Measuring inequality with Gini coefficients
I made this bubble chart using IBM’s super-useful Many Eyes. The website allows you to make all kinds of infographics with publicly available data or you can upload your own stats instead and just use their tools.
This infographic shows the different Gini coefficients of many developed countries. I thought this would make for an interesting infographic because I’ve always thought Gini coefficients were a really good way of measuring how well a country is doing. Essentially, the numbers represent how wealth is distributed within each country. Scores range from 0 (complete equality) to 1 (total inequality) and are based on a ratio, thus avoiding outliers from skewing the data as sometimes happens with other measures such as GDP.
After I’d put the infographic together, I began to think maybe this wasn’t the best data set for a bubble chart. With Gini coefficients, it’s best to have a smaller number, making Sweden the top-performer on this chart. That doesn’t seem obvious though, and is almost counter-intuitive for this type of chart, where a reader might assume that the biggest circle is the best performer.
Also, there’s the obvious problem that the names of many of the countries don’t fit inside their respective bubbles, which makes the chart look really mismatched. Many Eyes is a wonderful tool, but it’s important to use the right data with the right type of infographic. What types of data do you think make for the best bubble charts?
by E. Wolfe
Development Watch UK 