the gini coefficient

Over the past month, I’ve done an unholy amount of work with demographic data from the U.S. Census API.  Specifically, I was looking at what characteristics of a community affect broadband access in that community.  One of the features I looked at was economic inequality, which can be measured by the Gini coefficient.  Briefly, the Gini coefficient measures how equally incomes are distributed across a population.  The visual presentation is pretty intuitive, as you can see here:

(image source: wikipedia)

A perfectly equal community (everyone’s income is the same) will essentially trace the line of equality, and the greater the difference between the area under the line of equality and the cumulative share of income (y is the share of total income earned by the bottom x% of earners), the greater the inequality.


News organizations seem to love using the Gini Index to talk about the effects of taxation and relative economic inequality worldwide, just to name a few. It’s a really universal, powerful way to talk about inequality.  Here’s an example from the Washington Post, presumably for the internationally curious.

This is pretty interesting; since the countries and continents aren’t labeled, the authors of the map likely assumed basic geographic and historic knowledge; if you don’t know that the big dark red landmass in Asia is China and China is ostensibly a Communist country, for example, you won’t have the “huh” moment where you reflect on the way China’s brand of Communism has evolved to its present-day capitalist form.  Similarly, someone without a grasp of the history of colonialism in Africa, particularly the social woes of Southern Africa, might find the incredible economic inequality there anomalous. This map would succeed best in telling its story with expert commentary, some level of mathematical competence (to know what the Gini index is), and historical context; for that reason it’s probably speaking to a well-educated audience with the patience to pore over the map for at least a few minutes.  The problem, though, is that the map by itself places the onus on the audience to tell the story.  Sure, the Gini index is a powerful measure of inequality, but inequality is the result of many forces, both cultural and historical.  Without that context, and with so many stories, anonymous here, waiting to be told, the data isn’t as compelling as it can be, and that’s really a shame.



Now here’s our old friend the bar graph.  One of the things taxation can do is even out the distribution of wealth a little bit.  Scandinavian countries and, to a lesser extent, Western Europe, appear to employ taxation as an equalizing method.  Again, without being conversant with the paradigm a country uses to govern itself, this doesn’t mean much.  Nor do we know what the effects of this policy–which countries have a better quality of life? how many people live in poverty?  This is just one picture in a story about inequality that is rich in detail and nuance, all written in the same language thanks to the Gini coefficient.

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