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In recent weeks I’ve had cause to look at Moscow property prices.


There are basically three major socio-economic regions in Moscow:

  • The center – Upper middle class, very high property prices (300-400,000R/sq m), cosmopolitan, tilted against Putin and towards liberal parties like Yabloko, full of cafes with Macbook toting hipsters, do not discriminate against immigrants when renting out their properties (presumably because its not like Central Asian Gastarbeiters can afford the prices there anyway).
  • The south-west and west – Middle class, moderately high property prices (200,000R/sq m), tilted against Putin and towards liberals and Communists. This region traditionally hosted a large percentage of Moscow’s academic/R&D institutions and hi-tech factories, so the locals tend to be engineers and technicians and their well-educated children.
  • The east, south and north – Lower class, low property prices (150,000R/sq m), tilted towards Putin and especially the nationalist LDPR, majority proles – though very few work in factories, with a significant contingent of lumpenproles (one woman in my flat died from a drug overdose a few weeks ago) with a growing immigrant presence.

Whereas you see many Central Asians in the center of Moscow, there they are almost inevitably doing street sweeping or construction work, whereas in the outskirts there many of them start appearing out of work uniform. Most of them actually live in the cheaper outskirts, and are increasingly buying up property there. This is accompanied by ethnic tensions. One such region, Biryulyovo – which has Moscow’s second lowest property prices – was the site of a small race war back in 2013 provoked by the murder of a Russian by an immigrant. That said, it’s (still) a long way from the yearly “fireworks” you have in Paris.

In the meantime, I also suspect that many of the more successful locals from the prole areas are making their way to more prestigious regions. In the USSR, you tended to live where you worked (your apartment was assigned to you). With a free market in real estate, the way is clear for the sort of “cognitive clustering” that you see throughout the US and Europe, where the brightest, richest, most successful (all inter-correlated) converge onto good neighborhoods close to the center, while the duller and less successful elements are left behind in the Biryulyovo banlieues.

(Incidentally, this cognitive stratification is a microcosm of Russia as a whole – the average IQ in Moscow is ~106, versus ~96 in the rest of the country).

Here is a graph that I think supports this interpretation (left: Rubles / sq m, right: USD / sq m).


Vykhino and Zhulebino (blue, orange) are classic prole regions – at 120,000R per sq m, they are marginally more upscale than Biryulyovo, but only just. Sokol (red) is a solidly middle-class region, and contains two universities and an industrial museum within its boundaries. Its average property price is 200,000R per sq m. Tverskaya (green) is a super-elite central region that hosts many of Moscow’s tourist landmarks, including the Bolshoi Theater; property prices there are a cool 400,000R per sq m.

But the trend is even more interesting – note the steady stratification of property values of Sokol and Tverskaya relative to prolecore Vykhino-Zhulebino. Even as early as 2013, Sokol was only about 40% as expensive, whereas today it is more than 60% as expensive. The differential between Vykhino-Zhulebino and Tverskaya has increased from being twice as expensive a decade ago, to almost four times as expensive today.

(This isn’t due to any particularities of the chosen regions – the trend for the prole South-East as a whole relative to the middle-class South-West and the elite Center matches the specific example above).

london-house-price-map Nor do I think this is likely to change anytime soon. This differential in real estate prices is now approaching what you see in both Paris and London (see right), both of which are far more advanced on the diversity (and financialization/cognitive clustering) front than Moscow.

This isn’t great for me personally. For instance, while it was still possible to leverage my apartment, which is in one of the crappier regions, to jump into the center a decade ago and maybe 5 years ago, it’s no longer so realistic today. I suppose I should hurry up while regions like Sokol are still within reach.

I suspect these differentials will continue widening in the years ahead. Immigration will continue, and might intensify as the Russian economy emerges out of recession. Cognitive clustering has a momentum of its own and isn’t going to run out of steam anytime soon.

Finally, I suspect that the advent of driverless cars in one or two decades will produce another major uptick in housing prices in the central regions of the world’s metropolises. One of the few major downsides of life in big cities is that traffic congestion costs go up 34% with every doubling of the urban population. The much greater efficiency of driverless cars should largely nullify those costs, turbocharging property prices in big cities and especially the central, already very expensive cores of the big cities even further.

I am obviously not in the business of giving financial advice. That said, even all else equal, I suspect that in Russia as in most of the Western world, people who move to the big cities – especially the expensive, prestigious regions where high property prices form an effective wall against vibrant diversity – will do financially better than those who stay in the outskirts.

• Category: Economics • Tags: Moscow, Real Estate, Urbanization 
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My post on Indian IQ (max potential is low to mid 90′s) spawned an interesting analysis by commentator rec1man. It is not very well organized but he does have a ton of useful information that deserves to be highlighted. It’s reprinted in full below interspersed with occasional commentary by myself:

Caste Analysis

75% of the Indian population gets affirmative action quota in India and is genetically low IQ.
25% of the Indian population is upper caste and higher IQ and does not get quota.
Most of the upper caste population has Y-DNA = R1A = Russian / Slavic.

AK: The Slavic max. potential IQ appears to be around 100.

In North India there are 3 levels of quota, each quota level corresponding to a different IQ level:
In North India Upper caste > Other Backward Caste > Dalit – Untouchable – Tribal.

In South India, there are 4 levels of quota:
Brahmin > Dravidian Backward Caste > Dravidian Most Backward caste > Dalit-Untouchable-Tribal.

Upper castes and Brahmins dont get quota. In North India, upper castes and Brahmins are genetically the same of Aryan origin. In South India, the only Aryan origin caste is Brahmin.
The others are Dravidian.

5% of the Indian population is of Oriental race and they dont have a high IQ.

Next vegetarianism = Most upper castes, the higher IQ segment is vegetarian.
The lower castes are non-vegetarian and have lower IQ.

AK: This is interesting. I wonder to what extent (if any) Brahmans can improve their intelligence even further by switching to a meat-based diet? I know that among high-caste Indians adherence to vegetarianism is far from universal. I lived with two of them one. That said, being “non-vegetarian” may not mean that much for most Indians, as most are too poor to regularly afford meat anyway.

Jains are a 100% vegetarian merchant caste and they have beaten the Jews in the diamond trade, even in Tel Aviv.

The world chess champion Vish Anand comes from a vegetarian brahmin family.

AK: As I said, Brahmans appear to be the Jews of India. The most famous Soviet/Russian chess champion, Kasparov, is an Armenian Jew. Actually more than half of the Soviet chess champions were Jews. The most prominent exception was Anatoly Karpov.

In India, a non-vegetarian person is likely a low IQ affirmative action caste
You can check a persons caste rank by simply asking whether they are vegetarian, which implies higher caste

In the Indian Manhattan project team of 18, of which 15 were brahmin and 3 merchants.

AK: Thanks for confirming. :) “(I cannot find the source but I recall reading that almost all members of India’s version of the Manhattan Project were composed of Brahmins).”

Each Indian diaspora is different and has a different caste blend and a different IQ
The lowest level IQ diaspora is the agricultural laborer , 50% Shudra, 50% untouchable
This forms about 95% of the Indian population in South Africa, Fiji, Malaysia, Trinidad, Guyana etc

The Patels and Sikhs are Upper-Shudra / Vaishya and this is 80% of the diaspora in UK
In UK, they outperform whites academically and per Lynn , in the 2nd generation, measured and IQ of 97.

In the USA, 60% of the Indian diaspora is upper caste, and 40% from middle-level castes like Patels and Sikhs.

AK: The mean IQ of Indian immigrants to the US is 112.

Qatar School Rankings

Qatar School Ranking, top 30 schools out of 153

Mean PISA = 500 = IQ 100
SD PISA = 100 = IQ 15

Science, Math, Reading scores

For comparison Shanghai = 575, 600, 566 = 112 IQ

1. Al-Khor Indian Stream, ( GEMS ) = 566, 592, 604 = 113 IQ = Indian Hindu technicians and Engineers of NGL
2. The International School of Choueifat ( SABIS ) = 554, 562, 565 = 109 IQ = Lebanese Xtian
3. Doha College Private ( British Embassy ) = 572, 553, 563 = 109 IQ = UK
4. DPS Modern Indian School ( Delhi Public School Society ) = 552, 538, 563 = 107 IQ = Indian Hindu
5. Qatar Academy ( US educators ) = 540, 547, 562 = 107 IQ
6. American School of Doha, ( US Embassy ) = 553, 546, 559 = 108 IQ
7. Park House English ( UK ) = 568, 528, 552 = 107 IQ
8. Birla Public School = 586, 539, 549 = 108 IQ = Indian Hindu
9. Qatar Intl Private School ( UK ) = 539, 529, 540 = 105 IQ
10. Al Bayan Girls = 481, 464, 516 = Muslim Arab = 98 IQ
11. Cambridge Intl Private School = 531, 484, 514 = 101 IQ
12. Doha Modern Indian School ( Jai Gopal Jindal ) = 554, 525, 514 = 104 IQ = Indian Hindu
13. Al-Khor British Stream ( GEMS ) = 507, 505, 503 = 102 IQ
14. Dukhan English School ( UK ) = 529, 501, 500 = 102 IQ
15. Debakey High School for Health ( USA ) = 492, 467, 493 = 98 IQ
16. Qatar Canadian School = 451, 456, 491 = 95 IQ
17. MES Indian School ( Muslim Education Society ) = 484, 469, 490 = 97 IQ = Indian Muslim
18. Ideal Indian School Girls, ( Muslim ) = 481, 450, 489 = 96 IQ = Indian Muslim
19. Sudanese School = 463, 411, 488 = 93 IQ , remarkably high for black-arab mullatos
20. Al Arqam = 454, 451, 484 = 95 IQ
21. The Gulf English = 468, 448, 482 = 95 IQ
22. Philipine School = 466, 461, 480 = 96 IQ
23. Jordanian School = 446, 422, 472 = 92 IQ
24. Tunisian School = 459, 436, 463 = 93 IQ
25. Lebanese School ( Muslim ) = 444, 501, 463 = 96 IQ
26. Middle East Intl = 484, 452, 461 = 95 IQ
27. Al Andalus = 446, 397, 454 = 90 IQ
28. Ideal Indian School, boys ( Muslim ) = 462, 465, 453 = 94 IQ = Indian Muslim
29. Egyptian School = 463, 435, 434 = 92 IQ
30. American Academy = 462, 434, 434 = 92 IQ

Qatar, 153 school average = 379, 368, 372 = 81 IQ

School -1 and School-13 are both identical, run by GEMS, and solely for children of
employees of NGL

School-1 has Hindu students and School-13 has white students from UK
and the Hindu students are ahead of white students by nearly 1 SD

Indian muslims significantly lag behind Indian Hindus

California performance

In the California 2012 National Merit list, there were 184 Indian winners of which

Brahmin = 112
North Indian Aryan Upper castes = 40
Dravidian Upper castes = 25
Patels ( middle ranking ) = 3
Sikhs ( middle ranking ) = 4

In the US diaspora, Sikhs and Patels despite being 40% of the diaspora, win just 4%.
In the UK, these same Patels and Sikhs are 80% of the Indian diaspora and easily outperform whites academically.

The above data, also shows that sampling has to be very accurate to reflect the various caste IQs.

Future Indian IQ = 93

Calculating Average Indian IQ from PISA

TN raw math PISA score = 351
TN implied IQ = 100 – 1.5 x 15 = 78

HP raw math score = 338
HP implied IQ = 100 – 1.62 x 15 = 76

Indian Avg IQ based on raw PISA = 77

AK: Why only Math, and not also Reading and Science? (including them would bring down average IQ to 75.4).

Next step is to remove the bias caused by the PISA sample having 75% bilingual kids (Tibetan kids facing Hindi PISA exam and Telegu kids facing Tamil PISA exam).

TN mono-lingual = 378
Implied IQ = 500 – 1.22 x 15 = 82

HP mono-lingual = 401
Implied IQ = 500 – 15 = 85

AK: Fair enough – though this adjustment is needed not just in India.

Next there is a 40 point difference between scores for ‘Village’ and scores for ‘Large city’. In HP and TN, the village category is over-represented by a factor of 4. Even worse, in HP, City and Large City are entirely removed from the survey sample.

So adding an urban correction of 20 (half the village-large city difference).

TN semi-urbanised mono-lingual = 378 + 20 = 398
Implied IQ = 85

HP semi-urbanised mono-lingual = 401 + 20 = 421
Implied IQ = 100 – 0.79 x 15 = 88

Current Indian IQ = 86

Next we look to the future as malnutrition is removed. The only Indian kids who go to govt school is for the mid-day meal; if they are not starving they go to private school.

Private schools score 45 more than govt schools and that’s the future as poverty reduces.

AK: Not necessarily as it is richer (on average more cognitively endowed) Indians who are today sending their children to private schools. Disagree with this adjustment.

HP – future – semi-urbanised- mono-lingual = 401 + 20 + 45 = 466

Implied IQ = 95

TN – future – semi-urbanised – mono-lingual = 378 + 20 + 45 = 443

Implied IQ = 91

Future Indian IQ = 93

Given the huge bias in sampling towards over-representing the lower end IQ, by the poverty pimp NGOs, I am certain that none of the CBSE or Cambridge schools
that serve the top 15% are included in the survey.

And they have an entirely different IQ profile and cause an IQ bulge at the top end.

AK: A plausible estimate, with the IQ bulge at the top bringing up average Indian IQ by another point or two. But crucial flaw as far as I can see is the +45 point (+7 IQ points) adjustment, which assumes that the cognitive potential of private and public schoolchildren is essentially equal. That is very unlikely.


15% of the Indian population is Muslim, who are also highly inbred, and brainwashed in islamic madrasas, which lowers IQ potential. Another 15% is untouchable and another 10% is tribal.

These 3 groups as a whole have an IQ ceiling , even with nutrition of no more than 85, and these are 40% of the population.

The middle 40%, the Patels and Sikhs, based on UK performance, have an IQ ceiling of about 95.

However, Indians do not have unwed mothers, and Patels and Sikhs are thrifty, have the benefits of extended family and caste networks and save a lot and are a lot richer than whites in UK and Canada and USA.Ori

Averaging the lower 80%, gives an IQ ceiling of 90.

Anything beyond 90 IQ average ceiling, is a bonus and that’s due to the top 20%.

Most PISA type surveys are going to catch the bottom 80%.

The top 20% is extremely urbanised and goes to very good quality private schools. The Orissa TIMMS survey, showed that the 95th percentile was comparable to 95th percentile of Norway and Orissa is a very backward state.

On a system wide level, India is going to behave like 90 IQ ceiling.

On cutting level achievements, the top 20% is extremely world competitive.

Jing’s Counter-argument (8/18)

Orissa’s TIMSS 95% percentile compares favourably to Norway’s because Orissa’s is one of India’s higher scoring states and Norway is oddly enough Europe’s lowest by far. Norway’s 95% percentile was 573 (Orissa’s 577) but this is significantly lower than Bulgaria (611), Serbia (618), and Romania (619). To add some more perspective, neighboring Sweden is 614, Lithuania 628, Estonia 645, and Latvia 625. Russia is at 632, America 635 and England 627. To cap it off Hong Kong is at 691, Japan 697, South Korea 715, Singapore 723, and Taiwan tops the charts at a ridiculous 733.

India’s top 5% would not even make the 50% cutoff in any of the east Asian polities.

All data available here.

Rec1man’s Qatar comparisons are even more irrelevant because he is comparing the absolute HIGHEST ranked schools of high sigma Indian professionals in the country against OECD AVERAGES. Pick out the highest ranked school in Shanghai or the 10th for that matter and compare it against them and you will see just how far the gap is.

The data tables are available online for anyone who cares to delve more deeply into them for the 2009 PISA at the following link.

Selecting the two Indian participating states that (QTN and QHP) with the variable ST19Q01 as the student variable compares how well Indian students did based on the language of the test. Indian students who took the test in a language OTHER than the one spoken at home score higher than the ones who took the test in their native language.

By the way, Richwine’s backward digit span test correlated to a 112 IQ for India’s taken from the GSS survey had a sample size of less than 10 if I recall.

(Republished from by permission of author or representative)
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He writes:

These scores are indeed truly remarkable, and completely confirm the apparent pattern of Lynn’s IQ samples, in which desperately poor East Asians tend to score at or above the levels of the most successful and well-educated Western populations… But since the total population is at least well into the hundreds of millions, heavily rural as well as urban, the average PISA score of 520—corresponding to an IQ of 103—cannot be too dissimilar from the overall Chinese figure. And with China’s per capita GDP still only $3,700 and well over half the population still living in rural villages when the tests were conducted, these are absolutely astonishing results… Although opinions may certainly differ, I regard this new evidence as very strong support for my “East Asian Exception” hypothesis.

China isn’t anywhere near as backward as he portrays it.

(1) The urban-rural ratio was essentially 50/50 according to the 2010 Census. Furthermore, rural Chinese don’t really suffer from the absolute destitution common to peasants in Third World countries. They own their own land and it is almost impossible for them to lose it. Malnutrition is now close to non-existent. Slums are now very rare. According to a Gallup poll, Chinese now actually struggle less than Americans to buy food.

(2) Total Chinese meat consumption overtook US meat consumption in 1990, signifying a nutritionally adequate figure (as Americans eat a lot of and perhaps a bit too much meat anyway). Today Chinese meat consumption is half the US level. The PISA 2009 cohort would have been born in 1993, when Chinese nutrition had already essentially converged with the First World.

(3) He uses nominal GDP per capita which is quite meaningless. The PPP level of Chinese GDP per capita is $8,400 and that figure is probably underestimated.

Basically, if we adjust for the fact that in terms of basics (food, education, housing) China is now essentially equivalent to developed countries, it would make sense that its average IQ level is now only about 5 points from its potential maximum.

But really my fundamental problem with the “East Asia Exception” hypothesis is the huge paradox it exposes: Why was it Europe, and not China, that first underwent the Industrial Revolution? And the (initially unrelated) Scientific Revolution, for that matter? If as Ron Unz says the Flynn Effect barely applies to East Asian populations, then what you’d have had five centuries ago is 100mn Chinese, 20% of them urban – with an average IQ of maybe 95; and 100mn Europeans, only 5% of them urban – with an average IQ of 75. Sure Europe had various advantages (as chronicled by Jared Diamond, Kenneth Pomeranz, etc) but surely it couldn’t have trumped the effects of a 1 S.D. IQ advantage? That is why I believe the East Asia Exception to be historically implausible.

(Republished from by permission of author or representative)
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Via The Audacious Epigone: IQ scores by US ethnic groups. It is not very useful I think in theoretical or practical applications but it is interesting as a showcase of why IQ is more than just genetic inheritance, incorporating also Flynn, sampling issues, sense of popular identity, selection bias, etc.

Wordsum is basically a vocab test of 10 words (example). While one might not expect such a quick and simple test to accurately reflect IQ it actually does – the correlation between Wordsum scores and IQ is about 0.71. Respondents got to choose the one or two ethnic groups to which they belong. (See table below)

By and large, the results are as we would expect:

(1) Many of the Russians (about 50%) are, of course, Ashkenazi Jews. Explained.

(2) At first puzzling is the fact that Germans score considerably below the Irish, Mediterraneans (Italians, Greeks, French) and Slavics (Poles, Czechs). This is an inversion of European PISA results in which native Germans got 105 compared to France’s 101, Poland’s 100, Italy’s 98, Greece’s 96, etc. There are two reasons for this I think:

(a) As Ron Unz correctly points out, all other things equal rural people almost always score worse than urban people. Maybe rural life just isn’t as mentally stimulating. Almost certainly there exist strong dysgenic processes (every generation it is the brightest people who leave the farm). But this effect probably isn’t that strong as there are very few Americans still living on the farm today.

(b) As I think hbd chick pointed out, some lineages are more prestigious than others. The Irish and Italians, in particular, are cool. In practice almost everyone in American cities is mixed to some extent. So even if someone is primarily Germanic, he might pass himself off as a Latino or Irishman. So the higher scores of most Italians, Greeks, Irish, Swedes, Norwegians, Hungarians, etc., etc., may well reflect the Germanic substrate more than anything else.

(c) The Germans who came to America mostly came from the northern parts (e.g. the hamburger originated in… Hamburg). And various studies indicate that just as Germany is a genetically diverse nation by European standards so there are significant IQ differentials by regions. Generally speaking, the south is cleverer than the north.

So, we have three major factors: The regional specifics of German immigration to the US; the greater relative prestige of “exotic” ethnic groups together with the “melting pot” culture of American cities; their greater share of the rural population. They may explain why ethnic German IQ in the US is fairly low relative to other European population groups.

Ancestry IQ
Russian 105.2
Austrian 104.5
Swiss 103.6
Danish 102.6
English/Welsh 102.4
Norwegian 102.1
Hungarian 101.6
Scottish 101.5
Swedish 101.3
Czech 100.4
Italian 100.4
French 100.0
Irish 99.7
Polish 99.7
Greek 99.0
French Canadian 98.7
German 98.5
Other Canadian 96.2
Dutch 95.9
(White) “American” 94.2
Spanish 92.4
Native American 91.2
African 89.9
Puerto Rican 89.9
“American” 88.7
Mexican 87.7

(3) The Dutch are especially low, though I suppose that this could be partly accounted for by the fact that a substantial number of them are Pennsylvania Dutch, i.e. Amish. They reject modern technology. Presumably, the Flynn Effect isn’t strong in them.

(4) The Spanish are presumably a mix of real Spaniards (with IQ’s typical of the other Med nations – around 98) and Mexicans (who have an IQ somewhere in the mid to high 80′s, as a result of Amerindian admixture as well as some degree of under-development). They are thus exactly where one would expect them to be.

(5) White “Americans” are typically Scotch-Irish in the Deep South. Here there is an element of self-selection bias because they are people who either forgot or don’t care about their ethnic origins. I.e., rednecks.

(6) Africans score a respectable (by US black standards) 89. Probably because African immigrants tend to be far above the African average. They are also those people who didn’t answer “American”. Of the “Americans”, more than half are black. Since the white “American” average is 94, and the “American” average 89, that means that the black “American” average should be something like 85.

I don’t know how many black “Americans” and “Africans” there were relative to each other but at a minimum we can say that the average for this group is in the 85-89 range. This is exactly coincident with all other conventional estimates.

(7) The Swiss scores are not surprising. They are a very intelligent people. The Austrians might be something of an outlier as I imagine their sample size is quite low. Also the dumber Austrians may have identified themselves as Germans.

(8) All the other ethnic groups appear to be more or less where we would expect them to be based on the performance of their original home countries.

(Republished from by permission of author or representative)
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One of the most interesting emerging sciences today, in my opinion, is cliodynamics. Their practitioners attempt to come to with mathematical models of history to explain “big history” – things like the rise of empires, social discontent, civil wars, and state collapse. To the casual observer history may appear to be chaotic and fathomless, devoid of any overreaching pattern or logic, and consequently the future is even more so (because “the past is all we have”).

This state of affairs, however, is slowly ebbing away. Of course, from the earliest times, civilizational theorists like Ibn Khaldun, Oswald Spengler and Arnold Toynbee dreamed of rationalizing history, and their efforts were expounded upon by thinkers like Nikolai Kondratiev, Fernand Braudel, Joseph Schumpeter, and Heinz von Foerster. However, it is only with the newest crop of pioneers like Andrei Korotayev, Sergey Nefedov, and Peter Turchin that a true, rigorous mathematized history is coming into being – a discipline recently christened cliodynamics.

As an introduction to this fascinating area of research, I will summarize, review, and run an active commentary on one of the most comprehensive and theoretical books on cliodynamics: Introduction to Social Macrodynamics by Korotayev et al (it’s quite rare, as there’s only a single copy of it in the entire UC library system). The key insight is that world demographic / economic history can be modeled to a high degree of accuracy by just three basic trends: hyperbolic / exponential, cyclical, and stochastic.

Korotayev, Andrei & Artemy Malkov, Daria KhaltourinaIntroduction to Social Macrodynamics: Secular Cycles and Millennial Trends (2006)
Category: cliodynamics, world systems; Rating: 5*/5
Summary: Andrei Korotayev (wiki); review @; a similar text на русском.

Introduction: Millennial Trends

Google Books has the first chapter Introduction: Millennial Trends.

In 1960, Heinz von Foerster showed that the world’s population at any given time between 1-1958 CE could be approximated by the simple equation below, where N is the population, t is time, C is a constant, and t(0) is a “doomsday” when the population becomes infinite (worked out to be 13 November, 2026).

(1) N(t) = C / ( t(0) – t )

According to Korotayev et al, this simple formula of hyperbolic explains 99%+ of the micro-variation in world population from 1000 to 1970. Furthermore, a quadratic-hyperbolic equation of the same type accurately represents the increase in the GDP. Why?

He discusses the work of Michael Kremer, who attempted to build a model by making the Malthusian assumption that “population is limited by the available technology, so that the growth rate of population is proportional to the growth rate of technology”, and the “Kuznetsian” assumption that “high population spurs technological change because it increases the number of potential inventors”.

(2) G = r*T*N^a

(3) dT/dt = b*N*T

Above, G is gross output, T is technology, N is population, and a, b, and r are parameters. Note that dT, change in technology, is dependent on both N (indicates potential number of inventors) and T (a wider technological base enabled more inventions to be made on its basis). Solving this system of equations results in hyperbolic population growth, illustrated by the following loop: population growth → more potential inventors → faster tech growth → faster growth of Earth’s carrying capacity → faster population growth.

Korotayev then counters arguments dismissing such theories as “demographic adventures of physicists” that have no validity because the world system was not integrated until relatively recently. However, that is only if you use Wallerstein’s “bulk-good” criterion. If one instead uses the softer “information-network” criterion, noting that there is evidence for the “systematic spread of major innovations… throughout the North African – Eurasian Oikumene for a few millennia BCE” – and bearing in mind that this emerging belt of cultures of similar technological complexity contained the vast majority of the global human population since the Neolithic Revolution – then this can be interpreted as “a tangible result of the World System’s functioning”.

Then Korotayev et al present their own model that describes not only the hyperbolic world population growth, but also the macrodynamics of global GDP in the world system until 1973.

(4) G = k1*T*N^a

(5) dN/dt = k2*S*N

(6) dT/dt = k3*N*T

Above, T is technology, N is population, S is surplus per person (and S = g – m, where g is production per person and m is the subsistence level required for zero population growth), and k1, k2, k3, and a are parameters. This can be simplified to:

(7) dN/dt = a*S*N

(8) dS/dt = b*N*S

(9) G = m*N + S*N

As S should be proportional to N in the long run, S = k*N. Replace.

(10) dN/dt = k*a*N^2

Recall that solving this differential equation gives us hyperbolic growth (1).

(11) N(t) = C / ( t(0) – t )

Furthermore, replacing N(t) above with S = k*N gives (12), allowing us to work out the “surplus world product” S*N (13).

(12) S = k*C / ( t(0) – t )

(13) S*N = k*C^2 / ( t(0) – t )^2

Hence in the long-run, this suggests that global GDP growth can be approximated by a quadratic hyperbola. Other indices that can be described by these or similar models include literacy, urbanization, etc.

One finding is that after 1973, there world GDP growth rate itself falls (rather than just a slowing of the growth of the GDP growth rate, as predicted by the original model): the explanation is, “the literate population is more inclined to direct a larger share of its GDP to resource restoration and to prefer resource economizing strategies than is the illiterate one, which, on the one hand, paves the way towards a sustainable-development society, but, on the other hand, slows down the economic growth rate”. To take this into account, they build a modified model, according to which, “the World System’s divergence from the blow-up regime would stabilize the world population, the world GDP… technological growth, however, will continue, though in exponential rather than hyperbolic form”.*

The consequences for the future are that though GDP growth will reach an asymptote, technological improvements will continue raising the standard of living due to the “Nordhaus effect” (e.g. combine Moore’s Law – exponentially cheapening computing power, with the growing penetration of ever more physical goods by IT).

“It appears important to stress that the present-day decrease of the World System’s growth rates differs radically from the decreases that inhered in oscillations of the past… it is a phase transition to a new development regime that differs radically from the ones typical of all previous history”. As evidence, unlike in all past eras, the slowing of the world population growth rate after the 1960′s did not occur against a backdrop of catastrophically falling living standards (famine, plague, wars, etc); to the contrary, the causes are the fall in fertility due to social security, more literacy, family planning, etc. Similarly, the decrease in the urbanization and literacy growth rates is not associated this time by the onset of Malthusian problems, but is set against continuing high economic growth and the “closeness of the saturation level”.

(AK: This rosy-tinged analysis is persuasive and somewhat rigorous, but there is a gaping hole – they used only “technology” as a proxy for the carrying capacity. However, as Limits to Growth teaches us, part of what technology did is open up a windfall of energy resources – high-grade oil, coal, and natural gas – that have been used to fuel much of the post-1800 growth in carrying capacity (disguised as “technology” in this model), yet whose gains are not permanent because of their unsustainable exploitation. Furthermore, the modern technological base is underpinned by the material base, and cannot survive without it – you can’t have semiconductor factories without reliable electricity supplies – and generally speaking, the more complex the technology, the greater the material base that is needed to sustain it (this may constitute an ultimate limit on technological expansion). This major factor is also neglected in Korotayev’s millennial model. As such, the conclusion that the world has truly and permanently reached a sustainable-development regime does not follow. This is not to say that it is without merit, however – it’s just that it needs to be integrated with the work done by the Limits to Growth / peak oil / climate modelers.)

Chapter 1: Secular Cycles

Korotayev et al conclude that these millennial models are only useful on the millennial scale (duh!), and that typical agrarian political-demographic cycles follow Malthusian dynamics because in the shorter term, population tends to growth much more rapidly than technology / carrying capacity, which led to a plateauing of the population, growing stress due to repeated perturbations, and an eventual tipping point over into collapse / dieoff.

The basic logic of these models is as follows. After the population reaches the ceiling of the carrying capacity of land, its growth rate declines toward near-zero values. The system experiences significant stress with decline in the living standards of the common population, increasing the severity of famines, growing rebellions, etc. As has been shown by Nefedov, most complex agrarian systems had considerable reserves for stability, however, within 50–150 years these reserves were usually exhausted and the system experienced a demographic collapse (a Malthusian catastrophe), when increasingly severe famines, epidemics, increasing internal warfare and other disasters led to a considerable decline of population. As a result of this collapse, free resources became available, per capita production and consumption considerably increased, the population growth resumed and a new sociodemographic cycle started.

He notes that newer models are far more complex and predict the dynamics of variables such as elite overproduction, class struggle, urbanization, and wealth inequality with a surprisingly high degree of accuracy (e.g. see A Model of Demographic Cycles in a Traditional Society: The Case of Ancient China by Nefedov). Korotayev et al then list three major approaches to modeling agrarian political-demographic cycles: Turchin (2003), Chu & Lee (1994), and Nefedov (1999-2004).

1. Turchin has constructed an elegant “fiscal-demographic” model, in which the state plays a positive role by by a) maintaining armed order against banditry and lawlessness, and b) doing works such as roads, canals, irrigations systems, flood control, etc, – both of which increase the effective carrying capacity. However, as demographic growth brings the population to the carrying capacity of the land (in practice, the population plateaus somewhat below it due to elite predation), surpluses diminish. So do the state’s revenues, since the state taxes surpluses; meanwhile, expenditures keep on rising (because of the reasons identified by Tainter). Eventually, there sets in a fiscal crisis and the state must tax the future to pay for the present by drawing down the surpluses accumulated in better days; when those surpluses run out, the state can no longer function and collapses, which leads to a radical decline of the carrying capacity and population as the land falls into anarchy and irrigation and transport infrastructure decays.

2. The Chu and Lee model consists of rulers (including soldiers), peasants (grow food), and bandits (steal food). The peasants support the rulers to fight the bandits, while there is a constant flux between the peasants and bandits whose magnitude depends on the caloric & survivability payoffs to belonging in each respective class. However, it’s not a fully-formed model as its main function is to fill in the gaps in the historical record, by plugging in already-known historical data on warfare and climatic factors; they neglected to associate crop production with climatic variability (colder winters result in lesser crop yields) and the role of the state in food distribution (which staved off collapse for some time and was historically significant in China).

3. Nefedov has integrated stochasticity into his models, in which random climatic effects produce different year-to-year crop yields. One result is that as carrying capacity is reached, surpluses vanish and the effects of good and bad years play an increasingly important role – i.e. a closed system under stress suffers increasingly from perturbations. One bad year can lead to a critical number of people leaving the farms for the cities or banditry, initiating a cascading collapse. However, he neglects the “direct role of rebellion and internal warfare on cycle behavior”, so as the model is purely economic, each demographic collapse is, implausibly, immediately followed by a new rise.

The ultimate aim of Korotayev et al is to integrate the positive features of all three models (Chapter 3), but for now the take a closer look at the political-demographic history of China, the pre-industrial civilization that maintained the best records.

Chapter 2: Historical Population Dynamics in China – Some Observations

Below is a graph of China’s population on a millennial scale. Note the magnitude and cyclical nature of its demographic collapses. Note also that such cycles are far from unique to Chinese civilization (see collapse of the Roman Empire), and reflect for a minute, even, on the profound difference between the modern world of permanent growth, and the pre-industrial, “Malthusian” world.

Since it would be futile to repeat the fine details of every political-demographic cycle in China’s, I will instead just list the main points.

  • The cycles tend to be ones of a fast rise in population, when surpluses are high and people are prosperous. It plateaus and stagnates when the population reaches the carrying capacity, when there is overpopulation, much lowered consumption, increasing debilitation of state power, and rising social inequality and urbanization.
  • Sometimes, such as in the middle Sung period, population stress did not lead to a collapse, but instead to a “radical rise of the carrying capacity of the land” through administrative and technological innovations. This increased the permanent ceiling of Chinese carrying capacity from 60mn to around 120mn souls, and in doing so alleviated the population stress until the early 12th century (AK: e.g. in Early Modern Britain, the problem of deforestation was solved by coal). At that point, China may have once again solved its problems, even escaping from its Malthusian trap (AK: some historians have noted that it had many of the prerequisites for an industrial revolution). That was not to be, as “the Sung cycle was interrupted quite artificially by exogenous forces, namely, by the Jurchen and finally Mongol conquests”.
  • The Yuan dynasty would not reach the highs of the Sung because of the general bleakness of the 14th century – the end of the Medieval Warm Period, unprecedented floods and droughts in China, etc, which lowered the carrying capacity to a critical level. The resulting famines and rebellions led to the demographic collapse of the 1350′s, as well as the de facto collapse of the state, as China transitioned to warlordism.
  • Carrying-capacity innovations under the Ming did not, eventually, outrun population growth, and it collapsed during the turmoil of the transition to the Qing dynasty. The innovations accelerated throughout the 18th century (e.g. New World crops, land reclamation, intensification of farming). Indications of subsistence stress as China entered the 19th century were a) declining life expectancies, b) rising staple prices, and c) a huge increase in female infanticide rates in the first half of the 18th century. By 1850, China was again under very severe subsistence stress and the state grew impotent just as Europeans began to encroach on the Celestial Empire.
  • Huang 2002: 528-9, worthy of quotation in extenso. “Recent research in Chinese legal history suggests that the same subsistence pressures behind female infanticide led to widespread selling of women and girls… Another related social phenomenon was the rise of an unmarried “rogue male” population, a result of both poverty (because the men could not afford to get married) and of the imbalance in sex ratios that followed from female infanticide. Recent research shows that this symptom of the mounting social crisis led, among other things, to large changes in Qing legislation vis-à-vis illicit sex… Even more telling, perhaps, is the host of new legislation targeting specifically the ‘baresticks’ single males (guanggun) and related ‘criminal sticks’ of bandits (guntu, feitu), clearly a major social problem in the eyes of the authorities of the time”. See Diagram V.13. (AK: Interestingly, China’s one-child policy, by artificially restricting fertility in order to ward off a “Maoist dynasty” Malthusian crisis, has led to many of the same problems in the past two decades).
  • Speaking of which… China had further dips in its population after during perturbations in the 1850′s (the millenarian Taiping Rebellion), the 1930′s (Japanese occupation), and 1959-62 (the Great Leap Forward), each progressively smaller than the last in its relative magnitude. For instance, the latter just formed a short plateau.

Korotayev et al conclude the chapter by running statistical tests on China’s historical population figures from 57-2003. In contrast to linear regression (R^2 = 0.398) and exponential regression (R^2 = 0.685), the simple hyperbolic growth model described in “Introduction: Millennial Trends” produces an almost perfect fit with the observed data (R^2 = 0.968). So in the very, very long-term, the effects of China’s secular cycles are swamped by the millennial trend of hyperbolic growth.

Finally, the authors describe in-depth the general pre-industrial Chinese demographic cycle. Below is a functional scheme I’ve reproduced from the book (click to enlarge).

The main points are:

  • Fast population growth until it nears the carrying capacity, then a long period (100 years+) of a very slow and unsteady growth rate, accompanied by increasingly significant, but non-critical fluctuations in annual population growth due to climatic stochasticity (positive growth in good years, negative growth – along with dearth, minor epidemics, uprisings, etc – in bad years). These fluctuations get worse with time as the state’s counter-crisis potential degrades due to the drawdown of previously accumulated surpluses.
  • According to Nefedov’s model and historical evidence, the fastest growth of cities occurred during the last phases of demographic cycles, as peasants were driven off the land and there appeared greater demand for city-made goods from the increasingly affluent landowners (who could charge exorbitant rates on their tenants). Furthermore, some peasants are drawn into debt bondage because the landowner had previously given them food at a time of dearth. Other peasants turn to banditry.
  • Re-”elite overproduction → over-staffing of the state apparatus → decreasing ability of the state to provide relief during famines”. The system of state relief had been very effective earlier, e.g. in 1743-44 a state effort to prevent starvation in the drought-stricken North China core was successful. However: “By Chia-ch’ing times (1796-1820) this vast grain administration had been corrupted by the accumulation of superfluous personnel at all levels, and by the customary fees payable every time grain changed hands or passed an inspection point… The grain transport stations served as one of the focal points for patronage in official circles. Hundreds of expectant officials clustered at these points, salaried as deputies (ch’ai-wei or ts’ao-wei) of the central government. As the numbers of personnel in the grain tribute administration grew and as costs rose through the 18th century, the fees payable for each grain junk increased [from 130-200 taels per boat in 1732, to 300 taels in 1800, and to 700-800 taels by 1821]“. Similarly, the Yellow River Conservancy, whose task it was to prevent floods, degenerated into hedonistic corruption in the early 19th century; only 10% of its earmarked funds being spent legitimately.
  • So what you have is an increasingly exploited peasantry, a growing (and volatile) urban artisan class – e.g., the sans-culottes of the French Revolution, and more banditry. The bandits create a climate of fear in the countryside and force more outmigration into the cities, and the abandonment of some lands. At the same time, state power – military and administrative – is on the wane, displaced by corruption. The effects of perturbations are magnified due to the system’s loss of resiliency. There eventually comes a critical tipping point after which there is a cascading collapse that involves a population dieoff, the fall of centralized power, and a prolonged period of internal warfare.
  • Fast population growth does not resume immediately after collapse because things first need to settle down.

In my Facebook Note, Musings on the decline and fall of civilizations, I draw a link between the fast population increase / abundance of the “rise” period, and the concept of the “Golden Age” common to all civilizations. Also ventures a theory as to why cities (hedonism, conspicuous consumption, etc) have such a poor reputation as a harbinger of collapse… because they are, it’s just that the anti-poshlost preachers haven’t identified the right cause (i.e. overpopulation, not “moral decadence” per se).

Furthermore, a tentative explanation of the reason for differential Chinese – European technological growth rates (compare and contrast with Jared Diamond’s explanation):

Incidentally, a possible reason why Western Europe emerged as the world’s economic hegemon by the 19th century, instead of China, a civilization that at prior times had been significantly more advanced. But in China, the depth of the Malthusian collapses was deeper and more regular (once every 300 years, typically) than in W. Europe… Once the Yangtze / Yellow River irrigation systems failed, tens of millions of peasants were doomed; nothing on an equivalent scale in Europe, which is geographically and politically fragmented into many chunks and nowhere has anywhere near the same reliance on vulnerable hydraulic works for the maintenance of complex civilization (control over water was at the heart of “Oriental despotism” (Wittfogel); the Chinese word “zhi” means both “to regulate water” and “to rule”).

This theory that the reason China began to lag behind Western Europe technologically was because of its more frequent collapses / destructions of knowledge should be explored further.

Finally, about the nature of perturbations in a closed system under increasing stress… That is our world in the coming decades: even as Limits to Growth manifest themselves, there will be more (and greater) shocks of a climatic, terrorist, and military nature. The stochasticity will increase in amplitude even as the System becomes more fragile. As a result, polities will increase the level of legitimization and coercion, i.e. they will become more authoritarian.)

Chapter 3: A New Model of Pre-Industrial Political-Demographic Cycles

To address the shortcomings of other models and taking into account what happens in typical pre-industrial demographic cycles, Korotayev with Natalia Komarova construct their own model that includes the following three main elements:

(1) The Malthusian-type economic model, with elements of the state as tax collector (and counter-famine reservoir sponsor), and fluctuating annual harvest yields; this describes the logistic shape of population growth. It explains well the upward curve in the demographic cycle and saturation when the carrying capacity of land is reached. (2) Banditry and the rise of internal warfare in time of need are the main mechanism of demographic collapse. Personal decisions of peasants to leave their land and become warriors / bandits / rebels are influenced by economic factors. (3) The inertia of warfare (which manifests itself in the fear factor and the destruction of infrastructure) is responsible for a slow initial growth and the phenomenon of the “intercycle”.

Reproducing the model in detail will take up too much space, so just the main conclusions: “the main parameters affecting the period of the cycle are a) the annual proportions of resources accumulated for counter-famine reserves, b) the peasant-bandit transformation rate, and c) the magnitude of the climatic fluctuations. Hence, the lengths of cycles – and this is historically corroborated – is increased along with the growth of the counter-famine (more reserves) and law-enforcement (repress banditry) subsystems.

Chapter 4: Secular Cycles & Millennial Trends

Full version of Chapter 4: Secular Cycles and Millennial Trends.

The chapter begins by modeling the role of warfare, and challenges recent anthropological findings that denser populations do not necessarily lead to more warfare.

  • First, this is explained by the fact that it’s not a simple relation, but more of a predator-prey cycle described by a Lotka–Volterra equation. When warfare breaks out in a time of stress it leads to the immediate reduction of the carrying capacity and demographic collapse; however, warfare simmers on well into the post-collapse phase because groups continue to retaliate against each other.
  • Second, the methodology is flawed because it treats all wars the same, whereas in fact they tend to be far less devastating for bigger polities than for small ones. This is because bigger polities have armies that are more professional, and the length of their “bleeding borders” relative to total territory, is much smaller than for territorially small chiefdoms, for whom even low-intensity wars are demographically devastating. As such, more politically complex polities fight wars more frequently more frequently than smaller ones, but tend to be far less damaged by them.
  • Imperial expansions in territory coincide with periods of fast population growth and high per capita surpluses; later on, shrinking surpluses decimate the tax base and even defense proves increasingly hard (“imperial overstretch”). This correlation is very strong.

Now Korotayev et al combine their model from the last chapter with Kremer’s equation for technological growth (see the Introduction):

dT/dt = a*N*T

They also model a “Boserupian” effect, in which “relative overpopulation creates additional stimuli to generate and apply carrying-capacity-of-land-raising innovations”.

Indeed, if land shortage is absent, such stimuli are relatively weak, whereas in conditions of relative overpopulation the introduction of such innovations becomes literally a “question of life and death” for a major part of the population, and the intensity of the generation and diffusion of the carrying capacity enhancing innovations significantly increases.

Finally, they make the size of the harvest dependent not only on climatic fluctuations, but also on the level of technology.

Harvest i = H 0*random number i*T i.

Running this model with some reasonable parameters produces the following diagram, which reproduces not only the cyclical, but also the hyperbolic macrodynamics.


Note that it also describes the lengthening of growth phases detected in Chapter 2 for historical population dynamics in China, which was not described by our simple cyclical model. The mechanism that produces this lengthening in the model (and apparently in reality) is as follows: the later cycles are characterized by a higher technology, and, thus, higher carrying capacity and population, which, according to Kremer’s technological development equation embedded into our model, produces higher rates of technological (and, thus, carrying capacity) growth. Thus, with every new cycle it takes the population more and more time to approach the carrying capacity ceiling to a critical extent; finally it “fails” to do so, the technological growth rates begin to exceed systematically the population growth rates, and population escapes from the “Malthusian trap” (see Diagram 4.26):

The cycles lengthen, and then cease:

AK: some confirmation for my rough explanation of why Chinese technological growth rate fell below Europe’s prior to the Industrial Revolution (see end of Chapter 2 in this post).

Of special importance is that our numerical investigation indicates that with shorter average period of cycles a system experiences a slower technological growth, and it takes a system longer to escape from the “Malthusian trap” than with a longer average cycle period.

Finally, they also add in an equation for literacy:

l i+1 = l i*b*dF i*l i*(1 – l i)

Which has the following effect on population growth:

N i+1 = N i*(1 + α × dF’)*(1 – l) – dR i – rob*N i*R i

And all added together, it produces the following stunning reproduction of China’s population dynamics from ancient past to today.

And concludes:

Of course, these models can be only regarded as first steps towards the development of effective models describing both secular cycles and millennial upward trend dynamics.

The Meaning of Cliodynamics

Turchin, Peter & Sergey NefedovSecular Cycles (2008)
Category: cliodynamics, world systems; Rating: 5/5
Summary: Read the whole book (PDF) or in chapters

This is a free online, quasi-popular book about eight different pre-industrial secular cycles (including Tudor England, the Roman Empire, Muscovy, and the Romanov Empire). Knowing the facts of history and the proximate causes of Revolutions – Lenin’s charisma, Tsarist incompetence, the collapse of morale and of the railway system, etc – is all well and good, but an entirely different perspective is opened up when looking at late Tsarist Russia through a social macrodynamic prism. The interpretation shifts to one of how late imperial Russia was under a panoply of Malthusian pressure, and of how the additional stresses and perturbations of WW1 “tipped” the system over into a state of collapse.

Finally, my reply to someone who sent me a message suggesting that cliodynamics may “make old school idiographic history redundant”.

I don’t think these trends will make idiographic history redundant, because there are many elements that are irreducible to mathematical analysis; furthermore, a major and inevitable weakness of cliodynamics is our lack of numbers for much of pre-mass literacy history. To the contrary, I think cliodynamics will end up complementing the “old school” rather than displacing it.


* Ray Kurzweil, one of the high priest of the singularitarian movement, extends Moore’s observations to also model technological growth (computing power, to be precise) as doubly exponential, or even hyperbolic. See Appendix: The Law of Accelerating Returns Revisited,

On the other hand, Joseph Tainter noted that in many areas the rate of technological innovation is actually slowing down. This is an argument that Kremer’s assumption that the rate of technological growth is linearly dependent on the product of the population and the size of the already-existing technological base is too simplistic.

These observations are supported by Planck’s Principle of Increasing Effort – “with every advance [in science] the difficulty of the task is increased” (i.e. you’re now unlikely to make new discoveries by flying a kite in a thunderstorm). Furthermore, “Exponential growth in size and costliness of science, in fact, is necessary simply to maintain a constant rate of progress”, and according to Rescher, “In natural science we are involved in a technological arms race: with every ‘victory over nature’ the difficulty of achieving the breakthroughs which lie ahead is increased”.

(Republished from Sublime Oblivion by permission of author or representative)
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Anatoly Karlin
About Anatoly Karlin

I am a blogger, thinker, and businessman in the SF Bay Area. I’m originally from Russia, spent many years in Britain, and studied at U.C. Berkeley.

One of my tenets is that ideologies tend to suck. As such, I hesitate about attaching labels to myself. That said, if it’s really necessary, I suppose “liberal-conservative neoreactionary” would be close enough.

Though I consider myself part of the Orthodox Church, my philosophy and spiritual views are more influenced by digital physics, Gnosticism, and Russian cosmism than anything specifically Judeo-Christian.