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The Harmattan season.

By Afrosapiens, 555  words.


Harmattan haze in Niger.


The Harmattan season is a little known feature of the climatic characteristics of the West African subcontinent. Similarly to a temperate climate winter, it occurs between November and March,  5 months during which the region experiences dry, hazy, and colder conditions due to Saharan dust particles brought by the Harmattan trade wind.

Although the Harmattan season is sometimes referred to as a West African winter with temperature commonly dropping to a low 7°C (45°F) during the night and in the morning, typical tropical temperatures ranging from 25°C to 35°C (77°F – 95°C) are experienced during the afternoon. The humidity rate is below 15% and the region experiences no rainfall during the season. More than the drought, the colder temperature and occasional dust storms and wildfires, the dusty Harmattan haze is what makes the West African winter challenging by significantly reducing visibility and causing several health problems such as asthma, meningitis, skin and eye conditions. From an evolutionary standpoint, it is possible that the Harmattan season has caused various anatomical adaptations affecting brain characteristics.




Despite the common hereditarian claim that Sub-Saharan Africans average smaller cranial capacities than Eurasians due to the warmer climates of tropical Africa, the few studies that I’ve come across regarding West Africa paint a significantly different picture. In a 2011 sample of North-Eastern Nigerian adults likely of Kanuri ethnicity, the reported average cranial capacity was 1424cc for males and 1331cc for females, which makes a total average of 1378cc. In a 2013 sample of 527 Igbos aged 14-20 from Anambra State (Southeastern Nigeria), the reported cranial capacities were 1411cc for males and 1443cc for females and a combined average of 1427cc. In another study of Southeastern Nigerians (year 2011),  the reported values were closer to those usually claimed with an average of 1310cc among Edos, 1273cc among Igbos and 1256cc among Urhobos.


Beals et al.’s (1984) estimated distribution of cranial capacity.


Although these are only a few studies on West African cranial characteristics, they at least have the merit of being recent (less than 10 years old) and drawn from actual measurements on living persons contrary to Beals et al.’s 1984 reference study in which the West African values are inferred from simplistic climatic variables in the absence of actual skulls from the region. I have often shown these high cranial capacity West African samples as a refutation of the cold winter theory of brain size differences.  And whereas hereditarian debaters have commonly dismissed them as meaningless exceptions to the rule, there is no scientific rule with unexplainable exceptions.


Harmattan haze in the Ghanaian countryside


In fact, judging from the competing and more generally accepted theory that brain size and eyeballs grow in adaptation to low light environments, the high cranial capacity, especially in Northern Nigerian samples, makes perfect sense if seen as an adaptation to the low visibility caused by the Harmattan haze from November to March. Unfortunately I wasn’t able to find any data on West African eye characteristics, I can only anecdotally mention a higher frequency of epicanthic fold among West Africans relative to other non-Mongoloid populations. I acknowledge that this post is somewhat speculative and based on poor data. Nevertheless, I write it as a warning to all the bloggers and “scholars” making up theories and inferences based on a poor understanding of the complexity of the world’s current and past climatic conditions.


Dakar, Senegal under the Harmattan Haze.


Pumpkinperson on the history of Black Africa

By Afrosapiens, 3650 words.

This article is a point by point refutation of Pumpkin Person’s extremely inaccurate and unscientific depiction of Black Africa’s history based on astrophysicist Michael Hart’s book Understanding Human History. While it might sound like a waste of time to give any attention to the writings of a clueless blogger, I found it was the perfect occasion to share the main specialist views on civilizational development and Sub-Saharan African history that many are still unaware of.

Although a couple of our commenters have been citing a lot of the accomplishments of black Africa, Dr. Michael H. Hart paints a very different picture in his book Understanding Human History.

To begin with, we have to question the value of Michael Hart’s account of human history when we know that this man is only an astrophysicist with no specialist background in history or archeology of any region of the world. Aside from Hart’s blatant incompetence in these areas of study, I can’t fail to mention his close links with the white nationalist movement and his well known association with American Renaissance. Knowing this, it is hard to believe that an incompetent writer with known ideological leanings would have produced an objective description of human history.

Hart’s book was published in 2007 so some of his claims may no longer be accepted as the archeological record has since become more complete and politically correct.

Pumpkin Person needs to elaborate on how the archaeological record could be “politically correct” or incorrect. Archeology is factual, not political. Either way, Michael Hart’s claims were wrong in 2007 and are just as wrong as of 2017 as his account of African history is in contradiction with the earliest reports by explorers of the continent.

For starters, Hart claims that farming was not practiced in Africa until it was brought to Egypt by Southwest Asians in 6000 BC and from there it spread to Ethiopia, Sudan and then West Africa by 3000 BC.  Central and Southern Africa however, were still living in the paleolithic until 1000 BC, according to Hart.

This claim is obviously wrong and unsupported by genetic or botanical evidence. Pumpkin Person, doesn’t understand that societies do not invent “farming”, instead populations domesticate specific plants. The first plants were domesticated in West Africa around 5000BCE, these crops (sorghum, African rice, pearl millet, yam, fonio) are direct descendants of their local wild ancestors and were not grown in the Middle East and North Africa. This fact makes an introduction from those regions impossible, especially since transaharan communication was impossible before the domestication of the camel in the horn of Africa or southern Arabia around 3000 BCE. An other fact that Pumpkin Person seems to ignore is that populations do not transition from hunting and gathering to farming just from being exposed to agricultural populations, foreign crops are instead adopted by sedentary horticulturalist populations that have already domesticated non-grain plants. For this reason, the most common mode of diffusion of farming is not contact with another population but migration of the farmers themselves, which is why agriculture did not reach the southern half of Africa until the farming Bantu populations of West African origin gradually settled the region.

By 600 BC iron smelting occurred in Nigeria. Hart writes “It seems probable that knowledge of iron work had been introduced from the North or brought from the eastern Sudan.”  Introduced or brought in by Caucasoids?

Once again, the external introduction hypothesis is unsupported by any sort of evidence, especially since West Africans started smelting copper in the Sahel by 2000 BCE, which was the first stage of metal working development before iron-metallurgy. Evidence from metal working and crop domestication confirm West Africa’s status as a place of independent transition from the paleolithic to the neolithic.

Hart notes that prior to 1500,  sub-Saharan Africa could be divided into two wildly different sections.  The exposed zone and the secluded zone.  The exposed zone was all the parts that were in contact with Caucasoids, such as West Africa, Ethiopia, Somalia, small remote parts of the Indian Ocean colonized by Arab traders, and parts of the Atlantic coast where Portuguese traders  had set up shop.  The rest of sub-Saharan Africa was the secluded zone.  A terrifying region roughly twice the size of Europe.

Being familiar with African historiography, I was very surprised to learn about this pretended division between a “secluded” and an “exposed” zone marking the history of the continent before 1500. Such a division never appears in the literature and searching for it on the Internet only led me to Pumpkin Person’s article, which implies that this distinction only exists in Pumpkin Person and Michael Hart’s heads. Also, it’d be interesting to find an actual map of the pretended “secluded zone” as well as a scientific translation of its “terrifying” character.

Nevertheless, it is true that African regions that were more exposed to the outside world benefited from the exchange of goods, knowledge and technology more than the more isolated parts of the continent, similarly to the rest of the world. However, places like the pre-islamic Sahel and savanna (Ghana Empire, Nok Civilization, Djenne-Djeno), the Gulf of Guinea (Benin, Yoruba states, Igbo states), the Great Lakes regions (Rwanda, Buganda), the Lower Congo (Kongo Kingdom, Kuba Kingdom) and Zimbabwe developed complex organized societies independently from Eurasian contact before 1500.

While the exposed zone was not poor, and benefitted from written languages brought by Muslim slave traders, Hart feels the indigenous peoples still failed to make a single contribution to World civilization.

Bold claims here. Firstly, it is well acknowledged that the University of Timbuktu founded under the rule of the Mali Empire was one of the Islamic world’s main learning centers staffed by local West African scholars with thousands of religion, science, poetry, history and novel manuscripts still remaining to this day. Secondly, “the Muslim slave traders” were not foreigners of North African or Middle Eastern ancestry as Pumpkin Person or Hart seem to imply, these Muslims were Black African Muslims (close to half Sub-Saharans are Muslim) and they were not “slave traders” per se. They indeed traded slaves, along with gold, ivory and gum and other African products.

But it is the vast secluded zone that bears the brunt of Hart’s poison pen.   Described as a primitive and backward region until as recently as the 19th century,  Hart notes that there were:

Interesting, but what is this “secluded zone” that he’s referring to? Can someone name some populations of this “secluded zone”?

-no wheeled vehicles, nor even the potter’s wheel

Which is common to every isolated region with low population density.

– no method of even joining together pieces of wood

It’s extremely hard to understand what he’s referring to. Given that most Africans all over the continent built wooden houses, or assembled different pieces of metal and wood to manufacture tools, weapons or musical instruments, this allegation doesn’t stand the test of reality.

-no beasts of burden or draft animals (though cattle was raised)

These characteristics are far from being exclusive to Africa even if we’re only mentioning the “secluded zone”, these animals were absent in the Americas too. Keeping large animals was made even more difficult in tropical Africa by the presence of tsetse flies killing animals and humans by transmitting African trypanosomiasis also known as sleeping sickness.

-not a single written language in the entire region,  and thus no law codes, no philosophical works,  no literature or even oral epic-poetry

Before expanding on the topic of writing, I must quickly react on this obviously wrong assertion that any place in Africa was devoid of oral literature. Besides the very rich record of such an oral literary tradition, African societies all over the continent are noticed by the existence of a social caste of storytellers best exemplified by the West African griots. As for writing, although it is true that most of Subsaharan Africa remained illiterate until colonization and the literate parts adapted or adopted foreign scripts (writing was only invented in Egypt, Sumer, China and Mesoamerica according to most specialists), evidence, mainly from the Cross River region of current day Nigeria, shows that Africans had developed proto-writing with the Nsibidi system used in courts and were thus only a few centuries away from developing a complete, fluid and ideographic writing system, tonal languages making alphabets hard to use.

-no coins or money

This remark makes no sense since all forms of exchange were barter or a slightly improved form of it before the introduction of real, modern fiat money. Anyway, differently shaped pieces of metal, shells and salt were common means of transaction all over Africa in precolonial times.

– no math beyond simple arithmetic,

Although the absence of written language greatly limits the development of mathematic science, it is obviously wrong to claim that Africans anywhere in the continent were unable of calculation above the level of simple arithmetic. Indeed, as this paper demonstrates in the case of central Africa, various memorization techniques were used to execute complex calculations prior to colonization.

-no cities beyond small towns

Anyone who knows that the difference between a city and a small town or a village is not the intelligence but the number of its inhabitants can only wonder what this is supposed to prove besides the fact that Africa’s population density was and is still very low, which is in line with the leading and unchallenged position among specialists that complex societies are the consequence of population density.

no temples, large monuments nor domes, arches, schools, hospitals, libraries nor paved roads.

Same thing as with cities, monumental architecture was not the product of superior intelligence, instead, it relied on huge pools of laborers to erect such structures whose construction commonly spanned over several decades or centuries.

Hart credits the ruins of Great Zimbabwe as the most notable construction in the secluded zone, but feels it was nothing compared to the Machu Picchu in South America, or Cambodia’s Angkor Wat complex, or Mesoamerica’s large cities and religious buildings.  Hart notes that the giant statues on the tiny isolated Polynesian Easter Island were more impressive than anything found in the entire, secluded zone of black Africa.

This passage contains many inaccuracies. Firstly, Angkor Wat was by no means an isolated place. It was a Hindu, then Buddhist temple, and both religions went to Cambodia from India. As an assemblage of multiple stones to form a fortification system, Great Zimbabwe certainly necessitated more complex skills than carving statues in volcanic monoliths as seen in Easter Island. Machu Picchu, which is not a monument but an abandoned town is actually very similar to Great Zimbabwe, both are settlements built with locally available materials with a relatively simple architecture. Regarding Mesoamerican pyramids, they are absolutely not comparable to either Machu Picchu, Great Zimbabwe or Easter Island monoliths as they were built in a highly populated literate region with advanced mathematics and geometry which greatly facilitate architectural development.

-Almost no maritime skills.  Hart notes the stunning fact that took Indonesians from the other side of the Indian Ocean, coming from 3000 miles away, to inhabit Madagascar in 500 AD, because Africans still had not reached it, even though it was only 250 miles off the East African coast.   Nor did they reach the Cape Verde Islands, just a few hundred miles off the West African coast.

Africans are continental peoples, contrary to Indonesians who had spent millennia on archipelagos where seafaring across calm waters was the only means of communication between tiny islands. The African coast is straight and boarded by tumultuous oceanic waters, without peninsulas nor islands visible from the coast. For the same reason, Europeans never reached Madeira, the Azores or the Americas (apart from the Vikings, reaching Greenland and Newfoundland from Iceland at an unknown period) before the age of discoveries, Native Americans never reached Bermuda or the Galapagos and East Asians didn’t reach the Americas or any island in the Pacific. Continentality does not promote the development of seafaring skills whereas oceanic waters, with no island in sight from the coast give the impression of a boundary of the world. Nevertheless, Bantu peoples reached the Comoros in the sixth century soon after reaching the Eastern coast from their West African homeland, from there, they settled Madagascar at about the same time as Austronesians.

Hart also claims the secluded zone was primitive when it came to political and ethical matters, noting the lack of democracy and civil liberties and the common use of slavery and occasional cannibalism.

I wonder if this part has to be taken seriously. In fact, those things were common features in Africa, in and out of the “secluded” zone. But they were also common all over the world until someone invented and then enforced the idea of human rights. Duh!

Why was the secluded zone of black Africa so far behind virtually everyone else on Earth?  In Guns, Germs and Steel,  Jared Diamond argues that black Africa was simply cut off from the rest of the World, and thus didn’t have access to advances in knowledge, however Hart rejects this explanation because Native Americans were even more geographically isolated than black Africans, yet their societies were so much more advanced.

Wrong, Mesoamerica has impressive pyramids, the Andes have ancient ruined cities but those things definitely aren’t a common sight from Alaska to Patagonia. In fact, the area of the Americas where complex societies never existed is much larger than the whole Subsaharan part of Africa and is also much more “backward” since most populations did not acquire metallurgy and barely practiced agriculture, which makes them paleolithic societies.

Instead Hart favours the cold winters explanation.    Races who left Africa tens of thousands of years ago, and got at least some exposure to the ice age, evolved higher intelligence to survive the cold,  and once the ice age ended, this allowed them to create advanced culture and technologies.

This explanation is not supported by any genetic, archaeological, anthropological evidence. No Wurm-glaciation-related bottleneck has ever been reported by any geneticist, nor did any other related adaption on another trait. The only noticeable anthropometric change that came after the Wurm Glaciation is shrinking brain and body sizes. Also, the cradles of the neolithic revolution (all in low subtropical latitudes) were actually mildly affected by the last glacial maximum whereas such a scenario would have led one to expect agriculture or metallurgy to appear in more northerly regions.

Secondly, there is already a leading theory on the emergence and development of civilization that isn’t challenged and fits observable patterns. This theory does not focus on “qualitative” factors of human populations such as intelligence but simply on quantitative factors such as population density and exposure to long distance trade.

And it is no coincidence that complex societies emerged and developed in environments that were able to sustain high population densities thanks to their climate and vegetation, the quality of their soil, some characteristics of their terrain that prevented population dispersal and facilitated crowding, low disease burden and access to world trade. For these reasons, the different regions of the world experienced different trajectories of social development.

Australia and Oceania

Mostly lying in the tropical zone and greatly isolated from the Eurasian landmass, indigenous Australian and Oceanian societies developed in hostile environments and tiny islands preventing demographic expansion. Whereas Polynesia was settled by Austronesian populations that developed seafaring skills in the Indonesian Archipelago, Australia and Melanesia had no contact with the outer world until European exploration. No agriculture was ever developed in Australia, however Papua New Guinea is one place of independent domestication of crops, which along with continental Asian cultivates spread to Micronesia, Polynesia and Melanesia.

Central And Northern Asia

As a flat, arid and cold continental mass, Central and Northern Asia’s environments do not favor population density and complex social organization. Nomadism was traditionally a prevalent mode of subsistence in the region and and only a few complex permanent settlements emerged along the southern silk roads that were crucial to trans-Eurasian trade. Although the region has virtually no indigenous advancements, the Turkic and Mongolic warlike nomadic tribes have been able to take over the well established empires of Europe, East Asia, India, Persia and the Near-East.

East Asia

Benefiting from some of the most fertile soils of the world, a temperate climate enabling long growing seasons and lowering the prevalence of disease, as well as a terrain made of enclosed plains and valleys in Northern China and narrow peninsulas and islands in Japan and Korea. East Asia’s environment has allowed the development of very complex societies that still boast some of the highest population densities in the world. Expanding southwards to the subtropical area, the Han Chinese found the ideal conditions for year long high yield riziculture allowed by abundant rainfall and fertile soils. East Asia is noticeable within the Eurasian landmass for the indigenous development of its agriculture and writing and its historical resistance to foreign influences, which likely resulted in a delayed social development relative to the potential conferred by its excellent environmental conditions.


Receiving neolithic technologies from the near east, complex societies first developed in the southern peninsulas of Greece and Italy where the terrain and the Mediterranean climate quickly allowed high population densities. By the end of the first millennium AD, Mediterranean technologies were established in the northern areas and the large scale movement of deforestation cleared vast agricultural areas with fertile soils under an excellent temperate low-disease climate that led to a population boom that quickly allowed Europe to rise to global prominence.

Middle East and North Africa

Often called the cradle of civilization, the Middle East and North Africa reunite the optimal conditions for the emergence of complex societies. A warm, temperate climate with balanced rainfall combined with fertile valleys (Nile and Mesopotamia) allowing year-long irrigation, all enclosed by inhospitable deserts preventing population dispersal. Moreover, the region finds itself at the crossroads of the Old World and has dispersed then received technologies and commodities from neighboring eras.

North America and the Arctic

Similarly to South America and contrary to Subsaharan Africa as depicted by Pumpkin Person and Michael Hart, North America shows a stark contrast between highly developed Mesoamerica and the mostly paleolithic other regions and is a perfect illustration of the fundamental role of population density in the emergence of civilization. Whereas the narrow temperate highlands and warm lowlands of the Mesoamerican isthmus enabled highly productive yearlong agriculture and prevented population dispersal thus leading to very high population density and civilization in isolation from the Old World, the northern areas remained underpopulated due to a vast, flat continental landmass.

Nevertheless, the eastern temperate areas of Canada and the United States, benefiting from a balanced climate and fertile soils sustained some small scale agriculture but it is only after European settlement that these ideal conditions were fully exploited. Another specificity of the Americas compared to the Old World is the absence of disease due to lack of domestication of large animals. Whereas this could have been beneficial for demographic growth in precolumbian times, it proved to be fatal quickly after contact with the old-world as the indigenous populations had evolved no genetic immunity to resist Old World disease or to spread New World disease to the invaders.

South America

Comparably to North America, the development of complex societies is restricted to the very narrow Andine Altiplano, a temperate highland plateau where indigenous populations subsisted on the cultivation of potatoes. A colder climate and a shorter growing season prevented Andine societies to achieve population densities that equaled that of Mesoamerica and for this reason, they couldn’t develop complex technologies to the same level. The rest of the South American landmass has similar environmental characteristics to Subsaharan Africa which is a very warm area of flat arid grasslands and equatorial rainforests. The soils are poor for grain crops and the population densities are accordingly low.

South-East Asia

Entirely lying in the tropical zone, South-East Asia includes the Indochinese peninsula and the Indonesian Archipelago. Whereas some volcanic islands like Java as well as more temperate regions in Cambodia, Thailand and Vietnam were able to sustain high population densities, most of the local technology and cultural elements were acquired from the Indian Subcontinent and, to a lesser extant, East Asia. The constellation of islands in Indonesia led to the formation of thalassocracies whose maritime tradition allowed the settlement of Austronesian peoples in remote islands like Hawaii, Madagascar and Easter, but curiously not Australia.


Southern Asia

Southern Asia was for a long time the second most advanced region in the world after the Middle East and North Africa and similarly adopted and spread technologies and cultural practices from and to the neighboring regions. Centered around the Indus and the Ganges valleys as well as the Deccan plateau, Southern Asia has fertile soils, warm and moderately humid climates in that alternate with more arid climates where a large variety of crops can be grown all year thanks to powerful rivers that allow irrigation. Seasonal monsoon provide ideal rainfall for rice cultivation. Along with Eastern China, Northern India currently has some of the most densely populated areas of the world.


Subsaharan Africa

Subsaharan Africa is a vast, flat landmass under very warm climates which result in environments made of deserts, arid grasslands and equatorial rainforests. Short growing seasons, high disease burden and population dispersal (slave trade included) have made it particularly difficult to achieve sufficient population densities that lead to more complex societies compared to the rest of the world from which the region remained partially isolated. Nevertheless, centralized states were established in the Sahel and the northern Savannas of West Africa, in the Gulf of Guinea, the Lower Congo, the Ethiopian Highlands, the Great Lakes region, the Swahili Coast and Southern Africa. Contrary to the imaginary “secluded zone” theory proposed by Pumpkin Person and Michael Hart, social complexity is in Africa as elsewhere in the world more a function of population density than a function of exposure to “more evolved races”.



For further information on “secluded zones”, you will enjoy this documentary

Worldwide IQ estimates based on education data

By Afrosapiens, 2851 words

One of the leading theories to explain differences in cognitive test performance between time and place is that intelligence as measured by such tests is affected by exposure to formal schooling and the cognitive demands of a high-technology society (D. Marks, JR. Flynn). One of the strongest evidence for such an effect of schooling on IQ comes from a reform in the Norwegian school system in which an expansion of compulsory schooling was associated with a 3.7 points increase in IQ per additional year of education between pre-reform and post-reform cohorts. In order to test this relationship between years of schooling and commonly reported national IQ averages, I used data from the United Nation’s Development Program to estimate the average IQ of each country’s adult and school-age population. Adult IQs were estimated from mean years of schooling completed by adults aged 25 and older whereas School-population IQs were estimated based on the expected years of schooling that a student is supposed to complete if the enrollment ratios from primary through tertiary education remain constant. All variables were reported in year 2015. Great Britain was chosen as the reference country and assigned a default value of 100 on both variables. For each country, a difference of one year in completed or expected schooling added or removed 3.7 IQ points. Adult IQ and School-age population IQ were averaged to estimate the most probable mean IQ that would be found by randomly reviewing literature without controlling for the age or the health and socio-economic profile of the sampled individuals.


Country Main ancestry School age-Adult IQ average School age IQ Adult IQ
Australia West-European 107 115 100
Denmark West-European 104 111 98
New Zealand West-European 104 111 97
Iceland West-European 103 110 96
Ireland West-European 102 109 96
Norway West-European 101 105 98
Germany West-European 101 103 100
Netherlands West-European 101 107 95
United States West-European 100 101 100
United Kingdom West-European 100 100 100
Switzerland West-European 100 99 100
Canada West-European 100 100 99
Slovenia East-European 100 104 96
Lithuania East-European 99 101 98
Czech Republic East-European 99 102 96
Estonia East-European 99 101 97
South Korea North-East Asian 99 101 96
Israel West and Central Asian, North African 99 99 98
Sweden West-European 98 99 96
Poland East-European 98 100 95
Finland East-European 97 103 92
France West-European 97 100 94
Japan North-East Asian 97 96 97
Latvia East-European 96 99 94
Belarus East-European 96 98 95
Greece East-European 96 103 90
Hungary East-European 96 97 95
Spain West-European 96 105 87
Hong Kong North-East Asian 96 98 94
Austria West-European 96 99 93
Italy West-European 96 100 91
Slovakia East-European 96 95 96
Argentina West-European 95 104 87
Singapore North-East Asian 95 97 94
Liechtenstein West-European 95 94 97
Russia East-European 95 95 95
Kazakhstan West and Central Asian, North African 95 95 94
Ukraine East-European 94 96 93
Palau South-East Asian and Polynesian 94 93 96
Croatia East-European 94 96 92
Montenegro East-European 94 96 93
Chile West-European 94 100 87
Georgia West and Central Asian, North African 94 91 96
Cyprus East-European 93 93 94
Luxembourg West-European 93 91 95
Malta West-European 93 94 93
Bulgaria East-European 93 95 91
Barbados Black African 93 96 90
Fiji South-East Asian and Polynesian

South Asian

93 96 90
Cuba West-European 93 91 94
Saudi Arabia West and Central Asian, North African 93 99 86
Portugal West-European 92 101 84
Romania East-European 92 94 91
Tonga South-East Asian and Polynesian 92 93 92
Serbia East-European 92 93 91
Belgium West-European 91 90 93
Sri Lanka South Asian 91 91 91
Mongolia North-East Asian 91 91 87
Grenada Black African 90 98 83
Mauritius South Asian 90 96 84
Uzbekistan West and Central Asian, North African 90 85 95
Uruguay West-European 90 97 83
Armenia West and Central Asian, North African 90 87 93
Brunei South-East Asian and Polynesian 89 95 84
Azerbaijan West and Central Asian, North African 89 87 92
Bahrain West and Central Asian, North African 89 93 86
Andorra West-European 89 90 89
Kyrgyzstan West and Central Asian, North African 89 88 91
Albania East-European 89 92 86
Moldova East-European 89 83 95
Venezuela West-European 89 93 86
Trinidad and Tobago Black African

South Asian

89 87 91
Bahamas Black African 89 87 91
Iran West and Central Asian, North African 89 94 83
Seychelles Black African

South Asian


89 92 86
Belize Black African

Native American

88 87 90
South Africa Black African 88 88 89
Malaysia South-East Asian and Polynesian 88 88 88
Bosnia East-European 88 92 84
Samoa South-East Asian and Polynesian 88 87 89
Jordan West and Central Asian, North African 88 88 88
Qatar West and Central Asian, North African 88 89 87
Brazil West-European 88 96 79
Costa Rica West-European 88 92 83
Panama Native American 88 88 87
United Arab Emirates West and Central Asian, North African 87 89 86
Turkey West and Central Asian, North African 87 94 80
Peru Native American 87 89 84
Saint Lucia Black African 87 88 85
Jamaica Black African 87 87 86
Macedonia East-European 86 87 86
Ecuador Native American 86 91 82
Algeria West and Central Asian, North African 86 93 82
Saint-Kitts and Nevis Black African 86 90 82
Bolivia Native American 86 91 81
Mexico West-European 86 89 83
Saint Vincent and the Grenadines Black African 86 89 83
Lebanon West and Central Asian, North African 86 89 83
Oman West and Central Asian, North African 86 90 81
Botswana Black African 86 86 85
Palestine West and Central Asian, North African 85 87 84
Tajikistan West and Central Asian, North African 85 82 89
Tunisia West and Central Asian, North African 85 94 77
Thailand South-East Asian and Polynesian 85 90 80
Micronesia South-East Asian and Polynesian 85 83 87
Colombia West-European 84 90 79
China North-East Asian 84 90 79
Philippines South-East Asian and Polynesian 84 83 85
Suriname South-East Asian and Polynesian

Black African

South Asian

84 87 82
Dominican Republic Black African 84 89 79
Indonesia South-East Asian and Polynesian 84 87 80
Dominica Black African 84 87 80
Gabon Black African 84 86 81
Libya West and Central Asian, North African 84 89 79
Turkmenistan West and Central Asian, North African 84 80 87
Kuwait West and Central Asian, North African 83 89 78
Vietnam South-East Asian and Polynesian 83 86 80
Paraguay Native American 83 85 81
Egypt West and Central Asian, North African 82 88 77
Kiribati Melanesian 82 84 80
El Salvador Native American 81 89 75
Zambia Black African 80 86 76
Maldives South Asian 80 87 74
Guyana Black African

South Asian

79 78 82
Namibia Black African 79 83 76
Ghana Black African 79 82 76
Cabo Verde Black African 79 90 69
Nicaragua Native American 79 83 75
Swaziland Black African 79 82 76
India South Asian 79 83 74
Zimbabwe Black African 79 79 79
Vanuatu Melanesian 78 80 76
Honduras Native American 77 81 74
Congo Black African 77 81 74
Kenya Black African 77 81 74
Sao Tome and Principe Black African 77 84 70
Morocco West and Central Asian, North African 77 84 69
Guatemala Native American 77 79 74
Timor-Leste Melanesian 76 86 67
Lesotho Black African 76 79 73
Togo Black African 76 84 68
Iraq West and Central Asian, North African 76 77 75
Cameroon Black African 76 78 73
Angola Black African 76 82 69
Madagascar South-East Asian and Polynesian

Black African

76 78 73
Nepal South Asian 75 85 66
Laos South-East Asian and Polynesian 75 80 70
Nigeria Black African 75 77 73
Comoros Black African 75 81 69
DR Congo Black African 75 76 73
Uganda Black African 74 77 72
Bhutan South Asian 74 86 62
Cambodia South-East Asian and Polynesian 74 80 68
Bangladesh South Asian 74 77 70
Malawi Black African 73 80 67
Solomon Islands Melanesian 73 75 70
Equatorial Guinea Black African 72 74 71
Tanzania Black African 72 73 72
Rwanda Black African 72 80 65
Haiti Black African 72 73 70
Liberia Black African 72 76 67
Benin Black African 72 79 64
Papua New Guinea Melanesian 72 76 67
Syria West and Central Asian, North African 71 73 70
Cote d’Ivoire Black African 71 73 69
Myanmar South-East Asian and Polynesian 71 73 68
Afghanistan West and Central Asian, North African 70 77 64
Burundi Black African 70 79 62
Pakistan South Asian 70 70 70
Mauritania West and Central Asian, North African

Black African

69 71 67
Sierra Leone Black African 69 75 63
Mozambique Black African 69 73 64
Senegal Black African 68 75 61
Gambia Black African 68 73 63
Guinea-Bissau Black African 68 74 62
Yemen West and Central Asian, North African 67 73 62
Guinea Black African 66 72 60
Central African Republic Black African 66 66 66
Ethiopia North-East African 66 71 60
Mali Black African 65 71 59
Sudan North-East African 65 66 64
Djibouti Black African 64 63 66
South Sudan Black African 63 58 69
Chad Black African 63 67 59
Burkina-Faso Black African 62 68 56
Eritrea Northeast-African 62 58 65
Niger Black African 58 60


The values were rounded to the nearest unit.

In comparison to the mean national IQs mainly reported by Richard Lynn, 65 countries differed by less than 5 IQ points using the present methodology. It can be said that such small differences validate Lynn’s estimates since it is unlikely that years of education have the same cognitive value in every country and likewise, averaging adult IQ and school-age population IQ without controlling for a country’s age structure somewhat weaken the representativeness of my findings. Differences larger than 5 points were found for 30 countries, and in these cases, I suspect it is due to Lynn manipulating the data to fit racial patterns, Sub-Saharan African countries have been systematically under-estimated and East-Asian ones have been systematically over-estimated by Lynn, also, Some nations in Europe, the Middle-East, South-Asia and Latin America seem to have their scores manipulated in order to appear closer to what they would be based on their racial composition.

Such inconsistencies result in incoherences between the reported IQs and the educational and socio-economic outcomes (regardless of which variable influences the other) of the affected countries and support the accusations of racially-motivated fraud in Richard Lynn’s data collection. In the same way, estimating the mean IQs of countries for which direct data is missing by averaging the figures of neighboring countries of similar ethno-racial composition is unwarranted as race does not seem to play a role in a country’s cognitive performance.

In spite of all the deserved criticism that Lynn’s data met, it can be said that most of the commonly cited mean IQs out of Africa and East-Asia are reliable and that a strong relationship between human capital and human development exists whether we measure it by IQ or years of education. The causes of international variation in school quality and enrollment are well-known and come down to school and student characteristics. Schools in developing countries face numerous challenges: lack of basic amenities such as electricity, potable water, air-conditioning and heating, like of educational supplies (school rarely have enough textbooks and rely on chalk and blackboards), high student to teacher ratios (primary school classes with more than 50 students are common low-income countries), chronic teacher absenteeism (teachers usually have a business on the side), obsolete pedagogy, outdated or irrelevant curricula, multilingualism, exam-corruption, low public funding, misguided policies, gender and ethnic discrimination. Pupils are held back by poor health and nutrition resulting in developmental delays, tuition fees and supplies that poor families can’t afford, war, population displacement, absent educational resources at home, low parental education, lack of transportation, child labor, excessive use of grade repetition, mismatch between school curricula and daily life demands and many other factors. Differences in human capital have large implication in terms of workforce qualification and social behavior, which contribute for a large part to a country’s socio-economic development. The present findings provide evidence for large international inequalities in inter-generational change in educational outcomes which are probably the driving cause of the Flynn effect.

Intergenerational change in cognitive performance.

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Estimating IQs from the current school enrollment rates and the mean educational attainment of adults provide insights regarding intergenerational differences in cognitive performance. We can see from these figures that the countries that developed the fastest show large intergenerational differences in education/IQ favoring the younger cohorts, these countries are concentrated in South America, Southern Europe, West Africa, the Middle-East and Oceania, Ethiopia and China also show trends that are in line with their recent economic success. On the other hand, many ex-USSR countries, as well as Japan, Cuba, South Africa, Zimbabwe and the Philippines have been stagnant or even declining relative to the United Kingdom and this is also reflect in their poor socio-economic performance in the last decades. War-torn South Sudan and the Central African Republic experience alarming declines in their educational performance that expose them to grave humanitarian crises in the future. Although there is a clear relationship between socio-economic progress and gains in cognitive performance, a country’s ability to capitalize on its intellectual potential remains highly dependent on its leadership and the odds of the world-market, that’s why theories claiming that IQ is the main driver of global inequalities are not tenable in the light of the current evidence.


Update 09/07/2017 – Detailed comparison with Lynn’s Data


To test the predictive power of my estimates in comparison to Lynn’s, I decided to focus only on the world’s 20 most populous countries. The reason for that is that these countries are home to 70% of the world’s population and the law of large numbers says that they are likely more representative of whatever they could represent. On the other hand, the 100+ other countries are home to only 30% of humanity. They are a source of statistical noise due to extreme outlying values and differences in regional political fragmentation that would hide or weaken general trends better evidenced by considering large countries.




Correlations and averages:



Noticing an abnormal 22 points gap between Sub-Saharan African IQs and the world average on Lynn data, Suspecting that extremely low values would flaw the correlations, I tested if my estimates and Lynn’s would retain the same predictive power with the African IQs excluded. My assumption was that a strong causal relationship would leave the correlations unchanged no matter which countries were included whereas any change in predictive power resulting from excluding some countries would cast doubt on the accuracy of the reported data.

IQ-HDI correlation:

Similarly to my previous calculation including all the countries for which data were available, I found a 0.96 correlation between my estimates and HDI, Lynn’s estimates correlation with HDI was higher (+0.06) than with the worldwide data, but still largely inferior to mine. Removing African countries, the predictive power of my estimates remained the same (+02) whereas Lynn’s significantly decreased (-0.13) and left a predictive gap of 0.24 favoring my estimates. However, given the fact that my estimates are based on variables that are included in the calculation of HDI, such a high predictive power as to be met with caution.

IQ-GDP per capita correlation:

My previous calculation from the worldwide data yielded a correlation coefficient of 0.65 between my IQ estimates and GDP/capita and 0.60 for Lynn’s. Among the 20 most populated countries, the correlation rose by 0.24 points to 0.89 with my estimates and by 0.12 points to 0.72 with Lynn’s. Excluding Sub-Saharan African countries did not affect the predictive power of my estimates (+0.01) and further weakened Lynn’s by 0.04 points, resulting in a 0.22 gap in predictive power favoring my estimates again. This correlation of 0.89 between my IQ estimates and GDP per capita within the world’s population top 20 countries likely is the highest correlate of IQ ever reported in the psychological science and gives strong support to the relationship between schooling, economic development and cognitive ability.

IQ-Life expectancy correlation:

Compared with the worldwide database, the correlation between my IQ estimates and life expectancy was down 0.04 points within the world’s top 20 to 0.76, Lynn’s went up by o.o5 points to 0.84. However, removing Sub-Saharan Africa left the predictive power of my estimates unchanged whereas Lynn’s fell by 0.13 points to 0.71. My estimates again predicted life expectancy better by a small 0.4 points this time.

IQ-Homicide correlation:

Not estimated previously, my data finds an non-existent relationship between IQ and homicide rate (-0.01) and excluding Sub-Saharan Africa confirmed a null relationship between homicide rates and IQ in the rest of the world. Lynn’s estimates showed a low negative correlation between IQ and homicide (-0.35) and the exclusion of African countries further lowered the correlation (-0.25). Lynn’s estimates had a better predictive power which still remained in the range of low statistical significance.

IQ-Fertility correlation:

Adding a new variable, I found a negative correlation of -0.69 between my IQ estimates and Fertility, the correlation remained the same (-0.68) with the African countries excluded. The correlation between Lynn’s IQs and fertility was stronger (-0.84), but removing African data decreased it by 0.18 points to 0.66. My estimates ended up with a slightly stronger predictive power (+0.02).

General patterns:

In addition to having a stronger and globally consistent predictive power, my estimates reveal how Richard Lynn manipulates the data to fit desired racial patterns.

As expected from the 0.96 correlation between my IQ data and HDI, the ranking of countries by cognitive ability shows a perfect gradient from high to low development status. Moreover, the highest gap between two following countries is the 6 points separating Russia and Iran, showing a marked difference between the developed and the developing world.

Ranking countries by Lynn-estimated IQs results in a whole other pattern in which a country’s dominant ancestry seems to be the only variable that matters. East-Asians are on top, followed by Western Europeans, then Eastern-Europeans, South-East Asians, fair skinned Middle-Easterners (Turkey and Iran) and Latin Americans, Austronesians (Indonesia and the Philippines), South Asians and Arabs, and finally Sub-Saharan Africans far below, with a huge 10 points gap (the largest between two following countries in his dataset) separating Bangladesh from Nigeria.

The manipulation is quite apparent, Lynn largely over-estimated China (+22), Japan (+7) to make East-Asians cluster on top, thus protecting himself from accusations of nordicism and giving support to the inter-cultural validity of the IQs that he cherry-picked. The western European and Russian  data remained mostly unchanged. Vietnam (+11) and Thailand (+5) were given a bonus for their genetic proximity to North-East Asia that is supposed to make them score in the low 90s despite their lack of development. Little changes were brought to the scores of the Latin American, Middle-Eastern and Austronesian countries usually scoring in the mid-80s. Major fraud (+14 in Pakistan, +7 in Bangladesh) was done to lift up South-Asian countries out of the 70s range and excluding Sub-Saharan Africa as the only region scoring 70 or below and downgrading Nigeria (-4) and the DR. Congo (-7) in the process.

By pointing this out, I’m warning honest researchers and laymen about the dangers of relying on data resulting from undisclosed, unsystematic and un-replicable methodology. And although my estimates do not result from any actual IQ measurement beyond the relationship between IQ and schooling evidenced in Norwegian cohorts, my method uses a single, universal conversion factor applied to representative official data collected by professional demographers whereas Lynn’s and the likes’ cherry-picking of samples is only the hobby of a dozen scholars and pseudo-scholars. This is how I found out strong, consistent and meaningful correlations between IQ and various development variables.

Although they are likely more representative of the worldwide distribution of cognitive ability, my estimates still provide evidence that a large part (the largest part, actually) of the world’s population scoring below one standard deviation on Western-normed IQ tests, which is the case for 11 of the world’s 20 most populated countries. Although this may sound alarming, with Pakistan and Ethiopia scoring in the range of mental disability (70 and 66 respectively), I think this effect comes from using Western populations as a reference for standardization.

In fact, another picture emerges when we compare countries with the world’s average, replacing the eurocentric British Greenwich IQ of 100 by an universal IQ of 84 and thus giving a more accurate idea of what is normal cognitive ability by the standards of humanity. In this sample, China, the Philippines and Indonesia are representative of the top of the bell curve whereas Ethiopia, the United States and Germany are the only outliers left with respective Universal IQs of 81.6, 115.6 and 116.6. For this reason, I recommend the use of Chinese or South-East Asian normalization samples in international IQ comparisons.





Your brain on poverty.

By Afrosapiens, 1163 words.


Poverty has long been associated with educational under-achievement and various behavioral issues. Although the underlying causes of these differences have been at the center of a nature vs.. Nurture debate for decades, it’s only recently that insights from neuroscience have allowed better understanding of how poverty affects the brain. Observations from MRI scans show slower brain growth in children growing up in low SES households (poor and near-poor) which results in reduced volume and grey matter thickness in the frontal and parietal cortices as well as lower amygdala and hippocampus size. All those affected brain areas are crucial to learning and social functioning as they govern cognitive and executive functions such as language, working and long-term memory, attention, impulse control, emotional management and information processing.


Although research using animal experiments indicate that the relationship between poverty and altered brain development is causal, it is yet not clear which aspect of poverty impacts which function the most. The most cited factors are stress, trauma, low stimulation, poor child-parent relationship, poor nutrition and poor health. Although it is also possible that genetics play a role in individual susceptibility to these factors, the idea that genetic background cause people to be poor in the first place and then have their brains damaged by environmental factors is not supported by science and belongs to pseudo-Darwinian creationism, especially since such deficits appear to be reversible to a substantial degree due to brain plasticity.

Various interventions to improve or prevent decrease in cognitive and executive function have shown good and lasting results in reducing behavioral issues and increasing school performance and job market participation. Interventions can take various forms, first of all, since poverty is lack of financial resources, income supports to families with children are an obvious means of limiting children’s exposure to poverty-related adversity. Although this is absolute common sense, conservative ideologues have managed to convince a large part of the public that pro-poor policies would in fact be harmful to the needy whereas pro-rich ones would mysteriously benefit them.

Besides redistribution, executive function coaching in the form of computer or non-computer games, aerobic exercise and sports, music, martial arts and mindfulness practices as well as improvements in school curricula and teaching methods have been shown to improve social and educational outcomes. One last type of intervention that yielded good results is nurse home visits to low-income mothers of young children which had the effect of improving developmental outcomes of children by teaching mothers parenting skills and healthy practices.

These interventions aren’t to be confused with efforts at increasing IQ that caused little improvement beyond temporarily increasing IQ scores, which has no relevance in terms of life outcomes. IQ can probably benefit from increased language skills and executive function but it doesn’t seem to be the target of remedial intervention on those underlying abilities of which IQ test performance would only be a byproduct.

Now you might wonder how big a problem child poverty and its neurological consequences are in contemporary societies. Although the most extreme and widespread child poverty is seen in developing countries, industrialized countries like the USA, Israel, Turkey, Chile and Spain have rates of prevalence above 20%, whereas countries in Western Europe tend to maintain rates around or below 10%.

While informative, reported child poverty rates only include those who live below an arbitrarily defined poverty threshold in a given year, but the effects on poverty likely affect those living only slightly above poverty line and do not meet their developmental needs and those who have experienced poverty in the past but were living above the threshold when the figures were reported.

Within the United States, significant differences in the prevalence and the nature of child poverty exist between ethnic groups with 34% of Native Americans, 13% of Asians/Pacific Islanders, 36% of African-Americans, 31% of Hispanics and 12% of European Americans living under poverty line in 2015.

Comparing African-Americans and European Americans, the nature of poverty differed markedly with 77% of African Americans experiencing poverty at least once in their childhood and 37% living in poverty for more than 9 years.In comparison, only 30% of European American children experienced poverty while growing up, including 5% for more than 9 years. 40% of black children and 8% of white children were poor at birth. Among those born poor, 60% of African Americans and 25% of European Americans were still poor at age 17, among those not born in poverty, 20% of black children and 5% of whites were poor at age 17.

With the effects of poverty worse felt at a younger age and during long periods of time, such interracial differences in prevalence and persistence of child poverty are one plausible large contributor to the observed gaps in educational and behavioral outcomes between the two groups.

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