Despite the title, I will actually not be talking about London’s Tube this afternoon. Instead, I’d like to make you aware of a nifty little tool for comparing all sorts of economic data from across the world: gapminder.org.
Gapminder lets you compare a wide variety of information gathered from, for the most part, a very long period of time. For example, the default view compares income per capita to life expectancy in 2009, then graphs it out like this (bigger bubbles mean higher population):
Even cooler is that you can compare this data, shown in the year 2009, to any other year going back pretty far. With these default points of comparison, you can go back as far as 1800. Changing the data on the horizontal or vertical axis might limit the amount of years shown, but this still definitely enables one to get a very interesting picture of what’s going on in the world and how things have changed over time. For making comparisons, this tool also includes the ability to play the timeline, showing you the movement of data points over the course of history.
Suppose I wanted to get a visual representation of the link between the child mortality rate and the average number of children per woman. Here’s the difference between 1959 and 2009:
What’s especially interesting to me is how the link between these two variables is so clearly defined in current times. That is, there is a clear indication using the 2009 data that the higher the child mortality rate, the high the number of children that are born each year per woman. For the United States in particular, the shift leftward is a result of better healthcare practices and superior medicine in general, and the shift downward is reflected in the fact that parents simply aren’t having as many children as they used to.
That families are getting smaller is not particularly mind-bending information, as it’s actually a pretty well known fact. Furthermore, it just makes sense that if there is a higher rate of child mortality, women will have more children in order to increase the odds of having some that live to adulthood.
But what about less obvious things, like the relationship between the employment rate of females ages 15+ to the ratio of girls to boys in primary and secondary education? The intuition might be that the female employment rate rises with high ratios of girls in earlier education. However, actually plotting the data shows that there is very little, if any, correlation:
There are honestly way too many features to talk about here and far too many variables to list as potential comparisons, so I recommend you check this out now at gapminder.org. Once there, click on “Gapminder World” to get started. Change variables by clicking on the label of the respective axis.
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