10 Islamic articles on: China

Table of contents for the topic China
  1. Malaysia has overtaken Japan in scientific research output per capita
  2. Japan, South Korea and China’s scientific research output compared
  3. China’s scientific output per capita compared to Malaysia’s
  4. Comparing Iran’s scientific growth to South Korea’s
  5. Comparing China’s scientific output to South Korea’s
  6. Ending technological servitude: On the need for tariffs on Internet companies (and others)
  7. Failing empire barks
  8. Forecasting the World’s Top 50 Most Powerful Countries in 2035 Using the HQI
  9. Measuring Economic and Military Potentials of World Nations with the Human Genetic-Cultural Quality Index (HQI)
  10. The 12-Year Min-Max Average: A Simple Method for Calculating Real, Legitimate Economic Growth and Canceling Out Central Bank Manipulations and other Noise

Malaysia has overtaken Japan in scientific research output per capita

Below is the graph from my previous post where I discovered that Malaysia has been producing more scientific research per citizen than Japan since 2016. This is very interesting because Malaysia is a Muslim-majority country. Japan has stagnated since 2006 due to its aging population and stagnant economy while Malaysia, thanks to having a sustainable population (thanks to sustainable fertility rates, thanks in large part to religious faith) continues to grow steadily. We can expect South Korea to slowly fall as it goes the way of Japan, so that Malaysia may actually become the top science-producing country (per capita) of Asia in the next two decades.

In 2017, Japan published 892 scientific papers per million citizens, while Malaysia produced 936.

 

Data from the United Nations and Scimago.

Japan, South Korea and China’s scientific research output compared

As part of my series of posts comparing the scientific output per capita of different countries, in this post I will compare Japan, South Korea and China’s scientific output per capita, meaning the number of scientific research papers published per million citizens by each country. In the late 1990’s South Korea was going through industrialization but was greatly behind Japan. South Korea’s papers published per million citizens (PPMC) was 222.8 in 1996, compared to Japan’s 681, meaning that Japan was producing almost three times as much scientific research as South Korea. As South Korea industrialized, the gap between the two countries narrowed, so that South Korea actually overtook Japan in 2007. The sad story of Japan is that it has never been able to go significantly above its 2006 scientific output levels of 942 PPMC, perhaps due to its aging population (its median age is higher by 6 years today compared to South Korea’s), and perhaps due to its stagnant economy caused by the long-term effects of interest.

South Korea too appears to have entered the twilight zone in 2015 at a PPMC of 1523, we can probably safely assume that it will go through decades of slow decline from here on (unless immigration saves the day). China is slowly ramping up its scientific output, although it has yet to overtake South Korea’s scientific output per capita levels for the year 1999.

Adding Malaysia to the chart lead to something I hadn’t noticed before, Malaysia overtook Japan in scientific output per capita in 2016 (the green line):

Data from the United Nations and Scimago.

China’s scientific output per capita compared to Malaysia’s

As part of my series of posts comparing the scientific output per capita of different countries, in this post I will compare Malaysia and China. Malaysia is interesting for being the most industrialized Muslim-majority country in the world. It took China until 2001 to reach Malaysia’s scientific output per capita levels for 1996, meaning that China was five years behind Malaysia on this measure of scientific output. By 2005, the two countries were producing the same amount of scientific research per citizen. Then the gap started widening, so that it took until 2011 for China to reach Malaysia’s output levels for 2008. But even by 2017, China had not caught up to Malaysia’s 2009 output levels. Today Malaysia is producing almost three times as much scientific research per citizen than China is. I expect China to remain firmly 10 years or more behind Malaysia for the foreseeable future due to the time and energy costs of scaling scientific research output over the vast swaths of China, but China will likely ultimately catch up with Malaysia some day.

Note that China was producing 16 times as much scientific research as Malaysia in 2017, what we are comparing here is output per citizen, which tells us the entire country’s general state of development. Since China has highly undeveloped rural areas with hundreds of millions of citizens, those are going to seriously drag down its output per citizen until those areas are finally industrialized.

Data from the United Nations and Scimago.

Comparing Iran’s scientific growth to South Korea’s

In the previous post I compared China’s scientific growth to South Korea’s. In this post I do the same for Iran. Iran’s gap with South Korea is narrower than China’s gap. In 2017 it was only 13 years behind South Korea when it came to scientific research output per capita. This gap might continue growing due to Iran being larger and more difficult to industrialize. But now that South Korea’s per-citizen scientific output is in decline, Iran (and China) have a good chance of catching up with it within the next decade.

Data from the United Nations and Scimago.

Comparing China’s scientific output to South Korea’s

It has been said that China is twenty years behind South Korea in development, and that South Korea is or was twenty years behind Japan. A measure of development that I like to use for comparing the development level of different countries is PPMC, which stands for Papers Published Per Million Citizens, referring to the number of scientific and scholarly papers a country publishes per a million citizens in a year. In 2017, that number was 1491 for South Korea while it was 349 for China, meaning that South Korea produced more than four times more scientific research per capita than China, which means that South Korea’s infrastructure, institutions and educational attainment on average are four times more advanced than China’s. The reason for this is that besides the fact that China started industrializing later than South Korea, China is a much larger country and requires far more investment and work to where South Korea is. China’s coastal cities likely already far outpace South Korea technologically, but China’s interior is going to be dragging down the country’s averages for decades to come.

Below we have South Korea’s scientific output per capita compared to China’s. It took China until 2009 to reach the same scientific output per capita as South Korea had in 1996, meaning that on this measure China was 13 years behind South Korea. This lag only increased with time, so that by 2014 the gap was 15 years. By 2018, the gap was 18 years because China’s output per capita hadn’t risen significantly.

It is natural for this gap to grow with time because due to China’s much larger size, it is going to take it far more time to raise the country’s scientific production compared to the much smaller South Korea. The good news for China is that this gap is probably not going to be getting much larger because South Korea’s output has started falling (likely due to an aging population, i.e. fewer young people available to become scientists and scholars) while China’s continues to rise.

Data from the United Nations and Scimago.

Ending technological servitude: On the need for tariffs on Internet companies (and others)

Aircraft engines for airliners are some of the world’s most advanced technological products. Only a few Western nations can make them. China founded the Aero Engine Corporation of China in 2016 with $7.5 billion in funding to put an end to its reliance on Western-made engines.

In my view the most important reason why protectionism is necessary, or at least one of the most important reasons, is the prevention of technological servitude. A simple example illustrates the point: Your country cannot be a pioneer in aerospace technologies if your country is not producing airplanes. If all of your airplanes come from China, the research and development will happen in China. By buying any technological product from overseas, you are subsidizing research and development pertaining to that product in that country.

Most of the world’s countries are presently in a position of technological servitude when it comes to the products offered by internet companies like Google and Facebook. Short-sighted politicians think that it is sufficient to tax these companies in order to ensure that they partake in the local economy on a balanced basis. This is nonsense. When Google sells hundreds of millions of dollars worth of ads in the Philippines, this helps subsidize research and development in California. The technology and know-how to serve ads, to analyze data, to run data centers and networking hubs, all of these involve extremely advanced skills and tens of thousands of skilled workers, and most of them are going to be in California. The Philippines, by letting Google operate in the country without tariffs, is literally paying cash to Google to train people and develop technologies and ecosystems in California that helps it continue to maintain its technological edge over the rest of the world.

There is no way for a Filipino company to compete with Google in a free market, because Google is vastly ahead both in technology and in economies of scale. Without tariffs, the rest of the world will be stuck in a position of permanent technological servitude, the United States will always be ahead; it will have the most advanced technologies AND it will have tens of thousand of highly trained engineers with years of experience in using and developing these technologies. The United States becomes the “brain of the world” where all the interesting things happen, and every other nation has to beg it for its high-tech products, which it can use to force them to comply with its foreign policy.

In order to help create a Filipino innovation ecosystem in Internet advertising, the country will have to place tariffs on every ad Google sells in the country, making it more expensive to advertise in the country using Google’s services. This will help Filipino companies grow, hiring more people, but most importantly slowly acquiring the technological knowledge to innovate.

Instead of tariffs, the Philippines could ask Google to open engineering centers in the country. While that will certainly help in some ways, only a very small portion of Google’s skill-set will be transferred to the locals in this way, because the services Google offers in the country will still greatly rely on US infrastructure. To properly implement this, the country will have to require Google to serve the Philippines’ market entirely from inside the country, through data centers and networking and engineering hubs all inside the Philippines. But this may not be doable, because Google will have to replicate all of its major teams and technologies for the Philippines’ market (such as its search product). If this is done, it would create tremendous value for the country, helping create thousands of “Google veterans” who can go on to launch other technology companies.

It is true that tariffs cause inefficiencies and require reinventing wheels already invented in other countries. It is extremely costly to develop a local aerospace industry in a developing country. Why not just buy all of your airplanes from the US or Europe? Because that puts you in a position of permanent technological servitude. You will always have to go begging when you need new airplanes, as Iran and Turkey have to do today. No self-respecting nation should allow the US or Europe to hold this club over their heads.

Today, Iran and China are the two countries that recognize these facts better than any other country. Iran produces most of its cars locally, an industry that accounts for 10% of the country’s GDP and employs 700,000 people. It is true that this industry leads to various inefficiencies and the need to reinvent the wheel that could be avoided by importing foreign cars and helping subsidize German and US automakers. But so what? Iran’s car industry helps provide jobs for thousands of scientists and researchers in materials, electronics and robotics.

To be a technological slave or a technologically independent nation

Developing countries have two choices: either to remain technological slaves of developed economies and be treated with the usual contempt these countries have for developing nations, or use tariffs to build local technological innovation ecosystems.

What the US and Europe want from developing nations is free markets that enables them to keep these nations in a position of permanent technological servitude. This achieves four very important goals:

  • Providing markets for US and European products
  • Helping subsidize the jobs of hundreds of thousands of scientists and researchers in the US and Europe who will go on to build the next generation of technological products
  • The ability to use the threat of withholding these products as enforcement tools for making these nations do your bidding
  • Helping ensure US and European military superiority over these nations, since by lacking a powerful technological-industrial base, developing nations will be forever begging the US and Europe for military technology (and will own airplanes and satellites with US/European back-doors and vulnerabilities built in that prevents them from being a threat)

The attitude of the US and Europe toward countries like Indonesia is “We would love for you to develop, but don’t you dare develop in a way that makes you our equal!”

President Trump is really upset about China’s Made in China 2025 initiative because it is designed with the exact goal of escaping technological servitude. Until recently it was easy to dismiss China as a backward copycat that couldn’t innovate. The US and Europe incessantly made fun of China for its technological servitude, while using every tool in their toolbox to ensure that China remained that way.  This is the default Western attitude toward the rest of the world. China eventually took notice and decided to hit the US/Europe where it hurts by working to be an equal.

The problem with an equal is that an equal can hurt you. The West does not want equals, it wants the whole world to be one big family with them as the big brothers holding clubs over everyone else’s heads.

China, Iran and to a lesser degree Russia seem to be making the right decisions for escaping technological servitude. It remains to be seen whether other countries can follow their example.

Failing empire barks

How dare a sovereign state develop weapons technology that could prevent the American Empire from subjugating them and turning them into a client state?

For China the cat is out of the bag, so the US has to bark at North Korea and Iran and ask for China’s help in intimidating these countries.

And of course, something has to be done about the Iranian threat. Look how close to our military bases they have put their country:

Forecasting the World’s Top 50 Most Powerful Countries in 2035 Using the HQI

The following table is a list of 50 countries that are predicted to have the most economic, technological and military power in the world by the year 2035, according to HQI theory. The projected power of the United States is set to 100 to make it easy to compare other countries with it. China’s projected power is 251.6, meaning it will be more than double as powerful as the United States in 2035.

The 2035 populations are projected based on the average of a linear regression of population growth rates between 1995 and 2015. If a country’s population growth rate was 3% in 1995 and 2% 2015, it is assumed that in 2035 the population growth rate will be 1%. The average of the 2015 and 2035 growth rate is taken (1.5%), and this is recursively applied 20 times to arrive at the 2035 population. This is somewhat crude but good enough for our purposes.

The HQI is the Human Genetic-Cultural Quality Index, a measure of a population’s capacity for intellectual achievement and technological innovation, by taking into account a country’s scientific output and real (Smithian) economic growth. China’s HQI is 856 while the HQI of the United States is 1372, meaning each Chinese citizen adds a relative value of 856 to China’s economy, while each American citizen adds a value of 1372. The HQI indicates the “quality” (as opposed to quantity) of the human capital of a country.

By multiplying a population’s count by its HQI, we arrive at a number that indicates the total power for innovation in the population as a whole. In 2035, India will have more people than China (1.52 billion versus 1.46 billion), but since China’s HQI is higher (i.e. since its population is of higher genetic-cultural quality), its power and might will be consequently larger. In fact, China will be five times more powerful than India in 2035, and 2.5 times more powerful than the United States. It will be the most powerful country in the world by a wide margin.

Iran gets an advantage over Russia due to its higher economic growth, fast growing population, and its higher scientific output per capita (25% higher than that of Russia). However, many of Russia’s recent troubles have been due to economic warfare from Wall Street, therefore it is highly unlikely that it will ever be less powerful than Iran. As the HQI is updated over the next few years, Russia’s numbers should improve significantly.

Qatar and Saudi’s high HQI numbers are largely due to their importation of foreign scientists to carry out research in their universities and are not indicative of native capabilities.

It is unlikely that Germany will be less powerful than the United Kingdom in 2035. The HQI of the UK is inflated by the UK’s higher output in the “soft” sciences. Germany actually outdoes the UK in many important scientific fields, such as energy, engineering, physics, astronomy, mathematics and chemical engineering. The UK is superior in medical research.

South Korea produces far more science per capita than Japan, and its economy is growing fast. Both of these factors go toward its much higher HQI compared to Japan (1627 vs. 605). South Korea’s actual advantage may be smaller, and it seems unlikely that it will actually be more powerful than Japan.

How it Works

A country’s HQI shows its potential for growth. It says that after decades of infrastructure-building, urbanization and everything else that goes into producing a developed economy, that country can reach the level of output and innovation that another, fully developed nation of similar HQI has. What this means is that multiplying a non-fully-developed nation’s HQI will give us a number that reflects its power in a few decades, when it has finished developing.

The Chinese population’s HQI of 856 is close to that of Italy’s (945). What this means is that 20 years from now, once China has fully developed, it will have the same economic, military and technological power of an imaginary Italy that has 1.46 billion people. This thought alone should be sufficient to keep those Americans awake at night who think they will forever be the world’s biggest power. Can an America with 364 million people stand up to an Italy with 1.46 billion people? Italy’s 60 million people published 106000 scientific papers in 2015. If that population grows to 1.46 billion, an increase by a factor 24, that means they would likely be able to publish 2.5 million scientific papers per year, dwarfing America’s scientific output of 600,000 papers per year, and with that, dwarfing America’s ability at innovation and technological progress, and its economic and ultimately military power.

That imaginary Italy is very much what China is going to be in 20 years. The HQI of Italy and China are similar. All that remains for China to do to become an Italy with 1.46 billion people is to finish building its economic and scientific infrastructure, and this will probably be done in the 20 years, as the example of South Korea’s development shows.

As for already developed nations, their HQI can be multiplied by the present population to get its present level of power, and it can be multiplied by its projected future population to get its future power. This only makes sense for developed nations. For developing nations like China (and South Korea until recently), the population is high quality, but everything else isn’t, therefore the population is being held back by various factors from achieving what their HQI suggests. For this reason we give these fast-developing nations 20 years to reach their full potential.

Country Projected 2035 Population HQI Relative Economic, Technological and Military Power in 2035
1 China 1,464,562,493 856 250.46
2 United States 364,631,940 1372 100.00
3 India 1,520,438,646 162 49.24
4 United Kingdom 79,223,389 1818 28.79
5 Germany 93,984,408 1218 22.88
6 Australia 31,623,131 3561 22.51
7 France 72,157,368 1165 16.80
8 South Korea 50,400,996 1627 16.39
9 Canada 42,699,016 1859 15.87
10 Brazil 225,917,248 332 14.99
11 Japan 117,049,007 605 14.15
12 Iran 100,194,389 626 12.53
13 Italy 61,510,122 945 11.62
14 Spain 44,357,325 1277 11.33
15 Turkey 101,374,566 479 9.70
16 Russian Federation 149,971,486 312 9.35
17 Switzerland 10,987,401 3910 8.59
18 Poland 37,352,026 1120 8.37
19 Netherlands 18,189,750 2238 8.14
20 Taiwan 22,039,541 1717 7.56
21 Sweden 12,588,464 2775 6.98
22 Saudi Arabia 43,095,570 768 6.61
23 Nigeria 312,375,890 97 6.05
24 Singapore 5,924,284 4823 5.71
25 Malaysia 36,376,961 756 5.50
26 Israel 10,953,808 2410 5.28
27 Egypt 132,313,330 198 5.23
28 Belgium 12,642,382 2058 5.20
29 South Africa 70,569,040 292 4.12
30 Iraq 68,203,001 295 4.03
31 Norway 6,858,738 2925 4.01
32 Austria 10,735,422 1859 3.99
33 Czech Republic 11,684,419 1569 3.66
34 Pakistan 272,264,022 66 3.59
35 Denmark 6,374,946 2812 3.58
36 Mexico 152,508,904 110 3.37
37 New Zealand 6,972,004 2352 3.28
38 Hong Kong 7,811,688 1997 3.12
39 Qatar 4,879,996 2815 2.75
40 Argentina 50,278,252 253 2.54
41 Finland 5,873,345 2034 2.39
42 Portugal 8,783,800 1359 2.39
43 Thailand 68,077,965 174 2.37
44 Chile 21,146,173 535 2.26
45 Ireland 5,233,086 1959 2.05
46 Indonesia 314,805,429 29 1.84
47 Romania 19,228,586 475 1.83
48 Colombia 55,052,245 163 1.80
49 Greece 8,774,644 934 1.64
50 Algeria 58,570,388 129 1.51

Please see my essays on the HQI and the 12-Year Min-Max Average for the fine print regarding how the above numbers were calculated. Most of the data is from the World Bank. Taiwan’s population growth rate was taken from Worldometers.com as it is missing from the World Bank data.

Below is the same table with the nitty-gritty details exposed, and with seven bonus countries at the end. Download it as an Excel file or PDF.

Country 2015 Citable Scientific Documents 2015 Population 1995 Population Growth Rate 2015 Population Growth Rate 2035 Projected Population Growth Rate Projected Annual Population Growth Rate (Mean of 2015 & 2035 Rates) Projected 2035 Population Average Real Annual Economic Growth (2004-2015) [12-Year Min-Max Method] HQI Relative Power in 2035
1 China 416,409 1,401,586,609 1.1 0.5 -0.1 0.2 1,464,562,493 10.5 856 250.46
2 United States 567,007 325,127,634 1.2 0.8 0.4 0.6 364,631,940 0.5 1372 100.00
3 India 123,206 1,282,390,303 1.9 1.2 0.5 0.9 1,520,438,646 7.1 162 49.24
4 United Kingdom 169,483 63,843,856 0.3 0.8 1.4 1.1 79,223,389 -0.6 1818 28.79
5 Germany 149,773 82,562,004 0.3 0.5 0.8 0.7 93,984,408 -0.8 1218 22.88
6 Australia 82,567 23,923,101 1.2 1.3 1.5 1.4 31,623,131 3.0 3561 22.51
7 France 103,733 64,982,894 0.4 0.5 0.6 0.5 72,157,368 -0.1 1165 16.80
8 South Korea 73,433 49,750,234 1.0 0.4 -0.3 0.1 50,400,996 3.6 1627 16.39
9 Canada 89,312 35,871,283 0.8 0.9 0.9 0.9 42,699,016 0.1 1859 15.87
10 Brazil 61,122 203,657,210 1.5 0.9 0.2 0.5 225,917,248 3.7 332 14.99
11 Japan 109,305 126,818,019 0.4 -0.1 -0.7 -0.4 117,049,007 -0.4 605 14.15
12 Iran 39,727 79,476,308 1.4 1.2 1.1 1.2 100,194,389 5.0 626 12.53
13 Italy 95,836 61,142,221 0.0 0.0 0.0 0.0 61,510,122 -1.7 945 11.62
14 Spain 79,209 47,199,069 0.2 -0.1 -0.5 -0.3 44,357,325 0.3 1277 11.33
15 Turkey 39,275 76,690,509 1.6 1.5 1.4 1.4 101,374,566 2.3 479 9.70
16 Russian Federation 57,881 142,098,141 0.0 0.2 0.4 0.3 149,971,486 0.4 312 9.35
17 Switzerland 39,358 8,238,610 0.7 1.2 1.7 1.5 10,987,401 1.0 3910 8.59
18 Poland 37,285 38,221,584 0.1 0.0 -0.2 -0.1 37,352,026 4.2 1120 8.37
19 Netherlands 51,434 16,844,195 0.5 0.4 0.4 0.4 18,189,750 0.0 2238 8.14
20 Taiwan 34,011 23,381,038 0.8 0.1 -0.7 -0.3 22,039,541 4.5 1717 7.56
21 Sweden 35,039 9,693,883 0.5 1.1 1.6 1.3 12,588,464 0.4 2775 6.98
22 Saudi Arabia 17,529 29,897,741 2.6 2.1 1.6 1.8 43,095,570 5.9 768 6.61
23 Nigeria 5,112 183,523,432 2.5 2.6 2.8 2.7 312,375,890 18.2 97 6.05
24 Singapore 17,976 5,618,866 3.0 1.2 -0.7 0.3 5,924,284 7.3 4823 5.71
25 Malaysia 23,414 30,651,176 2.5 1.4 0.3 0.9 36,376,961 3.0 756 5.50
26 Israel 18,040 7,919,528 2.7 2.0 1.3 1.6 10,953,808 3.7 2410 5.28
27 Egypt 14,800 84,705,681 1.9 2.1 2.4 2.3 132,313,330 4.5 198 5.23
28 Belgium 29,180 11,183,411 0.2 0.5 0.8 0.6 12,642,382 0.7 2058 5.20
29 South Africa 17,409 53,491,333 2.2 1.7 1.1 1.4 70,569,040 2.0 292 4.12
30 Iraq 1,793 35,766,702 3.1 3.2 3.4 3.3 68,203,001 27.8 295 4.03
31 Norway 18,228 5,142,842 0.5 1.1 1.8 1.5 6,858,738 1.2 2925 4.01
32 Austria 21,818 8,557,761 0.2 0.8 1.5 1.1 10,735,422 -0.1 1859 3.99
33 Czech Republic 20,759 10,777,060 -0.1 0.3 0.6 0.4 11,684,419 1.0 1569 3.66
34 Pakistan 10,962 188,144,040 2.5 2.1 1.7 1.9 272,264,022 4.6 66 3.59
35 Denmark 23,081 5,661,723 0.5 0.6 0.6 0.6 6,374,946 -0.6 2812 3.58
36 Mexico 18,417 125,235,587 1.9 1.3 0.7 1.0 152,508,904 0.2 110 3.37
37 New Zealand 13,052 4,596,396 1.5 1.9 2.3 2.1 6,972,004 1.3 2352 3.28
38 Hong Kong 14,710 7,313,557 2.0 0.9 -0.2 0.3 7,811,688 3.1 1997 3.12
39 Qatar 2,766 2,350,549 1.2 2.9 4.6 3.7 4,879,996 14.9 2815 2.75
40 Argentina 11,815 42,154,914 1.3 1.0 0.8 0.9 50,278,252 2.2 253 2.54
41 Finland 17,551 5,460,592 0.4 0.4 0.4 0.4 5,873,345 -1.5 2034 2.39
42 Portugal 21,159 10,610,014 0.4 -0.5 -1.4 -0.9 8,783,800 -0.8 1359 2.39
43 Thailand 11,632 67,400,746 0.9 0.3 -0.2 0.1 68,077,965 3.4 174 2.37
44 Chile 10,347 17,924,062 1.5 1.0 0.6 0.8 21,146,173 2.5 535 2.26
45 Ireland 11,370 4,726,856 0.5 0.5 0.5 0.5 5,233,086 1.1 1959 2.05
46 Indonesia 6,280 255,708,785 1.5 1.2 0.9 1.0 314,805,429 5.5 29 1.84
47 Romania 13,053 21,579,201 0.0 -0.4 -0.8 -0.6 19,228,586 0.7 475 1.83
48 Colombia 7,500 49,529,208 1.7 0.9 0.2 0.5 55,052,245 4.3 163 1.80
49 Greece 16,616 11,125,833 0.5 -0.6 -1.7 -1.2 8,774,644 -1.7 934 1.64
50 Algeria 5,171 40,633,464 1.9 1.9 1.8 1.8 58,570,388 3.8 129 1.51
51 Serbia 6,540 9,424,030 -1.4 -0.5 0.5 0.0 9,490,218 3.0 663 1.26
52 Tunisia 6,228 11,235,248 1.9 1.0 0.1 0.5 12,525,418 2.2 493 1.23
53 Hungary 9,478 9,911,396 -0.1 -0.2 -0.3 -0.3 9,408,537 -0.8 653 1.23
54 Viet Nam 4,092 93,386,630 1.6 1.1 0.5 0.8 109,194,988 6.4 55 1.20
55 Slovakia 6,271 5,457,889 0.3 0.1 -0.1 0.0 5,463,349 2.7 1068 1.17
56 Morocco 4,079 33,955,157 1.5 1.3 1.1 1.2 43,445,867 5.0 134 1.16
57 Ukraine 8,868 44,646,131 -0.8 -0.4 0.1 -0.1 43,369,074 -1.4 129 1.12

 

Measuring Economic and Military Potentials of World Nations with the Human Genetic-Cultural Quality Index (HQI)

Introduction

What is the biggest predictor of a country’s scientific output, industrial capacity and military prowess? It is not geographic size. For instance, Kazakhstan and Mongolia are huge compared to Israel and Switzerland, yet Israel and Switzerland far outdistance them in all measures of intellectual, technological and military attainment.

It is not population. India’s 1.28 billion people are close in number to China’s 1.4 billion. And India has been a West-connected capitalist country since its independence in 1947, while China only started in the 1980’s. Yet China far outstrips India in all measures of technological and military power.

It is not natural resources. Russia has vastly more natural resources than Germany. Yet Germany’s economy is many times that of Russia, and its scientific output is double that of Russia, even though Russia’s population is close to double that of Germany.

The most important predictor of a country’s power and accomplishment is the nature of its population. A country’s most precious natural resource is its citizens. It is the genetic makeup of a population, enabled by supportive cultures, institutions and infrastructure, that predicts the country’s military-industrial power and capacity for innovation.

The book IQ and the Wealth of Nations by the professors Richard Lynn and Tatu Vanhanen makes a powerful argument for the importance of IQ in predicting a country’s power and prosperity, with IQ being a highly heritable (genetically-mediated) trait. While some of the data they use is not reliable, the general force of their argument is undeniable. The data used by Adam Smith and Charles Darwin were none too reliable either, but that didn’t stop their theories from being world-class accomplishments.

IQ is not everything. Japan’s IQ is in the same league as Germany and Sweden. Yet Swedes produce four times more science per citizen than the Japanese (measured in scientific papers published in peer-reviewed journals). Germans produce double the amount of science per citizen than the Japanese. An argument can be made that Japan’s infrastructure has yet to catch up with that of Western Europe. But Japan has had more than enough time (seven decades, in fact) to catch up. And a look at Japan’s infrastructure shows that they might even be ahead of Western Europe when it comes to infrastructure.

The factors that lead to Japan’s low accomplishment relative to Western Europe could be other genetic factors not widely studied. One factor could be Japan’s low testosterone levels compared to Europe, with testosterone being a significant contributor toward the drive for accomplishment1. Another factor could be aging. An aging population is going to be less productive than a younger one. Another factor could be non-genetic; for example cultural practices and ideals, although these factors are not independent of genetics and should be considered together with genetics2.

The Human Genetic-Cultural Quality Index

The HQI, short for the Human Genetic-Cultural Quality Index, takes account of both genetic and cultural factors to accurately predict a country’s real scientific, economic and military potential. It is a measure of the quality of human capital, a nation’s most important natural resource, and provides a single number that can be used to compare the quality of the human capital of different nations.

The math of the HQI will be explained below. For now, I will offer certain examples from it to illustrate the concept. Ukraine has an HQI of 129, while Russia’s HQI is 311. This means that a Russian citizen adds 2.6 times more value to Russia’s economy and scientific output than a Ukrainian citizen adds to Ukraine’s economy and scientific output. The quality of Russia’s human capital is 2.4 times that of Ukraine’s human capital. Even if Russia and Ukraine had exactly the same population (let’s say each had a population of 150 million), Russia would still be 2.4 times as powerful as Ukraine. Today, Russia’s population is 3.15 times as large as Ukraine’s (143.5 million vs. 45.49 million). Multiplying this by the 2.4 times HQI advantage, we arrive at a factor of 7.59. Russia is, or will be, 7.59 times as powerful as Ukraine when both countries reach their near-full development potential, perhaps in the next 30 years.

China’s HQI is 855. India’s is 162. Even if both countries had the same population, China would still be 5.2 times as powerful as India once both countries reach their near-full development potential.

The HQI uses two data points as predicting variables:

  1. Scientific papers per capita, which refers to the number of scientific papers published in a year by the nation, divided by its population. This measures the intellectual capacity of the nation’s population.
  2. Real economic growth rate. When a nation’s economy is growing fast (such as that of China), it shows that the nation’s institutions and infrastructure haven’t reached their full potential. The economic growth rate is used to correct for this fact. For instance, China’s papers per capita is only three times that of India’s. But China’s real 12-year average annual economic growth rate is 10.5%, while India’s is 7%. This means that the economic and scientific potential of China’s human capital has significantly more room to grow than India’s, as will be further illustrated down below.

The (real) economic growth rate of a nation expresses elasticity of human potential for a given nation. If growth is faster, improvements in infrastructure and institutions lead to big gains in the human potential of the nation, i.e. that the human potential of the nation is being held back by infrastructure and institutions, and that as these improve, so will the output of the nation’s human capital.

A slow economic growth rate indicates one of two things:

  1. The nation has reached close to its full potential, so that its human capital is already working at its full capacity. This is the case with slow-growing developed nations like Japan and the Netherlands.
  2. The quality of the nation’s human capital is so low that while theoretically there is much room for growth given the nation’s circumstances, that growth is being held back by human capital that’s not capable of achieving it. This is true in the case of various African and Latin American countries that have everything they could possibly need for growth, except a population that’s actually capable of said growth.

The history of colonization shows the importance of the genetic and cultural factors that go into the HQI. Any nation that becomes colonized by a high HQI population will quickly grow to reflect the home population’s intellectual prowess rather than the native population’s destitution. This has been true in the United States, Australia, Argentina, New Zealand and South Africa.

The most recent example comes from Israel. When Israel was colonized by Ashkenazi Jews that had been selected for high IQ in Europe during their 2000 year stay there3, Israel’s economy quickly grew to reflect a developed European nation, rather than a typical Middle Eastern one. It grew even above Eastern European nations, though it doesn’t seem likely it can surpass Germanic nations, as it has already had all the time and help it needs to achieve this.

The Mathematical Model

For any given nation and year, this is how the HQI basis number is calculated:
With a being the number of peer-reviewed scientific journal articles published in the year, b being the 12-year min-max average of economic growth4 for that year and the preceding 11 years, and c being the population of the nation in that year.

China published 416,409 peer-reviewed scientific journal papers in 2015. Its annual economic growth rate was 10.545 for the period 2004-2015 inclusive. And its population in 2015 was 1,401,586,609 (1.4 billion). The equation to calculate the HQI basis number thus becomes:

This results in a number of 0.001162729158. Since this is not a user-friendly number, the numbers for all nations are all multiplied by the arbitrary value of 735853.761, which gives an HQI of 1 to the lowest HQI country. This provides an easy-to-follow ground to which other countries can be compared. China’s HQI thus becomes 855.59. This means that a Chinese citizen adds a value to China’s economy that is 855.59 times greater than the value added by a citizen of the lowest HQI country to their respective economy (which happens to be the Democratic Republic of the Congo).

Raising the number of a country’s scientific papers to a power of (1 + its economic growth rate) is a mathematical trick that models both of these scenarios:

  1. Scientific output growth that continues at the rate of the country’s past 12-year economic growth rate for the next 11 years.
  2. Scientific output growth that starts off at the country’s average past 12-year economic growth, and then slows down by 6.95% every year over the next 30 years.

The assumption here is that a country’s scientific output will continue growing at the rate of the country’s economic growth over its past 12 years. This may seem a strange assumption, since a country’s future growth cannot be assumed to follow at the same rate as its past growth.

In fact, the correct assumption is that its economic growth will be quite lower. But what we are modeling here is not economic growth, it is growth in scientific output, whose future growth follows along the lines of a country’s past economic growth.

An example will make this clear. South Korea’s GDP grew from $771 billion USD in 2004 to $1.14 trillion in 2015 (all in 2000 dollars), a growth of about 48%. During the same period, its scientific output rose from 31182 papers in 2004 to 69469 papers in 2015, a growth of 122%, more than double its economic growth.

The growth in South Korea’s scientific output from 2004 to 2015 is actually similar to its GDP growth from 1989 to 2003 ($332 billion to $735 billion, a growth of 122%).

In short, scientific output is a lagging indicator of a country’s development, due to the amount of past investment necessary for its growth. No matter how much a country invests into increasing its scientific output, the tangible fruits of said investment will be over a decade in the future. The exception being countries like Saudi Arabia who rapidly increased their scientific output by importing foreign scientists.

India’s scientific output grew from 33031 papers in 2004 to 113144 papers in 2015, a growth of 242%. During the same period, its inflation-adjusted GDP grew from $0.971 trillion to $2.03 trillion, a growth of 109%. The growth in its scientific output was more than double the growth in its economic output.

Its growth, in fact, was similar to its GDP growth from 1981 to 2003. The reason for its slow economic growth over this period may have been its low-effectiveness gene-culture (low IQ, etc.), and its low urbanization rate accompanied by its vast size, that meant it took far longer than South Korea to build the infrastructure and institutions necessary to support effective scientific research.

This phenomenon of scientific growth growing far faster than economic growth can be seen throughout the world. Needless to say, a more rigorous study of the relationship between scientific output and past economic growth can be done. But we can take it as a general rule that past economic growth predicts future scientific output growth.

China’s Coming Supremacy

Raising China’s 2015 scientific paper count of 416409 to a power of 1.105 (1 + its annual economic output growth over 2004-2015) results in 1620204, or 1.62 million. What this means is that once China reaches close to its full economic potential (perhaps after 2030), it will be producing about 1.62 million scientific papers every year. Compare this to the 567000 scientific papers published by the United States in 2015, which, according to the same HQI calculation, will grow to 606000 during the same period. In other words, in the next 20 or more years, China’s scientific output will be 2.67 times as large as that of the United States.

While this may sound controversial to someone who has been wooed by the nascent racism of neocons, globalists and central bank usurer economists in their propagandizing the idea that the US can somehow maintain a permanent technological edge over China, that despite China’s enormous growth and a scientific output that is closely approaching that of the United States, that there is something wrong with the Chinese that will forever keep them as second-class citizens on the world stage, to someone who understands the history of Japan and South Korea’s growth, and who understands the realities of the gene-culture, this conclusion of China’s approaching supremacy is merely stating the obvious.

Using the HQI for Comparative Study of Gene-Cultures and National Potentials

Below is a list of the world’s highest HQI nations (the full list is at the end), for the reader’s viewing pleasure, and to help you follow along the rest of the essay.

Rank Country 2015 Citable Scientific Documents 2015 Population Average Real Annual Economic Growth (2004-2015) [12-Year Min-Max Method] HQI Predicted Scientific Output at Near-Full Potential (2035 and After)
1 Singapore 17,976 5,618,866 7.32 4823 36,824
2 Switzerland 39,358 8,238,610 1.01 3910 43,774
3 Australia 82,567 23,923,101 2.99 3561 115,764
4 Iceland 1,365 336,728 2.41 3549 1,624
5 Norway 18,228 5,142,842 1.17 2925 20,445
6 Qatar 2,766 2,350,549 14.88 2815 8,991
7 Denmark 23,081 5,661,723 -0.65 2812 21,633
8 Sweden 35,039 9,693,883 0.41 2775 36,556
9 Monaco 129 38,320 1.64 2683 140
10 Luxembourg 1,692 543,261 1.51 2564 1,893
11 Israel 18,040 7,919,528 3.71 2410 25,938
12 New Zealand 13,052 4,596,396 1.25 2352 14,693
13 Netherlands 51,434 16,844,195 -0.03 2238 51,239

While the Qatari population have a higher IQ, and are more liberal, than most other Arab populations (perhaps with the exception of Lebanese Christians), their high HQI is strongly a result of their importation of foreign scientists on the one hand, and their fast growing oil revenue on the other, the latter funding the former.

Germanic nations have the highest HQI in the world. Switzerland and Iceland, with their relatively low immigration rates, show the high productivity of Germanic genes and cultures. Australia, Denmark, Norway, Sweden, New Zealand and the Netherlands, with their Germanic roots, follow along the same lines.

The table shows how the HQI can be used to compare the genetic-cultural quality of any two countries. Iceland’s HQI of 3548, divided by the 2238 HQI of the Netherlands, results in a 58% advantage for the Icelandic people. Icelandic people are 58% more capable and productive than Netherlanders, and if the two countries had the same population, Iceland would be 58% more powerful militarily, technologically and economically.

Israel’s high HQI is influenced by its high economic growth, a large portion of which comes from its cozy relationship with the United States (US intelligence agencies, for example, are reliant on many Israeli technology companies). It’s highly unlikely that its population is more capable than that of the Netherlands or New Zealand. This shows that the HQI is not immune to aberrations, similar to all other methods, as reality is full of aberrations caused by disasters, sanctions, wars and political changes.

But regarding Israel, the HQI shows one very important result: Israel is already close to its full scientific potential. Over the next 30 years or so, its scientific output can grow from 18040 papers to 21631. No great improvement can be expected from Israel, and given its precarious political situation, even this much growth may not be possible, though stranger things have happened.

Changes in HQI Reflect Fundamental Genetic-Cultural Changes in a Nation

Far more interesting than a country’s economic growth is HQI growth. The HQI itself is a measure of growth potential, HQI growth means growth in the growth potential. When a nation climbs toward a peak in achievement, economic growth refers to this climb. HQI growth refers to an increase in the height of the peak, a removal of constraints that prevent a nation from reaching the heights reached by other nations.

China’s 2015 HQI was 6.4% lower than its 2010 HQI. This means that between 2010 and 2015, there were some forces in effect that reduced China’s genetic-cultural fitness, or economic fitness, so that while it continued to grow fast, its predicted near-full potential decreased. This was mostly caused by a large drop in the number of scientific papers published by China in 2015. I have contacted SCImago to find out whether this change was due to changes in their paper counting methodologies or whether it was due to a real drop in China’s output. If it was a real drop, maybe it was due to China’s best and brightest aging and retiring, or due to growing practice of usury creating a Western European-style of stagnation faster than expected, or due to some unknown dysgenic effect.

From 2010 to 2015, Ethiopia’s HQI increased by 173%. This is a very, very good sign. It means that there are genetic-cultural changes that are improving the nation’s future potential, or that there are bottlenecks that are being overcome. Not only is the nation climbing toward the peak, the peak itself is growing. Perhaps it is due to improvements in nutrition and health care, or beneficial cultural changes, or both. The actual beneficial change is probably smaller.

Poland has been Europe’s favorite backwater since at least Adam Smith’s time. What does the HQI tell us about what is going on in there? From 2010 to 2015, Poland’s HQI increased by 21.9%. There are forces at work in Poland that are increasing its population’s genetic-cultural fitness, so that whatever we believed Poland’s maximum growth potential to have been in 2010, in 2015 that maximum growth potential was 21.9% higher.

The HQI of the United States decreased by 12.3% from 2010 to 2015. This means that there are forces at work reducing the genetic-cultural fitness of America’s average citizen. One simple explanation could be the increase in immigration from lower HQI nations, who increase the population of the US without significantly increasing its economic and scientific output. Keeping population constant, the HQI still decreased by 8.9%, therefore immigration might be only causing a 3.4 percentage points of this dysgenesis.

Germany’s HQI increased by 0.2% from 2010 to 2015, meaning that no interesting structural change happened. These numbers are from before the recent migrant crisis, whose presence is sure to bring down Germany’s HQI.

Japan’s HQI fell 15.7% from 2010 to 2015. Not only is the nation coming down the peak (through its negative economic growth), the nation’s peak is also decaying.

Russia’s HQI increased by 31.1% from 2010 to 2015. Even though its economic growth was low (0.36%), its scientific output greatly increased during this period, from 38878 papers to 55500. Russia’s seeming low GDP growth is largely due to economic warfare from Wall Street. Its scientific growth shows its true economic fitness.

Why Scientific Output is Important

The HQI uses scientific output as the most important indicator of a nation’s genetic-cultural fitness. There are many good reasons for this, the two most important being:

  1. Producing science requires that a nation be prosperous enough to afford having a class of society who dedicate most of their time to research. And that prosperity can only come from high genetic-cultural fitness for most countries, excepting a few oil states that can afford to import scientists.
  2. Producing science requires very high intellectual capacity and drive for accomplishment (perhaps most importantly IQ and testosterone). If a nation’s population is incapable of producing science, they will be equally incapable of producing high-tech military equipment and industrial innovation, necessary for a nation to increase its power.

A nation’s scientific output is a very good indicator of its fitness. If a nation’s economy is growing fast, by raising the scientific output to the power of its real economic growth, the HQI gives the nation a fair chance at proving itself. China’s scientific output per capita is quite low compared to that of the United States. But its real economic growth is much higher. We can safely assume that China’s per capita scientific output is going to grow at a rate similar to its past economic growth.

The Bottleneck Effect in the Growth of Scientific Output

India’s example shows that there might be a bottleneck effect in the growth of scientific output in large and highly undeveloped nations. As mentioned, India’s scientific output grew by 242% from 2004 to 2015, similar to its economic growth from 1981 to 2003. 12 years of scientific output growth were equal to 23 years of past economic growth. While in the case of South Korea, its 2004 to 2015 scientific growth was roughly similar to its economic growth of the 12 years preceding that.

India may have already overcome the bottleneck. Indonesia, Bangladesh, Pakistan, Nigeria, the Philippines and Vietnam probably haven’t yet, and this probably partly accounts for their low HQI’s.

For a large, undeveloped and already low-HQI nation (low IQ, bad law enforcement, etc.), building the prosperity and infrastructure necessary for doing science takes far longer than it takes a smaller and higher HQI nation. Decades of education, infrastructure building and perhaps most importantly, urbanization, are needed before a country’s scientific output momentum gets going.

List of 203 Sovereign States by Human Potential

Below is a table of 203 sovereign states sorted by HQI, from highest HQI to lowest. Note that the HQI number shows the genetic-cultural quality of each individual citizen within that nation, not the nation’s power. While each additional Singaporean citizen adds a value of 4822 to the economy of Singapore, each additional US citizen adds a value of 1372. Since the genetic-cultural quality of Singaporeans is so much higher than the genetic-cultural quality of US citizens, what the HQI shows is that if Singapore had the same number of citizens as the United States, it would 3.5 times as powerful as the United States, as each individual citizen adds so much more to its power and productivity. Singapore’s real superiority is probably lower, as it is mostly its fast economic growth, aided by its geo-political situation, that’s contributing to its high HQI.

The HQI for certain nations, such as Indonesia and North Korea, are clearly inaccurate due to their exclusion from the world’s scientific community. The HQI of Indonesia and many former Central Asian Soviet states should increase considerably as they start to adopt Western scientific practices.

Please see below the table for the fine print regarding the numbers.

Rank Country 2015 Citable Scientific Documents 2015 Population Average Real Annual Economic Growth (2004-2015) [12-Year Min-Max Method] HQI
1 Singapore 17,976 5,618,866 7.32 4823
2 Switzerland 39,358 8,238,610 1.01 3910
3 Australia 82,567 23,923,101 2.99 3561
4 Iceland 1,365 336,728 2.41 3549
5 Norway 18,228 5,142,842 1.17 2925
6 Qatar 2,766 2,350,549 14.88 2815
7 Denmark 23,081 5,661,723 -0.65 2812
8 Sweden 35,039 9,693,883 0.41 2775
9 Monaco 129 38,320 1.64 2683
10 Luxembourg 1,692 543,261 1.51 2564
11 Israel 18,040 7,919,528 3.71 2410
12 New Zealand 13,052 4,596,396 1.25 2352
13 Netherlands 51,434 16,844,195 -0.03 2238
14 Belgium 29,180 11,183,411 0.68 2058
15 Finland 17,551 5,460,592 -1.55 2034
16 Hong Kong 14,710 7,313,557 3.12 1997
17 Liechtenstein 102 37,461 -0.40 1967
18 Ireland 11,370 4,726,856 1.09 1959
19 Canada 89,312 35,871,283 0.13 1859
20 Austria 21,818 8,557,761 -0.09 1859
21 Slovenia 5,428 2,079,085 -0.43 1851
22 United Kingdom 169,483 63,843,856 -0.60 1818
23 Taiwan 34,011 23,381,038 4.53 1717
24 South Korea 73,433 49,750,234 3.61 1627
25 Greenland 125 57,275 0.05 1610
26 Czech Republic 20,759 10,777,060 1.02 1569
27 United States 567,007 325,127,634 0.51 1372
28 Portugal 21,159 10,610,014 -0.77 1359
29 Macao 819 584,420 3.20 1278
30 Spain 79,209 47,199,069 0.30 1277
31 Estonia 2,620 1,280,227 -2.23 1264
32 Germany 149,773 82,562,004 -0.77 1218
33 France 103,733 64,982,894 -0.08 1165
34 Grenada 140 106,694 3.33 1138
35 Poland 37,285 38,221,584 4.23 1120
36 Cyprus 1,789 1,164,695 -0.51 1088
37 Slovakia 6,271 5,457,889 2.67 1068
38 Malta 559 431,239 0.75 1000
39 Italy 95,836 61,142,221 -1.74 945
40 Greece 16,616 11,125,833 -1.67 934
41 Saint Kitts and Nevis 62 55,376 1.83 889
42 Croatia 5,533 4,255,374 -1.12 869
43 China 416,409 1,401,586,609 10.55 856
44 Saudi Arabia 17,529 29,897,741 5.90 768
45 Malaysia 23,414 30,651,176 2.95 756
46 Brunei Darussalam 366 428,539 1.03 668
47 Serbia 6,540 9,424,030 2.97 663
48 Hungary 9,478 9,911,396 -0.81 653
49 Lithuania 2,973 2,998,969 -1.86 629
50 Iran 39,727 79,476,308 5.02 626
51 Japan 109,305 126,818,019 -0.41 605
52 New Caledonia 171 263,147 3.75 580
53 San Marino 22 31,802 3.26 563
54 Seychelles 59 93,754 3.79 540
55 Chile 10,347 17,924,062 2.50 535
56 Latvia 1,503 2,031,361 -1.23 498
57 Tunisia 6,228 11,235,248 2.17 493
58 Bermuda 43 65,578 0.13 485
59 Palau 14 21,291 -0.22 481
60 Turkey 39,275 76,690,509 2.27 479
61 Romania 13,053 21,579,201 0.70 475
62 Lebanon 2,076 5,053,624 5.60 464
63 Montenegro 316 621,556 2.50 432
64 Dominica 37 72,680 2.90 416
65 Bulgaria 3,441 7,112,641 1.73 410
66 United Arab Emirates 3,858 9,577,128 2.30 358
67 Uruguay 1,208 3,429,997 4.39 354
68 Macedonia 814 2,109,251 3.01 347
69 Barbados 128 287,482 0.83 341
70 Russian Federation 57,881 142,098,141 0.36 312
71 Jordan 2,313 7,689,760 5.45 338
72 Oman 1,461 4,157,783 3.56 335
73 Brazil 61,122 203,657,210 3.70 332
74 French Polynesia 122 282,764 0.11 319
75 Kuwait 1,327 3,583,399 1.84 311
76 Iraq 1,793 35,766,702 27.77 295
77 South Africa 17,409 53,491,333 2.03 292
78 Argentina 11,815 42,154,914 2.17 253
79 Bahrain 344 1,359,726 5.20 252
80 Georgia 1,067 4,304,540 4.28 246
81 Armenia 953 2,989,467 -0.14 232
82 Andorra 24 80,950 -0.26 216
83 Fiji 231 892,727 1.95 212
84 Trinidad and Tobago 285 1,346,697 5.03 207
85 Egypt 14,800 84,705,681 4.49 198
86 Cuba 1,760 11,248,783 6.76 191
87 Bosnia and Herzegovina 756 3,819,684 2.95 177
88 Thailand 11,632 67,400,746 3.39 174
89 Guam 36 169,885 3.00 174
90 Colombia 7,500 49,529,208 4.28 163
91 Belarus 1,554 9,259,666 3.78 163
92 India 123,206 1,282,390,303 7.08 162
93 Azerbaijan 676 9,612,580 17.29 160
94 Botswana 410 2,056,370 1.11 157
95 Panama 485 3,987,866 8.46 151
96 Tuvalu 2 9,916 2.01 150
97 Mauritius 208 1,253,581 3.57 148
98 Kazakhstan 2,062 16,770,447 5.95 142
99 Libya 352 6,317,080 21.21 142
100 Costa Rica 720 5,001,657 3.88 137
101 Cayman Islands 11 59,967 -0.20 134
102 Morocco 4,079 33,955,157 5.00 134
103 Ukraine 8,868 44,646,131 -1.35 129
104 Algeria 5,171 40,633,464 3.75 129
105 Puerto Rico 660 3,680,058 -0.79 125
106 Namibia 286 2,392,370 6.15 125
107 Albania 406 3,196,981 4.32 121
108 Bhutan 82 776,461 10.04 121
109 Mongolia 298 2,923,050 8.01 118
110 Vanuatu 34 263,888 4.84 112
111 Palestine (West Bank & Gaza) 475 4,548,815 6.18 112
112 Mexico 18,417 125,235,587 0.21 110
113 Marshall Islands 7 52,993 0.88 99
114 Nigeria 5,112 183,523,432 18.20 97
115 Ecuador 1,418 16,225,691 4.25 88
116 Bahamas 46 387,549 -0.39 86
117 Federated States of Micronesia 12 104,460 -0.51 83
118 Moldova 348 3,436,828 1.71 82
119 Antigua and Barbuda 10 91,822 0.67 81
120 Ghana 1,531 26,984,328 8.97 81
121 Aruba 12 103,889 -2.26 80
122 Gabon 174 1,751,199 1.72 80
123 Jamaica 305 2,813,276 -0.77 76
124 Congo 388 4,671,142 3.59 76
125 Belize 32 347,598 2.69 74
126 Swaziland 106 1,285,519 2.38 68
127 Sri Lanka 1,255 21,611,842 6.27 67
128 Pakistan 10,962 188,144,040 4.64 66
129 Cape Verde 35 508,315 6.95 65
130 Solomon Islands 43 584,482 4.10 63
131 Gambia 157 1,970,081 1.36 63
132 Peru 1,813 31,161,167 5.10 63
133 Suriname 38 548,456 5.38 62
134 Venezuela 1,473 31,292,702 7.55 60
135 Maldives 22 357,981 7.57 57
136 Viet Nam 4,092 93,386,630 6.40 55
137 Samoa 13 193,228 0.76 50
138 Kenya 2,215 46,748,617 4.32 49
139 Tonga 7 106,379 -0.20 48
140 Saint Lucia 11 184,937 3.25 47
141 Cameroon 1,116 23,393,129 4.09 47
142 Senegal 691 14,967,446 4.22 45
143 Uganda 1,270 40,141,262 7.03 38
144 Laos 226 7,019,652 7.81 36
145 Benin 409 10,879,828 4.30 36
146 Kiribati 5 105,555 1.51 36
147 Guyana 34 807,611 2.53 34
148 Nepal 922 28,440,629 4.73 33
149 Malawi 519 17,308,685 5.75 32
150 Zambia 432 15,519,604 6.76 31
151 Burkina Faso 508 17,914,625 5.81 30
152 Indonesia 6,280 255,708,785 5.49 29
153 Ethiopia 1,691 98,942,102 11.11 29
154 Tanzania 1,261 52,290,796 6.56 28
155 Saint Vincent and the Grenadines 4 109,374 2.15 28
156 Bolivia 290 11,024,522 5.08 26
157 Rwanda 278 12,428,005 7.92 26
158 Paraguay 182 7,032,942 5.04 25
159 Kyrgyzstan 142 5,707,529 5.23 24
160 Syrian Arab Republic 502 22,264,996 5.07 23
161 Equatorial Guinea 17 799,372 12.91 23
162 Zimbabwe 552 15,046,102 -2.88 23
163 Bangladesh 3,011 160,411,249 6.06 22
164 Cambodia 317 15,677,059 6.67 22
165 Philippines 2,091 101,802,706 4.39 21
166 Papua New Guinea 156 7,631,819 6.48 21
167 Togo 156 7,170,797 3.56 19
168 Lesotho 43 2,120,116 5.09 18
169 Uzbekistan 426 29,709,932 8.46 18
170 Sierra Leone 112 6,318,575 6.20 17
171 Guinea-Bissau 37 1,787,793 3.77 17
172 Timor-Leste 23 1,172,668 4.25 16
173 Niger 169 19,268,380 18.20 16
174 Côte d’Ivoire 386 21,295,284 3.16 16
175 El Salvador 135 6,426,002 0.39 16
176 Sudan 597 39,613,217 4.66 15
177 Guatemala 243 16,255,094 3.42 13
178 Mali 239 16,258,587 3.18 13
179 Mozambique 299 27,121,827 8.06 13
180 Nicaragua 100 6,256,510 1.74 13
181 Tajikistan 107 8,610,384 7.05 13
182 Liberia 63 4,503,439 4.92 13
183 Djibouti 13 899,658 4.59 12
184 Yemen 297 25,535,086 4.38 11
185 Dominican Republic 116 10,652,135 5.81 11
186 Madagascar 278 24,235,390 1.56 9
187 Honduras 92 8,423,917 2.07 9
188 Mauritania 35 4,080,224 8.92 9
189 Comoros 8 770,058 1.95 8
190 Haïti 112 10,603,731 0.01 8
191 Sao Tome and Principe 2 202,781 6.19 8
192 Central African Republic 41 4,803,082 3.45 7
193 Guinea 106 12,347,766 2.35 7
194 Angola 83 22,819,926 12.50 5
195 Myanmar 181 54,164,262 10.42 4
196 Chad 38 13,605,625 16.86 4
197 Eritrea 29 6,737,634 3.86 4
198 Burundi 42 10,812,619 1.42 3
199 Afghanistan 74 32,006,788 11.27 3
200 North Korea 52 25,155,326 1.85 2
201 Turkmenistan 9 5,373,487 9.85 2
202 Democratic Republic of the Congo 75 71,246,355 5.92 1

The economic growth rate data comes from my essay The 12-Year Min-Max Average. Please see this essay for technical details on how the numbers were calculated.

Only “citable” scientific documents are counted, citable documents are generally higher in quality and more important than non-citable ones. However, citable and no-citable documents tend to rise and fall together, so that including non-citable documents shouldn’t have a significant effect on the HQI numbers.

Population data comes the United Nations and the World Bank. Scientific output data from SCIMago. Economic growth data from the World Bank and Trading Economics.

* A country’s GDP growth rate could be said to somewhat take into account its population growth, since when all other things are equal, growth in population results in economic growth. However, this will only apply to a stagnant economy whose only increase or decrease is a result of population change rather than

The 12-Year Min-Max Average: A Simple Method for Calculating Real, Legitimate Economic Growth and Canceling Out Central Bank Manipulations and other Noise

Introduction

Real economic growth is different from the increase in GDP that comes from monetary expansion. The 12-year min-max method that I have come up with is a way of calculating a country’s GDP growth rate over a 12-year period that aims to cancel out “fake” growth caused by monetary expansion and boom-bust cycles by taking recent economic crashes into account.

To find out China’s real economic growth from 2004 to 2015 (inclusive), find the year with the highest economic growth and the year with the lowest economic growth over the 12 year period, then average the two numbers. Over the 2004-2015 period, China’s highest economic growth was 14.7%, in 2007. Her lowest economic growth was 6.9%, in 2015. To find her real economic growth:

14.7 + 6.9
__________ = 10.8
    2

China’s GDP in 2004 was $1.9417 trillion. According to the above calculation, her economy grew at an annual rate of 10.8%, meaning that in 2015, her true GDP would be $5.999 trillion.

According to the above, China’s real economy in 2015 was three times as large as its 2004 economy. This finding is remarkably close to the 3.3 times growth of GDP from 2004 to 2015 predicted by PPP calculations ($5.7 trillion to 19.4 trillion).

Since the dollar is not a reliable store of value, the $5.999 trillion number for 2015 doesn’t mean much of anything, unless we compare it to the 2004 number of $1.94. The result, as mentioned, is that the 2015 number is three times larger, meaning the 2015 economy was three times as large as it used to be in 2004.

Another way of thinking of the above graph is to draw a rectangle with the country’s highest and lowest economic growth rates at opposing corners, then taking the midpoint of the height of the rectangle:

Extending the calculation to other countries, I will start with India:

Over the 2004-2015 period, India’s lowest growth rate was in 2008 at 3.89%. Its highest growth was in 2010 at 10.26%. Averaging the two results in 7.075%.

In 2004, India’s GDP was $0.7215 trillion. An annual growth rate of 7.075% results in a 2015 real GDP of $1.53 trillion, meaning its economy grew by a factor of 2.12. Both nominal and PPP calculations say its economy grew by a factor of 2.7. The 2.12 is quite believable to me, the truth probably lies somewhere in between.

Moving on to the United State:

America’s highest GDP growth was in 2004, at 3.79%. Its lowest growth was in 2009, at -2.78%. Averaging the two, the result is a miserable annual growth rate of half of a percent. America’s GDP in 2004 was $12.27 trillion. According to this calculation, its real 2015 GDP was $12.97 trillion, which fits the feelings of America’s population. America’s 2015 economy was only 5% larger than its 2004 economy from an honest, Smithian point of view. During the same period, its population grew 9.77% larger, meaning the wealth or income of America’s average citizen actually fell during the period. The pie grew by 5%, the pie eaters grew by 9.77%.

Predictably, Japan is even more of a trainwreck than the United States.

Over the 2004-2015 period, Japan’s high point of growth was in 2010 at 4.71%. Its low point was in 2009, at -5.53%. Averaging the two, the result is an annual growth rate of -0.41%, meaning its economy actually shrunk by that amount every year. This is well in keeping with a stagnant economy and an aging and shrinking population count. Japan’s GDP in 2004 was $4.65 trillion. According to this economic shrink rate, Japan’s GDP in 2015 was $4.45 trillion, 4.3% smaller than in 2004.

Why 12 Years

The average length of a central-bank induced asset bubble cycle is 8 years (think the late 2000 and 2008 crashes). 12 years is a cycle and a half. The 2004 to 2015 window, as an example, captures the recovery from the last crash, and our current “recovery”, providing a rich picture of what brought us here. If we look at two cycles (2000-2015 inclusive), the data will be skewed by the previous cycle’s peak, which is quite irrelevant today. If we look at only one cycle (2008-2015), we will lose historical context that is still relevant today (the period that lead to the 2008 crash).

A market cycle and a half makes for a relevant and informative picture of most periods of a country’s economic history. The idea of using a market cycle and a half comes from John P. Hussman, manager of the billion-dollar Hussman Funds mutual fund.

Why It Works

The 12-year min-max average takes a realistic look at the “fakeness” factor within an economy and takes it out. It shows how much of an economy is a house of cards built on debt and investor euphoria, and how much of it is real and of lasting value.

The basic idea is that it measures the psychology of an economy’s participants. Crashes happen when a very large number of investors realize it wasn’t such a smart idea to buy those overpriced assets, thinking that they would be able to sell them for even higher. This moment of realization happened in 1929, 2000 and 2008.

After a crash, central banks use monetary easing to motivate investors to bid up the prices of assets. This creates a growing atmosphere of euphoria that increases spending in the economy and creates years of apparent growth. When the next crash inevitably comes, the atmosphere changes from one of euphoria to one of fear and panic. The market sobers up. In this year of sobriety, investors often underestimate the market’s worth. This acts as an antidote to the overestimation of the market during euphoric years. This is what happened in March 2009.

The momentum of an economy that’s truly expanding cannot be reversed by a financial crisis. It can slow a little, but it will continue growing, as China, India and Bangladesh’s numbers show. When we average out the years of most euphoria and least euphoria in a fast-growing economy, we still get a high rate of growth, because the growth is real, it is not entirely based on the feelings of investors.

But how does investor feeling affect GDP? The blaze of euphoria caused by monetary easing and fanned by complicit mass media makes it easier, and more likely, for businesses to borrow and expand their activities, regardless of supply-and-demand considerations. Overvalued corporations find it easy to convince lenders to give them large loans. The wealthy from around the country open their wallets and outbid each other in investing their wealth in all kinds of business activities in fear of being left behind.

In stagnant economies (where standards of living for the average citizen are no longer improving appreciably) like the those of the US, Japan and Western Europe, it is by and large the feelings of investors that drive economic expansion and recession.

Measuring Crashiness

One obvious difficulty with choosing only two points on a 12-year growth graph is that growth is cumulative. A 3% growth at the beginning of the period could mean a trillion dollars increase, while at the end of the period it could mean two or three trillions.

Regardless of where the low point is on the graph, it shows that it is possible, within that economy, to lose this much value. It shows that a great amount of volatility exists within the economy, so that even if in recent years the growth numbers have all been great, if there is one big crash year on the 12-year graph, it tells us that a similar crash is in the future.

The cumulative effect doesn’t take away from the idea, the idea contains it within itself. No matter how much growth we’ve had in recent years, a crash can take most of it away, as it is farther in the future, so that its percentage effect is going to be larger than the effect of the growth of the previous years.

The PPP GDP per capita of the United States over the 12-year period  (denominated in 2005 international dollars) illustrates this. In 2004 it was $48,597. Five years of “growth” later, it was $48,557 in the 2009 crash. The crash easily outweighed all the previous years of growth. In 2015, the number had grown to $52,549, and the coming crash can easily take it back close to square one again.

The 12-year min-max average shows the “crashiness” of an economy. And since there is always a bigger crash in the future, measuring a country’s greatest year of growth against its worst year of growth makes a lot sense.

Honesty requires that I admit I came up with these reasonings for why the 12-year min-max average works after I had discovered it. I needed a reliable way of comparing the true economic momentums of different countries for the HQI, the Human Genetic-Cultural Quality Index that I will announce later, and among the methods I tried out, this one brilliantly fit the true states of many economies I have studied.

List of Countries by Real 12-Year Annual GDP Growth

Below is a list of 203 sovereign states along with their real economic growth from 2004 to 2015 according to the 12-year min-max average. The countries are sorted by population.

The number I’m most skeptical of is Germany’s. Germany has appeared to grow fast in recent years. The 12-year min-max average says its growth has actually been negative 0.77% (i.e. that the next crash will wipe out all of its recent growth). This is possible, and only history will tell.

Russia gets a 0.36% growth rate. I expect its rate will be shown to be quite higher after the next crash, as it puts the 2008 crash behind, and what remains will be its recent real economic growth rate that cannot be wiped out by a crash.

Please see the fine print at the end of the table regarding certain corrections I’ve made.

Country Population Real 12-Year Annual GDP Growth Rate (2004-2015)
China 1,401,586,609 10.545
India 1,282,390,303 7.075
United States 325,127,634 0.505
Indonesia 255,708,785 5.49
Brazil 203,657,210 3.7
Pakistan 188,144,040 4.64
Nigeria 183,523,432 18.195
Bangladesh 160,411,249 6.055
Russian Federation 142,098,141 0.36
Japan 126,818,019 -0.41
Mexico 125,235,587 0.205
Philippines 101,802,706 4.39
Ethiopia 98,942,102 11.11
Viet Nam 93,386,630 6.4
Egypt 84,705,681 4.485
Germany 82,562,004 -0.77
Iran 79,476,308 5.02
Turkey 76,690,509 2.265
Democratic Republic of the Congo 71,246,355 5.915
Thailand 67,400,746 3.385
France 64,982,894 -0.075
United Kingdom 63,843,856 -0.595
Italy 61,142,221 -1.735
Myanmar 54,164,262 10.415
South Africa 53,491,333 2.025
Tanzania 52,290,796 6.56
South Korea 49,750,234 3.605
Colombia 49,529,208 4.275
Spain 47,199,069 0.3
Kenya 46,748,617 4.315
Ukraine 44,646,131 -1.35
Argentina 42,154,914 2.17
Algeria 40,633,464 3.75
Uganda 40,141,262 7.025
Sudan 39,613,217 4.655
Poland 38,221,584 4.23
Canada 35,871,283 0.13
Iraq 35,766,702 27.77
Morocco 33,955,157 4.995
Afghanistan 32,006,788 11.27
Venezuela 31,292,702 7.545
Peru 31,161,167 5.095
Malaysia 30,651,176 2.95
Saudi Arabia 29,897,741 5.895
Uzbekistan 29,709,932 8.46
Nepal 28,440,629 4.73
Mozambique 27,121,827 8.06
Ghana 26,984,328 8.965
Yemen 25,535,086 4.38
North Korea 25,155,326 1.85
Madagascar 24,235,390 1.56
Australia 23,923,101 2.985
Cameroon 23,393,129 4.085
Taiwan 23,381,038 4.53
Angola 22,819,926 12.5
Syrian Arab Republic 22,264,996 5.065
Sri Lanka 21,611,842 6.27
Romania 21,579,201 0.695
Côte d’Ivoire 21,295,284 3.16
Niger 19,268,380 18.195
Chile 17,924,062 2.5
Burkina Faso 17,914,625 5.81
Malawi 17,308,685 5.745
Netherlands 16,844,195 -0.035
Kazakhstan 16,770,447 5.95
Mali 16,258,587 3.18
Guatemala 16,255,094 3.415
Ecuador 16,225,691 4.25
Cambodia 15,677,059 6.67
Zambia 15,519,604 6.76
Zimbabwe 15,046,102 -2.88
Senegal 14,967,446 4.22
Chad 13,605,625 16.855
Rwanda 12,428,005 7.92
Guinea 12,347,766 2.35
Cuba 11,248,783 6.76
Tunisia 11,235,248 2.165
Belgium 11,183,411 0.675
Greece 11,125,833 -1.67
Bolivia 11,024,522 5.08
Benin 10,879,828 4.295
Burundi 10,812,619 1.42
Czech Republic 10,777,060 1.02
Dominican Republic 10,652,135 5.805
Portugal 10,610,014 -0.77
Haïti 10,603,731 0.01
Hungary 9,911,396 -0.81
Sweden 9,693,883 0.405
Azerbaijan 9,612,580 17.285
United Arab Emirates 9,577,128 2.3
Serbia 9,424,030 2.965
Belarus 9,259,666 3.78
Tajikistan 8,610,384 7.05
Austria 8,557,761 -0.09
Honduras 8,423,917 2.07
Switzerland 8,238,610 1.005
Israel 7,919,528 3.705
Jordan 7,689,760 5.45
Papua New Guinea 7,631,819 6.48
Hong Kong 7,313,557 3.12
Togo 7,170,797 3.56
Bulgaria 7,112,641 1.73
Paraguay 7,032,942 5.035
Laos 7,019,652 7.81
Eritrea 6,737,634 3.855
El Salvador 6,426,002 0.39
Sierra Leone 6,318,575 6.195
Libya 6,317,080 21.205
Nicaragua 6,256,510 1.735
Kyrgyzstan 5,707,529 5.225
Denmark 5,661,723 -0.645
Singapore 5,618,866 7.32
Finland 5,460,592 -1.545
Slovakia 5,457,889 2.67
Turkmenistan 5,373,487 9.85
Norway 5,142,842 1.17
Lebanon 5,053,624 5.6
Costa Rica 5,001,657 3.88
Central African Republic 4,803,082 3.45
Ireland 4,726,856 1.085
Congo 4,671,142 3.585
New Zealand 4,596,396 1.25
Palestine (West Bank & Gaza) 4,548,815 6.175
Liberia 4,503,439 4.915
Georgia 4,304,540 4.28
Croatia 4,255,374 -1.115
Oman 4,157,783 3.555
Mauritania 4,080,224 8.915
Panama 3,987,866 8.46
Bosnia and Herzegovina 3,819,684 2.945
Puerto Rico 3,680,058 -0.785
Kuwait 3,583,399 1.84
Moldova 3,436,828 1.705
Uruguay 3,429,997 4.39
Albania 3,196,981 4.32
Lithuania 2,998,969 -1.86
Armenia 2,989,467 -0.14
Mongolia 2,923,050 8.01
Jamaica 2,813,276 -0.765
Namibia 2,392,370 6.15
Qatar 2,350,549 14.875
Lesotho 2,120,116 5.09
Macedonia 2,109,251 3.005
Slovenia 2,079,085 -0.43
Botswana 2,056,370 1.105
Latvia 2,031,361 -1.225
Gambia 1,970,081 1.36
Guinea-Bissau 1,787,793 3.765
Gabon 1,751,199 1.715
Bahrain 1,359,726 5.195
Trinidad and Tobago 1,346,697 5.025
Swaziland 1,285,519 2.375
Estonia 1,280,227 -2.225
Mauritius 1,253,581 3.565
Timor-Leste 1,172,668 4.245
Cyprus 1,164,695 -0.505
Djibouti 899,658 4.585
Fiji 892,727 1.945
Guyana 807,611 2.53
Equatorial Guinea 799,372 12.905
Bhutan 776,461 10.035
Comoros 770,058 1.95
Montenegro 621,556 2.5
Solomon Islands 584,482 4.1
Macao 584,420 3.2
Suriname 548,456 5.38
Luxembourg 543,261 1.51
Cape Verde 508,315 6.95
Malta 431,239 0.75
Brunei Darussalam 428,539 1.03
Bahamas 387,549 -0.39
Maldives 357,981 7.57
Belize 347,598 2.69
Iceland 336,728 2.405
Barbados 287,482 0.825
French Polynesia 282,764 0.11
Vanuatu 263,888 4.84
New Caledonia 263,147 3.75
Sao Tome and Principe 202,781 6.185
Samoa 193,228 0.755
Saint Lucia 184,937 3.25
Guam 169,885 3
Saint Vincent and the Grenadines 109,374 2.145
Grenada 106,694 3.33
Tonga 106,379 -0.2
Kiribati 105,555 1.505
Federated States of Micronesia 104,460 -0.505
Aruba 103,889 -2.26
Seychelles 93,754 3.785
Antigua and Barbuda 91,822 0.67
Andorra 80,950 -0.26
Dominica 72,680 2.895
Bermuda 65,578 0.13
Cayman Islands 59,967 -0.2
Greenland 57,275 0.05
Saint Kitts and Nevis 55,376 1.83
Marshall Islands 52,993 0.875
Monaco 38,320 1.64
Liechtenstein 37,461 -0.4
San Marino 31,802 3.255
Palau 21,291 -0.22
Tuvalu 9,916 2.01

For Iran, ignored the 2012 crash, as it was a political event. Used the 2008 crash instead, as it was a shared world event. For Brazil too due to 2015. For Iraq 2014 due to CIA-ISIS. For Venezuela, 2009 was used due to recent mismanagement. For Syria, 3.23 lowest growth from 2010 was used, due to start of the Syria v. CIA war in 2011. For Yemen, ignored numbers from 2011 due to the crisis, used lowest number from 2006.

Data for New Caledonia taken from Trading Economics (World Bank data missing).

Monaco’s highest growth rate taken from World Bank (14.58 for 2007). Lowest number (-11.3 for 2009) missing from World Bank, taken from Trading Economics.

Data for Greenland after 2009 is missing. Used 2009 number as lowest growth rate (-5.41), and 2007 number as highest growth rate (5.51).

No reliable annual growth data for French Polynesia. Assumed a top growth of 4.42 and a bottom growth of -4.2, as hinted at by the CIA World Factbook.

For Sierra Leone, ignored 2013 and 2015 numbers, as they were aberrations caused by the mining sector’s boom and collapse.

For Liechtenstein, top and bottom growth numbers were taken from Trading Economics as World Bank seemed inaccurate and missed the years after 2009.

Guadelope, Cook Islands, Montserrat, Netherlands Antilles, Anguilla, Martinique, Réunion, Gibraltar, American Samoa, US and British Virgin Islands, Northern Mariana Islands and French Guiana not included as GDP growth rate is not. It is possible that the World Bank counts them toward their parent states, couldn’t verify this.

Faroe Islands not included as GDP growth rate data is unavailable.

Data for North Korea, Guam and Cayman Islands’ GDP growth rate taken from Trading Economics as World Bank doesn’t provide them.

For the Central African Republic, numbers after 2012 are ignored due to the civil war there. Used 2004’s GDP growth rate as the top number (5.99) and 2005 as the bottom (0.91).

For Eritrea, ignored 2008 GDP growth low as it was caused by US sanctions. Used 2006 low instead (-0.97).

Somalia is not included in the World Bank data, and other available data is none too reliable due to the fact that the country is a war zone. Decided not to include the country as no useful conclusions can be drawn from the data.

No reliable data found for Turks and Caicos Islands, Wallis and Futuna, Nauru, Mayotte, and Western Sahara, so omitted them.