World Happiness 2026

By shrijeetverma13 · April 28, 2026

This dataset provides the complete country-level rankings and happiness scores from the World Happiness Report 2026 — the most recent edition, published…

There is a very strong positive correlation (r = 0.957) between social support and healthy life expectancy across all countries. This means that countries where people feel they have more social support tend to have significantly higher healthy life expectancy. The scatter plot with a trend line visually confirms this strong relationship, with data points colored by region.

GDP per capita is a very strong predictor of happiness scores. With a Pearson correlation of r = 0.898 and an R² of 0.806, GDP per capita alone explains over 80% of the variation in happiness scores across all 147 countries. The scatter plot with a trend line visually confirms this strong positive relationship, with countries colored by region so you can also spot regional patterns.

The analysis produced a box plot showing the distribution and disparity of freedom scores across different regions. The visualization clearly highlights which regions have the widest spread of scores among their countries, making it easy to compare regional variation at a glance.

The analysis successfully identified the top 10 happiest countries and visualized what drives their rankings. Two charts were generated: a bar chart showing each country's overall happiness score, and a stacked bar chart breaking down the contribution of six key factors — GDP per capita, social support, healthy life expectancy, freedom, generosity, and corruption — for each of the top 10 nations.

The analysis identified the 10 lowest-ranked countries and measured the gap between their social support and freedom scores. A bar chart has been generated showing each country's gap size, color-coded by magnitude, making it easy to spot which countries have the most uneven balance between these two happiness factors.

Social Support is the single factor that most influences happiness scores, with an exceptionally strong correlation of 0.981 . This means countries with stronger social support networks tend to have significantly higher happiness scores. Two visualizations were generated: a horizontal bar chart comparing all six factors' correlations with happiness, and a scatter plot showing the relationship between Social Support and happiness scores with a trend line.

The analysis produced an interactive scatter plot showing happiness scores versus GDP per capita for all countries, with clear quadrant markers highlighting where high happiness meets low GDP. The chart uses color-coded regions and includes median reference lines (red for GDP, green for happiness score) to easily identify the overachievers in the lower-right quadrant.

The analysis successfully compared average happiness scores across all 10 regions. A horizontal bar chart has been generated showing each region ranked by its average happiness score, with color intensity reflecting the score level. A data table is also available with the detailed breakdown per region.

The analysis identified countries that have high GDP per capita but score lower on happiness than their wealth would suggest. A scatter plot was generated showing all high-GDP countries, with color coding to highlight the gap between economic wealth and happiness — countries in red have the largest disparity. The top 10 countries with the biggest GDP-to-happiness gap are annotated directly on the chart for easy identification.

There is a very strong positive correlation (0.878) between perceived corruption scores and happiness scores — meaning countries where people perceive less corruption tend to be significantly happier. Two visualizations were generated: a scatter plot showing individual countries colored by region, and a regional averages bubble chart comparing corruption and happiness across world regions.

Yes, countries with higher freedom scores do tend to have higher generosity scores, though the relationship is moderate rather than strong. The correlation coefficient of 0.422 suggests a meaningful but not overwhelming connection between the two variables.

The analysis identified the top-ranked country within each region based on happiness scores and highlighted each country's standout factor. A bar chart was generated showing these regional champions, color-coded by region, making it easy to compare scores across the world. A data table was also produced with the detailed results.

Yes, 17 countries appear multiple times (exactly twice each) in the dataset, and their scores do differ across entries. The tables generated show each country's score range, average, and the difference between their two entries.

The top 5 most generous countries are Cambodia, New Zealand, Nepal, Switzerland, and the United Kingdom. However, generosity has only a weak positive relationship with overall happiness — the correlation is just 0.258. Two charts were generated: a scatter plot showing generosity vs. happiness scores across all countries (with a trend line), and a bar chart highlighting the top 10 most generous nations. While generosity does nudge happiness slightly upward, it's far less influential than factors like GDP per capita, social support, and life expectancy.

The bar chart reveals which mid-ranked countries (ranked 40–100) could gain the most happiness score improvement by addressing their single weakest factor. Countries are ranked by their 'improvement potential' — the normalized gap between their weakest factor score and the maximum possible score. The chart color-codes each country by which factor is their weakest, making it easy to spot common themes.

Yes, countries naturally fall into three distinct happiness clusters — High, Medium, and Low — based on their happiness scores and key contributing factors. Two visualizations and a data table were generated to illustrate these groupings. The scatter plot shows how countries distribute across GDP per capita vs. happiness score, with bubble size reflecting social support levels. The bar chart compares average factor values (GDP, social support, life expectancy, freedom, generosity, and corruption) across all three clusters, making it easy to see what differentiates each group.