One notices a remarkable correlation between this, and the perceived attitudes of local women and their obesity rates.
(The map above was made by RVF commentator “durangotang” based on the geographic data here).
Tag Archives: maps
Analysis Of Russia’s PISA 2009 Results
A few months ago I posted a table and map of Russian IQ’s as derived from regional PISA performance. Those figures are based on Jarkko Hautamäki’s slideshow comparing regional PISA performance in Finland and Russia.
That material is a bit inadequate because, as had been my custom up that point, I was only making IQ estimates based on the Math and Science components of the PISA tests, and avoiding Reading to maintain reverse compatibility with my (now disused, in favor of just IQ) Human Capital Index. In light of some realizations that verbal IQ is no less important than numerical, I have updated the figures to include the verbal component as well. This doesn’t create any radical changes – the overall IQ only drops by 0.3 points – so I reuse the same map.
(Note that the legend on the map isn’t converted to IQ. “PISA scores, mean 500, SD 100, have to be transformed into IQ values, mean 100, SD 15, by adding or subtracting the deviation from the mean in the relationship 100 : 15 = 6,67.”)
Commentary
There are any numbers of comments one can make, but I will confine myself to the most important ones:
(1) In some regions, margins of error are high, as samples were low. Nonetheless, it is still possible to identify some concrete patterns. The overall estimate is very accurate because the sample was N=5,308 and representatively distributed across the country.
(2) Moscow pupils performed very well, at the level of the highest scoring OECD countries like Finland, Taiwan, and Korea. This is especially impressive considering the significant numbers of immigrants in that city from the North Caucasus and Central Asia, who come from poorly-scoring countries and rarely have good Russian. This is surely the result of a century of attracting Russia’s (the USSR’s) cognitive elite.
(3) St.-Petersburg and Tyumen oblast performed above the OECD average, while a few other regions performed at or only slightly below the OECD average.
(4) Among ethnic Russian republics, Siberian regions performed well, while the Urals and southern regions performed badly.
Graphing Influence
As today seems to be the day of cool visualizations on this blog, so on this note I’d like to highlight a really cool way of analyzing the influence of various people (philosophers, coding languages, etc) on history.
One of the basic strategies is to feed the information in Wikipedia info-boxes into a computer program called gephi that creates graphs of influence. The more connections a particular node has the bigger it appears, and distinct groupings of objects have the same color. I won’t reproduce the images here because they are typically so big (>10MB) but they are quite fascinating so here is a list of links to the relevant posts.
- Graphing the history of philosophy by Simon Raper. Note how the the algorithm successfully manages to recognize distinct schools just by analyzing the number of connections within them. The biggest nodes are those of Plato, Aristotle, Kant, Hegel, Nietzsche, Marx and Schopenhauer which is broadly consistent with general informed opinion on the greatest voices in Western philosophy.
- Following up on the The Graph of Ideas by Griff’s Graphs (who is also the author of all subsequent graphs linked to here). It goes beyond the above by also including authors (including sci-fi/fantasy) and comedians. We get an idea of the most influential authors – Hemingway, Kafka, Dostoevsky, Faulkner, Borges, Nabokov, Stephen King, H.P. Lovecraft; though the Big 7 philosophers both within philosophy and overall.
- This was followed up by a Graph of Ideas 2.0 in which nodes were sized not by direct influence but by the total number of other nodes with which they were connected with (so, theoretically, an obscure ancient Greek philosopher with just one connection to Plato would also have access to Plato’s entire network). This results in a pretty meaningless graph in which the influence of ancient philosophers is over-weighed.
- Graph of Mathematicians isn’t very useful because too many outright philosophers creep up and achieve prominent (Bertrand Russell? Avicenna?). There is no clearly dominant grouping.
- The Graph of Programming Languages is more interesting; Haskell, Java, C dominate, followed by a dozen or so of the likes of Algol-68, C++, Fortran, Perl, Python, Lua, Ruby, Smalltalk, Pascal, and Lisp. I do not have the background to assess if this is an accurate representation of reality, though I’ve never heard of Haskell, and would have guessed Fortran and Lisp would be higher.
- The Graph of Sports Teams.
- The Graph of Beer though they don’t really influence each other all that much.
- The Graph of Human Diseases is apparently dominated by colon cancer, breast cancer, leukemia, and deafness.
There is clearly a lot of scope to continue building on these graphs, especially involving ideas (philosophers, politicians, economists, sociologists, authors, etc) though finding or building the requisite databases is a time-consuming endeavour. Interesting patterns will also emerge. For instance, now that I think of it, the most influential person in history is Jesus Christ, and Karl Marx is surely in the top ten. Amazing really how deep Jewish over-achievement goes even on the biggest historical scale.
Another interesting project would be to build a graph of influence in the blogosphere perhaps based on some combination of blogroll connections and visitor numbers. This will of course be a very computationally demanding project given that there are something like 100 million blogs in existence today.
The World Economy’s Orbit
The map below shows the shifting location of the world’s economic center of gravity. It was compiled by McKinsey and reproduced by The Economist.
All is broadly as one might expect. In pre-industrial times, the world’s economic center of gravity was always basically triangulated between India, China, and the Roman Empire (later North-West Europe). By 1913, the US had became a significant world power, and in mid century it had drawn the center of gravity out into the North Atlantic. Since then the rise of the USSR, Japan, and then China, SE Asia, and India, started shifting the ball east and south at an accelerating pace. Today the speed of this transition is 140km per year. So there you have it: A cartographic representation of The Rise of the Rest.
By 2025, as shown on the map, the ball will be located somewhere in the Altai Mountains of Siberia. After that it will probably take a small dip south as India starts becoming much more prominent. Eventually however it will start going north and west again as the Arctic opens up and countries like Russia and Canada start growing much more rapidly as the century draws to a close. The cycle will retrace its ancient path.
Analysis Of China’s PISA 2009 Results
As human capital is so important for prosperity, it behoves us to know China’s in detail to assess whether it will continue converging on developed countries. Until recently the best data we had were disparate IQ tests (on the basis of which Richard Lynn’s latest estimate is an IQ of 105.8 in his 2012 book Intelligence: A Unifying Construct for the Social Sciences) as well as PISA international standardized test scores from cities like Shanghai and Hong Kong. However, the problem was that they were hardly nationally representative due to the “cognitive clustering” effect. The Chinese did not allow the OECD to publish data for the rest of the country and this understandably raised further questions about the situation in its interior heartlands, although even in 2010 I was already able to report a PISA representative saying that “even in some of the very poor areas you get performance close to the OECD average.”
As regards Chinese intelligence
Happily (via commentator Jing) we learned that the PISA data for Zhejiang province and the China average had been released on the Chinese Internet. I collated this as well as data for Chinese-majority cities outside China in the table below, while also adding in their PISA-converted IQ scores, the scores of just natives (i.e. minus immigrants), percentage of the Han population, and nominal and PPP GDP per capita.
| Reading | Math | Science | Average | (native) | IQ | (native IQ) | %汉族 | GDP/c (n) | GDP/c (P) | |
| China* | 486 | 550 | 524 | 520 | ~ | 103.0 | ~ | 91.6% | 5,430 | 8,442 |
| China: Shanghai | 556 | 600 | 575 | 577 | 589 | 111.6 | 113.4 | 99.0% | 12,783 | 19,874 |
| China: Zhejiang | 525 | 598 | 567 | 563 | ~ | 109.5 | ~ | 99.2% | 9,083 | 14,121 |
| Hong Kong | 533 | 555 | 549 | 546 | 557 | 106.9 | 108.6 | 93.6% | 34,457 | 49,990 |
| Macau | 487 | 525 | 511 | 508 | 514 | 101.2 | 102.1 | 95.0% | 65,550 | 77,607 |
| Singapore | 526 | 562 | 542 | 543 | 550 | 106.5 | 107.5 | 74.1% | 46,241 | 61,103 |
| Taiwan | 495 | 543 | 520 | 519 | 534 | 102.9 | 105.1 | 98.0% | 20,101 | 37,720 |
* Twelve provinces including Shanghai, Zhejiang, Beijing, Tianjin, Jiangsu totaling 621 schools, 21,003 students. Results have been released for Shanghai, and later on for Zhejiang (59 schools, 1,800 students – of which 80% were township-village schools) and for the 12-province average.
(1) Academic performance, and the IQ for which it is a good proxy, is very high for a developing nation. Presumably, this gap can largely be ascribed to the legacy of initial historical backwardness coupled with Maoist economics.
(2) The average PISA-converted IQ of the 12 provinces surveyed in PISA is 103.0. (I do not know if provincial results were appropriately weighed for population when calculating the 12-province average but probably not). We know the identities of five of the 12 tested provinces (Shanghai, Zhejiang, Beijing, Tianjin, Jiangsu). They are all very high-income and developed by Chinese standards. Furthermore, these five provinces – with the exception of Tianjin – all perform well above average according to stats from a Chinese online IQ testing website.
Through A Glass Ceiling Darkly: Racial IQ Disparities And The Wealth Of Nations
Now that I’m done with the Necessary Caveats, it’s time we had a look at why exactly HBD/IQ theories are both valid, and relevant to the real world. As I see it, their main import (as interpreted by me) can be distilled into a few logically consecutive, falsifiable statements:
- IQ tests are a valid, culturally fair measure of cognitive ability.
- It is hereditary.
- Race is real.
- There are racial/ethnic differences in average IQ that cannot be explained merely by reference to socio-economic or cultural factors.
- The US is an excellent “laboratory” to ascertain the average genetic IQ ceiling of different races and ethnicities.
- Average IQ influences prosperity, and general living standards.
- Consequently, knowing the racial constraints on average IQ’s – i.e., the IQ ceilings – we can estimate the relative development potential of different countries and regions.
All of them have have acquired a great deal of supporting evidence, even though they – or in particular, their linkage – remains taboo for the media and wider public discussion. By the numbers:
1. There is typically a large degree of correlation between various IQ tests, and academic achievement scores (1, 2). Nobody has yet discovered a test which has a negative correlation with a battery of other tests. This implies that there is a common “g factor” behind all types of cognitive ability.
Obviously this allows for very big variations within a single person. But within a group, someone who does well in one test will most likely also do well in another.
The argument that IQ tests are culturally biased is frequently made on the basis that they show differences in performance between racial/ethnic groups. This is a fallacy. In any case, there are IQ tests designed to be culturally fair insofar as they eschew words and test pattern recognition, such as Cattell Culture Fair III and Raven’s Progressive Matrices. These tests have a high correlation with the battery of other tests, i.e. they are valid reflections of g.
The Geography Of Global Human Capital
Today I discovered this really nifty tool, Target Map. It allows you to generate color-coded global and national maps just by uploading an Excel database.
In what will probably surprise no-one who follows my interests, my first map illustrates average PISA scores for Math, Reading, and Science for the 65 regions in the original 2009 study, 10 additional regions in a 2010 follow-up study, and the results from 12 of China’s provinces. The correlation between this map, a map of global IQ’s, and a map of GDP per capita - covered in detail on this blog – is startling to say the least.
(Click to enlarge). A table of PISA results, both average and by each of the three components, follows below the break.
The Geography Of Russia’s Talent
Human capital (primarily education) is the single most important factor behind long-term productivity gains, and hence economic growth. The relatively high human capital of Russia and China, which is comparable to developed country levels, is the most important reason why I rate their future prospects much higher than those of the other two BRIC’s, Brazil and India.
But the internal distribution of human capital is also very important. For instance, in Italy there is an almost perfect correlation between regional PISA scores in Math and Science, and regional GDP’s. I have long wanted to find a similar data set for Russia, and I finally did so today in Jarkko Hautamäki’s slideshow comparing regional PISA performance in Finland and Russia. Based on the figures there I estimated the PISA scores (Math and Science) for Russia’s regions and compiled the map below.
