to provide detailed evidence on the link
between individual productivity, approximated by wage level and psychological traits such
as competitiveness, persistence and risk tolerance. For this purpose, it employs data on 1,978
prime working-age employees in the Czech Republic.
The research design and data used in this article provide it with several advantageous
features. Firstly, the focus on competitiveness, persistence, and risk tolerance seeks to address
the insucient attention paid to these traits in recent empirical literature, despite their close
relationship to the diligence, eciency, and achievement orientation required by employers
in job applicants and employees (see Balcar et al., 2014). Secondly, measuring various psycho-
logical traits in one dataset makes it possible to explore the synergy eects of their interaction
with each other, as well as with cognitive skills. Thirdly, this article contributes to the limited
empirical research into the relationship between wages and psychological traits in the Czech
Republic, making it a solid foundation for comparing our results with ndings of other studies.

Prague Economic Papers, 2024, 33 (1), 79–102, https://doi.org/10.18267/j.pep.853
Psychological traits and wages intheCzech RepublicPsychological traits and wages intheCzech Republic
81
Our results suggest that competitiveness, persistence, and risk tolerance are signicantly
associated with higher wages, even when controlling for their eect on educational attainment
and job selection. However, this eect is not uniform across the labour market, as competitive-
ness and risk tolerance are more highly rewarded in high- and middle-income jobs, while per-
sistence is positively correlated with wages in low- and middle-income jobs. The investigation
of synergistic eects suggests that individuals who are both more competitive and persistent
may earn higher wages due to the combination of these traits. The synergy of persistence and,
to some extent, competitiveness with attained education is also conrmed. Since it appears
that competitiveness, persistence and risk tolerance are correlated with higher productivity and
wages, their development through education, sports or other activities should be encouraged
and supported.
2. Empirical Literature Review
Wage determinants have been studied systematically since the early 1970s, when Mincer
(1974) expressed the relationship between human capital, approximated by education and age/
potential work experience, and wages. His equation served as a methodological basis for re-
searching wage determinants. Gradually, more variables were added to the model to investigate
the inuence of other wage determinants, such as personal characteristics (gender, marital sta-
tus, number of children), labour status (occupation, working hours and type of job contract),
employer characteristics (rm size, private and non-private sectors, industries) and institutional
and regional factors (minimum wage, legal protection of employment, unions, and region).
Although these wage determinants were able to encompass many objective factors aecting
wages, a signicant portion of wage dierences remained unexplained. Therefore, over the last
two decades, psychological and sociopsychological factors have increasingly been considered
as possible explanations for labour outcomes.
Unfortunately, measuring personality and psychological traits is far from straightforward,
as noted by Heckman and Rubinstein (2001). Psychological traits are most commonly assessed
using personality inventories (e.g., the Big Five personality test), low-dimensional scores (e.g.,
Rotter’s locus of control scale, Rosenberg’s self-esteem), or single-item scales (e.g., Nichols
and Webster, 2013; Nagy, 2002; Robins et al., 2001). The limited number of valid and reliable
measurement tools adversely aects the diversity of traits examined in empirical literature.
As a result, we have substantial knowledge of the eects of the Big Five personality traits – ex-
traversion, agreeableness, openness, conscientiousness, and neuroticism (e.g., Nyhus and Pons,
2005; Salgado, 1997; Barrick and Mount, 1991) – as well as locus of control and self-esteem
(e.g., Feinstein, 2000; Goldsmith et al., 1997, 1998 and 2000) on productivity, wages, unem-

Prague Economic Papers, 2024, 33 (1), 79–102, https://doi.org/10.18267/j.pep.853
Pavlína Vydrželová, Jiří Balcar, Lenka Johnson Filipová
82
ployment, and other labour market categories. However, research focus on other psychological
traits, such as competitiveness, persistence, and risk tolerance – traits often sought by employ-
ers in job applicants and employees – is less common.
Willingness to take risks has been found to be associated with various benets on the la-
bour market. For example, Collischon (2020) discovered that a one-standard-deviation increase
in willingness to take risks is linked to a 0.8% higher probability of earning higher wages and
a 1.5% higher probability of full-time employment in Germany. Braakmann (2009) conrmed
the positive eects of risk tolerance on employment in Germany for women, showing a 3.8%
higher probability of full-time employment associated with a one-standard-deviation increase
in risk tolerance. However, he found no statistically signicant wage benets from this psycho-
logical trait for either women or men at the 0.05 signicance level. Di Mauro and Musumeci
(2011) demonstrated that individuals with higher risk tolerance tend to self-select into jobs with
more variable wages, such as those involving sales bonuses, while more risk-averse individuals
prefer jobs with xed wages. Farlie and Holleran (2012) provided evidence that more risk-tol-
erant individuals are more likely to participate in entrepreneurship training and derive greater
benets from it compared to their risk-averse counterparts.
Recent studies have increasingly focused on the impact of competitiveness and persistence
on labour outcomes. Karatepe et al. (2006) demonstrated that the competitiveness of front-line
hotel employees in Northern Cyprus signicantly predicts their performance. A subsequent
study conducted in Nigeria (Karatepe and Olugbade, 2009) provided evidence that compet-
itiveness enhances commitment, absorption in work, and enthusiasm among front-line hotel
employees. More competitive employees tend to be more energetic and fully engaged in their
work. Furthermore, Schrock et al. (2014) conducted an assessment of the direct and interactive
eects of competitiveness and the competitive environment on sales performance and organiza-
tional commitment. Their ndings indicate that this personality trait positively inuences sales
performance, particularly in highly competitive environments.
Díaz et al. (2013) focused on persistence and found that an increase in persistence by one
standard deviation is associated with a 15% wage premium on the Peruvian labour market, but
only for educated individuals or those with developed cognitive skills. However, it is also possi-
ble to observe an indirect eect of persistence on labour market success through education. Feng
and Papi (2020) provided evidence that more persistent students tend to set long-term goals, are
more motivated to study hard and achieve better academic results. Similarly to competitiveness,
persistence is also signicantly inuenced by the working environment. For instance, Buechel
et al. (2018) conducted an experiment revealing the positive impact of peers who discuss their
successes with motivation on persistence. The work environment can have a similar eect, al-

Prague Economic Papers, 2024, 33 (1), 79–102, https://doi.org/10.18267/j.pep.853
Psychological traits and wages intheCzech RepublicPsychological traits and wages intheCzech Republic
83
lowing employees to observe each other’s work performance (Gerhards and Gravert, 2020).
This suggests that more competitive individuals may also exhibit greater levels of persistence.
It is worth noting that Bertrand (2010) conducted a comprehensive review of experiments
investigating the role of psychological attributes in the gender wage gap. The review included
in-depth examinations of risk-taking, competition and attitudes towards negotiation. Bertrand’s
ndings indicate that women tend to be more risk-averse (Jung et al., 2018) and less competi-
tive than men, as supported by multiple studies (Gneezy et al., 2003; Datta Gupta et al., 2005;
Dohmen and Falk, 2006; Niederle and Vesterlund, 2007; Booth, 2009; Carpenter et al., 2018).
These psychological dierences may help explain a portion of the gender wage gap.
As this review suggests, there is limited knowledge regarding the eects of competitive-
ness, persistence, and risk tolerance on wages. Based on the studies above, we can anticipate
a positive correlation between each of these psychological traits and wages. Nevertheless, nu-
merous questions remain unanswered. Are these traits signicant when considered separately,
or is a combination of them necessary? Do their wage eects remain consistent across the entire
labour market, or do they vary across dierent market segments? “These questions will be ad-
dressed in the following paragraphs.”
3. Empirical Strategy
3.1 Data
The estimation of the relationship between competitiveness, persistence, risk tolerance, and
wage is based on individual data from a tailor-made survey of 1,978 employees aged 25 to 54
years in the Czech Republic. We focused on individuals aged 25–54 to capture the prime work-
ing-age population, as younger individuals are often in university studies, while those aged 55+
typically have stable careers or are entering retirement. The survey was conducted by the
FOCUS Social and Marketing Research Agency in October and November 2011 and provided de-
tailed data on Czech employees, such as income, personal characteristics, education, work expe-
rience, preferences related to the job, family and life roles, physiological traits and characteristics
of family background, household, and workplace2. The data were obtained through standardized
2 The survey questionnaire was designed (in alphabetic order) by Jiří Balcar (VSB-TU Ostrava),
Lenka Johnson Filipová (VSB-TU Ostrava), Jaromír Gottvald (VSB-TU Ostrava), and Mariola
Pytliková (VSB-TU Ostrava). Valuable comments were provided by Alicia Adsera (Princeton
University), Tor Eriksson (Aarhus University), Armin Falk (Bonn University), James Heckman
(University of Chicago), Leslie Stratton (Virginia Commonwealth University) and anonymous
respondents participating in two pilot surveys (the affiliations are relevant to the time
of questionnaire design).

Prague Economic Papers, 2024, 33 (1), 79–102, https://doi.org/10.18267/j.pep.853
Pavlína Vydrželová, Jiří Balcar, Lenka Johnson Filipová
84
interviews conducted by 481 interviewers from the FOCUS Agency. Due to the use of quota
sampling, where interviewers were instructed to reach predetermined quotas based on gender,
age, education, region, and the size of the municipality of the respondent’s residence, and with
only one member per household allowed to participate, the sample of respondents is representa-
tive in terms of the aforementioned characteristics. Subsequently, the FOCUS Agency veried
the data and supplemented the codes of individual jobs according to the ISCO classication,
as well as the identication number of the employer. The availability of employer identication
numbers allowed us to enrich the survey data with information on employers’ characteristics.
These data were sourced from the Albertina Firm Monitor database, which covers approximate-
ly 2.7 million economic entities in the Czech Republic and includes information on factors such
as the company establishment date, ownership, legal form, and the number of employees.
Denitions of variables used in this article and their descriptive statistics can be found
in Table A1.
The unique combination of wage determinants covered by this survey, particularly sel-
dom-captured psychological traits, gender roles, life preferences, etc., renders this survey ex-
ceptionally suitable for exploring the relationship between competitiveness, persistence, risk
tolerance and wage level in the Czech Republic. The authors of this article are not aware of any
other dataset from the Czech Republic that would allow an investigation into the interplay
of these psychological traits. This holds true even for a new wave of the survey on wage determi-
nation in the Czech Republic from 2022, which either lacked the psychological variables exam-
ined in this study or dened them dierently. Nonetheless, we employ data from 2022 to assess
alterations in the relationship between wage levels and competitiveness since it is the only psy-
chological trait that remains consistent across surveys (all control variables utilized in the model
are entirely comparable). The new wave of the survey was conducted in January 2022 to collect
individual data from the population of the Czech Republic. We specically focused on individ-
uals aged 25–54 years to capture the economic activity of the prime working-age population.
The online survey was conducted by advisory companies Nielsen Admosphere, a.s. (https://www.
nielsen-admosphere.cz) and Engage Hill s.r.o. (https://www.engagehill.com). The former compa-
ny was responsible for recruiting respondents using quota sampling based on sex, age, education,
labour market status, NUTS 3 region and size of the municipality of residence. The latter was re-
sponsible for gathering the data. The original sample consisted of 2,251 individuals with dierent
employment statuses, but we only utilize data on employees (N = 1,564) here.

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Psychological traits and wages intheCzech RepublicPsychological traits and wages intheCzech Republic
85
3.2 Psychological variables
The psychological traits examined in this article were assessed through self-assessment using
single-item scales. Numerous studies, such as Nichols and Webster (2013), Nagy (2002), and
Robins et al. (2001), have indicated that direct questions about the investigated traits are often
highly correlated with the results of established psychological scales. Therefore, respondents were
asked to provide answers to the following questions in order to evaluate their levels of competi-
tiveness, persistence and risk tolerance: (a) Do you feel a really strong need to excel in what you
do and be better than others? (answers on the scale Yes – Rather yes – Rather no – No); (b) Does
itoftenhappenthatyouabandonthegoalyousetwhenyounditdicult? (answers on the scale
Yes – Rather yes – Rather no – No); (c) How high a risk are you willing to take in your working
career? (answers on a scale from 0 for none to 10 for very high). Subsequently, these variables
were recoded to reect higher levels of the examined traits as the variable values increased, and
standardized using z-scores to facilitate easy comparison of the results.
It should be noted that there is no consensus regarding the treatment of variables based
on Likert-type or similar ordered scales as nominal or continuous in regression models. Both
approaches oer signicant advantages: treating a variable as nominal allows capturing non-
linearity in its relationship with the dependent variable, while treating it as continuous ena-
bles straightforward interpretation of the results (Gardner, 1975; Knap, 1990). Therefore, Pasta
(2009) and Williams (2016) suggest testing the linearity of the relationship between ordered
and dependent variables. If a statistically signicant non-linear relationship with the depend-
ent variable is found, the predictor should be treated as categorical; otherwise, it should be
treated as continuous. The testing procedure described by Williams (2016) was applied to both
the base and full models used in this article (Models 1 and 4 in Table 1). The results showed
no statistically signicant nonlinear relationship between the dependent variable and psycho-
logical variables. This nding supports the treatment of psychological variables as continuous.
Additionally, the Shapiro-Wilk normality tests conrmed this conclusion for competitiveness
and persistence, as a normal distribution further justies treating these variables as continu-
ous. Furthermore, we estimated both the base and full wage models using dierently specied
ordinal-type variables to assess the quality of these alternative specications. We evaluated
them based on the adjusted R2, Akaike’s information criterion (AIC) and Bayesian informa-
tion criterion (BIC). The psychological variables were included in the model as continuous
variables (Model 1: adj. R2 = 0.172, AIC = 1321.5, BIC = 1444.5; Model 4: adj. R2 = 0.473,
AIC = 466.5, BIC = 824.2), as well as dummy variables representing all categories/answers
(Model 1: adj. R2 = 0.170, AIC = 1338.5, BIC = 1534.2; Model 4: adj. R2 = 0.473, AIC = 482.3,
BIC = 912.8). All of the described procedures implied treating ordered variables as continuous,

Prague Economic Papers, 2024, 33 (1), 79–102, https://doi.org/10.18267/j.pep.853
Pavlína Vydrželová, Jiří Balcar, Lenka Johnson Filipová
86
allowing a more straightforward interpretation of results, which is the approach adopted in this
article.
3.3 Empirical model and estimation strategy
The data were used to estimate the Mincer-based regression model (Mincer, 1974), which ex-
plains the logarithm of the gross monthly wage (ln w) by n explanatory variables (x) that cap-
ture psychological traits, personal characteristics, family and background characteristics, job
and employer characteristics and location for individuals (i); see Equation (1), where βj are
regression coecients and ε residuals. A comprehensive description of all the control variables
used in our model can be found in Table A1.
ln wi = β0 +
0,
1
lnå
n
i j ji i
j
wx
ββ
=
=+ ×+
∑ βj × xj,i+εi (1)
Our empirical strategy relies on hierarchical regression analysis. Initially, we establish
a baseline model (Model 1), which includes psychological traits and location variables. This
model serves to estimate the overall relationship between wages and competitiveness, persis-
tence, and risk tolerance. We then enhance this model with gender (Model 2), education (Model 3)
and other supply-side characteristics (Model 4) in order to uncover the indirect eects of psy-
chological traits, particularly the eects mediated by the attainment of a higher level of educa-
tion. In Model 5, we introduce occupation controls to explore the indirect eects mediated by
gaining a better-paid occupation. Model 6 represents the full model, incorporating all relevant
controls and enabling the estimation of the net relationship between wages and psychological
traits. Subsequently, we explore the functional form of the relationship, assess the correlation
between wages and psychological traits at dierent income levels (employing unconditional
quantile regression; see Firpo et al., 2009) and investigate interactions between psychological
traits and between these traits and educational attainment.
The human capital theory (Becker, 1962) posits causality from individual characteristics,
such as education, health, and psychological traits, to their productivity and wages. However,
due to the cross-sectional nature of the data employed in this article, we are unable to denitive-
ly establish or disprove the expected causality. Instead, we can only draw conclusions regarding
the correlation between psychological traits and wages. This would not pose an issue if person-
ality and personal traits were innate and remained stable throughout one’s lifespan, as we could
easily assume a causal eect. However, these traits tend to evolve slowly over time (e.g., Hud-
son and Fraley, 2015; Hudson et al., 2012; Roberts and Mroczek, 2008). In such cases, we must
consider the possibility of reverse causality, wherein the performance of managerial or other

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Psychological traits and wages intheCzech RepublicPsychological traits and wages intheCzech Republic
87
well-paid positions requiring a high level of competitiveness, persistence or risk tolerance may
strengthen these psychological traits in individuals working in these roles. Furthermore, a high-
er salary can serve as motivation for increased persistence, competitiveness and risk tolerance.
Since our dataset lacks suitable instruments for employing IV regression, which could help
identify causality between psychological traits and wage levels, we refer to our ndings as cor-
relations rather than asserting causal relationships.
It is worth noting that considerable attention was devoted to data validation and model
specication in our study: (1) A data check was undertaken to exclude observations with un-
realistic (extreme) values and obvious measurement errors. (2) A check for empty and small
cells was conducted to support model stability. (3) All the models underwent rigorous test-
ing for specication errors (including the Ramsey RESET test and link test), multicollinear-
ity (assessed using the VIF test), heteroscedasticity (examined using the Breusch-Pagan test)
and autocorrelation (evaluated with the DW test). None of these tests revealed any violations
of the OLS assumptions.
4. Results
4.1 Competitiveness, persistence and risk tolerance are
correlated with higher wages
When considering dierences in psychological traits and location (see Model 1 in Table 1), the re-
sults clearly demonstrate that individuals who are more competitive, persistent, and risk-tolerant
tend to achieve higher levels of success on the labour market, as evidenced by their higher wages.
This relationship is substantial, with wages increasing by 6.8%, 6.5%, and 5.3% for a one-stand-
ard-deviation increase in competitiveness, persistence, and risk tolerance, respectively. The in-
clusion of additional control variables allows us to explore the indirect relationships between
psychological traits and wage levels. Initially, we introduced the gender variable (Model 2),
which had a negligible eect on the coecients of competitiveness and persistence. How-
ever, it had a signicant impact on the coecient of risk tolerance, decreasing it by −43.4%,
indicating that women tend to have signicantly lower risk tolerance. Expanding the model
to include years of schooling led to a further decrease in the regression coecients of com-
petitiveness, persistence, and risk tolerance by −32.8%, −26.6% and −20.0%, respectively
(compare Models 2 and 3). This suggests a positive relationship between the examined psy-
chological traits and the level of educational attainment, which in turn leads to higher wages.
That corresponds with the result of Feng and Papi (2020), who provided evidence about
the importance of persistence and competitiveness on better academic results of students.

Prague Economic Papers, 2024, 33 (1), 79–102, https://doi.org/10.18267/j.pep.853
Pavlína Vydrželová, Jiří Balcar, Lenka Johnson Filipová
88
Controlling for other personal, family and background characteristics (Model 4) did not re-
sult in signicant changes in regression coecients. However, given the expected substantial
indirect eect of occupations on the relationship between psychological traits and wage lev-
els, we estimated Model 5, which includes this variable. When we controlled for occupation
according to the 1-digit ISCO classication, there was a decrease in the correlation between
wage levels and competitiveness, persistence, and risk tolerance by −18.4%, −15.4% and
−17.4%, respectively. This indicates that individuals with these skills tend to occupy more
highly paid positions, as a higher ISCO category is associated with higher wages. Finally,
Model 6 reveals that the examined psychological traits remain signicantly related to wage
levels (at the 0.01 signicance level) even among employees who share the same education,
occupation, and many other characteristics. We observe that a one-standard-deviation increase
in competitiveness, persistence and risk tolerance is correlated with a 3.3%, 2.8%, and 1.9%
wage premium, respectively. The correlation of wages with these psychological traits is similar
to other results from the Czech Republic provided by Balcar (2016) for the mean of 15 soft
skills (a one-standard-deviation increase is correlated with a 3.4% wage premium; P < 0.05)
and by Balcar and Dokoupilová (2021) for communication skills (their one-standard-devia-
tion increase is correlated with a 2.6% wage premium; P<0.05). Our ndings are also con-
sistent with the study by Collischon (2020), which found that risk tolerance is associated with
a 0.8% higher likelihood of earning higher wages in Germany. This supports the general picture
of a positive relationship between non-cognitive skills and wage levels. The results provided
here raise the question of whether the described relationships are stable across the labour mar-
ket or whether they reect the fact that jobs at dierent ends of the wage distribution require
dierent psychological traits.
The re-estimation of Model 6 using unconditional quantile regression (Model 7 in Table 2)
suggests that the relationships between psychological traits and wages vary across dierent
income groups. At the 0.05 signicance level, we found that competitiveness and risk tolerance
are positively related to wage levels only for middle- and high-income individuals, but not
for low-income ones. In contrast, persistence is related to wage levels in the case of low- and
middle-income individuals, but not in the case of high-income ones. This aligns with theoreti-
cal expectations, as high-income employees are often found in managerial and specialist roles
where decision-making in competitive and risky environments is common, while low-income
employees typically work in positions requiring persistence, such as assembly line workers
or clerks.

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Table 1: Psychological traits and wages
(1) (2) (3) (4) (5) (6)
Variables
ln gross
monthly
wage
ln gross
monthly
wage
ln gross
monthly
wage
ln gross
monthly
wage
ln gross
monthly
wage
ln gross
monthly
wage
Psychological traits
Competitiveness 0.068*** 0.064*** 0.043*** 0.038*** 0.031*** 0.033***
(0.008) (0.008) (0.008) (0.008) (0.007) (0.007)
Persistence 0.065*** 0.064*** 0.047*** 0.039*** 0.033*** 0.028***
(0.008) (0.008) (0.007) (0.007) (0.007) (0.007)
Risk tolerance 0.053*** 0.030*** 0.024*** 0.023*** 0.019*** 0.019***
(0.008) (0.008) (0.007) (0.007) (0.007) (0.007)
Personal characteristics
Female −0.224*** −0.243*** −0.259*** −0.245*** −0.207***
(0.015) (0. 014) (0.014) (0. 015) (0. 015)
Years ofschooling 0.052*** 0.042*** 0.022*** 0.026***
(0.004) (0.004) (0.005) (0.004)
Other work experience 0.006** 0.007*** 0.009***
(0.003) (0.002) (0.002)
Other work experience squared −0.000*** −0.000*** −0.000***
(0.000) (0.000) (0.000)
Tenure 0.014***
(0.003)
Tenure squared −0.000***
(0.000)
Grades inmaths atage 15 −0.053*** −0.035*** −0.032***
(0.010) (0.010) (0.009)
Health limitation ofwork performance −0.077*** −0.062*** −0.070***
(0.023) (0 .021) (0.020)
Family and background
characteristics I. yes yes yes
Job and employer characteristics
Occupation according
to1-digit ISCO yes yes
Other job and employer
characteristics II. Yes
Location III. Yes yes yes yes yes yes
Constant 10.017*** 10.125*** 9.380*** 9.608*** 9.612*** 9.519***
(0.023) (0.024) (0.056) (0.078) (0.082) (0.085)
Observations 1,978 1,978 1,978 1,978 1,978 1,978
Adjusted R20 .17 2 0. 259 0.340 0. 367 0.424 0.473
Notes: robust standard errors inparentheses. *** p<0.01, ** p<0.05, * p<0.1. I. Partnership status, number
ofchildren in5 age categories, non-Czech mother tongue; II. Workload, prevailing economic activity accor-
ding toNACE classification, number ofemployees; III. NUTS 3 region, residence town size.
Source: authors’ calculations

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Pavlína Vydrželová, Jiří Balcar, Lenka Johnson Filipová
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Table 2: Unconditional quantile regression
Model 6 specification
(6) (7)
OLS 1st decile Median 9th decile
Variables ln gross
monthly wage
ln gross
monthly wage
ln gross
monthly wage
ln gross
monthly wage
Competitiveness 0.033*** 0.030* 0.030*** 0.031* *
(0.007) (0.017 ) (0.008) (0.016)
Persistence 0.028*** 0.036** 0.028*** 0.022*
(0.007) (0.017 ) (0.010) (0.012)
Risk tolerance 0.019*** −0.003 0.033*** 0.037***
(0.007) (0.015) (0.009) (0. 013)
Other variables ofModel 6 yes yes yes yes
Constant 9.519*** 8.903*** 9.550*** 9.873***
(0.085) (0.163) (0.108) (0.164)
Observations 1,978 1,978 1,978 1,978
Adjusted R20.473 0.220 0.307 0.231
Notes: robust standard errors (OLS) and bootstrapped standard errors based on 200 replications (UQR)
inparentheses. *** p<0.01, ** p<0.05, * p<0.1
Source: authors’ calculations
In the previous models, we approximated psychological traits using linear terms. However,
it is plausible to expect a non-linear relationship between the psychological traits in question and
wages, as extreme levels of competitiveness, persistence and risk tolerance may be associated
with reduced productivity. For instance, low competitiveness could result in insucient work
eort, while extremely high competitiveness might be linked to a lower willingness to cooperate
with others. We re-estimated Models 1 and 6, incorporating quadratic terms for the psychological
traits (not reported here). The results did not support this assumption, as the quadratic terms
were found to be statistically insignicant. This suggests that higher levels of competitiveness,
persistence and risk tolerance among employees are consistently correlated with higher wages.
This conclusion aligns with the ndings of the linearity test described in the methodology
section.

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4.2 Synergy effects ofcompetitiveness, persistence, risk
tolerance and cognitive skills
The existence of synergy eects between psychological traits represents another intriguing
question that could alter our perspective on their importance and functioning. Therefore, we
re-estimated both the base and full models (Models 1 and 6) by including respective interaction
terms between the examined psychological traits. The results presented in Table 3 indicate that
the regression coecients for competitiveness, persistence and risk tolerance are individually
statistically signicant, indicating a positive correlation between each trait and wage level. Ad-
ditionally, we observed that the interaction between competitiveness and persistence embodies
a positive relationship with wage level, reaching a signicance level of 0.064 for the base model
(see Model 8) and 0.085 for the full model (see Model 11). On the other hand, interactions in-
volving other psychological traits were found to be statistically insignicant.
Table 3: Interaction ofpsychological traits
Model 1 specification Model 6 specification
(8) (9) (10) (11) (12) (13)
Variables
ln gross
monthly
wage
ln gross
monthly
wage
ln gross
monthly
wage
ln gross
monthly
wage
ln gross
monthly
wage
ln gross
monthly
wage
Competitiveness 0.067*** 0.068*** 0.068*** 0.033*** 0.033*** 0.033***
(0.008) (0.009) (0.009) (0.007) (0.007) (0.007)
Persistence 0.066*** 0.065*** 0.065*** 0.028*** 0.028*** 0.028***
(0.008) (0.008) (0.008) (0.007) (0.007) (0.007)
Risk tolerance 0.053*** 0.053*** 0.053*** 0.018*** 0.018*** 0.018***
(0.008) (0.008) (0.008) (0.007) (0.007) (0.007)
Interactions
Competitiveness & persistence 0.012* 0.009*
(0.007) (0.005)
Competitiveness & risk tolerance −0.001 0.004
(0.007) (0.005)
Competitiveness & risk tolerance 0.001 0.005
(0.007) (0.006)
Other variables ofModel 1 yes yes yes
Other variables ofModel 6 yes yes yes
Constant 10.013*** 10.017*** 10.016*** 9.514*** 9.516*** 9.515***
(0.023) (0.023) (0.023) (0.085) (0.085) (0.085)
Observations 1,978 1,978 1,978 1,978 1,978 1,978
Adjusted R2 0.173 0.171 0.171 0. 474 0.473 0.473
Notes: robust standard errors inparentheses. *** p<0.01, ** p<0.05, * p<0.1
Source: authors’ calculations

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Pavlína Vydrželová, Jiří Balcar, Lenka Johnson Filipová
92
According to empirical literature (e.g., Díaz et al., 2013), we can anticipate that psycho-
logical traits do not only interact with each other but also with cognitive skills. In our dataset,
these skills are represented by two variables: grades in maths at age 15, approximating analyti-
cal and quantitative cognitive skills (e.g., Murnane et al., 2000; Murnane et al., 1995), and years
of schooling, reecting general cognitive skills. Both these variables are signicantly associat-
ed with wages. A one-grade increase in maths, i.e., worse math skills (where grade 1 is the best
and 5 is the worst), is linked to a −3.2% decrease in wages and each additional year of schooling
is associated with a 2.6% wage increase (see Model 6). When we re-estimated Model 6 with
the corresponding interaction terms, we found no evidence of a synergy eect between grades
in maths at age 15 and any of the psychological traits, regardless of whether grades in maths
were represented by a linear term or categories (not reported here). The interaction between
the linear term of years of schooling and psychological traits was also found to be statistically
insignicant (not reported here). However, when using categories of educational attainment
instead of a linear term, dierent results emerged. These results indicate that the correlation
between persistence and wage level is 0.079 to 0.102 higher for individuals with secondary
or higher education compared to those with primary education. The evidence for other psycho-
logical traits is less robust, as the correlation between competitiveness and wage level is higher
(at the 0.05 signicance level) only for individuals with secondary education compared to those
with primary education. No statistically signicant interaction was observed at the 0.05 signif-
icance level for risk tolerance (see Table A2).
4.3 Consistency oftherelationship between competitiveness
and wages over time
This article oers a detailed analysis of the relationship between psychological traits, specif-
ically competitiveness, persistence and risk tolerance and wages in the Czech Republic using
data from 2011. Given the temporal aspect, it presents an excellent opportunity for future re-
search to explore the evolution of this relationship over time. Regrettably, there is currently no
available dataset in the Czech Republic that would enable the replication of this study with up-
to-date data. As mentioned in the data section, the new wave of the wage determination survey
in the Czech Republic allows for a partial comparison of the results, with only “competitive-
ness” being measured identically in both the 2011 and 2022 surveys.

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Psychological traits and wages intheCzech RepublicPsychological traits and wages intheCzech Republic
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Table 4: Competitiveness and wage in2011 and 2022
Model 1 specification Model 6 specification
Year 2011 Year 2022 Year 2011 Year 2022
(14) (15) (16) (17)
Variables ln gross
monthly wage
ln net
monthly wage
ln gross
monthly wage
ln net
monthly wage
Psychological traits
Competitiveness 0.100*** 0.077*** 0.043*** 0.034***
(0.008) (0. 011) (0.007) (0.008)
Other variables ofModel 1 yes yes
Other variables ofModel 6 yes yes
Constant 10.026*** 10.229*** 9.507*** 10.343***
(0.024) (0.030) (0.085) (0.119)
Observations 1,978 1,564 1,978 1,564
Adjusted R20.117 0.058 0.466 0.553
Notes: robust standard errors inparentheses. *** p<0.01, ** p<0.05, * p<0.1
Source: authors’ calculations
We re-estimated Models 1 and 6 using data from 2011 and 2022, with competitiveness
as the only psychological trait (see Table 4). While the results are not entirely comparable due
to dierences in the dependent variable (natural logarithm of gross monthly wage in 2011 and
natural logarithm of net monthly wage in 2022), the regression coecients indicate a consistent
correlation between competitiveness and wage levels over time. This suggests that the conclu-
sions drawn from the comprehensive 2011 dataset remain relevant today.
5. Conclusion
The recent empirical literature has considered psychological traits to be important determi-
nants of individual productivity, which is reected in their employment and wage levels. Al-
though measuring psychological traits is far from straightforward, leading to potential issues
with reliability and validity of results in the case of individual studies, the body of empirical
research as a whole provides convincing evidence of the relationship between these traits and

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Pavlína Vydrželová, Jiří Balcar, Lenka Johnson Filipová
94
labour performance. This article aimed to provide evidence of the link between wages and com-
petitiveness, persistence, and risk tolerance – psychological traits closely related to diligence,
eciency and achievement orientation, which employers often demand from job applicants and
employees. We leveraged data from a representative sample of 1,978 employees in the Czech
Republic, which included information on psychological traits such as competitiveness, persis-
tence, and risk tolerance, along with a comprehensive set of demographic, family, job and em-
ployer characteristics of the interviewed employees. This dataset enabled us to analyse the link
between wages and psychological traits in a model that controls for a comprehensive range
of employee characteristics and provide robust and reliable results.
The Mincer-type wage equations employed in this article demonstrated a substantial
gross positive association between wages and the examined psychological traits. Specically,
a one-standard-deviation increase in competitiveness, persistence and risk tolerance is asso-
ciated with wage premiums of 6.8 %, 6.5 %, and 5.3 %, respectively (see Model 1 in Table 1).
Further analysis revealed that these relationships are largely mediated by higher educational
attainment and employment in better-paid occupations. It is worth noting that higher educa-
tional attainment is primarily linked to increased competitiveness, whereas securing a high-
er-paying occupation is associated slightly more with competitiveness and risk tolerance than
with persistence. Importantly, all three psychological traits maintain a signicant correlation
with wages (at the 0.01 level), even when controlling for numerous demographic, family, job,
and employer characteristics. The net link between psychological traits and wages amounts
to 3.3 %, 2.8 %, and 1.9 % for a one-standard-deviation increase in competitiveness, persis-
tence, and risk tolerance, respectively. These results align with ndings from other studies con-
ducted in the Czech Republic that have focused on correlating various types of non-cognitive
skills with wage levels. Our results are further consistent with the ndings in the analysis of risk
tolerance on the German labour market.
Furthermore, our analysis using unconditional quantile regression revealed that the asso-
ciation between these traits and wages varies signicantly based on income levels. Specically,
competitiveness and risk tolerance show positive correlations with wages among individuals
with medium and high incomes, whereas persistence is primarily relevant for individuals with
medium and low incomes. This observation aligns well with job characteristics at dierent
income levels, as lower-income jobs are often associated with routine work that requires per-
sistence, while higher-income positions are linked to high work performance and a tolerance
for risk. In our investigation of synergistic eects, we found that individuals who exhibit both
higher competitiveness and persistence receive a wage bonus. A similar eect was observed for
persistent individuals with secondary or higher education compared to those with only primary

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education. However, the evidence for synergistic eects involving other psychological traits
and education is less robust.
The results suggest that more developed competitiveness, persistence, and risk tolerance
are correlated with higher productivity, although this relationship diers for particular traits
across the labour market. Furthermore, the combination of these traits, both among themselves
and in conjunction with education, represents another factor correlated with higher productivity.
These ndings inevitably lead to a recommendation for systematic development of examined
traits, especially within the education sector. This can be achieved by supporting a competitive
environment, as well as an environment requiring independence and responsibility. Moreover,
competitiveness and persistence can also be eectively developed through sports activities.
It is important to note the limitations of our study as well. The theoretical literature as-
sumes a causal eect of psychological traits on employees’ productivity and wages. However,
the cross-sectional nature of the data and the lack of suitable instrumental variables prevent-
ed us from conrming this assumption. The second limitation is related to the measurement
of psychological traits, which were captured by single-item scales. Using dierent measure-
ments for these traits would be useful in supporting the robustness of our results. The last lim-
itation arises from the absence of a comparable dataset from recent years containing all three
examined psychological traits, which would allow us to assess whether their relationship with
wage levels has evolved over time. Competitiveness was included as one of the investigated
psychological traits only in the 2022 wage determination survey. This survey allowed at least
a partial comparison of the results, revealing a consistent correlation between competitiveness
and wage levels over time.
References
Balcar, J., Janíčková, L., Filipová, L. (2014). What General Competencies Are Required from theCzech
Labour Force? Prague Economic Papers, 23(2), 250–265. http://doi.org/10.18267/j.pep.483
Balcar, J. (2016) Is ItBetter toInvest inHard orSoft Skills? TheEconomic and Labour Relations Review,
27(4). http://doi.org/10.1177/1035304616674613
Balcar, J., Dokoupilová, L. (2021) Communication and Language Skills Pay off, but not Everybody
Needs Them. International Journal oftheSociology ofLanguage, 2021(270), 59–93.
Barrick, M. R., Mount, M. K. (1991). TheBig Five Personality Dimensions and Job Performance: AMeta-
analysis. Personnel Psychology, 44, 1–26. http://doi.org/10.1111/j.1744-6570.1991.tb00688.x
Beck, J. (2021). Exploring theLink Between Wages and Psychological Capital. Journal
ofInterdisciplinary Economics. http://doi.org/10.1177/0260107921989914

Prague Economic Papers, 2024, 33 (1), 79–102, https://doi.org/10.18267/j.pep.853
Pavlína Vydrželová, Jiří Balcar, Lenka Johnson Filipová
96
Becker, G. S. (1962). Investment inHuman Capital: ATheoretical Analysis. TheJournal ofPolitical
Economy, 70(5), 9–49. http://doi.org/10.1086/258724
Bertrand, M. (2010). New Perspectives onGender. In: Ashenfelter, O., Card, D.: Handbook ofLabor
Economics, Vol. 4b., pp. 1545–1592.
Booth, A. L. (2009). Gender and Competition. Labour Economics, 16, 599–606.
http://doi.org/10.1146/annurev-economics-111809-125122
Botea, I., Donald, A., Rouanet, L. (2021).InIttoWin It? Self-esteem and Income-earning Among
Couples. Journal ofEconomic Behavior & Organization, 187, 488–506.
http://doi.org/10.1016/j.jebo.2021.04.017
Braakmann, N. (2009). TheRole ofPsychological Traits for theGender Gap inFull-time Employment
and Wages: Evidence from Germany. SOEP Papers onMultidisciplinary Panel Data Research.
http://doi.org/10.2139/ssrn.1357346
Buechel, B., Mechtenberg, L., Petersen, J. (2018). IfICan DoIt, So Can You! Peer Effects
onPerseverance. Journal ofEconomic Behavior & Organization, 155, 301–314.
http://doi.org/10.1016/j.jebo.2018.09.004
Bühler, D., Sharma, R., Stein, W. (2020).Occupational Attainment and Earnings inSoutheast Asia:
TheRole ofNon-cognitive Skills. Labour Economics, 67,
http://doi.org/10.1016/j.labeco.2020.101913
Carpenter, J., Frank, R., Huet-Vaughn, E. (2018). Gender Differences inInterpersonal and Intrapersonal
Competitive Behavior. Journal ofBehavioral and Experimental Economics, 77, 170–176.
http://doi.org/10.1016/j.socec.2018.10.003
Cobb-Clark, D. A. (2015).Locus ofControl and theLabor Market. IZA Journal ofLabor Economics, 4(1).
http://doi.org/10.1186/s40172-014-0017-x
Collischon, M. (2020). TheReturns toPersonality Traits Across theWage Distribution. LABOUR, 34(1),
48–79. http://doi.org/10.1111/labr.12165
Datta Gupta, N., Poulsen, A., Villeval, M. C. (2005). Male and Female Competitive Behaviour:
Experimental Evidence. IZA DP No. 1833. http://doi.org/10.2139/ssrn.906766
DeBeaumont, R., Girtz, R. (2020). TheMediation Effect ofSelf-Esteem onWeight and Earnings. Atlantic
Economic Journal, 47(4), 415–427. http://doi.org/10.1007/s11293-019-09648-z
Di Mauro, C. Musumeci, R. (2011). Linking Risk Aversion and Type ofEmployment. TheJournal ofSocio-
economics, 40, 490–495. http://doi.org/10.1016/j.socec.2010.12.001
Díaz, J. J., Arias, O., Tudela, D. V. (2013). Does Perseverance Pay asMuch asBeing Smart? TheReturns
toCognitive and Non-cognitive Skills inUrban Peru. In: Conference IZA, Bonn, Germany.
Dohmen, T. J., Falk, A. (2006). Performance Pay and Multi-dimensional Sorting: Productivity,
Preferences and Gender. IZA Discussion Papers No. 2001.
Farlie, R. W., Holleran, W. (2012). Entrepreneurship Training, Risk Aversion and Other Personality Traits:
Evidence from aRandom Experiment. Journal ofEconomic Psychology, 33, 366–378.
http://doi.org/10.1016/j.joep.2011.02.001

Prague Economic Papers, 2024, 33 (1), 79–102, https://doi.org/10.18267/j.pep.853
Psychological traits and wages intheCzech RepublicPsychological traits and wages intheCzech Republic
97
Feinstein, L. (2000). TheRelative Economic Importance ofAcademic, Psychological and Behavioural
Attributes Developed inChildhood. Discussion Paper 443. Centre for Economic Performance,
London School ofEconomics, London, UK.
Feng, L., Papi, M. (2020). Persistence inLanguage Learning: TheRole ofGrit and Futures Elf-guides.
Learning and Individual Differences, 81. http://doi.org/10.1016/j.lindif.2020.101904
Firpo, S., Fortin, N. M., Lemieux, T. (2009). Unconditional Quantile Regressions. Econometrica, 77(3),
953–973. http://doi.org/10.3982/ECTA6822
Gardner, P.L. (1975). Scales and Statistics. Review ofEducational Research, 45(1), 43–57.
https://doi.org/10.3102/00346543045001043
Gerhards, L., Gravert, C. (2020). Because ofYou IDid not Give up – Peer Effects inPerseverance. Journal
ofEconomic Psychology, 81. https://doi.org/10.1016/j.joep.2020.102316
Girtz, R. (2015). TheEffects ofPersonality Traits onWages: AQuantile Regression Approach. Economics
Bulletin, 35(4), 2174–2183.
Gneezy, U., Niederle, M., Rustichini, A. (2003). Performance inCompetitive Environments: Gender
Differences. Quarterly Journal ofEconomics, 118(3), 1049–1074.
https://doi.org/10.1162/00335530360698496
Goldsmith, A., Veum, J., Darity, W. (2000). Working Hard for theMoney? Efficiency Wages and Worker
Effort. Journal ofEconomic Psychology, 21, 351–385.
https://doi.org/10.1016/S0167-4870(00)00008-8
Goldsmith, A. H., Veum, J. R., Darity, W. A. (1997). TheImpact ofPsychological and Human Capital
onWages. Economic Inquiry, 35(4), 815–829. https://doi.org/10.1111/j.1465-7295.1997.tb01966.x
Goldsmith, A. H., Veum, J. R., Darity, W. A. (1998). Race, Cognitive Skills, Psychological Capital and
Wages. TheReview ofBlack Political Economy, 26 (2), 9–21. https://doi.org/10.1007/s12114-998-10
Heckman, J. J., Rubinstein, Y. (2001). TheImportance ofNoncognitive Skills: Lessons from theGED
Testing Program. TheAmerican Economic Review, 91(2), 145–149.
https://doi.org/10.1257/aer.91.2.145
Heckman, J. J., Stixrud, J., Urzua, S. (2006). TheEffects ofCognitive and Noncognitive Abilities onLabor
Market Outcomes and Social Behavior. Journal ofLabor Economics, 24(3), 411–482.
https://doi.org/10.3386/w12006
Hudson, N. W., Fraley, R. C. (2015). Volitional Personality Trait Change: Can People Choose toChange
Their Personality Traits? Journal ofPersonality and Social Psychology, 109(3), 490–507.
https://doi.org/10.1037/pspp0000021
Hudson, N. W., Roberts, B. W., Lodi-Smith, J. (2012). Personality Traits Development and Social
Investment inWork. Journal ofResearch inPersonality, 46(3), 334–344.
https://doi.org/10.1016/j.jrp.2012.03.002
Jung, S., Choe, C., Oaxaca, R. L. (2018). Gender Wage Gaps and Risky vs. Secure Employment:
AnExperimental Analysis. Labour Economics, 52, 112–121.
https://doi.org/10.1016/j.labeco.2018.04.006

Prague Economic Papers, 2024, 33 (1), 79–102, https://doi.org/10.18267/j.pep.853
Pavlína Vydrželová, Jiří Balcar, Lenka Johnson Filipová
98
Karatepe, O. M., Olugbade, O. A. (2009). TheEffects ofJob and Personal Resources onHotel Employees’
Work Engagement. International Journal ofHospitality Management, 28, 504–512.
https://doi.org/10.1016/j.ijhm.2009.02.003
Karatepe, O. M., Uludag, O., Menevis, I., Hadzimehmedagic, L., Baddar, L. (2006). TheEffects ofSelected
Individual Characteristics onFrontline Employee Performance and Job Satisfaction. Tourism
Management, 27, 547–560. https://doi.org/10.1016/j.tourman.2005.02.009
Knap, T. R. (1990). Treating Ordinal Scales asInterval Scales: AnAttempt ToResolve theControversy.
Nursing Research, 39(2), 121–123. https://doi.org/10.1097/00006199-199003000-00019
Laible, M. C., Brenzel, H. (2021). Does Personality Matter? Noncognitive Skills and theMale Migrant
Wage Gap inGermany. International Migration Review, 56(2).
https://doi.org/10.1177/01979183211037315
Lee, S. Y., Ohtake, F. (2018). Is Being Agreeable aKey toSuccess orFailure intheLabour Market?
Journal ofTheJapanese and International Economies, 49, 8–27.
https://doi.org/10.1016/j.jjie.2018.01.003
Maczulskij, T., Viinikainen, J. (2018). Is Personality Related toPermanent Earnings? Evidence Using
aTwin Design. Journal ofEconomic Psychology, 64, 116–129.
https://doi.org/10.1016/j.joep.2018.01.001
Mincer, J. A. (1974). Schooling, Experience, and Earnings. New York: Columbia University Press.
Murnane, R. J., Willett, J. B., Duhaldeborde, Y., Tyler, J. H. (2000). How Important Are theCognitive Skills
ofTeenagers inPredicting Subsequent Earnings? Journal ofPolicy Analysis and Management,
19(4), 547–568.
Murnane, R. J., Willett, J. B., Levy, F. (1995). TheGrowing Importance ofCognitive Skills inWage
Determination. TheReview ofEconomics and Statistics, 77(2), 251–266.
Nagy, M. S. (2002). Using aSingle-item Approach toMeasure Facet Job Satisfaction. Journal
ofOccupational and Organizational Psychology, 75(1), 77–86.
https://doi.org/10.1348/096317902167658
Nichols, A. L., Webster, G. D. (2013). TheSingle-item Need toBelong Scale. Personality and Individual
Differences, 55, 189–192. https://doi.org/10.1016/j.paid.2013.02.018
Niederle, M., Vesterlund, L. (2007). DoWomen Shy Away From Competition? DoMen Compete Too
Much. Quarterly Journal ofEconomics, 122(3), 1067–1101. https://doi.org/10.1162/qjec.122.3.1067
Nyhus, E. K., Pons, E. (2005). TheEffects ofPersonality onEarnings. Journal ofEconomic Psychology, 26,
363–384. https://doi.org/10.1016/j.joep.2004.07.001
Pasta, D. J. (2009). Learning When toBe Discrete: Continuous vs. Categorical Predictors. Paper
248–2009, SAS Global Forum.
Roberts, B. W., Mroczek, D. (2008). Personality Trait Change inAdulthood. Current Direction
inPsychological Science, 17(1), 31–35. https://doi.org/10.1111/j.1467-8721.2008.00543.x
Robins, R. W., Hendin, H. M., Trzesniewski, K. H. (2001). Measuring Global Self-Esteem: Construct
Validation ofaSingle-Item Measure and theRosenberg Self-Esteem Scale. Personality and Social
Psychology Bulletin, 27(2), 151–161. https://doi.org/10.1177/0146167201272002

Prague Economic Papers, 2024, 33 (1), 79–102, https://doi.org/10.18267/j.pep.853
Psychological traits and wages intheCzech RepublicPsychological traits and wages intheCzech Republic
99
Salgado, J. F. (1997). TheFive Factor Model ofPersonality and Job Performance intheEuropean
Community. Journal ofApplied Psychology, 82, 30–43. https://doi.org/10.1037//0021-9010.82.1.30
Schrock, W. A., Hughes, D. E., Fu, F. Q., Richards, K. A., Jones, E. (2014). Better Together: Trait
Competitiveness and Competitive Psychological Climate asAntecedents ofSalesperson
Organizational Commitment and Sales Performance. Marketing Letters, 27 (2), 1–10.
https://doi.org/10.1007/s11002-014-9329-7
Trchalíková, P., Balcar, J., Johnson Filipová, L., Gottvald, J. (2022). Psychological Traits Have aSimilar
Impact onWorkers’ Productivity asSoft Skills. Paper presented attheFuture ofWork Conference
(ICEA), 24–25 February.
Williams, R. (2016). Ordinal Independent Variables.
Appendix
Table A1: Definition ofvariables and descriptive statistics
Variable description Coding Mean SD Min Max
Dependent variable
Ln income
Please state your gross monthly income from your main employment
(free answer)
Continuous variable 9. 84 0.369 8.29 11. 41
Psychological traits
Competitiveness
Doyou feel areally str ong need toexcel atwhat you doand be bet ter than
others? (No – Rather no – Rather yes – Yes)
Normalized variable 0 1 −1.91 1.78
Persistence
Ioften abandon thegoal Ihave set, when If ind itdifficult toreach
(Yes – Rather yes – Rather no – No)
Normalized variable 0 1 −2.36 1.50
Risk tolerance
How high risk are you disposed totake inyour wo rk career? Ple ase,
rank itonscale from 0 (no risk) to10 (very high risk).
Normalized variable 0 1 −2.16 2 .15
Personal characteristics
Female
(based onquota sampling)
Male 0.53 0.499 0 1
Female 0.47 0.499 0 1
Years ofschooling
(based onquota sampling; comp uted number ofyears necessary for
reaching respondent’s educational attainment)
Continuous variable 13.30 2.146 921
Tenur e
How many years have you been employed atyour current employer
(consider only your main employ ment)? Donot count maternity/
parental leave and long- term sick leave into this time. (free answer)
Continuous variable 7.4 3 6. 533 0.08 37
Other work experience
Please state thetotal number ofyears yo u have worked for all your
employers (excluding your current employer) orhave been self-
employed. Donot count maternity/parental leave, military service,
long-term sick leave, periods ofunemployment, aswell astemporar y/
summer jobs and contracts for services. ( fre e answer)
Continuous variable 8.97 7.9 6 9 035

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Pavlína Vydrželová, Jiří Balcar, Lenka Johnson Filipová
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Grades inmaths atage 15
What grade did you get from mathematics inyour last ye ar ofprimary
school (orcorresponding yea r oflong-term grammar school)? Use
grading scale from 1 (thebest) to5 (theworst).
Continuous variable 2.36 0.869 1 4
Health limitation ofwork performance
Please evaluate your long-te rm health condition. (a) Itdoes not
represent any limitation ofmy work performance atmy current job;
(b) Itlimits my work perform ance atmy current job
No health limitation 0.88 0.327 0 1
Health limitation 0.12 0.327 0 1
Family and background characteristics
Partnership status
Please indicate your marital/partnership status
(pre-def ined options, recoded)
Single 0.29 0.454 0 1
No partner cohabitation 0.07 0. 257 0 1
Partner cohabitation 0.64 0.481 0 1
Number ofchildren
Please state thenumber ofyour children aged
0–2, 3–5, 6–14, 15–18, 19+ years.
Number ofchildren 0–2
years ofage 0.07 0.272 0 2
Number ofchildren 3–5
years ofage 0.12 0.345 0 2
Number ofchildren 6–14
years ofage 0.34 0.632 0 2
Number ofchildren 15–18
years ofage 0.17 0.420 0 2
Number ofchildren 19+ years
ofage 0.46 0.791 0 2
Mother tongue
Please state your mother tongue. T hemother tongue is alanguage
that you were taught by your parents f rom birth. (pre-define d options,
re-coded)
Czech language 0.99 0.105 0 1
Other 0.01 0.105 0 1
Job and employer characteristics
Occupation (1-digit ISCO classification)
Please state thetitle ofyour job position inyour main employm ent
and describe it. (free answer, subsequently classified)
Legislators, senior officials
and managers (ISCO 1) 0.03 0.17 3 0 1
Professionals (ISCO 2) 0.11 0. 316 0 1
Technicians and associate
professionals (ISCO 3) 0.23 0.421 0 1
Clerks (ISCO 4) 0.15 0. 359 0 1
Service workers and shop
and market sales workers
(ISCO 5)
0.16 0.363 0 1
Skilled agricultural, forestry
and fishery workers (ISCO 6) 0.01 0.081 0 1
Craft and related workers
(ISCO 7) 0.14 0. 342 0 1
Plant and machine operators
and assemblers (ISCO 8) 0.11 0. 313 0 1
Unskilled workers (ISCO 9) 0.07 0.248 0 1
Scheduled working hours
Please state theextent ofyour workload. (free answer,
subsequently categorised)
Full-time 0.94 0.245 0 1
Part-time 0.06 0.245 0 1

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Psychological traits and wages intheCzech RepublicPsychological traits and wages intheCzech Republic
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Prevailing economic activity (NACE classification)
(added from Albertina Database)
Agriculture, forestry… +
mining… (NACE A+B) 0.02 0.14 9 0 1
Manufacturing + electricity,
gas,… + water supply…
(NACE C+D+E)
0.24 0.429 0 1
Construction (NACE F) 0.06 0.232 0 1
Wholesale and retail trade
(NACE G) 0.17 0.380 0 1
Transporting and storage
(NACE H) 0.06 0.242 0 1
Accommodation and food
service activities (NACE I) 0.04 0.205 0 1
Information and
communication (NACE J) 0.03 0.170 0 1
Financial and insurance
activities (NACE K) 0.03 0.16 6 0 1
Real estate activities (NACE L) 0.02 0.141 0 1
Professional, scientific and
technical activities (NACE M) 0.04 0.186 0 1
Administrative and support
service activities (NACE N) 0.03 0.164 0 1
Public administration and
defence,… (NACE O) 0.07 0. 247 0 1
Education (NACE P) 0.08 0.275 0 1
Human health and social
work activities (NACE Q) 0.06 0.235 0 1
Arts, entertainment and
recreation (NACE R) 0.02 0 .13 4 0 1
Other services activities
(NACE S) 0.03 0 .170 0 1
Number ofemployees
(added from Albertina Database)
0–49 employees 0.89 0 . 317 0 1
50–249 employees 0.05 0.216 0 1
250+ employees 0.06 0. 245 0 1
Location
Region ofliving atNUTS3 level
(based onquota sampling)
Prague 0.12 0.321 0 1
Central Bohemian region 0.11 0.315 0 1
South Bohemian region 0.06 0.243 0 1
Plzeň region 0.06 0.233 0 1
Karlovy Vary region 0.03 0.169 0 1
Ústí and Labem region 0.07 0.258 0 1
Liberec region 0.04 0.197 0 1
Hradec Králové region 0.05 0.214 0 1
Pardubice region 0.05 0.218 0 1
Vysočina region 0.05 0.222 0 1
South Moravian region 0.11 0. 319 0 1
Olomouc region 0.06 0.243 0 1
Zlín region 0.06 0. 231 0 1
Moravian-Silesian region 0.12 0.330 0 1
Size ofmunicipality ofresidence
(based onquota sampling)
Up to1,999 inhabitants 0. 21 0.407 0 1
2,000–4,999 inhabitants 0.15 0.360 0 1
5,000–19,999 inhabitants 0.18 0.387 0 1
20,000–49,999 inhabitants 0.12 0.322 0 1
50,000–99,999 inhabitants 0.11 0. 313 0 1
100,000+ inhabitants 0.23 0. 419 0 1
Source: authors’ calculations

Prague Economic Papers, 2024, 33 (1), 79–102, https://doi.org/10.18267/j.pep.853
Pavlína Vydrželová, Jiří Balcar, Lenka Johnson Filipová
102
Table A2: Interaction ofpsychological traits and educational attainment
Based onModel 4
(AP1) (AP2) (AP3)
Variables ln gross monthly
wage
ln gross monthly
wage
ln gross monthly
wage
Interactions between education and &… competitiveness persistence risk tolerance
Secondary education & ... 0.082**
(0.033)
0.092***
(0.032)
0.030
(0.028)
Higher vocational school & bachelor
degree & .. .
0.065
(0.039)
0.079**
(0.039)
−0.059*
(0.035)
Master & doctoral degree & . .. 0.062*
(0.037)
0.102***
(0.037)
0.021
(0.032)
Education
Primary education
(ISCED 2A/EQF 2) baseline baseline baseline
Secondary education
(ISCED 3C & 3A/EQF 3 & 4)
−0.065
(0.058)
−0.066
(0.054)
−0.10 4*
(0.054)
Higher vocational school & bachelor degree
(ISCED 5B & 5A/EQF 6)
−0.158*
(0.092)
−0.15 4*
(0.090)
−0.185* *
(0.089)
Master & doctoral degree
(ISCED 5A & 6/EQF 6, 7 & 8)
−0.145
(0.104)
−0.150
(0.103)
−0.187*
(0.102)
Psychological traits
Competitiveness −0.042
(0.032)
0.033***
(0.007)
0.034***
(0.007)
Persistence 0.028***
(0.007)
−0.061*
(0.031)
0.028***
(0.007)
Risk tolerance 0.018***
(0.007)
0.019***
(0.007)
−0.003
(0.028)
Other variables ofModel 6 yes yes yes
Constant 9.299***
(0.144)
9.309***
(0.143)
9.351***
(0.144)
Observations 1,978 1,978 1,978
Adjusted R20.476 0.476 0.477
Notes: robust standard errors inparentheses, *** p<0.01, ** p<0.05, * p<0.1
Source: authors’ calculations