Don't edit the README.md or html files directly. Your edits will be lost. They are generated by the iq.py script from the modules docstring. Edit the text there in the header of the file. HTML and MD files will be generated automatically when you run the script.
The motivation for this project was purely self-educational. The author aims to utilize and practice recently acquired skills in programming with Python, Pandas, and data visualization with plotly in particular, in a pilot project. While the data comes from probably reliable published sources, mostly scraped from the English-language Wikipedia, there is no pretense to any scientific rigor. The data sources and analysis software are provided without any legal restrictions.
The following data sources were combined:
a. What the study does not aim to do is to make any statements about the causes of the observed correlations. It is not about causes, but about correlations. The correlations might be causal, wealth increasing intellectual capacity, intellectual capacity increasing wealth, or both. Alternatively, both might be caused by a third factor. No opinion on that at all, less so one I would like to defend or just share. Probably the correlations are not just coincidental.
b, The study does not aim to make any statements about the quality of the data sources, neither positive nor negative. It is not about the quality of the data, but about the relationship between the data from different sources and their compatibility.
An interactive webpage with graphics is generated from the data sources. The data can be zoomed in, and individual data points can be inspected highlighting what can be done in plotly with relative ease. The process is entirely program-driven. The calculations are thus traceable, although not necessarily correct.
The top ranks in IQ and education, both according to Lynn & Becker's data and PISA results, are primarily occupied by East Asian countries, followed by European countries and culturally European-influenced countries such as Canada, the USA, Australia, and New Zealand. In the next group, you'll find mainly other Asian and South American countries. In the last group, you'll find most Sub-Saharan African countries, as well as many Central American and Caribbean states.
Regarding wealth, the distribution looks slightly different. A notable difference is that the median and average income of East Asian countries is not at the top, as would have been expected based on educational achievements and IQ data. They are primarily occupied by European countries, as well as the USA, Canada, Australia, and New Zealand. Only Singapore and Hong Kong are in the top group.
Average wealth increases exponentially with PISA-measured education points and IQ.
The data underwent a fitting process, and the data points were weighted according to the population of each country.
Therefore the numbers describe substantially the relationship between India and China, which are by far
the most populous countries. In the Pisa data, Indias is not available, hence
the results of the fits are noticeably different. Wealth/intellect correlations substantially coincide
when the Lynn & Becker data are used.
Alternatively, the data points could have been weighted according to the number of data points available for each country.
A 15-point higher average IQ, the standard deviation of personal IQ,
leads to a threefold average income and a 2.5 times higher median income.
A similar relationship exists between the dependence of median and average incomes on the education level measured by PISA.
A 100-point higher PISA result leads to a sixfold increase in the corresponding incomes.
The impact is slightly stronger on the median than on the average. This may either indicate that wealth slightly
increases equality or that below a certain intellectual metric threshold, wealth no longer gets worse.
A higher education level or IQ leads to a slight financial equalization of societies.
It should be noted for the PISA data that there is no data available for most populous but financially weak countries,
particularly India and Africa.
The correlation in all fits is close to 0.6-0.7, with the corresponding R^2 at 0.4. 40% of the variation
in incomes, on the one hand, and education and intelligence, on the other hand, are co-causal.
Lynn & Becker's numbers are compatible with those of PISA. The correlation is ..... Some outliers in both directions are noticeable, especially Cambodia and Saudi Arabia, but not to an extent that fundamentally questions one or the other measurement. 100 PISA points correspond to about 15 IQ points.
It is noteworthy that Western countries, Western Europe, USA, Canada, Australia, and New Zealand perform significantly better in terms of wealth than would be expected based on the collected education and intelligence data. The difference in wealth compared to the fit is a factor of two to five. East Asian countries, not only China but also Japan, are in the range of expected or below. Sub-Saharan countries also have incomes well below expected levels.
I am very grateful for any comments from the community, especially if they concern methodological errors, errors in the data, or inadequacies or errors in the areas of mathematics or programming. I have no competence in sociological matters, will read comments on this topic with interest, but due to a lack of qualifications, I will hardly respond to them.
Contact address: To avoid spam through web scraping, please replace the umlauts with the corresponding letters.
herr döt säckbauer ät gmäil döt cöm.
As to try the code, all you need is a computer running Python with Numpy, Pandas and Plotly installed and the Python file iq.py and the CSV files read in this script. The Pipfile and requirements can help you in the installation process with pipenv and pip respectively.
The program does not have any direct user interface. Just run it. It will try to start the webbrowser with a page showing the results. If this does not work on your system, just open the file iframes.html.
You can of course clone the directory so you can issue pull requests.