Analyzing Factors Impacting Happiness and Quality of Life

This essay delves into the multifaceted concept of a "good life" and the factors that contribute to it, drawing upon the perspectives of philosophers like Hobbes and Locke. It explores the evolution of human understanding of a good life, highlighting that it encompasses both social satisfaction and economic capability, often defined as happiness or subjective well-being (SWB). The essay examines the impact of income, education, employment status, age, and gender on individual happiness, referencing the Easterlin Paradox and other relevant studies. It analyzes the correlation between income and happiness, exploring theories like the adaptation theory and the theory of relative income. Furthermore, it discusses non-monetary factors such as education and employment, as well as the U-shaped relationship between age and happiness. The essay also touches upon the importance of gender equality in ensuring individual well-being. The research incorporates data from nine European countries to compare the variations among them.

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Introduction and literature
The purpose of every human being is to attain a life worth living. According to the
writings of Hobbes and Locke, the life of people that was in existence in the state of nature, was
not a life worth living (Fanning and O'Neill 2019). People had no rights and freedom and the
aspects of good life were unknown to them. Slowly, with the evolution of human civilization,
people started to understand the need for living a good life. As such, they started to undertake
many different activities in order to attain a quality standard of living. They started to indulge in
working activities, growing and cultivating various food crops and manufacturing clothes and
other necessities of life. Thus, for thousands of years people have thought about a good life or
how to achieve a quality standard of life (Oishi, Kushlev and Schimmack 2018). There are many
answers to this question; it could be by earning people's love, life satisfaction and other reasons.
People could think in different way about what express a good life; which could come from
within a person, to believe that he or she lives a good life (Flynn and MacLeod 2015). This
shows that simply materialistic pleasure is not enough. People cannot hope to achieve a quality
life through the use of money; it has to be supplemented by other factors of love, affection and
social validation (Jebbet al., 2018). As a result, good life is both social satisfaction and economic
capability; both complement each other. The way to define the good life is called happiness or
also known as subjective well-being (SWB). ''Subjective well-being refers to the evaluations of
the lives of the people based on both the aspects of affective and cognitive. ''In other words,
people can only find happiness when they are content with themselves and their surroundings. It
is quite difficult to measure the level of happiness experienced by the people as their perception
of a good life differs from one individual to another (Kushlev, Dunn and Lucas 2015). However,
there is a possibility to measure it with the aid of several parameters as that of the level of

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income of a person, the education received by the individual and the employment status of the
person (Walsh, Boehmand Lyubomirsky 2018). This research topic seeks to understand how the
different factors of income level of an individual, their age and gender have an impact on their
level of happiness. For this purpose, the different aspects are thoroughly looked in to in the next
section. In order to provide credibility to the research, data has also been collected from nine
different European countries and compared with each other. During this task, the European value
study was utilized which showed the variations among the different countries to the achievement
of such parameters. The research concludes with the analysis of the significance of attaining a
quality standard of life for the people of the world.
Factors affecting the level of happiness
Income as a factor
There is some evidence of an existing correlation of happiness with other factors which
includes 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 like 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 an individual.
However, the study of Easterlin (1974, updated in 1995) argues that income is not correlated
with happiness; it shows that even if there is increase in income it does not necessarily lead to a
higher level of happiness 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 that the relationship between income and happiness is
negligible or non-existentas (Diener, et al., 1993; Diener and Biswas-Diener, 2002)

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There are some theories that explain the finding of Easterlin Paradox. The first theory
explains that is the adaptation theory which states that people get adapted to their income. In
other words, people get used to their income and economic situation, so if there is any changes to
their income, it would only have a transient effect on their happiness or SWB (Easterlin, 2001).
Adaptation is defined by Frederick and Loewenstein (1999) as "a reduction in the affective
intensity of favorable and unfavorable circumstances''. This is referred to the hypothesis of
"hedonic treadmill". The second explanation of the Easterlin Paradox 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 income of the other people. This explains
why well-being does not increase despite the increase in income. So this comparison 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, using the conceptual-referent theory of happiness (CRT), a study done by
Rojas (2007) argues 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 a happy life
is. This weak relationship could be explained by the fact that 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 study
conducted by Frijters, Haisken-DeNew and Shields (2004) shows that as real income increases,
the happiness of an individual increases by about 35 to 40 percent in East Germany.

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Furthermore, by collecting data of some countries, Stevenson and Wolfers (2008) show that
there is a positive relationship between SWB and income in both developed and developing
countries. In fact, the happiness level of a person increase as the Gross Domestic Product per
capita of the economy increases. However, Easterlin and Angelescu (2010) rebut the study 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. Other studies focus on the relation between happiness with relative and absolute income.
They show that the changes in relative income has more effect on happiness of an individual
rather than the changes in absolute income as discussed before (Clark and Oswald, 1996).
However, Caporale et al. (2009) argue that both relative and absolute income have insignificant
or negligible effect on SWB compared to the non-monetary factors.
Non monetary factors:
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,
health of the person, his or her age, and others.
Education as a factor
Education is one of the most important aspects of a person’s life. Education is often
responsive to the inclusion of other variables within the model. It is positively correlated with
income and health, and if they are not controlled for, education is expected to be more strongly
positive (Blanchflower and Oswald, 2004b). It could affect the well-being of an individual either
directly or indirectly. Direct influences may include a positive impact on the aspect of self-
confident of a person and self-esteem by acquiring knowledge. Indirect effects may include
better job quality, expected higher income and better health. Many studies show a positive

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relationship between the level of education received by a person and his or her wellbeing. The
study of Blanchflower and Oswald (2004b) shows that the level of happiness or well-being of a
person increases at every higher level of education. However, there are some other studies which
argue that people with middle education have a higher level of happiness than those with a
higher and lower education (e.g. Stutzer, 2004). However, there is evidence that education has no
effect or considerably lower effect on the high income countries, while it has a positive effect on
the low income ones (Ferrer-i-Carbonell, 2005).
Employment as a factor
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
happiness and unemployment. 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 (Mogilner and Norton
2016). This may run in opposite direction; unhappy people do not perform well on the labour
market, but main causality is clearly run 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 (Hellevik 2017).
For example, the study by Household Panel Study in British in 99 shows that because of
the large financial aid provided by the state to the unemployed people, there are large number of
people who can choose or definitely chooses to be unemployed. The study shows that the
unemployed people in Britain have lower level of well-being than the employed people. Thus,
we can conclude that there is positive relationship between unemployment and unhappiness.

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Age as a factor
Many studies such as that conducted by Ferrer-i-Carbonell and Frijters (2007) show that
there is a negative relationship between age and happiness, but positive relationship between age
square and happiness (Morgan, Robinson and Thompson 2015). 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 (Li 2016). Study of Easterlin (2006)
shows that the U-shaped relationship of age and happiness 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 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 as a factor
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 who are by nature
compassionate and emotional. This was taken to a sign of weakness by the male gender who
were more rational and logical in their orientation. As a consequence of such gender divisions
which was evoked by their biological differences, the women had to face continual oppression
from the men; in other words, they were seen to be subservient to the male gender (Wong, Gong
and Fung 2019). This greatly impacted the way of life of the female gender who were even

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denied their rights and freedoms. As a result, there is a necessity on the part of the world to
ensure that everyone is guaranteed their rights and freedom, without any discrimination,
especially that of gender (Vlase and Sieber 2016). If the women of the world are guaranteed their
freedom and dignity then they will be able to live a quality life, which in turn would ensure their
well-being. Moreover, there is also a need on the part of the male gender to ensure that they do
not, willingly or unwillingly, encroach upon the dignity and integrity of their female counterpart
(Meisenberg and Woodley 2015).
Methodology
In this research paper, we will use data from European value survey (EVS) to explain the
relationship between wellbeing and income and the other factors from 2008 to 2010 for nine
European countries.
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 of
happiness here. The independent variables are a collection of variables such as income,
employment, age, and others, which could affect the happiness. We can write it in this way:
Y*ᵢ = β’ Xᵢ + ɛᵢ where ᵢ= 1,….., N (1)
Where Y*ᵢ is the dependent variable, in this case the happiness or SWB, Xᵢ is a vector of
explanatory variables which is the income, β is a vector of parameters of the model (to be

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estimated) and ɛᵢ is a disturbance or error term; which is random variable and normally
distributed.
As there are other factors that affect SWB other than income, the model could be written as;
Happiness= a0+ β1 income+ β2 employment+ β3 age+ YD+ ɛᵢ (2)
Where D represent many other factors such as; health, family size, education and others.
The observed and coded discrete dependent variable Yi is determined from the model as follows:
Yᵢ = 1 if -∞≤ Y*ᵢ ≤μ1 (not happy at all)
= 2 if μ1< Y*ᵢ ≤μ2 (not very happy)
= 3 if μ2< Y*ᵢ ≤μ3 (quiet happy)
= 4 if μ3< Y*ᵢ ≤∞ (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(Y*ᵢ≤μ1)= Pr(β’Xᵢ+ɛᵢ≤μ1)= Pr(ɛᵢ≤μ1− β’Xᵢ)= ᴓ(μ1− β’Xᵢ)
pi(2)= Pr(Yᵢ=2)= Pr(μ1< Y*ᵢ≤μ2)= Pr(ɛᵢ ≤ μ2 − β’Xᵢ) – Pr(ɛᵢ ≤ μ1− β’Xᵢ )= ᴓ(μ2 − β’Xᵢ) – ᴓ(μ1−
β’Xᵢ)
pi(k)= Pr(Yᵢ=k)= Pr(μk< Y*ᵢ≤ μk+1)= ᴓ(μk+1− β’Xᵢ) – ᴓ(μk−β’Xᵢ)
pi(K)= Pr(Yᵢ=K)= Pr(μK<Y*ᵢ)= 1– ᴓ(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

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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 Y*ᵢ shift to the right (higher level of happiness)
as the value of the associated variables increase. This changes could affect the probabilities of
happiness in a 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.
Data
In this paper we used the data from the European value study (EVS) for nine European
countries over a span of years from 2008 to 2010. The countries from where data is collected are;
Denmark, Norway, Sweden, Switzerland, the United Kingdom, 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 mentioned
European 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 the level of happiness 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 of low, medium and high level of income.
the education variable is label as the Highest educational level attained and it is divided into
many categories which are; Inadequately completed elementary education, Completed

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(compulsory) elementary education, Incomplete secondary school: technical/ Complete
secondary school: technical/vocational, Incomplete secondary: university-preparation, Complete
secondary: university-preparation, some university without degree/higher education and
University with degree/higher education. 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.
We can separate the nine countries into two different groups depending on their
economic situations as some countries have better economic situation than others. 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.
Appendix 1 and 2 shows basic descriptive statistics for each of the explanatory variables
of this model. Appendix 1 presents the variables for developed countries, while appendix 2 for
developing countries. The average income level of developed and developing countries is about
1.97198 and 1.954916 respectively. There is no big difference in average income for both groups
of countries. If we move to the second variable the 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. The third variable is employment; average unemployed
people in developed countries is 0.62 and 0.48 in developing countries. The average age of
people of developed countries is 50 while in developing countries is 47.
We can see the distribution of happiness for the nine European countries in Figure
1which shows four level of happiness from 1 to 4, where 1 indicates the lowest level of

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happiness and 4 indicates the highest level of happiness. In general 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 the highest 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. In general we can say that western European countries score higher level of happiness
than eastern European countries.
With respect of gender, it can be seen that a significant gap exist between the males and
the females with regard to the salary and wages they receive. While undertaking the survey in the
nine European countries, it was revealed that the conditions of the women gender was
considerably better in the regions of Norway, Sweden, Switzerland and the United Kingdom.
However, the rights and freedoms of the women were severely restricted in the countries of
Romania, Hungary and Poland.
Analysis of Empirical findings
The results for happiness regression shows in Table 1. Column (1) shows the regression
results of the developed countries and column (2) refers to the regression results of developing
countries.
There are 10, 995 people in these countries who answer the question of happiness (how
happy are you in your life?), the distribution of happiness show that there are 199 people or
around 2 % of population are not happy at all, there are 6,140 people or almost 55 % are

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relatively quite happy. Moreover, 3,368 who are very happy constitute around 30.63% of
population.
If we look to the distribution of happiness of the two groups of countries, we can see that
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 is represented by 3.48 % of the population, while 15.00 % 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, 36.58 % with
medium income and 30 % of population has high income level. On the other hand, in the
developing countries 36 % of population has low income level, 30.96 % with medium income
and 32 % of population has high income level.
Table 2, shows the correlation between happiness and income level for these nine
European countries. There is a positive correlation between happiness and income level for all
these countries. But the correlation for some countries is stronger than the others. For example
the correlation between happiness and income level for the Denmark is approximately 13 %,
while in Hungary it is about 19 %, 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 table 1, 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 8% for developed and developing
countries respectively. This finding is consistent with the literature part (Frijters, Haisken-

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DeNewand Shields, 2004) which argues that income has a positive impact on happiness. This
data indicates that “income buys happiness” across Europe.
Figure 3 shows the interaction between two groups of countries and income level (red
line=developed countries, blue line=developing countries), 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. There is a positive correlation between
happiness and education. We can observe a positive relationship between them by about 3.8 %
for the developed countries and for the developing countries it is about 15 %. Some old studies
show a negative relationship between SWB and higher level of education (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, there is no significant correlation between
happiness and education in developed countries; education has no effect on the happiness level
as is shown in table 1. While in developing countries it has positive correlation by about 1.75 %.
That was mentioned in the literature; the education has no effect in developed countries, but
affect the developing ones (Ferrer-i-Carbonell, 2005). 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
happiness level.
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

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between the two groups of countries is big, while the differences get smaller for each additional
level of education.
For the employment status there are 11,009 people who answer the employment question;
if they are employed or not. There are 6,210 people employed and 4,799 people who are
unemployed. In the developed countries 61.92 % of population who answered this question is
employed and 38.08 % are unemployed. Moreover, around 48.37 % of the population surveyed
in the developing countries are employed and 51.63 % are unemployed.
Employment status shows a positive correlation with happiness (Frey and Stutzer, 2000).
Both developed and developing countries are highly positive related with happiness by around
10 % and 5 % respectively. It could be explained by the fact 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.
With regards to the factor of gender, the level of happiness experienced by the female
gender is these countries is quite low. The government of such countries have taken certain pro-
active measurements in order to bridge the gap between the two genders. However, the real
situation is far from improving. 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. A negative correlation is noticed among the issue of
wage payment to the different genders and the impact of their happiness. Due to the low wage
that is received by the female gender, their quality of life is severely affected. This is because
they are unable to afford those things which might be easily affordable by a male person,
performing the same job. This can be especially seen in the countries of Poland and Romania.

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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. So, we use here the
concept of age square which has a positive effect on happiness in both developed and developing
countries. There is a U-shaped relationship between happiness and age. The happiness level is
higher for younger and older people and it is at the minimum point for the middle age ones. This
has been explained well in the literature, which derives from multivariate regressions of
happiness on age plus a number of life circumstances that vary systematically over the life cycle
(Ferrer-i-Carbonell and Frijters, 2007).
Conclusion
In conclusion it is observed that the achievement of happiness is the primary requirement
of all individuals. However, in the contemporary world of today, it is not possible to achieve this
quality since there are a lot of barriers that prevent an individual to be truly happy. There are a
number of different aspects that play a significant role in impacting the factor of happiness such
as the age of an individual, their income level, the highest education level achieved by them and
their gender. As a result, good life is the achievement of both social satisfaction and economic
capability as both complement each other. There is some evidence of an existing correlation of
happiness with other factors which includes income, education, employment and others. One of
the fundamental issues of economics is to explain whether income is related to happiness. 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, among a few others. Education is one of the most important aspects of a person’s life.
Education is often responsive to the inclusion of other variables within the model. It is positively
correlated with income and health, and if they are not controlled for, education is expected to be

16
more strongly positive. 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. As a consequence, it
may be said that monetary resources help a person to be happy. Moreover, the happiness of a
person is also dependent upon the age of a person. 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.

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