Can You Guess How Wealth is Distributed Around the World?
Here is how much wealth there is in the world.
In this article, I set out to improve on a MakeoverMonday challenge from an article titled “All the Worl’s Wealth in One Visual”. The original visualization is available here.
The data for my analysis is hosted by MakeoverMonday on their data.world account here.
I began by answering the question of how much wealth is controlled by all of the countries in our dataset. The answer is shown in the opening image.
I began my analysis by carrying out summary statistics. That is normally a good starting point. The minimum wealth is 1 Billion USD (no surprise there). The maximum is 106 Trillion USD (what country is that?). The 25th percentile is 12 Billion USD, the 50th percentile is 64 Billion USD and the 75th percentile is 377 Billion USD. Finally, the upper bound is 922 Billion USD.
Next, I took a look at the frequency distribution to see what outliers exist in the data. The image above shows you what countries have wealth above 922 Billion USD. These are the outliers. You can inspect the data later to see what countries those are. The visualization is a scatter diagram with a box-and-whisker superimposed on it.
The next thing that I did was plot a treemap. This visualizes each country as a rectangle, with the size representing the proportion of wealth held by each country. This visualization is good for showing you, at a quick glance, what countries have the largest wealth. It is a variation of the original visualization used in the article that I am trying to improve upon without cluttering the screen. The treemap is shown below.
We can see from the treemap that the United States accounts for the largest share of global wealth. How much is that, exactly?
That is right, the United States, being the wealthiest nation, accounts for 106 trillion USD!
The next bit of information I was interested in was “what countries account for 80% of global wealth?”. I had this question in mind because lists are boring. I could easily make a bar chart and say here are the Top 10, Top 15, or Top 20 countries in terms of wealth. But I wanted something better and more relatable.
To arrive at the answer, I would need to convert the wealth of the countries into a running total, and then get the percent of that total. I would finally need to find the countries that contribute the top 80% of the wealth. The required visualization is called a Pareto chart. The visualization is shown below.
Look at all those countries! In the final visualization, you will have a closer look. It shows 12 countries accounting for 80% of global wealth. Yes, 12!
Next, I decided to plot those 12 countries on a map of the world, just so we can see what the geographical spread looks like.
The map shows that no country in Latin America or in Africa makes that list. Time to take a look at Africa.
I decided to take a look at how much wealth all of the African countries account for when combined. Care to take a guess?
The figure above shows how much Africa accounts for as a region. It might take a few seconds for that to settle in.
Finally, let’s rank the regions of the world in terms of wealth contribution. A simple bar chart does that and is shown below.
We see that North America is at the top and Africa is at the bottom.
The data is sourced from Credit Suisse. The data is obtained by surveying 5.1 billion households and is published as a PDF report that goes into detail. The data was aggregated and utilized for a publication by howmuch.net. The data is hosted by MakeoverMonday here.
The actual data I worked with has only three fields: country, region, wealth.
The data could be subject to bias at different points. At the point of collection, we must note that a survey was carried out. Depending on the survey methodology, there could be selection bias. The question that comes top of mind here is whether members of the population were adequately represented.
Secondly, what exactly constitutes wealth? It is defined as assets, less liabilities. Again, what are those? There could be bias in how those are computed.
Thirdly, there could be missing variables in the data that was collected. This could lead to a wrong interpretation of the data or the extraction of wrong insights from the data. These are confounding variables.
Fourthly, there were outliers in our data. Some countries had so much wealth that the fell outside of the bounds of our box-and-whisker plot.
Finally, there could be confirmation bias. I am an African, living in Africa. That could explain why I zoomed into Africa.
Finally, I would like to invite you to interact with the data story so you can gain additional insights. It’s available here.
