Archive for October, 2013

Income Inequality: Cross sectional and Trends

October 5, 2013

Although there are many ways of measuring inequality of income and wealth, this time I’m only going to use one measure:  The Gini Index (or Gini Coefficient), to show income inequality.

The Gini index has a value from zero to one, but is usually shown as a percent from zero percent to 100 percent.  At zero, there is perfect equality and everyone in a population has the same income.  At 100 percent, only one person has all the income and nobody else has any income at all. Wikipedia has a very good explanation of it here

One has to be careful in interpreting the Gini Index, though, because it is a measure of dispersion not a measure of absolute well-being.  For example, there could be two countries, one very rich and one very poor.  Yet both of them may have a low Gini Coefficient.  Thus if everybody is equally poor, they will have a low Gini, and if everybody is equally rich, they, too, will have a low Gini.

If you want to look for the well-being of an entire population, you have to use other measures alongside the Gini, like, for example, the general health of the population, or the mortality rate, or food insecurity, or pandemics, or whether or not they are having civil wars, or the level of corruption in both government and business.

The Gini is also used to measure the dispersion of wealth, but I’m not going to delve into that here.

So, let’s begin.  The first two graphs show the twenty lowest Gini countries and the twenty highest Gini countries.  Data are from the World Bank and the U.N. Statistics Division.  The year being measured is 2011. (click the image to get a larger picture)

Twenty lowest Gini countries-2011

Twenty highest Gini countries-2011

The very lowest Gini Index was Iceland, and it is a relatively rich country.

The very highest Gini was Seychelles.  I know you may not have heard of this country, but it is a bunch of scattered islands off the east coast of Africa, not far from Madagascar.  If you want to know more about it, Here is the Wikipedia entry for it

Seychelles is somewhat of a mystery, since they have a high human development index, yet, according to Wikipedia,

It has the highest Human Development Index in Africa and the highest income inequality in the world, as measured by the Gini index. Seychelles is a member of the African Union.

Among those countries with a low Gini, there are the usual suspects: Sweden, Norway, Finland, and so on.  But looking at the top graph, you can see that Afghanistan is also in this group.  The others are relatively rich, but Afghanistan is very poor.

So you can see why there is a warning above about being careful in using the Gini as a measure of well-being.

Now, let’s turn to trends.  Since I am an American, my first impulse was to look at the U.S., as I assume that anybody in any country would look at theirs first.

But, really, the unemotional approach would be to look at the slopes of the curves and see which ones have an upward trend and which ones have a downward trend, and what the magnitude of those slopes were.

But I succumbed and here is what the long-run trend looks like in the U.S.

Gini index-US-1960 to 2011You can see that since 1960, the slope is upward, indicating an increasing rate of inequality.  But notice that the rate of upward change is slowing down, indicating some kind of limiting factor or factors.  I have no idea what those factors are, but if you do, please post them in the comments.

There are a number of ways to select trends in other countries.  One way is to bracket those within one or two standard deviations from the world mean (average).  Another way might be to include only OECD countries, and yet another could be to only highlight the extremes.  Another way might be to use the U.S. as a benchmark case and select those countries who are “nearest neighbors” and bracket anything that varies by a certain percentage from the U.S.

There is a technical problem with selecting trend lines based on the mean for all countries for all years in the dataset.  The reason is that before 1986 there were less than 30 countries in the sample universe.  A better starting year is 1997 with a population of 103 countries in the dataset, but the tradeoff is shorter trend lines.

So, given these limitations, here is the world average trend from 1997 to 2011.

(click on the graph to enlarge it)

World average Gini index-1997 to 2011You can see that worldwide, the distance between the rich and poor is declining.  Yet there are some countries where the slope is upward, while the bulk of them are downward.

I started with the idea of comparing all OECD countries, but that would make an unreadable graph, since there are 34 members.  So I decided to break the data into “chunks” to simplify things.

The most obvious candidates are the U.S. neighbors, Canada and Mexico, since they are members of NAFTA and are contiguous.  So here they are.

Gini index-Canada Mexico and US-1997 to 2011It’s difficult on this graph to see the slope in Mexico, but it is declining, even though it has the greatest gap between the rich and poor among the three.

Toward the end of the period, the U.S. also shows a small drop after 2009, but it is relatively insignificant.  Canada, on the other hand, is far below it’s neighbors, and the gap between the rich and poor is slowly declining.

Examining the extremes:

From the bar charts above, I’m going to pick the trends from four of the lowest Gini coefficients and four of the greatest, and show two trend charts.  Some of the data are short trends, so I’ll have to start with a target year and show them, even though some of the countries may have no data in the early years.  For the lowest index there is Iceland, Sweden, Norway and Finland, and for the greatest are Seychelles, South Africa, Comoros and Namibia.  The target beginning year is 1999.

Four lowest Gini index trends-1999 to 2011

Four greatest Gini indices

The first thing you notice is that the lowest Gini index countries are tracking together and are on average only three to four percentage points apart from lowest to highest.

The second thing is that the high Gini countries are converging, and are nearly the same at the end of the period.  From 2006 onward, the group is trending downward, indicating a trend toward less dispersion, even though the Gini is quite high compared to the world average, which has a beginning point of about 42 and an ending point of about 39.


As you can see, this subject could go on for quite some time, given a dataset of up to 191 countries.

This post has been simple descriptive statistics, and only a sketch at that, but it does give some insight into global trends.

The really interesting part will be when the correlates are introduced and analyzed.  But I am a long way from that stage, and it will take quite awhile and a lot of grunt work to put the pieces of this puzzle together.