Monetary and non-monetary determinants of happiness: evidence from Europe

This paper analyzes the impact of monetary and non-monetary determinants of happiness on individuals across nine European countries. It examines the relationship between happiness and income, finding a positive correlation. The study uses data from the European value survey and explores other factors such as education, employment status, age, and gender. The methodology includes an Ordered Probit Model to estimate the relationship between happiness and the independent variables.

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

countries is higher than others. For example, Denmark and
Norway show high level of happiness while other countries show
lower level of happiness like Ukraine and Romania since only a
few people belonging to these countries say they are very happy.
4 Estimation results
The results from the happiness regression for both sets of
countries are presented in table 4 in the appendix.
There are 10, 995 people in these countries who answer the
question of happiness (the feeling of happiness), the distribution of
happiness shows that there are 199 people or around 2 % of
population are not happy at all, there are 6,140 people or almost
55 % are relatively quite happy. Moreover, 3,368 who are very
happy constitute around 30% of population.
For the distribution of happiness of the two groups of
countries, there are 0.68 % of people who are not happy at all,
while 41.13 % are very happy with their life in developed countries.
On the other hand, people who are not happy at all with their life in
developing countries are represented by 3.48 % of the population,
while 15 % are very happy. We can see the distribution of
happiness for each country in figure 2.
For the distribution of income level, there is low, medium and
high level of income. In the developed countries there is about
33% of population who has low income, 36 % with medium income
and 30 % of population has high income level. While in the
developing countries 36 % of population has low income level,

31% with medium income and 32 % of population has high income
level.
Table 3, shows the correlation between the level of income
and happiness for these nine European countries. We can see that
the two variables are positively correlated for all these countries.
But the correlation for some countries is stronger than the others.
For example the correlation between the two variables for the
Denmark is approximately 13 %, while in Romania it is about 22%,
so we can say that this relationship is true which states that as
income level increases the happiness level shall increase as well.
In the regression results table 4, we can see that higher
income is positively correlated with subjective well-being. Both
developed and developing European countries is highly positive
and significantly correlated with happiness by almost 13 % and 7%
for developed and developing countries respectively. This finding
is consistent with the literature part (Frijters, Haisken-DeNewand
Shields, 2004) which argues that income has a positive impact on
happiness. This data indicates that “income buys happiness”
across Europe.
Countries and income interactions
Figure 3 shows the interaction between the two groups of
countries; developed and developing, and income level, we can
see that for a low level of income there is good differences

between two groups of countries, but as income level increases
the differences disappear.
If we move to the education distribution, there are 10,946
people who answer the question of the highest educational level
attained. Some old studies show a negative relationship between
SWB and higher level of education (e.g. Campbell, Converse and
Rodgers, 1976). This could be explained by the fact that education
increases people's ambition which is not easy to achieve.
However, table 4 shows that there is no significant correlation
between happiness and education in developed countries;
education has no effect on the happiness level. While in
developing countries it has positive correlation by about 1.75 %.
That was mentioned in the literature part by Ferrer-i-Carbonell
(2005) that the education has no effect in developed countries, but
affects the developing ones. Possible explanation for this could be
that while people in developing countries are in a bad economic
situation, so when they have a good education they may end up
with a good job and better income and that could increase their
level of happiness.
Countries and education interactions
Figure 4 shows interaction between developed and
developing countries and highest educational level attained. It

shows that in a situation of low level of education, the difference
between the two groups of countries is big, while the differences
get smaller for each additional level of education. The level of
education represents by numbers from 1 to 8, where 1 shows the
lowest level and 8 the highest level of education based on this
study.
For the employment status there are 11,009 people who
answer the employment question; if they are employed or not. In
the developed countries around 62 % of population who answered
this question is employed and 38 % are unemployed. Moreover,
around 48 % of the populations surveyed in the developing
countries are employed and 51% are unemployed.
Employment status shows a positive correlation with
happiness (Frey and Stutzer, 2000). Employment status in
developed countries is highly positive and significantly correlated
with happiness by around 9 % and for developing countries is
positively correlated by around 5 %. Possible explanation for this is
that, as people are employed and have fixed level of income and
they are financially stable, their level of happiness increases, and
the opposite occurs when they are unemployed.
Moving to the age factor, the distribution shows people aged
from 16 to 103 were surveyed. There is negative correlation
between the age and happiness level. There is a U-shaped
relationship between happiness and age as mentioned in the
literature (Clark and Oswald, 2006).The happiness level is higher
for younger and older people and it is at the minimum point for the

middle age ones.If we look at the regression in table 4, we can see
that there is positive and significantlycorrelated between age
square and happiness in the developed countries. However, there
is no significant correlation in the developing countries.
For the gender variable, we can see from the regression
table 4 that female in developed countries are happier than male.
While, female are less happy in developing countries than
male.The gender disability is considerably low in the countries of
Denmark, Norway, Sweden, Switzerland and the United Kingdom
but high in the countries of Hungary, Poland, Romania and
Ukraine. This may be because in developed countries there are
more opportunities for both men and women, while in developing
courtiers there could be inequality between the two genders which
lead to reduce the happiness level for female.So, we can conclude
that the conditions of the women were considerably better in the
developed countries. However, the rights and freedoms of the
women were severely restricted in the developing ones. However,
table 5 explains how other variables such as income, education
and others differ between male and female for developed and
developing countries. For example, in developed countries the
income level is highly and positive correlated with happiness for
both male and female by about 11% and 15% respectively. Also
for developing countries there is positive correlation by around 7%
for both male and female.

5 Conclusion
There are a number of different factors that play a significant
role in impacting the level of happiness such as people's age, their
income level, the highest education level achieved by them and
their gender. There is some evidence of an existing correlation of
happiness and these factors (Frey and Stutzer, 2018). One of the
fundamental issues of economics is to explain whether income is
related to happiness. And how other non-monetary factors are
correlated with happiness. It has been proved in the research
paper with the help of certain literature that the higher academic
achievements of an individual help the individual to gain a better
employment opportunity. This in turn, leads to the happiness of the
person. Moreover, the happiness of a person is also dependent
upon the age of a person.ref. When a person in the middle age, it
is hardly possible for him or her to be happy in the true sense.
They are too busy trying to build a career for themselves so that
they might be able to lead a fulfilling life. As a result, they are often
burdened with worry and trouble and cannot seem to find a break.
This situation takes a drastic turn when an individual reaches the
middle age. During this time, the person has been able to achieve
everything possible and this age defines his or true happiness.
Thus, the research conducted in the nine countries of Europe,
namely Denmark, Norway, Sweden, Switzerland, the United
Kingdom, Hungary, Poland, Romania and Ukraine shows the level
of happiness enjoyed by the people living in these countries in
accordance with the various determinates of happiness.
One of the main finding in this paper is the interaction between the
two set of countries with income level and interaction with

education level as shown in figure 3 and 4, it shown that as income
or education level are low the differences of two groups of
countries are big, however the differences get smaller as the
income level increase and as people get better education.
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Appendix:
Figure 1:

0 .2 .4 .6
Density
0 1 2 3 4
happy
Notes: Author’s calculations using EVS
Figure 2:
0 .2 .4 .60 .2 .4 .60 .2 .4 .6
0 5 0 5 0 5
Denmark Hungary Norway
Poland Romania Sweden
Switzerland Ukraine Great Britain
Density
happy
Graphs by Country/region
Notes: Author’s calculations using EVS
Figure 3:

2.6 2.8 3 3.2 3.4 3.6
Linear Prediction
<1800
1800 -3600
3600-6000
6000-12000
12000-18000
18000-24000
24000-30000
30000-36000
36000-60000
60000-90000
90000-120000
>120000
Annual household income
developing=0 developed=1
Predictive Margins of dummy with 95% CIs
Figure 4:
2.8 2.9 3 3.1 3.2 3.3
Linear Prediction
1 2 3 4 5 6 7 8
Highest educational level attained
developing=0 developed=1
Predictive Margins of dummy with 95% CIs
Notes: Author’s calculations using EVS
Table 3: Correlation between happiness and income:

Notes: Author’s calculations using EVS
Table 4: Regression of happiness:
CORRELATION BETWEEN HAPPINESS
AND INCOME LEVEL
Denmark 0.1380*
Norway 0.1998*
Sweden 0.2095*
Switzerland 0.1901*
UK 0.2098*
Hungary 0.1959*
Poland 0.1688*
Romania 0.2229*
Ukrain 0.2001*

(1) (2)
VARIABLES Developed Developing
Income level 0.137*** 0.0753***
(0.0121) (0.0150)
Education 0.000450 0.0175***
(0.00490) (0.00670)
Employment 0.0987*** 0.0500*
(0.0221) (0.0266)
Age -0.0210*** -0.0112***
(0.00302) (0.00368)
age2 0.000216*** 3.07e-05
(3.05e-05) (3.82e-05)
Female 0.0400** -0.0781***
(0.0170) (0.0227)
Constant 3.387*** 3.171***
(0.0742) (0.0971)
Observations 5,112 3,554
R-squared 0.040 0.087
Notes: Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Table5: OLS estimates for male and female:

(1)(2)(3)(4)
MaleMaleFemaleFemale
VARIABLESDevelopedDevelopingdevelopedDeveloping
Income level0.115***0.0708***0.157***0.0776***
(0.0179)(0.0223)(0.0163)(0.0202)
Education0.002860.0147-0.0006380.0185**
(0.00717)(0.0106)(0.00672)(0.00869)
Employment0.153***0.04540.0562*0.0556
(0.0339)(0.0403)(0.0291)(0.0356)
Age-0.0263***-0.0144**-0.0168***-0.00929*
(0.00453)(0.00558)(0.00403)(0.00489)
age20.000272***6.70e-050.000174***8.55e-06
(4.60e-05)(5.88e-05)(4.06e-05)(5.04e-05)
Constant3.537***3.177***3.367***2.968***
(0.103)(0.137)(0.0939)(0.120)
Observations2,4751,4802,6372,074
R-squared0.0380.0760.0440.087
Notes: Standard errors in parentheses
***p<0.01, ** p<0.05, * p<0.1