The World Happiness Report is a landmark survey of the state of global happiness that ranks 156 countries by how happy their citizens perceive themselves to be, and the latest report has just been released! This year’s World Happiness Report focuses on happiness and the community: how happiness has evolved over the past dozen years, with a focus on the technologies, social norms, conflicts and government policies that have driven those changes.
There are multiple data variables, the 3 main of which are:
The Cantril Ladder - a measure of life satisfaction.
Measure of positive emotion - comprises the average frequency of happiness, laughter and enjoyment on the previous day.
Measure of negative emotion - comprises the average frequency of worry, sadness and anger on the previous day.
Other variables that are calculated as contriubtions to the overall Happiness Score:
Perception of Corruption.
GDP per Capita.
Healthy Life expectancy.
Data source: http://worldhappiness.report/ed/2019/
The life evaluation used is the Cantril Ladder, which asks survey respondents to place the status of their lives on a “ladder” scale ranging from 0 to 10, where 0 means the worst possible life and 10 the best possible life.
So some of the variables most positively correlated with the Happiness Score are the economy (GDP per capita), social support, life expectancy, and freedom to make life choices.
The scatter plots above demonstrate that different indicators play more or less significant roles in overall happiness depending on the region. For example, the GINI coefficient (a measure of inequality within a country) shows that Europe, where the GINI coefficient is lowest (meaning lower inequality), is strongly correlated with a higher happiness score. In all cases, stronger social support is linked to higher happiness, as is GDP and life expectancy.
On the other hand, stronger perceptions of corruption are negatively correlated with a country's happiness score, most evident among Asian & African countries.
I will continue to extend the analysis above, looking at the other variables involved, and to break down the data to be more region-specific (Western Europe vs East, Middle East & Northern Africa vs Sub-Saharan Africa, etc.). Feel free to fork this post & continue the EDA!