On Day 4 of Dashboard Week, our team delved into the World Happiness Index data to identify correlations that could explain a country’s happiness score. As a data analyst, I was eager to uncover any meaningful insights.

Given the Chinese proverb “Not worried about being few, but worried about being unequal”, my initial idea was to investigate income inequality’s potential impact on happiness scores. I utilized data from the World Bank, cleaned it in Alteryx, and imported it into Power BI. I created a visualization that compared GDP per capita and GINI coefficient (a measure of income inequality) to happiness scores.

The Gini index: also known as the Gini coefficient, is a statistical measure of income or wealth inequality within a population. It is commonly used to assess the distribution of income or wealth in a society. The Gini index ranges from 0 to 1, where 0 represents perfect equality (i.e., everyone has the same income or wealth), and 1 represents perfect inequality (i.e., one person has all the income or wealth, and everyone else has none).

Data preparation

The data revealed that GDP per capita still had a stronger correlation with happiness than income inequality did. However, I did find a slight negative correlation between happiness scores and income inequality in Europe and Asia. In these regions, countries with higher levels of wealth inequality tended to have lower levels of happiness. This is likely due to social and economic disparities that can lead to social unrest, political instability and a lack of trust in institutions. However, in other continents such as Africa, North America, and South America, the correlation was less clear, suggesting that more complex and various country-specific and contextual factors could be at play.

Data visualization

Basically, it’s difficult to define what happiness is, and even harder to explain what factors contribute to it, since people have varying values and priorities. But exploring the data in Power BI was a fantastic opportunity to uncover new insights and visualization techniques.

The Data School
Author: The Data School