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Working-Time Regimes and Work-Life Balance
in Europe
Timo Anttila,
1,
* Tomi Oinas,
1
Mia Tammelin
1
and Jouko Na¨ tti
2
1
Department of Social Sciences and Philosophy, University of Jyva¨skyla¨, FIN 40014, Finland and
2
School
of Social Sciences and Humanities, University of Tampere, FIN 33014, Finland
*Corresponding author. Email: timo.e.e.anttila@jyu.fi
Submitted March 2014; revised June 2015; accepted June 2015
Abstract
The organization of times and places of work are key elements of working conditions, and define em-
ployees’ possibilities for balancing work and other life spheres. This study analyses several aspects of
temporal and spatial flexibility, and their associations with employees’ work-life balance. This study
separates four dimensions of temporal flexibility and one indicator of spatial flexibility. The dimen-
sions of temporal flexibility are the number of hours worked, when the hours are worked, work-time
intensity, and the degree of working-time autonomy. The workplace flexibility indicator is an index of
work locations. Work-life balance is analysed with work-hour fit. The analyses were based on the fifth
wave of the European Working Conditions Survey collected in 2010. We used data from 25 Member
States of the European Union (n ¼ 25,417). Based on the hierarchical cluster analysis, this study found
various types of flexibility regimes in Europe. Country clusters show a clear effect on perceived work-
life balance even after controlling for flexibility measurements at the individual level. This study con-
tributes to the existing research in analysing several dimensions of temporal and spatial flexibility at
the same time, as well as their associations to work-life balance.
Introduction
The flexibilization of working times and workplaces has
become an increasing focus for the analysis of quality of
work and life (Messenger, 2011). Instead of a standard
industrial working-time model that is characterized by an
8-h work day, a 5-day work week during the day, and
free evenings, weekends, and annual holidays, temporal
and spatial flexibility is becoming more common (Supiot,
1999; Rapoport and Le Bourdais, 2008; Craig and
Powell, 2011; Fagan et al.,2012). This study approaches
flexibilization as a transition from an industrial to a post-
industrial working-time regime. The new post-industrial
working-time regime is characterized by deregulation of
collective norms, diversification of the length (short and
long hours) and pattern of working time (unsocial hours),
increasing work intensity and time squeeze, and blurring
of the limits of working and leisure time (Clarkberg and
Merola, 2003; Brannen, 2005; Perrons et al.,2005;
Rubery et al.,2005; Gallie and Russell, 2009). At its best,
the new ‘working-time mosaic’ may provide more auton-
omy to employees. On the other hand, there are new risks
concerning the relationship between temporal flexibility
and private life, the time and energy available for per-
sonal, family, and social life (Presser, Parashar, and
Gornick, 2008; Bianchi and Milkie, 2010), material well-
being, and health (Ha¨rma¨ and Kecklund, 2010).
The first aim of this study was to empirically con-
struct European working-time regimes based on spatial
V
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European Sociological Review, 2015, 1–12
doi: 10.1093/esr/jcv070
Original Article
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and temporal dimensions of work. We present a data-
driven approach that analyses similarities among
European countries. This approach differs from many
earlier studies, in which the typologies of country clus-
ters (or regimes) served as a starting point for analysis.
Furthermore, our analysis looks at several aspects of
working time simultaneously, which differs from previ-
ous studies that only included one aspect, for example,
length of working time. Our second aim was to analyse
the linkages between temporal and spatial flexibility and
work-life balance. We assumed that temporal and spa-
tial flexibility was linked to work-life balance and ques-
tioned whether the linkages differed between country
clusters.
Flexibility of Working Time and Place
In everyday life, paid work has a major impact on living
conditions, use of time, social networks, and identity
(Supiot, 1999; Epstein and Kalleberg, 2001). Interna-
tional competition, accelerating technological change,
and the rise of the service economy are regarded as key
driving factors behind changing the organization of paid
work (Perrons et al., 2005; Green, 2006). At the same
time, the social organization of households, as well as
household production, is changing. The growing service
economy offers more employment options, particularly
for women. Consequently, increasing numbers of em-
ployed persons have to combine employment and caring
responsibilities (Fleetwood, 2007; McGinnity and Cal-
vert 2009). The increase of dual-earner families, and
changes in family structures, such as the increase in sin-
gle-parent families, have brought the work-life balance
into the agenda of national and European Union (EU)
policies.
Working-Time Regimes in Europe
The requirements for more flexible and lean forms of
production that are able to adapt to demand cycles,
both quantitatively and functionally, are common in all
advanced economies. International competition, bench-
marking practices, and the central managerial tenets
cross the borders of national states. Expanding com-
parative research literature has tried to discern if the im-
portant differences are among countries’ production
regimes or with countries’ welfare state institutions in
mediating the pressures of employment and households
(Gallie and Russell, 2009). The presumption is that there
are differences between national, political, and historical
compromises on industrial relations and production sys-
tems, and between societal institutions such as family
systems, educational systems, and security systems
(Bosch Rubery, and Lehndorff, 2007). Thus, policies
vary, and particular national institutional conditions
mediate globalization’s effects (Gornick and Heron,
2006). For example, national industrial relation systems
define to what extent working-time conditions are regu-
lated by industrywide collective bargaining, or by enter-
prise-level negotiations (Rubery, Smith, and Fagan,
1998).
Although countries face similar changes in the
restructuring of labour markets, production systems,
and international regulation, the existing national poli-
cies vary. Earlier, comparative research that concen-
trated on European working and production conditions
has classified European countries according to welfare
state regimes (Esping-Andersen, 1990, 1999) or forms
of capitalism (Hall and Soskice, 2001). In addition,
comparative analysis has applied more specific
approaches to discern countries according to their pro-
duction regimes (Hult and Svallfors, 2002; Gallie, 2007;
Gallie and Russell, 2009), forms of flexibility (Anxo and
O’Reilly, 2000; Kerkhofs, Chung, and Ester, 2008;
Chung and Tijdens, 2013), as well as employment sys-
tems, gender regimes, and working-time regimes (Lewis,
1992; Rubery, Smith, and Fagan, 1998; Bosh, Rubery
and Lehndoff, 2007).
As with other regimes, working-time regimes are
highly dependent on the cultural, institutional, and regu-
latory environments of the society (Anxo and O’Reilly,
2000). European companies are subject to institutional
regulations, which vary from one country to another.
They are also confronted with varying demands on part
of the employees. In addition, cross-national variation in
production systems has led to different employer strat-
egies for achieving a competitive advantage.
Our study contributes to the existing comparative
research in Europe by looking at several working-time
dimensions at the same time. Our research is particu-
larly related to two recent studies with a comparative
setting, which have also analysed working-time flexibil-
ity (Chung and Tijdens, 2013) and work-life balance
(Gallie and Russell, 2009) in Europe. Considering that
working-time practices are a changing landscape, our
findings are compared with the most recent studies,
while still maintaining the more theoretical classifica-
tions of welfare state, production, and gender regimes
Chung and Tijdens (2013) analysed working-time
flexibility in European companies using a European
Company Survey (2004–2005). Their analyses captured
working-time practices at the company level and identi-
fied ‘company-oriented’ and ‘worker-oriented’ flexibil-
ity, which is relevant to our study. Company-oriented
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flexibility (such as overtime or unsocial hours) would,
theoretically, serve the needs of the companies, not the
needs of the employees in balancing work and other
spheres of life. A three-cluster model divided European
countries into mainly northern (Denmark, Finland,
Sweden, the Netherlands, Poland, and the Czech
Republic), central (Austria, Belgium, France, Germany,
Ireland, Latvia, Luxembourg, and the United Kingdom),
and southern (Greece, Portugal, Spain, Italy, Cyprus)
countries according to the levels of company- and
worker- oriented flexibility. Whereas Chung and
Tijdens (2013) looked at companies, we were interested
in how working-time and -place flexibility is seen from
the perspective of employees.
The second study that is particularly related to our
study is Gallie and Russel’s (2009) study on working
time and work–family conflict in Western Europe,
which is one of the few comparative studies in Europe
that has analysed the relationships between work–family
conflict and working time. As expected, the research
found that working time and working conditions have a
strong influence on the level of work–family conflict,
particularly work pressure, which had the most negative
role. In addition to looking at the antecedents of the
conflict, the study looked at differences between em-
ployees in various countries. The study expected that
employees in countries where the production system is
best described as liberal would exhibit the highest con-
flict, and, consequently, more coordinated production
systems (such as in the Nordic countries) would be asso-
ciated with lower conflict. Based on the analysis, the re-
search found that Nordic countries were distinct
compared with other countries in the analysis, particu-
larly among male workers. Male workers in Northern
Europe report reduced work–family conflict. The au-
thors suggest that this result is because of shorter work-
ing hours and greater flexibility of hours. For female
employees, the same distinct pattern does not emerge.
The researchers argue that the origins of work-life con-
flict in the workplace may partly explain this disparity,
in other words, care and parental policies facilitate high
employment among women, but women’s employment
is associated with longer working hours and higher lev-
els of work pressure. In Britain and the Netherlands, on
the other hand, family pressures are lessened because of
the fact that many mothers work part-time.
Following the production regime approach that em-
phasizes differences in institutional settings defining em-
ployers’ strategies in how they use labour, we expect
that countries cluster in terms of spatial and temporal
flexibility and that employees’ perceptions of work-life
balance vary between clusters. At the same time, we are
seeking for the best possible combinations of flexibility
practices for work–family balance.
Temporal and Spatial Flexibility and
Work-Life Balance
Temporal and spatial flexibility form a complex rela-
tionship with work-life balance; flexible does not equal
family-friendly. Firstly it should be asked: flexibility for
whom? Some flexible work arrangements are driven pri-
marily by employers’ interests in promoting efficient use
of human labour. Other arrangements may be launched
through employees’ interest to enhance better balance
between work and other life spheres (Fleetwood, 2007).
In practice, it is difficult to define exactly in which cat-
egory arrangement counts. However, the nature of the
flexible arrangement reveals who the primary benefi-
ciary is, although theoretically, and sometimes in prac-
tice, both the individual worker and the employer can
benefit. Unsocial work hours are commonly used to
make the most of capital investments (process industry),
or to meet the various times of customer demand (ser-
vice sector); whereas, high work-time autonomy repre-
sents employee-friendly flexibility (see e.g. Chung,
Kerkhofs, and Ester, 2007).
Furthermore, flexible work practices, such as remote
working and individually defined work hours, which are
commonly considered as arrangements that facilitate
better work-life balance, can have unanticipated conse-
quences. Kelliher and Anderson’s (2008) study among
professional workers showed that employees tend to
trade workplace flexibility for effort. Employees re-
sponded to the ability to use flexible arrangements by
exerting additional effort in their work. Thus, high au-
tonomy in the use of working hours may be linked to
lengthening of working hours, additional hours worked
at home, and high work pressures.
While the research on work-life balance is
widespread, substantial differences occur based on
the concepts and measures (see Bianchi and Milkie,
2010; Fagan et al.,2012). Regardless of the concepts
or measures implemented, some universal trends seem
to hold. Earlier studies on the linkages between work-
ing-time dimensions show that a long working week
(Grzywacz and Marks, 2000; Crompton and Lyonette,
2006), unsocial working hours (Gallie and Russell,
2009), and high working-time tempo (Grzywacz and
Marks, 2000; McGinnity and Calvert, 2009) usually
have negative effects, and working-time autonomy has
positive effects on employees’ perceptions of the bal-
ance between work and other life spheres (Fagan et al.,
2012).
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Forms of Temporal and Spatial Flexibility
In this study, we operationalize the temporal and spatial
flexibility through four time-related dimensions and one
place-related dimension. The time-related dimensions
are the number of hours worked (duration), when (tim-
ing), the degree of time autonomy the individuals have
over their working hours (time autonomy), and work-
time intensity (tempo) (Adam, 1995; Fagan, 2001). In
addition to time-related dimensions, we analyse the
place-related dimension that exemplifies the flexibiliza-
tion of workspaces. This five-dimensional classification
of flexibility will be used for the empirical analysis.
Figure 1 illustrates the conceptual model of the study.
Duration
Although paid working hours, particularly in the more
developed countries, have declined dramatically during
the past 100 years, the length of working time remains a
topic of intense political debate (Messenger, 2011).
Several studies have reported direct and indirect, nega-
tive physical and psychological, health and well-being
impacts of long working hours (Joyce et al., 2010). Long
working hours are also increasingly discussed in the lit-
erature with regard to their impact on personal relation-
ships and home life (Moen, Kelly, and Huang, 2008;
Bianchi and Milkie, 2010) with contradictory findings.
A number of studies have shown that long working
hours are often done reluctantly, and employees perceive
the detrimental effects on their leisure time and personal
relationships as well as lower marital quality and less
time with children (Bianchi and Milkie, 2010; Warren,
2010; Chatzitheochari and Arber, 2012).
Timing
While non-standard work schedules have traditionally
been concentrated in the manufacturing sector, the ex-
pansion of operating hours in the service sector has
increased the demand for non-standard work hours
(Craig and Powell, 2011; Liu et al., 2011). Earlier
research has shown that working evenings, nights, or on
weekends is stressful for the worker and can have a
negative impact on the worker’s physical and psycho-
logical health and well-being (Costa, Sarton, and
A
˚
kerstedt, 2006). The studies have, however, shown
mixed effects of non-standard work hours on family
well-being. Some studies have reported that unsocial
work schedules are significantly related to perceived
conflict between work and family roles (Voydanoff,
2004; Beutell, 2010), with problems in functioning of
the family and in time use (Strazdins et al., 2004), paren-
tal well-being (Liu et al., 2011), and parent–child inter-
action (Wight, Raley and Bianchi, 2008; Mills and Ta¨ht,
2010). Still, some families may use non-standard hours
as a way to organize their family life (Strazdins et al.,
2004; Liu et al., 2011), but empirical evidence remains
scarce.
Tempo
Hurriedness and time pressure can be regarded as a
problem of the work environment. Several empirical
studies show that employees in the EU suffer from an
increasing intensity of work (Burchell et al., 2009).
Though a certain level of time pressure can be a natural
part of life, prolonged and severe time pressure is related
to health problems, as well as to job satisfaction, general
well-being, and leisure (Green, 2006). Time pressure can
be expected to increase employees’ negative emotions,
stress, and fatigue. These reactions may spill over into
family life, which may increase work-to-family conflict
by limiting employees’ abilities to perform family duties
(Voydanoff, 2004).
Autonomy
The literature demonstrates several concepts that em-
phasize workers’ control or agency in relation to work-
place flexibility. Autonomy includes the ability to
control one’s own time (start and end times of shifts,
breaks, days off, holidays, and total number of work
hours) and location in a way that meets individual needs
WORKING TIME FLEXIBILITY
Working time:
1) Duration
2) Timing
3) Tempo
4) Autonomy
Place of work:
1) Main place of work
2) Other locations
(past 3 months)
Research question 1:
Is it possible to
identify working time
regimes?
Research question 2:
Differences between
country clusters on
work-hour fit?
TEMPORALSPATIAL
Figure 1. Conceptual model of the study.
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and is closely aligned to the ability to achieve a satisfac-
tory work-life balance (Moen, Kelly, and Huang, 2008).
Hill et al. (2008) define a similar concept, workplace
flexibility, as the ability of workers to make choices
influencing when, where, and for how long they engage
in work-related tasks. As autonomy enables the adjust-
ment of working time to meet obligations, needs, and
activities in private life, it is expected to advance a better
work–non-work balance (Fagan et al., 2012). Based on
meta-analytic research, Byron (2005) concludes that in-
dividual schedule flexibility is negatively related to
work–family conflict. Hughes and Parkes (2007) found
that high individual work-time control buffered the
negative effect of longer hours on work–family
relations.
Spatial Flexibility
Earlier studies indicate that paid work is moving beyond
traditional places and timing of work, in other words,
the factory and office. One indicator of the phenomenon
is the research that found that only half of European
workers spent most of their working time at their em-
ployers’ premises (Parent-Thirion et al., 2007). It has
been argued that spatial flexibility and the opportunity
to work at home is central to the analysis of the work-
life balance (Felstead, Jewson, and Walters, 2002). A
hotly debated issue is the implications of spatial flexibil-
ity for satisfaction and well-being in relation to family
life and the care of children. The view that telework or
home-based work has the potential to enhance the
work-life balance is commonly based, either implicitly
or explicitly, on the idea that spatial flexibility offers
greater autonomy. The negative views emphasize that
the entry of work into private spheres may negatively af-
fect family relationships among partners and children
because of simultaneous demands to follow both work
and home roles (Maruyama and Tietze, 2012; Sullivan,
2012).
Data and Methods
Data
Empirical analyses were based on the fourth wave of the
European Working Conditions Survey collected in 2010.
In this study, we used data from 25 Member States of
the EU (EU-25, n ¼ 25,417 interviews) excluding Malta
and Cyprus (see more information on data in
Supplementary Appendix A).
For independent variables, we used measures of flexi-
bility in working times and places. Number of working
hours was measured by how many hours a person
usually works per week in his/her main job. Timing of
work was conceptualized as unsocial working hours.
This measure included questions on how many times in
a month a person worked at night, in the evening, on
Sundays, or on Saturdays. For multivariate analyses, we
combined all of these questions to a single index of un-
social work hours (a ¼ 0.69). Because of different scales,
variables were rescaled (0–1) before calculating the
index. The measure of work-time intensity (tempo) was
constructed from two 1–7 scales (‘never’ to ‘all of the
time’), which were used for the question of how often a
respondent had to work at either a high speed or to meet
tight deadlines (a ¼ 0.75). Time autonomy was meas-
ured with a statement addressing the extent to which re-
spondents had control over their working time. The
original response categories were collapsed to two cate-
gories (0 ¼ working times are set by organization or one
can choose from fixed schedules determined by organ-
ization; 1 ¼ can adapt to working hours within certain
limits or determined entirely by oneself). The flexibility
of workplace was measured with two questions about
the place of work. Respondents were asked what their
main place of work was and whether they also worked
in any other locations in the past 3 months. Response
categories were employers’ premises, clients’ premises, a
car or another vehicle, an outside site, own home, and
other place. The index for flexibility of workplace was
constructed by summing up the number of locations
where a person had worked, excluding employers’
premises.
As the dependent variable, we used a measure of
work-life balance. Work-life balance was measured by a
1–4 scale (‘not at all well’ to ‘very well’), with the ques-
tion of how well working times fit with family life or so-
cial commitments outside work. For individual-level
analyses, the variable was dichotomized (very well ver-
sus others). We introduced gender, age, level of educa-
tion, presence of children, and having a partner as
control variables in multivariate analyses. All analyses
are weighted with a supra-national weight to take into
account the differences between countries in the size of
their workforce. This ensures correct weighting of coun-
tries in each county group.
Methods
We used hierarchical cluster analysis for grouping coun-
tries according to flexibility of working time and place.
After clustering, we continued by examining the differ-
ences between country clusters in flexibility of working
time and place, and also in work-life balance. We con-
tinued the analysis using logistic regression analysis to
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account for the interconnection between independent
variables when analysing work-life balance. These ana-
lyses were conducted for the whole sample. We used lin-
ear probability models (LPMs), i.e. linear regression
with binary dependent variables to analyse the effects of
flexibility in working time and place separately for each
cluster (see more detailed description of methods in
Supplementary Appendix B).
Results
Working-Time Regimes
In Table 1, we present descriptive statistics of both inde-
pendent and dependent variables used in analyses. All
variables can be interpreted as interval-level variables,
except autonomy.
The next step in our analysis was to cluster EU-25
countries according to the measurements of flexibility in
working time and place. In the first step, country-level
averages of measurements were computed, and in the se-
cond step, hierarchical cluster analysis was used to clus-
ter countries with these country-level means as
clustering criteria. As a clustering method, we used
Ward’s method with squared Euclidean distance and
standardized variables. The dendrogram produced by
hierarchical cluster analysis is presented in Figure 2.
The dendrogram and agglomeration schedule suggest
that a five-cluster solution is adequate. In the first stage,
a group of northern and central European countries was
separated from other countries, and a group of mainly
eastern European countries (Lithuania, Slovakia, Latvia,
Poland, and Portugal) was separated from other coun-
ties. In the second stage, the first cluster was further sep-
arated into mainly northern (Finland, Sweden,
Denmark, and Netherlands) and central European coun-
tries (Austria, Germany, Belgium, Luxembourg, and
France). Furthermore, the United Kingdom, together
with Ireland, Italy, and Spain, was separated from the
second group of mainly eastern European countries
(Czech Republic, Estonia, Slovenia, Greece, and
Hungary). This clustering resembles those obtained ear-
lier on working conditions (Wallace et al., 2007) and
working-time flexibility at the company level (Chung,
Kerkhofs, and Ester, 2007), as well as widely used
Esping–Andersen typology (e.g. Parent-Thirion et al.,
2007), especially with regard to central and northern
Europe. However, there is one crucial difference be-
tween our solution and former typologies: the eastern
European countries no longer form one cluster, but are
grouped together with southern countries into two dif-
ferent clusters.
Using cluster analysis, we have identified five country
clusters or regimes according to measurements of flexi-
bility in working time and place. However, we do not
know exactly how these clusters differ from each other.
Kruskal–Wallis non-parametric analyses of variance
were used to test the significance of these differences in
country-level data, and Eta
2
to determine which dimen-
sions contributed most to the clustering of countries. In
analysis, we used five standardized indexes of dimen-
sions of flexibility in working time and place. These
cluster means are represented in Figure 3.
Figure 3 reveals clearly different patterns of working
time and place depending on the group. According to
Kruskal–Wallis, test differences between country groups
in all dimensions are statistically significant. The largest
differences between country groups are found in work-
ing-time autonomy (Eta
2
¼ 0.88) and workplace flexibil-
ity (Eta
2
¼ 0.78). The level of both working-time
autonomy and workplace flexibility is clearly highest in
northern Europe. The lowest level of autonomy is found
in the first eastern European group (Lithuania, Slovakia,
Latvia, Poland, and Portugal) and lowest workplace
flexibility in the UK-South group. Working-time inten-
sity (Eta
2
¼ 0.74) is above average in all other groups ex-
cept in the first eastern group. The level of weekly
working hours (Eta
2
¼ 0.74) is lowest in the northern
and highest in the eastern groups. Unsocial work hours
(Eta
2
¼ 0.48) are most common in the UK-South group,
and least common in the central and northern groups.
When dimensions are considered together, group-
specific profiles in flexibility emerge. The largest total
differences are found between the northern group and
the eastern groups, together with the UK-South group.
These groups differ from northern countries mainly in
their lower level of workplace flexibility and autonomy,
and higher level of unsocial work hours. In contrast,
northern and central Europe have similar profiles, but in
Table 1. Descriptive statistics of variables
Variables N Minimum Maximum Mean Standard
deviation
Duration
(work hours)
27,087 1 168 36.3 10.92
Timing
(unsocial)
27,363 0 1 0.1 0.15
Tempo 27,364 1 7 3.8 1.84
Autonomy 27,294 0 1 0.2 0.42
Workplace
flexibility
27,439 0 5 0.6 0.95
Work-life
balance
27,286 1 4 3.1 0.77
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the northern group all dimensions have higher scores
(except for working hours). The smallest total differ-
ences are found between the two eastern groups. The
main difference between these otherwise similar groups
lies in the level of working-time intensity, which is high-
est of all clusters in the group, including countries such
as the Czech Republic and Estonia.
The north appears as a group of countries where em-
ployees have a high level of autonomy in their working
hours and the possibility to work outside company
premises, but who, in addition, suffer from considerable
time-stress in work. The first eastern cluster (Lithuania,
Slovakia, Latvia, Poland, and Portugal) is quite the op-
posite. In this group, employees have only limited possi-
bilities to influence their working hours, have long work
weeks, and work during unsocial hours, but their work-
ing tempo is low. The analysis, hence, shows that high
time demands and high individual control are more
usual in the northern cluster, while low time demands
and low individual control characterize jobs in the first
eastern cluster. In contrast, high time demands and low
individual-control jobs seem to be most prevalent in the
second eastern cluster (Czech Republic, Estonia,
Slovenia, Greece, and Hungary).
Work-Life Balance and Country Regimes
Our second research aim concerned analysing the link-
ages between temporal and spatial flexibility and work-
life balance; can we notice differences between country
clusters in the level of work-life balance? On average,
that is in line with the welfare state typologies; em-
ployees in northern Europe are the most satisfied (45 per
cent well), and employees in the eastern groups are the
least satisfied (20 and 18 per cent well, respectively)
with how their working hours fit with their family or so-
cial commitments. The UK-South group scores are
close to the northern group (42 per cent well), and the
central group is somewhere between the two extremes
(34 per cent well). In other words, in northern Europe
Figure 2. Hierarchical cluster analysis for dimensions of working time and place in EU-25.
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and UK-South, employees are more than twice as satis-
fied with their work-life balance than employees in
Eastern Europe. However, there is a lot of within-cluster
variance in employees’ perception of work-life balance
(see Supplementary Appendix SB, Table SB2). This
implies that individual-level work characteristics may be
more important determinants of work-life balance.
Next, logistic regression analysis was used to test
whether these differences between country clusters in
perceptions of work-life balance remain after controlling
for individual-level background factors and measure-
ments of flexibility in working time and place. These re-
sults are presented in Table 2. The northern cluster is
selected as a reference category, where work-life balance
is expected to be the highest (e.g. Gallie and Russell,
2009). According to the results, the northern group does
differ from all other groups by a higher incidence of
work-life balance. This result stays virtually the same
after controlling for both background factors and meas-
urements of working-time and -place flexibility at the in-
dividual level. In addition, all measurements of
flexibility are connected to the experiences of work-hour
fit at the individual level. The more autonomy the em-
ployees have regarding their working hours, the more
often they feel that work hours fit well with family or
other social commitments. By contrast, the more work
hours, the more frequent the unsocial work hours or
working in multiple locations is, or the higher the work-
ing-time intensity the employees have, the less often
their work hours were deemed a good fit with family
and social factors. These results stayed essentially the
same when analyses were conducted separately for men
and women (see Supplementary Appendix C).
To gain further insight whether there are differences
between country clusters in which combinations of tem-
poral and spatial flexibility are most beneficial for good
work-life balance, we conducted additional analyses.
We used LPM to analyse how measurements of flexibil-
ity in working time and place were associated with work
hours fitting well with the other life spheres within each
of the country groups controlling for background fac-
tors. The significance of regime differences in effects was
tested with interaction terms. These results are presented
in Table 3, including measure of unique contribution
(sr
2
) of each variable on total variance. Overall, the ex-
planatory power of model was lowest in East-B group,
which is also evident from regression coefficients.
Spatial flexibility had no effect on work-hour fit in any
Figure 3. Country-level means of standardized working-time measures by country cluster.
a
a
Central: Austria, Belgium, France, Germany, and Luxembourg; East-A: Czech Republic, Estonia, Greece, Hungary, and Slovenia; Nordic: Denmark,
Finland, Netherlands, and Sweden; UK-South: Ireland, Italy, Spain, and United Kingdom; East-B: Latvia, Lithuania, Poland, Portugal, and Slovakia
8 European Sociological Review, 2015, Vol. 0, No. 0
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of the country groups. Interaction terms for work hours,
unsocial hours, and autonomy were statistically signifi-
cant, albeit their explanatory power was rather weak
(see Supplementary Appendix D). This indicates that
there are differences among country groups in how
strong these effects were.
Work hours had the strongest negative effect on
work-life balance in the UK-South and Central clusters.
The effect was clearly weaker in Nordic and Eastern
groups. Interestingly, the mean level of work hours was
one of the lowest in groups with stronger effects and
highest in Eastern groups. This suggests that working
hours might have stronger negative effect in countries
where the prevalence of long work hours is low and vice
versa. However, the Nordic group is different with low-
est mean work hours and weak negative effect on work-
hour fit. Unsocial work hours had strongest negative ef-
fect in Nordic and Central groups and weakest in East-B
group. As with work hours, it seems that the effect is
stronger in groups where unsocial hours are least preva-
lent. Working-time autonomy had strongest positive ef-
fect in UK-South and East-B groups and weakest in
Nordic and East-A groups. However, these differences
do not seem to be related to the prevalence of time au-
tonomy in country groups.
Discussion
With the coming of the post-industrial work-time re-
gime, the flexibility of work organizations and paid
work has been discussed extensively within Europe,
and is seen as a prerequisite of an organization’s ef-
forts to adapt to the fluctuation of global markets
and to economic trends in general. Also, policy dis-
cussion and research on work-life balance consider
the flexibility of work as a requirement for workers
to organize their daily lives to successfully meet the
demands of both work and family. Yet, as our study
discusses, the concept of flexibility refers to various
aspects of work, and does not hold only positive con-
notations for the individuals and their families. This
is particularly true when various indicators of flexibil-
ity are analysed simultaneously, which was the overall
focus of our study.
This study had two objectives: first, to make an em-
pirical exploration of the spatial and temporal flexibility
across European countries by asking if countries are
clustered based on these indicators of flexibility. In con-
trast to many previous explorations on the topic, our
starting point was empirical; this study explored the ac-
tual state of working conditions with regard to temporal
and spatial flexibility across Europe. Furthermore, un-
like much of the previous work, our study looked at
various working-time dimensions at the same time. This
analytical strategy captures a more comprehensive
understanding of working time. Secondly, our study
examined how work-life balance varied between flexi-
bility clusters, which gives insight into the daily life of
the employees and particularly contributes to the discus-
sion on the impact that flexibility has on work-life
balance.
The empirical findings of our study are clear. First,
countries are clustered based on temporal and spatial
Table 2. The effect of working-time and -place profiles on
good work-hour fit with family and other social commit-
ments (N ¼ 25 115)
a
Model 1 Model 2 Model 3
Cluster
b
Nordic 1 1 1
Central 0.69*** 0.70*** 0.71***
UK-South 0.70*** 0.72*** 0.77***
East-A 0.37*** 0.37*** 0.46***
East-B 0.27*** 0.27*** 0.29***
Gender
Male 1 1
Female 1.32*** 1.04
Age (years)
<35 1 1
35–49 0.94 0.92*
>50 1.14** 1.02
Spouse
No spouse 1 1
Spouse not working 0.93 0.93
Spouse working 0.88*** 0.89**
Children aged <7 years
No 1 1
Yes 0.84*** 0.81***
Education
Primary 1 1
Secondary 1.11 1.11
Tertiary 1.22* 1.14
Work hours 0.97***
Unsocial work hours 0.09***
Autonomy 1.37***
Work-time intensity 0.88***
Workplace flexibility 0.93***
Nagelkerke R
2
0.050 0.061 0.142
DR
2
0.011*** 0.081***
a
Coefficients are odds ratios. Values <1 indicate negative and >1 indicate
positive association.
b
Central: Austria, Belgium, France, Germany, and Luxembourg; East-A:
Czech Republic, Estonia, Greece, Hungary, and Slovenia; Nordic: Denmark,
Finland, Netherlands, and Sweden; UK-South: Ireland, Italy, Spain, and United
Kingdom; East-B: Latvia, Lithuania, Poland, Portugal, and Slovakia.
European Sociological Review, 2015, Vol. 0, No. 0 9
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flexibility. As expected, these clusters match partially
with earlier regime typologies (Gallie and Russell, 2009;
Chung and Tijdens, 2013), but contrary to earlier find-
ings, the division into southern (Mediterranean coun-
tries) and eastern (post-communist countries) regimes is
not straightforward. Mediterranean countries clustered
partly with the United Kingdom and Ireland and with
eastern regimes. Post-communist countries are mixed
and can be divided into two separate clusters. We as-
sume that this is because of the changes in the
eastern European countries that have reached the level
of southern European countries in temporal and spatial
flexibility. An alternative explanation is that the recent
economic recession has decreased employee-friendly
flexibility in some Mediterranean countries.
The second objective of the study was to analyse
how flexibility patterns are linked with work-life bal-
ance. Again, the findings are straightforward. Country
clusters show a clear effect on perceived work-life bal-
ance even after controlling for flexibility measurements
at the individual level. Thus, it seems that these country
clusters are able to gauge institutional differences that
are not directly related to work-time flexibility at the in-
dividual level. The significance of each regime for the
everyday lives of individual employees and families de-
pends also on culture and social policy, among other
factors
In particular, we find that the flexibility of working
time predicts perceived work-life balance, especially tim-
ing and duration of work, which are important. In con-
trast, spatial flexibility was not associated with the
perceptions of work-life balance. This is in line with
the existing literature (Maruyama and Tietze, 2012).
As the article has demonstrated via empirical approach,
temporal and spatial flexibility vary systematically
across countries. We suggest that in future, comparative
research on work-life balance should take into account
that there are working-time regimes that combine
employee-friendly or employer-driven work-time and
workplace arrangements in certain ways and that spe-
cific combinations may be more efficient in advancing
work-life balance.
This research raises new questions for further re-
search in terms of topics and methods. Although there is
comparative research on working time, flexibility, and
work-life balance, further studies should provide a more
detailed analysis on, for example, temporal and spatial
flexibility among different socio-economic groups of
workers in countries. Time-use surveys could be used to
explore actual daily patterns of work as well as actual
spatial flexibility of work.
Institutional and regulatory environments of the soci-
eties were beyond the scope of our study. An important
focus for future research is to look into the work-time
regimes identified and to analyse the similarities and dif-
ferences of the countries based on statutory regulation,
collective agreements, and policy orientations (Anxo
and O’Reilly, 2000). Another significant approach is to
look at how cultural differences, for example, in expect-
ations and values, contribute to the relationships
between working times and work-life balance (Pfau-
Effinger, 2005).
To summarize, this analysis has shown that the com-
parison of different dimensions of working time is
Table 3. The effects
a
of working time and place on good work-life balance by country cluster
b
(LPMs)
c
Nordic Central UK-South East-A East-B
Work hours B 0.004*** 0.008*** 0.008*** 0.004*** 0.003***
sr
2
0.008 0.025 0.032 0.007 0.005
Unsocial work hours B 0.554*** 0.399*** 0.325*** 0.353*** 0.199***
sr
2
0.021 0.013 0.012 0.015 0.005
Autonomy B 0.038 0.093*** 0.114*** 0.044 0.132***
sr
2
0.001 0.007 0.008 0.001 0.010
Work-time intensity B 0.023*** 0.028*** 0.024*** 0.019*** 0.015***
sr
2
0.006 0.012 0.009 0.007 0.004
Workplace flexibility B 0.009 0006 0.002 0.017 0.010
sr
2
<0.001 <0.001 <0.001 0.001 <0.001
R
2
0.058 0.091 0. 093 0.063 0.043
N 2 493 9 940 9 392 1 766 3 038
a
Coefficients are unstandardized regression coefficients (B) and squared semipartial correlations (sr
2
).
b
Central: Austria, Belgium, France, Germany, and Luxembourg; East-A: Czech Republic, Estonia, Greece, Hungary, and Slovenia; Nordic: Denmark, Finland,
Netherlands, and Sweden; UK-South: Ireland, Italy, Spain, and United Kingdom; East-B: Latvia, Lithuania, Poland, Portugal, and Slovakia.
c
Controlling for gender, age group, education, and presence of spouse and children <7 years of age.
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necessary, as they have different impacts on work-life
balance. Further studies should, to the extent possible,
include various indicators of flexibility in the analysis. It
is important to see that flexibility of working time
and place includes many dimensions, and that work-life
balance is enabled by a certain combination of working-
time and -place flexibility. Therefore, in the effort to
understand associations of spatial and temporal flexibil-
ity of work and personal life, it is essential to grasp a
holistic image of flexibility. The study does not suggest
abandoning the existing welfare or production regimes
and typologies, but it shows that employee-based ana-
lysis gives new information that supplements the exist-
ing typologies.
Funding
This work was funded by the Finnish Work Environment Fund
and the Academy of Finland, grant number 277376.
Supplementary Data
Supplementary data are available at ESR online.
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