The Heritage Foundation together with the Wall Street Journal has recently published the last report on the Index of Economic Freedom. The index captures in a series of factors like: “freedoms of movement for labor, capital, and goods, and an absolute absence of coercion or constraint of economic liberty, etc.” the degree of economic freedom of a given country.
Apart for the intrinsic interest such data has, in that it measures freedom, the case is very challenging in terms of graphical representation. Here is just an example of what I’ve been able to do so far, sincerely with little success.
The dataset (which can be downloaded directly from the website) contains for each country and year, form 1995 to 2009, the overall score and the individual factors (e.g., business freedom, trade freedom, fiscal freedom, etc.) that compose the score. Technically speaking it is, in fact, a multivariate time series, a quite tough object to handle indeed.
In my proposed solution I focus on the representation of the states that experienced the highest positive or negative changes in the whole time range. Beyond the obvious reading of best and worse countries in the overall score, which can be easily obtained from the website, I think representing measures of change is a lot more interesting.
I’ve created the chart with MicroCharts a wonderful little Excel add-on. Each sparkline represents the time variation of the overall score, so that it is possible to see ups and downs in the considered time span. Since the variation is represented in terms of the individual maximum and minimum values, the timelines cannot be compared in terms of their absolute values. But this is ok as long as the main goal is to covey messages like: “hey this country has significantly and steadily improved its index over the course of the years!”. The absolute values can be read on the right side where min and max are color-coded the same way the small dots are coded in the sparkline. The size of the dot represents the value and the bar chart the amount of variation.
I am by no means satisfied with my design, but I think it sheds some interesting light on the data. We can see that Armenia had an impressive improvement from 42.2 to 70.6. We can also see that many Eastern Europe countries like Moldova, Bosnia and Herzegovina, Lithuania, and Romania, had a great improvement as well, as highlighted in the report. Sad examples are Argentina, which experienced a sudden decrease, probably concomitant with the country economic breakdown, and Zimbawe which went from the already low 48.7 to 22.7.
A call to action!
The real challenge for these data is to represent the single factors together with the overall score and to represent the whole dataset, which I’ve not done. These factors can help explain for any major variation, if it is due to a specific sector or an overall change. I’m also convinced the same data can be seen under a myriad of other lenses different to mine. It is for this reason that I propose a “call to action“, inviting you to create a chart of this intriguing dataset.
In order to facilitate your task I have attached here a processed version of the file that contains the overall score organized by time in a single Excel sheet (the original data has one sheet for each year). If you go into some preprocessing too pay attention to some data inconsistencies the original file may have. Especially, note that Somalia in some years is removed from the dataset.