A quantitative analysis of components contributing each individual's happiness across 155 countries. The datasets were extracted from Kaggle and added some other elements to help this study more accurate. R and Tableau were used to support data analysis and visualization.

1. AN ANALYSIS OF WORLD HAPPINESS
WITH THE USE OF R
Authors: Huong Hoang, Blen Yirga, Daniel Marino, Joe Tristano
School of Business and Economics, SUNY Plattsburgh

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Table of Contents
1. Abstract......................................................................................................................................3
2. Introduction ...............................................................................................................................3
3. Research Design.........................................................................................................................4
3.1 Research questions and hypotheses...........................................................................................4
3.2 Data Collection ........................................................................................................................5
4. Data Analysis .............................................................................................................................6
4.1 One-way ANOVA....................................................................................................................6
4.2 Multiple Linear Regression ......................................................................................................8
4.3 Logistic Regression..................................................................................................................9
5. Visualization.............................................................................................................................10
6. Discussion and Concluding Remark........................................................................................13
References........................................................................................................................................14

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1. Abstract
Traditionally, countries have been mostly ranked based on their economic growth by evaluating
GDP. However, the United States, a nation with the highest GDP winning the first place among
the others, has lower standard of living than Germany. This means the German feel more
comfortable with their lives than those living in the U.S. This paper shows the experiment that
was conducted on the main components that contribute to happiness in different countries across
the world in 2019. The data set includes eight independent variables(GDP per capita, social
support, healthy life expectancy, Freedom to make life choices, Generosity and Perceptions of
Corruption, Classification, Sex of the Leader) and one dependent variable (Score). Statistical
tests including one-way ANOVA, Multiple Linear Regression, Logistic Regression were applied
to observe which factors contribute to happiness. The results show that GDP per capita, social
support, healthy life expectancy and freedom to make life choices were the factors that
contribute to happiness.
2. Introduction
The data set selected was designed to rank 155 countries based on a happiness score. The
Independent variable in the dataset is score. Along with eight dependent variables that consist of
Classification, Sex of a leader, GDP per capita, Social Support, Healthy Life Expectancy,
Freedom to Make Life Choices, Generosity, and lastly Perceptions of Corruption. The Score is
composed of variables that reflect on civilian social expectations as well as economic rankings.
Leading experts in fields such as economics, psychology, survey research, national statistics,
health, public policy and more can help explain how well-being metrics can be used efficiently to
determine nations' development. This dataset was chosen because of the importance of
understanding the people's happiness in a country. Furthermore, because it is fascinating that
people come up with quantitative values for the opinions of the people. The aim of this research
is to investigate the main components that contribute to happiness in each country across the
world in 2019.

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3. Research Design
3.1 Research questions and hypotheses:
In order to determine which elements directly contribute to the total scores of happiness, there
are four research questions that were created. At the same time, corresponding with those
questions, eight hypotheses were developed and aligned to the objective of the research. The
following are research questions and eight hypotheses:
Research Question 1: What components are related to happiness in each country?
Research Question 2: What are the effects of these components on the quality of happiness?
Research Question 3: Is happiness affected by the development of a country?
Research Question 4: Is the happiness score of a country affected by the sex of its leader?
Table 1. Research’s Hypotheses
Hypothesis
Address Research
Question
1 A higher GDP per capita will increase the score of happiness. Research question 1, 2
2 A lower social support will decrease the score of happiness. Research question 1, 2
3 A higher healthy life expectancy will increase the score of happiness. Research question 1, 2
4 More freedom to make life choices will increase the score of happiness. Research question 1, 2
5 Higher numbers of generosity will increase the quality of happiness. Research question 1, 2
6
Lower measures of perceptions of corruption will increase the quality of
happiness.
Research question 1, 2
7 Countries with female leaders have higher meaning happiness scores. Research question 1, 2, 4
8 Developed countries have higher happiness scores than developing countries. Research question 1, 2, 3

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Happiness is how people feel which is affected by many aspects in their lives. In other words,
standard of living is all elements that affect people’s happiness. Traditionally, a higher standard
of living would bring people more joy and happiness. With that said, in order to raise standard of
living, Santacreu (2015) recommended increasing GDP per capita of a country, which is a
potential measure for the standard of living as well as the economic growth. However, according
to Khan Academy, there are limitations of GDP as a measure of standard of living, because it
does not directly account for leisure, environmental quality, levels of health and education,
activities conducted outside the market, and other factors. Thus, apart from GDP per capita,
social support, healthy life expectancy, freedom, generosity, perceptions of corruption,
development, and leader’s gender of a country were taken into consideration. When the society is
supported by their government such as free health care and education, they would enjoy their
lives in that country. This is also true for ones living in a country that have high awareness of
maintaining good health and long-life expectancy. A country that gives their citizens freedom to
choose their lifestyle and shows generosity would make them feel happier. Also, a nation that
has few corruptions will bring more joy to their inhabitants’ lives. Last but not least, developed
countries or female leaders would contribute a higher score of happiness than developing nations
or men.
3.2 Data Collection:
The dataset World Happiness Ranking for 2019 was found on Kaggle. Kaggle enables data
scientists and other developers to engage in running machine learning contests, write and share
code, and to host datasets. In the World Happiness Ranking 2019 dataset there are 156 data
points and 10 columns that relate to happiness in each country. For better results, adding two
more variables to the dataset was crucial. Adding Classification as a zero or one to the dataset for
countries that were developed or developing made for more accurate results.

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Table 2. Variable Summary
Type Variable Name Expected Sign
Numeric - Dependent Score
Factor Country or Region
Dummy Variable Classification
Factor Sex of Leader
Numeric GDP per capita +
Numeric Social support +
Numeric Healthy life expectancy +
Numeric Freedom to make life choices +
Numeric Generosity +
Numeric Perceptions of corruption -
4. Data Analysis
The study was conducted through three statistical tests including One Way ANOVA, multiple
linear regression, and logistic regression. Since Classification representing the development
status of a country appeared as a discrete integer ranging from 0 to 1, logistic regression was
applied. While one way ANOVA was executed for the sex of leader based on its type as factor
with two levels, multiple linear regression was carried out for six other exogenous variables
(GDP per capita, social support, healthy life expectancy, freedom to make life choices,
generosity, perceptions of corruption) because they are all continuous numbers.
4.1 One-way ANOVA:
A one-way ANOVA test was applied because the variable of sex of leaders is a factor with two
levels, in other words, it is not continuous. The results indicate the leader’s gender is

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significantly correlated with the total scores of happiness as p-value of 0.000528 is less than
0.001 (Table 3). This means hypothesis 7 is supported. In addition, the mean difference of -
0.9105971 expresses countries led by female have higher score of happiness than those led by
male by 0.9105971 points.
Table 3. One-way ANOVA Results
Sex of Leader
Mean Difference p adj
Male-Female -0.9105971 0.000528
df Sum Square Mean Square F value Pr(>F)
Sex of Leader 1 14.46 14.458 12.54 0.000528***
Residuals 154 177.59 1.153
Significant levels: * - p < 0.05 ** - p < 0.01 *** - p < 0.001
T-test was also executed in order to determine the average score between nations with female
leaders and male leaders. The result was graphed in Figure 1 below.
Figure 1. Average Score among Two Groups of Gender

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These results, however, should not be misinterpreted as causation. In other words, countries with
female leaders have a higher happiness score does not imply that this higher score is due to the
female leaders. It is the high levels of social intelligence and social advancement that these
countries display and attribute to the happiness score as well as the progressive thinking required
in order to elect a female leader.
4.2 Multiple Linear Regression:
Table 4. Multiple Linear Regression Results
Hypotheses 1, 2, 3, and 4 are supported as they are significant with p-value less than 0.01 (Table
4). As the gross domestic product (GDP) per capita increases by one unit, the happiness score
will increase by 0.7754. If the society is supported, they will feel happier as 1.1242 times. The
score of happiness will increase by 1.0781 if people are not hampered by disabling illnesses or
injuries. People who have more freedom to make life choices believe it is 1.4548 times happier.
Noticeably, social support, healthy life expectancy, and freedom to make life choices are
positively correlated with the score of happiness. These three variables strongly affect one’s
happiness compared to the other independent variables.
df F P-value
Regression 6 87.62 < 2.2e-16
Residual 149
Total 155
Regression Statistics
Multiple R-squared 0.7792
Adjusted R-squared 0.7703
Standard error 0.5335
Min -1.75304
1Q -0.35306
Median 0.05703
3Q 0.36695
Max 1.19059
Coefficients t value p-value
(Intercept) 1.7952 8.505 0.00000000000001.77***
GDP per capita 0.7754 3.553 0.000510***
Social Support 1.1242 4.745 0.000004.83***
Healthy Life Expectancy 1.0781 3.223 0.001560**
Freedom to Make Life Choices 1.4548 3.876 0.000159***
Generosity 0.4898 0.984 0.326709
Perceptions of Corruption 0.9723 1.793 0.075053 .
Significant levels: * - p < 0.05 ** - p < 0.01 *** - p < 0.001 . – p < 0.1

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However, while perceptions of corruption were statistically significant at least p<0.1, hypothesis
6 is not supported as the resulting direction of the relationship is opposite to the hypothesis. The
result does provide useful insight indicating that as perceptions of corruption increase by one unit,
the score of happiness increases by 0.9723. Generosity does not affect one’s happiness.
The regression model has an F-statistic of 87.62 with a p<0.001, indicating the regression model
is a better model than an intercept-only model. In addition, the R-squared value is 77.92% and
the adjusted R-squared value is 77.03% depicting that the variability of the independent variables
does explain very well the variability of the dependent variable. The model has a good fit.
Table 5. Summary of Hypothesis with Decision based on Statistics Results
Hypothesis Variable p-value Supported
H1: A higher GDP per capita will increase the
score of happiness.
GDP per capita 0.000510*** Supported
H2: A lower Social Support will decrease the score
of happiness.
Social Support 0.00000483*** Supported
H3: A higher healthy life expectancy will increase
the score of happiness.
Healthy Life
Expectancy
0.001560** Supported
H4: More freedom to make life choices will
increase the score of happiness.
Freedom to Make
Life Choices
0.000159*** Supported
H5: Higher numbers of generosity will increase the
quality of happiness.
Generosity 0.326709 Not
Supported
H6: Lower measure of perception of corruption will
increase the quality of happiness.
Perceptions of
Corruption
0.075053 . Not
Supported
4.3 Logistic Regression:
A logistic regression was made to observe if happiness is affected by the development of a
country. Therefore, a classification was done by categorizing the developed countries as one and

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the developing countries as zero. According to the results from Table 6, GDP per capita and
Healthy Life Expectancy are significant due to the p-value being less than 0.05. At the same time,
Social Support has its p-value less than 0.1, so this element is significant as well. The other
variables such as Freedom to Make Life Choices, Generosity and Perceptions of corruption were
insignificant due to the p-value being higher than 0.05. Therefore, we neglected the insignificant
variables and focused on the significant variables. Consequently, hypothesis 8 is not supported,
because three exogenous variables are not significant as expected. The estimate coefficients from
the results explain that GDP per capita and Healthy Life Expectancy have a higher chance of
4.080 and 6.709 times that the development of the country will gain higher score of happiness,
respectively. In addition, developed countries will gain higher score when the government
supports the society than that of in developing nations. Finally, by calculating the Pseudo R
which was 54%, it was perceived that the model is a good fit but still not strong enough to
explain the variability of the endogenous factor.
Table 6. Logistic Regression Results
5. Visualization
The map (Figure 2) represents countries’ ranking which is based on total scores of happiness
evaluated by GDP per capita, social support, healthy life expectancy, freedom to make life
Regression Statistics
Pseudo R2
0.5477501
Min -2.1957
1Q -0.3787
Median -0.0388
3Q 0.2517
Max 4.4126
Coefficients z value p-value
(Intercept) -17.250 -4.282 0.0000186***
GDP per capita 4.080 2.289 0.0221*
Social Support 4.331 1.767 0.0772 .
Healthy Life Expectancy 6.709 2.205 0.0274*
Freedom to Make Life Choices 0.712 0.280 0.7796
Generosity -2.959 -0.759 0.4478
Perceptions of Corruption 3.801 0.877 0.3803
Significant levels: * - p < 0.05 ** - p < 0.01 *** - p < 0.001 . – p < 0.1

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choices, perceptions of corruption, and generosity. The scores range from 2.6 to 7.8
corresponding with red and green for low and high scores, respectively. According to the map,
Canada, Norway, Finland, Australia, and New Zealand have relatively high scores of happiness.
Meanwhile, most of the countries which have relatively low scores of happiness are in Africa.
Additionally, countries in America and Europe generally gain higher scores than those in Asia
and Africa.

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Figure 2. World Happiness Ranking Map

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6. Discussion and Concluding Remark
In conclusion, this study investigates the main components that contribute to happiness in
different countries across the world. On the data set, there are 155 countries that are both
developed and developing and 6 factors like GDP per capita, social support, healthy life
expectancy, Freedom to make life choices, Generosity and Perceptions of Corruption which
might contribute to happiness. Based on the results, Happiness is not only affected by how much
people earn but also affected by factors like GDP per capita, social support, healthy life
expectancy and freedom to make life choices. These components increase the quality of
happiness in different countries mentioned in the data set. When it comes to generosity and
perception of corruption by which these have no contribution. In addition, results were
observed on how the development of a country can affect happiness. Only GDP per capita and
healthy life expectancy were the ones that contribute to happiness because as people live longer,
they contribute to the country’s economy which then causes the country to be more developed.
Gender of leaders was further added to the dataset to see how this could affect happiness. The
result was that countries with female leaders are happier than other countries with male leaders.
Thus, based on the results it can be suggested that countries need to increase employment, reduce
inequality and provide better health services. In such a case, individuals stand to get great
wellbeing administrations they will be roused to work and if business expanded individuals will
be more joyful. If this happens, the components that were referenced before can have more
contribution to happiness. However, there were a number of limitations within the study. First,
the data was only relying on subjective indicators. It did not specify any geographical factors like
weather and climate, location, plants, animals, and frequency of disaster. Second, our data is
from a secondary source, which will cause problems such as lack of quality and accuracy of data
and source issues. Therefore, future researchers may investigate how geographical factors affect
world happiness. In addition, researchers should be able to refine models based on a more
relevant variable which will benefit in a qualitative approach.

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References
Federal Reserve Bank of St. Louis. (2016, May 31). What Causes a Country's Standard of Living to Rise?
Retrieved from https://www.stlouisfed.org/on-the-economy/2015/december/what-causes-
countrys-standard-living-rise
How well GDP measures the well-being of society (article). (n.d.). Retrieved from
https://www.khanacademy.org/economics-finance-domain/macroeconomics/macro-economic-
indicators-and-the-business-cycle/macro-limitations-of-gdp/a/how-well-gdp-measures-the-well-
being-of-society-cnx
Helliwell, J. F. (2019, March 20). World Happiness Report 2019. Retrieved from
https://worldhappiness.report/ed/2019/