Analysis of World Happiness Reports (2015-2019)

Date: 12 Oct 2021 Tag(s): Jupyter Notebook, Python, Pandas, Happiness Rank, Maps, Graphs Categories: Data Analysis, Geographic Plotting Download Project: 11 MB - Zip Archive

Overview #

The purpose of this project was to collect and analyze the data collected in the World Happiness Reports from 2015-2019 to get a deeper understanding of what the reports are collecting and what the data actually means. This project also explores the criticisms of the measured metrics of the reports to see if they hold true or not, specifically that a country’s GDP has a skewed impact on the overall happiness score. Meaning, countries with a higher GDP, like most western countries, will inherently have much higher happiness scores than those countries that are not as developed.

The metrics looked at in the consolidated reports are:

• GDP
• Family
• Life Expectancy
• Freedom
• Generosity
• Government Trustworthiness
• Overall Happiness Score

The notebook below goes through the process of parsing and merging of all the different reports together into a single dataset for easier usage and analysis, analyzing the metrics and graphing the data points on interactive map plots to help bring the data into a more holistic world view.

Findings #

Main points of the analysis of the reports, correlations/relationships of the metrics in the reports and some graphs that were created to help visualize the data.

Correlation Matrix of Happiness Score Metrics #

We can see from the below correlation matrix that GDP, Family, and Life Expectancy are strongly correlated with the Happiness_Score. Freedom also correlates very well with the Happiness score, but it’s also correlated quite well with all data columns (except Rank) and Gov_Trustworthiness has a moderate correlation with Happiness score.

Since its clear from the above that GDP, Family, and Life_Expectancy are the main drivers for a country’s happiness score, it will be the main focus of the report analysis.

Birds Eye of View of Column Distributions and Correlations #

Below is a pairwise comparison of our variables to give us a birds eye view of the distributions and correlations of the dataset. The color is based on quartiles of the Happiness_Score so (0%-25%, 25%-50%, 50%-75%, 75%-100%).

In the scatterplots, we see that GDP, Family, and Life_Expectancy are quite linearly correlated with some noise. It is to see interesting that the correlation of Gov_Trustworthiness has distributions all over the place, with no straightforward pattern evident.

Note: right-click the graph and select “Open Image in New Tab” to zoom in to get a better view.

Top Rankings #

It hs become no secret that the countries of Northern Europe (Denmark, Finland, Iceland, Norway and Sweden) are among the most developed and happiest countries in the world. As a result, they have consistently topped the world happiness report’s rankings, with Finland, Denmark and Norway trading places in the top 3 spots from 2015-2019.

It should also be noted that much of the countries that ranked in the top quarter of the report are mostly North American and European countries.

Bottom Rankings #

Given the countries that are consistently in the top of the rankings in the report, it should be no surprise that those countries that are not nearly as developed would be in the bottom rankings. As a result, the bottom rankings almost exclusively contains African and lesser developed Asian countries.

It becomes very evident from just looking at the rankings that there is an inherent bias towards countries that are more developed and have higher GDPs.

Maps #

Below are some map plots of the metrics in the report. There are interactive maps in the linked notebook that provides more information about each country and is more intuitive to the report timelines.

Happiness Score vs. GDP #

Happiness Score vs. Family #

Happiness Score vs. Life_Expectancy #

Overall #

From the analysis, it seems like some of the criticism for “The World Happiness Report” ring true, there is a high focus on a country’s GDP along with strongly correlated features such as Family and Life_Expectancy.

It does make sense to an extent that not only having money but also having a good social net (Family) is important and does make it easier for people to advance in life in whatever direction they so choose. This also translates quite well to Life_Expectancy because of a greater ability to provide for yourself (and your Family), thus having access to better options in general.

Suffice to say, money can indeed buy happiness.