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Lies, more lies, statistics

15 October 2012, 12:50
Every now and again the Government, or some other organisation, goes off about the Gini Coefficient that is so high in South Africa.

If you do not know by now what the Gini Coefficient is, let me give you a quote from wikipedia

"The Gini coefficient measures the inequality among values of a frequency distribution (for example levels of income). A Gini coefficient of zero expresses perfect equality where all values are the same (for example, where everyone has an exactly equal income). A Gini coefficient of one (100 on the percentile scale) expresses maximal inequality among values (for example where only one person has all the income)"

According to the Gini coefficient, South Africa has the highest score in the world, 63.1 as measured by the World Bank and 65.0 as measured by the CIA. (Wikipedia)

Wait a minute!!!! Why are there 2 values? 63.1 and 65.0? And this, boys and girls, is where the sh*t hits the fan.

To answer that I am going to show you what is the cause of the high Gini Coefficient in South Africa.

'Hau', you say 'Everybody knows that it is the huge difference in salaries between ordinary black and white people'.

'Uh?', I say....'Well, in that case, everybody is wrong because that it not what is causing it'

'Hau', you say, 'Everybody tells us that...everybody cannot be wrong at the same time?'

'Yes they can', I say. 'The High Gini coefficient in South Africa is caused by the +- 5 000 highest earners in South Africa. Take them out of the equation and South Africa's Gini Coefficient drops below 50 and South Africa becomes like any other ordinary country.'

To prove this I am going to give you a small lesson in how to calculate the Gini coefficient - actually I am going to cheat a bit because I am going to tell you how it works and then show you how to use an online Gini coefficient calculator. Furthermore, to make everybody understand it easier, I am going to assume that there are only 10 people in the country

Ok, let's start.

In this step you have group all the people according to their income per month or year.

Group1:   R0-R5000                7
Group2:   R5001-R10000        2
Group3:   R10001-R15000      0
Group4:   R15001-R20000      0
Group5:   R20001-R25000      0
Group6:   R25001-R30000      1

So, we have 7 people earning between R0-R5000, 2 people between R5001-R10000 and 1 rich guy earning R 30 000.

So, let's head over to
where we can calculate the gini coefficient. To do that , just type in the 7,2,0,0,0,1 on consecutive lines. When completed, click on Calculate.

Congratulations! You have just calculated your 1st Gini Coefficient. Look in the right of the graph next to the blue square and we see Gini = 70.00. How can it be so large? Surely there must be a mistake?

No mistake. Now do the following...Remove the 3 zeros and move the line with the 1 up so that you have only 7,2,1 on consecutive lines. Click on Calculate. Remarkably the Gini now comes down to Gini = 40.00

You can play around more if you like but what we see here is that the number of blocks with zero count an awful lot.

In South Africa, most people earn less than R 30 000 per month - blacks as well as whites. However, there are a few people earning R 100 000 and more per month. The more these rich people get, the more they increase the distance (number of 0 blocks)  between them and the rest of the population. Millionaires are bad for the Gini. Just think how billionaires mess it up!
Remove these guys from the calculation and South is just another ordinary Gini statistic.

The unusually large number of billionaires in South Africa compared to the number of ordinary citizens...the Ruperts, Openheimers, Motsepes..skew the Gini Coefficient considerably.

So, dear Government or other institutions, the next time you go off about white people being responsible for the high Gini.....know now that people know how to calculate it and can see through you.

Oh, and before I forget. The difference between the value for the World Bank and CIA is because they group it differently.

So, as they say...There are lies, more lies and then there is statistics.

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