A Comparative Analysis Of Global Happiness: A Perspective Analytics Using BI Technologies

MIS781 Business Intelligence

Unit Learning Outcome

The world happiness report is a land mark survey of the state of the global happiness. The major focus of the report of the happiness of countries or regions brings changes in happiness around the world. The overall ranking of the specified regions depends on the pooled outcomes from the ‘World Poll Surveys’ (Stavrova, Schlösser and Fetchenhauer 2013).

Happiness index of any country depends on some identified factors or parameters such as Economy (GDP per capita), life expectancy, generosity, freedom, attachment with family and public trust along with social support. The happiness score of a country is measured on the basis of 10-point scale. The happier countries mean that their government keep their people satisfied and happy. Whenever the problems arise, especially in government or non-government offices in the cities, in proper education, in proper medical facilities, in corruption issues, the government should consider the issues seriously. Sometimes the problems of immigration cause the significant unhappiness among citizens. The report and analysis develop the comparative and descriptive analysis that is applicable to any novel contribution towards human community or any business system. The year wise comparative analysis develops well-defined operational dashboards with outstanding visualization of data.

Happiness is essential to keep our brain calmer. Family life and availability of health as well as wealth directly or indirectly keep a human happy (Lelkes 2013). Happiness controls the regular secretion of hormones. It brings a ‘Feel good’ attitude to the human body along with a family. Reduction of stress, anxiety and agony are essential for happy thoughts (Easterlin 2013). Happier people makes a happier nation. Financial, economic and political stability brings the happiness in a country as well as a nation (Bartolini and Sarracino 2014). From eighteen to eighty, everybody must engage themselves in exercises and mental rest regularly. It is a way of stress relief (Cherlin 2016). The happier countries decreases the chances of domestic violence, manhandling, suicide or murder attempt (Heizomi et al. 2015). They like to enjoy their life as their life is neither critical nor full of crisis.

The business analytical report utilized for conducting analyses and decision-making. ‘IBM Watson Analytics’ was preliminary utilized for developing the dashboards and conduct perspective analytics (Watson 2014).

This analytical report is accomplished with the help of academically journals or articles and the data collected from the following website. The data is collected from ‘Kaggle’ data set. The link of the data set is-

Tasks

The data set in this report consists numerous countries and regions that are tabulated below. These countries are either developed or developing or under-developed (Rodionova 2015). The research report helps to figure out the key contributors to the problem at focus point and helps to identify these contributing factors. The parameters of the data set for two years 2015 and 2016 of different countries are visualized. Various kinds of graphical plots such as heat-map, word-cloud, bar chart, Global map, area chart and network plot are executed for visualization purpose. In the below table, some countries as per the region are being shown. All countries are not involved due to lack of scopes.

North America	Latin America and Caribbean	Australia and New Zealand	South-Eastern Asia	Southern Asia	Middle East and Northern Africa	Sub-Saharan Africa	Western Europe	Central and Eastern Europe	Eastern Asia
Canada	Brazil	Australia	Indonesia	Thailand	Saudi Arabia	Namibia	United Kingdom	Czech Republic	Taiwan
United States of America	Mexico	New Zealand	Vietnam	India	Qatar	Cameroon	Denmark	Uzbekistan	South Korea
	Uruguay		Laos	Bhutan	Iran	Ethiopia	Iceland	Russia	Hong Kong
	Haiti		Myanmar	Pakistan	Kuwait	Kenya	Norway	Moldova	China
	Honduras		Cambodia	Bangladesh	Bahrain	Zimbabwe	Switzerland	Belarus	Japan

Table 1: Example of countries region wise

Variables	Descriptions
Country	Name of the country
Region	Global region of the country
Dystopia Residual	An imaginary country of the world that has least happy people.
Economy (GDP per capita)	The ratio that measures the total output of a country (Gross domestic product-GDP) with respect to the number of people of the country.
Family	This variable defines the people’s average attachment with their family in a country.
Freedom	The mentality of freedom is required for happiness score.
Generosity	Generosity is a such kind of quality that uses unselfishness in case of time, money, kindness or food.
Happiness Rank	This ordinal variable ranks the countries as per happiness scores by ascending order.
Happiness Score	Happiness score of a country is determined by various predictors and perspectives that reflects sustainable well-being.
Health (Life Expectancy)	Life expectancy is the epidemiological measure of the average measures till a human is expected to live based on the condition at the time of birth.
Trust (Government Corruption)	The perception of Corruption that contributes happiness score.

Table 2: Variables and their descriptions

In general, the report focuses at governments around organizations, schools and societies. The target audience of this research report are the non-profit organizations to make them literate about current scenario of the world (Sevin 2015). People generally ignores the issues like frustration or depression simply because of the lack of systematic conditions. The outline indicates that humans must work and take necessary measures to retrieve the world from the anxiety and mental diseases. The report is also beneficial for the psychological researchers. It is often observed that tension and frustration are gradually growing in many prosperous countries of the world, which is a common consensus these days (Helliwell 2014).

The understanding of the report is very easy due to the usage of visualizations by dashboards.

The analytical report accomplishes the ‘Perspective analysis’. The comparison between two consecutive years 2015 and 2016 about the happiness among the people of various countries and various global regions are utilized in this report (Vozniuk, Govaerts and Gillet 2013). The report analytically lightens up the consciousness of people of various countries about the unhappiness issues and tries to make a stand to get rid of the problem. The dashboards constructed in this analysis come up with multiple analysis from a single or more than one data set. The capability of this report is to develop a better recommendation. IBM Watson Analytics comes up with the visualizations as per the types of queries asked (Stadler et al. 2016). The KPI approach has majorly three tasks that are-

• To analyze the dataset or data sets with proper visualizations
• To provide the comparative (descriptive and predictive) modelling to see the drivers that influence variation of happiness scores.

Excellent data visualization has become a key necessity to locate the decision support system of today’s world. Huge amount of data samples (Dashboards) are critically visualized with the data set in variety of formats (Verbert et al. 2013). This report in discussion section briefly studies the data visualization and offer the comparative analysis of two years (2015 and 2016) in the context of happiness score. Here, the comparative analytics simultaneously accomplishes the ‘Descriptive’ and ‘Predictive’ Analytics (Berger and Doban 2014).

Brief Literature Review

The descriptive analytics helps to find the insights of the big data and its components (Sun, Zou and Strang 2015). The data interpretation is very much easy with the help of descriptive analytics.

In 2015, the ‘Dystopia Residual’ is highest in Latin America and Caribbean (2.62) followed by North America (2.48). The least ‘Dystopia Residual’ is observed in Eastern Asia (1.68). In 2016, the ‘Dystopia Residual’ is greatest in Latin America and Caribbean (2.86) followed by North America (2.72). The least ‘Dystopia Residual’ is observed in Eastern Asia (1.9) also in 2016. Note that the ‘Dystopia Residual’ has increased from 2015 to 2016.

In 2015, the ‘Economy (GDP per capita)’ is highest in North America (1.36) followed by Western Europe (1.3). The GDP per capita is observed least in Sub-Saharan countries (0.38). In 2016, the ‘Economy (GDP per capita)’ is also highest in North America (1.47) followed by Western Europe (1.42). The least GDP per capita is observed in Sub-Saharan countries (0.47). Note that the ‘Economy (GDP per capita)’ has also increased from 2015 to 2016.

In 2015, the ‘Family’ in terms of size is highest in Australia and New Zealand (1.31) followed by North America (1.28). The family size is lowest in Southern Asia (0.65). In 2016, the ‘Family’ in terms of size is highest in Australia and New Zealand (1.14) followed by North America (1.07). The family size is least in Southern Asia (0.48). Note that the family size has decreased from 2015 to 2016.

In 2015, the ‘Freedom’ index is highest in Australia and New Zealand (0.65) followed by North America (0.59). The freedom is least in Central and Eastern Europe (0.36) and Middle East and Northern Africa (0.36). In 2016, the ‘Freedom’ index is highest in Australia and New Zealand (0.57) followed by North America (0.53). The freedom is lowest in Central and Eastern Europe (0.3). It is observed that freedom of the citizens has decreased in latest year.

In 2015, the ‘Generosity’ score is highest in Australia and New Zealand (0.46) followed by North America (0.43). The generosity is least in Central and Eastern Europe (0.15). In 2016, the ‘Generosity’ score is highest in Australia and New Zealand (0.48) followed by Southeastern Asia (0.45). The generosity is least in Central and Eastern Europe (0.17). It could be noted that generosity of the people among various countries has decreased from 2015 to 2016.

In 2015, the ‘Health’ index in terms of life expectancy is higher in Australia and New Zealand (0.92) followed by Western Europe (0.91). The average life expectancy is least in Sub-Saharan Africa (0.28). In 2016, the life expectancy is higher in Australia and New Zealand (0.84) followed by Western Europe (0.83). The life expectancy in 2016 is least in Sub Saharan African countries (0.24). Note that health expectancy among different countries has decreased in recent year.

Software used

In 2015, the trust on government is higher in Sub-Saharan Africa (4.96) followed by Western Europe (4.86). The trust is least in North America (0.49). In 2016, the trust on government is higher in Western Europe (4.88) followed by Sub-Saharan Africa (4.57). The trust on government is least in North America (0.46). It is observed that the trust on government has significantly decreased 2016 from 2015.

In both the years 2015 and 2016, the average happiness scores are region wise shown in ‘tabular’ format and graphical approach also. The regions as per descending order of average happiness Score are in the following way for both the years. These are- 1) Australia and New Zealand, 2) North America, 3) Western Europe, 4) Latin America and Caribbean, 5) Eastern Asia, 6) Middle East and Northern Africa, 7) Central and Eastern Europe, 8) Southeastern Asia, 9) Southern Asia and 10) Sub-Saharan Africa.

The important findings are that-

Average happiness score from 2015 to 2016 has increased in the following regions-

• Australia and New Zealand (7.29 to 7.32).
• Central and Eastern Europe (5.33 to 5.37).
• Southeastern Asia (5.32 to 5.34).

Average happiness score from 2015 to 2016 has decreased in the following regions-

• Northern America (7.27 to 7.25).
• Latin America and Caribbean (6.14 to 6.1).
• Eastern Asia (5.63 to 5.62).
• Middle East and Northern Africa (5.41 to 5.39).
• Southern Asia (4.58 to 4.56).
• Sub-Saharan Africa (4.2 to 4.14).  

Average happiness score from 2015 to 2016 is remained still in the following regions-

• Western Europe (6.69 to 6.69).

The Map of world indicates that Happiness Score is higher in large size countries like Canada, United States of America, Australia and Brazil. The countries like Russia or Mexico has moderate happiness score. The small countries of Europe such as Norway, Denmark, Finland or Switzerland have significant Happiness scores in 2015.

The main scenario is observed in 2016 also. Here also, North American countries, Australian countries, some European countries and Latin American countries has higher happiness. The Sub-Saharan countries and Asian countries are lesser happier in 2016 as in 2015.

The top 10 countries that are most happy as per the ‘happiness score’ are respectively- Switzerland (7.59), Iceland (7.56), Denmark (7.53), Norway (7.52), Canada (7.43), Finland (7.41), Netherlands (7.38), Sweden (7.36), New Zealand (7.29) and Australia (7.28). The first three positions are occupied by Switzerland (7.59), Iceland (7.56) and Denmark (7.53) respectively in 2015.

The top 10 countries that are most happy as per the ‘happiness score’ are- Denmark (7.53), Switzerland (7.51), Iceland (7.5), Norway (7.5), Finland (7.41), Canada (7.4), Netherlands (7.34), New Zealand (7.33), Australia (7.31) and Sweden (7.29) respectively. The first three positions are occupied by Denmark (7.53), Switzerland (7.51) and Iceland (7.5) respectively in 2016.

According to the Happiness scores, the countries that have higher happiness scores are leveled in the top of ranking list. In 2015, the ranking of top 10 countries as per higher happiness scores- Switzerland (1), Iceland (2), Denmark (3), Norway (4), Canada (5), Finland (6), Netherlands (7), Sweden (8), New Zealand (9) and Australia (10).

Research Sources

In 2016, the ranking of top 10 countries as per higher happiness scores- Denmark (1), Switzerland (2), Iceland (3), Norway (4), Finalnd (5), Canada (6), Netherlands (7), New Zealand (8), Australia (9) and Denmark (10).

Comparison of countries with ‘Lower’ Happiness Score and ‘Bottom’ Happiness Ranks:

The bottom 10 countries that are least happy as per the ‘happiness score’ are respectively- Togo (2.84), Burundi (2.91), Syria (3.01), Benin (3.34), Rwanda (3.37), Afghanistan (3.58), Burkina Faso (3.59), Ivory Coast (3.66), Guinea (3.66) and Chad (3.67). The last three positions are occupied by Togo (2.84), Burundi (2.91) and Syria (3.01) respectively in 2015.

The bottom 10 countries that are least happy as per the ‘happiness score’ are- Burundi (2.91), Syria (3.07), Togo (3.3), Afghanistan (3.36), Benin (3.48), Rwanda (3.52), Guinea (3.61), Liberia (3.62), Tanzania (3.67) and Madagascar (3.7) respectively. The last three positions are occupied by Burundi, Syria and Togo respectively in 2016.

According to the Happiness scores, the countries that have lower happiness scores are leveled in the bottom of ranking list. In 2015, the ranking of top 10 countries as per higher happiness scores- Togo (158), Burundi (157), Syria (156), Benin (155), Rwanda (154), Afghanistan (153), Burkina Faso (152), Ivory Coast (151), Guinea (150) and Chad (149).

In 2016, the ranking of bottom 10 countries as per lower happiness scores- Burundi (157), Syria (156), Togo (155), Afghanistan (154), Benin (153), Rwanda (152), Guinea (151), Liberia (150), Tanzania (149) and Madagascar (148) respectively.

In summary, this report undertakes predictive analytics is a form of advanced analytics that utilizes both past and recent data for estimating behavior, curriculum and trends (Waller and Fawcett 2013). It includes statistical analytical techniques, analytical queries and automated machine learning algorithms to the data sets for creating predictive models (Siegel 2013). Predictive analytics has increased in prominence alongside the emergence of the systems of big-data (Hazen et al. 2014). The dependent variable is predicted by multiple predictors in this model.

The predictive model of happiness score of the year 2015 shows that-

• For the Happiness rank less than or equal to 64, the predicted value of happiness score is 6.53.
• For the Happiness rank more than 64, the predicted value of happiness score is 4.59.

The predictive model of happiness score of the year 2016 shows that-

• For the Happiness rank less than or equal to 63, the predicted value of happiness score is 6.54.
• For the Happiness rank more than 63, the predicted value of happiness score is 4.61.

The ‘Happiness Score’ of all regions in 2015 are mostly significantly predicted by- 1) Happiness Rank (Single driver), 2) Family and Region (Two drivers) and 3) Freedom and Region (Two drivers).

The ‘Happiness Score’ of all regions in 2016 are mostly significantly predicted by- 1) Happiness Rank (Single driver), 2) Dystopia Residual and Economy (GDP per Capita) (Two drivers) and 3) Freedom and Region (Two drivers).

Countries used

Happiness Score by Happiness Rank:

The Happiness rank is the significant predictor of Happiness score; Happiness Score particularly determines it with 94% strength. The countries have average happiness score 5.38. Top ranked 32 countries have average happiness score more than 7, the countries of rank 32 to 64 have average happiness score more than 6, the countries of rank 64 to 94 have average happiness score more than 5, the countries of rank 95 to 127 have average happiness score more than 4.5 and the countries that have rank more than 127 have average happiness score lass then 4. The same kind of scenario is observed in both 2015 and 2016.

Happiness Score by Economy:

The Economy (GDP per capita) is also a significant factor that predicts Happiness score. The overall happiness score in 2015 is recorded as 5.38. The countries who have GDP per capita 1.23 have average happiness score more than 6, the countries who have GDP per capita in-between 1.01 and 1.23 have average happiness score more than 5 and the countries who have GDP per capita in-between 0.77 and 1.01 have average happiness score more than 5 but less the previous group.  Similarly, the countries who have GDP per capita in-between 0.42 and 1.01 have average happiness score more than 4 and the countries who have GDP per capita less than 0.42 have average happiness score more than 4 but less the previous group.  

The Economy (GDP per capita) is also a significant factor that predicts Happiness score. The overall happiness score in 2015 is recorded as 5.38. The countries who have GDP per capita 1.34 have average happiness score more than 6, the countries who have GDP per capita in-between 1.12 and 1.34 have average happiness score more than 5 and the countries who have GDP per capita in-between 0.89 and 1.12 have average happiness score more than 5 but less the previous group.  Similarly, the countries who have GDP per capita in-between 0.56 and 1.12 have average happiness score more than 4 and the countries who have GDP per capita less than 0.56 have average happiness score more than 4 but less the previous group.  

The word-clouds show the happiness score of different regions with respect to the single significant predictor Economy (GDP per capita). The two regions that have most countries of average happiness score are Australia and New Zealand as well as North America followed by Western Europe. The Economy in terms of GDP per capita is also higher in North America followed by Australia and New Zealand as well as Western Europe. Such kind of scenario are observed in 2015 and 2016 simultaneously.

Variables used

Happiness Score by Family and Region:

The happiness score is predicted by Family and Region by 81% in 2015. Almost in all the regions happiness score is higher when the average family size is greater (more than 1.23). All the countries of Australia and New Zealand as well as North America region have greater family size and hence greater happiness. Similarly, in Western Europe, Latin America and Caribbean and Central and East European countries, most of the countries have greater family size and hence higher happiness. However, the scenario is little bit of different in Eastern Asia Central and Eastern European Countries where moderate family size (0.78 to 1.09) provide greater happiness. The average family size is comparatively lower in Middle East and African countries, Southern Asian countries and Sub Saharan African countries. These countries are lesser happy with moderate family sizes. Central and Eastern Europe, Latin American and Caribbean as well as Sub-Saharan African countries has all kinds of family sizes.

The happiness score is predicted by Family and Region by 81% in 2016. Almost in all the regions happiness score is higher when the average family size is greater (more than 1.04). All the countries of Australia and New Zealand as well as North America region have greater family size and hence greater happiness. Similarly, in Western Europe, Latin America and Caribbean and Southeastern Asia, most of the countries have greater family size and hence higher happiness. However, the scenario is little bit of different in Eastern Asia Central and Eastern European Countries where moderate family size (0.77 to 0.90) provide greater happiness. Alike 2015 in 2016 also, the average family size is comparatively lower in Middle East and African countries, Southern Asian countries and Sub Saharan African countries. These countries are lesser happy with moderate family sizes in 2016 like 2015. Central and Eastern Europe as well as Latin American and Caribbean countries has all kinds of family sizes.

Happiness Score by Freedom and Region (2015):

Higher freedom is necessary for greater Happiness Score of any country as per this heat-map. Freedom and Region simultaneously predict 78% variability of Happiness Score. The higher level of freedom is observed in Australia and New Zealand as well as in North America. The regions like Central and East Europe, Latin America and Caribbean as well as Middle East and Northern Europe shows that as the score of freedom increases from less than 0.3 to 0.57 and above, the happiness score also increases. Southern Asian countries and Sub-Saharan African countries has comparatively lower freedom, therefore, the happiness score is not also high (less than 0.57). However, the countries in the regions like Eastern Asia or Northern America are happy with lower freedom index.

Target Audience

Happiness Score by Dystopia Residual and Economy (2016):

In 2016, two predictors ‘Dystopia Residual’ and ‘Economy (GDP per capita)’ effectively define 87% variability of the response variable ‘happiness score’. The heat map shows that higher Dystopia Residual and higher GDP cause greater happiness score in a country. The countries that have lowest happiness score have Dystopia Residual score 1.97 to 2.20 and GDP less than 0.565. The countries that have GDP more than 1.34 and Dystopia Residual more than 2.73 have higher happiness score. Besides, many countries who have GDP more than 1.34 or Dystopia Residual more than 2.73 individually irrespective of other factor, have significant level of happiness score.  

Both the connection plots find the similarly of GDP per capita and happiness score according to the regions. Hence, it interprets that the connectivity of GDP per capita and happiness score is almost similar as per region in both the years (2015 and 2016).

Conclusion:

The four different countries that consistently held the top spot in recent reports are Denmark, Switzerland, Norway and Iceland. All the top countries intend to have greater values for major predictive factors that are GDP per capacity, freedom index, family attachment score, Dystopia residuals and the happiness rank by no doubt. Among the top countries, differences are not high enough that year-by-year changes in rankings are to be expected. The two regions that are significantly happier than any other regions of the world are North America and Australia and New Zealand. The countries like Burundi, Togo, Syria and Benin are consistently possess the lower rank in the list year after year. The economical condition or the freedom indexes in these countries are not specifically high in those countries. Hence, the citizens of these states always stay in dissatisfaction, anxiety and disgust. The superb government policies and political stabilities are also not observed in those countries. The regions like South-East Asia, Southern Asia, Sub-Saharan countries and Middle-East Northern Africa are not enough stable in the context of political and economical conditions. Besides, population explosion in those regions has created the immense poverty and crisis.

Happiness among the people of any country year wise can change and cannot change according to the quality of the society in which people dwell. The social fabric features the extended income level of the citizens of the corresponding countries. However, the life expectancy and generosity of the people are the insignificant factors of generating happiness among common people. The year wise change shows that ‘Dystopia Residual’ and ‘Economy (GDP per capita)’ are getting higher. However, the effectiveness of ‘Family’, ‘Freedom’, ‘Generosity’, ‘Life Expectancy’ and ‘Trust on government’ are getting lesser year by year. However, as ‘Dystopia Residual’ and ‘Economy (GDP per capita)’ are the most significant factors along with ‘Region’ to bring a positive effect on the ‘Happiness Score’. The citizens of regions like ‘North America’ and ‘Australia and New Zealand’ are very happy as the significant factors are very high in those regions. The citizens of rest of the regions especially Sub-Saharan Africa, Southern Asia, South-East Asia and Middle and Northern Africa are not much happy because of the lower values of significant factors.

Analytical Approach

Summarizing the analysis, the data analysis shows that comparatively unhappier countries must look into economical and political prosperity (Ivlevs 2015). The upgradation of way of living is very much necessary in those regions. The poverty has swallowed the happiness of the countrymen in those countries as well as regions. The predictive analytics shows some insignificant factors that also to be enhanced.

More predictors of happiness would have define the variation of happiness among countries. The insignificant factors such as trust on government or generosity must be enhanced by stabilizing the political circumstances and by growing awareness among public about psychological and mental diseases. The economic growth should be maintained all over the world (Sarracino 2013). Also, the health care policy should be upgraded to increase the life expectancy of the people especially in Sub-Saharan and South-East Asian countries.

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