The Effect of Employment Status on Life
Satisfaction in Europe
Mehmet Fatih Aysan and Ummugulsum Aysan
Abstract There has been a growing interest in the concept of happiness in eco-
nomics, psychology, and sociology. The effect of employment status on life
satisfaction has been of particular interest in the empirical research of economics.
A substantial body of literature shows that unemployment is associated with lower
levels of happiness conceptualized as life satisfaction. This paper investigates life
satisfaction levels in three dimensions of life—social and demographic character-
istics, social inequality, and employment—using the third wave of the European
Quality of Life Survey (EQLS) conducted in 2011. Multiple regression results are
consistent with that of previous literature. Even when the ﬁnancial situation and
other individual characteristics are held constant, unemployment reduces people’s
life satisfaction. The ﬁnal model shows that the impact of social exclusion, depri-
vation, and ﬁnancial differences on life satisfaction proves to be higher than the
impacts of education level, marital status, age, and employment status. Hence,
welfare state policies affecting social inequalities and labor market have signiﬁcant
effects on life satisfaction.
Keywords Life satisfaction • Happiness • Employment • Social policy • Europe
Over the centuries thinkers have reﬂected on happiness and a good life. Neverthe-
less, due to lack of consensus on the deﬁnition of happiness and data sets, thinkers
have not been able to check their assumption about the determinants of happiness
and good life. Therefore, thinkers’understanding of what deﬁnes life satisfaction
has remained theoretical. Empirical studies introduced by social scientists,
M.F. Aysan (*)
Department of Sociology, Istanbul Sehir University, Istanbul, Turkey
Department of Social Services, Istanbul University, Istanbul, Turkey
©Springer International Publishing AG 2017
M.H. Bilgin et al. (eds.), Empirical Studies on Economics of Innovation, Public
Economics and Management, Eurasian Studies in Business and Economics 6,
particularly psychologists and economists, have brought innovative approaches to
comprehend the factors which affect life satisfaction, especially since the 1990s
(Veenhoven 1991; Easterlin 1995,2001; Christopher 1999).
The concept life satisfaction frequently denotes chances and capabilities for a
good life, such as high education level, health status, income level, leisure, social
relationship, and employment status. Efforts to create a better society after World
War II started with attacking the long term social problems: namely unemployment,
poverty, social security, and literacy Galbraith (1998) asserts three large goals
important for the life satisfaction of individuals: government investment in public
education, the poverty mitigation, and the growth of the new occupations that
consists of white collar workers such as doctors, engineers, and teachers. In order
to evaluate the welfare state programs and social development, new concepts were
introduced, such as happiness, wellbeing, and quality of life since the 1950s. This
new era of knowledge started to attract more researchers in the coming decades.
Hence life satisfaction became a chief topic in this new part of study. Another
central research area extensively studied for decades is the cause and consequences
of employment status (Amine and Dos Santos 2013; Constant and Zimmermann
2014; van Stel et al. 2014; Charfeddine and Mrabet 2015; Mitra and Jha 2015).
In this study, various factors affecting life satisfaction will be analyzed. A vital
determinant among these factors is the welfare state that mitigates inequalities and
unemployment. The research questions, hence, are what set of the country charac-
teristics best predict life satisfaction levels among European individuals? How do
social policies affect life satisfaction across countries? How does the life satisfac-
tion level change in accordance with the unemployment status?
This paper will ﬁrst start with a brief discussion of the life satisfaction and related
concepts will be brieﬂy discussed. Second, the relationship between employment
and life satisfaction will be analyzed. Third, data and method will be given. Fourth,
multiple regression results will be analyzed. In light of the ﬁndings, the importance
of social policies mitigating social inequality and deprivation will be discussed.
2 Life Satisfaction and Employment
The term life satisfaction denotes quality of life, well-being, and enjoyment.
Quality of life mostly denotes chances for a good life, such as having a good
education and career, working in a prestigious job. According to Sen (2001), an
individual’s capability to live a good life is determined in terms of the set of
valuable beings and doings such as being in good health or having good relation-
ships with others. Sen’s approach was one of the ﬁrst challenges to narrowly
economic thinking in explaining human development, happiness, and quality of
life with economic factors. Since the early 1990s, his approach has been employed
widely in the context of human development by the United Nations Development
Programme (UNDP). He claims that poverty must be understood as deprivation in
the capability to live a good life, and development can be understood as capability
expansion (Sen 2001).
336 M.F. Aysan and U. Aysan
In the literature, life satisfaction and happiness have been generally used inter-
changeably. In sociology, subjective indicators (life satisfaction and happiness) are
used to supplement traditional objective indicators (income, health, and education).
Life satisfaction and happiness have been utilized as a chief subjective indicator of
social performance since the 1970s (Andrews and Withey 1976). In addition to
sociologists, economists started to analyze the subjective indicators especially since
the 2000s (e.g., Frey and Stutzer 2010). While some are interested in latent vari-
ables such as empathy, self-worth, goal autonomy, discrimination and etc. (e.g.,
Anand et al. 2011), others focused on concrete factors such as employment status,
income, education, and health status (Diener and Suh 1997; Di Tella et al. 2001;
Frey and Stutzer 2010). The contemporary literature has shown that the effects of
personal characteristics and economic variables on the level of happiness are quite
strong. Among these studies, the effects of employment status on life satisfaction
have been heavily discussed (Veenhoven 2015).
Frey and Stutzer (2010) argue that being unemployed has a strong negative
impact on life satisfaction and happiness when other factors are controlled. Among
the active population, one of the most detrimental experiences is the lack of
employment opportunity. Many studies have proved the devastating economic
and psychological impacts of unemployment on one’s life satisfaction (Kossek
and Ozeki 1998; Lucas et al. 2004; Anand et al. 2011).
Di Tella et al. (2001) study how the unemployment rate, the inﬂation rate, and
the unemployment inﬂuence the level of happiness based on Euro-Barometer
Survey Series. They claim that when other factors are controlled for, unemployed
people are less happy than employed people. Clark and Oswald (1994) use the UK
micro data, and found that unemployment signiﬁcantly reduces people’s happiness.
Pittau et al. (2010) examined the role of economic factors on life satisfaction in the
regional level. They found that income has a strong effect in poor regions than in
rich regions. After having controlled individual characteristics and interaction
effects, regional differences in life satisfaction are signiﬁcant, conﬁrming that
regional dimension is very important in one’s life satisfaction. More importantly,
even after having controlled income variable, being unemployed is negatively
associated with life satisfaction (Pittau et al. 2010).
3 Data and Method
In the literature, the effects of unemployment on life satisfaction or happiness have
been used as a dependent variable. In this article, the effects of social and economic
factors on satisfaction are examined in three dimensions of life—social and demo-
graphic characteristics, social inequality, and employment—using the European
Quality of Life Survey (EQLS). These three domains are among the most central
determinants affecting one’s life satisfaction. The EQLS records many aspects of
the quality of life in Europe between 2011 and 2012. It includes social, economic,
and environmental determinants along with well-being and the quality of European
The Effect of Employment Status on Life Satisfaction in Europe 337
societies. This survey is the third wave of quality of life surveys started in 2003.
Through these longitudinal surveys, Eurofound has developed a uniﬁed methodo-
logical approach and quality assurance system not only for the European Union but
also for other countries in the region. This survey covers 43,636 people from the
27 EU Member States plus seven candidate countries (Croatia, Iceland, Kosovo, the
Former Yugoslav Republic of Macedonia, Montenegro, Serbia, and Turkey), in
total of 34 countries.
The sample size for each country ranges from 1001 to 3055, while the seven EU
countries with the largest population, making up altogether 75% of the EU popu-
lation, had a higher sample size so as to help to advance the accuracy of estimates at
the national and European level. Survey interviews were conducted face to face in
respondents’houses. The target population were all residents of the 34 European
countries aged 18 and older (for details, see Eurofound 2013).
Respondents’answers to the dependent variable life satisfaction questions are
coded on a 10-point scale ranging from 1 for “very dissatisﬁed” to 10 for “very
satisﬁed.” The reason for using life satisfaction variable to understand the good life
or well-being of individuals is that subjective measures such as life satisfaction and
meaning of life have been heavily used and considered as reliable measures by
international studies and guidelines.
There are three sets of independent variables used in the multiple regression
analysis. The ﬁrst set of variables can be considered as control variables including
age, education level, marital status, gender, place of residence, and health status.
The second set of variables consists of social inequality variables namely, self-
perceived ﬁnancial situation (Could you please evaluate your ﬁnancial situation in
comparison to most people in your country?), social exclusion index, and depriva-
tion index (number of items household cannot afford). Social exclusion can be
deﬁned as negative experience particularly about being left out or looked down
upon. It captures recognition of one’s activities, the sense of connectedness, and a
sense of barriers to participation in broader society due to one’s social position
(Eurofound 2013: 81). The third item is employment status. Employment status
may have signiﬁcant impact on life satisfaction when other individual characteris-
tics are held constant. Employment status and other categorical variables are
recoded as dummy. Four dummies (unemployed, retired, disabled student and
other, and homemakers) were created for employment status variable.
4.1 Descriptive Results
Before examining the multivariate results of life satisfaction scores, some descrip-
tive results on various variables can be insightful. Table 1shows the average life
satisfaction scores by selected variables. Overall, education level, health status
338 M.F. Aysan and U. Aysan
Table 1 Average life satisfaction scores by selected variables
Min Max Mean S.D.
18–24 1 10 7.34*** 1.98
25–34 1 10 7.17*** 2.03
35–49 1 10 6.93*** 2.19
50–64 1 10 6.88*** 2.23
65+ 1 10 7.10*** 2.28
No education 1 10 6.50*** 2.51
Primary 1 10 6.66*** 2.43
Lower secondary education 1 10 6.78*** 2.34
Upper secondary 1 10 6.95*** 2.15
Post-secondary 1 10 7.13*** 2.23
Tertiary education (ﬁrst level) 1 10 7.51*** 1.81
Tertiary education (advanced level) 1 10 7.74*** 1.61
Married or living with partner 1 10 7.22*** 2.12
Divorced 1 10 6.58*** 2.25
Widowed 1 10 6.67*** 2.37
Unmarried 1 10 7.00*** 2.08
Male 1 10 7.05*** 2.16
Female 1 10 7.02*** 2.18
Place of residence
Rural 1 10 7.00*** 2.20
Urban 1 10 7.07*** 2.14
Very low 1 10 4.74*** 2.69
Low 1 10 5.75*** 2.34
Medium 1 10 6.66*** 2.2
High 1 10 7.27*** 1.93
Very high 1 10 7.68*** 2.00
Much worse 1 10 4.86*** 2.61
Somewhat worse 1 10 6.04*** 2.32
Neither worse nor better 1 10 7.15*** 1.98
Somewhat better 1 10 7.77*** 1.75
Much better 1 10 8.20*** 1.80
Employed 1 10 7.27*** 1.95
Unemployed 1 10 5.87*** 2.50
Retired 1 10 7.02*** 2.27
Homemaker 1 10 6.89*** 2.34
Disabled, student, and other 1 10 7.04*** 2.17
Note: *p <0.5, **p <0.01, ***p <0.001 (t-test and ANOVA)
The Effect of Employment Status on Life Satisfaction in Europe 339
level, and ﬁnancial situation increases or become better, the life satisfaction also
All age groups reported a higher life satisfaction compared to the 50–64 age
group, reinforcing the U-shaped relationship between age and happiness
(Blanchﬂower and Oswald 2007). Life satisfaction scores of young people aged
18–24 is 7.34 on average, while it is 7.17 for 25–34 age group, 6.93 for 35–49 age
group, and 6.88 for 50–64 age group. Life satisfaction level is higher for the old
people aged 65 and over with 7.1 compared to adults. Education is one of the most
important determinants of life satisfaction. The higher the education level is, the
higher the life satisfaction score. While the score of uneducated people was 6.5, it
was 7.7 for those people who have advanced tertiary education degree in 2012.
Marital status also has an impact on life satisfaction. People who are married or
living with a partner had 7.22 life satisfaction score, while divorced or separated
had 6.58 and unmarried had 7.0 in 2012. There were small life satisfaction differ-
ences with regard to gender and place of residence. Health status is an important
factor of life satisfaction score. People who had very low health status were scored
4.74, while very healthy people scored 7.68 on their life satisfaction score on
average. Descriptive results of Table 1display that ﬁnancial situation is crucial
on life satisfaction. While people who evaluate their ﬁnancial situation worse in
comparison to most people in their country had only 4.86 life satisfaction score on
average, while it was 8.2 for those evaluated their ﬁnancial situation better in
comparison to most people. Last, employment status can give us insight about
life satisfaction. Employed people had much higher life satisfaction scores with
7.27 on average compared to unemployed people with 5.87 on average.
As a precursor step towards the ﬁnal analysis, bivariate correlation coefﬁcients
between and within (the numerical) independent and dependent variables are
presented in Table 2. All of the correlations presented here are signiﬁcant
(p <0.01). In addition, Table 2does not report any correlation coefﬁcient equal
Table 2 Correlations between dependent and independent variables
1. Age 1
2. Education 0.22*** 1
3. Health status 0.42*** 0.24*** 1
0.01*** 0.15*** 0.20*** 1
0.05*** 0.24*** 0.23*** 0.28*** 1
0.08*** 0.27*** 0.29*** 0.38*** 0.48*** 1
0.04*** 0.13*** 0.29*** 0.41*** 0.34*** 0.41*** 1
Note: *p <0.5, **p <0.01, ***p <0.001 (2-tailed)
340 M.F. Aysan and U. Aysan
to or greater than 0.7, which would otherwise suggest the presence of a problem of
Table 2conﬁrms previous research which found that more educated and wealth-
ier people are more likely to have higher life satisfaction levels compared to those
with lower education and income levels (Easterlin 2001; Diener and Diener 2009).
There is a negative correlation between life satisfaction level and the following
variables; age, social exclusion index, and deprivation index. Combating ﬁnancial
problems and social exclusion is mainly the responsibility of welfare states and can
explain the life satisfaction variances among individuals as well as societies. Last,
employment status can be considered as the third dimension explaining life satis-
faction variations. As mentioned above, employment can explain the variation in
life satisfaction, since unemployment or fear of unemployment reduces people’s
life satisfaction and happiness.
4.2 Results of Multiple Regression Analysis
In multiple regression analysis, we can estimate the net effect by controlling for any
other factors. Table 3shows three different models predicting the life satisfaction
scores in Europe. When controlling for age, gender, health status, marital status,
and education, gender has no signiﬁcant effect on life satisfaction. Therefore,
gender was not included in the models.
Variables included in the ﬁrst model explained around 10% of variation (R
¼0.097) in the distribution of life satisfaction variable. The intrusion of social
exclusion index, ﬁnancial situation, and deprivation index variables brought an
additional 19% of explanatory power (R
¼0.282). Introduction of employment
status variables in the third model also brought an extra statistically signiﬁcant
explanatory power of 0.7%. The ﬁnal model and variables in the regression explains
almost 29% (R
¼0.289) of variation in life satisfaction in Europe.
In the ﬁnal model, social exclusion index variable, with a standardized Beta of
0.26 (p <0.001), emerged as the strongest predictor of life satisfaction contrary to
the assumption presented above. As social exclusion score increases, life satisfac-
tion decreases signiﬁcantly and substantially. One level increase in social exclusion
index is associated with 0.65-point decline in the predicted life satisfaction level.
The second strongest predictor of life satisfaction is the deprivation index variable
(Beta ¼0.20), which also negatively affects life satisfaction. One level increase
deprivation index is associated with 0.24-point decline in the predicted life satis-
faction level. The third strongest predictor is health status with standardized Beta
coefﬁcient of 0.17. Parallel with the previous literature, health status appeared to be
a strong predictor of life satisfaction. In line with the descriptive results, we can see
that health status has an important effect on life satisfaction. One-unit increase in
health status may lead to 0.37-point increase in life satisfaction scores on average.
Respondents’perception of the ﬁnancial situation in their country also exerts a
stronger effect (Beta ¼0.13). The higher the perception of ﬁnancial situation, the
The Effect of Employment Status on Life Satisfaction in Europe 341
higher the life satisfaction is. Those people who evaluate their ﬁnancial situation
better in comparison to most people in their country have higher life satisfaction
scores. One-unit increase in ﬁnancial situation score leads to 0.32-point increase on
average in life satisfaction scores after controlling for the other variables in the
Even though employment status variables can only explain 0.7% of variance in
the life satisfaction level, employment status is still important to understand life
satisfaction levels in Europe. Employed people are predicted to have, on average,
0.36-point more life satisfaction scores than the unemployed. Nevertheless, con-
trolling for all other variables, the retirees are predicted to have 0.36-point more and
homemakers are predicted to have 0.24-point more life satisfaction scores com-
pared to employed people.
In the ﬁnal model, signiﬁcant coefﬁcient values show interesting results
explaining the effect of various determinants on life satisfaction levels. One level
increase in education degree leads to an average of 0.03 decline in life satisfaction
scores. People who are married or living with a partner are predicted to have, on
average, 0.24-point more life satisfaction scores than divorced or separated people,
0.20-point more life satisfaction scores than widowed, and 0.19-point more life
satisfaction scores than unmarried people.
Table 3 Linear regressions of life satisfaction levels in 34 European countries in 2011
Model 1 Model 2 Model 3
Coeff. Beta Coeff. Beta Coeff. Beta
Constant 3.59*** 0.08 6.65*** 0.09 6.65*** 0.09
Age 0.17*** 0.10 0.06*** 0.03 0.01 0.01
Education 0.13*** 0.08 0.05*** 0.03 0.03*** 0.02
Marital status (married ref.)
Divorced 0.52*** 0.07 0.27*** 0.04 0.24*** 0.03
Widowed 0.19*** 0.03 0.10*** 0.01 0.20*** 0.03
Single 0.21*** 0.04 0.15*** 0.03 0.19*** 0.04
Health status 0.67*** 0.30 0.35*** 0.16 0.37*** 0.17
Rural (urban ref.) 0.01 0.00 0.00 0.00 0.01 0.00
Social exclusion index 0.67*** 0.26 0.65*** 0.26
Financial situation 0.34*** 0.14 0.32*** 0.13
Deprivation index 0.24*** 0.21 0.24*** 0.20
Unemployed 0.36*** 0.03
Retired 0.18*** 0.07
Homemaker 0.36*** 0.03
Disabled, student and
R square 0.097*** 0.282*** 0.289***
R square change 0.185*** 0.007***
Note: *p <0.5, **p <0.01, ***p <0.001
342 M.F. Aysan and U. Aysan
Even though age appeared to be a statistically signiﬁcant predictor of life
satisfaction in the ﬁrst and second models, its impact was explained out in the
ﬁnal model which added employment status variables. That is to say, age does not
have a signiﬁcant effect on life satisfaction when employment status is taken into
consideration. Place of residence (rural versus urban) does not yield any signiﬁcant
explanatory power in any of the three models. Everything else being equal, indi-
viduals living in rural and urban areas have similar levels of life satisfaction. The
ﬁnal model shows that the impact of social exclusion, deprivation, and ﬁnancial
differences on life satisfaction proves to be higher than the impacts of age, educa-
tion level, marital status, and employment status. Hence, social policies affecting
social inequality across countries matter. In the next part, the impact of welfare state
policies on life satisfaction will be discussed.
5 Life Satisfaction Levels Across Welfare States
The extent that unemployment causes unhappiness depends on individual, social,
and institutional circumstances. According to some researchers, some factors which
are affected by positional differences of individuals such as employment, educa-
tion, and income have little relationship with life satisfaction in European societies
(Veenhoven 2015). Poverty reduction or social inclusion is mainly the responsibil-
ity of national governments. Large social welfare policy differences among these
countries are apparent. The impact of welfare states and their various social policies
on well-being of citizens are crucial. As Di Tella et al. (2003) claim, countries with
more generous beneﬁt systems are happier than those countries, which have
rudimentary and remnant social policies.
The importance of social policies and institutional differences on life satisfaction
necessitates considering welfare state variations across Europe. Esping-Andersen
(1990) constructed the threefold welfare state regime classiﬁcation (namely the
Liberal, the Social Democratic, and the Corporatist) to explain cross-national
variations inﬂuenced by the role of the state, the market, and the family in the
management of social risks. Later, some researchers included Southern European,
Eastern European, Antipodean, East Asian, and Latin American welfare states to
the welfare state typology.
The Liberal welfare regime (such as in Ireland and the UK) is well-known
through the power of the market in the management of social risks, modest public
transfers, and means-tested social assistance. The Social Democratic welfare
regime (such as Denmark, Finland, and Sweden) emphasizes the role of the state
with de-commodiﬁcation and de-familization policies for its citizens’social well-
being, rather than the market or the family. This welfare regime promotes a high
social equality where all people are incorporated under universal social provisions.
The Continental European welfare regime (such as in Austria, France, and Ger-
many) has a conservative welfare tradition heavily inﬂuenced by the institutional-
ization of rights attached to social class rather than social citizenship. The Southern
The Effect of Employment Status on Life Satisfaction in Europe 343
European welfare regime (such as in Greece, Italy, Spain, and Turkey) is based on
strong familialism, a residual form of public support and social assistance, patron-
age, and clientelism. The Eastern European welfare states (such as in Bulgaria,
Hungary, and Romania) mostly have the social welfare contract based on commu-
nist system. It consists highly subsidized prices on food and housing, guaranteed
employment, and universal health care and education. These welfare states, how-
ever, have experienced a rapid privatization and removal of already enjoyed social
All of these welfare states, which evolved through different historical and
institutional paths, have very diverse labor market policies and life satisfaction
levels. In both the 2003 and 2011 EQLS, Europeans gave quite a positive life
satisfaction score with about 7.0 on average. In 2011, life satisfaction average of
selected European countries remained stable, with a slight decrease to 7.0. Table 4
shows the changes in life satisfaction scores for each of the 34 countries in Europe.
Some countries which had lower life satisfaction scores in 2003 increased their
average life satisfaction scores, while the average life satisfaction score declined in
some countries, particularly in Continental Europe and Southern Europe, during
this period. Bulgaria, Lithuania, and Turkey experienced the highest life satisfac-
tion increase, while average life satisfaction declined in Greece, Ireland and
Germany. Nevertheless, Social Democratic countries (Denmark, Finland, Iceland,
and Sweden) which have strong welfare provisions get the highest life satisfaction
Table 4 Life satisfaction across Europe in 2003 and 2011
2003 2011 2003 2011
Austria 7.8 7.8 Lithuania 5.4 6.7
Belgium 7.5 7.4 Luxembourg 7.7 7.8
Bulgaria 4.5 5.5 Macedonia – 6.7
Croatia – 6.8 Malta 7.3 7.2
Cyprus 7.2 7.2 Montenegro – 6.9
Czech Republic 6.6 6.4 Netherlands 7.5 7.7
Denmark 8.5 8.4 Poland 6.2 7.1
Estonia 5.9 6.3 Portugal 6.0 6.8
Finland 8.1 8.1 Romania 6.1 6.7
France 6.9 7.2 Serbia – 6.3
Germany 7.4 7.2 Slovakia 5.7 6.4
Greece 6.7 6.2 Slovenia 7.0 7.0
Hungary 5.9 5.8 Spain 7.5 7.5
Iceland – 8.3 Sweden 7.8 8.0
Ireland 7.7 7.4 Turkey 5.6 6.6
Italy 7.2 6.9 UK 7.4 7.3
Kosovo – 6.2 EU 25/EU 34 7.1 7.0
Latvia 5.6 6.2
Source: Eurofound (2013)
344 M.F. Aysan and U. Aysan
scores in Europe, while many Eastern and Southern European welfare states have
lower life satisfaction scores.
There are also signiﬁcant variations in terms of employment and active labour
market policies across Europe. Active labor policies are the state regulations to
improve the access of unemployed people to the labor market and the functioning of
the labor market in general (Powell and Barrientos 2004). According to Gallie and
Paugam (2000), welfare regimes can be classiﬁed on the basis of criteria of
coverage, level of compensation and expenditure on active labor market policies.
The authors claim that expenditure on active labor market policies can be barely
seen in the Southern European and the Liberal countries, while these policies are
very widespread in the Social Democratic welfare states, particularly in Denmark
and Sweden. Having universal welfare provisions, which mitigates social exclusion
and inequality, as well as strong active labor market policies, Social Democratic
countries prove that providing new employment opportunities and direct welfare
state supports hand in hand.
This paper empirically analyzes what determines the life satisfaction level with
particular focus on employment status, using the individual data obtained from the
third wave of the EQLS. Multiple regression results are consistent with that of
previous literature. Even with the same ﬁnancial situation, those who are currently
employed are more satisﬁed in their life than those who are unemployed. Never-
theless, employment status can only explain 0.7% of the variance in the life
satisfaction level. The main ﬁnding from the empirical analysis is that while
unemployment reduces people’s life satisfaction, the most important determinant
of life satisfaction is social inequality (social exclusion, and deprivation, and
ﬁnancial situation) along with health status. Social Democratic welfare states,
which have strong and universal welfare provisions and good active labor policies,
have the highest life satisfaction scores among 34 European countries. The South-
ern European welfare states which are considered as “late comer” welfare states due
to their rudimentary welfare provisions and Eastern European welfare states
experiencing the rapid liberalization process after the cold war have relatively
lower life satisfaction scores compared to rest of the Europe. In light of aforemen-
tioned variations across Europe it can be concluded that social policies do matter.
Acknowledgments This research was supported by a Marie Curie FP7 Integration Grant (Project
no: 618792) within the 7th European Union Framework Programme. The authors thank to editors,
anonymous referees, Dr. Zubeyir Nisanci, David Albachten, and Suada Aga for their valuable
comments and contributions.
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