IQ-GDP II: Curve fitting

Systematic outliers

GDP is a flawed measure, but it is a very popular one. We choose it to represent wealth production per nation to establish continuity with preceding investigations. Of course, the mean national IQs, introduced in the last post, also correlate with GDP per capita (0.6167787349944118, p<1.4581315729112548e-16). In this post we are going to take a closer look at this relationship.

In the figure above, we can see an upward sloping curve described by the bulk of the data points. But there is also no shortage of outliers. Now, sometimes outliers are just noise and the only honest way to remove them is to get better data. Most of our outliers here are systematic. They group into countries that are biased in the same direction and for the same reason.

The gulf states (QAT,KWT,ARE,SAU,BHR,OMN) stand out with their IQ in the 80ies and very high GDP. Obviously, their high GDP is due to oil. China and several ex-communist countries are still catching up after decades of planned economy. The North-East Asian countries are too smart for their GDP. It seems to be the case that their unusually high mathematical-spatial IQ exaggerates their full-scale IQ or whatever aspect of IQ is essential for the GDP-relationship. If we wanted to get at the underlying relationship that drives that bulky upward sloping curve in its purest form, we might want to exclude countries rich in natural resources, tourism or tax haven fueled economies, (ex-)commies and the North-East Asians.

That leaves us with roughly half the countries, and we can claim that we examine the relationship between national IQ and GDP in non-North-East Asian countries, whose economy is based on the industrial production of goods in a market economy.
Here, we still see some outliers: The USA (maybe the dollar), Puerto Rico (due to being part of the US), South Africa and Namibia (we’ll see why in a later part of this series) and Panama (no idea why).

Fitting a curve and telling a story

The Pearson correlation we calculated above, assumes a linear relationship. In case of a non-linear relationship the correlation undersells the actual connection between the variables. Finding a fitting function for the curve described by our datapoints allows us to correct for that.

There are different ways to fit this curve and they come with different narratives.

La Griffe Du Lion proposes the smart fraction theory of IQ [1]. According to his theory, GDP is directly proportional to the size of the fraction of the population above a certain IQ-threshold. This theory entails that GDP gains would level off once most of the population is above the IQ-threshold. The threshold that fits our filtered data (70 countries) best is an IQ of 106. This results in a correlation between the size of the smart fraction and GDP of 0.932, p<8.15e-32.

Richard Dickerson proposes an exponential fit of the form a*10^(b*IQ) [2]. This results in a virtually identical fit with a correlation of 0.931, p<1.875e-31. The story changes somewhat, however. There is no reason to expect a leveling off of the curve and no smart fraction gets to play an essential role.

While these two ways of fitting the data come with different narratives and predictions, they both show that the IQ data explains the vast majority of the variation in GDP produced by industrial production of goods in a market economy.

I have another two competing theories how to fit the data. However, we first have to cover more important ground. In the next post, we are going to look at the question of causality.

[1] The smart fraction theory of IQ and the wealth of nations

[2] Richard E. Dickerson: Exponential correlation of IQ and the wealth of nations.

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