bmij (2021) 9 (2):561-578
doi: https://doi.org/10.15295/bmij.v9i2.1801
ISSN: 2148-2586
Research Article
Socio-demographic determinants of happiness in Turkey
Türkiye’de mutluluğun sosyo-demografik belirleyicileri
Selay Giray Yakut1
N. Ece Bacaksız2
Ceren Camkıran3
1 Assoc. Prof. Dr., Marmara University,
Faculty of Economics, Istanbul, Turkey,
selaygiray@marmara.edu.tr
ORCID: 0000-0003-4002-7956
2 Dr.,
Muğla, Turkey
ecebacaksiz@gmail.com
ORCID: 0000-0003-0534-6011
RA, Marmara University, Faculty of
Economics, Istanbul, Turkey,
Abstract
This study examines the relationship between the perception of happiness and socio-demographic
characteristics in Turkey using the Life Satisfaction Survey conducted by the Turkish Statistical
Institute in 2017. For this purpose, nonlinear canonical correlation analysis was performed on a dataset
of 4261 employees. Socio-demographic characteristics evaluated the variable set consisting of both the
individual's direct perception happiness and the conceptual/personal sources of happiness.
Consistent with the literature, it was seen that socio-demographic variables impacted the levels of
happiness in Turkey. The main findings show that marital status has the highest effect on perceived
happiness, and married people are happier than the unmarried ones. Age has a negative, educational
background has a positive effect. For employment status, it is notable that per diem employees are
unhappy. Besides, a detailed perspective to researchers working towards increasing perceived
happiness by evaluating the identified sub-groups of working individuals living in Turkey is
provided.
3
ceren.camkiran@marmara.edu.tr
Keywords: Happiness, Determinants of Happiness, Nonlinear Canonical Correlation, Optimal
Scaling
Jel Codes: I31, C40, C30
ORCID: 0000-0001-8675-5890
Corresponding Author:
Ceren Camkıran,
Marmara University, Istanbul, Turkey
ceren.camkiran@marmara.edu.tr
Submitted: 21/03/2021
Revised: 15/05/2021
Accepted: 30/05/2021
Online Published: 25/06/2021
Öz
Bu çalışma Türkiye'de mutluluk algısı ile sosyo-demografik özellikler arasındaki ilişkiyi Türkiye
İstatistik Kurumu tarafından 2017 yılında gerçekleştirilen Yaşam Memnuniyeti Araştırması verileri
kullanılarak incelemektedir. Bu amaçla 4261 çalışandan oluşan veri setine doğrusal olmayan kanonik
korelasyon analizi uygulanmıştır. Hem bireyin doğrudan mutluluk algısını hem de kavramsal /
kişisel mutluluk kaynaklarını içeren değişken seti sosyo-demografik özelliklere göre
değerlendirilmiştir. Literatürle uyumlu olarak sosyo-demografik değişkenlerin Türkiye'deki
mutluluk düzeylerini etkilediği görülmüştür. Temel bulgular, medeni durumun algılanan mutluluk
üzerinde en yüksek etkiye sahip olduğunu ve evli insanların daha mutlu olduğunu göstermektedir.
Yaşın olumsuz, eğitim geçmişinin olumlu etkisi bulunmaktadır. İstihdam durumu için, yevmiyeli
çalışanların mutsuz olması dikkat çekicidir. Ayrıca, Türkiye'deki çalışan bireylerin belirlenen alt
gruplarını değerlendirmek suretiyle algılanan mutluluğu artırmaya yönelik çalışmalar yapan
araştırmacılara detaylı bir bakış açısı sağlanmaktadır.
Anahtar Kelimeler: Mutluluk, Mutluluğun Belirleyicileri, Doğrusal Olmayan Kanonik Korelasyon,
Optimal Ölçekleme
JEL Kodları: I31, C40, C30
Citation: Giray Yakut, S. & Bacaksız, N.E.
& Camkıran, C., Socio-demographic
determinants of happiness in Turkey, bmij
(2021) 9 (2): 561-578, doi:
https://doi.org/10.15295/bmij.v9i2.1801
© 2021 The Author(s).
This article was prepared in line with research and publication ethics and scanned for plagiarism by using iThenticate.
Selay Giray Yakut & N. Ece Bacaksız & Ceren Camkıran
Introduction
Humans have an inherent tendency to avoid pain and seek pleasure. This search is one of the
fundamental rules of human nature. Even this is not the case for everyone, and happiness is the ultimate
goal for most people (Veenhoven, 1984). Having made significant contributions to the literature with
happiness studies, Ruut Veenhoven defines happiness as 'the overall appreciation of one's life-as-awhole'. In the literature, the concepts of life satisfaction and subjective well-being are also used
interchangeably with the term “happiness” (Graham, 2004).
Happiness, in other words, subjective well-being, is based on the person's feeling that they are well,
dwelling on their positive criteria and qualities, and elements that include a global assessment of all
aspects of their own life (Diener, 1984). Therefore, the concept of happiness can be defined as the “degree
to which an individual judge the overall quality of his life favourably” (Veenhoven, 1991). Besides the
different definitions of happiness, there are also different theories on happiness (Abdullah and Zulkifli,
2016). As said in the first sentence in the introduction, the Theory of Hedonism is the philosophy of
pleasure (Veenhoven, 2003). Another theory called the Theory of Desire says that having a desire for
something that can be something straightforward, like loving tea, moves you and makes you act to that
thing. This acting can be obtained in various types, like action-based, learning-based, pleasure-based
etc. (Schroeder, 2006). It can be said that the theory of desire owes its currency to the emergence of the
welfare economy. (Rodogno, 2016). Apart from that, the Authentic Happiness Theory embraces all of
the three traditional theories above. It satisfies the three theories mentioned before. The theory is
believed as the “Full Life” when Hedonism is called the “Pleasant Life”; the Desire as a “Good Life”
and Objective List as a “Meaningful Life” (Seligman and Royzman, 2003; as cited in Abdullah and
Zulkifli, 2016). There is also Objective List Theory, which can be understood either as substantive or as
both formal and substantive theories of well-being. In either case, the substantive claims these theories
make is that there is an irreducible plurality, consist of prudential goods such as pleasure and the
absence of pain, achievement, friendship and other deep personal relations, autonomy, knowledge, etc.
(Rodogno, 2016). In this respect, objective list theories look like Hedonism, unlike desire theories or
authentic happiness.
Research on the concept of happiness (or subjective well-being) also started to find an area as a
multidisciplinary subject after Easterlin (1974) brought together the concepts of economy and
happiness. The consensus was that income and happiness had a linear relationship; namely, money
would buy happiness, but the relationship between happiness and income was more puzzling
(Easterlin, 2001). Although economists associated income per capita and higher income levels with more
welfare and increased happiness (Clark and Oswald, 1996; Tella, MacCulloch and Oswalds, 2003; Ferreri-Carbonell, 2005; Scoppa and Ponzo, 2008; Senik, 2009), it was even proven that there was no
relationship between income and happiness (Hyun, Bauer and Hogan, 1993; Moghaddam, 2008). The
realization that income was limited in explaining happiness led to socio-demographic characteristics
being heavily promoted in happiness research.
At this point, some factors determine the concept of happiness, which is defined as the level of
evaluating the total quality of one's own life, includes health, family status, social relationships, status
in the labour market, working conditions, having/or not leisure time, security, ethical values etc. (Ahn,
Garcia and Jimeno, 2004). These characteristics, which are thought to lead to happiness status and may
vary from one individual to another, started to become the subject of many research studies conducted
from different perspectives and using different methods. To give an example, while some of the
literature focused on international happiness -or well-being- (Bonasia, Napolitano and Spagnolo, 2018;
Seghieri, Desantis and Tanturri, 2006; Tucker, Ozer, Lyubomirsky and Boehm, 2006; Alesina, Tela and
MacCulloch, 2003), others were designed for different groups of individuals, e.g. heads of household
(Lodhi, Rabbani, Khan, Irum and Naieni, 2020), parents (Schwarze and Winkelmann, 2005), students
(Takebayashi, Tanaka, Sugiura and Sugiura, 2018; Öztürk, Meral and Yılmaz, 2017; Tucker et al., 2006).
Also some literature studies about teenagers (Bassi, Steca, Monzani, Greco and Delle Fave, 2014), elderly
(Nieboer and Cramm, 2018; Tran and Vu, 2018), mothers (Hamplová, 2019), white-collars (Karabati,
Ensari and Fiorentino, 2019), farmers (Markussen, Fibæk, Tarp and Tuan, 2018). All these studies
utilized national or survey-specific data (Devine, Hinks and Naveed, 2019; Sironi, 2019; Vang, Hou and
Elder, 2019; Susanlı, 2018) or data sets that combine the survey data with the time dimension
(Venetoklis, 2019; Graafland and Lous, 2019; Ngoo, Tey and Tan, 2015). Additionally, probit analysis
(Devine et al., 2019; Susanlı, 2018; Mangeloja and Hirvonen, 2007; Frey and Stutzer, 2000), logit analysis
(Lodhi et al., 2020; Dumludag, Gokdemir and Giray, 2016; Arı and Yıldız, 2016; Selim, 2008), data mining
analysis (Babadağ, Uyanık and Yılmaz, 2009), regression analysis (Markussen et al., 2018; Kümbül Güler
and Emeç, 2006; Çopur, Çiçek and Pekmezci, 2015), instrumental variables (Sironi, 2019; Tran and Vu,
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2018) and other statistical analyses (Brzezinski, 2019; Öztürk et al., 2017; Amole, 2009; Gitmez and
Morçöl, 1994) have been among the most commonly used methods in happiness studies.
Amongst all the comprehensive literature on happiness and well-being, this study focuses on happiness
within the context of possible socio-demographic characteristics that may affect itself. The study aims
to investigate the socio-demographic characteristics that affect the perceived happiness of individuals
who live in Turkey, particularly the relationships with their sub-categories. For this purpose, the survey
data obtained from the Life Satisfaction Survey conducted by the Turkish Statistical Institute
(TURKSTAT) in 2017 were used.
The beginning point of the paper is whether socio-demographic characteristics and perceived happiness
are related or not, which is the research questions. So, this study examines socio demographic
characteristics that affect happiness, using the case of Turkey. What makes the study significant is the
fact that the data it uses is relatively recent, and this current data set provides insight as to which of
these characteristics make people in Turkey, which is not ranked very highly in the world happiness
index, happy. Moreover, nonlinear canonical correlation analysis is not among the most commonly used
methods in happiness studies. This method interprets the sub-categories of multiple variables together
and provides a more detailed perspective, and this study will make a substantial contribution to the
literature.
In this study, firstly, the concept of happiness, being the subject of many studies from different
disciplines, was explained within the scope of socio-demographic characteristics, also considering its
relationship with life satisfaction. In the next section, the methodology, data and empirical strategy of
the study were introduced. Then, the relationship structure of socio-demographic variables that affect
the perceived happiness of individuals was examined in detail. In order to obtain more detailed
information about each category of these variables, categorical interactions were presented in detail
with the help of nonlinear canonical correlation. Then, the information obtained by the Chi-Square Test
of Independence was supported with statistics. In the last section, remarkable findings are given, and
the results are discussed.
The effect of characteristics that constitute quality of life-on-life satisfaction and
happiness
Happiness studies attract the interest of thinkers, artists and behavioural scientists as well as political
scientists, business owners (considering life satisfaction increases productivity) and economists
(considering one objective of economic policies is to make the majority happy and the fact that economic
growth is associated with the state of happiness). Therefore, it can be said that happiness is a centre of
attention for almost all social scientists. Most of the studies on life satisfaction are categorized under
two headings: personality factors, which include genetic and innate characteristics, and environmental
factors, such as living conditions, events in life. It is suggested that there is a significant relationship
between one's life satisfaction and personality variables such as psychological resilience, assertiveness,
control etc. Besides, recent studies reveal that the interactions of the individual with the surrounding as
environmental factors affect life satisfaction (Sousa and Lyubomirsky, 2001). As a combination of these
features, one can consider that socio-demographic characteristic is an essential determinant of
happiness. The socio-demographic characteristics that apply to people who are relatively closer to
happiness and the primary sources of happiness sought by such people are a curiosity. As mentioned
above, by disregarding the studies that initially associated income with happiness, it can be seen that
some of the characteristics that affect happiness include health, family, social relationships, status in the
job market, leisure time outside of work, educational background, security, the characteristics of the
district/region/country the person lives in, and religious and ethical stance.
The fact that people from different age groups have different levels of satisfaction also causes them to
have different levels of pleasure in life. To quote the oldest person in the world, Jeanne Calment: “Every
age has its happiness and troubles”. Studies in the literature in which the relationship between age and
happiness is proven show that this relationship is U-shaped (Blanchflower and Oswald, 2004; Beja, 2018)
and has a hyperbolic form (Fischer, 2009). Another factor associated with happiness is the individual's
status in social life. The social relationships of an individual has contributed to emotional well-being
and absorb the destruction caused by negative events, leading the person towards happiness
(Quoidbach, Taquet, Desseilles, Montjoye and Gross, 2019). If the individuals conduct healthy human
relationships with their family, friends, acquaintances, i.e., their social circle, they internalize that they
have their social protection network whenever they need it (Li and Kanazawa, 2016). This feeling of
security, in turn, provides the individuals with some form of psychological support. Therefore, social
relationships such as marriage, friendship, neighbourhood and kinship are associated with happiness.
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For instance, it is thought that marriage typically provides both emotional and financial security for
both men and women (Hori and Kamo, 2018).
For this reason, the consensus is that married individuals are happier than unmarried. (Cid, Ferres and
Rossi, 2008; Park, 2009; Ngoo et al., 2015). This, of course, stems from the fact that the quality of marriage
(Saphire-Bernstein and Taylor, 2013), i.e., “a happy marriage” makes the individuals happy (Gilbert,
2010), rather than marriage itself. Besides, an unhappy marriage also triggers unhappiness (Babadağ et
al., 2009). In terms of the power bestowed upon the individual by social relationships, it is known that
people who have secure and good relationships with friends (Gitmez and Morcöl, 1994; Requena, 1995;
Demir, Ozdemir and Weitekamp, 2007; Demir and Özdemir, 2010; Acar, 2020) family-namely relatives(Lelkes, 2006; Botha and Booysen, 2014) and neighbours (Cheng and Smyth, 2015) effect happiness
positively. The literature that examines happiness also mentions its relationship with the level of
education. Typically, this relationship is directly proportional. There is no doubt that increased levels
of education would also increase income and status (Veenhoven, 1996), thus increasing happiness.
However, when a high level of education prevents the individuals from being content with what they
have, or when the individuals cannot reap the benefits of the effort they put into their education, this
relationship may become inversely proportional (Clark and Oswald, 1996). The effects of gender have
also been the subject of researches on the way of being happy. Studies that include the effect of gender
on happiness show that women are happier than men (Blanchflower and Oswald, 2004; Bozkuş, Çevik
and Üçdoğruk, 2006; Çopur et al., 2015). With this said, it can be asserted that being a woman would
decrease happiness when women are active in the labour market, and this prevents them from fulfilling
their responsibilities relating to the household (Hori and Kamo, 2018). Venetoklis (2019) conducted a
well-being study for 16 countries for 2002-2014, using the European Social Survey data set, which
includes most of the characteristics listed above. This study, which uses individual characteristics such
as age, marital status, having children, education, health, employment status, time spent with friends
to explain happiness; having a job, being a woman, being married, having good health, interaction with
friends and good financial standing were studied as factors that affect well-being. The main findings
from the study point that being employed, gender (being female), good social relationships (with
friends), marital status, health status and financial standing have a positive impact on well-being. Lodhi
et al. (2020) also looked at happiness from a broad perspective. This study was conducted to explain the
quality of life for Pakistani heads of household with their demographic characteristics, type of family,
housing status, employment status, physical and psychological condition. It was found that living in
urban settlements, being married, being free of illness, and being employed were significantly
correlated with life quality.
Similarly, for China, Ding, Salinas-Jiménez and del Mar Salinas-Jiménez (2020) have found that income
and the interaction factors that include income, factors of gender, age, health, and education also
impacted the well-being of the Chinese people. One of the studies happiness of Turkish people
comprehensively, including demographic, social and labour determinants, is Acar (2020), concluding
that women, married people, private sector employees are happier than opponents. Additionally, health
satisfaction, job satisfaction, earning satisfaction, social life satisfaction makes people happier.
It is also known that the position of individuals in the job market also affect happiness by way of
providing life satisfaction. An individual is being employed both supports the positive correlation
between income and happiness and increases individual happiness by providing them with a sense of
satisfaction due to their surroundings. Even if an unemployed person has the same level of income as
when they were employed, being unemployed can still have a negative effect on well-being of this
person (Frey and Stutzer, 2002). Frey and Stutzer (2000) proved that unemployment is the most critical
factor affecting micro and macro scale happiness for 11 European countries. Considering this subject
from a work satisfaction standpoint, Sironi (2019) examined optimal well-being metrics for 24 countries
and proved that work satisfaction affected optimal well-being. According to Lodhi et al. (2020), being
employed in Pakistan affects both physical and psychological health positively and increases the quality
of life. Another factor discussed as having a direct impact on the living standard, and thus life
satisfaction of individuals is the environment in which such individuals spend their lives, i.e., residence.
The characteristics of the place of residence are among the determinants that have been proven to affect
happiness. Tran and Vu (2018) directly examined the effect of housing satisfaction on the happiness
levels of the elderly, also taking into account the effect of both demographic characteristics and
household characteristics. In parallel with the expectations, housing satisfaction is a significant
predictor of life satisfaction. A person's having religious beliefs is also among the factors that affect life
satisfaction. In psychology, subscribing to any belief system is thought to increase an individual's
spirituality and instil a sense of security, thus affecting the degree to which they take pleasure in life.
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Devine et al. (2019) analysed the data obtained from Bangladesh to examine the role religion plays in
people's well-being and life chances. The findings from this study that relate to religion indicate that
religious identity in Bangladesh is an important determinant of happiness. Mateu, Vásquez, Zúñiga and
Ibáñez (2020) have reported that for Peruvians, the existence and importance of God contributed to
increasing levels of happiness. Once again, these findings affirm the existing literature, which suggests
that income is insufficient in explaining happiness, in that the sample of this study was made up of
answers given by heads of household who were highly impoverished.
If the empirical studies are classified with the socio-demographic characteristics listed above as
explanatory factors to create a happiness profile for the Turkish people, the most accurate categorization
will be based on data sets. In the empirical studies conducted in Turkey on well-being/happiness/life
satisfaction, the TURKSTAT Life Satisfaction survey data, in addition to regional/study-based survey
data, are most commonly used. For example, Bozkuş et al. (2006) used the data set from the TURKSTAT
Life Satisfaction survey to determine that in Turkey, for the year 2004, women were happier than men,
people between the ages of 45 to 60 were having the unhappiest time of their lives, that income, health
and living in urban settlements increased the level of happiness, while an increased level of education
was a factor that reduced happiness. Selim (2008) deals with life satisfaction and Happiness in Turkey.
She uses the dataset of the European Values Study Group and World Values Survey Association 2006
for the case of Turkey. The main findings pointed that income and health had a positive correlation with
happiness. There was a U-shaped relationship between age and happiness. Moreover, unemployment
caused unhappiness. Babadağ et al. (2009) used the TURKSTAT Life Satisfaction Survey data from 20032007. According to the findings of the analysis in which the data mining method was used; they
highlighted that there was a significant correlation between hope and happiness; and asserted that
especially the unhappiness felt from marriage triggered hopelessness and unhappiness, while higher
income levels increased hope and affected happiness in a positive manner. Bülbül and Giray (2011)
examined the perception of happiness using the data set from the TURKSTAT Life Satisfaction Survey
2008. Main findings pointed out that married women, homemakers and young women, and men who
live in the city, employed and belong to the low education and low-income group, were at the mediumhigh happiness level. It was seen that retired middle-aged individuals who had primary school
education were also at the medium-high happiness level, and the most important concept that made
them happy was health. Korkmaz, Germir, Yücel and Gürkan (2015) determined a causal relationship
between personal happiness and family happiness and source of happiness, satisfaction and hope
factors for the period of 2004-2014, where the general happiness of the family was affected to the greatest
extent by love and healthcare services. Additionally, whereas the family was affected to the lowest
extent by success and security services, individuals were made happy the most by money and
education; and the least by health and healthcare services. The most important factor in both the
happiness of individuals and the family was the happiness of women. Similarly, Arı and Yıldız (2016)
also examined the TURKSTAT Life Satisfaction survey data for 2014 using the ordered logistic
regression method. Variables such as demographic characteristics, relationship with the immediate
environment (neighbours, relatives), social life, satisfaction arising from government services
(healthcare services, educational services etc.) were used to explain happiness. Dumludag et al. (2016)
examined the effect of income comparisons on life satisfaction, which is characterized by 'collectivism'
or 'low individuality' in Turkey. They identified a positive correlation between happiness and income,
rural living and marriage based on the TURKSTAT Life Satisfaction Survey data from 2011. A U-shaped
relationship was identified between age and happiness. Another data set used in the happiness studies
conducted within Turkey is obtained from surveys completed by different groups of individuals. Çopur
et al. (2015) used the perception of the adequacy of resources to explain life satisfaction based on their
design survey data. The level of perception in terms of adequacy of resources, subjective/general
happiness and life satisfaction was found to be higher in women who are married, whose spouse was
employed, who had a high level of education, who were homeowners, who lived in the city and who
had a higher level of income, as compared to men. In a study that compared the years 2004 and 2014 in
Turkey, Servet (2017) examined the effect of economic and socio-demographic variables such as
household income, age, sex, educational background, health satisfaction, welfare and the degree to
which the household can meet their needs with their available income on the level of happiness. The
findings showed that within these ten years, the happiness level of men increased. People became
happier as they get older. Also the happiness levels of married people decreased over time. In a study
that examined the data set from the TURKSTAT life satisfaction survey about the period of 2004-2013,
Susanlı (2018) supported the assertion that people who were unemployed in Turkey were subjected to
significantly lower levels of welfare compared to the people who were employed. The study also
showed that the unemployed individual had a negative and significant impact on the employed
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individual within the household. Finally, it was proven that the effect of the labour market on “welfare”
varied according to “job expectations”.
When the studies conducted on the subjects of happiness and life satisfaction are examined, it can be
seen that these studies have a broad perspective and that these concepts are subjects of research in many
countries. In this study, Turkey, which has a medium level of happiness according to global research
studies (World Happiness Report, 2019), was considered the subject. A notable fact is that although
Turkey has seen rapid economic growth in recent years, it has been unable to go up in happiness
rankings. This study was conducted to represent the perception of happiness in Turkey, based on the
socio-demographic characteristics of the Turkish people and based on the fact that economic growth
does not necessarily increase the happiness ranking.
Methodology
The number of variables in statistical studies being more than one also brings about differences in
variables. Especially in social sciences, mixed data sets that are measured metrically and categorically
are commonly encountered. As categorical data sets do not fulfil the assumptions of classical techniques,
these are included in the analyses by performing certain nonlinear transformations. In this study, to
find categorical variables, nonlinear canonical correlation analysis (OVERALS), one of the nonlinear
multivariate analysis techniques, is used.
The characteristic features of nonlinear canonical correlation analysis were designed by De Leeuw in
1973 and were first defined by Gifi in 1981, Van Der Burg, De Leeuw and Verdegaal in 1984, and Van
Der Burg, De Leeuw and Verdegaal in 1988 (Gifi, 1989).
In nonlinear multivariate analysis techniques, optimal scaling and alternating least squares of optimal
scaling techniques are used to analyse categorical data. Nonlinear canonical correlation analysis
corresponds to a definite canonical correlation with the optimal scaling method. Nonlinear canonical
correlation analysis is a method used to reveal similarities between two or more variable sets consisting
of metric and non-metric scale levels by applying a nonlinear transformation (Golob and Recker, 2003).
Nonlinear canonical correlation analysis makes no assumptions about the distribution of variables with
different measurement levels or the linearity of correlations. No assumption is made other than that the
variables in sets have no outliers.
The operation of the analysis is defined in various ways according to the differences in the way it is
introduced, and in this study, it is described based on the Homogeneity Analysis. In the Gifi
methodology, Homogeneity Analysis is the foundation of multivariate analysis techniques with
Optimal Scaling, and the goal is to construct a map of objects and categories in low-dimensional
Euclidean space. This mapping is done for each variable category and each object by obtaining graphs
based on minimum distance (Michailidis and de Leeuw, 1998).
When Gj indicator matrix (𝑛𝑛 𝑥𝑥 𝑘𝑘𝑗𝑗 ) is a matrix that indicates to which category of the concerned variable
all objects belong, 𝑌𝑌𝑗𝑗 category quantifications matrix (𝑘𝑘𝑗𝑗 𝑥𝑥 𝑝𝑝) is a matrix that includes new scale points
assigned to the categories of the 𝑗𝑗. variable and 𝑋𝑋 object scores matrix (𝑛𝑛 𝑥𝑥 𝑝𝑝) is a matrix calculated based
on category quantifications and indicator matrices; the loss function is defined as follows:
𝑚𝑚
𝑚𝑚
𝑗𝑗=1
𝑗𝑗=1
𝜎𝜎(𝑋𝑋; 𝑌𝑌1 , … , 𝑌𝑌𝑚𝑚 ) = 𝑚𝑚−1 � 𝑆𝑆𝑆𝑆𝑆𝑆�𝑋𝑋 − 𝐺𝐺𝑗𝑗 𝑌𝑌𝑗𝑗 � = 𝑚𝑚−1 � 𝑡𝑡𝑡𝑡�𝑋𝑋 − 𝐺𝐺𝑗𝑗 𝑌𝑌𝑗𝑗 ��𝑋𝑋 − 𝐺𝐺𝑗𝑗 𝑌𝑌𝑗𝑗 �
𝑅𝑅𝑅𝑅𝑅𝑅𝑡𝑡𝑡𝑡𝑅𝑅𝑅𝑅𝑡𝑡𝑅𝑅𝑅𝑅𝑛𝑛𝑅𝑅:
𝑋𝑋’𝑋𝑋 = 𝑛𝑛𝑛𝑛,
𝑢𝑢’𝑋𝑋 = 0
The goal here is to minimize the loss function simultaneously over 𝑋𝑋 and the 𝑌𝑌𝑗𝑗 ’s.
In Nonlinear Canonical Correlation Analysis, as different from Homogeneity Analysis, the relationship
between variable sets is examined. Since each set includes multiple variables in Nonlinear Canonical
Correlation Analysis, the loss function must be individually taken into account for all variables included
in the sets. The generalized formula for the Nonlinear Canonical Correlation Analysis is expressed as
follows,
𝑀𝑀𝑅𝑅𝑛𝑛𝑅𝑅𝑚𝑚𝑅𝑅𝑧𝑧𝑅𝑅
𝐾𝐾
𝜎𝜎(𝑋𝑋, 𝑌𝑌) = � 𝑡𝑡𝑡𝑡 �𝑋𝑋 − �
𝑘𝑘=1
𝑗𝑗∈𝐽𝐽𝑘𝑘
′
𝐺𝐺𝑗𝑗 𝑌𝑌𝑗𝑗 � �𝑋𝑋 − �
𝑅𝑅𝑅𝑅𝑅𝑅𝑡𝑡𝑡𝑡𝑅𝑅𝑅𝑅𝑡𝑡𝑅𝑅𝑅𝑅𝑛𝑛𝑅𝑅:
𝑋𝑋’𝑋𝑋 = 𝑛𝑛𝑛𝑛,
𝑢𝑢’𝑋𝑋 = 0
𝐹𝐹𝑅𝑅𝑡𝑡 𝑅𝑅𝑅𝑅𝑚𝑚𝑅𝑅 𝑣𝑣𝑣𝑣𝑡𝑡𝑅𝑅𝑣𝑣𝑣𝑣𝑣𝑣𝑅𝑅𝑅𝑅:
𝑌𝑌𝑗𝑗 = 𝑦𝑦𝑗𝑗 𝑣𝑣𝑗𝑗′ 𝑣𝑣𝑅𝑅 𝐺𝐺𝑗𝑗 𝑦𝑦𝑗𝑗 ∈ 𝐶𝐶𝑗𝑗
bmij (2021) 9 (2):561-578
𝑗𝑗∈𝐽𝐽𝑘𝑘
𝐺𝐺𝑗𝑗 𝑌𝑌𝑗𝑗 �
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where 𝐽𝐽𝑘𝑘 indicates the number of variables included in the 𝑘𝑘. set and 𝐶𝐶𝑗𝑗 indicates the nominal, ordinal
or quantitative transformation set that fits the variable hj. The analysis is essentially an optimization
problem that minimizes the loss function under certain restrictions (Gifi, 1996).
Data set and variables
Life satisfaction and happiness are usually measured through surveys by conducting one interview
with the individuals. There are survey studies used in happiness research in the literature, e.g.,
European Social Survey, Chinese General Social Survey, Asia Barometer, Vietnam Aging Survey,
German Socio-Economic Panel, etc. In this study, the data acquired within the scope of the Life
Satisfaction Survey conducted by the Turkish Statistical Institute (TURKSTAT) in 2017 are used. Since
this study does not require “Ethics Committee Approval”, Ethics Committee Permission document
was not obtained. The data set of this study was obtained using the stratified two-stage systematic
set sampling method. The samples were obtained due to computer-aided face-to-face interviews with
individuals living in household addresses who are at or over 18 years of age. After sorting out missing
data, the analysis conducted was based on 4261 observations.
The main goal of the study is to examine socio-demographic characteristics that affect happiness. In this
context, the nature of the relationship between the sub-categories of socio-demographic characteristics
and happiness will be analysed, and the relative positions of associated categories will be interpreted
geometrically.
The income variable is included in addition to socio-demographic characteristics content, however as
the focus of the study was not the effect of income, the study was performed only on working
individuals to minimize variability. In addition, the variables used were included in the study according
to the information obtained from the literature review. The categories of the variables used in this study
are shown in Table 1.
Table 1. Variables and Categories
Variable
Age
Categories
A1: Y Generation (18-36)
A2: X Generation (37-56)
A3: Baby Boomer (57-71)
A4: Silent Generation (72+)
Gender
G1: Male, G2: Female
Marital status
M1: Never married, M2: Married, M3: Divorced, M4: Dead spouse
E1: Did not graduate
E2: Primary school
E3: General secondary school,
Vocational or technical secondary school, Elementary education
E4: General high school, vocational or technical high school
E5: 2 or 3-year college, 4-year college or faculty
E6: Master (5 or 6-year faculty etc.), Postgraduate degree, PhD
ES1: Wage or salary earner
ES2: Casual (seasonal or daily workers)
ES3: Employer
ES4: self-employed
ES5: Unpaid family worker
Education
Employment Status
The income per person
in the household
How happy you are when
you think of your life as a
whole
I1: Low, I2: Middle Lower, I3: Middle-Upper, I4: High
H1: Very unhappy, H2: Unhappy, H3: Medium, H4: Happy, H5: Very happy
Who makes you happiest
in life?
Who1: Myself, Who2: Children, Who3: Parents Who4: Friends,
Who5: Nephews, Who6: Grandchildren, Who7: Partner – Spouse,
Who8: Whole Family
What makes you happiest
in life?
What1: Success, What2: Work, What3: Health, What4: Love
What5: Money
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Among the variables included in Table 1, the choices of the variables aside from age, income and
educational background were used in the analyses in the form determined by TURKSTAT. Whereas
the values of the age variable were determined numerically by TURKSTAT as the actual ages of
individuals, in this study, these values were grouped into categories in order to determine the
differences between the perceived happiness of individuals who belong to specified age groups. This
categorization was made by taking into account different generations. In determining the generations,
the most widely accepted generations (Yıldız and Giray Yakut, 2019; Weber and Urick, 2017; Zemke,
Raines and Filipczak, 2013; Costanza, Badger, Fraser, Severt and Gade, 2012) were taken into account.
The following generations include children who were born in the following periods:
•
Silent Generation: between 1925 and 1945,
•
Baby Boomers: between 1946 and 1960,
•
Generation X: between 1961 and 1980,
•
Generation Y: between 1981 and 2000.
Additionally, for the income variable, the two highest groups were unified together in the study. As for
the educational background variable, since some individuals only graduated from primary school and
some graduated from elementary school (which is primary and secondary schools combined), the
choices of elementary school and secondary school were unified; and also, open education university,
vocational school and college graduates were grouped into the same category. Moreover, the data
within the scope of the study only relates to individuals who were still employed and had jobs within
the last week leading up to the study.
Frequencies related to the variables and means for appropriate variables are shown in Table 2. In the
original study, age and income are continuous. Although it is used categorically in the analysis, the
means of the original variables are given to provide general information about the sample. The age
variable was presented in the survey as an open-ended value. However, since it was made categorical
for analysis purposes, Table 2 provides information for both cases.
Table 2. Descriptive Statistics
Variable
Mean
Age
39,80
%
Variable
Mean
Income
1207,62
Age categorical
-
A1: 38,9
A2: 49,5
A3: 11
A4: 0,6
Gender
-
G1:69,6
G2: 30,4
How happy you are?
3,52
-
M1: 20,4
M2:74,7
M3:3,5
M4: 1,3
Who makes you happiest in life?
-
Marital status
Education
-
Employment Status
-
E1: 5,6
E2: 30,4
E3: 15
E4: 20,8
E5: 25,5
E6: 2,8
ES1: 65
ES2: 5,9
ES3: 6,5
ES4: 15,4
ES5: 7,3
Income categorical
-
What makes you happiest in life? -
bmij (2021) 9 (2):561-578
%
I1: 24,2
I2: 18,7
I3: 33,5
I4: 23,6
H1: 1,7
H2: 8,4
H3: 33,4
H4: 49
H5: 7,5
Who1: 3,4
Who2: 12,2
Who3: 3
Who4: 0,5
Who5: 0,3
Who6: 1
Who7: 4,4
Who8: 75,2
What1: 10,9
What2: 2,2
What3: 65,9
What4: 16,5
What5: 4,5
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According to Table 2, it can be deduced that most of the 4261 working individuals who participated in
the survey belonged to generation X, were married, with low educational background and from the
middle-income group. It can also be said that the general tendency of these individuals was to be happy.
Since the analysis only considers individuals who were still employed and had jobs within the last week
leading up to the study, it can be thought that this sample group was a good representation of Turkey.
Empirical findings
In this section of the study, findings related to the categorical relationships between the question set
consisting of “How happy are you when you think of your life as a whole”,” Who makes you happiest
in life?” and “What makes you happiest in life?” which concerns happiness and the question set
consisting of socio-demographic variables were presented. With nonlinear canonical correlation
analysis, interpretations obtained from the geometric positions of the categories were included. SPSS
package program version 21.0 was used while conducting the analyses. The information relating to the
scale types of the variables included in the analysis was shown in Table 3.
Table 3. List of Variables
Set
Age categorical
Gender
Marital Status
1
Education
Employment Status
Income
How happy are you?
2 Who makes you happiest in life?
What makes you happiest in life?
Number of Categories
4
2
4
6
5
4
5
8
5
Optimal Scaling Level
Ordinal
Single Nominal
Single Nominal
Ordinal
Single Nominal
Ordinal
Ordinal
Single Nominal
Single Nominal
When Table 3 is examined, it is seen that the scale type of all the variables in both variable sets is
categorical. The iteration history of the analysis process used in this study is shown in Table 4.
Table 4. Iteration History
0
89
Loss
1,972273
,688688
Fit
,027727
1,311312
Difference from the Previous Iteration
,000010
According to Table 4, during the analysis process, the loss function is minimized using iteration,
ensuring stationarity, meaning that object scores and category quantifications are determined.
Convergence was achieved with 89 iterations, category digitization and object scores were determined,
and the loss function was minimized.
The values of the analysis summary Table 5 can be considered as an indication of the general
significance of the analysis, i.e., to what extent the data fits the analysis. Loss and fair values indicate
the goodness of the solution.
Table 5. Summary of Analysis
Dimension
Loss function
Eigenvalue
Fit
Set 1
Set 2
Mean
1
,279
,279
,279
,721
2
,410
,410
,410
,590
Sum
,689
,688
,689
1,311
The eigenvalue for the first dimension (0,721); is equal to the difference between the value of 1 and the
mean loss value of the first dimension (0,279). The eigenvalue for the second dimension is similarly
obtained as 0,590. The sum of eigenvalues gives the total fit value. Accordingly, the total fit value is
1,311. The maximum fit value is equal to the number of dimensions (2). The mean loss value is 2 - 1,311
= 0,689, which is the difference between maximum fit and actual fit values. In two sets, the canonical
correlation coefficient per dimension is obtained by the following formula. ρd = 2.Ed - 1 The canonical
correlation coefficient calculated in the first dimension is 2 x 0,721 - 1 = 0,442. In other words, according
bmij (2021) 9 (2):561-578
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Selay Giray Yakut & N. Ece Bacaksız & Ceren Camkıran
to the first dimension of the solution, there is a moderate correlation (44,2%) between happiness and
socio-demographic characteristics.
Figure 1. Graphical representation of component loadings
Component loadings are the correlation coefficients between the digitized variable and the object scores.
The fact that the load values of the variables are high is an indicator of their usefulness and significance
for the solution. If in Figure 1, a vector is drawn from the origin to the relevant variable points; the
length of the vector will be an indicator of the usefulness and significance of the variable for the solution.
As seen with the help of Figure 1, the main variables with the highest load value are "marital status",
"who makes you happiest in life" and "how happy you are". Component loadings of these variables are
respectively 0.830, -0.806 and 0.693. Single and multiple fits are given in Table 6.
Table 6. Single and Multiple Fit
Set
1 Age
Gender
Marital status
Education
Employment status
Income
2 How happy you are?
Who makes you happiest in life?
What makes you happiest in life?
Multiple Fit
Dimension
1
2
Sum
,000
,317
,317
,032
,005
,037
,696
,130
,825
,002
,064
,066
,001
,078
,079
,002
,089
,091
,006
,483
,489
,606
,008
,614
,068
,112
,179
Single Fit
Dimension
1
2
,000
,317
,032
,005
,695
,127
,002
,064
,000
,078
,001
,089
,005
,483
,606
,003
,066
,111
Sum
,317
,037
,822
,066
,079
,090
,489
,609
,177
If there are significant differences between single and multiple loss values, the single nominal variable
should be made multiple. According to Table 6, differences between single and multiple loss values is
minor for each variable. Therefore, a single nominal variable was used.
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Figure 2. Graphical representation of centroids
In light of the 44,2% correlation between socio-demographic characteristics and the perceived happiness
categories, to better understand the correlation structure between the categories of variables, the
locations of the category points in the Centroids Graph are examined. With the help of Figure 2, groups
with the highest correlation and relatively homogeneous behaviour may be identified. Firstly, sociodemographic categories that are located strikingly close to the categories that relate to the questions
concerning the perception of happiness were interpreted:
* Master's/Ph.D. graduates are located closest to the excellent category. It is seen that master's and PhD
graduates are pleased when they considered their life as a whole.
* It is seen those casual employees are the unhappiest group when they considered their life as a whole.
* Wageworkers and individuals with medium to high-income level defines themselves as happy, while
the factor that made them happiest is health.
* It is seen that individuals who never married were happiest when they were on their own and with
their friends. Additionally, categories of mother-father and nephews-nieces relatively interacted with
these categories.
* For individuals whose spouse is dead, it is found that the people who made them happiest were their
children.
* As far as age categories are concerned, it is remarkable that the factor of success is located the closest
for individuals who belonged to generation Y.
* It is found that the people who make them happiest were their grandchildren who have not graduated
from any school and belong to the baby boomer’s generation.
* It is found that the concept that made the men who belong to the medium to low-income group
happiest is health.
* For divorced men, it is seen that the concepts that make them happiest are work and money.
Aside from those above, the following category points are also found to be closely located:
* Individuals whose employment status is 'employer' is located close to the college graduate category.
* It can be said that individuals who have not graduated from any school or only graduated from
primary school belonged to the baby boomer’s generation and generation X.
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* It is notable that the depressed category is separated and located far from other categories.
Finally, the object scores scatterplot is also examined, and the cases weighted by the number of objects
of the analysis are shown in Figure 3.
Figure 3. Cases weighted by the number of objects
According to Figure 3, no outlier observations were detected within the scope of the analysis. To test
the validity of the variables interpreted in this analysis, the Chi-Square test of independence was used
as an additional test. For variables that showed dependency, the analysis results performed to
investigate the variables that are the source of this dependency were shown in Table 7.
Table 7. Chi-square independence analysis results
Variables
Gender* How happy you are?
Gender* Who makes you happiest in life?
Gender* What makes you happiest in life?
Marital Status* How happy you are?
Marital Status * Who makes you happiest in life?
Marital Status * What makes you happiest in life?
Employment status* How happy you are?
Employment status * Who makes you happiest in life?
Employment status * What makes you happiest in life?
Age* How happy you are?
Age* Who makes you happiest in life?
Age* What makes you happiest in life?
Income* How happy you are?
Income * Who makes you happiest in life?
Income * What makes you happiest in life?
Education* How happy you are?
Education * Who makes you happiest in life?
Education * What makes you happiest in life?
Chi-square
17,685
139,662
25,227
48,212
974,396
157,300
48,974
70,839
42,373
54,270
327,055
82,264
49,186
159,918
39,266
57,430
172,687
91,549
Sig.
0,01
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
For the three question groups that investigate happiness, an analysis was carried out to see whether or
not they are related to all socio-demographic variables. According to Table 7, the happiness question
set and all socio-demographic questions have a statistically significant relationship.
When Table 7 and Fig 2 are evaluated together, it was seen that gender is related to happiness in Turkey
and that while women are closer to the happy category, men are close to the medium happiness
category. This result parallels studies in the literature that conclude that women are happier than men
(Venetoklis, 2019; Ding et al.,2020).
It is found that there is a statistically significant relationship between marital status and happiness in
Turkey; married individuals are happier than unmarried. This result is also parallel with the existing
literature (Selim, 2008; Mateu et al., 2020). When the relationship between the variable’s marital status
and the person who makes the individual happiest, it is noted that individuals who never married are
happy on their own and with their friends. This marks the importance of being in social relationships
for an individual to feel happy. When the variables of marital status and the concept that makes the
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Selay Giray Yakut & N. Ece Bacaksız & Ceren Camkıran
individual happiest are examined, it is notable that divorced individuals associated happiness with
work and money. It sounds reasonable that people who failed to find happiness in their marriage
resorted to seeking work and money.
When the categorical structure between educational background and happiness in Turkey is examined,
it can be seen that as the level of education went higher, the level of perceived happiness also increased
(Bülbül and Giray, 2011; Lodhi et al., 2020). It is observed that especially individuals with a
master's/PhD degree are closer to a high level of happiness.
When the variables educational background and the person who makes the individual happiest are
examined, it can be seen that individuals who have not graduated from any school are happy with their
grandchildren. It was also found that secondary school and high school graduates were happy with
their whole family.
The relationship between age and perceived happiness was also found to be significant (Blanchflower
and Oswald, 2004; Dumludag et al., 2016), and upon examination, it can be seen that the concept that
makes the individual happy and the person who makes them happiest vary from generation to
generation. It is seen that, while the perceived happiness is reduced in older ages, these individuals
were happy with their children and grandchildren. The responsibilities assumed by individuals at early
ages reduce the time they spend with their children. With this, combined with dead spouses died in
older ages, especially in Turkey, the grandchildren become the source of happiness for the elderly.
Another result associated with older age is the concept of health becoming a more prominent for the
source of happiness. This result is also very reasonable when the fact that health issues encounter in old
ages remind not to take health for granted is considered.
The relationship between socio-demographic characteristics and perceived happiness has yielded
consistent results in studies conducted both in Turkey and worldwide. Explaining the nature and
structure of the relationship in detail by category and presenting all sub-categories together, this study
allowed to examine the subject from a different perspective.
Conclusion
In this study, the structure of the relationship between the perception of happiness and sociodemographic characteristics in Turkey is examined based on the data collected within the scope of the
Life Satisfaction Survey conducted by TURKSTAT. The degree of the relationship between the variable
set consisting of three questions used for the perception of happiness and the variable set consisting of
socio-demographic characteristics is examined. Considering the relationship between happiness and
income in keeping with the literature, this is also included in the variable set. However, as the focal
point of the study is not income, the study is performed only on working individuals to minimize
variability to a certain extent. It is investigated whether or not the individuals fit into homogenous
structures in terms of the perception of happiness and the conceptual and personal sources of happiness.
Because the methods available in the literature do not allow the examination of the relationship between
all variables when there are multiple dependent variables, nonlinear canonical correlation analysis is
used in the study. In this analysis, the structure of the relationship between the sub-categories of
variable sets is revealed and visualized in a lower-dimensional space.
A significant number of studies investigate the effects of socio-demographic characteristics on the
perception of happiness, and the findings obtained have highlighted that these characteristics are
essential factors in determining personal happiness. However, this study builds on previous studies as
it allows the examination of multiple variable sets and the examination of clustering sub-categories.
When the effect of employment status was examined, it is notable that per diem employees define
themselves as unhappy. Health is the most central concept in happiness for men who belong to the
medium to low-income and wage workers who belong to the medium to high-income group. When the
age variable was examined, it can be seen that there are specific differences from generation to
generation. It is seen that the level of happiness reduced in older ages, also, their children and
grandchildren come up the source of happiness for these individuals. Another notable finding is that
the concept of success is prominent for individuals who belong to generation Y.
In conclusion, this study is essential for interpreting different groups by evaluating different sociodemographic characteristics together. Findings were corresponding to the socio-demographic structure
of Turkey and were consistent with the literature. According to the study results, the detection of
categories that are located close to each other enables detailed studies on specific categories. In a survey
data set similar to TURKSTAT Life Satisfaction Survey, nonlinear canonical correlation analysis can be
suggested to use more commonly to remove the disadvantages of different methods when there are
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Selay Giray Yakut & N. Ece Bacaksız & Ceren Camkıran
multiple dependent variables and allow for more detailed interpretations to be made. Besides, further
studies can be done using this method for other countries to determine the sub-categories for happiness.
The use of this method can be strengthened by using logistic regression, chi-square independence test
and similar techniques.
Peer-review:
Externally peer-reviewed
Conflict of interests:
The authors have no conflict of interest to declare.
Grant Support:
The authors declared that this study has received no financial support
Author Contributions:
Idea/Concept/Design: S.G.Y., N.E.B., C.C. Data Collection and/or Processing: S.G.Y., N.E.B., C.C.
Analysis and/or Interpretation: S.G.Y., N.E.B., C.C. Literature Review: S.G.Y., N.E.B., C.C. Writing the
Article: S.G.Y., N.E.B., C.C. Critical Review: S.G.Y., N.E.B., C.C. Approval: S.G.Y., N.E.B., C.C.
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