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Journal of Occupational and Environmental Medicine, Publish Ahead of Print
DOI : 10.1097/JOM.0000000000001528
Who is engaged at work?
A large-scale study in 30 European Countries
Jari J. Hakanen, PhD1
Annina Ropponen, PhD1
Wilmar B. Schaufeli PhD 2,3
Hans De Witte PhD2,4
1 Workability and Working Careers, Finnish Institute of Occupational Health,
Topeliuksenkatu 41 B, BOX 40, 00032 Finnish Institute of Occupational Health, Helsinki,
Finland
2 Research Unit Occupational & Organizational Psychology and Professional Learning, KU
Leuven, Dekenstraat 2 - box 3725, 3000 Leuven, Belgium
3Department of Social, Health & Organizational Psychology, Utrecht University,
Padualaan14, 3584 CH Utrecht, The Netherlands
4Optentia Research Focus Area, North-West University, Vaal Triangle Campus, PO Box
1174, Vanderbijlpark, 1900 South Africa
Revised manuscript for Journal of Occupational and Environmental Medicine
December 4, 2018
Words in the main text: 3961 (+ refs.)
Corresponding author: Jari Hakanen, Finnish Institute of Occupational Health, Workability
and Work Careers, Topeliuksenkatu 41 B, BOX 40, 00032 Finnish Institute of Occupational
Health, Helsinki, tel. +358 40 562 5433, email jari.hakanen@ttl.fi
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Conflict of Interest. None declared.
Acknowledgements. The authors gratefully acknowledge the European Foundation for the
Improvement and of Living and Working Conditions as the original data creators.
Sources of funding. This study was supported by SWiPE research consortium 303667,
which is funded by the Strategic Research Council of the Academy of Finland.
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Abstract
Objective. The aim was to investigate differences in the levels of work engagement across
demographic and work- and organization-related factors, and their relative importance for
work engagement.
Methods. The study was based on a sample of 17 498 male and 17 897 female employees
from the sixth European Working Conditions Survey collected in 2015. Linear regression
models and dominance analysis were used.
Results. Several significant differences between the levels of work engagement in different
demographic and work- and organization-related groups. Employees working in human
service occupations reported higher levels of work engagement than employees in other
industries. Relatively, occupational group (68%) and industry (17%) contributed most to
work engagement.
Conclusions. It is important to focus on enhancing work engagement, particularly among less
educated employees, among those with non-permanent contracts, and in certain occupations.
Key words: Work engagement; employment arrangements; population study; employee
well-being; epidemiology; Europe
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Introduction
Work engagement also called employee engagement is an important and popular
issue, in both academia and business. For instance, since its introduction at the turn of the
century, the number of scientific papers on the topic has increased steadily year by year and
currently amounts to over 7000 (Google Scholar). However, despite the overwhelming
number of scientific publications, valid and reliable information on the epidemiology of work
engagement is lacking. So far, scientific research has focused on specific occupational
samples or organizations rather than on the workforce as a whole. The current study fills this
gap by using epidemiological data on work engagement from the sixth European Working
Conditions Survey (EWCS) carried out in 2015.
Work engagement is defined as a positive, fulfilling state of mind that is characterized
by vigor, dedication, and absorption,and operationalized by the Utrecht Work Engagement
Scale (UWES).1 Vigor refers to high levels of energy and mental resilience while working,
the willingness to invest effort in ones work, and persistence in the face of difficulties.
Dedication refers to a sense of significance, enthusiasm, inspiration, pride, and challenge.
The third defining characteristic of engagement is absorption, which is characterized by being
fully concentrated on and happily engrossed in ones work, a sense that time passes quickly,
and possible difficulties in detaching oneself from ones work.
The antecedents and consequences of work engagement have been intensively
investigated using the Job Demands-Resources (JD-R) model. 2-5 This research has shown
that different job resources (e.g., skill variety, job control, learning opportunities) and
personal resources (e.g. self-efficacy, proactivity, optimism) are the main drivers of work
engagement, whereas job demands (e.g., workload, role conflicts, emotional demands) play a
minor role. 6,7
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Multiple studies suggest that work engagement is beneficial for both employees and
organizations. For instance, work engagement has been associated with better mental and
physical health among employees in terms of low levels of depression8 and anxiety,9 healthy
cardiac autonomic activity10, better workability11, better cortisol suppression in response to
dexamethasone12, lower systolic blood pressure,13, and better sleep quality. 14 In addition,
work engagement has been found to predict less from work to family conflicts and more
positive from work to home enrichment experiences. 15
Research also suggests that work engagement is beneficial for employee performance,
and hence also for organizations. For instance, work engagement is related to a low risk of
sickness absences, 16, 17 several indicators of job performance, 18-22 and workplace safety. 7
As work engagement seems to be valuable for both organizations and employees,
knowledge regarding the prevalence of work engagement in different occupational groups
would be important to be able to take specific, targeted measures to increase it. Identifying
(dis)engaged employees is not only important at the European and national governmental
level (e.g. Engaging for successin Britain, 23 but also for employers, trade unions, and non-
governmental organizations such as the ILO. However, very little is known about how
demographic and occupational factors (other than immediate working conditions) relate to
work engagement. According to the validation study of the Utrecht Work Engagement Scale,
which used samples from 10 countries, 24 work engagement was weakly positively related to
age, whereas the relation with gender varied across countries. Of occupational groups,
educators, managers, and police officers reported the highest levels of work engagement,
whereas groups such as blue-collar workers and social and health care workers reported the
lowest levels. 24 However, these results are based on non-representative studies conducted at
different times and in different occupational and organizational samples. In addition, the
present dataset showed that work engagement differs across countries, and that these
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differences are related to various economic, cultural and governance indicators. 25 Generally
speaking, the workforce is more likely to be engaged at work in well-governed,
individualistic countries with a strong democracy, low corruption and gender inequality.
The aim of this current, unique paper is to investigate the prevalence of work
engagement at the individual level across different socio-demographic, work (e.g. type of
contract, working hours) and organizational (e.g. size of the company, sector) groups in 30
European countries. Another aim is to compare the relative importance of these factors to the
level of work engagement to determine the most important contributors to feeling engaged at
work.
Methods
Participants and study design
We used the data of the 6th European Working Conditions Survey (EWCS), collected in
2015. 26 EWCS is collected every five years from random samples of the workforce and
focuses on their occupation, working conditions and health. The target population for the
EWCS consists of all residents from EU countries aged 15 or above and in employment at the
time of the survey. A stratified (by region and degree of urbanization) multi-stage, random
sample is drawn in each country, using individual-, household- and address-level registers. In
each stratum, primary sampling units are randomly selected, in proportion to the size of the
country. Subsequently, a random sample of households is drawn in each of these units.
Finally, in each household, the selected respondent is the working person whose birthday is
next. More details on sampling can be found elsewhere. 26
The survey interviews were carried out face-to-face using computer-assisted personal
interviewing. The average duration of these interviews was 45 minutes. The minimum sample
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size per country was 1000. The overall response rate was 43%, ranging from 11% in Sweden
to 78% in Albania, and resulting in a total of 43 850 responses. All 28 EU Member States
were included, as well as Norway and Switzerland. EWCSquality assurance24 meant that
five EU-associated counties had to be excluded. The EWCS is representative of those aged
15 years and above (16 and above in Bulgaria, Norway, Spain and the UK) who are in
employment and are resident in the country that is being surveyed. We also excluded those
not working at the time of the data collection. Table 1 presents the demographic details of the
participants as well as the mean levels of work engagement in the different groups.
Measurements
Work engagement was assessed using three items from the Utrecht Work Engagement
Scale (UWES) 24: At my work, I feel full of energy(vigour), I am enthusiastic about my
work(dedication), and Time flies when I am working(absorption). Recently, a similar
three-item version of the UWES was validated and shown to be psychometrically as sound as
the nine-item version (UWES-3). 27 The present survey uses two items that differ from
UWES-3. 27 However, using the third authors database, which includes 109 975 employees
from 25 countries, both the slightly different three-item UWES versions correlated at .88. 27
Across countries, the correlations varied between .68 and .92. In eight countries, the
correlations were .90 or higher (> 80% shared variance). In short, both the ultra-short three-
item versions of the UWES were similar. The items were rated on a five-point scale, ranging
from 1 (always) to 5 (never). We reversed the scale so that the higher value referred to higher
work engagement.
In addition, we utilized sociodemographic and work-related factors regarding gender,
age, highest level of education (ISCED) and country, and included design weights to adjust
for different selection probabilities, sectors of economic activity (NACE), occupational
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groups (ISCO), part-time vs. full-time work, being self-employed, type of labour contract,
public or private sector, size of workplace, length of tenure, restructuring during last three
years (yes/no), and number of work hours/week. These were categorized in accordance with
the EU working time directive into four classes: < 35 hours/week, 3540 hours/week, 4048
hours/week, and > 48 hours/week. The purpose of using weights was to ensure that the
samples were comparable and representative of each country.
Statistical analyses
The statistical analysis included three steps. First, we studied each demographic and
occupation-related factor separately, both unadjusted and adjusted for age and gender, in
linear regression models to predict work engagement. Second, we investigated all factors
were simultaneously as independent variables in the same model to predict work engagement.
As some of the sociodemographic categories included less than 500 individuals, which might
add bias or chance to the estimations, we chose to present only the categories with 500 or
more individuals, to add comparability and reliability. However, to avoid losing information
and to maximize the number of individuals in the models, we retained these categories in the
models but did not present the results. Consequently, we did not use them for making any
interpretations. Third, to assess the relative importance of different demographic and work-
related factors, we conducted dominance analysis (DA) to determine the most important
contributors to work engagement. 28 This analysis compares all independent variables in the
model to each other and ranks them by their relative importance in predicting work
engagement. 29
DA is used to overcome methodological difficulties, such as the multicollinearity
related to traditional regression models with several correlated predictors. Regression,
including stepwise and hierarchical approaches with several overlapping independent
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variables, may overestimate the importance of the strongest predictors, underestimate the
importance of the less important predictors, reverse the signs of predictors (i.e. suppressor
effect), and allow slight differences in inter-predictor correlations to change the pattern of
derived regression weights. 28 In addition to overcoming multicollinearity problems, DA is
particularly useful when the number of predictors is large. 29 We also reported the domin
value, which means the proportion of each factors explained variance (%) of the variance
explained (100%) by the whole model.
In all analyses, the reference category for demographics and work-related factors was
the largest category (i.e. including most employees). In addition, we weighted all the analyses
with design weights to adjust for different selection probabilities of EWCS to control for the
impact of the country. We used Stata 14.0 software (Stata Corporation) and its DOMIN
module to conduct the analyses. 30
Results
Table 2 presents the crude and age- and gender-adjusted associations between
demographic and work-related factors and work engagement. Women and workers over 60
years of age were significantly more engaged than men and those in younger age groups,
respectively. After adjusting for age and gender, the results remained similar, indicating a
clear linear trend in education: the lower the educational attainment, the lower the level of
work engagement. For example, those with primary education were clearly less likely to feel
engaged at work [Exp(coef) = 0.89, 95%CI 0.83-0.96], whereas those with a doctorate or
equivalent education were more likely to feel engaged [Exp(coef) = 1.39, 95%CI 1.24-1.57].
Of the work-related factors, longer tenure in the same company and being self-
employed were positively related to work engagement. Working more than 48 hours/week
was also associated with work engagement [Exp(coef) = 1.12, 95%CI 1.08-1.16] both in the
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crude and age- and gender-adjusted models. In regard to the type of employment contract, a
clear trend was observed, as contracts with a limited duration [Exp(coef) = 0.93, 95%CI 0.89-
0.97], temporary agency contracts [Exp(coef) = 0.83, 95%CI 0.73-0.94], no contract
[Exp(coef) = 0.92, 95%CI 0.86-0.98], or other type of contract [Exp(coef) = 0.80, 95%CI
0.70-0.91] were all associated with lower work engagement in comparison to permanent
work contracts.
The differences between industries indicated that working in health care and social
work [Exp(coef) = 1.15, 95%CI 1.09-1.20] and other service activities [Exp(coef) = 1.16,
95%CI 1.09-1.24], and in education [Exp(coef) = 1.25, 95%CI 1.19-1.32] were associated
with higher levels of work engagement. In contrast, working in manufacturing and transport,
storage and communications [Exp(coef) = 0.92, 95%CI 0.88-0.97] was associated with lower
levels of work engagement. Of the occupational groups, managers [Exp(coef) = 1.17, 95%CI
1.11-1.23] and professionals [Exp(coef) = 1.08, 95%CI, 1.04-1.13] reported higher levels of
work engagement, whereas plant and machine operators [Exp(coef) = 0.76, 95%CI 0.72-0.81]
and elementary occupations [Exp(coef) = 0.74, 95%CI 0.70-.0.78] reported lower work
engagement. Moreover, in the private sector, the likelihood of work engagement was
somewhat lower than in other sectors. We found no association between work engagement
and the size of the company, nor between work engagement and recent company
restructuring or reorganization.
In the multivariate analysis (Table 3), being a worker over 60 years of age [Exp(coef) =
1.18, 95%CI 1.06-1.18] was positively associated with work engagement. In addition,
working in health care and social work [Exp(coef) = 1.17, 95%CI 1.07-1.28], education
[Exp(coef) = 1.15, 95%CI 1.03-1.30], and agriculture, hunting or forestry [Exp(coef)=1.22,
95%CI 1.01-1.48] increased the likelihood of work engagement. In contrast, working in
manufacturing and transport, storage and communications [Exp(coef) = 0.92, 95%CI 0.88-
⇐°↓ƒ↑∂÷•↔ ϖ ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
0.97] was associated with lower levels of work engagement. Of the different occupations,
plant and machine operators and assemblers [Exp(coef) = 0.80, 95%CI 0.72-0.89],
elementary occupations [Exp(coef) = 0.77, 95%CI 0.69-0.85], clerical support workers
[Exp(coef) = 0.91, 95%CI 0.84-0.99], and service and sales workers [Exp(coef) = 0.91,
95%CI 0.85-0.98] were likely to be less engaged, and managers [Exp(coef) = 1.16, 95%CI
1.06-1.26] were more likely to feel engaged at work. Finally, working in the public sector
was positively related to work engagement [Exp(coef) = 1.10, 95%CI 1.00-1.20], and
working in a company with 500 or more employees was negatively [Exp(coef)=0.91, 95%CI
0.83-0.99] related to work engagement. We found no other significant associations.
DA (Table 3) revealed that occupational group and industry were relatively the most
important factors associated with work engagement, explaining 68.1% and 17.1% of all the
explained variance of the model, respectively. Far less important were sector (4.3%) and
employment contract (3.0%), followed by education (2.5%) and gender (2.1%). Individual
factors such as age, job tenure, employment contract (part-time/full-time and self-
employed/salaried employee), and similarly organization-related factors such as size of the
company, and restructuring or reorganization during the last 12 months played almost no role
in explaining the variance of work engagement.
Discussion
The purpose of this large-scale study in 30 European countries among over 35 000
employees was twofold: to examine the prevalence of work engagement in different socio-
demographic and work- and organizational-related groups, and to investigate the relative
importance of these factors for work engagement. To the best of our knowledge, this is the
first prevalence study on work engagement to be based on national representative data from
various countries.
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Our study found that work engagement, a positive affective-motivational state at work,
was related to many sociodemographic and work-related factors, particularly to educational
attainment, employment contract, occupation, industry, and sector. In addition, the results of
DA indicated that occupation and industry made the strongest contribution to work
engagement.
We found a clear social gradient related to the prevalence of work engagement: those
who had better educational attainment were also more likely to be engaged at work, whereas
less educated employees were less engaged. Research on social inequalities in health which
indicates that poor socioeconomic status (education, social class) is associated with mortality
and morbidity is abundant. Already two decades ago, Marmot et al. 31 suggested that the
inequality gap in health and health promotive mechanisms may be much more profound and
persistent than was assumed on the basis of mere mortality and register-based disease studies.
Previously it was found that the origins of job burnout may lie in childhood socio-economic
status. 32 Our study suggests that a positive state of employee well-being, i.e., work
engagement may also indicate an inequality gap. Previous research has also found a positive
relation between work engagement and physical and mental health8-14 and work-family
balance15, and thus (lack of) work engagement may be one mechanism linking
socioeconomic status differences to poorer health in the long term.
Similarly, employment contract was related to work engagement, so that those with
permanent employment contracts were more likely to report higher levels of work
engagement than those with other types of contract or no contract at all. Those with no
permanent contract may experience work stress due to several reasons. For instance, they
may be considered peripheral workers, and employers may not be willing to invest in them
by, for example providing training. They also may lack job control and social supportnd have
monotonous jobs with less challenges33-35 all these job characteristics are known to
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influence work engagement. 36 More research is needed that both compares stable, permanent
and increasingly untypical, precarious employment contracts, and their impacts on work
engagement.
Interestingly, we also found curvilinear relationship between working hours and work
engagement, meaning that particularly those working more than 48 hours per week but also
those working less than 35 hours per week were more likely to be engaged than those
working between 35-48 hours per week. Previously, work engagement has been found to
correlate positively with working hours. 37 As engaged employees have high levels of energy
and are enthusiastic about their work, they also often work voluntarily more hours than
required by their organizations. In the present study, among those who worked more than 48
hours per week there were relatively more managers (12%) than in the whole sample (4%)
and also more self-employed (42% vs. 14%) both occupational groups scored high in work
engagement in the present study. The higher likelihood of feeling engaged among those
working less than 35 hours is likely to be explained by the fact that it was more typical to
work shorter hours in highly engaged industries, i.e., in social and health care (16% in the
group working less than 35 hours per week vs. 11% in the whole sample) and in education
(14% vs. 8%). Indeed, the (curvilinear) association between working hours and work
engagement disappeared after controlling the impact of other factors, such as occupational
group and industry.
The DA results showed that the most important contributors to work engagement were
occupation and industry, explaining 68% and 17% of the variance of work engagement,
respectively. This left only 15% for all remaining factors, such as economic sector (4.3%),
employment contract (3%) and education (2.5%). As regards occupations, not surprisingly,
managers and professionals were clearly more engaged than, for example, plant and machine
operators, assemblers, other elementary occupations, and clerical support workers. These
⇐°↓ƒ↑∂÷•↔ ϖ ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
differences are likely due to variances in the job resources available in these jobs. Several
studies3-5 have confirmed the assumptions of the Job Demands-Resources model2: that job
demands are the primary antecedents of job burnout, and that job resources (e.g., job
autonomy, skill variety, feedback, social support) are the main drivers of work engagement.
Managers, professionals and many other well-educated employees are more likely to be able
to draw upon these resources, whereas other jobs may lack resources.
Employees in human service jobs such as health and social care and education, as well
as those in agriculture, hunting and forestry reported more work engagement than employees
in other types of industries such as manufacturing, transport, storage, and communication.
Interestingly, chronic job burnout was long considered typical only among human service
professionals because of the emotional and interpersonal stressors in these jobs. 38 According
to this study, those employed in human services may feel particularly highly engaged in their
work. Working with and for people, and helping them, is often experienced as meaningful
and many may consider these jobs a calling. These jobs often also include many job resources
such as skill variety, professional development, receiving immediate feedback, good climate,
and colleague support, which all positively impact work engagement. 3,39,40
A possible explanation for the likelihood of higher work engagement in agriculture,
hunting and forestry is that these fields have a high ratio of self-employed people (60% in the
present sample). Self-employed individuals have been found to be more engaged in their
work than salaried employees. 41 This may be because individuals in entrepreneurial jobs are
usually proactive and have achievement-related personality characteristics, and/or because
the self-employed by definition have more autonomy.
Finally, being over 60 years of age was positively related to work engagement. The
healthy worker effect may have impacted this result, in that the more engaged aged
⇐°↓ƒ↑∂÷•↔ ϖ ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
employees stay longer in the labour force, whereas those who are less engaged and less
healthy drop out at an earlier age. Interestingly, women reported slightly more work
engagement than men. This is surprising, as research usually shows that although women live
longer, they report more symptoms and suffer from more diseases than men. 42 However,
positive and negative feelings and states of ill-being and well-being are not necessarily
opposites. Research on employee well-being (work engagement vs. job burnout) and on
general mental well-being (emotional, social and psychological well-being vs. depression)
has shown that positive and negative well-being constitute separate although correlated
unipolar dimensions. 4,43 More research is needed on the mechanisms that explain why
employed women may be more engaged than men.
Many large private companies have human resource policies to assess and boost work
engagement. However, not all organizations have such policies. As work engagement is
beneficial for all types of organizations and for employees themselves, it is important that
particular attention is paid to developing more resourceful working conditions (e.g. more job
autonomy, skill variety and job security), especially among those who lack higher education,
have blue-collar jobs, and no permanent contract.
Strengths and limitations
The major strength of this study was its large and representative sample size, which
consisted of employees from 30 European countries. In addition, the response rate was
satisfactory (43%). Although work engagement is currently widely investigated all over the
globe, very little is known about its prevalence. This unique dataset enabled us to investigate
this prevalence and to assess the relative importance of various individual, work-related and
organizational factors for work engagement after controlling for the impact of country.
⇐°↓ƒ↑∂÷•↔ ϖ ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
Our study also has some limitations. First, as it was cross-sectional, it was not possible
to investigate how different factors predicted future work engagement, or how changes in
these factors (e.g. in job status and employment contract) would influence engagement over
time. Second, it was based on self-reports. However, we assume that common method bias
did not considerably influence the results, as work engagement was the only variable based
on subjective experiences; all the other variables (e.g. age, employment contract and sector)
were more factual by nature. Third, despite the large sample size, some of the
sociodemographic categories included less than 500 employees, so we were restricted in
making any interpretations based on these if we wished to maintain comparability with the
categories with up to 4000 employees. Hence, an even larger and representative sample
would be needed to confirm findings across all sociodemographic categories with sufficient
statistical confidence. Finally, although the study included 30 countries, they were all
European, so it is uncertain how far our results can be generalized to other parts of the world.
Conclusion
According to this first large-scale prevalence study on work engagement, an
employees level of work engagement depends on individual (e.g. education), work-related
(e.g. contract) and contextual (e.g. occupation, industry) factors. Job-related and contractual
factors seem to be the most significant determinants of work engagement. In the changing
world of work it is important to strive for enhancing work engagement, especially among
those who lack higher education, have blue-collar jobs, and have no permanent contract.
⇐°↓ƒ↑∂÷•↔ ϖ ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
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Table 1. Work engagement by sociodemographic and work-related factors (including only
those reporting being at work and limited to EU 28 countries, excluding candidate countries
but Switzerland and Norway included)
Work engagement
n Mean SD
All 35395 3.95 0.70
Gender
Men 17498 3.94 0.70
Women 17897 3.96 0.70
Age groups
< 30 years 5221 3.91 0.72
30-40 years 8240 3.94 0.71
40-50 years 9645 3.95 0.70
50-60 years 8924 3.96 0.70
> 60 years 3080 4.03 0.68
Education
Early childhood education 164 3.76 0.84
Primary education 1083 3.76 0.84
Lower secondary education 4443 3.87 0.77
Upper secondary education 14542 3.91 0.72
Post-secondary non-tertiary education 2903 3.94 0.70
Short-cycle tertiary education 3511 4.04 0.63
Bachelor or equivalent 4393 4.05 0.64
Master or equivalent 3706 4.06 0.61
Doctorate or equivalent 372 4.14 0.59
Years worked in the company
1 year 1986 3.96 0.75
2-5 years 9196 3.92 0.71
5-10 years 7037 3.96 0.69
10-15 years 4282 3.96 0.66
15-20 years 2964 3.97 0.69
over 20 years 5557 4.02 0.67
Employment contract (main job)
Contract of unlimited duration 24181 3.94 0.69
Contract of limited duration 3366 3.85 0.78
A temporary agency contract 380 3.79 0.86
An apprenticeship or other training schedule 141 3.98 0.76
No contract 1721 3.82 0.72
Other 228 3.83 0.81
Working hours (limits by EU Working time directive)
< 35 hours/week 8021 3.98 0.72
35-40 hours/week 17727 3.94 0.69
40-48 hours/week 4172 3.90 0.69
>48 hours/week 4451 4.00 0.71
Part-time work 7178 3.95 0.73
Full-time work 27965 3.95 0.70
Self-employed 5294 4.11 0.66
Employee 29839 3.92 0.71
Occupational group
⇐°↓ƒ↑∂÷•↔ ϖ ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
Plant and machine operators, and assemblers 2445 3.74 0.79
Elementary occupations 3365 3.76 0.82
Clerical support workers 3228 3.88 0.69
Service and sales workers 7411 3.91 0.73
Craft and related trades workers 4248 3.94 0.70
Skilled agricultural, forestry and fishery workers 958 3.97 0.73
Technicians and associate professionals 4267 4.03 0.63
Professionals 6760 4.09 0.60
Managers 2353 4.15 0.61
Industry
Manufacturing 4982 3.85 0.74
Transport, storage and communication 2372 3.88 0.72
Wholesale and retail trade 5267 3.89 0.72
Hotels and restaurants 1823 3.93 0.74
Agriculture, hunting and forestry 1380 3.94 0.73
Real estate activities 4126 3.94 0.71
Public administration and defense 2058 3.94 0.69
Construction 2220 3.97 0.67
Financial intermediation 1011 4.00 0.63
Health and social work 3778 4.04 0.64
Other service activities 2011 4.09 0.69
Education 2825 4.13 0.60
Sector
Private sector 24276 3.93 0.72
Public sector 8509 4.00 0.67
A joint private-public organization or company 1254 4.00 0.68
Other 630 4.01 0.69
Not-for-profit sector or NGO 385 4.03 0.65
Size of the company
1 (participant works alone) 552 4.05 0.72
2-4 1149 3.99 0.70
5-9 1480 3.95 0.69
10-49 4172 3.94 0.69
50-99 1802 3.90 0.70
100-249 1753 3.91 0.71
250-499 1002 3.90 0.71
500 and over 1672 3.91 0.70
During last year, restructuring or reorganizing (yes) 8053 3.93 0.70
no 26067 3.96 0.70
⇐°↓ƒ↑∂÷•↔ ϖ ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
Table 2. Crude and age- and gender-adjusted regression coefficients with 95% Confidence
intervals (CI) for demographic and work-related factors predicting work engagement
Work engagement
Crude Age- and gender-adjusted
Regression
coefficient*
95%CI Regression
coefficient*
95%CI
Gender (men as reference) 1.03 1.01, 1.06 1.03 1.01, 1.06
Age groups (40-50 years as
reference)
< 30 years 0.99 0.95, 1.03 1.05 0.97, 1.15
30-40 years 0.99 0.96, 1.03 1.03 0.97, 1.08
50-60 years 1.01 0.98, 1.05 0.98 0.93, 1.03
> 60 years 1.09 1.04, 1.14 1.02 0.94, 1.12
Education (Upper secondary
education as reference)
Early childhood education 0.86 0.70, 1.05 0.86 0.70, 1.05
Primary education 0.89 0.83, 0.96 0.89 0.83, 0.96
Lower secondary education 1.01 0.96, 1.05 1.01 0.96, 1.05
Post-secondary non-tertiary education 1.04 1.00, 1.09 1.04 0.99. 1.09
Short-cycle tertiary education 1.14 1.04, 1.19 1.09 1.10, 1.19
Bachelor or equivalent 1.15 1.10, 1.20 1.15 1.10, 1.19
Master or equivalent 1.20 1.15, 1.24 1.19 1.15, 1.24
Doctorate or equivalent 1.39 1.24, 1.57 1.39 1.24, 1.57
Years worked in the company (2-5
years as reference)
1 year 1.01 0.95, 1.09 1.02 0.95, 1.09
5-10 years 1.04 1.00, 1.08 1.04 1.00, 1.08
10-15 years 1.02 0.98, 1.06 1.02 0.98, 1.07
15-20 years 1.05 1.00, 1.10 1.05 1.00, 1.10
over 20 years 1.10 1.06, 1.15 1.11 1.06, 1.15
Working hours (limits by EU
Working time directive, 35-40
hours/week as reference)
< 35 hours/week 1.03 1.00, 1.06 1.02 0.99, 1.05
40-48 hours/week 0.99 0.96, 1.03 1.00 0.96, 1.03
>48 hours/week 1.11 1.07, 1.15 1.12 1.08, 1.16
Employment contract (main job,
Contract of unlimited duration as
reference)
Contract of limited duration 0.94 0.90, 0.98 0.93 0.89, 0.97
A temporary agency contract 0.83 0.73, 0.94 0.83 0.73, 0.94
An apprenticeship or other training
schedule
1.05 0.81, 1.35 1.05 0.81, 1.36
No contract 0.92 0.87, 0.98 0.92 0.86, 0.98
Other 0.81 0.71, 0.92 0.80 0.70, 0.91
Part-time work (Full-time work as
reference)
0.97 0.94, 1.00 0.95 0.92, 0.98
Self-employed (Employee as 1.21 1.17, 1.25 1.21 1.17, 1.25
⇐°↓ƒ↑∂÷•↔ ϖ ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
reference)
Occupational group (Technicians
and associate professionals as
reference)
Plant and machine operators, and
assemblers
0.75 0.71, 0.80 0.76 0.72, 0.81
Elementary occupations 0.74 0.70, 0.79 0.74 0.70, 0.78
Skilled agricultural, forestry and
fishery workers
1.05 0.97, 1.14 1.06 0.98, 1.14
Clerical support workers 0.87 0.82, 0.91 0.86 0.82, 0.91
Service and sales workers 0.91 0.87, 0.95 0.91 0.87, 0.95
Craft and related trades workers 0.95 0.91, 1.00 0.97 0.92, 1.01
Professionals 1.09 1.04, 1.13 1.08 1.04, 1.13
Managers 1.16 1.10, 1.23 1.17 1.11, 1.23
Industry (Wholesale and retail trade
as reference)
Agriculture, hunting and forestry 1.12 1.04, 1.20 1.12 1.04, 1.20
Activities of households 0.92 0.83, 1.02 0.92 0.83, 1.03
Manufacturing 0.92 0.88, 0.97 0.92 0.88, 0.97
Transport, storage and communication 0.93 0.87, 0.98 0.92 0.87, 0.98
Hotels and restaurants 1.07 1.00, 1.15 1.07 1.00, 1.15
Electricity, gas, and water supply 1.02 0.90, 1.16 1.02 0.90, 1.15
Real estate activities 0.99 0.94, 1.04 0.99 0.94, 1.04
Public administration and defense 1.00 0.94, 1.06 1.00 0.94, 1.06
Construction 1.05 0.99, 1.12 1.05 0.98, 1.12
Financial intermediation 1.11 1.03, 1.20 1.11 1.02, 1.20
Health and social work 1.14 1.09, 1.24 1.15 1.09, 1.20
Other service activities 1.16 1.09, 1.24 1.16 1.09, 1.24
Education 1.25 1.19, 1.32 1.25 1.19, 1.32
Sector (Private sector as reference)
Public sector 1.07 1.04, 1.10 1.06 1.00, 1.13
A joint private-public organization or
company
1.06 1.00, 1.13 1.05 0.96, 1.14
Other 1.14 1.03, 1.26 1.14 1.03, 1.26
Not-for-profit sector or NGO 1.05 0.96, 1.15 1.07 1.04, 1.10
Size of the company (10-49 as
reference)
1 (participant works alone) 1.16 1.05, 1.29 1.10 0.98, 1.23
2-4 1.08 0.99, 1.17 1.02 0.93, 1.12
5-9 1.06 0.98, 1.14 0.95 0.88, 1.02
50-99 1.00 0.94, 1.06 0.95 0.88, 1.02
100-249 0.99 0.92, 1.06 0.94 0.86, 1.02
250-499 0.97 0.88, 1.06 0.92 0.83, 1.02
500 and over 0.98 0.92, 1.05 0.94 0.86, 1.01
During last year, restructuring or
reorganizing (no as reference)
0.97 0.94, 1.00 0.97 0.94, 1.00
*Statistically significant regression coefficients and 95%CIs in boldface
⇐°↓ƒ↑∂÷•↔ ϖ ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
Table 3. Multivariate regression coefficients with 95% Confidence intervals (CI) for
demographic and work-related factors predicting work engagement and standardized
dominance estimates (Domin = % R2explained) and the position in the ranking of dominance
analysis (Domin rank).
Work engagement
Multivariate model Domin Domi
n
rank
Regression
coefficient*
95%CI
Gender (men as reference) 1.04 0.95, 1.13 2.1% 6
Age groups (40-50 years as reference) 0.4% 10
< 30 years 1.04 0.97, 1.12
30-40 years 1.03 0.97, 1.09
50-60 years 1.05 0.99, 1.11
> 60 years 1.18 1.06, 1.29
Education (Upper secondary education
as reference)
2.5% 5
Early childhood education 1.27 0.85, 1.89
Primary education 1.12 0.97, 1.29
Lower secondary education 1.04 0.96, 1.13
Post-secondary non-tertiary education 0.98 0.91, 1.05
Short-cycle tertiary education 1.06 1.00, 1.13
Bachelor or equivalent 1.05 0.98, 1.12
Master or equivalent 1.04 0.97, 1.12
Doctorate or equivalent 1.06 0.88, 1.26
Years worked in the company (2-5
years as reference)
0.4% 9
1 year 1.06 0.94, 1.20
5-10 years 1.03 0.97, 1.10
10-15 years 1.03 0.96, 1.10
15-20 years 0.98 0.90, 1.06
over 20 years 1.03 0.95, 1.11
Working hours (limits by EU
Working time directive, 35-40
hours/week as reference)
1.2% 7
< 35 hours/week 1.02 0.93, 1.11
40-48 hours/week 0.98 0.93, 1.04
>48 hours/week 1.05 0.99, 1.12
Employment contract (main job,
Contract of unlimited duration as
reference)
3.0% 4
Contract of limited duration 0.95 0.88, 1.02
A temporary agency contract 0.89 0.73, 1.09
An apprenticeship or other training
schedule
1.08 0.85, 1.37
No contract 0.90 0.77, 1.04
Other 0.86 0.68, 1.08
Part-time work (Full-time work as 1.04 0.95, 1.14 0.7% 8
⇐°↓ƒ↑∂÷•↔ ϖ ⇒″≡↑∂…± ⇐°≥≥≡÷≡ °≠ ∠……♠↓↔∂°±≥ ±≈ ∨±♥∂↑°±″≡±↔≥ ≡≈∂…∂±≡〉 ⊇±♠↔•°↑∂∞≡≈ ↑≡↓↑°≈♠…↔∂°± °≠ ↔•∂← ↑↔∂…≥≡ ∂← ↓↑°•∂∂↔≡≈
reference)
Self-employed (Employee as reference) 0.99 0.92, 1.05 0.0% 13
Industry (Wholesale and retail trade as
reference)
17.1% 2
Agriculture, hunting and forestry 1.22 1.01, 1.48
Activities of households 1.23 0.88, 1.73
Manufacturing 0.94 0.86, 1.03
Transport, storage and communication 1.01 0.91, 1.11
Hotels and restaurants 1.04 0.92, 1.18
Electricity, gas, and water supply 1.07 0.91, 1.25
Real estate activities 0.96 0.87, 1.05
Public administration and defense 1.02 0.92, 1.14
Construction 1.00 0.89, 1.12
Financial intermediation 1.10 0.99, 1.22
Health and social work 1.17 1.07, 1.28
Other service activities 1.08 0.94, 1.25
Education 1.15 1.03, 1.30
Occupational group (Technicians and
associate professionals as reference)
68.0% 1
Plant and machine operators, and
assemblers
0.80 0.72, 0.89
Elementary occupations 0.77 0.69, 0.85
Skilled agricultural, forestry and fishery
workers
0.89 0.69, 1.16
Clerical support workers 0.91 0.84, 0.99
Service and sales workers 0.91 0.85, 0.98
Craft and related trades workers 1.04 0.94, 1.14
Professionals 1.01 0.94, 1.09
Managers 1.16 1.06, 1.26
Sector (Private sector as reference) 4.3% 3
Not-for-profit sector or NGO 0.99 0.92, 1.07
Public sector 1.10 1.00, 1.20
A joint private-public organization or
company
0.88 0.78, 1.00
Other 1.15 0.94, 1.38
Size of the company (10-49 as
reference)
0.1% 12
1 (participant works alone) 0.98 0.84, 1.14
2-4 0.98 0.90, 1.08
5-9 0.94 0.87, 1.01
50-99 0.94 0.87, 1.01
100-249 0.92 0.85, 1.01
250-499 0.93 0.84, 1.03
500 and over 0.91 0.83, 0.99
During last year, restructuring or
reorganizing (no as reference)
0.95 0.91, 0.99 0.2% 11
100%
*Statistically significant regression coefficients and 95% CIs in boldface
