An examination of contribution to happiness including wealth, health, and other social factors
Over the last five years there appears to be a slight decrease in overall world happiness. This is particularly evident from the years 2017-2018 when (as seen in the visual) the majority of countries experienced a decline in national average happiness. While this is evident visually, the numbers communicate a different story with the national world happiness level remaining in the range of 5.358 - 5.387 for all five years. This discrepancy can be attributed to the fact that the data set did not provide the same countries for each year. Several low scoring countries could have been removed thus inflating the average and not communicating the change. Zooming in on the United States the trend is very clear, average happiness declined for every year (7.119 → 7.104 → 6.993 → 6.886 → 6.892) except 2019. This is why we chose to illustrate the trend on a per country basis (visually) to make it evident where happiness increased or decreased. Across the years, the Western Hemisphere and Northern Europe exhibited the highest happiness levels, in contrast to Africa and Southern Asia turning in majority lower happiness scores. Our observations led us to investigate some reasons why different areas of the world are experiencing such different levels of happiness.
One major national metric used in many comparisons is the GDP (Gross Domestic Product) per capita of a country. It is an accurate measure of a country's overall level of wealth as well as per household income and spending. In a depiction of each country's GDP combined with their Happiness Score a visible trend appears. As GDP increases for a country, it is expected that their happiness score will also increase. This is most evident in the 0-20k range, where the impact of marginal happiness increase is greatest. Within this range there is a strong upward trend indicating that perhaps the biggest indicator of happiness is simply having enough income to sustain day to day life. Once countries are beyond this range the increase of happiness compared to GDP is less strong showing the law of diminishing marginal returns. From this data logical assumptions can be made that happiness is attributed more so to having the necessities than on how much disposable income a household has.
Another major metric recorded for every country is the National Life Expectancy. Our data specifically measured life expectancy at birth, measuring how long the average lifespan of a newborn baby would be. This metric, while it seems very specific, is a broad indicator of other social factors such as access to healthcare, diet and lifestyle, and crime rate among others. Broadly, life expectancy depicts the day to day social environment of a specific country. When compared against countries' happiness scores another strong trend is visualized. Considering the bulk of the data from life expectancy ranges of 60-85 there is a strong positive correlation that remains consistent throughout – illustrating that a country with a higher life expectancy is expected to have a higher happiness score. This indicates that citizen happiness is perhaps more dependent on social programs and factors such as health and safety than the previous trend of wealth. This insight offers a next step if we were to continue our research to focus on specific factors such as healthcare, crime, freedom, to observe which have the most impact on national happiness.
Our final analysis was conducted between the Coffee Consumption per Capita and Happiness Scores. While this comparison was based more on personal interest, in smaller environments such as at home or at college we have made personal observations about many people’s dependencies on caffeine for mood and productivity. This can be extrapolated to a much larger context seeing as caffeine is the most used psychoactive substance worldwide, and consumption data was readily available. By comparing this data we hoped to see if social standards of caffeine consumption had a larger effect on happiness on a global level. From the visualization we observed that there is a weak-moderate correlation between coffee consumption and happiness levels. It was interesting to see that within the 0-5 kg range, data varied, showing potential for happiness with or without coffee; however, within the 5-10 kg range (a small sample size), all of the countries with this consumption reported happiness above the middle of the scale (5). After thinking more critically about this data and then comparing it to our other visualizations we realized that there might be some confounding factors in our analysis.
We decided to cross analyze our data of Happiness compared to Coffee Consumption per Capita by including the GDP per capita of each country (represented in the size of each bubble). This illustration made it clear that Coffee was not the sole indicator behind the slight positive correlation, and there was a confounding effect from GDP. The countries that consumed more coffee generally tended to have higher GDP per capita, demonstrating that coffee consumption was based primarily on having more disposable income. This means that countries who consumed a lot of coffee likely could afford other amenities such as higher quality food, better health services, or other non-necessities. Perhaps a better observation would be that countries with higher GDP per capita have more money to spend on additional services and convenience goods (such as coffee), which is indicative of a happier lifestyle.