If a lie is repeated often enough it turns into truth. Neoliberal economists with Nobel Prizes in their knapsacks have hammered into our policymakers’ heads that we need widened income gaps (“a more flexible income spread”) in the developed world as well as in the developing world. A simple but powerful analysis shows that they are wrong and that we should not increase gaps for better economic performance.
Download attached file: wealth.xls (mimetype: application/excel )It is often said that people inclined to the political left argue with their hearts rather than with their minds. That we lack common sense and the ability to draw conclusions. We get patronizing pats on our heads and are told that, well, actually, it’s like this and that, you see, my little friend. What you say doesn’t work in reality.
Nothing wrong with having a big heart and ability for compassion. But how about the logical arguments of the political right wing? Every once and again we hear economy experts urge countries, rich and poor, to meet the needs in making the economy more flexible and letting the rich get a little bit richer, let the energetic ones through in society, and just like the engine in front of the train, they will increase the speed even for the slower wagons. They call it science, but how scientific is it?
It is fairly easy to check this by using a mathematical analysis. All you need is the Internet and a simple statistics program. Microsoft Excel will do. We will now compare the world’s countries in 1999 and see how income gaps collaborate with the economy. For income gaps we use the so-called Gini index, which describes the distribution of the resources in society. A high Gini index means wide gaps, and vice versa.
For economic efficiency we will use purchase power parity GDP per capita, which indicates how much goods and services a country produces in a year per citizen, corrected for the country’s relative price level. If the experts are right, we should thus get a positive relationship between these data, which means that a high Gini index should render a high PPP GDP per capita.
Now, let us download the Human Development Report 2001, produced by the United Nations body UNDP, from http://www.undp.org/hdr2001/completenew.pdf
In table 11 on page 178 we find theGDP/Capita (PPP), and in table 12 on page 182 we find the Gini index, for 162 countries. It is a bit tricky to fit this into Excel. First you must paste it into a text document, which should then be opened in Excel. Some more fine mechanics is needed to get the two data series into two neat columns next to each other.
Create two new columns with the logarithms of the interesting values through the function ”=LOG10(A1)” as an example for the logarithm of cell A1. We do this in order to get a more compact amount of data pairs, which gives us a better overview of the data, without changing any results. Select one of the new columns, press and hold Ctrl and select the other one. Click on the Diagrams icon. Choose “XY(scatter)” and “Finish”. This gives us a pretty little graph. Use the right mouse button to click on one of the points and choose ”Add trendline”. As Type, select Linear, and as Options, select Display equation and Display r-square value.
Now we can actually start drawing conclusions! We see that with 111 data pairs for a corresponding number of countries we have received a negative relationship! When Gini increases BNP/Capita decreases and vice versa (both positive numbers)! An equal distribution and a good economy match! The r-square value is 0.19 – which means that our equation explains the relationship to 19 %. This, together with the number of data tells us that the result is significant on commonly used confidence levels, or in plain English: the result is true.
We can ask ourselves what happens if we switch axises, i.e., suppose that the dependency is the opposite. I won’t go into these thoughts any further, but the result turns out to be similar. They belong together anyway. A similar conclusion can be drawn if we omit the ”outliers” U.S., Rwanda, Uzbekistan and Tanzania, which in our little study turn out to be somewhat extreme compared to other countries. The reason why those very countries differ is another interesting discussion.
But the data series are not exactly time compatible! The GDP is for 1999, while the Gini index is calculated for another year. Sure, let us delete those countries that have a Gini index from earlier than 1994, and suppose that the Gini index hasn’t changed substantially during the last five years, and add the reasoning that income gaps can also have a long-term impact on the GDP. We now get a degree of explanation of 13 % based on 86 countries, and we still have a significant relationship between social justice and GDP per capita! We should honestly say that the relationship is unknown for a Gini index of less than 22 or more than 61.
I tell you again, comrades! Equal distribution and economic efficiency like each other! Income gaps are hostile to economic growth! The experts speak with split tongues!
“It’s fake mathematics that make the poor so poor and the rich so damned rich” (Swedish singer Peps Persson)
(If you’re lazy, check my excel file, which is calculated in the Swedish version that uses commas instead of points.
Get the file at http://houston.indymedia.org/front.php3?article_id=2846&group=webcast