Thinking

# The Story of Hominin, Part I: pre-Homo

In a contemplative article, Bernard Wood, the doyen of hominin taxonomy, identified three boundary problems in the business. The first is, of course, where to draw the line between ape and man; more precisely, the boundary between “ape-like” hominid and man-like hominin fossils. [Hominid includes the great apes; hominin does not.] It is not simply a matter of lineage for there are many species (by which we simply mean morphological types or taxa; not the biological definition—the ability to produce fertile offspring—which cannot be determined on the basis of extant evidence) in the fossil record that are neither the ancestors of modern humans nor that of modern chimps. So the question is above all where to place these dead-ends. In practice, the boundary is fuzzy and boils down to the degree to which the species is arboreal (adapted to living in the canopy) or bipedal. Committed bipeds are regarded as closer to modern humans; committed arboreals as closer to apes; with fossils displaying both adaptations somewhere in between.

Source: Wood and Collard (1999).

The second is the boundary of the genus Homo. Drawing this boundary is an equally fraught enterprise for now we are talking about demarcating which species can be regarded as human senso lato (in a loose sense)Committed bipedalism is not enough, it is reasonable to demand that species in our genus approach the human form if not behavior. Human-like behavior, especially tool manufacture, is of course enough to guarantee you a spot in the genus. But things are complicated due to the fact that multiple hominin species coexisted in eastern Africa when the first tools appear around 2.5 Ma (millions of years ago) so that it is impossible in general to attach the first industries to particular species. In practice, paleoanthropologists regard big brains as the ticket to entry with cranial capacity above 600 cubic centimeters (cc) regarded as the conventional boundary. The defining exception or marginal case is H. habilis (“handy man”, 2.5-1.4 Ma) which has definitely been tied to the first lithic technology but sports an average cranial capacity of only 552 cc. (Although Gamble reports an average of 609 cc. Compare figures 1 and 2.) We will presently return to encephalization in human evolution.

Source: Gamble (2013).

The third is the boundary between fully-human, H. sapiens senso stricto (in the strict sense), and near-human hominin. This is perhaps the most fraught boundary of the three. It is also uncomfortably close to the question of the origin of the races, that perennial obsession of high racialism. There is a virtual consensus that what distinguishes humans from other hominins is behavior—we are more sophisticated, behaviorally-plastic, and dynamic than those other guys. But this is very far from being free of problems. First, the appearance of anatomically modern humans considerably predates the evidence for behavioral modernity however defined, so that the origin of our species senso stricto is thereby shrouded in mystery. Second, some but not all late archaic hominins, in particular Neanderthals, are associated with advanced lithic technology (including Levallois tools) indistinguishable from contemporaneous anatomically modern humans (say around 100 Ka).

It would seem that there are two ways of resolving this boundary problem. Either we impose a less stringent criteria for behavioral modernity (say tool manufacture requiring multiple processes in a specific sequence) and therefore be generous with inclusion. Or we impose a more stringent criteria for behavioral modernity (say art, ornaments, burial, colonization of extreme environments, sea-faring, projectile weapons, and so on; in short, culture, versatility and dynamism) and be thereby stingy with inclusion. But both generous and stingy definitions are deeply-problematic.

For if you say advanced tool manufacture (say Acheulean tools c. 1 Ma, in particular, bifacial hand-axes) is sufficient criteria for inclusion then archaic hominin (including our ancestors) in the western Old World would be included but not those in eastern Eurasia for the spread of the Acheulean industry after 1 Ma was confined to west of the infamous but accurate Movius line. The eastern Old World, populated by late archaic hominins, continued to manufacture unsophisticated Oldowan tools developed c. 2.5 Ma for hundreds of thousands of years after Acheulean industry becomes dominant in the western Old World.

Source: Lycett and Bae (2010).

If on the other hand you say let’s be stingy and restrict H. sapiens senso stricto to much more recent hominin populations that display the full suite of modern behavior, then that would imply that populations in western Eurasia (Europe and the Near East) and Sahul (Australia, Tasmania, New Guinea) were fully-human by 50-40 Ka, tens of thousands of years before the rest of the world (say 20 Ka). No doubt this is extremely controversial. But the empirical evidence for global polarization in the Late Pleistocene is overwhelming. Basically, apart from some ephemeral early evidence in southern Africa around 90 Ka, the appearance of the full-package of behavioral modernity around 40 Ka is confined to western Eurasia to which the term Upper Paleolithic is properly applicable. To reach Sahul, of course, required sea-faring so that those populations were definitely fully-modern. A different term, Late Stone Age, applies to Africa from 50 Ka on. It is defined in terms of advanced lithic technology and does not sport evidence of the full-package of behavioral modernity. I’m walking on coals here … many Africanist prehistorians would be furious. But my goal is to problematize the modern-premodern dichotomy in prehistory.

My general point is that all three dichotomies are necessarily fuzzy. Any schema involving sharp boundaries in hominin taxonomy is guaranteed to be shot through with contradictions and glaring anomalies. In short, it’s a fool’s errand. In what follows we will not take a position on these boundary questions.

The title of the present dispatch is also a nod at another problem in narrative accounts of the human career. Namely, the temptation to take a teleological approach is very strong in this domain. ‘The Story of Man’ presumes that tracing how we got to where we are is a sufficient account of the hominin career. But that ignores the evolutionary dead-ends; more sympathetically, it ignores hominin forms and strategies different from ours that went out of business. The explosion of taxa is a testament to the extraordinary variation in hominin morphology (and therefore life history and survival strategy) that is not only interesting in itself, but also informs our own story. Just as a liberal order after the failure of Communism is quite different from the counterfactual without the Communist experiment, the story of us that emerges from a full consideration of alternative lifeways pursued by sister species is different and richer than a story that emerges from the tenuous assumption of telos in human evolution. In other words, we must tell the story of the others as well as of us because the story of the others informs the interpretation of our own story. What follows is a sketch of a broad-brush history of our tribe, the Hominini.

The story begins with the Planet of the Apes. During the Miocene, 23-5 Ma, the climate was much warmer and wetter. Siberia and Greenland were not glaciated and rainforests covered much of the Old World. Apes emerged out of Africa and colonized much of the Old World presumably jumping from canopy to canopy.

Source: Gamble (2013).

By the time of our last common ancestor with the chimps c. 7-6 Ma, it was still warm but the temperatures had fallen dramatically. Greenland was glaciated, the rainforests had receded, and apes had become confined again to Africa. This is where the first hominin began, very tentatively, to walk. The emergence of committed bipedalism was an excruciatingly slow process. Although we have scant fossil evidence for the period, it is clear that for millions of years, hominin refused to commit to bipedalism. They retained skeletal features like opposable toes that show that they were still arboreal and only occasional bipeds. Very little is known about early pre-australopith hominins c. 7-4 Ma other than they sport an ape-like morphology. Indeed, the only thing that distinguishes them from apes is that they were occasional bipeds. In fact, some experts suggest that they are too ape-like to be considered hominin. Regardless, one or more of these hominins, most likely from the genus Ardipithecus, evolved into Australopithecines, when things start to get really interesting.

Source: Liberman (2013).

Towards the end of the Pliocene, 5.3-2.6 Ma, the climate became much cooler. Rainforests disappeared from eastern Africa and Woodland and savannah expanded. A vast number of hominin taxa suddenly explode in the fossil record at this time. Most of them have been placed in the genus Australopithecus c. 4-1 Ma. The most famous australopith, of course, is Lucy (named after the Beatles’ song Lucy in the Sky with Diamonds) who lived in Ethiopia 3.2 Ma and belonged to the taxon A. afarensis. It is clear from her skeleton that Lucy was an obligate biped, eg the absence of opposable toes. But the earliest evidence for obligate bipedalism is from the Laetoli footprints made c. 3.6 Ma that have also been associated with A. afarensis.

There is no consensus on why bipedalism emerged at this time. Some have claimed that bipedalism might have been a postural adaptation with those able to stand upright being able to gather more fruit. Others have suggested that selection of bipedalism was due to the thermodynamic efficiency of bipedal locomotion in the expanding savannah or woodland habitats where sustenance was more sparsely distributed. Still others have emphasized the thermoregulatory advantage of upright walking. It has been suggested that australopiths engaged in midday scavenging when competition from quadruped scavengers (disadvantaged because they expose a much greater surface area to the sun) was absent or less intense. In all cases, the logic leads straight to the question of foraging strategy and therefore diet. We’ll return to this question shortly.

Australopiths, like all early hominin, had small bodies and small brains. Australopith females, for instance, were just 1.1m tall and weighed 28-35kg. Male Australopiths averaged 1.4m in stature and weighed in at 40-50kg. Thus, males were about 40-50 percent larger than females. From figure 2 we see that the index of sexual dimorphism for Australopiths (1.53) is closer to gorillas (1.68) than modern humans (1.16). This suggests that male Australopiths fought each other for access to females.

With an average cranial capacity of just 464 cc, their brains were nearly as small as that of modern chimps. Their brains were small not just in absolute volume but also relative to body mass. Indeed, their encephalization quotient comes to just 2.6, or less than half as much as modern humans who sport an EQ of around 6-7. Given their brain size, Dunbar’s social brain hypothesis suggests a network size of 67 individuals, less than half that of contemporary human population (136). They also had a much faster life history; taking about 12 years to reach adulthood.

Source: Liberman (2013).

While all early hominin were small-bodied and small-brained, they differed markedly in their dietary strategies. Both gracile Australopiths and robust Australopiths (the latter have recently secured their own genus, Paranthropus) ate a wide-variety of fruit, insects, leaves, tubers, roots, and the occasionally scavenged meat as attested by their greater molar size (compared to Ardipithecus). But what distinguishes the two is their masticatory (chewing) apparatus.

Source: Evans et al. (2016).

Taxa in the genus Paranthropus in general, and the taxon P. boisei in particular, were what Wood called megadonts. Their powerful masticatory muscles and very large molars allowed them to crush and grind hard foods such as nuts, seeds, roots, and tubers in the back of the jaw. Since the genus Homo emerged from the gracile Australopiths, the megadonts are the classic dead-end. None of their descendants survived. They vanish from the fossil record after 1.3 Ma. So the temptation is rather strong to see the roots of their doom in dietary specialization in hard-to-digest and poor quality foods. That temptation must be resisted. Microwear evidence from their tooth enamel suggests that their diets were just as varied as the gracile Australopiths. The decisive difference in dietary strategy between the two was in fallback foods, ie what they resorted to eating when their preferred food was unavailable. However, it is clear that their strategy did not generate the sort of feedback loop between foraging strategy, gut morphology, and encephalization that emerged in our lineage. That will be the subject of part II when we examine the career of the genus Homo. Stay tuned.

Source: Aiello and Wheeler (1995).

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# Population History and European Morphology since the Upper Paleolithic

Christopher Ruff, a paleoanthropologist at the Johns Hopkins University School of Medicine and the director of the Center for Functional Anatomy and Evolution, has been very generous with his time. He has helped me greatly in refining my understanding on human morphology and our population history. Ruff and coworkers have recently published Skeletal variation and adaptation in Europeans: Upper Paleolithic to the Twentieth Century, 2018. The study examines a total of 2,179 individual skeletons since the Upper Paleolithic beginning 33 thousand of years ago (henceforth, Ka). He has been kind enough to share their data with me. What follows is based on my interrogation of this data in light of our population history.

The basics of west Eurasian population history are now well-understood. The following account is based on the scheme presented in David Reich’s Who We Are and How We Got Here, 2018. The picture that emerges from ancient-DNA studies is straightforward. Basically, there are three major departures in west Eurasian population history. (Here we focus specifically on Europe.) The first is the arrival of Homo sapiens during the early Upper Paleolithic around 40 Ka into a continent already populated by Neanderthals. H. sapiens had already mixed with Neanderthal populations in the Near East immediately upon their exit from Africa. There was further admixture in Europe.

Estimates of the precise degree of admixture are quite sensitive to assumptions about the neutrality of genomic sequences acquired from the Neanderthals since even genes acquired in a single admixture event could rapidly get fixed throughout the population if they come under selection; conversely, the prevalence of genomic sequences not under selection contains information on the degree of admixture. Almost all estimates however fall into single digits. This is perhaps not because mating was infrequent despite near-continuous contact but rather because of the large disparity in population sizes. The colonizers were dramatically more populous than the natives so that very high degree of mixing for the latter is consistent with low rates of mixing for the former. (Similar to interracial marriage rates in the present-day United States.)

By the Last Glacial Maximum 26 Ka, the Neanderthals had long vanished. It is not clear if they went extinct or were simply absorbed into H. sapiens populations. Upper Paleolithic populations of Europe were already morphologically-adapted to the macroclimate with more northern populations displaying bigger bodies in accordance with Bergmann’s Rule. This population is basically swamped by a second pulse around 9 Ka when the Neolithic Revolution generates a major population pulse of farmers in the central Eurasian region who explode out in both easterly and westerly directions. The former would go on to found the Dravidian-Harappan Civilization. In Europe, the hunter-gatherers survived in isolated pockets; especially at northern latitudes. Since the Neolithic farmers came from the Near East their morphology was adapted to the much warmer macroclimate of the central region. In accordance with Bergmann’s Rule, we expect them to be smaller than the more cold-adapted populations of the Upper Paleolithic. We’ll presently see what the data has to say about this.

A third major population pulse was triggered by the Secondary Products Revolution in the fourth millenium BCE. In the central region, this dramatic transformation in the material possibility frontier gives rise to the very first state society at Uruk. The introduction of these advanced technologies—especially the wagon, as David Antony has argued—from the core of the Uruk world-system to the periphery, in this case, north of the Caucasus, makes the systematic economic exploitation of the sparsely-endowed steppe possible for the first time. This material revolution in the hitherto very sparsely-populated steppe is in turn responsible for the ethnogenesis of the Yamnaya, the speakers of Proto-Indo-European (the mother tongue whose descendents are spoken by half the world’s population today).

Yamnaya pastoralists explode outward almost immediately from their homeland. By 5 Ka, a massive population pulse reaches Europe, another India, and a third the Altai mountains in Kazakhstan. The Yamnaya are an extremely violent and hierarchical rank-society; obsessed with martial glory, competitive feasting, and other male bonding rituals. They conquer the first-farmers of Europe and eventually the isolated pockets of hunter-gatherers (in particular, in Scandinavia). These migrations are extremely sex-biased. Yamnaya warrior-pastoralists likely took the women and slaughtered the men in raids and skirmishes as the horizon moved inexorably westward.

Source: David Reich (2018).

The end-result of this population history is that contemporary European populations are a sex-biased admixture of Pleistocene hunter-gatherers, Neolithic farmers, and Yamyaya pastoralists; in the reverse order in terms of weight in the population structure. These three populations were morphologically-adapted to very different macroclimates during the Late Pleistocene. Specifically, the first can be expected to be adapted to local conditions in Europe that were highly polarized by latitude (southern Europe was never glacial whereas northern Europe witnessed the massive glacial-interglacial whipsaw), the second to the considerably warmer conditions of the Late Pleistocene in the Near East, and the last, the Yamyaya, to the more-permanently glacial conditions of the Russian steppe. We thus expect systematic time-variation in European morphology consistent with this population history. More precisely, we expect the slower-moving morphological parameters (eg, pelvic bone width, femur head diameter) to fall after the invasion of the farmers from the central Eurasian region and rise after the invasion of the pastoralists from the Eurasian steppe.

Figure 1 and 2 display the pelvic bone width of the skeletons in the Ruff et al. (2018) dataset. We have resized the points by the number of skeletons in the dataset for given region and period. We have also merged some periods in the original dataset for simplicity. [Early Upper Paleolithic (33-26 Ka) and Late Upper Paleolithic (22-11 Ka) have been folded into Upper Paleolithic (33-11 Ka); Mesolithic (11-6 Ka) and Neolithic (7-4 Ka) into Neolithic (11-4 Ka); Bronze (4-3 Ka) and Iron/Roman (2.3-1.7 Ka) into Yamnaya (4-1.7 Ka); Early Medieval (c. 600-950) and Late Medieval (c. 1000-1450) into Medieval; and Early modern (c. 1500-1850) and Very recent (c. 1900-2000) into Modern (c. 1500-2000).]

Figure 1. Pelvic bone width in Europe, Men. Source: Ruff et al. (2018).

The evidence that emerges is pretty unambiguous. For both men and women there is a significant fall in pelvic bone width during the Neolithic transition, and a substantial rise contemporaneous with the Yamnaya transition. Since there was no major population replacement in the Medieval-Modern passage, the decline in body size cannot be traced to population history. Note the French outlier that attenuates the modern decline for women. Without the outlier, the modern decline in women’s pelvic bone width would be as significant as men’s.

Figure 1. Pelvic bone width in Europe, Women. Source: Ruff et al. (2018).

Similar results hold if we look at femur head diameter which is also strongly canalized (very slow-moving). Femur head diameter is the main weight-carrying parameter of the human body and as slow to change as pelvic bone width.

The length of the thigh bone (femur) is much more developmentally-plastic than either pelvic bone width of femur head diameter. Yet, we know that even femur length (and hence stature) exhibits morphological adaptation to the macroclimate. The evidence that emerges from this dataset is consistent with our previous findings.

Finally, for the sake of completeness, we include graphs for stature. These ought to be congruent with the results for femur length since the former is a linear function of the latter.

The evidence that emerges is consistent with the idea that population history confounds the interpretation of the time-variation (as opposed to the just the cross-section as we have argued until now) of morphological parameters of the human body. In order to make valid inferences, population history must be kept in mind.

I constructed an index of body size by adding up the z-scores of femur head diameter and pelvic bone width. It is a less noisy measure of body-size that those considered above. The overall pattern revealed by the Body-Size Index is very compelling. We observe that size falls in the Upper Paleolithic-Neolithic passage and rises with the arrival of the Yamnaya precisely as predicted by population history. The modern decline does not correspond to any major population movement and therefore cannot be explained by population history.

The decrease in body-size is consistent with other evidence of gracialization during the transition to modernity. Men have not only become smaller, even their faces have become less aggressive (not to speak of manners and behavior). Could it be that changing social norms against the warrior code rewarded variants with traits less associated with aggression with greater reproductive success? Or did the rewards for body-size decline with better technology for grunt work? We don’t know. There is certainly a case to be made for a general process of gracialization related to modernity.

P.S. On second thoughts, it may not be wise to combine sexes after all. There is good reason to think that women’s pelvic bone width is more plastic than men’s because maternal mortality can adjust the latter quite rapidly. The best measure we have is men’s pelvic bone width. Here I graph the mean pelvic bone width of European men without combining periods. The evidence is consistent with the population history noted above. The overall pattern suggests strong gracialization after the Last Glacial Maximum, a further fall in body-size with the arrival of Neolithic farmers, a dramatic rise with the arrival of Yamnaya pastoralists; followed by slow upward drift until the end of the Middle Ages, strong gracialization in the early modern period, and very partial restoration after c. 1900. The big question thrown up by the present investigation is of course the dramatic decline in European body-size after the Black Death. But the overall elephant shaped pattern is very interesting as well.

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# Notes on the Escalatory Logic of the Mid-Century Passage, 1928-1962

JFK and SAC’s Curtis LeMay (center) in 1962.

I’m interested in writing a violent history of the mid-century passage that does justice to the unprecedented nature of the interwar armament race and the unprecedented stakes in the mid-century struggle; one that does not get stuck in victims’ point-of-view (a risk that comes with the terrain in writing a violent history); one that treats the victors and the vanquished symmetrically (not ethically but analytically). Organized massive violence at mid-century is not limited to the eastern front; although that it where its most extreme version obtained in earnest. I want to focus on how the specter of total war pulls the war-strategy of insurgents and defenders alike inexorably towards the annihilation of the adversary’s civilian population. Not just National Socialist Germany, the communist great power and Imperial Japan, but also the Anglo-Saxon powers.

As Adam Tooze has emphasized, the demands of total war compelled the insurgent powers to mobilize their entire societies for war precisely because of Anglo-Saxon geopolitical supremacy. To paraphrase Adam, it was a compliment to the massive fist concealed behind the velvet glove of the liberal democratic discourse. Nicholas Mulder has studied the understanding and deployment of the economic weapon whose only logical target was the entire body-politic of the adversary. This was no coincidence for Anglo-Saxon warfighting strategy at mid-century also went all-in on strategic bombing. Unlimited bombardment of the adversary’s population was expected to undermine the adversary’s will to resist. That was the Anglo-Saxon formula for winning wars even after its failures became manifest in World War II. That’s how we got to the Strategic Air Command. It was an integral component of what David Edgerton calls the ‘liberal-democratic way of war.’

David has emphasized how the British warfare state was in rude health at mid-century. In particular, Britain was the dominant terror bombing power of the world in the 1940s. But I think it is a mistake to see the peak of this movement in 1941-1945. The escalatory logic culminates not in the greatest war in history (the Soviet-German War) but with omnicidal American nuclear warfighting strategy; specifically, the SAC’s operational plan for a massive preemptive first-strike on the Sino-Soviet bloc. That only emerges in 1952. There is really only one weapon of omnicide in the fifties and sixties: Curtis LeMay’s SAC.

I want to use the logic of escalation for narrative control. The mid-century passage is of course the culmination of a very long process of systemically-driven escalation in organized violence. Successive tournament rounds in Western history have been conducted at exponentially greater powers of destruction whose graph looks like a hockey stick. The process extends back before the onset of the global condition in the mid-to-late nineteenth century, to the cauldron of Europe in the early modern period, with Germany as the playing field; and earlier still … perhaps all the way back before the Black Death to the arrival of Indo-European warrior societies (the Yamnaya) into Europe after 3000 BC. What is going on in Western history is the unfolding of the hard realist logic identified with Ashley Tellis in his PhD dissertation. Big fish actually eating small fish so that the roster of great powers dwindles until it is reduced to a singleton.

But let’s stick to how the logic of escalation comes to a head at mid-century. The onset of material modernity, say in 1930 when the settler premium in stature vanishes and Britons become taller than Americans for the first time (ie, precisely when Braudel timed the baton’s crossing of the Atlantic), raises the cost of hegemonic war by an order of magnitude. And the political stakes go up in tandem. Here I want to anchor the narrative on Max Werner’s observations on the 1930s armament revolution. Werner insisted that the arms race of the 1930s was no mere arms race. It was an existential struggle. The loser was going to be wiped off the face of the earth. As a German military journal explained in 1935, “totalitarian warfare is nothing but a gigantic struggle of elimination whose upshot will be terrible and irrevocable in its finality” (quoted in Werner, 1939).

The narrative, I think, should therefore begin in 1928 when Stalin declares the existential arms race open; five years before Hitler. We could begin with Fisher’s declaration in 1906 when the introduction of the Dreadnaught makes the war fleet of all great naval powers obsolete, and developments in firepower identified in real-time by Ivan Bloch revolutionize the nature of great power war. But I want to focus the narrative on the escalation end-game at mid-century. Werner is right. The stakes were considerably higher in World War II than in World War I and understood to be so. This then would be a fitting sequel to Adam Tooze’s The Deluge: The Great War, America and the Remaking of the Global Order, 1916-1931.

The perfection of organized violence is closely-tied to the search for ‘total death’ (Gil Elliot, Twentieth-Century Book of the Dead, 1972) that I think must be framed as culminating not at Auschwitz but when, for the first time in in the entire history of planet earth, we faced an anthropogenic mass extinction. Kennedy did not know it during the Cuban Missile Crisis but in the 1980s climate scientists established that if his threat had actually been carried out it would have caused a nuclear winter. Temperatures would’ve fallen precipitously in all the world’s food producing regions with the result that the anthropocene would terminate in a spectacular orgy of hunger, chaos and cannibalism. Kennedy only knew that the massive preemptive first-strike he threatened would kill hundreds of millions in Eurasia, as Daniel Ellsberg documents. (On the Cuban Missile Crisis see Trachtenberg.) But the very possibility of a credible anthropogenic threat of mass-extinction marks a decisive break in the natural history of the planet; and as much an existential break in history as that great trauma of modernity, the Holocaust.

This long-running controlling logic of Western history runs smack into MAD by the mid-sixties. The logic of Darwinian elimination is frustrated by the iron logic of mutually assured destruction. Frustrated too are the Soviets who find that strategic parity does not deliver prestige, as Wohlforth notes in The Elusive Balance. So MAD provides the natural coda of the narrative. The central question raised by the twentieth century on this reading then is, How do you get over MAD and actually fight a thermonuclear war? At what stakes does it become a real possibility? Can we rule out that this rubicon would not be crossed in the twenty-first century?

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# Morphological Adaptation to the Macroclimate during the Late Pleistocene

Previous estimates of correlations of anthropometric variables and latitude appearing on these pages and in the draft paper (“Ruff’s surface law holds for Holocene population but Bergmann’s size law does not”) were incorrect as a result of a bug in the code. The estimates that appear below supercede all previous estimates.

I sent my results to Christopher Ruff. His comments were extremely helpful and led me to discover a devastating flaw in my estimates. My earlier computations were compromised by an unforeseen bug in Matlab. Specifically, I did not know that the function grpstats permutes the groups in a different manner from the function categories so that all the correlation estimates were attenuated. In order to make sure that my estimates were kosher, this time I computed the unweighted coefficients independently twice; the first time in Matlab and the second time in Excel. Their congruence gives us good confidence that the numbers are kosher.

A second issue was my growing appreciation that morphological adaptation of the human body to the macroclimate could very well be gendered. I therefore decided to finally take my father’s suggestion and work exclusively in a sex-specific setting so to not rely on OLS or WLS corrections for the sex ratio.

A third issue was that some of our variables (stature and BMI) were measured with more noise than others (femur length, pelvic bone width, femur head diameter) since the former are estimates while the latter are direct measurements of the skeletons. We can therefore expect coefficient estimates of the former to be attenuated relative to the latter. We must therefore rely more on the latter than the former.

A final issue, pointed out by Ruff, was that New World populations can be expected to be adapted to the macroclimate of Siberia (from whence they migrated to the New World at the end of the Pleistocene) and not their New World locations. Since the skeleton samples for them are attached to the wrong latitude, this would tend to attenuate our coefficient estimates due to additional noise. We should therefore rely more on the Old World sample than the full sample.

Our preferred test statistic is weighted Spearman’s rho for the Old World sample where the weights are given by the number of fossils at the location. In light of the manifest heteroskedasticity due to the tremendous variation in sample size at each location, weighted rho is a more powerful test statistic than the unweighted version.

The results that emerge are very strong. Essentially, all the main anthropometric variables are strong correlates of latitude in the Holocene sample. This means that the economic interpretation of the cross-section of not only BMI but stature as well is confounded. The evidence is consistent with a thermoregulatory theory, or more generally, a Heliocentric theory of morphological adaptation of geographically-situated populations to the macroclimate during the Late Pleistocene (ie, since the dispersal of Homo sapiens out of Africa 130-50 Ka).

All the information contained in anthropometric variables can be extracted from the seat of the pants. Specifically, femur (thigh bone) length controls stature and pelvic bone width and the diameter of the femur head (the knob at the top of the thigh bone) controls BMI. The main results can therefore be read off Table 1.

 Table 1. Sex-specific Spearman’s correlation coefficients with latitude. Full sample Old World N 42 39 25 24 Male Female Male Female Tail Femur length rho 0.369 0.201 0.545 0.301 pVal 0.009 0.110 0.000 0.076 Right weighted rho 0.264 0.132 0.477 0.349 pVal 0.045 0.212 0.000 0.015 Right Femur head diameter rho 0.657 0.607 0.703 0.610 pVal 0.000 0.000 0.000 0.001 Right weighted rho 0.720 0.757 0.867 0.739 pVal 0.000 0.000 0.000 0.000 Right Pelvic bone width rho 0.460 0.662 0.539 0.684 pVal 0.001 0.000 0.002 0.000 Right weighted rho 0.719 0.792 0.741 0.780 pVal 0.000 0.000 0.000 0.000 Right Source: Goldman Osteometrics Dataset, author’s computations.

The evidence is astonishingly unambiguous. By our preferred test statistic, the correlation coefficient of latitude and femur length for is around 0.55 (p<0.001) for men and 0.33 (p=0.020); for femur head diameter, around 0.80 (p<0.001); and for pelvic bone width, about 0.87 (p<0.001) for men and 0.72 (p<0.001) for women. Note that all the estimates are attenuated in the full sample relative to the Old World sample. This suggests that the “wrong latitude” problem for the New World is very serious; implying that the adaptation is encoded precisely in Late Pleistocene population history. But there is very little wriggle room in the evidence. What we have under the null hypothesis is the statistical equivalent of a straight flush in poker. The probability of observing these rank correlations in these measured postcranial variables by chance is negligible.

As expected, the correlations of the controlling variables in the seat of the pants translate into very high correlations for estimated height and BMI. See Table 2. Note that the estimates for stature are identical to that for femur length, as it indeed ought to be since the former is computed as a linear function of the latter. The full sample estimates are pairwise smaller than the Old World estimates, reemphasizing the “wrong latitude” problem in the dataset. By our preferred test statistic, the correlation of latitude with stature is around 0.60 (p<0.001) and for BMI is about 0.80 (p<0.001).

 Table 2. Sex-specific Spearman’s correlation coefficients with latitude. Full sample Old World N 42 39 25 24 Male Female Male Female Tail Estimated stature rho 0.369 0.201 0.477 0.301 pVal 0.009 0.110 0.007 0.076 Right weighted rho 0.264 0.132 0.545 0.349 pVal 0.045 0.212 0.000 0.015 Right Estimated BMI rho 0.425 0.640 0.351 0.638 pVal 0.004 0.000 0.043 0.001 Right weighted rho 0.659 0.757 0.525 0.698 pVal 0.000 0.000 0.000 0.000 Right Source: Goldman Osteometrics Dataset, author’s computations.

Thus, BMI and stature are both confounded by morphological adaptation to the macroclimate. We cross-check our work by presenting our independent Excel-based estimates for unweighted Spearman’s rho together with Pearson’s r (although we know that Pearson’s r has a potentially severe liberal bias due to the nonnormality of latitude in the sample—but this does not seem to be a problem). Table 3 displays the results for Holocene men, and Table 4 that for Holocene women.

 Table 3. Correlation coefficients with latitude for Holocene Men. Pearson Spearman Men Full sample Old World Full sample Old World Femur length 0.395 0.459 0.371 0.571 Femur head diameter 0.589 0.640 0.655 0.674 Pelvic bone width 0.448 0.497 0.461 0.583 Stature 0.395 0.459 0.371 0.571 BMI 0.474 0.592 0.423 0.619 Source: Goldman Osteometrics Dataset, author’s computations in Excel. Estimates in bold are significant at the 5 percent level.
 Table 4. Correlation coefficients with latitude for Holocene Women. Pearson’s r Spearman’s rho Women Full sample Old World Full sample Old World Femur length 0.110 0.330 0.201 0.509 Femur head diameter 0.590 0.590 0.607 0.699 Pelvic bone width 0.630 0.660 0.662 0.729 Stature 0.110 0.330 0.201 0.509 BMI 0.630 0.660 0.640 0.760 Source: Goldman Osteometrics Dataset, author’s computations in Excel. Estimates in bold are significant at the 5 percent level.

The fate of the surface law is very interesting. (Chris asked me not to refer to it as “Ruff’s surface law” since it had been proposed by others before him; notably Mayr.) Turns out, once we look at sex-specific data, body surface and body weight are linear functions of each other. They both scale together at the individual level so it is not clear how to interpret correlation estimates for the ratio of surface area to body weight (although they are significant in a signed one-tailed test). See Figure 1.

Figure 1. Surface law.

In sum, the evidence is consistent with manifest adaptation of the basic parameters of the human skeleton to the macroclimate specifically during the Late Pleistocene. This means that the economic interpretation of the cross-sectional variation in both stature and BMI is confounded by our population geohistories. We must therefore rely exclusively on the time-variation of the variables to extract economic information, for instance, by looking at the cross-section of percentage changes in stature and BMI across nations.

There is massive variation in human morphology. ANOVA reveals that, depending on the particular anthropometric variable, roughly three-fifths of the variation is between individuals within populations; and two-fifths is systematic variation between populations at different geographic locations. Race is a poor way to understand the latter. What we have instead are geographic clines. Indeed, there is continuous geographic variation in human morphology as attested by a very significant gradient for latitude. We have suggested that the most parsimonious interpretation of this pattern is in terms of the imperatives of thermoregulation, as Ruff has argued.

The overall pattern suggest a Heliocentric theory. While it is tempting to try to connect this to our Heliocentric theory of the spread of the secondary industrial revolution and therefore of contemporary global polarization, that temptation must be resisted. The latter is a very specific theory that ties the rate of intensity of work performed on the same machine at different temperatures to explain the geographic distribution of global production.

Ruff’s thermoregulatory theory is also a Heliocentric theory in that it ties the heat of the sun directly to human morphology. The empirical evidence is consistent with the idea that Holocene bodies reflect a population history of differential survival not of discrete types, but of highly-varied and continuously-distributed morphological “parameters” of the human skeleton during the Late Pleistocene in a manner that was systematically differentiated by latitude in accordance with the generalized Bergmann’s rule. Although the pace of change may have accelerated in the recent past, our skeletons have the wrath of Ra written all over them.

Many thanks to Christopher Ruff for his guidance.

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# Ruff’s Surface Law: Evidence from the Holocene

In the previous dispatch we argued that throughout the Holocene, stature was not correlated with latitude and mean temperatures, but BMI was. Consequently, the gradient of stature along latitude in modern data encodes information on global polarization in everyday living standards. We can thus interpret the cross-sectional variation in modern stature as reflecting the differential health transition in the mid-twentieth century. Conversely, the interpretation of cross-sectional variation in BMI is confounded by the fact that BMI is a linear function of pelvic bone width, and pelvic bone width encodes information on the very long run morphological adaptation of geographically-clustered populations to the macroclimate in accordance with Ruff’s surface law. In what follows we introduce a new anthropometric measure that captures this morphological adaptation. And we refine our empirical strategy in light of our lack of precise knowledge of the antiquity of the fossils in the Goldman Osteometrics Dataset.

Ruff’s surface law asserts that because body heat is a function of body mass (or volume) and the rate of heat dissipation is a function of surface area, in colder climes, surface area ought to be minimized relative to volume; conversely, in warmer climes, surface area ought to maximized relative to volume. We operationalize this observation by constructing a new metric that we label Ruff’s ratio defined as the ratio of surface area in squared centimeters (SA) exponentiated to the power 1.5, to body mass in kilograms (BM),

$\textup{Ruff's ratio}:=\frac{\text{SA}^{1.5}}{\text{BM}},$

where we have chosen the power of the numerator to equalize the dimensions of the two quantities. If Ruff’s surface law holds for human beings, Ruff’s ratio should be a strong correlate of latitude. Ruff argued that the surface area of the human body is well-approximated by the area of the curved surface of a cylinder with diameter equal to pelvic bone width and height equal to stature. We therefore estimate the surface area via the formula,

$\text{SA}:=\pi\cdot BW\cdot ST,$

where BW is pelvic bone width and ST is stature; both measured in centimeters. Table 1 presents summary statistics for our three sex-combined variables.

 Table 1. Summary statistics. Stature (cm) BMI Ruff’s ratio Mean 158.7 22.0 27.0 SD 8.698 1.709 0.778 CV 0.055 0.078 0.029 Source: Goldman Osteometric Dataset, author’s computations.

Note that the coefficient of variation of Ruff’s ratio is extremely low. Since there is such little variation in this metric, small differences in Ruff’s ratio between populations can be expected to be correspondingly significant.

Before we examine the empirical correlations it must be observed that the location of the fossils is not always a good indication of population geohistory. Specifically, it may well be the case that New World populations were not fully adapted to the macroclimate of the location where the fossil was recovered. We will therefore report both full sample estimates and those obtained by restricting the sample to the Old World. Also, Spearman’s rank correlation is a more robust measure in the presence of heteroskedasticity than the standard Pearson correlation coefficient; as will be clear from the stability of the estimates. For the same reason we report robust standard errors for our gradient estimates.

Table 2 reports Spearman’s rank correlation coefficients for latitude. The results are consistent with our hypothesis. Moreover, Ruff’s ratio is a stronger correlate of latitude than BMI in accordance with Ruff’s surface law. Spearman’s rho for Ruff’s ratio is 25% higher than the same for BMI in the full sample. Note also the extraordinary stability of the coefficient for Ruff’s ratio vis-à-vis BMI when we restrict the sample to the Old World. While Spearman’s correlation coefficient for BMI jumps by 15% when we restrict the sample to the Old World (consistent with our interpretation of the New World data points being noiser), the coefficient for Ruff’s ratio is unchanged to the third decimal point. Ruff’s ratio is therefore a less noisy measure of climatic adaptation than BMI.

 Table 2. Spearman’s rank correlation coefficients for latitude. Stature BMI Ruff’s ratio Full sample (N=45) 0.211 0.493 0.616 pVal 0.164 0.001 0.000 Old World (N=27) 0.218 0.567 0.616 pVal 0.274 0.003 0.001 Source: Goldman Osteometric Dataset, author’s computations.

Table 3 reports Pearson’s correlation coefficients. Stature is now a (just barely) significant correlate of latitude in the full sample. But not after we restrict the sample to the Old World. Also, consistent with our interpretation, BMI is a more significant correlate of latitude once we restrict the sample to the Old World. Meanwhile, Ruff’s ratio remains unchanged to the second decimal. Note that Pearson’s correlation coefficient is less reliable than Spearman’s in the presence of heteroskedasticity, which probably explains why it shows Ruff’s ratio to be only a weakly stronger correlate of latitude than BMI.

 Table 3. Pearson’s correlation coefficients for latitude. Stature BMI Ruff’s ratio Full sample (N=45) 0.298 0.516 0.548 pVal 0.047 0.000 0.000 Old World (N=27) 0.331 0.544 0.554 pVal 0.092 0.004 0.003 Source: Goldman Osteometric Dataset, author’s computations.

Table 4 displays our OLS gradient estimates and robust standard errors. Stature again just barely clears the threshold for significance in the full sample, while falling into insignificance in the restricted sample. Meanwhile, latitude is strongly priced into BMI and Ruff’s ratio. And the gradient estimate exhibit absolute stability when we restrict the sample. Latitude explains roughly 10 percent of the variation in stature, 25-30 percent of the variation in BMI, and 30 percent of the variation in Ruff’s ratio. Note also the extraordinary stability of the parameter estimates for our last two variables when we restrict the sample to the Old World.

 Table 4. Gradients of latitude. Stature (cm) BMI Ruff’s ratio Full sample (N=45) Gradient 0.083 0.047 0.020 Robust std error 0.049 0.014 0.006 pVal 0.048 0.001 0.001 R^2 0.089 0.267 0.300 Old World sample (N=27) Gradient 0.094 0.047 0.021 Robust std error 0.058 0.015 0.007 pVal 0.057 0.002 0.002 R^2 0.109 0.296 0.307 Source: Goldman Osteometric Dataset, author’s computations.

Again it is very hard to reject our hypothesis. The evidence marshaled in the present and previous dispatch are consistent with the hypothesis that climatic information is encoded in body shape rather than stature. We have seen thus far that both BMI and Ruff’s ratio are strong correlates of latitude. The Spearman coefficients in Table 1 suggest that the latter is a stronger correlate of latitude than BMI in accordance with Ruff’s surface law. But the evidence from least squares is more equivocal in this regard. In order to test this more carefully, we run a horse race between our three variables. Specifically, we run “reverse regressions” with latitude as the dependent variable and see “who kills whom” among our three regressors. Table 5 displays our estimates.

 Table 5. Reverse multivariate OLS regressions. Dependent variable is latitude. All regressors have been standardized to have mean 0 and variance 1. N=44 Estimate Robust Std Error pVal Model 1 Const 33.72 2.22 0.00 Stature 3.05 2.44 0.11 BMI 8.02 2.43 0.00 Model 2 Const 33.67 2.21 0.00 Stature 0.58 2.84 0.42 Ruff’s ratio 9.44 2.59 0.00 Model 3 Const 33.65 2.18 0.00 BMI 4.43 3.44 0.10 Ruff’s ratio 6.53 3.21 0.02 Model 4 Const 33.67 2.16 0.00 Stature 0.89 2.62 0.37 BMI 4.51 3.42 0.10 Ruff’s ratio 5.93 3.26 0.04 Source: Goldman Osteometric Dataset, author’s computations.

A very consistent picture emerges from the multivariate regression estimates displayed in Table 5. First, BMI kills stature (Model 1). Second, Ruff’s ratio kills stature even more thoroughly (Model 2). Compare the coefficient and standard error of stature in Model 1 and 2. The gradient estimate for stature falls from three to one-half and the pValue jumps from 0.11 to 0.42. Third, and most importantly, Ruff’s ratio kills BMI (Model 3). This is also true of the “kitchen sink” regression (Model 4). In sum, neither stature nor BMI is a statistically significant correlate of latitude once we control for Ruff’s ratio. This is very strong evidence indeed in favor of Ruff’s surface law.

The following two graphs display the variation in Ruff’s ratio across Holocene populations labeled by their modern location.

Figure 2. Boxplot for Ruff’s ratio. Source: Goldman Osteometric Dataset, author’s computations.

Figure 2. Latitude and Ruff’s ratio. New World observations in red; Old World in blue. Source: Goldman Osteometric Dataset, author’s computations.

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# Do stature and BMI contain information on living standards or adaptation to climate?

We have previously argued that the cross-section of stature and BMI contain information on the polarization of living standards between nations. We have also documented the dramatic revolution in Western living standards during 1860-1960 as revealed by secular gains in stature. The picture that emerges from these analyses suggests that living standards were considerably less polarized as late as the late-nineteenth century; that the hockey stick was not hit in earnest until after 1920; and that the morphological transition to modernity was largely complete by 1960-1980. The interpretation of time-variation is much less problematic than the interpretation of cross-sectional variation. There is a virtual consensus among physiologists and scholars of public health that gains in stature and BMI track gains in net nutritional status (nutrition and disease burdens considered jointly). No such consensus exists on the interpretation of population differences in stature and BMI.

Do population differences in stature and BMI reflect morphological adaptation to the macroclimate or do they reflect differences in living standards?

Two tribes of scholars are deeply interested in this question. On the one hand you have economic historians who are interested in the economic information contained in anthropometric data. On the other you have paleoanthropologists (more generally, physical anthropologists) who are interested in the information on population history and climatic adaptation contained in anthropometric data. For the latter tribe, anthropometry is the bread and butter of their discipline. Each side insists that their component dominates. The first insist that biological information gets averaged away when we take population means; the latter argue that, to the contrary, and just like all other species, humans have faithfully displayed morphological climatic adaptations throughout the entire human career. Who is right?

Paleoanthropologists argue that bigger bodies in colder climates (from the Neanderthals on) reflect Bergmann’s Rule: within a morphologically variable species spanning a wide geographic range, larger-bodied variants will be found in the colder parts of the range, and smaller-bodied variants in warmer parts. In a seminal paper, Ruff (1994) argued that Bergmann’s Rule is a particular instance of a more general, thermoregulatory “surface law”:

Simply stated, because surface area increases as the square of linear dimensions, and volume increases as the cube, the ratio of surface area to volume decreases as an object (or body) becomes larger. In terms of thermoregulation, because heat production is proportional to body mass (or volume, since body density varies only relatively slightly) and heat dissipation is proportional to exposed body surface area, a larger body will have a higher ratio of heat production to heat dissipation than a smaller body (of the same shape), which is the physiological basis for the clinal variation incorporated in Bergmann’s “Rule.”

In order to get to the bottom of this conundrum I taught myself some physical anthropology. (I also had some fun with debunking racial craniology on the way.) In the past few days I got my hands on substantial anthropometric datasets. And now I think I have solved the problem.

I will argue in a forthcoming paper bridging the two literatures that it can be demonstrated econometrically—using very large datasets for the entire Holocene as well as data on a large number of living indigenous groups—that both groups are right. More importantly, and happily for us, economic and climatic information are largely contained in different measures of the human body. Specifically, in the Holocene, stature does not contain significant climatic information; it contains economic information. Conversely, BMI contains information on climatic adaptation and population history which confounds the economic interpretation of the cross-sectional variation in this variable. The bottomline is that cross-sectional variation in BMI should not be used by economists as a measure of relative living standards and differences in stature should not be used by physical anthropologists to investigate climatic adaptation. It is shape not size of human bodies that reflects climatic adaptation.

In what follows I will document the main results. We begin with some established relationships in anthropometry. The basics of long bone estimation are straightforward. Stature (ST) is a linear function of femur length (FL). (Femur is the thigh bone.) We use the estimates from Ruff et al. (2005),

Male:     ST=2.72*FL + 42.85,
Female: ST=2.69*FL + 43.56,

where both stature and femur length are measured in centimeters. Similarly, body mass (BM) is bilinear in stature and pelvic bone width (BW). Also from Ruff et al. (2005),

Male:     BM=0.422*ST + 3.126*BW – 92.9,
Female: BM=0.504*ST + 1.804*BW – 72.6,

where body mass is measured in kilograms, and both stature and pelvic bone width are measured in centimeters. From body mass and stature, we immediately obtain BMI via the well-known formula, BMI equals weight in kg divided by squared height in meters.

We estimate these three variables from the Goldman Osteometrics Dataset that contains detailed information on 1,538 skeletons spanning the globe across the Holocene. Our first result is that BMI turns out to be a linear function of pelvic bone width. This is not an artifact of circularity in the formulas (BMI as computed above is a highly nonlinear function of femur length and pelvic bone width). Rather, it is an empirical regularity with important consequences. Namely, even if there exists an optimal BMI (which is highly unlikely) or an optimal range (which is much more reasonable), it is not independent of your body type. Rather, Figure 1 suggests that it is a function of your pelvic bone width. This information should be included in estimating “optimal BMI” and optimal range for BMI from mortality and morbidity data.

Figure 1. Pelvic bone width and BMI. Source: Goldman Osteometrics Dataset; author’s computations.

Our second result (in no sense original) is that BMI is independent of stature. This fact will be very important for our main results. It holds both at the individual level and for grouped means. (The data can be grouped by geographic location; a fact that will come in handy in what follows.) See Table 1. Estimates in bold are significant, and those in italics are insignificant, at the 5 percent level.

 Table 1a. Spearman’s rank correlations for individuals. Males N=924 Weight BMI Stature 0.756 0.063 Weight 0.664 pValues 0.000 0.054 0.000 Females N=516 Weight BMI Stature 0.810 0.008 Weight 0.554 pValues 0.000 0.851 0.000 Table 1b. Spearman’s rank correlations for group means. Males N=41 Weight BMI Stature 0.736 0.229 Weight 0.769 pValues 0.000 0.149 0.000 Females N=39 Weight BMI Stature 0.814 0.184 Weight 0.687 pValues 0.000 0.263 0.000 Source: Goldman Osteometric Dataset, author’s computations.

Since BMI and stature are statistically orthogonal, they could plausibly contain different information. Is that the case? More precisely, is it the case that pelvic bone width (and hence BMI) reflects climatic adaptation while femur length (and hence statute) reflects net nutritional status as promised? In order to test this hypothesis we interrogate both the Goldman Osteometric Dataset and the data in Ruff (1994). We can reject our hypothesis if there is a significant gradient of latitude or thermal burdens in the cross-section of femur lengths/stature, or an insignificant gradient for pelvic bone width/BMI. On the other hand, if latitude is priced in the cross-section of pelvic bone width/BMI but not femur length/stature for ancient and indigenous skeletons, that would be consistent with our hypothesis. For then it would imply that the polarization of human stature in modern data is a consequence of the differential health transition across the globe. We begin with the Ruff (1994) data.

 Table 2. Gradients of latitude. Dependent variable: Stature Pelvic bone width Weight BMI Estimated gradient 0.092 0.065 0.255 0.076 Robust standard error 0.084 0.009 0.043 0.017 t-Statistic 1.097 7.429 6.009 4.553 p-Value 0.139 0.000 0.000 0.000 Intercept Yes Yes Yes Yes N 56 56 56 56 Source: Ruff (1994), author’s computations.

Table 1 reports our estimates. We compute heteroskedasticity-robust standard errors as is appropriate for comparing geographically-grouped population means. We see that latitude is not priced into stature but is priced into pelvic bone width, weight and BMI. We cannot therefore reject our hypothesis.

Betti et al. (2009) furnish data for minimum and maximum temperatures (as well as distance from Africa along postulated Out-of-Africa migration routes which we include here to avoid the bias resulting from omission of null results) that we matched with the Goldman data by hand. Table 3 presents our estimates for sex-combined population means.

 Table 3. Sex-combined Spearman’s rank correlations for group means. N=45 Femur length Pelvic bone width Min Temp -0.089 -0.430 p-Value 0.584 0.006 N=45 Femur length Pelvic bone width Max Temp -0.046 -0.261 p-Value 0.777 0.104 N=45 Femur length Pelvic bone width Distance from Africa 0.032 0.065 p-Value 0.845 0.689 Source: Goldman Osteometric Dataset, Betti et al. (2009).

We see that, consistent with our hypothesis, minimum temperature is significantly correlated with pelvic bone width but not stature. We find no statistically significant correlation between maximum temperature or distance from Africa and either of our anthropometric measures. This is consistent with our work with modern data where we found that winter lows were especially significant. Table 4 repeats this analysis with stature and BMI separately for the sexes.

 Table 4a. Spearman’s rank correlations for group means (female). N=39 Stature Weight BMI Min Temp -0.090 -0.301 -0.399 p-Value 0.606 0.079 0.018 N=39 Stature Weight BMI Max Temp 0.044 -0.160 -0.424 p-Value 0.802 0.358 0.011 N=39 Stature Weight BMI DFA -0.001 0.054 0.129 p-Value 0.996 0.758 0.461 Table 4b. Spearman’s rank correlations for group means (male). N=42 Stature Weight BMI Min Temp -0.124 -0.243 -0.337 p-Value 0.452 0.142 0.039 N=42 Stature Weight BMI Max Temp -0.079 -0.165 -0.178 p-Value 0.633 0.324 0.285 N=42 Stature Weight BMI DFA -0.069 0.054 0.183 p-Value 0.677 0.748 0.271 Source: Goldman Osteometric Dataset, Betti et al. (2009).

Table 4 is consistent with Table 3 with a single exception. For females, max temperature is also priced into BMI. This is entirely congruent with our hypothesis.

The evidence marshaled above is uniformly consistent with our hypothesis that cross-sectional variation in stature contains information on net nutritional status or living standards whereas the cross-sectional variation in BMI is potentially confounded by climatic adaptation. Specifically, BMI is a function of pelvic bone width (and not just under or over-nutrition) which correlates with the macroclimate throughout the Holocene in accordance with Ruff’s “surface law.” This is not true of stature, which was not polarized along latitude until the differential exit from the Malthusian world during 1860-1960. It is shape not size of human bodies that reflects climatic adaptation.

Figure 3. Gradients of latitude and cattle per capita in the cross-section of human stature, 1880-1960.

Figure 4. Western stature, 1710-1990.

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# An Illustrated Guide to Racial Anthropometry: Or, the Narcissism of Small Differences

Despite the long-standing scholarly consensus that race is a social construct — more precisely, that it is a real abstraction; a fiction with all-too-real social consequences — essentialism refuses to relinquish its hold on the popular imaginary. Everyday folk suspect that the scholarly consensus is an artifact of rampant political correctness in academia. The vast majority of Westerners, while somewhat unsympathetic to notions of an unchanging racial hierarchy rooted in biology, imagine that there are indeed biological differences between Whites, Blacks, and Asians; differences that are relevant to understanding the social order.

Samuel G. Morton (1799-1851) pioneered the techniques of craniology in 1839, thereby founding the American school of ethnology. Morton and his followers were the advance vanguard of scientific racialism. He was the first to make inferences about racial hierarchy on the basis of measured cranial capacity. Big brained Caucasians, he argued, were demonstrably superior to small-brained Negros. Whatever the validity of the brain size=intelligence equation, is there any empirical support for the differences in skull-size championed by Morton and his intellectual descendents all the way down to Rushton today?

Physical differences between say national populations are in fact not a function of biology. Rather, they reflect nutritional status and disease burdens. Indeed, anthropometric measures like (the population means of) stature and BMI capture everyday living standards much more reliably than per capita income. We’ve previously interrogated the time-variation and international cross-sectional variation in these indicators to get an handle on global polarization that is largely independent of, and complementary to, national economic statistics. Following in the footsteps of Fogel, Waaler, and Kim, our investigation yielded a measure of living standards that we called effective stature that combined height and BMI. But if we are wrong and physical differences are in fact a function of biology, then that confounds our interpretation of at least the cross-sectional variation in effective stature. So independent of testing the claims of high racialists, it is very important for us to get to the bottom of this. (We’ll revisit that issue in a future post.)

The top panel in Table 1 displays the racial averages of a number of anthropometric measures for men in the US military. BMI is the ratio of weight in kilograms to squared height in meters; cranial capacity (CC) is the volume of the interior of the braincase measured in cubic centimeters estimated via the Lee-Pearson-Rushton formula for men from head length (L) and head breadth (B) [CC=6.752(L-11)+11.421(B-11)-1434.06]; brain mass is then computed via the Ruff et al. (1997) formula [brain mass=1.147CC^0.976]; encephalization quotient (EQ) is calculated from Martin’s relationship in mammals [EQ=brain mass/(11.22 body weight^0.76)]; and the cephalic index equals 100 times the ratio of head breadth to head length. The last has a particularly sordid history in the annals of racial craniology. And as opposed to brain size, which is only problematic, the cephalic index as a measure of intelligence potential has been totally debunked. We only include it here for the sake of completeness.

 Table 1. Anthropometric measures of American men by “race.” Means “Race” Sample size Stature (cm) Weight (kg) BMI Cranial capacity (cc) Head Circumference (cm) Brain Mass (g) EQ CI White 2,817 176.4 85.8 27.6 1,474.3 57.4 1,419.3 7.6 77.1 Black 642 176.2 87.5 28.2 1,489.3 57.8 1,433.4 7.5 76.9 Hispanic 440 171.8 83.7 28.3 1,465.3 57.2 1,410.9 7.7 78.7 Asian 117 169.9 74.8 25.9 1,470.6 56.5 1,415.9 8.4 82.4 Other 66 172.9 84.0 28.0 1,481.4 57.1 1,426.0 7.7 79.3 All 4,082 175.6 85.5 27.7 1,475.7 57.4 1,420.7 7.6 77.4 Z scores “Race” Stature Weight BMI Cranial capacity Head Circumference Brain Mass EQ CI White 0.11 0.02 -0.03 -0.02 0.00 -0.02 -0.04 -0.09 Black 0.08 0.14 0.12 0.16 0.23 0.16 -0.07 -0.14 Hispanic -0.56 -0.13 0.15 -0.12 -0.17 -0.12 0.07 0.36 Asian -0.83 -0.75 -0.45 -0.06 -0.59 -0.06 0.91 1.39 Other -0.40 -0.11 0.08 0.07 -0.19 0.07 0.18 0.53 Source: ANSUR II (2012), author’s computations.

The second panel in Table 1 displays the z scores that tell us how much the racial means differ from the overall population means. Z scores within (-1.96,+1.96) indicate insignificance at the standard 5 percent confidence level. All the entries in the second panel lie within this interval, so right off the bat we can tell that anatomical differences between the “races” are statistically insignificant. As for brain size, whatever trivial differences that exist are in favor of African-Americans.

We have seen the means, but what do the distributions look like? In order to visualize these distributions, we assume that all measures are normally distributed. We collect and display the graphs for the distribution of all these variables in the following slideshow.

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From Table 1 and the distributional graphs we can see that Asians are a bit shorter and skinnier than Whites and Blacks. But despite their smaller bodies, their brain size is comparable to Whites and Blacks (since their encephalization quotient is a bit higher). And their heads are a bit more globular (their cephalic index is also a bit higher). But again these are minor differences. Interracial variation is swamped by intraracial variation.

The most extreme differences we can detect are in the encephalization quotient and the cephalic index between Asians and non-Asians. The next figure displays the two indices for Asians and Blacks. (There are too many observations for Whites, which clutters the scatter plot.) We see that even here, the overlap is substantial. Moreover, there is no evidence whatsoever that either measure has anything to do with intelligence or anything else of relevance to the social order.

The same is true of head circumference and cranial capacity. See next figure. Yes, Asian heads a bit more globular. But the overlap is still substantial. Note also the minor differences in the intercepts (12cc) and slopes (0.6cc per cm).

From the distributions it is clear that the most extreme phenotypic differences (although still statistically insignificant) are between Asians and non-Asians. This doesn’t mean that Asians can be identified as a biological race (in the sense of subspecies). There is substantial variation in human populations. But race is a poor way to understand it. Human populations vary continuously. What we have is geographic clines (which look like differences between discrete races in settler countries where geographically distant populations live cheek by jowl). However, this is not so much due to climatic adaptations (as skin reflectance would suggest) as due to population bottlenecks and founder effects (along with genetic drift) in human dispersal from Africa. Basically our race left our African homeland in small bands that had by definition much less genomic variation than the population left behind. This is why Africa has more genomic variation than the rest of the world combined. And why the best handle we have on phenotypic variation is distance from Africa. But more on that another time.

The bottomline is that race gives us no handle whatsoever on variation in human bodies, and above all, claims of racial differences in brain size have no basis in empirical reality.

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