19th International Conference on Cultural Economics (ACEI 2016)
21st -24th June, University of Valladolid, Spain
On the determinants of happiness in Spain: The role of cultural and
leisure consumption1
Nela Filimon
Department of Economics
Universitat de Girona, Spain
E-mail: [email protected]
Malgorzata Bartosik-Purgat
Department of International Management
Poznan University of Economics and Business, Poland
E-mail: [email protected]
Abstract
This research aims to add recent evidence on the determinants of Spaniards’ perception
of happiness with a special focus on the role of cultural and leisure consumption.
Research evidence on the nature of happiness identifies to broad theoretical approaches:
one according to which happiness is a predominantly subjective phenomenon, a
manifestation of the Western modernity’s individualism, inducing some authors to
adopt a rather critical attitude towards it (“happiness as a problem”); a second approach
focuses on happiness as a complex concept, involving both a subjective and a social
(co-produced and collective) dimension. Empirical works have shown that there is a
bias in favor of the individualistic values as being more important than the social ones.
Concerning the role played by cultural consumption and leisure activities, research
evidence is less rich and this applies also to the case of Spain. The empirical analysis
uses a representative data set of 2,465 Spaniards and foreigners of both genders and age
18 years and older, all resident in Spain in 2014. The methodology applied, based on
multivariate techniques of analysis, allows distinguishing between individualistic and
social patterns of cultural consumption. Findings show that cultural and leisure
consumption is an important determinant of Spaniards’ happiness through its social (coparticipative) dimension; the less happy people seem to root their unhappiness into
more individualistic patterns of behavior when comes to cultural activities, socializing
mainly with close family and relatives. As expected, socioeconomic correlates play an
important role too, with gender and age being particularly significant.
Key words: cultural consumption patterns; individualistic vs. collective happiness;
multivariate analysis; leisure
1
Preliminary results, please do not quote. 1 1. Introduction
Being happy is a key feature of life in most societies today (Cieslik 2015:421). Research
evidence on the nature of happiness identifies to broad streams: a first one, according to
which happiness is, predominantly, a subjective phenomenon, a manifestation of the
individualism that characterizes Western modernity and which induced some authors
(see i.e., Furedi 2004; Ahmed 2010, etc.) to adopt a rather critical attitude (“happiness
as a problem”); a second stream of research presents happiness as a complex concept,
involving both subjective and social (co-produced and collective) dimensions (Abbot
and Wallace 2012; Cieslik 2015; etc.). Overall, existing evidence indicates that there is
a bias towards individualistic values in the happiness research. Concerning the research
on the determinants of happiness, many researches supply supporting evidence on the
important role of socioeconomic demographics, some with a direct and indirect impact
–age, gender, education, income, social class, and occupational and personal status–
(Argyle 1999; Inglehart 1990; Requena 1995; Cuñado and Perez de Gracia 2011; etc.).
Other authors paid special attention to the “non-work” satisfaction (leisure, religion,
social relationships; life events) though without clear-cut results. Concerning the role
played by cultural and leisure activities and their impact on happiness, research
evidence is less rich and this applies also to the case of Spain.
Therefore, this research aims to analyze the impact of socialization patterns
derived from cultural and leisure consumption on individuals’ perception of happiness,
in order to add some more recent evidence on its social dimension, with a special focus
on Spain. In this respect, the findings returned by the latent class analysis identified
three probabilistic patterns of individuals’ happiness (very happy, pretty happy, not too
happy) and supplied evidence in favor of a close relationship between the degree of selfperceived happiness and the individualistic or co-participative nature of the engagement
in various cultural and leisure activities. Thus, on average, a higher level of socialization
in cultural/leisure activities is more likely to be associated with a higher level of
happiness.
The paper unfolds as follows: the second section is dedicated to the a brief
review of the literature on happiness and cultural consumption, in section three it is
presented the methodological approach and the data, section four discusses the results
and in section five some conclusions are provided.
2 2. On the determinants of happiness: brief literature review
Most of the existing research evidence on happiness or subjective wellbeing is dated in
the last three decades (see i.e., Jugureanu 2016 for a review of the happiness studies in
the field of social sciences). Several definitions of happiness can be found across the
literature –“feeling good” (Layard 2005), “positive emotional state” (Haybron 2008),
etc.–, highlighting the existence of cross-country cultural differences, and positioning
the research on happiness as an interdisciplinary field. Several studies have focused on
the potential measures of happiness with various findings: Andrews and Withey (1978),
distinguish among three possible components to happiness such as positive emotion, life
satisfaction and the absence of negative emotions or psychological distress; Ryff
(1989:4) mentions purpose in life and personal growth; while Lu and Argyle (1994)
focus on individuals’ engagement in leisure activities, getting on well with the loved
ones and feeling overwhelmed by the beauty of nature. Other researches (see Diener
1984, for a review) proposed happiness measures based on the distinction between the
emotional and cognitive aspects of happiness.
A substantial bulk of research provides evidence on the role played by various
social indicators such as demographic and environmental variables: many country
surveys found that they all correlate with happiness or satisfaction (subjective wellbeing) although the magnitude and direction of the causality varied across countries
(Cantril 1965; Campbell et al. 1976; Inglehart 1990; Argyle 1999; etc.); some authors
found evidence for a small increase in happiness with age; older people were more
satisfied with their past and current life but less satisfied with their future prospects
(Cantril 1965); Nimrod (2007) found evidence on the impact of age on happiness;
Inglehart (1990) worked with Eurobarometer surveys and found that the effect of age on
increasing satisfaction was greater after controlling for lower rates of marriage,
education and income for older people; gender differences applied too, with women
becoming less happier with age while men more happy (Spreitzer and Sneyder 1974,
etc.); education level was found significant too though country effects varied as well,
the effect on happiness being stronger in the lower income group countries (Campbell
1981); recent evidence on Spain (Cuñado and Pérez de Gracía 2011) identified direct
and indirect effects of education on happiness through income and labor status; in the
same fashion, social class was found indirectly significant for happiness (Argyle 1999);
concerning the economic capital (income), overall findings point to a rather small effect
on happiness (see Di Tella and MacCulloch 2006, for a review); labor status seem to
3 affect negatively the unemployed individuals while no significant difference was found
on the level of happiness of the employed women and the housewives (Inglehart 1990);
personal status returned evidence in favor of a direct relationship between ‘being
married’ and happier than otherwise (Veenhoven et al. 1994).
Concerning leisure, several authors have found that the frequency of engaging in
such activities is increasing overall life satisfaction and happiness (Lloyd and Auld
2002); some researchers worked on the impact of structured leisure activities such as
reading on happiness with positive findings (Ragheb 1993); Ateca-Amestoy et al.
(2008) define ‘leisure satisfaction’ and provide findings in favor of a positive
correlation between satisfaction with leisure and happiness (see also Nawijn and
Veenhoven 2013, for a literature review). Frey (2008) focused on the influence of
cultural attendance showing that a higher frequency is increasing individuals’ life
satisfaction. Thus, according to this author, happiness studies are adding to the political
actions, usually derived from the research on the economics of the arts, arguing in favor
of the public support of the arts as a ‘source of happiness’ (see also Frey and Stutzer
2001; Layard 2005; Stutzer and Frey 2007, etc.).
3. Methodology
3.1. Data and variables
The empirical analysis uses a representative data set of 2,465 Spanish and foreigners of
both genders and age 18 years and older, all residents in Spain in 2014, provided by the
Centre for Sociological Research (CIS). Apart from the basic socioeconomic indicators
(see Table 1 for basic statistics), the questionnaire elicited information on several items:
a) cultural and leisure activities to be performed alone or in group –with friends, in
couple, and/or with relatives– (see Table 2); b) a set of indicators assessing the most
important things for a happy life (see Figure 1); c) the level of self-perceived
happiness: very happy (48.6%), pretty happy (46.4%), and not too happy (5.1%); d) and
the personal economic situation (good-28.5%; regular-49.3%; bad-22.2%); among
others.
4 Table 1. Basic Socio-demographic statistics
Variable
Gender
Male
Female
Education level
Primary School
Secondary School
Bachelor
%
48.9
51.1
24.3
24.2
14.2
17.6
7.2
12.5
Vocational training
Technical college
University and above
Status
Married
Single
Divorced/Separated
Widowed
Age
18-35 years
36-50 years
>50 years
53.7
31.3
7.1
7.9
27.4
30.4
42.2
Variable
Occupational Status
Employed
Unemployed
Retired
Student
House works
Household income (€)
%
41.1
22.9
23.6
4.4
8.0
<=1200
1201-2400
2401-4500
>4500
Citizenship
Spanish
Other EU countries
Latin America
M&A
Social class
High/middle-high
Middle class
Working class
49.5
35.2
13.5
1.8
97.7
0.4
1.8
0.1
20.3
37.4
42.3
Table 2. Cultural and leisure activities (%)
Variable (YES)
Go to bars and discos
Go to cinema and theatre
Do sports
Go to a sportive show
Go to concerts or music shows
Go to country side, excursion
Play something
Go for a walk
Watch TV
Go shopping
Reed books, magazines, comics
Listen to music
Listen to radio
Participate in an association or clubs
Do handmade works
Study
Alone
3.6
1.6
28.4
2.7
1.0
5.4
5.4
16.3
37.0
29.4
73.1
65.8
67.9
7.2
28.4
29.9
Friends
37.8
17.7
18.3
23.1
23.6
21.7
28.7
21.3
2.3
9.8
0.6
2.5
1.1
19.4
3.7
1.1
Couple
23.7
36.2
6.9
10.1
22.8
34
9.8
45.8
37.2
38.4
3.9
10.1
8.7
8.2
2.9
0.8
Relatives
3.8
6.2
2.1
7.8
5.1
18.8
15.3
12.7
20.9
15.4
2.3
4.1
3.3
2.9
5.4
1.5
NO
31.1
38.3
44.3
56.3
47.5
20.1
40.8
3.9
2.6
7.0
20.1
17.5
19
62.3
59.6
66.7
5 Figure 1.
1 Most imp
portant thin
ngs for a happy life
A baattery of beehavioral an
nd cultural indicators measured
m
th
he amount of averagee time
alloccated to be spent
s
during
g the week or at the week-end: in couple, witith children,, with
parennts, friends/neighbors, other relattives, with colleagues at work oor at schooll and,
alonee (see Figurre 2). The time
t
was sc aled in fivee levels, as follows:
f
1- nno time at all;
a 2less tthan half daay; 3- half day;
d 4- moree than half day;
d and 5- the whole dday; religio
on has
identtified a largge group off Catholics (70.1%); nonbelievers
n
s (14.8%); atheists (9.7%);
and oother religioous confessiion (3.1%).
Figure 2. Average tim
me allocateed to be speent with…
15,00
10,00
5,00
Sunday
0,00
Saturday
During the w
week
Data analyssis
3.2. D
The m
methodologgy applies an
a exploratoory latent claass (LC) mo
odel (Lazarrsfeld and Henry,
H
19688) to identiify patternss of self-peerceived haappiness which
w
can bbe explaineed by
cultuural and leissure activities, socioecconomic vaariables and
d other impoortant thing
gs for
6 happy life. The LC model, allows identifying the unobserved heterogeneity in the data
and the procedure consists of splitting the original sample into T clusters or classes, the
association between the indicators being fully explained by probabilistic class
membership. In the model expressed by the equation below, the segmenting indicators
(the three indicators of self-perceived happiness and the economic situation) are denoted
with Yi (i=1,…,4) and Y stands for the entire set of indicators. Hence, the model will
estimate the following parameters for the population analyzed: the cluster size P(t), and
indicators’ probabilities conditioned to cluster membership P(Yi=yi|t), for each cluster t.
In the LC models each observation can be assigned to only one cluster (Magidson &
Vermunt 2001).
PY  y    Pt  PY i  yi t 
T
4
t 1
i 1
The LC analysis estimates first the null model (T=1), with one latent class, and under
the assumption that the null hypothesis is rejected (the null model is not a good fit for
the data) the process continues by incrementing each time the number of latent classes
by 1 in order to find the model which is an adequate fit for the data. The analysis was
performed with LatentGold 4.5 (Vermunt & Magidson 2008). The statistics used to
select the number of latent clusters are reported in Table 3. The measures of goodness
of fit used were: the chi-squared likelihood-ratio statistic (L2), the Bayesian information
criterion (BIC), and the Akaike’s Information Criterion (AIC) (see Raftery, 1986).
According to these criteria (the lower their values, the better the model), the model with
three latent classes is the best one, as it is the one providing the most information with
the lowest number of parameters. In the same fashion, the p-value criterion (>0.05)
indicates that the model with 3 clusters would be a good fit for the data as well.
Table 3. Goodness of fit statistics for the LC model
1‐Cluster
2‐Clusters
3‐Clusters
4‐Clusters
LL
‐6420.7958
‐4986.649
‐4555.1091
‐4555.0325
BIC(LL)
12880.6026
10059.1223
9242.8557
9289.5158
AIC(LL)
12851.592
9995.298
9144.2181
9156.065
Npar
5
11
17
23
L²
3734.039
865.7457
2.6658
2.5127
df
18
12
6
0
p‐value
5.4e‐790
1.30E‐177
0.85
.
Class Error
0.00
0.00
0.00
0.0011
7 3.3. Parameters of the model and patterns of self-perceived happiness behavior
In Table 4 are shown the estimates of the parameters for the three-cluster model. The
first row indicates the proportion, P(t), of individuals classified in each cluster and the
following row indicates the probability of happiness behavior given individuals’
classification on the t cluster, that is, P(Yi=yi|t). The results (in percentages), presented
here as row profiles, indicate whether individuals classified in cluster t are over (see
bold numbers) or underrepresented among individuals with that profile of happiness
behavior. In this fashion, our model suggests two bigger clusters, one (48.5% of the
sample) of very happy individuals, who have a good/regular economic situation; a
second cluster (46.4%) of pretty happy individuals whose economic situation was rated
as regular/bad; and, a third cluster (5.1%) of not too happy individuals, whose economic
situation is rather bad.
Table 4. Probabilistic patterns of happiness
(row profiles, %)
Size
Indicators
Very happy
no
yes
Pretty happy
no
yes
Not too happy
no
yes
Economic Situation
Good
Regular
Bad
Cluster 1
48.5
Cluster 2
46.4
Cluster 3
5.1
0.0
100.0
90.07
0.0
9.93
0.0
90.44
0.0
0.0
100.0
9.56
0.0
51.06
0.0
48.94
0.0
0.0
100.0
61.23
49.87
28.69
36.62
47.14
57.57
2.15
2.99
13.73
4. Main results and discussion
4.1. Lifestyle correlates
The association between the socioeconomic covariates and the patterns of happiness
presented in Table 5, below shows that, on average, individuals’ propensity to rate
themselves as more or less happy depends on social status, gender and generational
patterns: women, young (below 35 years), married, either employed, studying or
dedicated to house works, with a high level of cultural (education), economic and social
8 capital (high/middle-high social strata) are more likely to be part of cluster 1 (very
happy); the pretty happy segment is integrated by men, above 35 years of age, and with
low economic (unemployed), social (middle/working class) and cultural capital
endowments most of them with primary school degree only and some university
graduates); on average they are also more likely to be either single or
separated/widowed. The unhappy cluster exhibits some characteristics similar to those
of cluster two except for the gender (women) and social status (working class).
Table 5. Sociodemographic characteristics
(row profiles, %)
Size
Indicators
Occupational Status
Employed
Retired
Unemployed
Student
Housework
Status
Married
Single
Widowed
Separated/Div.
Social class
High/middle-high
Middle class
Working
Age
18-35
36-50
>50
Education
Primary or less
Secondary
Bachelor
Vocational training
Technical college
University
Gender
Men
Women
House income
<=€1,200
€1,201-2,400
€2,401-4,500
>€4,500
Cluster 1
48.5
Cluster 2
46.4
Cluster 3
5.1
53.26
44.98
40.63
58.33
50.25
44.55
49.2
49.01
38.89
45.17
2.18
5.81
10.36
2.78
4.58
54.56
46.01
31.21
31.42
42.55
47.71
57.66
58.27
2.89
6.29
11.13
10.31
55.19
48.16
45.77
42.16
47.83
47.14
2.65
4.01
7.09
55.5
47.19
44.73
40.03
47.32
49.99
4.47
5.49
5.28
45.57
46.7
48.99
52.76
50.56
49.34
47.78
45.69
47.55
43.78
46.06
48.04
6.65
7.61
3.47
3.46
3.38
2.62
47.65
49.19
47.74
45.19
4.61
5.61
40.79
51.6
66.22
60.0
49.75
44.84
32.89
40.0
9.46
3.56
0.89
0.0
9 4.2. Perceived happiness and the main determinants of a happy life
For these indicators, the estimates are given in Table 6. Overall, the clusters’ profiles
show that while for the very happy cluster both individual (couple relationship, good
health, clear values in life) and family/social factors (good relationship with relatives,
good friends, feeling useful to others) are important; for the pretty happy and not so
happy cluster, it is more important the others’ opinion, guarantees to freedom, having
enough money for a comfortable life and house and being physically active; for the
unhappy ones, being in good health and studying/intellectually active are significant
determinants of happiness too.
Table 6. Probabilistic patterns of happiness indicators (row profiles, %)
Cluster 1
Size
48.5
Indicators: Most important things for a happy life (Yes)
good friends
53.84
good consideration of others
36.95
feeling useful to others
52.06
couple relationship
52.32
guarantees to freedom
44.09
good health
49.50
good relationship with fam. relatives
53.81
enough money for a comfortable life
31.72
children
44.00
professional career
44.44
studying, being intellectually active
41.37
physically active
36.83
comfortable house
50.00
religious beliefs
42.86
clear life values
49.01
Cluster 2
46.4
Cluster 3
5.1
43.35
54.34
45.45
40.69
50.30
45.12
42.97
61.37
52.00
52.77
44.81
52.62
50.00
57.14
46.07
2.80
8.71
2.49
6.99
5.60
5.38
3.22
6.91
4.01
2.78
13.82
10.55
0.00
0.00
4.91
4.3. Cultural and leisure activities
Data reduction techniques distinguishes among four factors: a) shared or group cultural
consumption (go to bars, disco; cinema and theatre; music concerts; do sports; go to
country side or play something); b) individual cultural consumption (read books; listen
to music; listen to radio); c) relaxing leisure activities (go for a walk; go shopping;
watch TV); and d) volunteering and self-esteem enhancing activities (take courses;
membership in clubs and associations; do handmade works). In line with previous
findings, the probabilistic patterns presented in Table 7 show that, on average, the very
happy people (cluster 1) exhibit a more predominant family/social dimension of
happiness, for them being important the co-participation of couple/relatives in this
activities; the collaboration of friends is significant in the case of volunteering activities;
10 the pretty happy segment, is on average, less sociable, as they prefer not to perform
most of the cultural activities presented in Table 7, and if they do it, they share them
with friends and relatives or they perform them alone (volunteering). The third cluster
(not too happy people) is on average, culturally inactive as it is overrepresented in the
no-alternative of all the indicators; if they get engaged in any cultural/leisure activity
they prefer to share it with their relatives (individual cultural consumption) or with
friends (volunteering).
Table 7. Perceived happiness and cultural/leisure activities
(row profiles, %)
Cluster 1
Cluster 2
Size
48.5
46.4
Indicators
Group entertainment and cultural consumption
with friends
43.4
51.7
couple
45.8
51.6
relatives
42.4
53.0
no
41.4
50.0
Individual cultural consumption
alone
48.6
46.5
with friends
41.7
53.5
couple
44.7
50.9
relatives
44.1
50.0
no
40.0
52.0
Relaxing leisure activities
alone
31.3
60.5
with friends
41.2
52.1
couple
44.7
51.3
relatives
40.6
54.2
no
37.5
50.0
Volunteering & self-esteem enhancing activities
alone
35.0
63.3
with friends
38.7
54.9
couple
42.8
53.0
relatives
47.1
48.6
no
46.6
47.5
Cluster 3
5.1
4.9
2.6
4.6
8.6
4.9
4.8
4.4
5.9
8.0
8.2
6.8
4.0
5.2
12.5
1.7
6.4
4.2
4.4
5.9
5. Conclusions
The findings returned by the LC model have supplied evidence in favor of a close
relationship between the degree of self-perceived happiness (very happy, pretty happy,
not too happy) and the individualistic/co-participative nature of the engagement in
various cultural and leisure activities. Thus, the higher the individuals’ socialization
degree in cultural/leisure consumption, the higher their level of happiness will be.
Furthermore, both generational (age) and gender patterns are adding evidence in favor
11 of young women (below 35 years) being more prone to belong to the very happy
cluster. The socioeconomic covariates confirm the existing evidence too, with higher
economic, cultural and social capital endowments associated to a higher level of
happiness and self-rated wellbeing. In the same fashion, personal and family values and
a good health are also associated with a higher level of happiness. All in all, findings
could supply useful information to policymakers in favor of increasing the public
support to cultural/leisure activities as they can increase people’s perception of life
satisfaction and hence, their self-rated happiness too.
References
Abbot, P. and Wallace, C. (2012). Social Quality: A Way to Measure the Quality of
Society, Social Indicators Research, 108(1):153-167.
Ahmed, S. (2010). The Promise of Happiness. Durham, Duke University Press.
Andrews, F. M., Withey, S.B. (1978). Social indicators of Well-Being. New York:
Plenum.
Argyle, M. (1999). Causes and correlates of happiness. In Kahneman, D. (Ed); Diener,
Ed. (Ed); Schwarz, N. (Ed), (1999). Well-being: The foundations of hedonic
psychology (pp. 353-373). New York, NY, US: Russell Sage Foundation.
Ateca-Amestoy, V., Serrano-del-Rosal, R., ¬ Vera-Toscano, E. (2008). The leisure
experience. Journal of Socio-Economics, 37(1):64-78.
Cieslik, M. (2015). ‘Not Smiling but Frowning’: Sociology and the Problem of
Happiness. Sociology, 49(3), 422-437.
Cuñado, J. and Pérez-Gracía, F. (2011): Does Education Affect Happiness? Evidence
for Spain. Social Indicators Research, 108(1): 185-196.
Diener, E. (1984). Subjective well-being, Psychological Bulletin 95(3):542-575.
Di Tella, R., MacCulloch, R. (2006). Some uses of happiness data in economics. The
Journal of Economic Perspectives 20 (1), 25-46.
Frey, B.S., Stutzer, S. (2001). Happiness and Economics: How the economy and
institutions affect human well-being, Princeton: Princeton University Press.
Frey, B.S. (2008). Happiness: A Revolution in Economics. Cambridge, MA and
London: MIT Press.
Furedi, F. (2004). The Therapy of Culture. Cultivating Vulnerability in an Uncertain
Age. London and New York: Routledge.
Haybron, D.M. (2008). The Pursuit of Unhappiness: The elusive Psychology of WellBeing. Oxford: Oxford University Press.
Inglehart, R. (1990). Culture Shift in Advanced Industrial Society. Princeton, N.J.:
Princeton University Press.
Jugureanu, A. (2016). A short introduction to happiness in social sciences. Belvedere
Meridionale 28(1):55-71.
Layard, R. (2005). Happiness: Lessons from a New Science. New York: Penguin Press.
Lazarsfeld, P.F.; Henry, N.W. (1968). Latent Structure Analysis, Boston: Houghton
Mifflin.
Lloyd, K, Auld, C.J. (2002). The role of leisure in determining quality of life: Issues of
12 content and measurement. Social Indicators Research, 57(1):43-71.
Lu, L., Argyle, M. (1994). Leisure satisfaction and happiness as a function of
leisure activity. Kaohsiung Journal of Medical Sciences, 10, 89-96.
Magidson, J.; Vermunt, J.K. (2001). Latent class factor and cluster models, bi-plots, and
related graphical displays. In M. Sober & M. Becker (eds.), Sociological
Methodology, 31:223-264. Boston: Blackwell Publishers.
Nawijm, J., Veenhoven, R. (2013). Happiness through Leisure, in Freire, T., ed.,
Positive Leisure Science (pp. 193-209), Springer, Netherlands
Nimrod, G. (2007). Expanding, reducing, concentrating and diffusing: post retirement
leisure behavior and life satisfaction. Leisure Sciences, 29(1): 91-111.
Raftery, A.E. 1986. Choosing models for cross-classifications. American Sociological
Review 51:145-146.
Regheb, M.G. (1993). Leisure and perceived wellness: A field investigation. Leisure
Sciences, 75(1): 13-24.
Requena, F. (1995). Friendship and subjective well-being in Spain: A cross-national
comparison with the United States, Social Indicators Research, 35(3):271-288.
Ryff, C.D. (1989). Happiness is everything, or is it? Exploration of the meaning of
Psychological well-being. Journal of Personality and Social Psychology,
57:1069-1081.
Stutzer, A., Frey, B.S. (2007). “What Happiness Research Can Tell Us about SelfControl and Utility Misprediction”, in Frey, B.S., Stutzer, S. eds., Economics
and Psychology (pp 169-95). Cambridge, MA and London: MIT Press.
Veenhoven, R. and co-workers (1994). Correlates of happiness: 7838 findings from 603
studies in 69 nations 1911-1994. RISBO, Erasmus University Rotterdam,
Netherlands.
Vermunt, J. K. and Magidson, J. (2008). LG-Syntax User's Guide: Manual for Latent
GOLD 4.5 Syntax Module, Belmont, MA: Statistical Innovations Inc.
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