Chart of the day

Posted: 5 April 2014 in Uncategorized
Tags: , , , , ,

latin+america+5

I often explain to students that Gini coefficients should be used with more than a few grains of salt.

One reason, as Timothy Taylor explains, is that changes in the Gini coefficient don’t tell us where the changes come from.

because the Gini boils down the overall distribution of income to a single number, it also loses some detail. For example, if the Gini coefficient has risen, is this because the share going to the top 20% went up, or the top 10%, top 1%, or top 0.1%? You can see these kinds of differences on a Lorenz curve, if you know what you’re looking for, but the Gini alone doesn’t tell you which is true.

The other reason, which Taylor does not discuss, is that Gini coefficients should not be compared across countries. That’s because it’s blind to different economic and social structures. Thus, a coefficient of .52 in one country, where all goods and services are private commodities, means something quite different from the same number in a country in which many of those commodities (such as education, healthcare, and so on) are provided as public goods.

So, what is the Gini coefficient good for? There are two acceptable uses for that simple, convenient number.

One is to look at the degree of inequality before and after fiscal policy, as in the chart above (from the World Bank [pdf]. There, we can see that fiscal policy in Latin American countries does very little to alter the before-fiscal-policy, or market, distribution of income.

The other acceptable use is to look at the changes over time for the same country, as in the chart below from the same report.

LA-Gini

What we can see, once we resist the temptation to compare numbers across countries, is that there is a wide range of experiences across Latin America: while Honduras’s distribution of income became slightly more unequal (increasing by 2.1 percent between 2007 and 2011), Mexico’s became a bit more equal (falling 2.3 percent from 2008 to 2012) and Bolivia’s fell quite dramatically (by 15.9 percent from 2007 to 2012).

So, yes, go ahead and look at changes in Gini coefficients for individual countries—before and after fiscal policy, and over time—but, by all means, resist the temptation to compare the coefficients across countries. Such comparisons are, at best, meaningless and often can be quite misleading.

Comments
  1. […] to that of the 1920s (if, that is, we include nonmarket income). It’s the same point I made when I argued we can’t compare Gini coefficients across countries—precisely because […]

  2. […] years”) even more so (because, as I’ve argued before, the underlying data, such as Gini coefficients, mean very different things depending on the countries […]

  3. […] of comparing inequality indicators (e.g., Gini coefficients) across countries (as I explained here), what is most relevant is the change in inequality indicators for individual countries and, […]

  4. […] of comparing inequality indicators (e.g., Gini coefficients) across countries (as I explained here), what is most relevant is the change in inequality indicators for individual countries and, […]

  5. […] **The Gini coefficient (named after the Italian statistician Corrado Gini) is a measure of statistical dispersion intended to represent the distribution of income or wealth among the members of a group (e.g., a nation). Inequality on the Gini scale is measured between 0, where everybody is equal, and 1, where all the income or wealth is captured by a single person. I have expressed my own reservations about comparing Gini coefficients across countries or regions here. […]

  6. […] **The Gini coefficient (named after the Italian statistician Corrado Gini) is a measure of statistical dispersion intended to represent the distribution of income or wealth among the members of a group (e.g., a nation). Inequality on the Gini scale is measured between 0, where everybody is equal, and 1, where all the income or wealth is captured by a single person. I have expressed my own reservations about comparing Gini coefficients across countries or regions here. […]

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