Determinants of Happiness in Europe: A Research Report

This report investigates the monetary and non-monetary determinants of happiness across nine European countries, utilizing data from the European Value Study. The research explores the relationship between income and subjective well-being (SWB), revealing a positive correlation between income and happiness in all nine countries analyzed. The study reviews existing literature, including the Easterlin Paradox, which suggests a weak correlation between income and happiness, and examines various factors such as education, employment status, age, and gender. Methodologically, the research employs an Ordered Probit Model to analyze the impact of these determinants on happiness levels. The findings highlight the complex interplay of economic and social factors in shaping individual well-being, contributing to a broader understanding of happiness dynamics in Europe. The report also discusses how education, employment, age, and gender influence happiness levels, drawing on various studies to support its findings and conclusions.

Monetary and non-monetary determinants of
happiness: evidence from Europe
Abstract
The aim of this paper is to use the European value study to
analyses the impact of the determinants of happiness on the
individual's level of happiness across nine European countries.
The paper examines the relationship between happiness and
income. Our finding shows that income is positively correlated with
the level of happiness in all nine countries.
Key words: happiness, well-being, income, non-monetary factors
1 Introduction
This research seeks to understand how the different
determinants of happiness as income level of an individual,
education, employment status, people's age and gender have an
impact on their level of happiness or their subjective well being
(SWB). For this purpose, this paper presents empirical evidence
for the determinants of happiness using the European value
survey. The different factors are thoroughly looked in the literature
part which includes some studies show the correlation between the
SWB and these factors. Easterlin (1974) examined the hypothesis
of Esterline paradox which shows the negative relation between
real income growth and happiness level. However, some studies
asFrijters, Haisken-DeNew and Shields (2004) shows that income
has positive effect on SWB across Europe. Other studies show

that how happiness level vary across the European countries
depends on the change of the determinants of happiness, the
study of Caporale et al, (2009) reports that western European
countries show higher level of happiness compare to eastern
European countries.
In order to support the research, data has been collected from nine
different European countries from 2008 to 2010 and compared
with each other.The structure of this paper is as follow. Section 2
provides a brief discussion of literature on the determinants of
happiness. Section 3 describes the methodology model and the
data used in this research. Section 4 shows the empirical results
based on these data, and finally section 5, the conclusion.
2 Survey of the literature
2.1 Income effect
There is some evidence of an existing correlation between
happiness and other factors such as income, education,
employment and others (Frey and Stutzer, 2018). One of the
fundamental issues of economics is to explain whether income is
related to happiness. Some studies as Frijters, Haisken-DeNew
and Shields (2004) shows that income is correlated with happiness
and that money plays a significant role in impacting the well-being
of people's life. However, the study of Easterlin (1974) argues that
income is not correlated with happiness; it shows that the
happiness level is notnecessarily increase as the income level
increases. And that happiness level stays flat even with a positive

growth of the real income or society’s economic.This aspect is
known as the Easterlin Paradox. There are many studies which
support this finding; the relation between income and happiness is
negligibleor non-existentas (Diener and Biswas-Diener, 2002;
Diener, et al., 1993)
There are some theories that explain the finding of Easterlin
Paradox. The first theory explains that is the adaptation theory, it
states that people get adapted to their income. In other words,
people get used to their income and economic situation, so if there
are any changes to their income, it wouldonly have a transient
effect on their happiness or SWB (Easterlin, 2001). Adaptation
could be explained as the most important aspects of life have
small effect on people's happiness. This is referred to the
hypothesis of "hedonic treadmill" (Loewenstein and Ubel, 2008).
The second explanation of this finding is the theory of relative
income. Relative income determines utility rather than absolute
income. People care more about relative income than absolute
income because it has more impact on happiness. It tells how the
person feels about her or his income relative to the other’s income.
This explains why well-being does not increase despite the
increase in income. So thiscomparison of the economic situation
with the others could weaken the relation between happiness and
income which could be observed only through absolute income
(Clark and Oswald, 1996).
Furthermore, a study done by Rojas (2007) explains the
conceptual-referent theory of happiness (CRT), itargues that a
weak relationship exists between the well-being of a person and
his or her income level. This theory explains that people have

different concept about what represent a happy or good life. This
weak relationship could be explainedby that the income is not the
most important thing for some people and that happiness comes
from within a person and not from material things.
On the other hand, several studies contradict the idea of the
Easterlin Paradox; the negative relationship between wellbeing of
a person and the level of income. The studies show that as real
income increases, the happiness of an individual increases by
about 35 to 40 percent in East Germany (Frijters, Haisken-DeNew
and Shields, 2004). Furthermore, by collecting data of some
countries, the studies show that there is a positive relationship
between SWB and income in both developing and developed
countries (Stevenson and Wolfers, 2008).In fact, the happiness
level of a person increase as the Gross Domestic Product per
capita of the economy increases. However,other studies rebut the
argument of Stevenson and Wolfers (2008) and show that there is
no relation between income and happiness at least in the long run;
however, in the short run the evidence show a positive relation
between them (Easterlin and Angelescu, 2009). Other studies
focus on the relation between happiness with relative and absolute
income. Theyshow that thehappiness levelof an individualis
influenced by relative income more than absolute income as
discussed before (Clark and Oswald, 1996). However, other
studies as Caporale et al, (2009) argues that the two sides of
income; relative and absolute have insignificant or negligible effect
on SWB compared to the non-monetary factors.

2.2 Non-monetarydeterminants:
There are also certain non-monetary factors that determine the
level of happiness of an individual such as the level of education
received by a person, the employment status of an individual, his
or her age and gender and others (Frey and Stutzer, 2018).
Education
Education is an important determinant of happinessand
important factor for people’s life. There are several variables which
usually effect education, such variables as health and income.
There is positive correlation between education and these two
variables. However if these two variables are not controlled
for,education is expected to be more strongly positive
(Blanchflower and Oswald, 2004). Education could affect the well-
being of an individual either directly or indirectly. Direct influences
may include a positive effect on the aspect of self-confident of a
person and self-esteem by acquiring knowledge. Indirect effects
may include the expectation of higher income, get better quality
work and others.Many studies show a positive relationship
between the level of education received by a person and his or her
wellbeing. The study of Blanchflower and Oswald (2004) shows
that the level of happiness or well-being of a person increases at
every higher level of education. There are some other studies as
Stutzer (2004) which argues that people who receive a middle
level of education are happier than those who receive lower or
higher education level.However, the study of Ferrer-i-Carbonell
(2005) shows that the education has no effect or considerably
lower effect on the countries with high level of income, while it has
a positive effect on the low income ones.

Employment
In addition, employment status of an individual is a very
important determinant of happiness as it provides the person with
income, and makes him or her financially independent. The study
of Frey and Stutzer (2000) shows that there is a negative
relationship between unemploymentandhappiness. It shows that
people who have full-time work are happier than those who are
unemployed. In other words, the studies show the negative
correlation between happiness and unemployment as
unemployment leads to unhappiness. This may run in opposite
direction; people who are less happy are not perform well in the
labour market, but the causal relationship is from unemployment to
unhappiness. Some studies show that unemployment has stronger
effect on men than women and on the middle aged people than
older and younger ones (Arrosa and Gandelman, 2016)
Age
Many studies show that there is a negative relationship between
happiness and age, but positive relationship between happiness
and age square (e.g. Brereton, Clinch and Ferreira, 2008)
These studies show that there is a U-shaped relationship
between age and happiness which means that the level of
happiness is high at younger and older age, but it is at the
minimum level in the middle age that is between the ages of thirty
five and fifty, depending on the study Clark and Oswald (2006).
Study of Easterlin (2006) shows that the U-shaped relationship is
found when many variables that control the life of a person such as
income, employment, health and others, could be misleading.
Other studies argue that the U-shaped relationship between the

two variables disappear when using certain fixed variables;
variables that could increase happiness such as getting a better
job, an increase in a person’s income and others, which is usually
experienced by the middle aged people and this contributes to the
increase in their level of happiness (Frijters and Beatton, 2012).
Gender
The factor of gender plays a vital role in ensuring the well-
being of an individual (Biedaet. al., 2017). Since the inception of
the human civilization, the male gender has always assumed a
dominant role over the female gender (Arrosa and Gandelman
2016). This was made possible because of the inherent
characteristics of the female gender that are by nature
compassionate and emotional. Some studies show that men are
happier than women in some European countries(e.g. Matteucci
and Vieira Lima, 2014). However, the study of Blanchflower and
Oswald (2004) reports that, women are happier than men in some
countries as the United States and Britain. Other studies as Arrosa
and Gandelman (2016) show that happiness and gender relation
could be affected by other variable as income level.
3 Methodology and data
3.1methodology model
In this research paper, we will use data from European value
survey (EVS) to explain the relationship between wellbeing and
income and the other non-monetary factors.

In this paper we will use data model which relies on datasets
to test how income could affect happiness in some European
countries. As there are some characteristics or factors have impact
on the level of happiness, we will estimate the Ordered Probit
Model or Ordered Logit Model. The Ordered Probit Model is used
to estimate the relationship between the dependent and
independent variables. The dependent variable is one ordinal
variable which is the variable ofhappiness here. The independent
variables are a collection of variables such as income,
employment, age, and others, which could affect the level of
happiness (Kockelman and Kweon, 2002). We can write it in this
way:
yi
¿=β' xi+ εiWhere i= 1, …, N (1)
Where yi
¿
is the dependent variable, in this case the happiness or
SWB, xiis a vector of explanatory variables which is the income, β
is a vector of parameters of the model (to be estimated) and ε iis a
disturbance or error term; it is random and normally
distributedvariable.
As there are other factors that affect SWB other than income, the
model could be written as;
Happinessi=a0+ β1 income+ β2 employment + β3 age+ y D +εi (2)
Where D represent many other factors such as; health, gender,
education and others.
The observed and coded discrete dependent variable yi is
determined from the model as follows:

yi= 1 if −∞ ≤ yi
¿ ≤ μ1(not happy at all)
= 2 if μ1 ˂ yi
¿ ≤ μ2(not very happy)
=3 if μ2 ˂ yi
¿ ≤ μ3(quiet happy)
=4 if μ3 ˂ yi
¿ ≤ ∞(very happy)
Where μi represent the threshold to be estimated (along with the
parameter vector β).The probabilities of yi from the Ordered Probit
Model are as follows:
pi ( 1 ) = pr ( yi=1 ) = pr ( yi
¿ ≤ μ1 ) = pr ¿
pi ( 2 ) = pr ( yi=2 ) = pr ( μ1 ˂ yi
¿ ≤ μ2 ) =pr ( εi ≤ μ2 −β' xi ) − pr ( εi ≤ μ1−β' xi )=∅ ( μ2−β' xi ) −∅ ( μ1 −β' xi )
pi ( k ) = pr ( yi=k ) = pr ( μk ˂ yi
¿ ≤ μk +1 )=∅ ( μk+1 −β' xi ) −∅ ( μk−β' xi )
pi ( K )= pr ( yi=K )= pr ( μK ˂ yi
¿ )=1−∅ (μK−β' xi )
Where i is an individual, k is a response alternative, pr =¿ (yi=k) is
the probability that individual i responds in manner k, and ᴓ ( ) is
the standard normal cumulative distribution function. We could
explain the parameter set β of this model as follow; if the sign of a
particular parameter β is positive it means that the whole
distribution of yi
¿
shift to the right (higher level of happiness) as the
value of the associated variables increase. These changes could
affect the probabilities of happiness in different levels, while that
the probability of the highest level pr ( yi=4 ) is absolutely increased
and the probability of the lowest level pr ( yi=1 ) is decreased. Other
probabilities could determine numerically. To see how income

could affect the life satisfaction we should calculate the marginal
effects based on the estimated regressions coefficients
(Kockelman and Kweon, 2002)
3.2 Data
In this paper we used the data from the European value
study (EVS) for nine European countries over a span of yearsfrom
2008 to 2010. The countries from where data is collectedare;
Denmark, Norway, Sweden, Switzerland, the United Kingdom,
Hungary, Poland, Romania and Ukraine.
We can separate the nine countries into two different groups
depending on their economic situations as some countries have
better economic situation and reported higher level of happiness
than others(Caporale et al,2009).As a result, we can divide them
into developed and developing countries, where the developed
countries are Denmark, Norway, Sweden, Switzerland and the
United Kingdom, and the developing countries are Hungary,
Poland, Romania and Ukraine.
Data from EVS have some information about the dependent
variable; the happiness level is used in our regression as a
dependent variable. The data was collected based in some
questions about the variables. The questions were asked to those
people who lived in the above mentionedEuropean countries. Such
questions included the following; how happy are you in your life?
“Feeling of happiness”, are you very happy, quite happy, not very
happy or not at all happy.

The EVS data also provides some information about other
independent variables that could affect thehappinesslevel of the
people living in these countries. The variables that were taken in to
consideration are income, education, employment, age and
gender. The income variable here is divided into three categories
that are low, medium and high level of income. The education
variable is label as the highest educational level attained and it is
divided into eight categories. For the employment status the
chosen people divided into employed and unemployed. For age it
mentioned people aged from 16 to 103. And gender as male or
female.
Table 1 shows the summary statistics for developed
countries, while table 2 shows the relevant statistics for developing
countries. The average income level of developed and developing
countries is about 1.97 and 1.95 respectively. There is no big
difference in average income for both groups of countries. If we
move to the second variablethe education, the average score of
this variable in developed countries is 5.17 and 5.22 in developing
countries. No big differences between the two groups, but we can
say that people in developing countries care more about education
as it could improve their lives in the future. For the average value
of employment; there are 62% of people are employed in
developed countries and 48% are employed in developing
countries. The average age of people in developed countries is 50
while in developing countries is 47. Finally for the gender; female
represent 53% of the population in developed countries and 58%
in developing countries.

Table 1 Summary statistics for developed countries
MaxMinStd. Dev.MeanObsVariable
31.79596751.971985,282Income level
811.8812685.1661266,471Education
10.4856116.61923726,529Employment
1031617.3263149.527156,592Age
106092561782.3092753.0956,592Age2
21.49917881.5292966,605gender
Notes: Author’s calculations using EVS
Table 2 Summary statistics for developing countries
MaxMinStd. Dev.MeanObsVariable
31.82978321.9549163,682Income level
811.8432125.2216764,475education
10.4997902.48370544,480employment
931817.5082346.873744,475Age
86493241712.922503.6174,475Age2
21.49355851.5803374,506gender
Notes: Author’s calculations using EVS
We can see the distribution of happiness for the nine
European countries in Figure 1 in the appendix, it showsfour level
of happiness from 1 to 4, where 1 indicates the lowest level of
happiness and 4 indicates the highest level of happiness.
Ingeneral the graph shows the different levels of happiness among
the people from these European countries.
However, in figure 2 we can see the distribution of happiness
for each country; it shows that the level of happiness in some