This is the third and final part of my response to Emil Karlsson at Debunking Denialism. In this post, I will look at how race and evolution relate to national differences in success both today and in the past.

Modern Racial Differences in Success

First, let’s be clear about what the racial differences in national success are today. In terms of technology, White nations and East Asian nations are both over-represented in patents and trademarks relative to their share of the global population, with the over-representation being largest for white nations.

WIPO (2015)

(Europe is about 10% of the world’s population)

In terms of Nobel prize winners, we see a similar pattern but with Whites doing far better than Asians:

Kanazawa (2006)

Lynn (2008)

A similar pattern of wealth emerges with Whites being the richest, followed by East Asians, followed by Blacks:

It is worth noting that this chart, based on world bank data, makes the East Asian/White wealth gap seem smaller than it really is because it is an average not weighted for population size. China is a very poor country, and takes up a huge proportion of all East Asians on earth.

The White People Are Mean Theory

There are many different theories about why Africa has done so poorly relative to Europe and East Asia. One such theory is the White People are Mean hypothesis. This theory posits that White people have historically been really mean to Africans and that is why they are so poor.

Karlsson invokes this theory when he is asked why Africa is poor and responds by saying:

“colonialism, slavery and discrimination. It is not difficult to see how these factors can radically shift potential for national and personal income.”

Karlsson is right in saying that it is not hard to come up with speculative stories about how these factors may have hampered Africans. Showing this with evidence, however, is quite a bit more challenging.

Colonialism

Let’s start with colonialism. Speculation about colonialism can go many different ways. You can sit back and imagine that Europeans came in, killed huge numbers of Africans, stole natural resources, and redefined nations in a way that brought instability to the continent, and thus screwed Africa over. On the other hand, you can just as easily imagine that Europeans brought western government, education, values, and living standards to Africa and thus, through an admittedly ugly method, civilized them. Since speculation can lead us in either direction, I think it’s best to try and stick to data.

The first thing to say here is that if we look at the change in Africa’s wealth over time there is no obvious effect of colonialism:

Nor, despite the killings associated with colonialism, is there an obvious impact on population growth:

We can also break this down country by country and ask whether nation’s that were more colonized ended up being poorer or richer. The answer, whether you measure colonization by the number of Europeans who went there or the amount of time that a colony lasted there, is that more colonized nations ended up being richer.



Eaverly and Levine (2012)

Feyrer and Sacerdote (2006)

Now, you might say that this is all because more Europeans went to, and what stay longer at, richer African nations. You can say that, but you’re going to need some evidence. And that evidence will need to take into account the fact that the amount of precious metals in an area does not predict the degree to which it was colonized by Europeans (Eaverly and Levine 2012).

The pro-colonialism case is strengthened further by the fact that the degree to which an area was colonized also predicts its future quality of education and government quality, and these variables moderate the relationship between colonization and modern wealth (Eaverly and Levine 2012).

In sum, there is no proof that colonialism helped Africa, but it sure looks like it. During colonialism Africa got richer and its population exploded. Moreover, the more specific areas were colonized the richer they ended up being. If someone wants to say that everything is actually other than it seems, and colonialism made Africa poorer, they are going to need some powerful empirical evidence.

Slavery

Okay, so what about slavery? Well, in terms of Africans in Africa, slavery doesn’t seem to have made them poorer. Looking at the charts above, we can see no discernible negative impact on Africa’s GDP per capita or population growth.

In fact, slavery was seen as an essential part of the economy of many African nations and it’s abolition was opposed by African leaders:

“The great majority of slaves transported to the Americas were already slaves in Africa. Slavery was the most important component of the West African economy, so much so that tribal leaders from a number of African kingdoms sent delegations to London and Paris in the 1830’s and 1840’s to protest its abolition in territories controlled by Britain or France (Davidson, 1980).” – Walsh (2006) Pg 101

With respect to those made into slaves, it’s hard to see how slavery made them, or their descendants, poorer. Had they not been European slaves they probably would have been slaves in Africa, and there is no obvious reason to think that African slaves were richer than European slaves. There are, however, painfully obvious reasons to assume that the descendants of European slaves are far richer than the descendants of African slaves.

Some people prefer to look at this question in a peculiar way: they chose to speculate about how rich African Americans would have been if they somehow had all been free citizens of America in the early 19th century. Of course, there is no plausible way that this ever could have happened historically and so it really has nothing to do with what caused Blacks to become poor. That is, this counter factual is not relevant to the effects of slavery because, had they not been salves, Africans would not have been free citizens of European nations, they would have been (probably enslaved but maybe free) in Africa.

That being said, it isn’t obvious that Black slaves were all that worse off than the average White at the time economically speaking. This is not to say that slavery was okay, it wasn’t, but the point is that being a factory worker or farmer in the 19th century was really crappy too, so it isn’t obvious that this conferred an advantage on Whites over Blacks.

This is not to say that Whites were not freer, it’s just that freedom doesn’t automatically give someone a long term economic advantage.

There is no easy way to measure this, but two possible metrics are height and life expectancy. Both these variables are heavily impacted by nutrition and general quality of life. Perhaps surprisingly, the Black/White height and life expectancy gaps in the early 1800’s were roughly what they are today.

Something we can all probably remember being taught in school is that slavery separated families. The fact of the matter is that Black children were more likely to grow up with both their parents under slavery than they are today.

In summary, I don’t know of any data which straight forwardly shows that slavery made Blacks poorer than they would have been had they never been enslaved by Europeans.

Discrimination

Finally, let’s look at discrimination. Does discrimination make African American’s poorer? I don’t think so. For one, the Black/White wealth gap is larger today than what is was in 1963, even though the amount of racism in society has obviously decreased since then.

Pathe (2015)

Consider also that if you apply some basic controls for reigonality and labor type we find that Blacks made 89% of what Whites did in the south in 1880.

Ng and Verts (1993)

Given that the rural 19th century south is supposed to be the epitome of American racism, it’s hard to see how racism could have a huge effect. Finally, consider that the Black/White wage gap completely disappears if you control for IQ:

Herrnstein and Murray (1996)

At this point, some people will be quick to point out “call back” experiments in which applications with identical credentials but names implying difference races (IE Richard vs Jamal) are sent to employers and it is found that White applicants are more likely to get called back. These experiments rest on the assumption that Whites and Blacks with the same credentials posses the same skills, and this s simply not true. In fact, by many measures, Black graduate students are about as skilled as White college drop outs.

NALS 1992

Given this, employers have perfectly rational, non racist, reasons for preferring White employees over Black ones even after they are matched for credentials. The totality of evidence I am aware of doesn’t suggest that racism causes Blacks to make less money, and Karlsson offered no evidence to this effect.

Psychological Correlates With National Wealth and Innovation

So, if racism, slavery, and colonism, can’t explain why Africa’s failure what can? Well, a good place to start is with population differences in psychological variables such as IQ.

IQ and Economic Policy

Some people might be inclined to point to economic policy as an important part of what explains national wealth variation. There are two things to note about this: first, IQ correlates strongly with economic policy:

Lynn and Vanhanen (2012)

Thus, some populations may have better economic policy because they are smarter.

Secondly, racial variables such as the mean skin tone of a nation or its IQ predict national wealth better than variables like tax policy, public debt, and government spending do.

Further still, IQ and skin color continue to predict national wealth when national differences in these economic policies are held constant:

Last (2015)

Clearly then, economic policy is, at best, only one part of the story. With that said, let’s look some more at IQ.

IQ and National Success

IQ predicts not only current national wealth, but also economic growth:

Economic inequality:

And measures of institutional quality, such as how democratic a nation is and how corrupt its government is:

Lynn and Vanhanen (2012)

IQ also predicts a nation’s patent rate and it’s per capita rate of researchers engaged in R&D, and this is especially true for the IQ of the top 5% of a nation:

Burhan, Mohamad, Kurniawan, and Sidek (2014)

In fact, IQ continues to predict patent rates even after controlling for differences in national wealth and the number of researchers in country:

Burhan, Mohamad, Kurniawan, and Sidek (2014)

Now, the fact that smart countries are more prosperous does not mean that being smarter causes them to be more prosperous. It may be, for instance, that being rich allows nations to set up a more cognitively stimulating environment and this is why their citizens tend to be smarter.

However, it is hard to take seriously the idea that people being smarter doesn’t help them invent more stuff or preform better in today’s cognitively demanding economy. The direction of causality almost surely goes both ways.

One line of evidence which supports this conclusion is that even within the same country IQ predicts wealth, and it does so better than parental socio-economic status does:

Strenze (2007)

Moreover, IQ is a great predictor of job performance, beating out many other metrics including job experience and interviews. Further still, even within the same family smarter siblings end up making more money (Murray 1998).

Given all this evidence, I think that IQ is a pretty decent partial explanation for why African nations are so poor. That being said, differences in intelligence cannot explain why White nations tend to do better than East Asian ones. After all, East Asians are, on average, smarter than Europeans.

In large part we do not know why Europeans are richer and more advanced than East Asians, but I think racial differences in individualism and creativity probably play some role.

Individualism

Richer nations are more individualistic.

Gordonichenko and Ronald (2012)

This association is also true when only comparing nations within the same continent, as well as when comparing different regions of Italy, and it does not go away after controlling for national differences in social cohesion and ethnic composition (Gordonichenko and Ronald 2012)

Some have theorized that individualism may cause nations to be richer by increasing innovation. Collectivism may discourage innovation with its emphasis on conformity and lack of focus on individual success.

Gordonichenko and Ronald (2012) measured innovation by comparing nation’s patents per person, the size of the advanced technology industry in a nation, the share of GDP taken up by royalty and licensing fees, and the number of citations in scientific and technical journals a nation produces. They found that more individualistic nations tended to have higher levels of innovation. By several of these measures the association was quite large: individualism explained over 40% of national variation in several innovation metrics.

Moreover, these are significant racial differences in individualism such that Whites are more individualist than East Asians

In the past several years, researchers have found that population differences in gene variants associated with increased social sensitivity, a key feature of a collectivist culture, also predict population differences in individualism.

Way and Leiberman (2010)

Chaio and Blinzinsky (2009)

Moreover, Gordonichenko and Ronald (2012) confirmed that the more genetically distant a population is from the United Kingdom, the second most individualistic country in the world, the more collectivist they tend to be.

There is currently no way to estimate exactly how much of national differences in individualism is explained by genetics. However, the evidence we do have suggests the answer is greater than zero, and it is highly plausible that this difference gives Europeans an economic advantage over Asians.

There is also some research to suggest that East Asians are less likely than Whites to describe themselves as creative and this, independent of individualism, may also play a role.

Historical Differences in Prosperity

The next step in explaining long term differences in national success is to look at how persistent said differences have been over time. It should go without saying that much of the data we have on the technology and wealth of nations in the distant past is less than perfect. However, it’s the best we’ve got, and it is surely better than anecdote based history.

Below, we can see a comparison of the degree of technological advancement of different civilizations at different points in history. Technological advancement was measured by how many goods in a defined basket of goods each civilization had. Different goods were worth a different amount of points which contributed to a total score which good vary between 0 and 1. This was the basket of goods used for the years 1000 BC and 0 AD:

And this was the basket of goods used for 1500 AD:

Here is the average score of different populations for each era:

Comin, Easterly, and Gong (2010)

As can be seen, in every era White people scored higher than any other group, or, in 0 AD, tied with Asia. If we restrict our analysis to the most advanced civilizations within each continent a different picture emerges:

Comin, Easterly, and Gong (2010)

Of course, none of these groups are representative samples of total races. Nonetheless, it is note worthy that in 1000 BC China and the Arab word had better technology than Europeans. By 0 AD this difference went away and by 1500 Europeans were more advanced than even the most advanced civilizations else where.

Another way of measuring innovation is to ask how much different populations have contributed to world technology. This is different than asking how much technology they had, since you can always adopt technology made by others.

Murray (2004) attempted to do this by collecting 183 comprehensive encyclopedias, histories, etc., of innovation in various fields and analyzing those individuals who were included in at least 50% of the qualified sources within a given field. Murray found that this measure had an extremely high degree of statistically reliability (.93) by showing that arbitrarily breaking the sources into two groups produced two sets of basically identical results. He also noted that basically the same picture emerged when he compared sources from different part of the world, suggesting that eurocentrism did not significantly plague the analysis.

He called individuals included in 50% of the sources “significant individuals”. He referred to events mentioned in 50% of sources as “significant events”. His analysis included all such individuals who lived, or events which took place, between the years 800 BC and 1950 AD. His results, as can be seen below, show that almost all innovation has come from Whites:

Breaking down this analysis by year, we can seen that Europe drove innovation in the BC period, but then stopped leading the world in this respect until around 1500. We can also see that almost all innovation that has happened in human history happened after the year 1500.

With wealth we see a similar story. The leading authority on ancient GDP estimates is the economist Angus Maddison. Below we can see his 2007 data set, expressed in 1990 dollars:

(Maddison 2007)

Once again, we see Europe leading the world in the BC period, weakening during the dark ages, and taking the lead again by the year 1500.

The above estimates for European GDP are somewhat controversial. Recently, it has been argued that they are too low, suggesting that the wealth gap between Europe and the rest of the world may have historically been larger than previously thought.

Bolt and Zanden (2014)

This newer data set shows that Europe was clearly the richest place on earth by the year 1300. There is a significant gap in data between the years 1 AD and 1300 AD, but it seems almost certain that Europe became the richest part of the world sometime during this period. Ironically, this implies that Europe retook its place as the richest place in the world during Islam’s “Golden age” and Europe’s “Dark age”.

A few important points about this and related data can be made. First, all most all the wealth, innovation, and population growth, that has ever occurred in history occurred within the last 500 years:

Murray (2004)

Clark (2009)

Delong (2014)

Secondly, as can be seen in all the datasets above, Europe took the lead over the rest of the world before the industrial revolution took place. This is perhaps most surprising with respect to East Asia. Because of this, I think the economic historian Gregory Clark is worth quoting at length on this topic to compliment the data we have already seen:

“In terms of wages, stature, diet, and occupations Japan, China, and India seem

much poorer in 1800 and earlier than Europe… There are suggestions in the genetic data that this disparity in living standards between Europe and East Asia may go back over thousands of years. Hunter-gatherers consume meat but not milk. Thus the arrival of settled agriculture with animal domestication created the possibility of large-scale consumption of milk from animals for the first time. However, people at very low income levels do not typically consume many dairy products. Milk, butter, and cheese are all expensive ways of getting calories, favored only by the rich. Grains and starches are much cheaper calorie sources. Geographic factors that affect the relative cost of production of animals and arable crops also play a role, but in general only richer preindustrial agrarian economies consumed milk regularly. Consequently populations that never developed settled agriculture, such as Australian Aboriginals, almost all lack a genetic mutation that permits adults to digest lactose, a sugar found in milk. In contrast most people from northwestern Europe have this mutation. However, Chinese adults, despite their very long history of settled agriculture and the variety of climate zones within China, generally lack the ability to absorb lactose, suggesting that milk was never a large part of the Chinese diet, and that by implication Chinese living standards were generally low in the preindustrial era.” Clark (2009) Page 70

A final thing to note is that Western Europe has consistently been more prosperous than Eastern Europe. Maddison’s GDP data shows that this has been the case for thousands of years. We see a similar pattern with respect to innovation when we look at the number of significant figures that came from different European nations in Murray’s data set:

I think this lends credence to the long term importance of individualism, because the west is also more individualist than both East Asia and Eastern Europe.

Getting Statistical

So far, I have been comparing rather macro level populations. If we break populations down into smaller units, roughly corresponding to the size of nations, we can make even more definite statements about the long term consistency of wealth and technology.

First, let’s note that the technological adaption index referenced above only weakly correlates with an area’s level of technology or wealth today:

Comin, Easterly, and Gong (2010)

This makes since. Europe was ahead early on, but not by nearly as much as it was later, and the order of populations after Europe has changed a lot over time. That being said, the predictive power of an area’s ancient technology increases by a lot if we adjust for ancestry. This means that we predict a current area’s technology level not based on the technology level of that area in the past but, rather, the technology level of the ancestors of the area’s current population.

These two things can be very different. For instance, the technology level of the ancestors of the current populations of Australia, and the United States, is very different than the technology level that was historically present in Australia and north america. Adjusting the predictions for ancestry vastly improves their accuracy:

Comin, Easterly, and Gong (2010)

Ancient levels of technology are not the only ancient predictor of modern success. For instance, the years an area has had agriculture, or a state, correlates with their 2005 GDP per capita at .23 and .26. Adjusting for ancestry raises these correlations to .46 (agriculture) and .48 (state history).

Spolaore and Wacziarg (2013)

In fact, these variables, in addition with a population’s latitude, and their status as either being land locked or not and being an island or not, can statistically explain 52%-59% of current variation in national income.

Spolaore and Wacziarg (2013)

Similarly, the genetic distance between a population and the United states, in conjunction with the previously mentioned geographic variables, statistically explains 50% of current national variation in wealth. If you add to this model the percentage of a nation that is White, 55% of national wealth variation can be statistically accounted for.

Spolaore and Wacziarg (2013)

These results are extremely impressive. As with all statistical associations, they do not prove causality. However, the direction of causality can only one run way. It is not possible that current wealth variation caused differences between populations thousands of years ago. Thus, these statistical associations only leave open the possibility that some ancient variable, either the ones measured in these models or one’s correlated with them, causally explain around the majority of modern differences in national wealth.

Moreover, the fact that these models are improved when adjusting for ancestry shows that what ever ancient variable impacted these populations did so in a way that sticks with them when they migrate.

Thus, something happened to people thousands of years ago which strongly impacts their ability to create successful civilizations, and the effects of this move with them when they migrate. There are basically two, non mutually exclusive, explanations for this: geographically determined culture and biological evolution.

Geographic Explanations

The most popular version of geographic determinism was offered by Diamond (1997). Diamond argued that variation in the amount of domesticable plants and animals in regions explains why some regions started civilization earlier than others which in turn explains long term differences in success. Diamond also argued that culture diffusion was easier across an East-West axis than a North-South axis, and that variation in climate and latitude also played a role.

This theory was empirically tested by Olsson and Hibs (2003). Olson and Hibbs showed that an index of an area’s biological conditions and its geological conditions can predict with almost prefect accuracy how early a society transitioned into agriculture. These variables can also explain about 40% of modern variation in national wealth.

Biological conditions were defined by the number of plants and animals suited for domestication that a region had.

Olsson and Hibs (2003)

The geological condition index included the following variables:

“Axis is a rough measure of the East–West orientation of the major landmasses and

is obtained by dividing each continent’s distance in longitudinal degrees between the

eastern and westernmost points with its North–West distance in latitudinal degrees. Climate takes four discrete values; with 4 denoting the best climate for agriculture (Mediterranean and West Coast climates) and 0 denoting the worst

(tropical dry). Latitude. Size is the number of square kilometers of the landmass to which each country belongs.”

Their results can be seen below:

Olsson and Hibs (2003)

In summary, the number of domesticable plants and animals predicts a population’s transition into agriculture which in turn predicts their modern wealth.

Unfortunately, this theory was, necessarily, formulated with diamond already knowing which populations ended up successful and which did not. Because of this, this isn’t really predictive science so much as an after the fact explanation. However, the theory does have a high level of intuitive plausibility.

Poor Competitors

Unfortunately, judging between this theory and theories of biological evolution is extremely difficult. This is because the same geographic and biological variables which geographic determinists say should predict national wealth also drive evolution. That is, differences in climate and diet lead to differences in selective pressures for different traits which in turn are postulated to drive national success.

For instance, how cold a region is, and the mean skin tone of a nation (which is a function of climate) are both very good predictors of national IQ scores which is in turn a very good prediction of national wealth (Templer and Arikawa 2005).

The geographical determinists will look at this data and argue that the climates that lead to light skin or cold winters provided a better environment in which to build a civilization. The evolutionist will use the same data to argue that these climates selected for high IQ.

Interpreting this data is complicated even further by the fact that geography was a major barrier of gene flow between populations and so geographic differences between populations correlate with genetic differences (Pemberton et al 2013; Wang et al 2012; Becker and Rindermann 2016).

As of now, there is no real way to decide between these theories. Happily, they are not mutually exclusive. In fact, it seems almost certain that both theories are correct to some degree.

I argued in earlier parts of this response series that racial differences in behavioral traits are due to genes. If this is true, then evolution clearly played a role in national differences in success. The only other option is that traits like intelligence, individualism, and self control, have nothing to do with how a nation does, in spite of the fact that this is intuitively implausible and each of these traits predict national differences in wealth.

On the other hand, it is obviously implausible that early differences in how easy it was to transition in advanced civilization had no impact on historic differences in national success. And, as we’ve seen, there is some basic empirical evidence which aligns with this hypothesis.

Great Companions

But these theories are more than just non mutually exclusive; They are complimentary.

This is true firstly because the ecological differences between prehistoric populations described by geological determinists would obviously have an effect on evolution. After all, even a very slight difference in the mating success associated with different levels of intelligence, self control, etc., will lead to huge phenotypic differences over the 2000 generation period that the races have been evolving (mostly) apart.

This can be shown with a simple model. The breeder’s equation states that the difference between one generation within a population and the next which is due to selection will be equal to a traits heritability multiplied by its “selection differential”, which is the association between variation in that trait and variation in reproductive fitness. This is most commonly operationalized as the mean difference in the trait between people who successfully reproduce and those who do not.

We can alter this formula slightly to predict the difference that will emerge between populations by defining the selection differential as the difference in the mean value of a trait between people who successfully mate in one population and those who successfully mate in another. Doing so makes a number of simplifying assumptions, but this does not matter for the points I am making here.

As can be seen, very low selection differentials and heritability figures would lead to very large differences between the races over the amount of time that the races have been evolving separately.

Of course, this model could also predict racial differences much larger than anything we actually see. This is because the model assumes that evolution would work in a much more consistent way than it actually does. However, the point remains that slight differences in selection pressures can lead to the sorts of differences between the races we see today, around 1 SD, which in turn have been shown to account for a significant proportion of differences in national outcomes.

This is especially true because the heritability of traits was probably very high in pre-historic time when there was little environmental variation within populations.

Gene-Culture Coevolution

The connection between culture and evolution is even stronger. To the degree that culture impacts the association between a trait and reproductive success it will necessarily impact natural selection. Some people think that evolution happens to slowly for culture to have had an impact, but the above chart shows that this is not true if the selection differentials aren’t tiny. In fact, geneticists estimate that evolution has sped up by a factor of 100 within the last 5000 years, suggesting that culture may have had an extremely dramatic impact on evolution (Hawks et al 2007).

Examples of culture impacting evolution are not hard to think of. For instance, between the years 1500 and 1750 England killed between 1% and 2% of it’s population every generation for violent crime. This undoubtedly had a strong effect selecting against gene variants associated with violent crime (Frost and Harpending 2015).

One can come up with lots of stories about how virtually any culture event impacted evolution. Regardless of what specifics of these stories, everyone agrees that culture varied across populations, and so everyone should agree that selection pressures differed between populations for all sorts of behaviors. In short, everyone should agree that egalitarianism is obviously false.

Thus, in more ways than one, theories based on geography and evolution actually go hand in hand, and there is no reason to think that one is true while the other is not.

Genetic Diversity

Karllson offers one really silly argument against the idea that biological evolution explain’s national differences in success. Specifically, he cites a paper which found no relationship between how genetically diverse a population was an it’s level of economic development and from this concludes ” we have to look elsewhere than genetics to figure out why countries differ economically”.

Of course, this is a ridiculous inference because race realists typically argue that national success differences are caused by differences in allele frequencies relating to behavioral traits, not differences in the degree of within population genetic diversity.

Conclusion

In conclusion, Europeans tend to make the most prosperous civilizations. With the exception of the dark ages, this has always been true, and it has especially been true of western Europeans.

The relevant empirical evidence does not back up the idea that White sins account for the poverty of Africa. Instead, the evidence suggests that much of national variation in wealth can be attributed to factors which exerted an influence long before Whites made contact with southern Africans.

The two most plausible candidates for such a factor are genetics and geography. These theories are often pitted against one another, but in reality they go hand in hand and probably both tell us something important about why some nations have been more successful than others.