We also travel to Namibia to visit with the Himba tribe.  They have different color categories than we do in the West, making them “color blind” to certain distinctions we easily parse out, but revealing ways in which we, too, can be color blind.

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Also notice that the five countries with the most income inequality are all in sub-Saharan Africa (the grey countries have no data available; as far as I can tell, the only grey nations outside of Africa are North Korea and Burma).

National debt as a percent of Gross Domestic Product:

These data are a bit out of date, so I went to the CIA Factbook and got the most recent debt data, from 2009. Here are the top 15 most indebted countries, as a percent of GDP:

Anyone know what’s going on with Zimbabwe that makes it stand out so much compared to other nations?

Another map from the site, this of major recipients and donors of development aid:

UPDATE: Reader Marianne points out that that image could be a bit misleading visually:

The chart with major recipients and donors of development aid, is showing off the data somewhat wrong. France + Germany = approx USA, if you look at the numbers. The chart, however, might get the reader to assume that US gives away a LOT more, because US’ column is not only taller, but also wider. On the chart, if you compare the volume of the columns, Alaska gives away more than all of the other countries added together. I do see why they did it this way, but it will present the data with a bias.

On the same topic, Katrin sent in this graph from AidWatch showing where the bulk of U.S. aid money goes (but without specific percentages):

UPDATE: JRichman says,

I think the last 2 graphs are misleading – or, at least, leave out some information people might find interesting. Both include only developmental aid, rather than military aid. Military aid is vastly larger and goes to much different recipients; i think a graph showing the distinction between the two would be very interesting.

I think the comments about how the data are visually represented are great. How we choose to represent numerical data affects the perceptions people take away about the topic and can, whether intentionally or unintentionally, lead to a biased view of the information.

I should have picked up on that when I was writing the post–as I think the commenters picked up on, the representation of the data is at least as interesting as the information itself. Thanks, everybody!

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