A first look at happiness and the data behind it
Hi all! Hope the semester has been kind to everyone so far :) For the intent of penning down my thoughts, medium will be used as a platform to gather my ideas over the week. So let’s get down to business for this week…
When was the last time you felt truly happy? How has the environment around you change your perspective of the way you view happiness? Is there even a tangible method of measuring happiness? Why bother?
These are some of the questions that come to mind when contemplating about the meaning of happiness. Nevertheless, these are difficult questions. In recent news, New Zealand has announced that it will report on how national budget impacts well-being. There are merits to looking at happiness as a form of measurement for countries. Most of which are intangible at first glance. However, a study by Diner and Martin have shown that happiness leads to increase productivity in the workforce, which correlates the relevance of happiness as a measurement for countries. For the intent and purpose of the class, as you all are familiar with, I will be looking at the data from World Happiness Report 2018.
First of all, I have visualised the changes in Happiness Score. Using Tableau (it’s free for students), I manage to craft out a world view of the changes from 2017 to 2018. Following the concept of heat mapping as advised by Dr Charles Burke, I followed the use of warm colours to represent the increase in variable of interest, and cool colours for a decrease.
Alas, that was not enough as I wanted to dwell further search up for any correlation with other variables in the data provided by the World Happiness Report.
When changes with happiness was plotted against changes in dystopian residual, most countries follow a positive trend of improvement of happiness score against improvement in dystopian residual. To put it simply, a happier country translate to a less dystopian country.
However, Singapore seems to be rather exceptional here as it falls quite significantly off from the plot. As a nation, we became less happy but also less dystopian.
To try to explain this phenomenon, let’s take into account the definition of dystopian by the World Happiness Report: “a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors”. These factors are lowest incomes, lowest life expectancy, lowest generosity, most corruption, least freedom and least social support. Between 2017 and 2018, Singapore did better for life expectancy, social support and freedom, pulling up the total score for dystopian residual.
As such, even though the six factors contribute to dystopian residual, it is by no means a given that it will translate to happiness score in a country despite being a trend when plotted as a scatter plot.
To end this week’s insight, I would like to quote renowned industrial designer Dieter Rams, “simplicity is best”.
One of the intent of a visualisation is to make information accessible to the masses in order to put across your idea. As such, it pays to be able to convey your data in a simple fashion without diluting the meaning behind the data. I find the idea of “simplicity is best” as the most relevant principle when it comes to visualisation. (which I am still working on) XD
Thanks for reading!
Citationns
- “Budget 2019: Focus on Wellbeing — Budget 2018–17 May 2018 — New Zealand Government.” NZ 2018 Budget. Accessed January 28, 2019. https://www.budget.govt.nz/budget/2018/economic-fiscal-outlook/budget-2019-focus-on-wellbeing.htm
- Diener, Ed, and Martin E.p. Seligman. “Beyond Money: Toward an Economy of Well-Being.” Social Indicators Research Series The Science of Well-Being, 2009, 201–65. doi:10.1007/978–90–481–2350–6_9.
- “World Happiness Report 2018.” United Nations Sustainable Development Solutions Network. http://worldhappiness.report/download/