Brief exploration on World Happiness
The objective for this is to explore data on happiness world-wide in an interactive manner.
First we need to outline what makes people happy, I need to preface that the answer is too complex, this has been a question that has been relevant since ancient Greece, so attempting to answer this in a short article is futile. But what we can do is analyse some easily available data to see if we can draw some conclusions and set ourselves up for success when exploring happiness data.
Before we even decide to explore any data, we need to explore happiness as a concept.
For this we need to visit a concepts from ancient Greece, Aristotle made a critical differentiation, when it comes down to happiness there are two types:
Hedonia, which refers to pleasure or happiness from pleasure seeking activities, this can be donating to charity or winning a competition.
The second type is Eudaemonia, this comes from seeking virtue, a more casual way of explaining is “meaning” so Eudaemonic happiness is linked to self-fulfilment. If you have ever taken any type of business management class, this might sound familiar since Maslow’s hierarchy (or pyramid) of needs is based on this concept.
In a study, titled “Being good by doing good: Daily eudaemonic activity and well-being. Journal of Research in Personality, by (Steger, Michael & Kashdan, Todd & Oishi, Shigehiro. (2008))”, the researchers conclude that there is a difference between the satisfaction that people obtained in engaging in hedonic behaviours and eudemonic. “Contrary to the prevalent popular cultural support for pleasure-seeking, those who engaged in more hedonic behaviours did not consistently report more well-being.” For clarity, hedonic behaviour or happiness exists in the lower half of Maslow’s pyramid, and it covers the basic necessities of a person, while eudaemonic happiness or behaviour often relates to the top of the aforementioned pyramid and where self-actualisation reside. Put in simpler words we do not tend to obtain happiness from actions that just keep us alive. This might come into play again once we look at our data.
In economics we have a bad habit of quantifying happiness, we camouflage by calling it utility so that we don’t attract attention to our strange models. To understand utility as a general concept we need to understand Opportunity cost, it is defined by the “Concise Encyclopedia of Economics” as: “the value of the next-highest valued alternative use of that resource”. In lame man’s words what we miss out by doing something other than what we are currently doing or going to do.
Most economic theories are based around on how consumers will maximise their utility. So, if we already determined that eudaemonic actions bring satisfaction, therefore when the consumer is presented with a choice, he will pick the one that will bring them more utility. This will be different for every consumer since not everyone will derive the same utility of consuming a certain good and hence cannot be accurately measured unless you are an economist in an extremely controlled theoretical environment.
So why is this important when looking at data? Well, we need to be able to interpret our data and why it might be biased, without introducing any concepts you probably already know what countries are the happiest on earth.
You are most likely thinking of Scandinavia or some extremely rural country in Asia or the amazon. Trying to interpret subconscious behaviour this is above our current scope for this little article sadly, but we can use the conclusions drawn by on Ed Deiner in a talk he gave in 2008 at the APS 20th annual convention. Ed Deiner is an American psychologist and has dedicated over thirty years to researching happiness, moreover, he also works at Gallup, this means nothing now, but will be relevant in a a couple of paragraphs.
Diener identified five factors that contribute to happiness: social relationships, temperament/adaptation, money, society and culture, and positive thinking styles.
This are what we will be focusing most of our attention on, we will include some other brief connections, but this will be what our analysis of the data will revolve around.
Finally, it’s time to introduce our data, this study revolves around the results of a survey about the state of global happiness, this survey is conducted every year by Gallup and it’s known as the Gallup World Poll. The main number we will focus here is the “Happiness Score”, as well as the other parameters that compose it. We will not focus on exploring how it is calculated but more so the results of the poll and some basic data exploration.
We will divide our analysis in to two sections following Dieners conclusions.
First money, probably one of the most important factors that impact happiness.
Second social relationships, society and culture. We omitted temperament and adaptations as well as positive thinking styles since those are too subjective for our analysis.
The Survey Results (2018):
All of the results have been compiled into an online app for easier exploration. Since happiness is subjective, the interpretation of the data might be too. But some strong conclusions can be drawn.
Finland comes in at nº1 with a score of 7.6 and is closely followed by the rest of Scandinavia. Notable results would be Canada at nº7 and Costa Rica at nº13.
Money / Basic needs:
We are not interested in a country’s overall wealth. So we will be focusing on GDP per capita, this is measured by grabbing the overall GDP of a country and dividing it by its population. This metric even do it benefits smaller richer countries, it is not coincidental that smaller or less dense countries scored higher in the happiness index.
In an article by Jasper Bergink he outlines how smaller countries with a higher taxation rate usually score higher in the World Happiness report, and the same tendencies are present in out data.
We can see a positive correlation between happiness and GDP per capita, the richer the people in the country the higher it scored in the Happiness index.
Our main outliers will be Qatar, Singapore and Luxembourg, which have an extremely high GDP per capita.
As we have outlined before GDP per capita comes highly conditioned by the population size, so rich city-states will score higher than larger countries.
Our other outlier will be Costa Rica scoring extremely high in the happiness index but not so much in GDP.
One of the reasons this phenomena occurs is due to the previously outlined concept of happiness types, the more an individual needs to focus on tasks centerer on survival (Hedonic), the less benefit they obtain from them. With money comes security and those tasks are stripped away or become less prevalent in day to day activities.
An easier visualisation of this is Maslows pyramid or hierarchy of needs. The more an individual can climb the pyramid the happier it will be. With other factors stripped away, money will be the main contributor to climb out of the bottom two steps at-least.
Society / Psychological needs:
This is the second part of our analysis and therefore the more subjective one. We have already showcased Maslow’s pyramid and how it explains an individual’s path to happiness. This part of the analysis focuses on the next two steps of the pyramid, hence the title of society. We will also include political climate using the perception of corruption variable even do a strong argument could be made that it should fall in the basic needs category.
For my personal exploration of the data Imostly looked at Freedom to make life choices, social support and Generosity. Three hard to measure metrics but I felt they would represent well the psychological needs/social side of our exploration.
Social support:
Again we can see a pretty strong positive correlation of the data. One thing we need to keep in mind is that this is global data. The US and Finland might seem like polar oposites to us since we live in a western world but their score in the Social support index is only separated by 0.2. But we need to keep in mind again that this is global data hence most of the time having a stable government and some sort of social support infrastructure such as health care or unemployment benefits will be enough to score highly.
Freedom to make life choices:
If you are starting to see a pattern in all of the graphs, you are not mistaken, it’s pretty telling that something is going on by looking at how our data is behaving. Freedom to make life choices follows this pattern, perhaps not as strongly as the previous variables we have looked at, but there is still a positive correlation.
This is due to the nature of our data. The fact that we are looking at the components that form an index means that our data will always be positively skewed. This is why this is just an exploration of the index of happiness and we are not trying to prove anything other than what the data shows.
Generosity:
A breath of fresh air when it comes to our data, there is no strong correlation present, meaning happier countries aren’t scoring higher in the generosity variable. There is some notable outliers that are worth mentioning. Myanmar leads the charge, this results are from 2018, so sadly this index has probably fallen due to the current political situation in the region. Second in this index is Indonesia, both Myanmar and Indonesia score rather low in the overall happiness index both falling in the bottom half of the overall rank.
Conclusions:
Finally after taking a brief look at the data, there is a few conclusions i drew from it. Keep in mind this are subjective and based purely on my personal data exploration.
Money doesn’t bring happiness, after a certain point. As we saw in the “Money” section of our exploration, we pointed out there were some extreme outliers in the GDP per capita score. This even do it might be compromised by population, the fact that the correlation with happiness wasn’t stronger paired with our exploration about happiness and the factors that contribute to it helps us conclude that once our basic needs are met and we can stop worrying about them, money stops playing such a strong role in overall happiness.
Happy people aren’t necessarily generous. There might be more factors in play in the generosity index than what we might initially think, such as religion or cultural traditions. Still the point stands, there is no correlation between happiness and generosity.
Perhaps a better way to measure happiness would be to isolate it by continents or cultural patterns. What we value as happiness in the west might be different to the east. The survey is done by Gallup, an American company. Perhaps if this analysis was done by an Indian company, the results would be very different. Negating cultural differences and trying to turn it into an index for the sake of an analysis is always going to be a flawed method of measuring something as subjective as happiness.
