Race, genes, & intelligence, part 0

This is divided into three sections:
A. Clarifications
B. The plausibility of the Hereditarian Hypothesis
C. The likelihood of socially important genetic differences between populations
D. The reasonability of positing the Hereditarian Hypothesis

A. Clarifications

Motivation

My interest is not in convincing anyone that this or that subpopulation phenotypic difference has a partial genetic etiology, but rather in forcing an empirical test of the genetic hypothesis and resolving this issue once and for all. To do that, it is necessary to demonstrate (to the public and misinformed members of academia) that this is a yet unresolved issue of pressing social importance. Levin (1997), Jensen (2000), Gottfredson (2005), Sesardic (2005), and Hunt and Carlson (2007) have laid out the case for bringing closure to the issue; those interested are left to bring the matter to public consciousness and to challenge those believers in the reigning paradigm to subject their prejudices to investigation. This issue, for the most part, can be resolved in a matter of months. With regards to the questions of evolved ancestral differences, Rowe (2005), Rushton and Jensen (2005), Murray (2005), Hunt and Carlson (2007), and Lee (2009) have already discussed the proper tests that would provide dispositive results: admixture studies. Such studies, which are now commonly done (see: Winkler et al., (2010), Admixture Mapping Comes of Age) to locate the origins of medical disparities would, if properly done, end this debate.

Clarification about the Concept of Race

On the matter of biology and race, there seems to be considerable confusion, no doubt artfully sowed. Given that, let me clarify: When it comes to the hereditarian hypothesis, we are not discussing the philosophy of biology — I discuss some aspects of that here — we are discussing the etiology of differences between socially identifiable ethnoracial groups. These groups represent different biological population structures. The assumption here is not that these ethnoraces are taxonomically identifiable groups or that there are clear boundaries between these groups, but rather that the said groups have different population structures. When the Human Genome Project states that “DNA studies do not indicate that separate classifiable subspecies (races) exist within modern human,” they are talking about biological taxonomic classifications and saying that there are no population specific genes that would warrant classifying various populations as subspecies. That is a separate issue. (See: 70-71). With regards to the current discussion, we are starting with socially identified ethnoracial groups which have different population structures and asking: “Why are there behavioral differences?”

McEvoy, et al., 2010. Whole-genome genetic diversity in a sample of Australians with deep Aboriginal ancestry

Trishkoff, 2010. The Genetic Structure and History of Africans and African Americans

Race concepts, nonetheless

Since the mantra of “race is a social construct (RISC)” is often intoned to ward of the specter of Jensenism, a little more clarification is, perhaps, needed. The “RISC” concept comes by way of Lewontin. Unlike many of our contemporaneous academic charlatans who peddle the “RISC” concept, Lewontin was kind enough to provide criteria for falsifiablity. According to Lewontin (1972), RISC means that racial classification are of “virtually no genetic or taxonomic [superfamily to subspecies] significance.” The scientific community has since rejected the notion that race, whether delineated by continental, sub-continental, or regional ancestry (lumpers and splitters), is of no such significance. [2, 17, 22, 28, 42, 44, 62, 69, 71]. (The root of “race” is “ancestry,” word games about “lactose intolerant races” notwithstanding). The RISC hypothesis has been falsified and Lewontin’s claim about race is now know as Lewontin’s fallacy.

None of the above has stopped many (every other social “scientist”) from asserting that RISC is, nonetheless, true. Often, a sleight of hand is pulled and, while the pretense that race is of “no genetic or taxonomic significance” is maintained, RISC is redefined to mean that racial classifications do not represent subspecies; some go so far as to baldly claim that “the social construction of race,” now redefined to mean the lack of consensus concerning the taxonomic status of race, contradicts the biogenetic concept of race (e.g. Smedley, 2005). (It’s worth noting that in many parts of the world,, the race concept has wide currency; see: Lieberman et al., 2004. The race concept in six regions: variation without consensus).

To emphasize again, with regards to the Hereditarian hypothesis and the question of mean differences between socially classified subpopultions, whether or not there are human subspecies and whether everyone fits neatly into some grouping is immaterial. What is presupposed is that different racial (read: regional ancestral) classifications describe, on average, sets of individuals with different (average) ancestries. See: A defense of the Race concept.

Clarification about the meaning of general intelligence

General intelligence (g) can operantly be defined as the property that both IQ and Reaction time tasks measure. A more technical definition is the “substantial covariation among diverse measures of cognitive ability as indexed by an unrotated first principalcomponent score, which typically accounts for about 40% of the total variance of diverse cognitive tests, or by a total score across diverse tests as is done in intelligence tests ” (Plomin and Spinath, 2004. Intelligence: Genetics, Genes, and Genomics).

The educational, social, and psychological, and neurophysiology correlates of g are well established; no other phenomena has been as well researched in psychology as that of general intelligence. For a good review of the empirical findings refer to Deary et al. 2010. The neuroscience of human intelligence differences. There is some debate as to whether g represents a causal entity or whether it is epiphenomenal. For a good discussion of this, refer to: Gottfredson, 2010. Intelligence and Social Inequality: Why the Biological Link?. This is an interesting question, but as differences in g are highly predictive of social outcomes, it does not matter if g represents a causal entity, if g represents a set of correlations tied to causal entities (e.g different neural functions), or if g magically recruits the environment to cause the predicted social outcomes. Differences in g matter.

Clarification about the Meaning of Average differences

When we talk about group differences, with respect to population genetics, we are talking about mean averages. Mean averages are an abstraction. They do not say anything about individual performance. (Whether or not probabilistic calculations based on these means is morally acceptable, is another issue.) As such, if you are naturally at the right tail end of some measure, you are at the right tail end.

Gottfredson, 2005. Social Consequences of Group Differences in Cognitive Ability

Clarification about the meaning of heritability

To say that a difference is heritable is to say that, given equal conditions, individuals or groups will differ as a result of genetically conditioned endogenous factors. With regards to the issue of heritability and malleability, we can quote Jensen (1973):

The proportion of variance indicated by [environmentality] , if small, does in fact mean that the source of environmental variance are skimpy under the conditions that prevailed in the population in which h^2 was estimated. It means the already existing variations in environmental conditions are not a potent source of phenotypic variance, so that making the best variations available to everyone will do little to reduce individual differences. This is not to say that yet undiscovered (or possibly already discovered but rarely used) environmental manipulation of forms of intervention .. cannot in principle markedly reduce individual differences in a trait which under ordinary conditions has very high heritability (Jensen, 1973).

Johnson, Penke, and Spinath have a nice introductory discussion on the concept of heritability. (Johnson, Penke, and Spinath, 2011. Heritability in the Era of Molecular Genetics: Some Thoughts for Understanding Genetic Influences on Behavioral). The only comment I would make is that they seem to error in this statement: “First, it is not 60% of the phenotype that is passed on to the next generation, or even 60% of genes related to deviation from the original population average in any way that has any meaning for the individual in that next generation.” They seem to suggest that heritability estimates only concern populations and are irrelevant to individuals. This is not the case, however, as individual genetic value is probabilistically related to population heritablility. For a good discussion of this, refer to Tal, 2009. From heritability to probability.

Race, genes, & intelligence, part 1

B. The Hereditarian Hypothesis and plausibility

The issue being discussed is called the hereditarian hypothesis (HH from now on).(8) The hypothesis is that some of the well established statistically average ethnoracial differences in cognition (9, 24, 33, 35, 50, 52, 75, 76, 99) have a partial genetic etiology and that these differences result in some of the pervasive average performance (and behavioral) differences that we find in our society (24, 52, 53, 75, 76). Refer g and group differences.

Specifically, according to the hypothesis as often formulated, some of the average performance differences between (and within) groups result from biological differences related to general intelligence which develop, to some extent, as a result of genotypic difference. Here, general intelligence (the g factor) refers to the latent trait that IQ and others cognitive tests measure and is, while closely related, conceptually distinct from the term “intelligence” as generally used; it is conceived as being a psychometric construct with neurophysiological functional and structural correlates.

Templer, Tomeo, Arikawa, Willians, 2002. Asian-Black differences in aptitude and difficulty of chosen academic discipline (75)

Roth, Huffcutt, Bobko, 2003. Ethnic Group Differences in Measures of Job Performance:
A New Meta-Analysis (76)

For the Hereditarian Hypothesis to be robust 5 prerequisites must be fulfilled:

1) The said populations must represent different average genetic populations and there must be plausible mechanisms by which the differences in intelligence could have developed (I-V). 2) Tested differences in psychometric intelligence, specifically in g, must represent measurable, functionally important differences within and between populations (VI-VII). 3) Intelligence must be heritable within populations and map neurophysiologically (VIII-X). Finally, 4) the differences must be relatively consistent across time and nation, given the parameters of population genetics (XI).

It’s a rather audacious hypothesis! With regards to the Black-White difference in the US, 1-3 (I-IX) are no longer in serious question. 4 (X) is generally supported. I will outline these below:

The reality of race

I. The HH presupposes that there are descendant populations, which there are. Humans split from their most recent primate cousin ~300,000 years ago; Eurasians split from west SubSaharans ~100,000 years ago and left Africa 40-80,000 year ago; West Eurasians split from East Eurasians 30-40,000 years ago (3, 21, 22, 64, 71).

Campbell and Tishkoff, 2009. The Evolution of Human Genetic Review and Phenotypic Variation in Africa

II. It presupposes that 40-100,000 years was sufficient time for evolving intrahuman differences and that socially significant differentiation did occur; 40-100,000 years was enough time and differentiation did occur (1, 2, 13 15, 20, 21, 22, 42, 62, 63, 66), some of which is socially significant. This is why race (or regional ancestry) is medically relevant (22, 28, 69). Human evolution has, in fact, rapidly accelerated over the last 40,000 years (48). And selection is still ongoing (61).

Coop, Pickrell, and Novembre, 2009. The Role of Geography in Human Adaptation

Voight, et al., 2006. A map of recent positive selection in the human genome

III. It presupposed that descendant populations could theoretically differ genetically in some cognitive dispositions on average (refer to Lewontin’s second fallacy), which there is every reason to believe they could (7, 13, 16, 14, 30, 45, 59, 60, 65, 66). (See Appendix 1: Major theories supporting the evolution of population specific difference in cognition). And which there is direct evidence that they, in fact, do in some manner (13, 20, 44). For example, there has been recent intense differential selection for genes that code for the NRG–ERBB4 pathway, a pathways which is involved in for neural development and which is associated with various psychiatric phenotypes (44). Additionally, as in the case of DRD4, there is direct evidence that population specific alleles affect social behavior and academic performance (64-68). As for alleles that are associated with intelligence, two SNPs of DTNBP1 (rs1018381 and rs2619522) have consistently been found to influence general cognitive ability (109); the frequencies of these alleles vary by regional ancestry.  Other genes that affect cognition and have alleles that vary by regional ancestry include PKU (110) and APOE (111). 

Genes under recent selection for nervous system, brain function and development; language skills and vocal learning
CDK5RAP2, CENPJ, GABRA4, PSEN1, SYT1, SLC6A4, SNTG1, GRM3, GRM1, GLRA2, OR4C13, OR2B6, RAPSN, ASPM, RNT1, SV2B, SKP1A, DAB1, APPBP2, APBA2, PCDH15, PHACTR1, ALG10, PREP, GPM6A, DGKI, ASPM, MCPH1, FOXP2 (see: 59-64)
Laland, Odling-Smee, Myles, 2010. How culture shaped the human genome: bringing genetics and the human sciences together (13)

Wang, et al., 2006. Global landscape of recent inferred Darwinian selection for Homo sapiens

There is also convergent evidence supporting the hypothesis that populations differ in cognitive genetics. Beal et al. (1984) found that cranial capacity varied across populations and that cranial volume was positively correlated with temperature (92). Bailey and Geary (2010) found that human brain evolution was driven by population density, variation in paleoclimate, and temperature variation (72); Odokuma et. al. (2010) found that average Nigerian cranial capacity was less than average European cranial capacity (73); Rushton and Ankney (2009) and Jensen (1998) report numerous studies which consistently found that cranial capacity correlates with general intelligence (74). McDaniel (2005) conducted a meta-analysis on all MRI studies of brain size and intelligence and found a .30 correlation (103). Connecting this convergent evidence back to the findings above, ASPM and MCPH1, two genes under recent positive selection, have been found to be associated with sex-specific increases in brain size (106). For an elaboration of this paradigm refer to Ash and Gordon’s Brain Size, Intelligence, and Paleoclimatic Variation (77) and Jensen’s Population Differences In Intelligence: Causal Hypotheses (84).

IV. It presupposes a social understanding of ‘race’ which refers to groups with different average population ancestry, which we have (11):

According to the Journal of Philosophy account, the logical core of the ordinary concept of race is the concept of a group of human beings:
H1. Who are distinguished from other human beings by visible physical features of the relevant kind
H2. Whose members are linked by common ancestry
and
H3. Who originate from a distinctive geographical location.
Hardimon, 2009. Wallis Simpson was Wrong

See also A semantic defense of the ordinary race concept for an elaboration of this.

V. It presupposes that ‘race,’ in fact, refers, in part, to descendant populations, which it does (17,28, 43).

Zakharia, et. al. 2009.Characterizing the admixed African ancestry of African Americans

Nelis, et al., 2009. Genetic Structure of Europeans: A View from the North–East

The meaningfulness of intelligence

VI. The HH presupposes that there are experimental means of measuring individual cognitive differences, which there are (4, 10, 11, 23), and that these measures are fairly well defined, which they are.
McGrew, 2009. CHC Theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research (15)

VII. It presupposed that differences in intelligence within and between populations are functionally important, which they are (7, 10, 19, 24, 33, 35, 46, 50, 52, 75, 76, 99) — though the scope of the importance is still in debate.

The heritability and biological basis of intelligence within populations

VIII. The HH also presupposes that intelligence has neurophysiological correlates which, in fact, it does (49, 102, 103).

Gläscher, et al. 2009. Lesion mapping of cognitive abilities linked to intelligence

IX. It presupposes that some of the various forms of cognitive processing (4, 5, 15) are heritable, and that differences between individuals within a population could be attributed, in part, to genes; which they can (12,20, 36).

Within population heritability of General Intelligence across age.

Bouchard, 2009. Genetic influence on human intelligence (Spearman’s g): How much?

From: Deary et al. 2010. The neuroscience of human intelligence differences

“Basic genetic influences on intelligence Investigation of the presence of genetic influences on general intelligence dates back to the nineteenth century, when Francis Galton published two papers concluding that mental abilities were transmitted by heredity from one generation to another15. Despite an intermittently hostile political reception, many studies since then — based principally on twin and adoption samples — have replicated this observation, and none has contradicted it16. estimates of how much of the total variance in general intelligence can be attributed to genetic influences range from 30 to 80%. General intelligence factors, in the form of latent traits from which measurement error has been removed, fall at the high end of this range. Broad domains of cognitive ability — such as verbal and perceptual– organizational abilities — generally show similar amounts of genetic influence, although the genetic influence on memory tends to be somewhat smaller.”

From: Hayworth, et al., 2009. Generalist Genes and High Cognitive Abilities

“We found strong support for the Generalist Genes hypothesis for high cognitive abilities in that genetic ‘group’ correlations were substantial between g, reading, math and language. The average genetic correlation of 0.58 is comparable to genetic correlations found in multivariate genetic studies in unselected samples, which are about 0.60 between g and learning abilities and about 0.70 between cognitive abilities. A direct comparison can be made with our previous analyses of the entire distribution using the same sample, measures and methods as in the present analysis of high cognitive ability (Haworth et al. 2009b). In that report, we found that the average genetic correlation was 0.68 for the entire distribution.”

From Bouchard, 2009. Genetic influence on human intelligence (Spearman’s g): How much?

“The history and conceptual background of the heritability statistic is briefly discussed. The construct of heritability is embedded in the method of structural equation modeling widely used in modern population genetics and in human behavior genetics. The application of structural equation modeling to behavioral phenotypes is shown to be a useful and informative analytic tool, as it implements the research strategy of ‘strong inference’. I describe the application of ‘strong inference’, via the use of structural equation models in the domain of human intelligence, and demonstrate its utility as a means of refuting well formulated scientific hypotheses. The construct of Spearman’s g is shown to be a strongly confirmed scientific hypothesis. Genetic and environmental influences are shown to influence g differentially over time, with shared environmental influences predominating early in life, but dissipating to near zero by adulthood. The hypothesis of substantively significant genetic influence on adult g is documented by multiple lines of evidence and numerous replications.”

X. While most contemporary hereditarians argue that the said performance differences result from genetically encoded differences for general intelligence, it’s worth noting that the psychometric and biological existence of g, and the g-loadedness of group difference, is not a necessary precondition for the hereditary hypothesis to be true. As the environmentalist James Flynn noted:

Gould’s book evades all of Jensen’s best arguments for a genetic component in the black-white IQ gap, by positing that they are dependent on the concept of g as a general intelligence factor. Therefore, Gould believes that if he can discredit g, no more need be said. This is manifestly false. Jensen’s arguments would bite no matter whether blacks suffered from a score deficit on one or 10 or 100 factors. I attribute no intent or motive to Gould, it is just that you cannot rebut arguments if you do not acknowledge and address them. (Flynn 1999a, 373)

The existence of g just makes the implications of the hereditarian hypothesis, if true, unavoidable. As Jensen (2000) pointed out, g “lies at the heart of the whole problematic nexus involving the nature of group differences, the merits of meritocratic selection in a diverse society, the legitimacy of using tests, their adverse impact on certain groups, and its redress by group preferences in college admissions and employment.” It also significantly strengthens the hypothesis. The existence of g loaded differences makes implausible a number of environmental arguments (including virtually all purely sociological ones, such as test bias, motivation, stereotype effect, etc.) and it allows for a number of hereditarian arguments. This is now a moot point, of course, because it is now known that g is not a psychometric artifact, that g has a robustly biological dimension, and that the said differences (at least in the US) are g-loaded (4, 5, 15, 36, 84).

Deary and Johnson, 2010. The neuroscience of human intelligence differences

As for g, it’s psychometrically structurally similar across populations, sexes, ages, and cultures it’s structurally similar (101). The basic structure of g can be found in other primates (102). Structurally and functionally, it correlates with neural organization, the volume of white and grey matter, total brain size, rate of cerebral glucose metabolism, and nerve conduction velocity, resting EEG and average evoked potentials, cerebral glucose metabolic rate, speed and efficiency of brain functioning inferred from reaction time (103).

The relative consistency of ethnoracial differences

XI. It presupposes that ethnoracial populations differences are relatively consistent, from the perspective of population genetics, across time and nation. There is support for this (54, 55 — but see also 56, 57).

Internationally, the basic pattern below can be seen (54, 55, 56, 79, 80, 81, 82). The intelligence of nations is intercorrelated with technological advancement, academic achievement, GDP, patent rates, numbers of scientists, rates of HIV, etc. National intelligence also predicts the productivity of individual immigrants coming from a particular nation (112). (Global IQ table as summarized by Steve Sailer).

Rindermann, 2007. Relevance of education and intelligence at the national level
for the economic welfare of people

The highest values for the smart fractions are found in East Asia …followed by Western and Eastern European and North American countries, by South European countries, Arab or Muslim and Latin American countries and finally by sub-Saharan countries.
Rindermann, Sailer, Thompson, 2009. The impact of smart fractions, cognitive ability of politicians and average competences of peoples on social development

When it comes to causality and national differences, the causal pathway runs from cognitive capacity out:

Rindermann and Thompson, 2011. Cognitive Capitalism : The Effect of Cognitive Ability on Wealth, as Mediated Through Scientific Achievement and Economic Freedom

Within highly mixed nations, with no explicit racial classes, the expected pattern predicted by Hereditarians can also be seen between individuals with different proportions of ancestral mixture. Villarreal (2010) found them in Mexico:

…Despite this ambiguity, I found evidence of profound social stratification by skin color in contemporary Mexico. Individuals with darker skin tone have significantly lower levels of educational attainment and occupational status, and they are more likely to live in poverty and less likely to be affluent, even after controlling for other individual characteristics. Differences in socioeconomic status between Mexicans of different skin tones are indeed large. Although measurement differences preclude precise cross-country comparisons, the differences between Mexicans in the three color categories used in this study, and particularly between individuals classified as white and non-white, are comparable to the differences between African Americans and non-Hispanic whites in the United States……
Villarreal, 2010. Stratification by Skin Color in Contemporary Mexico

The differences are ubiquitous. The Black (West Sub-Saharan African)-White (European) difference, for example, can be seen in Britain (87, 88), Canada, Brazil (89), the Caribbean, the Netherlands, and throughout Africa (56, 57). Of course, it matters what specific ancestral African and European (or East Asian or Mixed Amerindian) populations one is comparing. (See: Lynn, 2006. Race differences in intelligence)

Overall, the global pattern of differences is as predicted by the Hereditarian Hypothesis. As Wendy Johnson noted, when commenting on Richard Lynn’s The Global Bell Curve, “Lynn’s data are essentially correct and do reflect the general state of the world.”

Race, genes, & intelligence, part 2

B. The likelihood of the Hereditarian differences

These studies do not prove that blacks and whites would have exactly the same scores if they were raised in the same environment and treated the same way. But we find it hard to see how anyone reading these studies with an open mind could conclude that innate abilities played a large role in the black white gap
–The Black-white test score gap, Jencks and Phillips, 1998

Nisbett’s book may well be the most sophisticated exposition of the non-hereditarian thesis on the origin of IQ and achievement differences written to date. It is instructive, entertaining, and hopeful. Many readers will be encouraged by Nisbett’s thesis that IQ differences among individuals, and between classes and racial and ethnic groups, are fundamentally cultural and thus, perhaps, reducible by cultural and environmental means. There is an insatiable thirst for this message, and the book will no doubt be highly successful. Given the strengths of the hereditarian case, however, the author’s claims may well be illusory.”
–Nielson, 2010. Intelligence of Culture. Contemporary sociology.

The HH, as formulated by Jensen and Rushton, a) holds that >50% of the African American-European American ~1 SD psychometric gap (and the correlates thereof) is due to average genetic differences. A more generic form applies the same logic above to some differences found between other populations. As for the US B-W gap, the alternative explanations are that a) the gap has a smaller genetic component (<50%), b) the gap is the result of gene-environment interactions which can not be disentangled (technically 0-heritability), or c) that gap is totally the result of some unknown environmental factors and has no genetic basis whatsoever.

The support for the genetic hypothesis was outlined by Jensen in Population Differences In Intelligence: Causal Hypotheses(1998), Rushton in Races differences in g and the “Jensen Effect (2003),” and Jensen and Rushton in Race and IQ: A Theory-Based Review of the Research in Richard Nisbett’s Intelligence and How to Get It (2010). (Critiques: Nisbett, 2005. Hereditary, environment, and race differences in IQ: A Commentary on Rushton and Jensen; Flynn, 2010. The spectacles through which I see the race and IQ debate; Brody, 2003. Differences in intelligence: Critical Evaluation)

I used the following scale with (-) to designate support for environmentalism and (+) to designate support for hereditarianism:

Decisive, 5 (strong), 4 (intermediate strong), 3 (intermediate weak), 2 (weak), 1 (very weak), 0 (equivocal)

In sum, after looking at 20 lines of evidence, I conclude that the hereditarian hypothesis (+17) is more supported than the environmental hypothesis (-10).

(1) Inductive Probability and Parsimony. Inductive probability (Jensen, 1973; Jensen 1998; Jensen and Rushton 2005) : Given that over 30,000 polymophics genes, almost 1/3rd of the total, encode for neural function, that there was differential selection pressure (due to culture, population density, and environment), and that racial populations genetically differ in a number of physical ways, it’s improbable that over 30,000 to 100,000 years (population depending) no non-trivial cognitive differences developed between racial populations — and it is, at this point, certain that some non-trivial differences did, in fact, develop; given that non-trivial genetically coded cognitive differences developed between racial populations, it’s improbable that all currently observed differences (between populations) in general intelligence, a highly polygenic trait, are wholly non-genetic. Parsimony: Given that there are both within racial population and between racial population differences in general intelligence and given that within racial population differences are highly heritable, that is, due to differences in gene frequencies within a population, the most parsimonious explanation for differences between racial populations is differences in average gene frequencies between populations, that is, that some of the differences are heritable.

This conclusion is supported by the fact that there is a whole pattern of differences. (As to this logic, we can quote the early Franz Boas: “Differences of structure must be accompanied by differences of function, physiological as well as psychological; and, as we found clear evidence of differences in structure between the races, so we must anticipate that the differences in mental characteristics will be found. Boas, 1911. The Mind of Primitive Man).

We can take this argument one step further and turn Lewontin’s etc. famous argument against the environmentalists:

Cavalli-Sforza and Lewontin claim, respectively, that the total genetic variance between continental races (CR) and continental races + populations (CR+P) is insufficient to allow for socially significant differences in general intelligence. To determine how much between variance in genotypic IQ the total between genetic variance can allow for, we have to make an assumption about the distribution of IQ genes within the total variance. For now, let’s assume that IQ genes are randomly distributed throughout the total genetic variances. Under this assumption, how much between genotypic IQ variance would we predict? Using Cavalli-Sforza’s estimates (in Barbujani et al., 1997), the total between genetic CR and CR+P variance is 10.8% and 15.5% respectively.

Now there’s a caveat: Lewontin’s second fallacy. When we’re talk about genotypic IQ variance between CR and CR + P, we’re talking about variance between populations of individuals. Likewise, when we’re inquiring about the amount of genotypic IQ variance that the total between genetic variance would predict (given our assumption), the relevant total between genetic variance is the total between individual, between CR and CR + P genetic variance. Since we are diploid organisms, Cavalli-Sforza’s estimates refer to a) the between CR and CR + P genetic variance, b) the between individual within CR and CR + P genetic variance, and c) the within individual genetic variance. As the later component is not relevant to us, we have to extract it. Roughly, the within population variance should spit equally between inter-individual variance (CR= 44.6%; CR + P = 42.25%) and intra-individual variance (CR= 44.6%; CR + P = 42.25%). Adjusting accordingly, the between individual, between CR and CR + P total variance is 24% and 36%.

How much between CR and CR + P genotypic IQ variance (in Sds) would this predict? Assuming within population SDs of 15 (variance = 225), the predicted between population SDs would be roughly CR= 1.1 SD and CR+P =1.5 SD [.24/.36 = between variance for IQ/(225 + between variance for IQ); solve for between group variance = 68/127. Assuming equally numerous populations, (Sqrt (between group variance)) = [(Mean population A – joint mean)^2 + (Mean population B – joint mean)]^2/N =2. Solve for Mean A, B difference =16.5/22.5 IQ points; transform to SD: 16.5/15 = 1.1 SD, 22./15 = 1.5 SD.]

This statistical prediction coheres with the empirical finding of Jensen (1980): the between race IQ variance in the sample was 14% and the within race variance was 86%; the between race difference was 12 points.

Of course, again, this follows from the assumption that IQ genes are randomly distributed throughout the total genetic variances. As it is, we don’t know how they are distributed. That said, the following point need to be made: There is evidence that there has been recent positive selection for neurologically based phenotypes. See: Pickrell, Coop, Novembre, et al., (2009) “Signals of recent positive selection in a worldwide sample of human populations” and Wu and Zhang (2011) “Different levels of population differentiation among human genes.”

Overall, Lewontin’s estimate, interpreted naively as environmentalists are wont to, supports the hereditarian position.

[At most, this only establishes the prior plausibility of the global hereditarian hypothesis. 0. Equivocal]

(2) Global consistency. There is no parsimonious environmental account for the global consistency of differences in general intelligence and its correlates across descendant populations. The environmental explanations used to account for this global difference are an ad hoc patchwork of conflicting ideas. For example, noting that there are no explicit racial categories in Mexico and that the “differences between Mexicans in the three color categories used in this study.. are comparable to the differences between African Americans and non-Hispanic whites in the United States,” Villarreal (2010) posits an ad hoc model of “color privileged”; yet, in the US, racial differences are explained by explicit racial categories, or racial castes. Alternatively, differences in the US are explained in terms of a “legacy of racism,” (a hypothesis not supported by evidence) while differences (87, 88) in Britain are explained by contemporaneous institutional racism (a hypothesis not supported by evidence), this, even though many first generation self-selected African immigrants (the right side of the bell curve) in Britain have superior performance (86). None of these explanations, of course, account for why members of Black African dominant countries (in the Caribbean and Saharan Africa) have low IQs or why N.E Asians in Western countries overachieve.

Refer to National G differences

[Hereditarians have to strain themselves to explain the high performance of many globally southern immigrants [e.g., African immigrants in the UK] just as much as the environmentalists have to strain themselves to explain the broad pattern of differences (see: # 17). For example, in the UK, based on a recent analysis of age 11 CAT scores, there is only a 0.5 SD IQ difference.

(Source: GL assessment (2010). Cognitive Abilities Test (CAT) and GCSE grades: 2009/10. Table 4.)

A similar magnitude of difference can be found on UK SATs, Situational Judgement Tests, the UKCAT, Military tests, and the LNAT. Refer here. 0. Equivocal]

(3) Lack of plausible environmental explanations.
a) The validity of IQ and particularly GQ (general intelligence quotient), that is, the fact that g predicts numerous nonpsychological, noneducational, and nonsociological outcomes makes implausible test bias explanations (e.g. cultural bias, lack of test practice, test unfamiliarity, and stereotype threat).

b) The relation between g and neurophysiological g correlates such as brain neural conduction velocity, latency and amplitude of evoked electrical brain potentials, brain size, white and grey matter volume, cortical volume, reaction times, etc. rules out virtually all purely sociological explanations, such as racial difference being due to motivation, low self esteem, social caste perception, and contemporaneous cultural (i.e. ones that don’t influence individual development). The difference is robustly biological.

Given a) and b), there are only three plausible possibilities:
#1 Genetic differences, both within and between populations, express themselves during the developmental process; this results in (substantially biological) differences in intelligence both between and within populations (i.e. the hereditarian hypothesis).
#2 Genetic differences interact with environmental differences, guiding the developmental process which results in (substantially biological) differences in intelligence both between and within populations (g-e hereditarian or g-e environmental hypothesis).
#3 While #1 is true within populations, between population environmental differences manifest themselves during the developmental process; this results in (substantially biological) differences in intelligence between populations (i.e. the o-genetic environmental hypothesis).

# 2 and #3 are implausible because….

The within group heritability of general intelligence (in the United States) is high; this places constraints on within-between group variable environmental explanations; given that between group X-factor explanations have been empirically ruled out, a 0-genetic explanation is highly improbable.

For an elaboration of this refer to:
The many causes hypothesis
The gene-environment hypothesis

Here’s a path diagram of the argument:

[I fail to see how this does not provide some evidence against the US environmental hypothesis. By my estimate this entails at least a .2 SD geneotypic g gap + 2. Weak support for the US hereditarian hypothesis.]

(4) Spearman’s hypothesis and the Jensen Effect (83). The within group heritability of g is high; the between group difference in the US is g-loaded (33, 52, 99). And the between group difference correlates with heritability. Given that a 0-genetic explanation would predict no correlation between heritability and group differences but would, instead, predict a correlation between environmentality and group differences, a 0-genetic explanation is implausible. Moreover, the g-loadeness of the group differences constrains the possible environmental explanations. For an elaboration of this refer Spearman’s hypothesis and the Jensen Effect

[Independently, the g-loadedness provides no evidence for the US hereditarian hypothesis; IQ (g) can be substantially environmental; from this it follows that IQ (g) differences do not imply a genetic etiology. The g-loadedness of the gap does, however, limit environmental explanations; see above. As for the correlation between the B-W gap and heritability, the most parsimonious explanation is a partial genetic one.]

+1. Weak support for the hereditarian hypothesis]

(5) Dysgenic reproduction. Even assuming an initial identicalness between populations in the US, given the high heritability of intelligence, differential reproductive patters would have produced average genetic based differences (84, 97, 98, 100). Vining (1982) found that Blacks had more dysgenic reproductive patters (defined as low-IQ members producing more children and high-IQ members producing less children) throughout the 20th century; Meisenberg (2010) found that the patter continues to hold. (See: Jensen, 1998. Genetic Implications of IQ and Fertility for Black and White Women.)

Meisenberg, 2010. The reproduction of intelligence

[Statistically, this is an unavoidably conclusion. Given a non-zero h^2 and differential breeding (and immigration) rates, it will follow that genetic based subpopultion differences will emerge. This, though, is somewhat tangential to the global hereditarian hypothesis. + 1. Very weak support for the hereditarian hypothesis].

(6) Regression towards the mean studies. Regression towards the mean has been found to occur for all degrees of kinship; siblings correlate at 50%. Black and white children regress towards their respective population means (whether upwards or downwards). The hereditarian hypothesis predicts this and the environmental hypothesis predicts the opposite: black or white children of low IQ parents would not regress up, and black or white children of high IQ parents would not regress down. (84)

[I re-summarized this: Argument by way of regression towards the mean. In my estimate, this provides intermediate support for the US hereditarian hypothesis, given the fact that the findings have been replicated a number of times and that environmentalists have not been able to give an alternative explanation. + 3. Intermediate support for the hereditarian hypothesis]

(7) Structural equation modeling Two studies have been conducted to determine the heritability of the black-white difference using structural equation modeling. The results of these where discussed by Jensen (1998) — here and here. The studies found between group heritabilities ranging from .36 to .74. As Jensen (1998) and Rowe (2005) have discussed though, these studies are open to alternative interpretations.

[The structural equation modeling findings do seem to support a genetic hyposthesis, but, as noted, the findings are open to alternative interpretations. +1. Very weak support for the hereditarian hypothesis<]

(8) Cranial capacity, Brain size, and correlates with IQ. a) Across and within species, organisms with larger brains relative to body size are more intelligent (103); b) there has been intense selection pressure for cranial capacity and brain size during human evolution; (c) this pressure was not uniform (refer to 72, 73, 74, 77, 92); d) there are differences is cranial capacity and brain size between ethnic and racial populations, controlling for sex — (the Human species is dimorphic, so within sex differences do not translate to between sex differences; d) there are between race differences in gene frequencies in genes know to be associated with brain size (106); e) Cranial capacity correlates with IQ, particularly with g; f) based on twin studies, one can infer that the relation between genes and general intelligence is partially mediated by brain size (with a .30 component); f) with regards to cranial size and intelligence, N.E Asian, African, and European Americans fall on the same regression lines. When they are matched for IQ, there is virtually no difference in size. For an elaboration of this, refer to Brain size, and correlates with IQ.

[This is based on the following syllogism:
1. Within subpopulations, brain and cranial capacity correlates with IQ.
2. Within subpopulation differences in brain and cranial capacity have a partial genetic basis.
3. Between subpopulations there are differences in brain and cranial capacity and these differences correlate with differences in IQ.
4. There is anthropological evidence that population differences in cranial capacity resulted from natural selection over the last 50 thousand years.

Given this, it’s reasonable to conclude that the between population differences in cranial capacity have a genetic basis and that this contributes genetically to differences in IQ.

1 and 2 are no longer disputed by those informed with the research. The evidence for 3 and 4 is now overwhelming. The major problem with this argument is that the IQ variance explained by differences in cranial capacity would be small (Between .2 to .4 — for the correlation between IQ and cranial capacity — times .6 SD to 1 SD — for the standardized differences in cranial capacity; in standard deviations, the IQ differences explained would be only between 0.12 and .4)

Weak evidence for a genetic hypothesis.

(9) US Intra African-American Skin color-IQ correlations

Nisbett (1998); (Nisbett (2005). Summary: The HH predicts that Caucasian admixture will positively correlate with IQ differences within the African-American population. Various studies have set out to test the hypothesis by using skin color as a measure of Ancestry. The found correlations between skin color and IQ within the African-American population is only .15%. This indicates that the HH is false.

It’s rather odd that environmentalists bring this up since the found mean IQ-skin correlation amongst African Americans is consistent with the HH. (See: Jensen, 1973)

The expected mean correlation between IQ and skin color (SC) would be the square root of the product of the reliabilities (i.e., square) of the correlation between IQ and individual ancestry (IA) and SC and individual ancestry (IA), assuming some between group heritability (BGH) of IQ. The average SC-IA correlation for African Americans is around .44 (ranging from .34 to .54); the reliability of skin color as a predictor of African American Ancestry is, therefore, .19.

The average IQ-IA correlation obviously has yet to be determined. According to Zakharia, et al. (2009):

“Numerous studies have estimated the rate of European admixture in African Americans; these studies have documented average admixture rates in the range of 10% to 20%, with some regional variation, but also with substantial variation among individuals [1]. For example, the largest study of African Americans to date, based on autosomal short tandem repeat (STR) markers, found an average of 14% European ancestry with a standard deviation of approximately 10%, and a range of near 0 to 65% [1], whereas another study based on ancestry informative markers (AIMs) found an average of 17.7% European ancestry with a standard deviation of 15.0% [2].…
…These results were confirmed in the estimation of IA by using the program frappe (also in Figure 1).(Zakharia, et al., 2009. Characterizing the admixed African ancestry of African Americans)”

If this is the case, US Blacks, who are 20% White, differ in White Ancestry from hypothetical US Blacks who are 99% White by 5.3 Standardized differences. If we propose that there is a genotypic IQ difference of 1 Standard deviation, at maximum, between US Blacks and hypothetical Blacks who are 99% White, we might suppose that the correlation between IQ and ancestry in the US Black population is 1/(5.3) or 0.19, since the correlation would be the change in X (IQ) over the change in Y (White Ancestry). Using .44 as the SC-IA correlation and 0.19 as the IQ-IA correlation, the SC-IQ correlation would be around 0.8. The weighted average IQ-SC correlation found to date in published studies is .17 (N = 1130, p < .001), which falls comfortably above the predicted range.

Below is a list of studies to date, published and unpublished, on skin color and IQ. The N-weighted correlation at 0.15 (N= 3694).

Herskovits (1926)/r=0.17/n=115
Klineberg (1928)/r=0.12/n=139
Peterson and Lanier (1929)/r=0.18/n=83
Peterson and Lanier (1929)/r=0.3/n=75
Scarr et al. (1977)/r=0.155/n=288
Lynn (2002)/r=0.17/n=430
NLSY97 (unpublished)/r=0.12/1433
ADD Health (unpublished)/r=0.17/n=1131

And the average Cohen’s d between the upper and lower 4rths of the spectrum is about 0.5 n = >6,000.

Feguson (1919)/d= about 0.7/n=657
Feguson (1919)/d= about .9 SD/n=667
Kock and Simmons (1926)/d= about 0.15/n=1078
Klineberg (1928)/d= about 0.15/n=200
Young (1929)/ d= about 0.8 and 0.33/n=277
Peterson and Lanier (1929)/d = about 0.66/n=83
Peterson and Lanier (1929)/d= about 0.2 SD/m=83
Bruce (1940)/d=about 0.25/n=72
Codwell (1947)/d= about 0.33/n=480
Lynn (2002)/d= about 0.5/n=430
NLSY97 (unpublished)/d= about 0.4/n=1433
ADD Health (unpublished)/d=about 0.5/n=1131

Environmentalists, of course, would maintain that the intrablack IQ-SC and other related correlations (see: Rushton and Templer, 2011) are due to “colorism.” This can’t be ruled out. Yet, in no way is the IQ-SC correlation evidence against the HH. As it is, hereditarian hypothesis offers a ready explanation for the paradox of pigmentocracy in the US:

Dark-skinned blacks in the United States have lower socioeconomic status, more punitive relationships with the criminal justice system, diminished prestige, and less likelihood of holding elective office compared with their lighter counterparts. This phenomenon of “colorism” both occurs within the African American community and is expressed by outsiders, and most blacks are aware of it. Nevertheless, blacks’ perceptions of discrimination, belief that their fates are linked, or attachment to their race almost never vary by skin color. We identify this disparity between treatment and political attitudes as “the skin color paradox.”

Hochschild and Weaver, 2008. The Skin Color Paradox and the American Racial Order

Moreover, it makes sense the presence of pigmentocracies (Lynn, 2008) throughout Latin America and the Caribbean (cf. Hunter, 2007; Harris, 2008; Villarreal, 2010) and of the international correlation between skin color and national intelligence (see: Templer and Arikawa, 2006; Templer, 2010).

0. Equivocal.

(10) US blood groups and Racial Ancestry

Nisbett (2005). Summary: The HH predicts that Caucasian admixture will positively correlate with IQ differences within the African-American population. Loehlin et al. (1973) and Scarr et al. (1977) set out to test this hypothesis using blood group indexes. Neither found a significant correlation between ancestry index and test scores. This indicates that the HH is false.

The methodologies that Scarr et al. (1973) and Loehlin et al. (1977) used precluded the ability to detect an IQ-IA correlation. T. H Reed, an expert on blood group difference, provided a technical discussion of this which is replicated below. One of the problems with using blood group indexes as proxies for individual ancestry is that blood groups assort independently from other traits. This is why the authors of the Loehlin study note that: “this result may not, however, be a very strong test of the genetic basis of the between-group IQ difference, because of independent assortment of blood group and ability genes over a number of generations among U.S. Negroes.”

The study by Scarr et al. (1977) perhaps deserved more discussion since it is cited so frequently.

Scarr et al. (1977) tested the genetic hypothesis in two ways. First, they looked to see if g was associated with an index of African ancestry and found a statistically non-significant -.05 correlation (which was reduced to -.02 after controlling for SES and Skin color) and, second, they divided their subjects into thirds based on their index of ancestry and compared the g scores of the top third to the bottom third; the latter analysis showed a non-significant difference of .11 SD between the groups.

Scarr et al. concluded: “An extrapolation from the contrast between extremes within the hybrid group to the average differences between the races predicts that not more than one third of the observed difference between the races could be due to genetic differences. In view of the negligible correlations between estimated ancestry and intellectual skills even this seems unlikely.[Emphasis added]”

As Scarr et al. pointed out, the findings from their second test are consistent with a weak version of the genetic hypothesis, a version which proposes a between group heritability (BGH) of less than .5. It’s not particularly clear how they derived their “that not more than one third,” though. In a footnote, they give the following rationale:

“The rough calculation for the estimate of the difference between upper and lower thirds of the black group proceeds as follows. If the resultant difference in standard deviations is 0.9 between the races when the mean difference in degree of Caucasian ancestry is about 0.77 (0.99 – 0.22 = 0.77) then the difference between upper and lower thirds of the black group alone should be about 0.23SD when the difference in Caucasian ancestry is about (0.35 – 0.15) = 0.20. Furthermore, if three-fourths of that mean difference is due to racial genetic differences alone the smallest expected difference is (0.75 x 0.23) = 0.18. So, about one-fifth to one-fourth of a SD would be the expected mean difference between upper and lower thirds of the black group.”

Based on this reasoning (expected BGH x 0.23 = difference between upper and lower thirds), and their findings of a .11 SD difference, the BGH could be as high as ½. not 1/3. Of course, this assumes a difference of 20% in admixture between the upper and lower thirds of the sample and an average admixture of about 20%. Based on more recent data (e.g., Parra et al, 1998), which indicates that the admixture in Philadelphia, from where the subjects came, is lower than average, this may represent an overestimate of admixture and therefore underestimate of the possible BGH. Even if we grant the .33, though, as the average age of the study’s subjects ranged from 10-16, and as the heritability of IQ increases with age, the findings, taken as such, could still be consistent with a strong version of the genetic hypothesis, at least one that takes into account the heritability x age effect.

All of this, of course, assumes that the index of ancestry used by authors had a high reliability. As Reed (1997) pointed out, it likely didn’t. To some extent, we can see this simply by comparing the correlation Scarr et al. found between their index of ancestry and skin color (.27) with the correlation found between more sophisticated indexes of African Ancestry and skin color (.44) (Parra, Kittles, Shriver, 2004.) The correlation Scarr et al. found was significantly lower than that found using modern techniques.

Whatever the case, Scarr et al. went onto contend that the found -.05 correlation between the test scores and their index of ancestry argued against even a weak genetic hypothesis. Putting aside the issue of the problematic nature of their index as pointed out by Reed (1997), the trouble with authors’ contention is that it depends on an assumed predicted magnitude of the IQ-individual ancestry (IA) correlation, given some proposed BGH of IQ. The IQ-index correlation should be the product of the index-IA and IQ-IA correlations. If the environmental hypothesis is correct, the IQ-IA correlation would be 0, and so the index-IA correlation should also be 0 or not significantly different from that. It’s not clear, though, what the IQ-index correlation would be, were genetic hypothesis correct, since it’s not clear what the predicted IQ-IA correlation would be. In making their case, Scarr et al. cite a speculation made by Arthur Jensen about the predicted magnitude of the IQ-IA correlation. In “Educability and Group differences,” Jensen speculated that the correlation between IQ and IA in the African American population would be higher than that between IA and skin color (~.40) “since more genes are involved in intelligence.” This was just a speculation though. Later, in reply to Scarr et al, Jensen argued and provided a deduction — refer to the notes section — demonstrating that the predicted correlation would be less than .10 and, as such, that the predicted IQ-index correlation would be less than .05. Scarr (1981) objected to these low estimate but was unable to provide a defense of the estimate her group used.

The uncertainty about the predicted IQ-IA correlation – in addition to the study’s methodological problems as noted by Reed (1997) — has left the findings open to interpretation.

Currently, with modern genetic methodologies an accurate assessment of ancestry admixture can be made and the HH can be unequivocally tested as discussed by Rowe (2005), Rushton and Jensen (2005), and Lee (2009). The fact that purported environmentalists are not calling for such tests suggests that they are less certain about their position than they make out.

[It would probably be a mistake to leave this at that, without clearly demonstrating that the results are consistent with a genetic hypothesis. We can do this several ways:

(1).

a. According to Scarr et al. the difference between the upper and lower thirds of the distribution was 0.11 SD. Assuming a normal curve approximation, the upper and lower thirds of a distribution are 2.2 SD apart. If the upper and lower thirds are 2.2 SD apart and the correlation between IQ and ancestry is, according to Scarr et al. 0.05, we would expect that the difference between the thirds would be 0.11 SD (2.2 x o.05). So the low correlation is consistent with the mean difference.

b. Now it’s clear that Scarr et al.’s index of ancestry was unreliable so we have to correct for that. Based on partial correlations, Jensen calculated the validity of the index to be 0.49. We can calculate it alternatively by simply dividing the mean found skin color-ancestry correlation in the US Black population (0.44) to the skin color-index correlation that Scarr et al. found (.27). We get a validity of .61, which might be an overestimate, as some of the correlation between skin color and Scarr et al.s index of ancestry could be due to the correlation between blood groups and skin color genes as Scarr et al. noted (quoted below.)

c. Using the higher estimated reliability (.61), the corrected mean difference is 0.18 (0.11/.61).

d. Plugging this into Scarr et al.’s formula above (expected BGH x 0.23 = 0.18), we get a between group heritability of 0.78 on a measure that showed a between race difference of 0.9 SD.
(We should also correct for the test reliability which is typically 0.9 –correcting for this, the expected heritability would be 0.866)

(2).

a. According to recent analyses, the mean African admixture is 20% and the standard deviation of admixture is 15%. According to Zakharia, et al. (2009):

“Numerous studies have estimated the rate of European admixture in African Americans; these studies have documented average admixture rates in the range of 10% to 20%, with some regional variation, but also with substantial variation among individuals [1]. For example, the largest study of African Americans to date, based on autosomal short tandem repeat (STR) markers, found an average of 14% European ancestry with a standard deviation of approximately 10%, and a range of near 0 to 65% [1], whereas another study based on ancestry informative markers (AIMs) found an average of 17.7% European ancestry with a standard deviation of 15.0% [2].…
…These results were confirmed in the estimation of IA by using the program frappe (also in Figure 1). The amount of European ancestry shows considerable variation, with an average (± SD) of 21.9% ± 12.2%, and a range of 0 to 72% (Table 1).”

Based on this we can calculate an expected IQ-ancestry correlation.

b. One interpretation of a correlation coefficient is: amount of change in x, change y or, in this case, the amount of change in admixture per change in genetically conditioned test score. 
In this case the genetically conditioned difference between Blacks and White would be 0.75 SD, since we are proposing that 75% of the gap is genetic; the ancestry difference would be 5.3 SD, which is the number of SDs separating Blacks who are 20% White and Whites, given that 1 SD of admixture equals 15% Whiteness ((100-20)/15=5.3). The correlation between test scores and genotypic ancestry, would then be 0.75/5.3 or 0.14.

c. This would be the correlation for an index that had perfect reliability. Correcting for the unreliability (see 1b), the the correlation between IQ and the index would be 0.085

d. This would be the correlation between IQ and Scarr’s index, assuming that the within population heritability was 1, as a lower within population heritability will attenuate the correlation. According to Scarr et al., the within population heritability was 0.48. Correcting for the lowered correlation between IQ and genes, we get 0.06 (0.085*SQRT(.48)), which is approximately the correlation found. (We should also correct for the test reliability which is typically 0.9 –correcting for this, the expected correlation would be 0.05.)

e. The difference between the upper and lower thirds would then be .13, which was approximately what was found.

The above demonstrates that Scarr et al. (1977) does not contradict a genetic hypothesis. It doesn’t support it, because the findings were non-significant, but the findings are nonetheless in agreement with a genetic hypothesis of substantial magnitude.]

0. Equivocal.

(11) a. Reported US white ancestry

Nisbett (2005); Flynn (1980). Summary: The HH predicts that Caucasian admixture will positively correlate with IQ differences within the African-American population. Witty and Jenkins (1936) set out to test this by comparing the racial admixture of a group of high IQ black school children. They found that white racial ancestry was not positively correlated with intellectual superiority. This indicates that the HH is false.

(For more detailed analysis of this study, refer here.)

The study, which had two components, was a subpart of a larger study by Witty and Jenkins on intellectually superior black children in the Chicago Public schools. Using Terman’s methodology, Jenkins was able to identify, and then study the characteristics and demographics of 103 intellectually superior (IQ >120) children out of a population of ~8000. To address the genetic hypothesis, Witty and Jenkins looked at the relationship between genealogy and IQ for a subsample of these. Witty and Jenkins reasoned that, were the genetic hypothesis true:

In a mixed group such as we have in the United States those individuals having the largest amount of white ancestry should on the average stand higher in tests, other things being equal, than persons of total or larger amounts of Negro ancestry. (Witty & Jenkins, 1936, p. 180).

They conducted two tests of this hypothesis. In the first, they estimated the racial admixture of 63 of the children on the basis of parental report* and then compared the average amount of admixture found to that found in a supposedly nationally representative sample of blacks discussed by Herskovits (1930). Herskovits estimated racial admixture from reported parental and grandparental admixture and in some instances genealogical records going back two generations.

Table 1. Herskovits’ Ancestral data (A) and Methodology (B)

Witty and Jenkins determined that the superior children had less White ancestry and concluded that the genetic hypothesis was falsified. Unfortunately for their conclusion, as Mackenzie (1984) pointed out, Herskovits’ sample was not representative. The sample had a substantially higher than average SES, with 50% of the individuals being either Howard University students or well-to-do professionals. Worse, as discussed by Loehlin et al. (1975), Herskovits’ sample seems to have had more White admixture than the national average. If we translate Herskovits’ ordinal ancestry data into percentages (e.g., N= 100% African; NNW=66% African, 33% Caucasian, etc.), we find that his sample had a White admixture rate of 31%; this is compared to the current national estimate of 20% based on DNA markers (e.g., Zakharia et al. 2009) and to an estimate of 13% for Chicago blacks — the more relevant comparison population — again based on DNA markers (e.g., Reed, 1969). Using the same method of conversion, as above, we see that Witty and Jenkins’ sample had a 33% admixture rate. Applying this method to a sample of mostly college students reported by Meier (1949), with which Herskovits’ method of tabulating ancestry was used, we get an admixture rate of 35%. Witty and Jenkins, of course, were right that their intellectually superior sample didn’t have a higher percent of White ancestry than Herskovits’ — or Meier’s — but both samples, nonetheless, seem to have had a higher percent than found in both the national and Chicago populations as determined by DNA. (Jenkins’, Herskovits’, and Meier’s samples were more admixed, as determined on the basis on genealogy, than the national average, as determined on the basis of DNA markers, by a standardized difference of or over 0.35 SD. See table 2.) And all samples had higher social economic statuses than average; in Jenkins’, 2/3rds of the children hailed from families in which the fathers were in the “upper occupational levels”; in Herskovits’, 50% of the sample was highly selected; in Meier’s, the individuals were mostly college students.

Table 2.


Witty and Jenkins’ results for their first test, thus, seem to support the genetic hypothesis. Higher IQ African-American children were found to have a higher percent of White ancestry than both the national population and the population from which they were drawn. The comparison data set which Witty and Jenkins rely on, likewise, supports the genetic hypothesis. Herskovits’ sample of African Americans were found to have both a higher SES, an IQ correlate, than average and a higher percent of white admixture. The same holds with Meier’s sample. The literature on “colorism” corroborates the finding of a correlation between SES and admixture and admixture and IQ. As I noted elsewhere:

From 1850 to the early ‘1900s, US census takers were instructed to classify African Americans as Black or Mullato. They were given the following directions: “in all cases where the person is white, leave the space blank; in all cases where the person is black, insert the letter B; if mulatto, insert M” and “Be particularly careful in reporting the class Mulatto. The word is here generic, and includes quadroons, octoroons,and all persons having any perceptible trace of African blood” (Snip, 2003).

Hill (2000) found that those African-Americans classified as Mullato had a higher SES (judged by profession — e.g., white collar workers versus domestic workers) than those classified as Black and that this difference remained after controlling for social origins. Hill (2000) rejected a genetic interpretation, arguing that “[e]xplanations for a cultural or genetic origin can not be supported. Research has failed to uncover any association between white ancestry and intellectual ability among African Americans” and citing Scarr et al.; yet, as we noted above, those studies were inconclusive.

The case could reasonably be made, of course, that it’s invalid to compare admixture rates based on geneological information with those based on DNA markers because rates based on genealogy are much less accurate. If we grant this, though, we are left with no standard with which to compare Jenkins’ prodigious youth. Not only were Herskovits’ and Meier’s samples unrepresentative but, more problematic, Herskovits and Meier used a different methodology than Jenkins in calculating admixture. Given the differences in methodology, the samples can only be compared on the assumption that they accurately capture admixture. Jenkins asked both parents to estimate their own racial admixture. From this he estimated the children’s. Alternatively, Herskovits and Meier calculated admixture based on the reported genealogy. An example of the latter method can be seen in table 1. As Loehlin et al. (1975) noted, this method lends itself to overestimation. To quote:

“The figure in both Jenkins’ and Herskovits’ sample … suggest a somewhat higher proportion of caucasian ancestry (approximately 30 percent) than one might expect from Reed’s data based on blood group genes (Reed, 1969). But it is quite possible that this discrepancy is due in part to a bias in the method of classification used — for example a person reporting all four grandparents as “mixed” would be classified as “about equally negro as White” (Herskovits, 1930 p. 14) even though the odds are that such a person would have more black than white, since more “mixed” blacks in the current generation were “more negro than white” then were “more white than negro”.

Loehlin et al.’s point applies only to Herskovits’ (and Meier’s) data. Loehlin et al. didn’t realize that Jenkins used a different method, which did not suffer from this bias — but undoubtably did from others. So either we grant that Jenkins’, Herskovits’, and Meier’s results were accurate, in which case it’s valid to compare them with results based on genetically informed methods or we don’t, in which case we have no standard against which to compare Jenkins’ results. Either we have found support for a genetic hypothesis or we have found no admissible evidence against it.

In the second test, Witty and Jenkins took a gifted (IQ > 140) subset of the 63 children and compared the average ancestry of the subset to that of the larger group (IQ = 125-140). They found no average difference in ancestry and concluded, again, that the genetic hypothesis was falsified.

On problem with their methodology was that they compared the gifted subset (>140) with the larger group (>125) instead of, more properly given the small sample size, with the non-gifted subset (125 to 140). When the proper comparison is made there is a slight, but nonetheless, non-significant difference as shown in the figure below.


Witty and Jenkins’ results for their second test, thus, seem to more support the environmental hypothesis. How strongly, though? To put this question otherwise: what difference in admixture would a genetic hypothesis have predicted — 2%, 5%, 10%, 20% — given an approximately 1 Standard deviation difference in IQ? And how large of a sample size would have been needed to detect a statistically significant difference (or the absence of one)? It’s not at all clear. To answer this, one would need to know the predicted correlation between IQ and individual ancestry, in addition to the means and variance of admixture, in this population and that’s unknown. Whatever the case, there can be no doubt that this is a much weaker test than the former. Here we had 28 kids drawn out of an already IQ selected sample of 63, with an approximately 1 standard deviation difference between the groups. In the former test, we had 63 kids drawn from an unselected sample of 8000, with an approximately 3 standard deviation difference between groups. While the first test, the results of which seem to support a genetic hypothesis, probably had the power to reject the null, this second certainly did not.

It might be worthwhile to explore the results in some more detail to show how consistent they are with a genetic hypothesis. To do this properly, we would need an estimate of the mean and variance of admixture in the 1930 Chicago Black population, which we obviously don’t have. We do have estimates for the 1990 to 2000 national population, which we can use as a substitute. According to Zakharia, et al. (2009):

“Numerous studies have estimated the rate of European admixture in African Americans; these studies have documented average admixture rates in the range of 10% to 20%, with some regional variation, but also with substantial variation among individuals [1]. For example, the largest study of African Americans to date, based on autosomal short tandem repeat (STR) markers, found an average of 14% European ancestry with a standard deviation of approximately 10%, and a range of near 0 to 65% [1], whereas another study based on ancestry informative markers (AIMs) found an average of 17.7% European ancestry with a standard deviation of 15.0% [2].…
…These results were confirmed in the estimation of IA by using the program frappe (also in Figure 1). The amount of European ancestry shows considerable variation, with an average (± SD) of 21.9% ± 12.2%, and a range of 0 to 72% (Table 1).”

If, based on this, we assume a 1930 Chicago admixture of 20% with a standard deviation of about 15%, we can infer a predicted IQ-ancestry correlation, given a genetic hypothesis which proposes that 75% of the 1 SD Black-White difference is genetic. From this we can calculate how much more admixed we would have expected Jenkins youth to be.

One interpretation of a correlation coefficient is: amount of change in x, change y or, in this case, the amount of change in admixture per change in genetically conditioned test score. 
In this case the genetically conditioned difference between Blacks and White would be 0.75 SD, since we are proposing that 75% of the gap is genetic; the ancestry difference would be 5.3 SD, which is the number of SDs separating Blacks who are 20% White and Whites, given that 1 SD of admixture equals 15% Whiteness ((100-20)/15=5.3). The correlation between test scores and genotypic ancestry, in this population, would then be 0.75/5.3 or 0.14. This means that Blacks, in this population, who were selected 1 SD for intelligence would be selected 0.14 SD for white ancestry or that they would be 2% more admixed. This is a little more than what was seen in Jenkins’ 2nd test results but not significantly so. It’s worth noting, at this point, that other studies of admixture and IQ in the African-American population have show a correlation between genealogy and cognitive ability (e.g., Tanser (1939); Tanser (1941)). These are, of course, ignored by proponents of radical environmentalism.

In the case of Jenkins’ first test, which was the more powerful one, the difference between the selected and unselect children was about 3 SD, so the children should have been 3 X 0.14 SD more admixed or 6.3% more admixed than the reference population. If we compare these results with those found from genetic analysis, we will see that in no way do they contradict a genetic hypothesis – rather they are quite consistent with it.

Notes

*Jenkins (1934) tells us: “The racial composition of sixty-three subjects of 125 IQ and above was determined from genealogical data provided by parents…The following procedure was utilized in determining the racial composition of the children. Parents were asked to state to the best of their ability, their racial composition i.e., the approximate proportion of Negro, white, Indian, or other racial Ancestry. The racial composition of each child was then computed from that of his parents. The subjects were divided into four groups: 1) N (those having no white ancestry), 2 (NNW (those having more negro ancestry than white ancestry), 3) NW (those having about an equal amount of Negro and white ancestry), 4) NWW (those having more white ancestry than Negro.”

b. Estimated US negroness

Nisbett (2005). Summary: The HH predicts that Caucasian admixture will positively correlate with IQ differences within the African-American population. Based on the studies conducted, as reported by Shuey (1966), the average correlation between IQ and judged “Negroidness” is low, therefore the hereditarian hypothesis is false.

For a summary of the studies under discussion refer to: Admixture studies discussed in Shuey (1966)

To support his claim, Nisbett sidesteps 7 studies that showed a large relation between indexes of white admixture and IQ — Feguson (1919), Peterson and Lanier (1929), Young (1929), Tanser (1939), Tanser (1941), Codwell (1947); Grinder et al (1964) –and 4 studies that showed a moderate to small relation –Davenport (1928), Klineberg (1928), Peterson and Lanier (1929), Bruce (1940). He draws attention, instead, to the low correlations found between indexes of admixture and IQ in 3 studies – Herskovits (1926), Peterson and Lanier (1929), and Klineberg (1928) — and argues that the low correlations stand as evidence against the genetic hypothesis.

Nisbett points out that the skin color-IQ correlations found are low: Indeed. Based on the 3 studies mentioned, the average correlation is .16, which is about the same as that for all 6 studies to date which report a correlation, the n-weighted average being .17. What Nisbett neglects to mention is that the genetic hypothesis predicts only a slight correlation. Of what magnitude? The correlation predicted would be the product of the correlation between skin color and African ancestry (.44) times the predicted correlation between IQ and African ancestry for some between group heritability (undetermined but most likely under .50), attenuated for restriction of range (range unknown, but most definitely not, in any of the studies, zero white admixture to complete white admixture). In short, the correlations found are not low at all from the standpoint of the genetic hypothesis, rather they are quite high, suggesting some other factors involved (i.e. cross assortative mating for color and IQ; “colorism”)

Likewise Nisbett points out that the correlations between other indexes of admixture and IQ are low. Based on the 3 studies that used indexes of admixture aside from skin color, the weighted average correlation was .08 (indicating that the more admixed individuals scored higher). Low indeed. (Interpupillary Span, – 0.01 (N=75); Nose Width, 0.05 (N=329); Ear height 0.2 (N=75); Lip thickness 0.1 (N=329).

But just as above, the expected correlations, given the genetic hypothesis, would most likely be even lower. (It’s difficult to say because no one has determined the average correlation between “nose width” or “lip thickness” and white admixture in the African American population. Presumably, these correlations would be lower than the skin color-ancestry correlation, if only because the measure of the latter is more reliable than the measures of the former.)

In short, the findings of the three studies that Nisbett highlights provide no evidence against the genetic hypothesis; rather, they are consistent with it. Now, turning to the whole set, of the 16 studies predating 1965, 13 showed a positive relation between indexes of white admixture and IQ (7 large, 6 moderate to slight), 2 samples show no such relation, and 1 was equivocal. Of the 2 studies that showed no relation, in one — Kock and Simmons (1926) – light colored blacks nonetheless outscored darker colored backs. In all, the findings are not inconsistent with a genetic hypothesis.

It could be argued that the low difference is some of the studies is inconsistent, though. For example, Klineberg (1928) found that less African looking blacks scored 2 points above more African looking blacks in New York. However, a small difference is no more than would be expected. If pure Africans and Europeans differ genotypically by 15 points (1 SD), the difference between African-Americans with very low admixture (10 percentile) and African Americans with very high admixture (90 percentile) as indexed by color, which has a validity of about .44, would only be .about 7 points (.44 x 15). But the admixture of African-Americans is not evenly distributed. There’s range restriction. The average admixture of African-Americans at the upper end is probably about 40 percent and the average admixture at the lower end is probably about 5 percent, with a difference in admixture of about 35 percent. As the range is restricted by about 2/3rds, the difference in scores between African Americans at the upper and lower end of the admixture distribution as indexed by color would only be a couple of points. The expected difference, of course, would be larger if multiple indezes were combined and if the range was less restricted.

Of course, the above means that a genetic hypothesis can not account for some of the large differences found, for example, Feguson (1919). Clearly some other force was at work. The point though is that the relations found in these studies, as a whole, are not inconsistent with the existence of genetic differences.

Whatever the case, there are consistent findings of a relation between indexes of African admixture and IQ or IQ correlates. For example, From 1850 to the early 1900’s, US census takers were instructed to classify African Americans as Black or Mullato. They were given the following directions: “in all cases where the person is white, leave the space blank; in all cases where the person is black, insert the letter B; if mulatto, insert M” and “Be particularly careful in reporting the class Mulatto. The word is here generic, and includes quadroons, octoroons,and all persons having any perceptible trace of African blood” (Snip, 2003).

Hill (2000) found that those African-Americans classified as Mullato had a higher SES (judged by profession — e.g. white collar workers versus domestic workers) than those classified as Black and that this difference remained after controlling for social origins. Hill (2000) rejected a genetic interpretation, arguing that “[e]xplanations for a cultural or genetic origin can not be supported. Research has failed to uncover any association between white ancestry and intellectual ability among African Americans” and citing Scarr et al.; yet, as we noted above, those studies were inconclusive.

The findings of Hill (2000) concord with the beliefs (or observations) of the times:

Mulattoes always have enjoyed opportunities somewhat greater than those enjoyed by the rank and file of the black Negroes. In slavery days, they were most frequently the trained servants and had the advantages of daily contact with cultured men and women. Many of them were free and so enjoyed whatever advantages went with that superior status. They were considered by the white people to be superior in intelligence to the black Negroes and came to take great pride in the fact of their white blood…. The higher the standard of success, the lower the per cent [sic] of full-blooded Negroes. (378-79)
–Reuters, 1918

Comparing them by their faculties of memory, reason, and imagination, it appears to me, that in memory they are equal to the whites; in reason much inferior….The improvement of the blacks in body and mind, in the first instance of their mixture with the whites, has been observed by every one, and proves that their inferiority is not the effect merely of their condition of life. We know that among the Romans, about the Augustan age especially, the condition of their slaves was much more deplorable than that of the blacks on the continent of America…Yet notwithstanding these and other discouraging circumstances among the Romans, their slaves were often their rarest artists. They excelled too in science, insomuch as to be usually employed as tutors to their master’s children. Epictetus, Terence, and Phaedrus, were slaves. But they were of the race of whites. It is not their condition then, but nature, which has produced the distinction.
–T. Jefferson, 1781

While the differences could potentially be explained by “colorism” or cross assortative mating in no way do assessments of African ancestry contradict the genetic hypothesis. (They do, however, make difficult cultural only explanations for racial differences.)

0. Equivocal

(12) Growth of gap with age

Flynn (2010). Summary: The gap increases with age. Moreover, that gaps increase steadily with age. Given this, it’s likely that the gap is completely environmental. (“At just 10 months old, the average score is only one point behind; by the age of 4, it is 4.6 points behind, and by the age of 24, the gap is 16.6 points. This could be due to genes, but the steady rate after the age of 4 (about 0.6 IQ points lost every year) suggests otherwise, since genetically driven differences such as height differences between males and females tend to kick in at a certain age.”)

This is an argument by way of cumulative deficit theory (or more neutrally progressive achievement gap). Cumulative deficit theory was first proposed in the 1960’s. Accordingly, the gap is due to accumulating early age cultural disadvantages:

It appears that, as Negro children get older, the discrepancy between their IQ scores and those of white children increases, while the discrepancy between the two groups’ scores on the language measures of this research decreases. At first grade level, the disadvantaged child’s experience seem .. [Deutch, 1967]

One problem with the cumulative deficit theory is that the gaps have not historically systematically increased with age. Were cumulative deficit theory correct, the first grade IQ gap found by Coleman (1966) should have magnified to 2 SD by the time those kids were 24. Obviously, it didn’t. Instead of saying that the gaps increase with age, we could also say that in recent years the gaps have been decreased with youth. The cumulative deficit theory has no parsimonious explanation for this. Why prior didn’t the gap increase and why currently doesn’t the decrease lastingly remain?

The hereditarian hypothesis offers a simple solution (see Murray, 2005; Jensen 1998, p. 178). The HH proposes that the between population gap is of the same nature as the within population gap. Within populations the H^2 of IQ (or at least GQ) increases near linearly with age (Haworth, et al., 2009). As such, one would expect something akin to the following between population curve ceteris paribus, environmentally speaking:
Accordingly, a significant percent of the gap for younger children, whose IQs have a high e^2, is predicted to be a function of their parent’s IQ, that is, as a result of cultural intergenerational IQ transference. While the IQs of the children can be raised by outsourcing parenting (preschool, Head Start, early intervention, etc.), as the children age, it is predicted that their IQs will regress towards that of their parental population’s mean. This is, in fact, what is seen in Flynn’s IQ computations and in both adoption and early intervention studies (e.g. Perry, Abecedarian, and Chicago Early Childhood program).

Flynn’s argument that the steadily increasing difference with age makes a genetic hypothesis improbable is curious. One strains one mind to think of an environmental factor or set of them that can produce a steadily widening differences with age form early childhood to adulthood and then abruptly level off at appropriately the same time that the increase in the heritability of IQ does so. Were the cause parental environment at age 4, blacks youth culture at age 12, and something else at age 20, one would expect a volatility in the differences which, according to Flynn, does not exist.

0. Equivocal.

(13) Gaps are minuscule at very young ages

Levitt and Fryer (2006). Summary: At age 1 the gap is only .02 SD. This proves that the gap is due to accumulating environmental affects.

As noted above, the hereditarian hypothesis predicts that the between race genotypic gap, and therefore phenotypic gap all things environmentally equal, will increase with age. The nice thing about this explanation is that it can readily account for the puzzle Levitt and Fryer (2006) find:

The primary puzzle raised by our results is the following: how does one generate small racial gaps on the BSID test scores administered at ages 8-12 months and large racial gaps in tests of mental ability later in life, despite the fact that these two test scores are reasonably highly correlated with one another ( =.3), and both test scores are similarly correlated with parental test scores ( >.35)

0. Equivocal.

(14) The US gap can be explained environmentally

Yeung and Pfeiffer (2008). Summary: There are environmental factors, particularly early home environmental factors, at each stage of development which can account for the gap. Regression analysis shows that the mother’s vocabulary does not explain a significant portion of the gap. Given this, it’s likely that the gap is environmental.

Controlling for parental environmental factors also controls for parental IQ (see below). As noted above, at young ages the gap is predicted to be substantially environmental; as such, environmental factors are expected to cause a significant portion of the gap at young ages.

Also, Controlling for SES isn’t the only way to take SES into account. Another method is comparing between SES stratum. When this is done, it is seen that the Black-White gap increases — not decreases — with SES.

0. Equivocal.

(15) Reassessed international African Scores

Nisbett (2010); Wicherts (2010). Summary: Richard Lynn originally found continental African IQs below 70. Reanalysis shows that the continental African IQs might be above 80. Given that the Flynn effect has yet to take hold in Africa, it’s probable that continental African scores will increase dramatically and close the continental European-African gap. Given that the continental European-African gap is most likely environmental, it’s most likely that the US African-Americans-White gap is environmental.

This is a Flynn effect argument through the back door.

While Lynn’s data provides support for the HH, the US HH is neither proved nor disproved by continental African scores. Rather, what matters is the genotypic intelligence of the specific populations from which African-Americans came and, specifically, the genotypic intelligence of the ancestors that were selected for slavery and sold to Europeans. To quote Eysenck (in Modgil, 1986): “Thus there is every reason to expect that the particular sub-sample of the negro race which is constituted of American Negroes is not an unselected sample of Negroes, but has been selected throughout history according to criteria which would put the highly intelligent at a disadvantage. The inevitable outcome of such selection would of course be the creation of a gene pool lacking some of the genes making for high intelligence.”

Regardless, based on Wicherts et al (2010)’s rigorous exclusion criteria, the average West S.S. African IQ is ~77. This is somewhat of an inflated number. The true average African IQ centers around 75. (For a more complete discussion of this, refer here.)

Table 1. African IQs based on IQ tests as calculated using three different sets of inclusion criteria. The following countries have at least 1 data point: Congo, Ethiopia, Ghana, Mali, Nigeria, South Africa, Zaire, Kenya, Malawi, Nambia, Sudan, Uganda, Zimbabwe, Madagascar

Table 2. African IQs based on International test scores as calculated by direct transformation, equalization of means and standard deviations, and regression. The following countries have at least 1 data point : Mozambique, Nigeria, Swaziland, South Africa, Botswana, Ghana, Zimbabwe The following assessments were included: TIMSS 95‘, TIMSS 97‘, TIMSS 03‘, PIRLS 06’ Reading, IAEP Math 90‘, IAEP Reading 91‘, SISS 83‘, SIMS 81)

Wicherts et al. likely would argue that the Flynn effect is bound to hit Africa and reduce that ~1.7 SD difference to zero. The Flynn effect, though, has been in effect in Africa. In 1929, Fick found a Zulu IQ of 65 relative a British IQ of 100; in 2009, eighty years latter, Wicherts (2009) estimated that the Black South African IQ was 77 relative to a British IQ of 100. Were the Flynn effect yet to hit Africa, based on Wicherts estimates, the 1929 South African IQ should have been 53 [77 – (8×3)] and based on Fick’s data, Wicherts’ 2009 estimate should be 41 [65 – (8×3)]. By plotting the change in IQ across time using the full African IQ data bank, we can see how the Flynn effect is behaving in African relative to the West.

Figure 1. The African IQs over time based on the complete data bank

The regression lines show that the African IQs have been fairly constant across time relative to UK norms. If an accelerated Flynn effect was occurring in Africa, such that the African and Western averages were due to intercept in the near-term, we would expect a positive slope of more the negligible magnitude. If the Flynn effect had yet to hit Africa, we would expect a negative slope, as the Western scores should have risen over time relative to the African scores. The Flynn effect seems to have occurred in African in tandem with the West.

For further confirmation of the last statement, we can plot the international test score equivalents (calculated using equalization of the means) over time (K = 17). Were Wicherts et al. correct the African student scores should show an increase corresponding to their supposed IQ increase. They don’t. Rather, the African student scores show a decrease in tandem with increased enrollment and increased test sophistication.

0. Equivocal.

(16) Closing of the IQ gap

Flynn (2010). Summary: The IQ gap between African Americans and European Americans has narrowed over the last 40 year. This shows that the gap is environmental in nature.

A narrowing of the gap no more implies a complete environmental etiology than a constancy would imply a total or partial genetic etiology. Regardless, based on the selective data that Flynn and Dickens deem appropriate, the phenotypic gap across all ages, is .9SD. (My analysis here.) This is down only 0.1 SD from the 1 SD phenotypic gap that Shuey found over 40 years ago, and it is in line with the hereditarian estimate of a .5 to .8 SD geneotypic gap. Moreover, contra Flynn, the gap for adults shows no trend towards closing. In 1978, the WAIS R standardization showed a 1.01 SD gap; in 2008, 30 years latter, the WAIS IV standardization showed a 1.06 SD gap.

0. Equivocal.

(17) Success of some African immigrant groups

Various: Some African (etc.) Immigrants in some parts of the world fair quite well; for example, West Indian African immigrants in the US do well. Therefore the hereditarian hypothesis is false.

Africa is a rather genotypically diverse place. As such, one can’t generalize across all African populations, particularly Saharan, West, and East African populations. (This holds for environmentalists just as much as for hereditarians). As for West Indian African immigrants to the West, immigrants who are of West African stock, they represent, as Susan Model has exhaustively demonstrated, a self selected elite who perform no better than internal African American immigrants. Presumably, both West African immigrants to the US and US African American internal migrants, have a higher than average genotypic IQ (g). To quote from Model (2008):

To review, West Indian immigrants have long fared better economically than African Americans. This generalization holds even when immigrants and natives are assigned the same age, education, location, etc. Experts have proposed four distinct explanations for this state of affairs: West Indians are positively selected immigrants, Caribbean slavery taught West Indians valuable skills, socialization in an all-black society is psychologically beneficial for blacks, and white Americans discriminate less against West Indians than African Americans. When the four explanations are tested empirically, only positive selection receives support. This is not to say that growing up in an all-black society might not provide psychological benefits or that whites might not respond positively to blacks with a Caribbean accent. But even if these relationships hold (which has yet to be demonstrated), there is no empirical evidence that they enhance West Indian economic attainment. Rather, West Indian success can be attributed entirely to the greater talent and ambition of those who choose to move. Similarly, the subset of African Americans who are voluntary internal migrants are better off than their less venturesome counterparts. Once this point is clear, it is easy to see why West Indian success offers no lessons for African American improvement.

The mean to which the offspring of these immigrants regress would be informative (refer back to 6).

0. Equivocal.

(18) Adoption Studies

(Scar and Weinberg, 1976; Scar et al., 1992)

The Scarr et al. study provides strong support for the hereditarian hypothesis (See: Levin, 1994). It shows: a) the predicted white-biracial-black relative differences, b) the predicted white, biracial, and black averages as compared to the national norms, c) close to the predicted biological child/parent IQ and academic performance correlation, d) and the predicted regression to the population mean. Relative to the Moore (1986) study, this study had the advantage of having larger N’s, being longitudinal, having records of the biological parent’s IQ and school performance, and having multiple measures of achievement.

The only aspect slightly out of discord with the hereditarian hypothesis was the relatively low Asian/Indian scores. These, however, are difficult to interpret as N.E. Asians are lumped together with Amerindians.

+ 5. Strong support for the US hereditarian hypothesis

(Moore, 1986)

(The Standard deviations of the groups were ~10)

Moore (1986) studied both traditionally and transracially adopted Blacks and Biracial children. While the IQs of the traditionally adopted black and biracial kids conform to the hereditarian predictions, the IQs of the transracialy adopted kids do not. The latter provides intermediate weak support for the environmental hypothesis for two reasons: a) no significant difference was found between the transracially adopted Black and biracial children and 2) the transracially adopted black and biracial children tested with high IQs, relative to the national norms.

With regards to the traditionally adopted children, the average age of the kids was 8.61. If we assume that the WGH^2 (with group heritability) of IQ (g) increases linearly with age within populations (Haworth, 2009) and that the between group H^2 (BGH) acts in the same manner, we would estimate an age 8.61 B-W BGH^2 of around .4. If we assume that the B-W genotypic gap is around 1 SD at its adulthood peak, at age 8.61, with environment controlled, the age 8.61 B-W gap should be around .4SD. Accordingly, the biracial-Black gap should be roughly around .2SD as opposed to 0SD. The found gap was about .25 SD, taking into account the lower SDs, and so is consistent with a genetic hypothesis.

With regards to the transracially adopted children, given a B-W maximal genotypic gap of 1 SD, at age 8.61, we would expect significantly lower IQs relative to the national norms (assuming average environment)– something to the effect of BB=94, BW=97 (almost exactly what was found for the traditionally adopted kids) instead of BB= 109, BW=108. To effect the near 1SD difference in the found and predicted scores for the adopted black and biracial kids, given an e^2 of .6, 1.3 SD of superior cognitive environmentality (1/squrt .6) would be needed. (As oppose to the 1 SD the environmentalists would have to posit).

The study can not, however, be considered as counter evidence of equal weight to the Minnesota study for five reasons: 1) it was not longitudinal, 2) the BB/BW Ns were much smaller (N=23 to N=98), 4) there was a lack of multiple assessments, and 5) biological parental IQ’s are unknown.

– 3. Intermediate weak support for the environmental hypothesis

(19) Racial Hybrid studies

[I have since found numerous large samples in which mixed race kids of Black and White parentage score intermediate to their parental populations. Refer: here (NAEP 2003 to 2011), here (NAEP 2003 to 2011), and here (PISA 2009; PISA 2006; PISA 2003; here (NLSF); here (GSS). I consider it to now be an established fact that mixed race Black-White kids perform intermediate to their parental populations.]

In the IQ wars, Environmentalists (e.g., Nisbett and Flynn) make much out of a few studies which show little or no significant difference between biracials and Whites. Others (e.g., Murray and Rushton) point to studies which show their hypothesis’ predicted gap and contend that the biracial data largely supports a genetic interpretation. The studies present a conflicting picture and it’s difficult to adjudicate between the environmentalist and hereditarian claims because the subject numbers are rather small. One way to approach such situations is by conducting a meta-analysis. To that end, I did a literature search and located 6 studies that contain data on the IQs of American biracials. I then computed the standardized difference for Biracial-Whites and Biracial-Blacks across studies and found respective d’s of .34 (N= 479. P < .01) and .58 (N= 557, p<.01).* (Different methodologies, of course, will lead to slightly different results.)

(SD = d =standardized mean difference.)

As can be see, on the whole the genetic hypothesis is supported. Below I discuss the studies included not already discussed above. To that I add a discussion of two studies from outside the US.

(Willerman et al., 1974)

The Willerman et al study is often cited as evidence against the hereditarian hypothesis. Originally it was argued that were the hereditarian hypothesis true, the scores of White mothered biracial children would not be significantly different from the scores of Black mothered biracial children. As it can be seen they were, but not consistently so.

The difference between the Black and White mothered biracial children was due to the extremely low scores of a few children and doesn’t warrant the attention environmentalists give it. What is of interest is the difference between the White and biracial children’s scores. The biracial scores fall intermediate to the scores of the parental populations and are consistent with a predicted maximal genotypic gap of 1SD.

To see this we can analyze the scores of the biracial kids with white mothers. White mothers are, in effect, our environmental control. The controlled BW-W gap is .2 SD (which is equivalent to a .4 SD Black-White gap). The h^2 at age 4 and therefore the approximate BGH^2 at age 4 is no more than .3. Given a maximal 1 SD gap, the predicted age 4 BW-W genotypic gap would be .15 SD (i.e. one half of the predicted B-W gap). Our found gap is consistent with our predicted gap.

While this study provides some evidence for the hereditarian hypothesis, it is nonetheless very weak evidence.

+ 1. Very weak support for the US hereditarian hypothesis

(Rushton 2008)

This is rather weak evidence. Environment wasn’t controlled for and only two assessment were given. Given the history of South Africa it could plausibly argue that coloreds are socially privileged relative to Black South Africans. Nonetheless, the study provides the hereditarian hypothesis with some support and itself is supported by other similar findings (e.g Fick 1929, Coloreds = 83 (N = 6196), Blacks = 65 (293); Owen 1992, coloreds = 80 (N=778), Blacks = 74 (N=1093)).

+ 1. Very weak support for the global hereditarian hypothesis

(Rowe, 2002)

Rowe’s study is based on an analysis of the ADD health wave 1 data. I commented on this study elsewhere.
In his study, to control for the influence of appearance (i.e. colorism), he only included mixed race individuals whom the interviewers recorded as looking ‘Black.’ The same data has been analyzed by Fryer, Kahn, Levitt, and Spenkuch in their 2008 paper, The Plight of Mixed Race Adolescents. Using a more inclusive criteria, they found a .33 SD Mixed-White gap. In the same paper, Fryer et al. note:

Mixed race adolescents are less likely than blacks or whites to have a learning disability. Their AHPVT scores are roughly in the middle of blacks and whites. While blacks fare .89 standard deviations worse than whites, mixed race children lag .33 standard deviations behind. On our other two achievement variables (grade point average and whether or not a student repeated a grade), mixed race adolescents are between blacks and whites but more similar to blacks

+ 2. Weak support for the US hereditarian hypothesis

Gullickson (2004).

Gullickson (2004) analyzed the data from the National Longitudinal Survey of Youth. He identified 79 biracial children and found a Biracial-white gap of .49 SD and a biracial black gap of .46 SD.

+ 2. Weak support for the US hereditarian hypothesis

(Eyferth, 1961)

Notes:
1. 20-25% of the Black fathers were North African soldiers
2. There was a 30% rejection rate for African-American solders
3. Children were matched for racial community, age, sex, SES, family characteristics, etc.
4. The Flynn effect corrected IQ of the kids are 2.5 points lower. White boys = 98.5; White girls = 90.5; Biracial boys =94.5; Biracial girls 93.5

Old Eyferth. This Eyferth: “Dickens, 2005: “In the author’s view, Flynn’s exhaustive 1980 analysis of Eyferth’s work provides close to definitive evidence that the black disadvantage is not genetic to any important degree.”

Where do we start?

Let’s start with the hereditarian hypothesis’s predictions. According to the HH, the adult black-white genotypic gap is greater than 0.5 SD. Let’s assume that the contemporaneousness US adult (1.1 SD) gap has a between population heritability of .5 to 1, that is, that the maximal (additive) genotypic gap is .55 to 1.1 SD. What would the magnitude of the predicted age 11 (mean age of the Eyferth kids) gap be relative to the matched white group and the national norms.

As for the gap relative to the matched white group, let’s assume an age 11 H^2 and BGH^2 of 0.6 (refer to point 12 & 13); let’s also assume that the North African adult genotypic IQ is intermediate to the African-American and European American genotypic IQ. If the African american soldiers were genotyocially representative of the US population, with environment controlled for, the predicted difference between the biracial and White kids would be -3/4 x (1/2 African-American) ( x .6 age heritability x .55 Between group heritability) – 1/4 x (1/2 North African) (.6 age heritability x .5 Between group heritability) if the current gap has an BGH of .5 or -3/4 x (1/2 African-American) ( x .6 age heritability x 1.1 Between group heritability) – 1/4 x (1/2 North African) (.6 age heritability x .5 Between group heritability) if the current gap has a BGH of 1 = -.16 to -.29 SD or -2.5 to -4.3 points.

Flynn (1980) argues that the soldiers were genotypically representative of the US population, even though there was a 30% rejection rate (i.e the application gap was 1.5 SD and the bottom .5 SD were rejected). He bases his argument on evidence showing that the phenotypic difference between black and white soldiers was 1 SD. For this conclusion to follow, the rejection of the bottom 1/3 of black applicant distribution would have to have no impact on the genotypic distribution; accordingly, amongst the applicants, phenotype and genotype would have to have been uncorrelated.

If we assume that the rejection of the bottom phenotypic 1/3 resulted in the rejection of the bottom genotypic 1/3 (and shifted the mean genotypic IQ of the soldiers up +.333), given regression the the mean, predicted gap would be approximately -.116 to -.24 SD or -1.74 to -3.6 points. [-3/4 x (1/2 African-American) ( x .6 age heritability x .55 Between group heritability (minus .6 x .33 for selection and regression) – 1/4 x (1/2 North African) (.6 age heritability x .5 Between group heritability) if the current gap has an BGH of .5; -3/4 x (1/2 African-American) ( x .6 age heritability x .55 Between group heritability (minus .6 x .33 for selection and regression) – 1/4 x (1/2 North African) (.6 age heritability x .5 Between group heritability) if the current gap has a BGH of 1]

The expected magnitude of the predicted age 11 (mean age of the Eyferth kids) gap relative to the IQ norms, depends on our assumption of the environmental condition. If we assume that the kids were raised in an IQ envrionmentality equivalent to 100 (i.e average), the expected gap would be our 1.74 to 4.3 points. From an environmentalist perspective the lowest, in IQ metrics, that the cognitive environment could have been is 85. (i.e 1 SD of environmentality below the norm). [94 = (.6 H^2 x 100) + (.4 e^2 x E); E = 85.] This would give us a predicted BW-Normative gap — assuming representativity — of [.6(1.74) + .4(15)] to [(.6 x 4.3) + (.4 x 15)] 7 to 9 points. Needless to say, the biracial kids’ IQ gap with respect to the norms (6 points, Flynn corrected) is about what would be predicted by the hereditarian hypothesis (low for all scenarios 1.74 to high 9).

The real issue is that gap relative to the control group (-.05 SD instead of -.12 to -.29 SD). One possibility is that African Americans don’t have a genotypic IQ too much lower than European Americans. (The .05 SD difference is consistent with a 4 point adult difference, assumptions depending).

Another possibility is that there was sampling error. With environments controlled for, the white girls were about 1/2 of a SD below the white boys. How do environmentalists explain that, even though the German Welcher norms indicate the sexes performs equally on the test? They must say that either the white girls had a lower genotypic IQ than the white boys or that the white girls had an inferior environment relative to the white boys (and that the Biracial girls and boys had superior and inferior environments to the white girls and boys, respectively.) Either way necessitates experimental error.

Given the above, it’s difficult to see how someone could conclude that Eyferth provides “close to definitive evidence” for anything. Do Dickens et al. really believe that the low scores of ~40 American-German white girls definitatively proves the global and US environmental hypothesis?

– 4. Intermediate strong support for the environmentalist hypothesis

(20) Controlled environment Study

(Tizard et al., 1974)

This study provides weak support for the environmental hypothesis. Environment was more or less controlled for and across all assessments the pattern predicted by the hereditarian hypothesis was not found. The average age of the orphans, though, was 3.5 . At that age the H^2 (and BGH^2, we might suppose) would be rather low– less than .2 (refer to point 12 & 13).

To put the results in perspective, we can ask how much would the hereditarian hypothesis constrain us when it comes to explaining the difference, assuming the following IQs (W=101, BW= 109 BB= 106)?

First, we have to make some assumptions about the genotypic IQs of the parents, assuming a hereditarian point of view. The ancestrally African parents were immigration selected UK West Africans. Their IQ’s were unknown but Tizard tells us that “in both studies genetic aspects were neither controlled for nor adequately known, that is, the children had not been randomly assigned to different environments and no parental IQs were available. It could therefore be argued that the non-white parents may have been of higher IQ than the white parents…[w]e found no evidence to support such hypotheses…the occupations of a third of the natural fathers was unknown, but for the rest there was not significant difference between the proportion of manual and non-manual workers in the different racial groups or different home or nursery environments.” Tizard makes the case that the parents of the Black and Mixed children did not have higher IQs than the parents of the White children; by the same logic, of course, it follows that they did not have lower IQs. Any difference between the offspring, therefore, would have to be due to regression towards the mean (.6 x n; where n is the parental SD above the population mean).

Let’s assume that the adult European-West African genotypic gap is 1.3 SD. Based on this, we would predict that the Black and biracial orphans (mean age 3.5) would have IQs .16 SD and .08 SD below the White IQs, respectively. (Black: 1.3 (parental SD above the population mean) x .6 (regression) x .2 (heritability at age 3.5); Biracial: 1/2 Black.)

Now, to account for the gaps, environmentalists would have to maintain that the Black and biracial orphans had a superior environment equivalent to .6 SD and .37 SD, respectively. (Black = .53 SD above white; .53/sqrt of environmentality at age 3.5 (.8) = .6SD; Mixed = .33 above white; .33/sqrt of environmentality at age 3.5 (.8) = .37.) We know they likely did have a superior environment, because Tizard tells us “..one the other hand, in both studies the relationship between the children’s test scores and measured aspects of the environment was shown to be large and significant.”

What about hereditarians? Hereditarians would have predicted that the gaps would be slightly less in favor of the Blacks and Mixed orphans (Black= .6-.16 SD = .44 SD; Mixed .37-.08 SD = .29SD. (Environmental advantage minus genetoypic disadvantage.) This is an important and often missed point: given a Black and mixed environmental advantage and given the low H^2 at this age, hereditarians would not have predicted that the Black and Mixed orphans would have lower IQs.

Effectively, to account for the difference, hereditarians would have to maintain that the Black and biracial orphans had a slightly extra superior environment — equivalent to .77 SD and .46 SD instead of the environmentalists .6 and .33 SD. (Black = .53 + .16 SD above white; .69/sqrt of environmentality at age 3.5 (.8) = .77SD; Mixed = .33 + .08 above white; .41/sqrt of heritability at age 3.5 (.2) = .46.) Given the N’s, the difference of +.17 and + .13, relative to what the environmentalists themselves have to maintain, is not statistically significant.

In general, While this study does support the environmental hypothesis (against the global hereditarian hypothesis), it doesn’t provide more than weak support. As demonstrated by the many US early intervention programs (e.g Perry, Abecedarian, and Chicago Early Childhood program) and adoption studies (Scarr and Moore), environmental differences can lead to substantial differences in intelligence at young ages but this difference later washes out. Basically, studies that measure differences at low ages without controlling for environments or genetics are not too informative and so can not be given much weight.

-2. Weak support for the environmental hypothesis

References

Dickens and Flynn, 2006. Black Americans reduce the racial IQ gap

Eyferth, 1961. Leistungern verscheidener Gruppen von Besatzungskindern in Hamburg-Wechsler Intelligenztest für Kinder.

Fick,1929. Intelligence test results of poor white, native (Zulu), coloured and Indian school children and the social and educational implications.

Flynn, 2010. Where Have All the Liberals Gone? Race, Class, and Ideals in America

Gullickson, 2004. Amalgamations, New and Old: The Stratification of America’s Mixed Black/White Population

Harris and Thomas, 2002. The educational cost of being Multiracial: evidence from a National survey of Adolescence

Harris, 2008. From color line to color chart?: Racism and colorism in the new century

Haworth, 2009. The heritability of general cognitive ability increases linearly from childhood to young adulthood

Hill, 2000. Color Differences in the Socioeconomic Status of African American Men: Results of a
Longitudinal Stud

Hunter, 2007. The Persistent Problem of Colorism: Skin Tone, Status, and Inequality.

Jensen, 1969. How Much Can We Boost IQ and Scholastic Achievement?

Jensen, 1981b. Obstacles, problems, and pitfalls in differential psychology. In S. Scarr (Ed.), Race, social class, and individual differences in IQ

Jensen, 1998. The G-Factor: the science of human mental ability

Lee, 2009. Review of intelligence and how to get it: Why schools and cultures count, R.E. Nisbett, Norton, New York, NY (2009)

Levitt and Fryer, 2006. Testing for racial differences in the mental ability of young children

Lynn, 2008. PIGMENTOCRACY: RACIAL HIERARCHIES IN THE CARIBBEAN AND LATIN AMERICA

Lynn, 2006. Race Differences in Intelligence: An Evolutionary Analysis

Mackenzie, 1984. Explaining race differences: in IQ The Logic, the Methodology, and the Evidence

Modgil, Modgil, and Eysenck, 1986. Hans Eysenck: consensus and controversy, page. 117

Model, 2008. The Secret of West Indian Success

Moore, 1986. Family socialization and the IQ test performance of traditionally and transracially adopted black children.

Nisbett, 1998. Race, genetic, IQ.

Nisbett, 2005. Heredity, environment, and race differences in IQ: A commentary on Rushton and Jensen

Nisbett, 2010. Intelligence and How to Get It: Why Schools and Cultures Count

Parra et al. 1998. Estimating African American admixture proportions by use of population-specific alleles

Reed, 1997. The Genetic Hypothesis: It Was Not Tested but It Could Have Been

Rowe, 2002. IQ, birth weight, and number of sexual partners in White, African American, and mixed race adolescents.

Rushton, 2008. Testing the genetic hypothesis of group mean IQ differences in South Africa: racial admixture and cross-situational consistency.

Ruston and Templer, submitted 2011. IQ, Pigmentocracy, Crime, and Income in 50 U.S. States

In 50 U.S. states, we found a positive manifold across measures of IQ, skin color, violent crime, birth rate, infant mortality, life expectancy, HIV/AIDS, and GDP with the first principal component accounting for 47% of the variance (median factor loading = .78). The correlation with total violent crime was higher with skin color (r = .55), a more biologically influenced variable, than with GDP (r = -.17), a more culturally influenced variable. These results corroborate those we reported at the international level using INTERPOL crime statistics.

Scarr and Weinberg, 1976. IQ test performance of black children adopted by white families.

Scarr et al., 1977. Absence of a relationship between degree of white ancestry and intellectual skill in a black population.

Scarr et al., 1992. The Minnesota Transracial Adoption Study: A Follow-Up of IQ Test Performance at Adolescence.

Snip, 2003. RACIAL MEASUREMENT IN THE AMERICAN CENSUS: Past Practices and Implicationsfor the Future

Templer and Arikawa, 2006. Temperature, skin color, per capita income, and IQ: An international perspective.

Templer,2010. IQ and Skin Color: The Old World Reexamined and the New World

Tizard, 1974. Race and IQ

Villarreal, 2010. Stratification by Skin Color in Contemporary Mexico

Wicherts, et al. (2009). A systematic literature review of the average IQ of sub-Saharan Africans

Wicherts, et al. (2009). Why national IQs do not support evolutionary theories of intelligence

Willerman et al., 1974. Intellectual development of children from interracial matings: Performance in infancy and at 4 years.

Yeung and Pfeiffer, 2008. The black–white test score gap and early home environment

Notes

Reed, 1997. “The Genetic Hypothesis: It Was Not Tested but It Could Have Been

I wish to comment on “The genetic hypothesis” (p. 95; for the Black-White difference in psychometric intelligence) in the Neisser et al. (February 1996) article, particularly the reference to two studies that used blood groups to estimate the degree of African ancestry in American Blacks in relation to their IQ scores (they found no relation). I have experience in such admixture estimation (e.g., Reed, 1969, 1973) and, as mentioned in the target article (Reed & Jensen, 1992, 1993), in studying biological factors in intelligence. My 1969 article gave the fast estimate of the proportion of White ancestry in American Blacks (Pw) with a standard error, 0.220 ± 0.0093 (using the Duffy blood group gene Fy~), and because it was based on large samples (more than 3,000 each of Blacks and Whites), it remains the best single estimate for non-Southern American Blacks. I contend that, because of their methodology, the two studies cited above—Loehlin, Vandenberg, and Osborne (1973) and Scarr, Pakstis, Katz, and Barker (1977)–did not adequately test the possible association of cognitive ability with Pw” Consequently, their negative results provide no evidence against the genetic hypothesis. I suggest a method that, had it been used with data of the second study and if the genetic hypothesis is true, probably would have confirmed the genetic hypothesis. The methodologies of these two studies share a basic misconception–that all blood (and serum) groups are useful in estimating P. This is plainly false, as I (Reed, 1969) showed. The P estimate in this population, w using the A and B genes of the ABO blood groups, was 0.200 ± 0.044; the above esti- mate with Fy’ provides (.044)V(.0093) 2 = 22 times more information than this ABO estimate. If I had estimated Pw using the MN blood groups (both the Loehlin and Scarr groups used them), the standard error would have been even much larger than for ABO and would have been worthless (see below). The racial informativeness of a gene used to estimate P (measured by the reciprocal of the variance of Pw) is a function of its relative frequencies in the two ancestral populations, African and White. A genetic Locus I is perfectly informative (an “ideal locus”; MacLean et al., 1974; Reed, 1973) when it has two codominant alleles (genes; say I and 12), with one allele being homozygous (i.e., PI ]) in all individuals of one ancestral population and the other allele being homozygnus (I 2I 2) in all individuals of the other ancestral population. Thus, when testing an American Black, every allele at this ideal locus derived from a White ancestor is recognized as such. The Gm serum group locus (testing for nine factors) closely approximates such an ideal locus, but with multiple alleles; three are White alleles and four are sub-Saharan African alleles (Roychoudhury & Nei, 1988). The Fy ~ allele alone, with a frequency of about .43 in Whites and about 01 in Africans, is not ideal. When present in. an American Black person, we are reason- ably sure that it came from a White ancestor, but other White matings could have contributed an Fy b allele (frequency about .57 in Whites and about .01 in Africans) and so would not be recognized (when testing only for Fy~). But contrast this with the situation using the MN blood groups: In both Whites and Africans, the M and N alleles each have frequencies close to .50. This locus provides essentially no information on the ancestry of American Blacks! The consequences of using all blood and serum groups available, without regard to their great differences in racial informativeness, as the Loehlin (Loehlin et al., 1973) and Scarr (Scarr et al., 1977) teams did, are severe.

Loehlin Group

Of the eight blood-group systems used, only Duffy (using Fy ~) has some utility in Loehlin et al.’s (1973) small sample of Black persons (42 twins). Assuming that they had the equivalent of 60 unrelated individuals (their sample contains monozygous and dizygous twins), one can calculate that a P for their sample would have a standard error of about .064 and, therefore, a large 95% confidence range (about .24). Other blood groups would have considerably larger standard errors and confidence intervals and, so, give little or no information. Yet Loehlin et al. performed rank-correlation between blood-group genes (arranged in descending order of the difference between frequencies in Whites and Blacks) and association with cognitive ability. The small sample size and non-informativeness of most blood groups mean that, except for Fy ~, they were usually dealing with noise, and their negative result was to be expected.

Scarr Group

Scarf et al. (1977) used Black twins from the Philadelphia area, and the number (181) was large enough, using both Fy ~ and Fy b of the Duffy group, to give a useful estimate ofP w. Gm serum groups were determined (testing for four factors) and could also have given a useful estimate of P,. Ten other groups were also tested. Scarr et al. attempted to obtain for each individual a measure of individual ancestry to associate with an estimate of cognitive ability, but this measure is deeply flawed. They used an “odds coefficient,” log[AtAzA3…/BtBzB3…], in which A was one ancestral population (e.g., African) and B was the other ancestral population, and the subscripts were the loci of the different blood and serum groups. A t was the frequency of an individual’s phenotype (group) at Locus 1 in Population A, Bt was the frequency of his or her phenotype in Population B at that locus, and so on. This coefficient was intended to give a rank ordering of individuals according to their degree of ancestry from one population, say A. Now consider the effect of one uninformative Locus X, for example the MN blood groups, on this coefficient. Because A/B x varies essentially at random, and Ax/B x multiplies all the other ratios, the odds coefficient acquires considerable randomness. Add the random effects of other only slightly informative loci, such as the ABO, and the coefficient will necessarily lose much of its potential for ancestry identification. Scarr et al.’s (1977) procedure for dealing with zero phenotype frequencies—replacement by .0001- further distorts the coefficient, particularly for the informative Duffy and Gm groups. This is because, with the usual sample sizes, absence of a phenotype at Locus Y does not mean that its true frequency is not of the order of.01 -.001. This procedure would often bias log(A/By) by about ± 1 to ±
2. With the above problems, it is not surprising that the correlations between the odds coefficient and the measures of cognitive skills were nonsignificant; it would be surprising if they were otherwise. (Incidentally, although Scarf et al. thanked me and others for “consultation on the design and analysis of the study” [p. 86], I did not have any part in the design or analysis.)

For a More Powerful Test of the Genetic Hypothesis MacLean et al. (1974) studied 372 adult Blacks in the Rochester, New York, area for possible correlation of diastolic blood pres- sure (DBP) with proportion of African ancestry (Pa = 1 -Pw). They used 10 blood and serum groups, including Duff), and Gm, and corrected the DBP readings for gender, age, and obesity. Although they recognized that accurate individual estimates of P were not a possible (Reed, 1973), such estimates were made anyway, and the corrected DBP values were regressed on them. A very significant (p < .001) positive linear regression was found: Increasing DBP accompanied increasing P. Evidently, the overall information on P was more than adequate, although individually inaccurate. But the point of this account is that DBP is a surprisingly good surrogate for IQ score: Both are quantitative traits, are moderately heritable (h: for DBP is estimated to be 0.37 by Cavalli-Sforza & Bodmer, 1971), and have similar relative changes going from 100% African ancestry to 100% White ancestry. Furthermore, the value (3.4) for their regression is still signifi-cant at the .001 level for 120 degrees of freedom. Therefore, ~fthe genetic hypothesis was true, I predict that the MacLean et al. methodology applied to the Scarr et al. (1977) data would show this.
Scarr et al. (1977) knew of the MacLean et al. (1974) study (they referred to it), but they chose to use their own method. After expending so much effort in collecting their data, it is a pity not to analyze them properly.

Jensen (1981b).

Paraphrased:
1. The Predictive validity of the blood groups is 0.49
2. Given a normal distribution, the upper and lower thirds of the sample are 2.2. SD apart. If the upper and lower thirds of the sample, respectively, have 35% and 15% European admixture, with a difference of 20%, then 1 SD of difference is equivalent to .09 % ancestry.(.2/2.2 = .09).
3. Scarr’s group gives 0.9 SD as the average difference in test scores between Whites and Blacks in the sample. If we suppose a between group heritability of IQ of .625, on the same scale the genetic difference would be .565 SD (.625 x .9). Since Blacks in sample only have 80% African genes, the genetic difference between a European population and a 100% African population would be, on the same scale, .7SD (.56SD/.8).
4. One interpretation of a correlation coefficient is: amount of change in x, change y.
Accordingly, one shift in ancestry equals a 0.063 shift in test scores (.09 x .7 =.063) and .063 would be the correlation between test scores and genotypic ancestry.
5. Given our test score-genotypic ancestry correlation, the expected correlation between test scores and ancestry as indexed by blood groups, which have a predictive validity of 0.49, would be 0.031 (0.063x.49=.031), which is lower than the correlation found.
6. Given our predicted correlation of 0.031 the difference between the top and bottom thirds of our distribution would be 0.07SD (.031 x2.2 =.07SD), which is lower than the difference found.

Race, genes, & intelligence, part 3

The more one thought about it the less one saw any justification for the evasive, self-defeating attitude of conservatives, or the outright self-betrayal of Anglo-American liberals. One might attribute it to mawkish sentimentality, venality, blindness, or cowardice, but none of these seemed sufficient to account for the situation. Perhaps the element that appeared from my experience to come the closest to the root cause was ignorance, but a strange, self-perpetuating kind of ignorance bordering on hypnosis, an ignorance nourished by the pervasive power of the news and entertainment media after it had first been instilled by the academic hierarchy-an ignorance buttressed by feelings of guilt which the ignorance itself created.
—Putnam, 1967. Race and Reality

D. The reasonability of positing the Hereditarian Hypothesis

Given various aspects of history, why argue the hypothesis?

For a scientific justification of positing this Hypothesis or any biologically based group differences refer hereL (7, 12, 32). For a sociopolitical justification and implications, refer here (14, 18, 32, 34, 35). For a discussion of the fallacies used to derail this hypothesis, refer here (5, 6, 7, 26, 27, 41).

On the sociopolitical level, one policy of western societies is to monitor ethnoracial participation in various fields and enforce, directly or indirectly, group based quotas (38) — whether these quotas are framed in terms of Affirmative Action or Diversity Policy (37). These quota are often justified on the basis of various claims of current discrimination, when not the legacy of historic discrimination. To the extent that endogenous factors within an ethnorace are the cause of the differences, this justification does not stand (47). Hunt and Carlson (2007) give a cogent discussion of this concern (107):

A great deal of public money has been spent on programs to improve the performance of African American and Latino children by making changes within the school system. One of the most important measures in effect at the time of this article, the No Child Left Behind Act, requires school systems to reduce the scholastic achievement gap between White, African American, and Latino children or risk losing substantial federal subsidies. Other federal and state legislation requires that schooling be “equal,” in the sense that the same content and type of instruction be offered to all ethnic groups. In the Larry P. v. Riles (1984) case, a federal appellate court ordered the State of California not to use intelligence tests to place African American children in special-education classes, on the grounds that systematically poorer performance of these children on intelligence tests was inherently prejudicial.

If we take the logic of the act and the court decisions together, the California schools are simultaneously required to (a) reduce the achievement gap and (b) do so without offering separate forms of instruction to African Americans based on screening for intelligence. The requirements are reasonable, providing that three implicit assumptions are correct. The first is that the achievement gap can be reduced by offering better instruction, the second is that the difference in the test scores of African American and White children does not reflect any difference in underlying mental competence at the time of testing, and the third is that children at different levels of intelligence do not need different forms of instruction.

…

The same argument applies to anti-discrimination laws. The principle that no one should be denied social benefits or opportunities on the basis of racial/ethnic status is accepted in the United States and in most developed countries. Discrimination is proscribed. But what counts as evidence for discrimination? The law is quite clear on this point.

The Equal Opportunity Employment Commission (EEOC) defines “adverse impact” as the use of a selection instrument that results in a selection ratio (ratio of hires to applicants) for a minority group that is less than .8 of the ratio of that group in the nonminority applicant population. Consider the following example, which is based on realistic numbers.

The Wonderlic test is a short intelligence test frequently used in employee selection. On the basis of a meta-analysis of data available in the early 1970s (Wonderlic & Wonderlic, 1972), Roth et al. (2001) estimated that, in an applicant pool for a job with moderate cognitive demands,2 the difference between the African American and White applicants is .72 standard deviation units (Roth et al., table 3).3 The applicant–hiring ratio for such jobs varies greatly, depending upon the amount of training required, area of the country, and general economic conditions. For simplicity, let us assume that it is 19:1, so that the top 5% of the applicant pool is being hired. This translates to a standard score of 1.65. If the two racial/ethnic populations are assumed to have an identical distribution of talent, the EEOC rule demands that if the top 5% of the White applicants are hired, at least the top 4% of the African Americans be hired. If we apply the .73- deviation-unit difference, a score of 1.65 in the White population would translate into a score of 2.38 in the African American applicant population, and would be achieved by slightly less than 1% of the applicants—well below the EEOC’s guideline. But this is not the end of the story!

Intelligence tests are never perfect guides to performance. Suppose that, as is reasonable to believe, the correlation between test scores and job performance in the applicant population is approximately .5. (This is in the range established by Schmidt and Hunter, 1998, in their meta-analysis.) One could argue, then, that the mean difference in estimated job effectiveness is .36 instead of .72. If this reasoning is accepted, the top 2.5% of the African American population represents a pool of talent equivalent to the top 5% of Whites.
Depending on the assumptions that we make, the ratio of qualified applicants in the two applicant pools is either 1:1 (assuming d = 0), 1:5 (assuming d = .72) or 1:2 (assuming d = .36). Should the EEOC demand an impact ratio of not less than .8, .16, or .4? The choice is not a trivial one, for the choice made, combined with the proportions of the two racial/ethnic groups in the applicant population, will determine the mean effectiveness of the workforce (see Hunt, 1995, pp. 69–79). Disregarding such arguments in order to achieve equality of representation in a workforce or student body could lead to serious economic consequences (Gottfredson, 1994; Hunt, 1995). This is the point at which policy considerations come into play.

Average genetic differences are possible endogenous factor; there are, of course, others more significant factors such as culture and social capital (29, 40).

In addition to the above, to the extent the hypothesis is true some of the historic claims of discrimination will need to be revisited, along with the criticism of the ethnoeuropean culture. To quote Levin (14):

“The quota debate is patently about compensatory justice, since quotas are meant to redress injuries said to have been inflicted on blacks by whites. Lyndon Johnson introduced affirmative action for government contractors via the analogy of a man released from shackles required to run a foot race, and who (Johnson reasoned) deserves a head start to make up for his unjust handicap. Many quota advocates say they reject the redress rationale. Johnson’s metaphor has lost its vigor as it has been realized that an edge for the lame runner cheats his competitors, who are in no way responsible for his plight. In literal terms, affirmative action, particularly when state-imposed, is recognized as prima facie unfair to whites who never discriminated. To dodge this objection, defenders of affirmative action now say it is necessary to create role models or prevent renewed discrimination. Ronald Dworkin offers an elaborate rationale based on a distinction between personal and external preferences. Race spends a number of pages arguing that virtually all of these rationales tacitly rely on compensation, or share the compensatory premise that the race gap in attainment is due to harm done to blacks by whites. The compensation rationale and its many avatars are no firmer than the causal premise.”

“Because affirmative action rests on a theory of the cause of the racial attainment gap, Race argues, only the race differences in intelligence and motivation, which explain the gap more plausibly than racism, offers a convincing case against it. Negatively, racial discrimination cannot explain black failure because there is not enough of it.”

“That the attainment gap is explained by race differences in phenotypic intelligence and motivation allows heads-up compensation theorists to reply (as some have) that this phenotypic variance is itself caused by racism. Blacks don’t try because trying is pointless when the deck is stacked against them. And were racism thus indirectly responsible for the attainment gap, the compensation argument would reenter at one remove, with blacks deserving redress for their wrongfully caused dysfunctional traits. That is why letting the issue rest at the phenotypic level is inconclusive. The case for reparation can be judged only by looking at whether racism offers a better account than biology of the lower IQs and higher time preferences of blacks. The topic of genes is unavoidable, Race insists. No matter what the black shortcoming, some will insist that racism is its cause, or the cause of its cause, or the cause of that cause. Sooner or later, the genetic question must be faced.”

“In fact—this theme permeates Race—play of the gene card, far from being a gratuitous swipe at blacks, has been forced on defenders of justice by the constant diabolization of whites. It is impossible to be silent when silence amounts to an admission of guilt”

“Race contends that the “equal protection” clause of the Fourteenth Amendment is consistent with state raceconsciousness. The Fourteenth Amendment excludes only racial classifications irrelevant to any vital government function. (Inborn, involuntary and immutable traits might well be relevant, as when the state denies driver’s licenses to the congenitally blind.) The Constitutional powers-that-be have decided that compensating blacks is a sufficiently central government function to support laws burdening whites.”

(I elaborate on this in Appendix IV: The Moral basis for arguing the Hereditarian Hypothesis)

Is positing the Hereditarian Hypothesis ‘racist’?

The claim that the Hereditarian hypothesis is ‘racist,’ is either based on a gross misinterpretation of the hypothesis or a gross misinterpretation of the meaning of “racism.” With regards to the hypothesis and its meaning, let me quote Levin (14):

“For one thing, I wished to make clear that no empirical facts about race imply that whites are better than blacks, a judgment so often imputed to hereditarians that only a full airing of the issue of value can put the imputation to rest. To this end Race presents a resolutely “naturalistic,” non realist view of values. There being no empirical phenomena requiring objective value for their explanation, we have no more reason to believe in it than to believe in phlogiston, or the little man that wasn’t there. A God’s-eye view favors neither high nor low time preferences; investing $150 may be more prudent than spending it on running shoes, but it is not inherently better.”

It is, however, not uncommon for people to either think or argue that hypothesizing a genetic etiology for group differences is “racist.” For example, take this excerpt from a recent article by psychologist Jerry Hirsh:

For example, since the mid-1850s, unscrupulous authors have purported to show that intelligence is determined by race. More recently, Nobel Prize winner William B. Shockley1 of Stanford University and educational psychologist Arthur Jensen1-2 of the University of California at Berkeley have argued that genetics accounts for alleged IQ differences among the races. There is neither valid scientific justification for this alleged inferiority nor for either of its presumed explanations. (Hirsh, 2010. Race, Genetics, and Scientific Integrity.

When it comes to popular debates, definitions such as Merriam-Webster’s are often citied:

Racism is the belief that the genetic factors which constitute race are a primary determinant of human traits and capacities and that racial differences produce an inherent superiority of a particular race.[

The claim, never clearly spelled out, is either that positing in genetic etiology for some group differences is, ipso facto, racist, or that only a racist would posit a genetic etiology, and therefore, positing a genetic etiology represents a racist act of some sort.

With regards to the former, the operant phrase in the definition of racism is and that racial differences produce an inherent superiority of a particular race. While a belief that genetic factors determine traits and capacities across populations is empirical, the belief that a group is “inherently superior/inferior” is metaphysical. The later belief is a necessary part of the given definition, yet the Hereditarian hypothesis, and it’s principle advocates, make no such contention. (See Appendix II.)
(For a discussion of this refer to Appendix II

This leaves the claim that the hereditarian hypothesis is racist because it is something that only racists would consider. Obviously, given that the HH is not ipso facto racist, positing it can’t be the ground for which its proponents are deemed to be racist in the first place, so an inference needs to be drawn based on other considerations. For a discussion of this refer to: Appendix III.

Is positing the Hereditarian Hypothesis ‘dangerous’?

To quote Linda Gottfredson (39):

“Some social scientists have written that certain scientific conclusions about intelligence are too ‘‘dangerous’’ to report freely or that society is better served by well-meaning lies. But are they correct? None has provided any reasoned argument or evidence, just allusions to evil and catastrophe, to justify this stance. Might they have it backwards? To my eye, efforts to hide or shade the truth about human variation are doing grievous harm to the body politic (e.g., Gottfredson, 2005b, 2007a). Keep in mind that false belief in infinite human malleability led to some of the worst horrors of the 20th century. I also think it is patronizing and usually self-serving when elites contend that the American public cannot be trusted with certain facts. The onus, in my view, is on those who would withhold information that is so relevant to public life. We should not be surprised that the public often misunderstands the results on intelligence, because it has been systematically miseducated by media accounts, many college textbooks, and other ostensible sources of enlightenment. We should worry about the destruction that disinformation wreaks, as when critics suggest that giving credence to Black– White IQ differences would mean believing that ‘‘Blacks are inferior.’’ My experience is that people get more serious-minded about IQ differences when they start to appreciate the greater practical difficulties and health risks faced by individuals of below-average intelligence, regardless of race.”

Saying that some who posits the Hereditarian hypothesis is ipso facto racist is defamatory and libelous.

Race, genes, & intelligence, references

References

(1) Barreiro, Laval, Quach, et al., 2008. Natural selection has driven population differentiation in modern humans
(2) Beckman, 2006. The Race for Ancestral Genetics in Clinical Trials
(3) Campbell and Tishkoff, 2009. The Evolution of Human Genetic Review and Phenotypic Variation in Africa
(4) Gottfredson, 2010. The General Intelligence Factor
(5) Gottfredson, 2009. Logical fallacies used to dismiss the evidence on intelligence testing. (appendix here; for the summary and discussion refer here)
(6) Gottfredson, 2007. Flynn, Ceci, and Turkheimer on Race and Intelligence: Opening Moves
(7) Gottfredson, 2005. Suppressing intelligence research: Hurting those we intend to help
(8) Gottfredson, 2005. What if the Hereditarian Hypothesis is True?
(9) Gottfredson, 2004. Social Consequences of Group Differences in Cognitive Ability
(10) Gottfredson and Sakloske, 2009. Intelligence: Foundations and Issues in Assessment
(11) Hardimon, 2009. Wallis Simpson was Wrong
(12) Hunt and Carlson, 2007. Considerations Relating to the Study of Group Differences in Intelligence
(13) Laland, Odling-Smee, and Myles, 2010. How culture shaped the human genome: bringing genetics and the human sciences together
(14) Levin, 1997. Why Race Matters (For a review, refer here: http://mises.org/misesreview_detail.aspx?control=117)
(15) McGrew, 2009. CHC Theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research
(16) Meisenberg, 2003. IQ Population Genetics: It’s not as Simple as You Think
(17) Mountain, Risch, 2004. Assessing Genomic contributions to phenotypic differences among “racial” and “ethnic” groups
(18) Murray, 2005. The Inequality Taboo
(19) Deary, Penke, and Johnson, 2010. The neuroscience of human intelligence differences
(20) Pickrell, Coop, Novembre, et al., 2009. Signals of recent positive selection in a worldwide sample of human populations

“NRG–ERBB4 pathway Among the top selection candidates shown in Figure 1, we noticed that two—ERBB4 and NRG3—are, in fact, binding partners (Zhang et al. 1997). … Further inspection of genes in the NRG–ERBB4 pathway (Kanehisa et al. 2008) revealed a striking alignment of selection signals (Fig. 5A). ERBB4 shows extreme extreme iHS signals in all non-African populations (Fig. 5B,C), NRG3 shows extreme iHS signals in West Eurasian populations, and two other binding partners of ERBB4—NRG1 and NRG2—fall well into the 1% tail of iHS scores in East Asia (Fig. 5A). Further, ADAM17, the gene encoding the enzyme that converts NRG1 to its active form (Mei and Xiong 2008), falls in a region that contains some of the most extreme XP-EHH scores in East Asia (maximum value of XP-EHHinthe region of 4.2 at rs2709591, empirical P = 2 3 10 5). The NRG–ERBB4 signaling pathway is well-studied and known to be involved in the development of a number of tissues, including heart, neural, and mammary tissue (Gassmann et al. 1995; Tidcombe et al. 2003). Variants in genes in this pathway have been associated with risk of schizophrenia [comment:refer here] and various psychiatric phenotypes (Stefansson et al. 2002; Hall et al. 2006; Mei and Xiong 2008). We suggest that an unidentified phenotype affected by this pathway has experienced strong recent selection in non-African populations”

(21) Pritchard, Pickrell, and Coop, 2009. The Genetics of Human Adaptation Hard Sweeps, Soft Sweeps, and Polygenic Adaptation
(22) Race, Ethnicity, and Genetics Working Group, 2005. The Use of Racial, Ethnic, and Ancestral Categories in Human Genetics Research
(23) Rindermann, 2007. The Big G-Factor of National Cognitive Ability
(24) Schmitt and Quinn, 2009. Reduction in Measured Subgroup Mean Differences: What is possible?
(25) Shermer, 2009. A Noble Conception
(26) Sesardic, 2010. Nature, nurture, and politics
(27) Sesardic, 2000. Philosophy of Science That Ignores Science: Race, IQ and Heritability
(28) Soo-Jin Lee, et al., 2008. The ethics of characterizing difference: guiding principles on using racial categories in human genetics
(29) Sowell, 1995. Race and culture: a world view
(30) Templer and Arikawa, 2006. Temperature, skin color, per capita income, and IQ: An international perspective
(33) Singer, 2007. Should We Talk About Race and Intelligence?
(32) Lahn and Ebenstein, 2009. Let’s Celebrate Human Genetic Diversity
(33) Roth, Bevier, Bobko, et al., 2001. Ethnic Group Differences in Cognitive Ability in Employment and Educational Settings: A Meta-Analysis
(34) Rushton and Jenson, 2005. Thirty years of research on race differences in cognitive ability
(35) Gottfredson, 1987. The practical significance of black–white differences in intelligence
(36) Deary, Johnson, and Houlihan, 2009. Genetic foundations of human intelligence
(37) Gottfredson, 1994. From the ashes of affirmative action
(38) Gottfredson, 1994. The science and politics of race-norming
(39) Gottfredson — Interview by Howard Wainer and Daniel H. Robinson, 2009. Profiles in Research. Page 422.
(40) Mises Institute Media Podcast, 2009. Block Defends Against Charges of Racism and Sexism
(41) Rushton and Jensen, 2005. Wanted: More Race Realism, Less Moralistic Fallacy
(42) Li, Absher, Tang, et al., 2008. Worldwide Human Relationships Inferred from Genome-Wide Patterns of Variation.
(43) Zakharia, Analabha, Basu, et al., 2009. Characterizing the admixed African ancestry of African Americans
(44) Chiao and Blizinsky, 2009. Culture-gene coevolution of individualism-collectivism and the serotonin transporter gene
(45) Green, Krause, Briggs et. al., 2010. A Draft Sequence of the Neandertal Genome
(46) Gottfredson, L. S. (in press). Intelligence and social inequality: Why the biological link?
(47) Refer to Justice Ginsburg’s dissenting opinion in the Ricci v. DeSTEFANO case: http://www.law.cornell.edu/supct/html/07-1428.ZD.html And here for context.

That pretension, essential to the Court’s disposition, ignores substantial eviden of multiple flaws in the tests New Haven used. The Court similarly fails to acknowledge the better tests used in other cities, which have yielded less racially skewed outcomes.

By order of this Court, New Haven, a city in which African-Americans and Hispanics account for nearly 60 percent of the population, must today be served—as it was in the days of undisguised discrimination—by a fire department in which members of racial and ethnic minorities are rarely seen in command positions. In arriving at its order, the Court barely acknowledges the pathmarking decision in Griggs v. Duke Power Co., 401 U. S. 424 (1971) , which explained the centrality of the disparate-impact concept to effective enforcement of Title VII. The Court’s order and opinion, I anticipate, will not have staying power.

(The claim that the tests are flawed and the implication of disguises racism is obviously predicated on the conception that test disparities are a result of the tests itself.)

That pretension, essential to the Court’s disposition, ignores substantial evidence of multiple flaws in the tests New Haven used. The Court similarly fails to acknowledge the better tests used in other cities, which have yielded less racially skewed outcomes.

By order of this Court, New Haven, a city in which African-Americans and Hispanics account for nearly 60 percent of the population, must today be served—as it was in the days of undisguised discrimination—by a fire department in which members of racial and ethnic minorities are rarely seen in command positions. In arriving at its order, the Court barely acknowledges the pathmarking decision in Griggs v. Duke Power Co., 401 U. S. 424 (1971) , which explained the centrality of the disparate-impact concept to effective enforcement of Title VII. The Court’s order and opinion, I anticipate, will not have staying power.

(In principle,Title VII’s disparate-impact provision, concerns unintentional as well as deliberate discrimination, where discrimination is not defined as equal representation, but exclusion on the basis of an individual’s race alone, and for which average group differences are not directly relevant — consitutional justification would be needed to redefine disparate-impact in terms of equal group representation, per se.)

(48) Hawks, Wang, Cochran, Harpending, and Moyzis, 2007. Recent acceleration of human adaptive evolution
(49) Gläscher, Tranel, Paul, and Rudrauf, 2009. Lesion mapping of cognitive abilities
(50) Flynn, 2010. The spectacles through which I see the race and IQ debate
(51) Hirsh, 2010. Race, Genetics, and Scientific Integrity
(52) Roth, Huffcutt, Bobko, 2003. Ethnic Group Differences in Measures of Job Performance:
A New Meta-Analysis
(53) Stevens, 2007. Researching Race/Ethnicity and Educational Inequality in English Secondary Schools: A Critical Review of the Research Literature Between 1980 and 2005
(54) Rindermann, 2007. Relevance of education and intelligence at the national level
for the economic welfare of people
(55) Rindermann, 2007. The g-factor of international cognitive ability comparisons: The homogeneity of results in PISA, TIMSS, PIRLS and IQ-tests across nations
(56) Wicherts, Borsboom, Dolan, 2010. Why national IQs do not support evolutionary theories of intelligence
(57) Wicherts, Dolan, and van der Mass, 2010. A systematic literature review of the average IQ of sub-Saharan Africans
(58) Laland, Odling-Smee, and Myles, 2010. How culture shaped the human genome: bringing genetics and the human sciences together
(59) Enard, et al., 2002. Molecular evolution of FOXP2, a gene involved in speech and language
(60) Enard, et al., 2009. A humanized version of Foxp2 affects cortico-basal ganglia circuits in mice
(61) Nielsen, et al. 2007. Recent and ongoing selection in the human genome.
(62) Lopez, et al., 2009. Genetic variation and recent positive selection in worldwide human populations: evidence from nearly 1 million SNPs.

“We also used a maximum-likelihood method, implemented in the software frappe [24], to assign ancestry components to each individual without any prior assumptions about clustering of individuals into groups. In principle, the sharing of an inferred ancestry component among individuals could reflect recent admixture, ancient shared ancestry, or both. Although additional knowledge concerning the likely history of the populations involved can help distinguish among these possibilities, this is a descriptive rather than a statistical analysis. As the method does not allow inference of the most likely number of clusters (K), we ran the analysis multiple times and observed highly concordant results across multiple runs for values of K up to 6. For K = 7 and beyond different runs of the software gave different results, although continental subdivisions remained largely similar. We therefore present the results obtained with K = 6 (Fig. 4), which are largely concordant with the PC analysis and with previous such analyses of the HGDP-CEPH[13], [21]. The six clusters roughly correspond to the Americas, sub-Saharan Africa, North Africa/Europe/Middle East, Central/South Asia, East Asia, and Oceania.”

(63) Voight, et al., 2006. A map of recent positive selection in the human genome.
(64). Wang, et al., 2006. Global landscape of recent inferred Darwinian selection for Homo sapiens
(65) Way and Lieberman, 2010. Is there a genetic contribution to cultural differences? Collectivism, individualism and genetic markers of social sensitivity
(66) Harpending and Cochran, 2002. In our genes

“These selective forces must not be the same in all populations, because the 7R allele is quite common in some populations (South American Indians), exists at intermediate frequencies in others (Europeans and Africans), and is rare to nonexistent in yet others (East Asia, !Kung Bushmen).”

(67) Settle et al. 2010. Friendships Moderate an Association Between a Dopamine Gene Variant and Political Ideology

“We find that the number of friendships a person has in adolescence is significantly associated with liberal political ideology among thosewith DRD4-7R”

(68) Beaver et al., 2010. Three dopaminergic polymorphisms are associated with academic achievement in middle and high school

“The 7R allele was coded as the risk allele…DRD4 was related to grades in all four subjects at wave 1 and history and science atwave 2. The reasons why these genes had different effects on different subjects at different times are not immediately obvious. The differential genetic effects, however, likely are produced, in part, by the fact that performance in certain academic subjects depends on the use of specific regions of the brain.”

(69) Beckman, 2006. The Race for Ancestral Genetics in Clinical Trials; Race, Ethnicity, and Genetics Working Group, 2005. The Use of Racial, Ethnic, and Ancestral Categories in Human Genetics Research; Rotimi, 2005. Understanding and Using Human Genetic Variation Knowledge in the Design and Conduct of Biomedical Research. (Discusses the relevance of Race in biomedical research)

(70) For a defense of the concept of human subspecies refer to: Woodley, 2009. Is Homo sapiens polytypic? Human taxonomic diversity and its implications; Crow, 2002. Unequal by nature: a geneticist’s perspective on human differences; Mayr, 2002. The biology of race and the concept of equality; Sesardic, 2010. Race: a social destruction of a biological concept

(71) Cluster analysis shows that there are 5-7 main human populations. For example, look at figure 1 in Bastos-Rodriguez, Pinmenta, Penal, 2006. The Genetic Structure of Human Populations Studied Through Short Insertion-Deletion Polymorphisms

Given enough loci to analyze, individuals can be definitively assigned to populations or a set of them. Refer to: Edwards, A.W.F. (2003). Human genetic diversity: Lewontin’s fallacy

(72) Bailey and Geary, 2010. Hominid Brain Evolution: Testing Climatic, Ecological, and Social Competition Models

Abstract: Hypotheses regarding the selective pressures driving the threefold increase in the size of the hominid brain since Homo habilis include climatic conditions, ecological demands, and social competition. We provide a multivariate analysis that enables the simultaneous assessment of variables representing each of these potential selective forces. Data were collated for latitude, prevalence of harmful parasites, mean annual temperature, and variation in annual temperature for the location of 175 hominid crania dating from 1.9 million to 10 thousand years ago. We also included a proxy for population density and two indexes of paleoclimatic variability for the time at which each cranium was discovered. Results revealed independent contributions of population density, variation in paleoclimate, and temperature variation to the prediction of change in hominid cranial capacity (CC). Although the effects of paleoclimatic variability and temperature variation provide support for climatic hypotheses, the proxy for population density predicted more unique variance in CC than all other variables. The pattern suggests multiple pressures drove hominid brain evolution and that the core selective force was social competition.

(73) Odokuma, Igbigbi, Akpuaka, and Esigbenu, 2010. Craniometric patterns of three Nigerian ethnic groups

The findings in this study are similar to previous studies (Morton, 1839) where the mean cranial volume of the skulls of whites was 1,425 cm³, while that of the Blacks was 1,278 cm³. Based on the measurement of 144 skulls of Native Americans, Morton (1839) reported a figure of 1,344 cm³. Gould (1981) and Rushton (1995) have also showed very similar figures. Tribe had a significant effect on cranial volume at 0.05 levels of significance. Intercultural comparisons demonstrated significant variation as reported by Howells (1989), Froment (1992) and Lahr (1996). While the Ibo’s had an average cranial capacity of 1273.39 cm3, that of the Urhobo’s was 1255.89 cm3. The Edo’s was 1310.08 cm3. This may be attributable to a common ancestral origin of the Ibo and Urhobo people or inter marriages which are very common between these cultures with interchange of physical characteristics over the years since these people have been cordial neighbours. Also the cranial volume of male (1334.34 cm3) was significantly different from that of female (1204.54 cm3) in all the studied tribes, male being larger than that of female p< 0.05. This important characteristic which was also previously observed (Rushton, 1995) is very important in sex determination. Cranial volume has demonstrated strong sexual dimorphic patterns and thus individuals from the studied populations can be differentiated from those of other races…

The craniometric patterns of three indigenous Nigerian ethnic groups have been presented highlighting certain features common to Nigerians and perhaps indeed West African populations. It has also been shown that craniometric patterns are significant indices for inter ethnic differentiation of population groups. In spite of these observations, similarities which enabled intracultural differentiation did occur as exhibited by craniometric patterns in this study. Inevitably therefore, craniometric studies are most essential in the study of population dynamics especially with respect to quantitative variables. This study has further demonstrated the well established genealogy that the three studied populations may have evolved from a common ancestral origin.

(74) Rushton and Ankney, 2009. Whole Brain Size and General Mental Ability: A Review

(75) Templer, et al. 2002. Asian-Black differences in aptitude and difficulty of chosen academic discipline

(76) Ash and Gordon. Brain Size, Intelligence, and Paleoclimatic Variation. In Geher and Miller, 2008. Mating intelligence: sex, relationships, and the mind’s reproductive system/

(77) Rushton and Jensen, 2010. Race and IQ: A Theory-Based Review of the Research in Richard Nisbett’s

(78) As Neisser et al., 1996. Intelligence: Knowns and unknowns

Implications. Estimates of h* and c* for IQ (or any other trait) are descriptive statistics for the populations studied. (In this respect they are like means and standard deviations.) They are outcome measures, summarizing the results of a great many diverse, intricate, individually variable events and processes, but they can nevertheless be quite useful. They can tell us how much of the variation in a given trait the genes and family environments explain, and changes in them place some constraints on theories of how this occurs. On the other hand they have little to say about specific mechanisms, i.e., about how genetic and environmental differences get translated into individual physiological and psychological differences. Many psychologists and neuroscientists are actively studying such processes; data on heritabilities may give them ideas about what to look for and where or when to look for it. A common error is to assume that because something is heritable it is necessarily unchangeable. This is wrong. Heritability does not imply immutability. As previously noted, heritable traits can depend on learning, and they may be subject to other environmental effects as well. The value of h* can change if the distribution of environments (or genes) in the population is substantially altered. On the other hand, there can be effective environmental changes that do not change heritability at all. If the environment relevant to a given trait improves in a way that affects all members of the population equally, the mean value of the trait will rise without any change in its heritability (because the differences among individuals in the population will stay the same).

(79) Johnson, 2009. The global bell curve: Race, IQ, and inequality worldwide, Richard Lynn, Washington Summit

(80) Rindermann, Sailer, Thompson, 2009. The impact of smart fractions, cognitive ability of politicians and average competences of peoples on social development

Because average, upper and lower levels are correlated there are at first sight no large differences: The highest values for the smart fractions are found in East Asia …followed by Western and Eastern European and North American countries, by South European countries, Arab or Muslim and Latin American countries and finally by sub-Saharan countries.

(81) Jensen, A. R. (2006). Comments on correlations of IQ with skin color and geographic–demographic variables. Intelligence, 34, 128–131.

A large number of national and geographic population samples were used to test the hypothesis that the variation in mean values of skin color in the diverse populations are consistently correlated with the mean measured or estimated IQs of the various groups, as are some other physical variables, known as an ecological correlation. Straightforward statistical analyses clearly bear out the hypothesis, showing a significant positive ecological correlation between lightness of mean skin color and mean IQ across different populations. The main limitation of such a study design is that correlations obtained from this type of analysis are completely non-informative regarding any causal or functional connection between individual differences in skin pigmentation and individual differences in IQ, nor are they informative regarding the causal basis of the correlation, e.g., simple genetic association due to cross-assortative mating for skin color and IQ versus a pleiotropic correlation in which both of the phenotypically distinct but correlated traits are manifested by one and the same gene.

(82) Gelade, G. A. (2008). IQ, cultural values, and the technological achievement of
nations

(83) Rushton and Jensen, 2010. The rise and fall of the Flynn Effect as a reason to expect a narrowing of the Black–White IQ gap

(84) Jensen, 1998. The G-Factor

(85) Anonymous, 2008. Why Family Income Differences Don’t Explain the Racial Gap in SAT Scores. In: The Journal of Blacks in Higher Education; Winter 2008/2009.

(86) Cross, 1994. Black Africans are the most highly educated group in British society. In: The Journal of Blacks in Higher Education; Spring.

(87) Department of education and skills, 2005. Key stage 1 data.

(88) Leslie, 2005. Why people for the UK’s minority ethnic community achieve weaker degree results than whites.

(89) Burgard, 2002. Does race matter? Children’s Height in Brazil and South Africa.

(90) Rushton, 1998. Secular gains in IQ not related to the g factor and inbreeding depression Ð unlike Black±White differences: A reply to Flynn

(91) Rushton, 2003. Race differences in g and the “Jensen Effect.”

(92) Beals, et al., 1884. Brain Size, Cranial Morphology, Climate, and Time Machines

(93) Rowe and Cleveland, 1996. Academic achievement in Blacks and Whites: Are the developmental processes similar?

(94) Rowe, Vazsonyi, and Flannery, 1994. No more than skin deep: ethnic and racial similarity in developmental process.

(95) Waldman, Weinberg, Scarr, 1994. Racial-group differences in IQ in the Minnesota Transracial Adoption Study: A reply to Levin and Lynn

Second, whatever researchers’ beliefs are regarding the etiology of racial-group differences in IQ, terms such as hereditarianism and environmentalism do not do justice to what is likely to be the true state of affairs in nature regarding the etiology of racial-group differences in IQ. We think that it is exceedingly implausible that these differences are either entirely genetically based or entirely environmentally based. The true causes of racial-group differences in IQ, or in any
other characteristic, are likely to be too complex to be captured by locating them on a single hereditarianism-environmentalism dimension. Furthermore, such terms represent a qualitative shorthand for issues that are explicitly quantitative and should be expressed as such (Loehlin, 1992). We feel that terms such as hereditarianism and environmentalism blur important quantitative differences rather than increase their clarity.

(96) Levin, 1994. A Comment on the Minnesota transracial adoption study

(97) Meisenberg, 2010. The reproduction of intelligence

(98) Vining, 1982. On the possibility of the reemergence of a dysgenic trend with respect to intelligence in American fertility differentials

(99) Hartmann, Kruusea, and Nyborg. Testing the cross-racial generality of Spearman’s hypothesis in two samples

(100) Shockely, 1972. Dysgenics, Geneticity, Raceology: A Chalenge to the Intelectual Responsibility of Educators

(101) Johnson, W., te Nijenhuis, J., & Bouchard, T. J., Jr. (2008). Still just 1 g: Consistent
results from five test batteries. Intelligence, 36, 81-95.

(102) Chabris, 2007. Cognitive and neurobiological mechanisms of the law of general intelligence. In M. J. Roberts (Ed.), Integrating the mind: Domain general versus domain specific processes in higher cognition/

(103) Jung and Haier, 2007. The Parietal-Frontal Integration Theory (P-FIT) of
Intelligence: Converging neuroimaging evidence.

(105) McDaniel, 2007. Big-brained people are smarter: A meta-analysis of the relationship between in vivo brain volume and intelligence.

(106) Montgomery, 2010. Brain Evolution: Microcephaly Genes Weigh In.

(107) Hunt and Carlson, 2007. Research on group differences in intelligence is scientifically valid and socially important

(108) Posthuma et al., 2002. The association between brain volume and intelligence is of genetic origin

(109) Zhang et al., 2010. Meta-analysis of genetic variation in DTNBP1 and general cognitive ability

(110) Hardelid et. al., 2007 The Birth prevalence of PKU in populations of European, South Asian and Sub-Saharan African Ancestry living in South East England

(111) Eisenberg  et. al., 2010 World wide allele frequencies of the human apolipoprotein E gene: climate, local Adaptations, and evolutionary history

(112) Jones, 2008. IQ
in
the
Production
Function:

Evidence
from
Immigrant
Earnings

(113) Brown, et al., 2011. Evolutionary accounts of human behavioural diversity

g and group differences

The size of the average Black–White (B–W) difference (expressed in standardized units) on various psychometric tests varies as a direct function of the tests’ differing g loadings. In other words, the B–W difference on psychometric tests (and their many external correlates as well) is mainly a function of g, so that the more a test measures g, the more it discriminates between unselected groups of Blacks and Whites. No other feature of tests is as highly correlated with the variable size of the mean B–W difference on various tests. Nor are socioeconomic status or other social background factors as sharply predictive of B–W differences on a given test as is the g factor. Because g is the primary effective factor both in the practical validity of tests and in the magnitude of the B–W difference in unselected groups, the conjunction of these two effects is the unavoidable cause of adverse impact when g-loaded tests are used in selection. This is a more serious matter than if the tests were merely biased in cultural content: first, because it is not possible to rid cognitive tests of g and still have them remain valid for any practical purpose; and second, because individual differences in the level of g resist intentional change.

–Jensen, 2000. The Dilemma of group differences: Testing: The Dilemma of Group Differences

Average differences in g as of the 90’s, d = .8 to 1.1 (age >16)

Academic

Industrial and Military

Differences in Job performance (2), d = .27 as of the 2000’s. See also (12).

Notice, objective tests are significantly more “biased”:

Differences on work sample test (i.e. job simulation exercises) as of the 2000’s (3), d = varying. See also (12).

The difference is larger in cognitively loaded tests:

Differences in trainability as of the 2000’s (4), d = varying

The difference is larger for cognitively loaded skills:

Differences on Academic Achievement tests (grades k-12) as of the 2000’s (5), d (averaged) = .7 to .8

Differences in IQ across all ages (6) as of the 2000’s, d (averaged) = 0.9; d (cumulative across age) = .95

Differences on various non-IQ psychological tests as of the 2000’s, d = varying

Differences in pyschomotor ability (7), d = – .7

Different rates in mild mental retardation (9), d = .66 SD (see: 10)

Different rates in Giftedness (11)

Implications of differences, were the US to embrace meritocratic equal opportunity, abolish ethnoracial quotas, and use standard forms of assessment (8):

References

(1) Roth et al., 2001. Ethnic group differences in cognitive ability and educational setting: A meta-analysis

(2) Roth, Bobko, and Huffcut, 2003. Ethnic Group Differences in Measures of Job Performance: A New Meta-Analysis

(3) Roth, et al., 2008. Work Samples tests in personal selection: A meta-analysis of Black-White differences in overall and exercise scores

(4) Roth, 2010. Updating the trainability tests literature on black-white subgroup differences and reconsidering criterion-related validity.

(5) Sackett and Shen, 2008. Subgroup differences on Cognitive tests in contests other than personal selection

(6) Dickens and Flynn, 2006. Black Americans reduce the racial IQ gap; Gottfredson, 2005. Implications of cognitive differences for schooling within diverse societies

(7) Ployhart, 2008. THE DIVERSITY–VALIDITY DILEMMA: STRATEGIES FOR REDUCING RACIOETHNIC AND SEX SUBGROUP DIFFERENCES AND ADVERSE IMPACT IN SELECTION

(8) Gottfredson, 2006. Social consequences of group differences in cognitive ability

(9) Chapman, et al., 2008. Public Health Approach to the Study of Mental Retardation

(10) Plomin and Spinath, 2004. Intelligence: Genetics, Genes, and Genomics

Although no twin studies of severe mental retardation have been reported, an interesting sibling study shows no familial resemblance. In a study of over 17,000 children, 0.5% were moderately to severely retarded (Nichols, 1984). As shown in Figure 4 (dotted line), siblings of these retarded children were not retarded. The siblings’ average IQ was 103, with a range of 85 to 125. In other words, moderate to severe mental retardation showed no familial resemblance, a finding implying that mental retardation is not heritable. In contrast, siblings of mildly retarded children (1.2% of the sample) tend to have lower than average IQ scores (see Figure 4, solid line). The average IQ for these siblings of mildly retarded children was only 85. Similar findings—that MMR is familial but moderate and severe retardation are not familial—also emerged from the largest family study of MMR, which considered 80,000 relatives of 289 mentally retarded individuals (Reed & Reed, 1965).

(11) Yoon and Gentry, 2009. Racial and Ethnic Representation in Gifted Programs: Current Status of and Implications for Gifted Asian American Students.

(12) Roth et al., 2011. TOWARD BETTER META-ANALYTIC MATRICES: HOW INPUT VALUES CAN AFFECT RESEARCH CONCLUSIONS IN HUMAN RESOURCE MANAGEMENT SIMULATIONS

The many causes hypothesis

Given the absence of X-factors, the high within group heritability of intelligence poses a problem for environmentalists. Briefly, the variance between two groups can either result from factors which only act between but not within groups (X-factors) or it can result from factors which also cause variance within both groups (variable factors). Since X-factors have been empirically ruled out (1) in the said case, the difference between the groups must be due to variable factors. High heritability, though, constrains the possible influence of individual variable factors.

Given this situation, environmentalists must either maintain that gene-environment interactions confound heritability estimates (let’s call this the gene-environment hypothesis) or argue that numerous variable factors, in aggregate, cause the gap (let’s call this the many causes hypothesis). I will address the many causes hypothesis.

The many causes hypothesis was recently argued by Nisbett (2010). Nisbett holds that the gap could be due to the additive effect of wealth disadvantage, teenage motherhood, discrimination, caste-like status inhibiting “effort optimism,” unstable marriages, stereotype threat, etc. Hunt and Carlson in Hunt and Carlson (2007) agree with this idea, stating: “It is quite possible that the present discrepancy in achievement is due to multiple small and subtle social effects, many of which may be due to cultural practices in the affected groups, such as attitudes toward education, indirect effects of health practices, and relative degrees of family solidarity.”

How many possible causes have been proposed? Quite a few. How does the intricate web of Hereditarian logic stand in wake of these 5 score and some possible causes? Let’s review that web of logic:

A. As the various measures have high validity, the gap is not due to test bias of any sort. It’s real.
B. There’s no minority specific cause to the gap.
C. The gap, both in IQ and IQ correlates, persists across SES (See note 2).
D. The gap is general intelligence loaded and general intelligence has substantial structural and function neurophysiology correlates. (See note 7.) As such, the gap has a substantial biological component. According to Flynn (2010), the GQ gap is slightly larger than the IQ gap and the adult IQ gap is over 1SD.
E. If gap is 100% environmental, African-Americans adults would have to live in an “cognitive environment” equivalent to that of the bottom white 2.2 percent.

Let’s revisit that last point. Given the function BGH = WGH(rg(1-rp)/rp(1-rg), between group heritability (BGH), within group heritability (WGH), and the genetic/ environmental variance within and between groups (rg) are mathematically related. As such, the amount of within group difference needed to cause a between group difference (assuming no X-factors) can be determined given a specific BGH and WGH value. [You can use the simple formula: xSD between divided by the square root of the environmental variance = y SD within].

If we assume that the BGH is zero and that the WGH is zero:

1 SD of between group difference would require 1 SD of within group difference (1/1) or that the mean black cognitive environment was equivalent to the cognitive environment of the white lower 16 percent. You can use this calculator to transform SD to percentile.

If we assume that the adult BGH is zero and that the WGH is .75 (see note 3):

1 SD of between group difference would require 2 SD of within group difference (1/.5)or that the mean black cognitive environment was equivalent to the cognitive environment of the white lower 2.2 percent.

And this, as we said, precludes single variable factors. Imagine if the difference in IQ between college age blacks and whites was said to be due to hours of college homework done (h). If so, since h is a factor that varies within groups, we would have: 1 SD of between group difference = 2 SD of within group difference = 2 SD of difference in (h) or that the mean number of hours of homework done by African-American students is equivalent to the number of hours done by the white lower 2.2 percent. That this is not the case can easily be verified (4).

However, the above does not preclude the possibility that many factors add up to create the difference. 1 SD of between group difference = 2 SD of within group difference = 2 SD of difference in (h + A + B + C + D + etc.). There are two reasons why this is improbable. The first is conveyed by the often referred to graph of SAT differences across SES and the second follows from the second half of Jensen’s with-between group heritability argument.

1) A and D effectively rules out all testing bias explanations (Test Performance and Test-Taking Motivation, Performance of African Americans Suffers in Stereotype-Priming Situations, etc.); B, as we said, effectively rules out minority specific explanation (Castelike/Involuntary Minority Groups, Racism in Young Children, etc). This is important because such factors would more or less be equally distributed across SES. Other possible causes like poverty (e.g. Prenatal Exposure to Pollutants, Effects of Early Povert, etc) vary across SES. Since the factors which would have been equally distributed across SES have been effectively ruled out, the gap for high SES blacks would have to be caused by a different combination of factors that cause the the gap for low SES blacks. Yet (C) the difference (at least in SAT scores) is relatively constant across SES (see note 2). It’s improbable that roughly the same magnitude of difference would be caused by rather different sets of factors (Number of Toys + Postnatal Exposure to Pollutants + Inequality in Health Care) for low SES blacks and (Cultural Expectations + Oppression Resistance + Subconscious Racial Stereotypes) for high SES blacks.

More still, the number of possible explanations for the high SES black-white gap are severely constrained. Given Dthe gap must be partially developmental in nature; it is a biological gap the emerges across the development periods of Black and white children and young adults. Poverty explanations (Prenatal Exposure to Pollutants, etc) could account for this amongst low SES blacks but obviously not high SES blacks. For high SES blacks, the explanations (for differences in g) are limited to “cultural” factors which could influence the developmental process and create substantially biological differences (e.g a history of not exercising one’s brain). These “cultural” explanations, in turn, are constrained by heritability.

If we control for SES, the adult B-W g-gap is .8 SD. To explain that difference, 1.6 SD of general intelligence (i.e. biological) affecting “cultural” is needed; this “culture” of course must be unrelated to SES. This means that black SES independent “culture” must be equivalent to the “culture” of the lower white 6 percent; it isn’t.

2) Since, assuming BGH = 0, 1 SD of between group difference = 2 SD of within group difference, and since 1 SD of IQ = 15 points, an adult between group difference of 15 points would be equivalent to a within group difference of (15 x 2) = 30 points. As BGH is said to be zero, groups are considered to be genetically equal. So the 20 point difference would be equivalent to a 30 point difference between pairs of Monozygotic twins as opposed to the expected ~6.5 points. The largest ever reported difference between MZ twins, however, was 24 points (or 1.5 SD). Moreover, the average absolute difference (due to both genetics and environment) between all random pairs of individual in a population would be 17 points (or 1.1284 SD). Given this, it’s highly improbable that BGH = 0.

[We can continue with the line of thinking and derive what we found above in another manner. To say that the B and W subpopulations are genetically identical, is to say that these subpopulations are equivalent to being sets of identical twins. As we said, the total average difference between twins raised in modern Western environments is ~6.5 points (as compared to ~17 for any random pair of individuals). Given a total average difference of 6.5 points, the standard deviation between twins is about 5 points (see note 6). Since the B-W difference is 15 points, there must be 3 SD (15/5) of environmental effect between them, which is to say that Bs must live in a cognitive environment equivalent to the Ws 0.14 percent. Which they don’t.]

This is why Jensen (1981) concluded that “[t]he constraining implication of h^2/W for h^2/B can be escaped only by making a different assumption (note that it is presently an assumption) — namely, that the phenotypic difference between the groups is attributable to a source of monogenetic variation (other than measurement error and sampling error) that does not contribute to phenotypic variation (i.e individual differences within either group.”

Taken together these two reasons make the many factors (h^2=0) hypothesis highly improbable — Which is why Flynn decided to explore the gene-environment hypothesis. Unlike Nisbett (2010) and Hunt and Carlson (2007), Flynn grasped the strength of Jensen’s argument.

[Now, it could be argued that the IQ difference is no longer 1 SD and when it was 1 SD, that the heritability of IQ for African American adults was significantly lower than 75%. One could, for example, cite Flynn and Dickens (2006); when projecting their data out to year 2002, Flynn and Dickens found an average IQ of 88 for African Americans below the age of 24. This would give a within difference of only 1.29SD (.8/.62) or 15.5 points; this is higher than the expected 6 points but not unexplainable by environmental causes. Yet Flynn and Dickens, nonetheless, found an average IQ of 83.5 for African Americans above the age of 24 (see note 5); and now there is no reason to think that the heritability of IQ for African Americans is lower than 0.75. Moreover, when one focuses only on the average between family environmental difference (< .12), which is the only relevant portion of the difference when it comes to B-W gap, one gets (1/.34) or ~ 3 SD of needed environmental affect! [Note: I recalculated the average IQ gap and derived and average of .9 to .95.

Notes

(1) Both Flynn and Nisbett have conceded this. This is substantial given that an X-factor explanation was long held to be the cause of the gap. For example, refer to Sandra Scar’s A reply to some of professor Jensen’s Commentary. In: Race, social class, and individual differences in I.Q.

X-Factors were empirically ruled out by the following studies:

Lubke, et al. 2003. On the relationship between sources of within- and between-group differences and measurement invariance in the common factor model

Rowe, 1994. No more than skin deep. American Psychologist; Rowe and Cleveland, 1996. Academic achievement in Blacks and Whites: Are the developmental processes similar?

Rowe, et. al., Vazsonyi 1994. No more than skin deep: Ethnic and racial similarity in developmental process.

Rowe, et. al., 1995. Ethnic and racial similarity in developmental process: A study of academic achievement.

(2) Controlling for SES, there is a 12 point (0.8 SD) IQ difference. If we assume that none of the SES difference is due to genetics, and assume a .75 within race heritability at age 24 (since at the same SES, there is no reason to assign a lower black h^2 than white h^2), we get 1.6 SD of difference, meaning that controlling for SES, for a 0 genetic hypothesis to be true, blacks must live in a cognitive environment equivalent to the white lower 5.5 percent. (.8/sqrt .2) = 1.6 SD.

(3) The h^2 of IQ (and GQ) increases with age. By adulthood the heritability of general intelligence approaches .80 in industrialized countries (Deary, Penke and Johnson, 2010). Similar heritabilities have been found elsewhere; Pal, Shyam and Singh (1997) found a heritability of .81 for rural adult (mean age 21) Indians; Nathawat and Puri (1995), as reported by Pal, Shyam and Singh (1997), found a heritability of .9 for urban adult Indians.

(4) From U.S. Department of Education survey of the conduct and attitudes of the parents of American students from kindergarten through grade 12 (2005):
93% of Black parents but only 82% of White parents regularly check to see that their children complete their homework assignments.
46% of Black parents but only 32% of White parents help their children with their homework three or more days a week.
49% of Black parents but only 41% of White parents visited a library with their children.

(5) I’ll address this in another post. Basically the “default hypothesis” (i.e. Hereditarian hypothesis) proposes that the between group differences are caused by the same factors which cause within group differences (genes and environment). Given that the heritability of IQ increases with age within groups, it should increase with age between groups. (Though, to note, Rushton has often argued (in error in my opinion) that the differences are more or less constant from age 6 up.) Another way to say this is that genetic differences impose themselves more as one ages. Differences in children at younger ages that are out of proportion to the expected genetic affect, accordingly, are due to environmental influences (the IQ of parents) — differences which can be reduced (e.g. by outsourcing parenting by means of preschools, etc).

(6) Jensen (1973) gives the twin standard deviation formula:

The MZ Twin SD (4.74) is equal to sqrt of the environmental variance [sqrt (22.5)]. The environmental variance (22.5) is equal to the total variance (15^2) minus the genetic variance (.85 x 15^2) and the error variance (-1.95(15^2). The genetic variance is the twin correlation X the population SD (15). The twin correlation (.85) is r = 1-(|dk|/|dp|)^2, where
|dk| is the mean absolute difference between kin
|dp| is the mean absolute difference between all random people
and
|dp|=2pi/sqrt(pi) = 1.13SD

(7) In representative studies Karama et al (2009) and Karama et al (2011) found that general intelligence correlated with cortical thickness and that general intelligence completely mediated the correlation between cortical thickness and cognitive performance.

The same relation between psychometric g and neurological g exists for both Blacks and Whites.

References

Deary, Penke and Johnson, 2010. The neuroscience of human intelligence differences

Flynn and Dickens, 2006. Black Americans reduce the racial IQ gap.

Hunt and Carlson, 2007. Research on group differences in intelligence is scientifically valid and socially important

Jensen, 1981. Obstacles, problems, and pitfalls in differential psychology

Jensen, 1998. Population Differences In Intelligence: Causal Hypotheses. In: The g Factor: The Science of Mental Ability

Karama, et al., 2009. Positive association between cognitive ability and cortical thickness in a
representative US sample of healthy 6 to 18 year-olds

Karama et al., 2011. Cortical thickness correlates of specific cognitive performance accounted for by the
general factor of intelligence in healthy children aged 6 to 18

Pal, Shyam and Singh (1997), Genetic analysis of general intelligence ‘g’: A twin study

The gene-environment hypothesis

When it comes to the B-W IQ gap there are three possible types of non-hereditarian explanations: the gap is due to different frequencies of IQ affecting environmental factors (VE-factors) which vary within both populations (e.g. SES or average savings or bad schools), the gap is due to some gene-environment factors which are as yet undetected by heritability estimates, or that gap is due to IQ affecting environmental factors (X-factors) which are unique to one or the other population and are more or less equally distributed across that population.

X-factor explanations were always theoretically implausible. For one, Blacks and Whites more or less live in the same environment unlike, for example, Americans and North Koreans or Americans now and Americans 50 years from now; given this, it’s rather hard to think of factors which could uniquely affect one or the other population and affect that population more or less equally. Additionally, X-factor explanations require a rather unparsimonious dual hypothesis for intellectual differences within the US; accordingly, the causes of differences within populations are completely distinct from the causes of differences between populations.

Given this situation, and given that high within group heritability of intelligence precludes any one VE factor from accounting for the Gap, environmentalists are left with either arguing that numerous VE factors sneak through the cracks of heritability and cause the gap (let’s call this the many causes hypothesis) or maintaining that gene-environment correlations confound heritability estimates (let’s call this the gene-environment hypothesis). Unlike Nisbett (2010), Flynn (2010) concedes that Jensen’s within-group/between group heritability argument makes implausible the many causes hypothesis. Instead, he tries to escape Jensen’s “steal chain of ideas,” as he calls it, by proposing a COVGE model of heritabiltiy. This is a seductive proposition because it offers a single unified explanation for within, between, and across cohort variations in intelligence. Jensen long argued that a between group genetic explanation should be treated as the default explanation as it offers the most parsimonious account of differences within and between groups (X-factors need not apply). With the gene-environment model, Flynn one ups the genetic explanation and offers a single account which can incorporate individual, group and cross cohort differences in IQ.

Let’s look at what Flynn (2010) has to say about intelligence and within group heritability:

Originally, Jensen argued: (1) the heritability of IQ within whites and probably within blacks was 0.80 and between family factors accounted for only 0.12 of IQ variance — with only the latter relevant to group differences; (2) the square root of the percentage of variance explained gives the correlation between between-family environment and IQ, a correlation of about 0.33 (square root of 0.12=0.34); (3) if there is no genetic difference, blacks can be treated as a sample of the white population selected out by environmental inferiority; (4) enter regression to themean — for blacks to be one SD below whites for IQ, they would have to be 3 Sds (3×.33=1) below the white mean for quality of environment; (5) no sane person can believe that — it means the average black cognitive environment is below the bottom 0.2% of white environments; (6) evading this dilemma entails positing a fantastic “factor X”, something that blights the environment of every black to the same degree (and thus does not reduce within-black heritability estimates), while being totally absent among whites (thus having no effect on within-white heritability estimates). [comment: When it comes to X-factors. Flynn is being disingenuous — years before developing his 2001 model with Dickens, he considered the Flynn effect and X-factors, in general, to be theoretically plausible and, presumably, not “fantastic” accounts for the said racial group differences]

I used the Flynn Effect to break this steel chain of ideas: (1) the heritability of IQ both within the present and the last generations may well be 0.80 with factors relevant to group differences at 0.12; (2) the correlation between IQ and relevant environment is 0.33; (3) the present generation is analogous to a sample of the last selected out by a more enriched environment (a proposition I defend by denying a significant role to genetic enhancement); (4) enter regression to the mean — since the Dutch of 1982 scored 1.33 SDs higher than the Dutch of 1952 on Raven’s Progressive Matrices, the latter would have had to have a cognitive environment 4 SDs (4×0.33=1.33) below the average environment of the former; (5) either there was a factor X that separated the generations (which I too dismiss as fantastic) or something was wrong with Jensen’s case. When Dickens and Flynn developed their model, I knew what was wrong: it shows how heritability estimates can be as high as you please without robbing environment of its potency to create huge IQ gains over time.

To sum up: Jensen used the high heritability of IQ to argue that, in absence of X-factors, differences between populations were not likely due to variable environmental factors and therefore most probably had a partial genetic basis. Flynn turns this logic around, using it to construct the IQ paradox and argue that the conventional understanding of heritability must be wrong. Accordingly, given the high heritability of IQ, the rapid secular increase in IQ can not be due to variable environmental factors and so must be due to genetics or X-factors; as the latter two explanations are supposedly implausible, there must be something incorrect about the conventional ( g + e) understanding of heritability.

Flynn concludes that high heritability doesn’t constrict vE explanations for differences (both between cohorts and between populations) because there must be some variable environmental factors which go undetected by heritability studies (i.e. variable environmental factors that are recursively correlated with genotype). That is, heritablility estimates must miss gene x environment correlations (rGE).

From Dickens and Flynn (2001):

“Thanks to industrialization, it is likely that the cognitive complexity of the average person’s job has increased over the last century. There is no doubt that more-demanding educational credentials control access to a wide range of jobs. There are far more people in scientific, managerial, and technical positions than ever before.6 Increased leisure time is another possible trigger for IQ gains, as some activities undertaken during extended“

“Between generations, the mask slips. For it to do its work, the worse environment of the earlier generation would have to be matched by worse genes for IQ; and the better environment of the later generation would have to be matched by better genes for IQ. However, because the two generations are equivalent for genes, there is no matching and therefore no masking. The potency of environmental factors stands out in bold relief.”

Initially, the COV GE model seems reasonable; when it comes to individual differences, it doesn’t seem outlandish to suppose that naturally born intellectuals might increase their verbal IQ through bookish behavior. On the subpopulation level, likewise, it’s doesn’t seem implausible that a propensity for studiousness might lead to cognitive enhancement. Yet, there are a few bumps which preclude a simple GE model for the US subpopultion differences.

When it comes to subpopultion differences in the US, the differences are general intelligence-loaded. As such, a COVGE explanation for these differences would entail a GE explanation for the high heritability of g. Arguing for the necessity of a GE explanation for g by way of the Flynn effect doesn’t work because the cause of g differences are qualitatively different from the cause of the Flynn effect (See: Wicherts, Dolan, Hessen, et al. 2004) and g has shown no secular rise. Flynn’s argument is reduced to a plead for the possibility of a g GE explanation. This is problematic.

When it comes to rGE explanations (the more a posteriori plausible of the two GE possibilities), GE theorists are forced to maintain that g is created from the outside in. (See theoretical diagram). Since g is structurally the same across individuals, cultures, sexes, and subpopulations, not only would the patterns of one’s environment have to construct g, the patterns of everyone’s environment would have to construct the same g.

Additionally, IQ g has numerous endophenotypic correlates, such as the volume of white and grey matter, the mass of the prefrontal lobe, and total brain size [1, 11, 13] and the overlap between IQ (g) and many of these endophenotypes is entirely due to genetic influences [1, 13]. To explain this genetic covariation, rGE theorists must maintain that genetics sets the parameters for environmental selection, which leads to the development of different cognitive phenotypes, which, in turn, molds the endophenotypic differences, thus creating the three way correlation.

Since the Phenotypic/endophenotypic correlations have been found to be a function of differential rates of change during the development process [ 9] and correspondingly since IQ differences are stable after adulthood, rGE theorists must maintain that this environmentally induced endophenotypic molding occurs primarily during the developmental process and starts early on. If we kept in mind what we said above, that the genes that lead to slight genetic IQ differences in infancy are the same genes that lead to large genetic based differences in adulthood, and note that the heritability of many dispositions also increases with age [4], we can readily identify the problem with this conception. Somehow, dispositional differences, which are under heavy environmental influence early on, must set the phenotypic/endophenotypic molding (environmental) parameters in a way that happens to correspond to the genetic driven phenotype that the individual will later express.

None of the above logically precludes a rGE explanation for g; the explanation would just have to be exceedingly complex. That said, there is a growing body of evidence against the active GE model. Since according to the active rGE model, environmentally conditioned phenotypic differences cause endophenotypic differences, the active rGE model predicts that environments will correlate with endophenotyes [13]. This was found to not be the case by Posthuma et al. (2003), De Moor et al. (2008), van Leeuwen et al (2009), and Betjeman (2009), disconfirming the model.

In addition to the above, Shikishima et al. (2009) found evidence of a causal genetic g. This effectively rules out the possibility of a purely active rGE created g:

Accordingly, our findings could furnish an argument against the typical criticisms offered by those who are opposed to the concept of g; in other words, g is an “artifact” (Simon, 1969) of the statistical methods that psychologists apply to the data. Gould (1981) argued that g, as a factor extracted from the factor analysis, is neither a “thing with physical reality” nor a “causal entity”, but is a “mathematical abstraction”, maintaining that “we cannot reify g as a ‘thing’ unless we have convincing, independent information beyond the fact of correlation itself.” Although the present study also draws information from correlations, we were able to depict the structure of human intelligence beyond the fact of phenotypic and genetic correlations with an explicit comparison between the independent pathway and the common pathway model; and as a “causal entity”, as a highly genetically driven entity…

…Several recent reports have shown that g is also correlated with a variety of neural mechanisms, such as glucose metabolism (Haier, 2003), cortical development (Shaw et al., 2006), and biochemical activity (Jung et al., 2005), along with the identification of promising endophenotypes for intelligence such as working memory and processing speed (van Leeuwen, van den Berg, Hoekstra, & Boomsma, 2007). These studies allow us to assume that it is now reasonable to consider g to be a physiological or biological, genetic entity.

Given this, while some GE interactions and correlations might occur for g, they’re unlikely to be substantial causes of differences. When it comes to the Flynn effect, it’s likely that this effect is accounted for my some variation of:

a) across cohort bias (i.e apparent rises in IQ are a measurement artifact). (See: Wicherts, Dolan, Hessen, et al. 2004; Gottfredson, 2009; Kaufman, 2010; Beaujeana and Osterlind, 2008)

b) rises in IQ which do not represent rises in general intelligence. (See: Gottfredson, 2007; Gottfredson, 2009, Jensen, 2011.) When it comes to comparisons across time, Jensen (2011) makes the key point:

The central issue is that methodology by which the dependent variable (viz., secular gains in IQ scores) has been measured, fails to meet the standard of the advanced sciences on an absolutely critical point! Despite the popular inference drawn from all the IQ data collected, this research can neither confirm nor reject the existence of the FE. Doubling the amount of the already massive data (other conditions being unaltered) could not resolve the issue. But whatever the outcome of a proper investigation of the FE, the gentleman– scholar philosopher James Flynn deserves recognition as an important figure in the history of psychometrics. The term Flynn Effect, however, will go down in history as a blind alley in psychometrics, viz., trying to answer a basic, nontrivial factual question using wholly inappropriate data.

Suppose a study were performed on the secular trend in the mean height (measured in either centimeters or inches) of 10- year old school children born and reared in a given locality over the past century. The result per se is not controversial and provides a valid basis for research on its causes. Indeed, such studies are among the least controversial findings in the science of human growth and development. Why? Because ‘height’ can be defined objectively by describing the physical operations used to measure it. The problem with IQ tests and virtually all other scales of mental ability in popular use is that the scores they yield are only ordinal (i.e., rank-order) scales; they lack properties of true ratio scales, which are essential to the interpretation of the obtained measures.

…

1.1. Minimum requirements for the scientific study of secular changes in psychometric variables

Four conditions are essential for advancing scientific knowledge about intelligence: (a) clearly formulated coherent theory of intelligence; (b) instrumentation for the ratio-scale testing of theory-driven hypotheses; (c) a standard protocol for administering the use of this equipment; and (d) appropriate statistical analysis of the raw data so obtained.

c) heterosis (See: Mingroni, 2007)

d) plan old X-factors working across time (e.g. nutrition). These are hardly implausible since different times represent different environments.

With regards to that last point, Flynn (and Taylor) fallaciously argued that since between population X-factors were theoretically implausible and empirically found not to exist, then between generation X-factors were likewise implausible. The fallacy of this argument is readily shown by the findings of Ang et al., 2009. The authors find no significant difference in the rate of the Flynn effect between ethnic groups, sexes, urbanity, age, family interaction, household income, and income x race. In this data, the Flynn effect indeed behaves like an x-factor.

Of course, even if one did maintain that differences between groups were due to rGE correlations, one would still be left with the basic problem found in the case of X-factor explanations. The rGE factors would have to differentially hit one population living more or less in the same environment — as such they can not be population generic rGE factors like those Flynn argues are behind the secular rise (of IQ). To get around this, Flynn seems to posits average genetic difference between populations!

“The standard model that poses the paradox assumes that environment and genetic endowment are uncorrelated. Applied to basketball, this implies that good coaching, practicing, preoccupation with basketball, and all other environmental factors that influence performance must be unrelated to whether genes contribute to someone being tall, slim, and well coordinated. For this to be true, players must be selected at random for the varsity basketball team and get the benefits of professional coaching and intense practice, without regard to build, quickness, and degree of interest”

So, in short, at minimal Jensen’s dilemma forces Flynn et al. to accept a genetic component to the difference. (What are the cognitive equivalents to well built, quick, attuned, “tall, slim, and well coordinated”?) While this technically might not be a hereditarian account of the gap, it surely is not a 0-genetic account (2).

Notes

1. X-factors accounts for groups differences when the groups are living in approximately the same environment are not just theoretically implausible, but, in the US, they have been empirically ruled out. Both Flynn and Nisbett have conceded this. This is substantial given that an X-factor explanation was long held to be the cause of the gap. For example, refer to Sandra Scar’s . In: Race, social class, and individual differences in I.Q.

X-Factors were empirically ruled out by the following studies:

Lubke, et al. 2003. On the relationship between sources of within- and between-group differences and measurement invariance in the common factor model

Rowe, 1994. No more than skin deep. American Psychologist; Rowe and Cleveland, 1996. Academic achievement in Blacks and Whites: Are the developmental processes similar?

Rowe, et. al., Vazsonyi 1994. No more than skin deep: Ethnic and racial similarity in developmental process.

Rowe, et. al., 1995. Ethnic and racial similarity in developmental process: A study of academic achievement.

Rowe, et al. (1994) found that that Blacks, Hispanics, Asians, and Whites have the same relation between background variables and developmental outcomes; Rowe and Cleveland (1994) found that the achievement tests of Black and White siblings and half sibling had the same structure of variances and covariances and that a quantitative genetic model was the best fit explanation. Based on an analysis AFOQT scores; Ree and Carreta (1995) found that the above ethnoracial groups had approximately the same general intelligence loadings — finding effectively ruling out x-factors. X-factors accounts for cohort differences across time are both theoretically plausible and, in many cases, empirically certain (differences in nutrition, etc).

(2) A gene x environment explanation which does not allow one to parse out a specifically genetic components would be non-hereditarian.

(3) There are two types of GE interactions. The first involves non-additive genetics. When it comes to the IQ wars, environmentalists hold out for the possibility of this. With non-additive GE:

What constitutes a good environment for one genotype in terms of the development of the phenotype may constitute a bad environment for some other genotype in terms of the development of the phenotype OR Environmental advantage, through acting in some phenotypic direction for all genotypes may have unequal phenotypic effects on different phenotypes

This is the type of GE that I’m maintaining plays an insignificant role in the heritability of IQ. The second type of GE involves additive genetics. With this type of GE, environmental differences interact with the heritability of a trait, such that poor environments can depress heritability and rich environments can increase heritability. This is the type of GE that is commonly found. What one notices is that with age the h^2 if IQ for impoverished kids nonetheless increases. My guess would be that by adulthood no gene-environment interaction of this sort is found. Whatever the case, this type of GE is irrelevant to the above discussion.

Images from:

Tucker-Drob et al., 2011. Emergence of a Gene× Socioeconomic Status Interaction on Infant Mental Ability Between 10 Months and 2 Years

Harden, 2007. Genotype by environment interaction in adolescents’ cognitive aptitude

References:

Ang et al., 2009. The Flynn Effect within subgroups in the U.S.: Gender, race, income, education, and urbanization differences in the NLSY-Children data

Beaujeana and Osterlind, 2008. Using Item Response Theory to assess the Flynn Effect in the National Longitudinal Study of Youth 79 Children and Young Adults data

Borsboom and Dolan, 2006. Why g is not an adaptation: A comment on Kanazawa (2004).

Dickens, W. T., & Flynn, J. R. (2001). Heritability estimates versus large environmental effects: The IQ paradox resolved.

.

Gottfredson, 2007. Shattering logic to explain the Flynn Effect.

Gottfredson, 2009. Of what value is intelligence?

“The puzzle of the Flynn Effect remains unsolved in large part because intelligence tests lack ratio-level measurement, that is, a scale that starts at zero quantity and counts in equal-size units from there. Without that capability, we cannot know whether the observed secular rise in average IQ reflects an absolute change in the level of any of the constructs captured by IQ tests, either in g itself or some normally inconsequential source of variance in the composite IQ score. Critics often implicitly assume that IQ scores can register changes in absolute ability level when they criticize intelligence tests. A currently popular critique in this vein is that the Flynn Effect disproves what had come to seem incontrovertibly true, namely, that IQ tests measure a stable, heritable, psychometrically unitary general intelligence. Their argument is that g cannot at once be heritable, unitary, and yet some subtest scores increase dramatically over time. Flynn (2007), for example, argues that the rise has “shattered g.” It obviously has not. The problem is not with our conception or measurement of g, but with our measurement technology not yet allowing us to measure absolute rather than just relative positions along any ability continuum. This is the severest limitation of current IQ tests, yet to be overcome (Jensen, 2006), but it does nothing to invalidate the construct of g or the utility of current IQ tests for many diagnostic, selection, placement, training, and treatment purposes.”

Flynn, 2010. The spectacles through which I see the race and IQ debate

Hulshoff Pol, et al. 2004. Genetic Contributions to Human Brain Morphology and Intelligence

Jensen, 1998. Population Differences In Intelligence: Causal Hypotheses. In: The g Factor: The Science of Mental Ability

From Jensen (1998):

[Given BGH = WGH*(rg(1-rp)/rp(1-rg))] …To accept the preponderance of evidence that WGH > 0 and still insist that BGH = 0 regardless of the magnitude of the WGH, we must attribute the cause of the group difference to either of two sources: (1) the same kinds of environmental factors that influence the level of g but that do so at much greater magnitude between groups than within either group, or (2) empirically identified environmental factors that create variance between groups but do not do so within groups. The “relaxed” default hypothesis allows both of these possibilities. The dual hypothesis, on the other hand, requires either much larger environmental effects between groups than are empirically found, on average, within either group, or the existence of some additional empirically unidentified source of nongenetic variance that causes the difference between groups but does not contribute to individual differences within either group. If the two groups are hypothesized not to differ in WGH or in total phenotypic variance, this hypothesized additional source of nongenetic variance between groups must either have equal but opposite effects within each group, or it must exist only within one group but without producing any additional variance within that group. In 1973, I dubbed this hypothesized additional nongenetic effect Factor X. When groups of blacks and whites who are matched on virtually all of the environmental variables known to be correlated with IQ within either racial population still show a substantial mean difference in IQ, Factor X is the favored explanation in lieu of the hypothesis that genetic factors, though constituting the largest source of variance within groups, are at all involved in the IQ difference between groups. Thus Factor X is an ad hoc hypothesis that violates Occam’s razor, the well-known maxim in science which states that if a phenomenon can he explained without assuming some hypothetical entity, there is no ground for assuming it.

Jensen, 2011. The theory of intelligence and its measurement

Kaufman, 2010. In What Way Are Apples and Oranges Alike?” A Critique of Flynn’s Interpretation of the Flynn Effect

Lenroot, et al., 2007. Differences in genetic and environmental influences on the human cerebral cortex associated with development during childhood and adolescence

Miele, F.: 2002, Intelligence, Race, and Genetics: Conversations with Arthur Jensen.

Mingroni, 2007. Resolving the IQ Paradox: Heterosis as a Cause of the Flynn Effect and

Other Trends

“First, as Dickens and Flynn (2001) pointed out, estimates of the heritability (h2) of IQ are high, at about .75 in adults (Neisser et al., 1996). Without positing genetic change, this would seem to require positing environmental factors that cause large changes over time yet do not vary enough at any single point in time to reduce heritability estimates very much. Dickens and Flynn referred to such an implausible aspect of the environment as a “factor X.” Of note, although Dickens and Flynn carried out their analysis using a value of .75 for h2, they suggest that assuming values as low as .60 would still necessitate positing implausibly large change in those factors that do create environmental variance within generations (i.e., non–factor Xs). The magnitude of IQ heritability estimates, however, is only the first part of the problem. In addition to the magnitude of IQ heritability, the fact that estimates appear to have remained stable over time (Jensen, 1998, pp. 322–323) is also a problem for environmental hypotheses. Some hypotheses, such as nutrition, suggest that IQ-depressing environmental factors kept individuals of the past far below their maximum genetic potential for IQ. However, unless these factors depressed everyone’s IQ by the same amount, their removal from the IQ environment should have also removed an environmental source of variance, thereby causing heritability estimates to rise over time. Conversely, if the trend is due to something like a practice effect that has artificially raised IQ, this should represent the introduction of a new source of environmental variance that should have caused heritability to decline over time, unless, that is, everyone today is practicing the same amount. The consistency of heritability estimates would therefore still pose a problem for environmental hypotheses, even if the estimates were lower, because t would suggest that large evironmental change has occurred, without either the addition or subtraction of any noticeable ource of environmental variance.”

Panizzon et al., 2009. Distinct Genetic Influences on Cortical Surface Area and Cortical Thickness

Ree and Carretta, 1995. Group differences in aptitude factor structure on the ASVAB

Rowe, et al., 2001. Expanding variance and the case of historical changes in IQ means: A critique of Dickens and Flynn

We find a model including a phenotype-environment correlation, like that in Dickens and Flynn’s (2001) Model 2, to be reasonable and plausible. However, features of the model raise three important questions. First, the model implies increasing IQ variance under typical parameter specifications. Yet empirical evidence suggests that IQ variance has not increased, and may even have declined.

Second, the model has not been fit to empirical data. Rather, its performance was evaluated by specifying different (nonoptimized) parameter values and observing the results. Then, they were compared with patterns in the literature. Dickens and Flynn discussed the difficulty of finding appropriate data. Although we are not as pessimistic about data availability as they are, we recognize the problem. However, we also have concern over model fixes and adjustments when those were created to match external empirical patterns, without mechanisms to evaluate their legitimacy. Further, in a related concern, we wonder whether the model can be falsified. What types of patterns would do so? Can we distinguish between incorrectness at a fundamental level, as opposed to problems in some particulars (to which mathematical fixes can be applied)?

Third, we note that more conventional processes—based on more parsimonious models—can account for some IQ gain. Rowe, Jacobson, and Van den Oord (1999) found that genetic and environmental components of IQ variance were moderated by socioeconomic status (SES). In the bottom 20% of the SES distribution, c2 =.40, whereas it was approximately zero in the remainder of the distribution (a result replicated by Thompson, Tiu, & Detterman, 1999). To further elaborate, shared environmental effects were strongest where there was greatest potential of improvement in environmental circumstances. Some part of the Flynn effect may have derived from improved environmental conditions for poor families. As Dickens and Flynn suggested, this effect size may be no more than one third of a standard deviation, too little to be viewed as a complete explanation of the historical change. However, the simplicity of this explanation (and others reviewed earlier) provides a stark contrast to the complexity of Dickens and Flynn’s Model 3.

Rowe and Cleveland, 1996. Academic achievement in Blacks and Whites: Are the developmental processes similar?

Rowe, Vazsonyi, and Flannery, 1994. No more than skin deep: ethnic and racial similarity in developmental process.

This study investigated the similarity of developmental processes in Hispanics, blacks, and whites using correlation matrices. The matrices contained PIAT scores at two time points and a measure of environmental quality specific to each child. All measures were completed by siblings; hence, the correlational structure included all family effects through sibling psychological resemblance and all effects through the HOME measure of family environment. These correlation matrices were statistically equal across Hispanics, blacks, and whites. From this equality of correlation matrices, we concluded that developmental processes that determine variation in PIAT scores were similar across ethnic and racial groups. Statistical power, of course, limits the ability of this study to detect ethnic and racial group differences. Each ethnic or racial group, however, had more than 100 sibling pairs. Small developmental ef- fects may have gone undetected, but certainly larger ones would have appeared as differences in the correlational structures. As a second step, we proposed a specific structural equation model to explain variation in achievement. It postulated an achievement latent trait specific to each child and treated any association between achievement and family environment as noncausal. Other research on the NLSY (Rodgers et al., 1994) found that heritable effects on PIAT subtests were moderate, whereas shared environmental effects were relatively weak. Thus, our model emphasizing genetic effects but minimal family environment effects on PIAT achievement variation is consis tent with these direct behavior genetic analyses of the PIAT subtests in the NLSY. Nonetheless, the family environment (i.e., the HOME score) may also contain some environmental effects on achievement, but ones weaker than the family environment-achievement correlation parameter d (.314), which may contain genetic as well as environmental components. This study’s findings bear upon earlier studies of the construct validity of IQ across ethnic and racial groups. This previous research consisted essentially of showing the equality of 2×2 co variance matrices. In each such matrix, one variable was IQ and another was a theoretically related developmental outcome (e.g., course grades, job performance ratings). In general, such 2×2 matrices were statistically equal for blacks and whites (the groups most frequently studied; Barrett & Depinet, 1991; Cole, 1981; Jensen, 1980). By these statistical criteria, IQ was determined to be an equivalent psychological construct in different ethnic and racial groups. In this study, however, the argument goes considerably further by proposing that the determinants of achievement are identical across ethnic and racial groups. Our explanation for the similarity of developmental processes is that (a) different ethnic and racial groups possess a common gene pool, which can create behavioral similarities, and that (b) among second- generation ethnic and racial groups in the United States, cultural differences are smaller than commonly believed because of the omnipresent force of our mass-market culture, from teleision to fast-food restaurants (see Rowe et al., 1994).

Certainly, a burden of proof must shift to those scholars arguing a cultural difference position. They need to explain how matrices representing developmental processes can be so similar across ethnic and racial groups if major developmental processes exert a minority-specific influence on school achievement. Further research on this topic should consider replacing the more distal categories of ethnicity and race with more proximal cultural variables to measure and identify local cultures. Although local cultures (e.g., a ghetto or barrio culture) may moderate developmental processes, this claim, like the claim about ethnicity and race, remains one that has been widely accepted in the social sciences without strong empirical evidence.

Shaw, 2007. Intelligence and the developing human brain

Taylor, 2006. Heritability and Heterogeneity: The Irrelevance of Heritability in Explaining Differences between Means for Different Human Groups or Generations

Wicherts, Dolan, Hessen, et al. 2004. Are intelligence tests measurement invariant over time? Investigating the nature of the Flynn effect

This clearly contrasts with our current findings on the Flynn effect. It appears therefore that the nature of the Flynn effect is qualitatively different from the nature of B–W differences in the United States. Each comparison of groups should be investigated separately. IQ gaps between cohorts do not teach us anything about IQ gaps between contemporary groups, except that each IQ gap should not be confused with real (i.e., latent) differences in intelligence. Only after a proper analysis of measurement invariance of these IQ gaps is conducted can anything be concluded concerning true differences between groups.

[1] Betjemann, et al., 2009. Genetic Covariation Between Brain Volumes and IQ,

[2] Boomsma, et al., 1998. Genetic influences on childhood IQ in 5- and 7-year-old Dutch twins

[3] Bouchard, 2009. Genetic influence on human intelligence (Spearman’s g): How much?

[4] Gardner, 2007. A meta-analysis of age-related changes in heritability of behavioral phenotypes over adolescence and young adulthood.

[5] Flynn, 2007. What is intelligence? Beyond the Flynn effect.

[6] Haworth, 2009. The heritability of general cognitive ability increases linearly from childhood to young adulthood

[7] Plomin, 1987. Development, genetics, and psychology.

[8] Posthuma, et al., 2003. Brain volumes and the WAIS-III dimensions of verbal comprehension, working memory, perceptual organization, and processing speed.

[9] Shaw et al., 2006. Intellectual ability and cortical development in children and adolescent

[10] Shikishima, et al., 2009. Is g an entity? A Japanese twin study using syllogisms and intelligence tests.

[11] Smit et al., 2010. Endophenotypes in a Dynamically Connected Brain.

[10] van Leeuwen, 2008. A twin-family study of general IQ

[13] van Leeuwen et al., 2009. A genetic analysis of brain volumes and IQ in children.

[14] Jensen, 1973, Educatability and group differences.

Spearman’s hypothesis and the Jensen Effect

Group differences and g. There are numerous psychometric tests of intelligence. It has been found that there is a central common factor. This central factor is called general intelligence or g. G is of interest because: 1) it is psychometrically structurally similar across populations, sexes, ages, and cultures, (and several species), 2) it represents a behavior-psychometric manifold with numerous educational, psychological, and sociological correlates, 3) it correlates with the cognitive complexity of activities, 4) it is highly heritable (within populations) and correlates with inbreeding depression scores, and 5) it has numerous neurophysiological correlates such as brain neural conduction velocity, cerebral glucose metabolic rate, the latency and amplitude of evoked electrical brain potentials, the speed and efficiency of brain functioning inferred from reaction time, neural organization, the volume of white and grey matter, the mass of the prefrontal lobe, total brain size, and cranial capacity.

In the history of the debate, the truth of 1-5 were vigorously fought by environmentalists for a reason. If it turned out that g was just a “test artifact” or if it was shown that there was no such physiological manifold — or that groups did not differ in that regards — the hereditarian hypothesis, and its implications, would have been weakened (see: Brand, 2001; Jensen, 2000). Flynn (2007), in fact, recently made the later environmental case, holding that psychometric intelligence is a conglomerate of physiological functions which can “swim free of g,” and, years before (1987), he made the former case, arguing that IQ was only “a correlate with weak causal links to intelligence” (i.e. there’s no central factor binding the various manifestations of intelligence). Steve Rose (1995) articulated one of the environmentalist reasons for opposing the reality of g, worrying that “if intelligence is one thing, it becomes appropriate to seek a single causative agent.” Indeed. As such, Flynn’s (2010) concession, in wake of the mounting evidence, that general intelligence “does have some root in brain physiology” is significant. For a nice summary of the g affair refer to Brand, Constales, and Kane (2003).

The reality of g does two things: it makes the implications of the hereditarian hypothesis, if true, unavoidable, and it substantially strengthens the hereditarian argument. With regards to the former, as Jensen (2000) pointed out, g “lies at the heart of the whole problematic nexus involving the nature of group differences, the merits of meritocratic selection in a diverse society, the legitimacy of using tests, their adverse impact on certain groups, and its redress by group preferences in college admissions and employment.” With regards to the latter, the reality of g loaded differences makes implausible a number of environmental arguments (including virtually all purely sociological ones, such as test bias, motivation, stereotype effect, etc.). Murray (2005) articulates this point well:

A concrete example illustrates how Spearman’s hypothesis works. Two items in the Wechsler and Stanford-Binet IQ tests are known as “forward digit span” and “backward digit span.” In the forward version, the subject repeats a random sequence of one-digit numbers given by the examiner, starting with two digits and adding another with each iteration. The subject’s score is the number of digits that he can repeat without error on two consecutive trials. Digits-backward works exactly the same way except that the digits must be repeated in the opposite order.

Digits-backward is much more g-loaded than digits-forward. Try it yourself and you will see why. Digits-forward is a straightforward matter of short-term memory. Digits-backward makes your brain work much harder.

The black-white difference in digits-backward is about twice as large as the difference in digits-forward.[60] It is a clean example of an effect that resists cultural explanation. It cannot be explained by differential educational attainment, income, or any other socioeconomic factor. Parenting style is irrelevant. Reluctance to “act white” is irrelevant. Motivation is irrelevant. There is no way that any of these variables could systematically encourage black performance in digits-forward while depressing it in digits-backward in the same test at the same time with the same examiner in the same setting.

The g-loadedness of group differences also allows for the argument from Spearman’s hypothesis.

Jensen and Rushton used the method of correlated vectors to show that the B-W gap correlates with more heritable IQ subtests and that the gap is gloaded. They argue that the correlation with heritability implies that there is a genetic etiology to these differences. Additionally, the g-loadedness of the gap supports the hereditarian position. First, given that the environmental hypothesis predicts a relationship between environmentality and group differences and no relationship between heritability and group differences, the correlation with heritability supports the hereditarian hypothesis. Second, environmental explanations or combinations thereof are found wanting when it comes to explaining the said average differences in general intelligence, given that g stands at the nexus of a whole web of psychological, social, and neurophysiological factors; this leaves genetic explanations as the default.

The environmentalists’ reply. Flynn (2006) makes the case that the GQ (general intelligence) gap is closing, which implies that environmental factors are effecting a change; as such, the current lack of coherent environmental explanations does not imply genetic origin to the gap. With regards to the latter argument, Flynn (2010) replies that since g is a proxy for cognitive complexity and since environmental deficits increasing impose disadvantage with complexity, the environmental hypothesis can offer a plausible explanation to the gloaded specificity. As such, a genetic etiology to the differences is not implied. To quote Flynn:

(1) g would be of no interest were it not correlated with cognitive complexity. (2) Given hierarchy of tasks, a worse performing group (whatever the cause of its deficit) will tend to hit a “complexity ceiling” — fall further behind a better group the more complex the task. (3) Heritability of relevant traits will increase the more complex the task. (4) Thus, the fact that group performance gaps correlate with heritability gives no clue to the origin of group differences. (5) When a lower performing group gains on a higher performing one, their gains will tend to diminish the more complex the task. Thus, blacks have gained 5.50 IQ points on whites since 1972 but only 5.13 GQ points. (6) Recent achievement test data confirm these IQ gains but the data as a whole pose problems for the external validity of black IQ. (7) The FE is irrelevant to showing that the racial IQ gap is environmental but it was historically valuable in clarifying the debate.

Both of Flynn’s counters are flawed. With regards to the closing of the GQ gap, the best explanation to date for this was put forth by Murray (2006) and Chay et al. (2009). Accordingly, the g gap was partially closed by health improvements (i.e . environmental influences that had immediate biological impact). This explains the change in the substantially biological g and leaves environmental explanations wanting to explain current g-loaded differences, at least between mid to upper SES members of the said populations. With regards to the g-loadedness of group differences, Flynn’s cognitive explanation does not hold in wake of the vast social, psychological, and neurophysiological manifold that general intelligence represents.

We could use a basketball analogy to capture both positions on this matter. Flynn argues that g is analogous to general basketball ability; it’s important because it correlates with the ability to do complex moves, say like making reverse two-handed dunks. Flynn’s point is that to do a reverse two-handed dunk, one needs to learn all the basic moves. Since environmental disadvantages (poor coaches, limited practicing space, etc.) handicap one when it comes to basic moves, they necessarily handicap one more when it comes to complex basketball moves. Rushton and Jensen argue the g is analogous to a highly heritable athletic quotient; it’s important because it correlates with basic physiology, generalized sports ability, and basic eye-motor coordination. Their point is that it’s implausible that disadvantages in basketball training would lead to across the board disadvantages in all athletic endeavors and, moreover, lead to a larger handicap in general athleticism than to a handicap in basic basketball ability. Rather than disadvantages in basketball training leading to disadvantages in general athletic ability, it’s much more plausible that disadvantages in general athletic ability would lead to a reduced effectiveness of basketball training.

Flynn and other environmentalists can only circumnavigate g by insisting that a web of g affecting environmental circumstances, in effect, constructs g from the outside in. Given that g is psychometrically structurally similar across populations, sexes, ages, and cultures this seems implausible as it would necessitate that either everyone happened to encounter the same patter of g formative environmental circumstances just at different levels of intensity or that environmental circumstances were themselves intercorrelated.

Given the weakness of environmental accounts of GQ differences, the hereditarian hypothesis is a more plausible explanation than the environmental (0-genetic) hypothesis.

References

Brand, 2001. The g Factor – General Intelligence and its Implications (This is a free, downloadable book)

Brand, Constales, and Kane, 2003. WHY IGNORE THE G FACTOR? — Historical considerations.

Chay, et al., 2009. Birth cohort and the black-white achievement gap: The roles of access and health soon after birth

Dickens and Flynn, 2006. Black Americans reduce the racial IQ gap

Flynn, 2010. The spectacles through which I see the race and IQ debate

Murray, 2005. Inequality taboo.

Murray, 2006. Changes over time in the black-white difference on mental tests: Evidence from the children of the 1979 cohort of the National Longitudinal Survey of Youth

Rushton and Jensen, 2010. Race and IQ: A Theory-Based Review of the Research in Richard Nisbett’s

Brain size and correlates with IQ

When it comes to racial differences in intelligence, the average differences in cranial capacity are an important piece to the puzzle. Whatever their ultimate cause (i.e. whether due to environmental differences or genetic differences that resulted from environmental differences), they establish the deep roots of general mental ability differences. In their recent discussion of race, genes, and IQ, Hunt and Carlson (2007) maintain that differential brain size is a reasonable line of investigation:

Rushton (1995) maintained that one of the reasons for the White–African American disparity in IQ scores is that Whites have larger brain sizes than African Americans. Leaving aside the issue of whether or not one accepts this particular argument, the argument itself illustrates a useful principle. Differences in brain size are associated with intelligence (McDaniel, 2005). Rushton has stated a hypothesis about a biological mechanism, known to influence intelligence, that might explain the difference. Rushton’s claim for a racial disparity in brain sizes was based on exterior skull measures. Further studies, using modern imaging techniques, may provide a more sensitive test of the hypothesis. It would not be appropriate to enter into a detailed discussion here. Our point is simply that discussing this sort of claim is far more likely to increase our understanding of the disparity than is arguing about the percentage of variance associated with biological or environmental variables.

What is the status of this line of investigation?

Obviously, for the brain size explanation to be plausible brain size, cranial capacity, and head circumference need to correlate with differences in intelligence within populations; moreover, brain size, cranial capacity, and head circumference need to be partially heritable. Rushton and Ankney (2009) summarize the findings to date with regards to brain size and intelligence: based on 28 non-clinical published brain imaging samples (N= 1,389) a .40 correlation between IQ and brain size measured by MRI was found; based on 59 published samples (N= 63,405) a .20 correlation between IQ and head circumference was found. These findings are consistent with others. In a meta-analysis McDaniel (2004) found an in vivo brain volume/IQ correlation of 0.33 based on 37 published studies (N= 1535); Reviewing all the data to date (N = 935), Miller and Penke (2007) found a in vivo brain volume/general intelligence (GQ) of .41; the heritability of adult brain volume (N =2494) was found to be .89. Pietschnig, Zeiler, and Voracek, (submitted), found an in vivo brain volume/IQ correlation of .24 based on a meta-analysis of 94 studies published and unpublished.

With regards to the heritability of brain size, based on a review of 14 twin studies using CT to measure brain size, Peper et al. (2007) found a .81 heritablity of global brain measures, .66-97 heritability of brain volume, and .82/.88 heritability of grey/white matter. Moreover, based on a twin study, Posthuma et al. (2002) found a .30 genetic correlation between general intelligence and brain volume, replicating the findings of Pennington et al. (2000), who found a genetic correlation of 0.48 (as cited in Gignac et al, 2003). So, within populations, one can infer a genetic basis to intelligence differences, partially mediated by differences in brain size.

This conclusion is confirmed by the preponderance of the evidence which shows a within family correlation between brain size and IQ (Gignac et al, 2003; Rushton and Jensen, 2010).

In addition to the above two prerequisites, there must be consistent findings of between population differences.

Cranial capacity differences between continental populations

As for cranial capacity differences between regional populations, Beals, et al. (1984) found an average Asian, European, and African cranial capacity of, respectively, 1380, 1362 (sd = 35), and 1276 (sd =85) (N= 20,000); they attributed the selection to cold weather adaptation. (See: Smith and Beals (1990) for population means and standard deviations.) Rushton (2005) summarizes previous global findings: East Asians 1,364, Europeans 1,347, S.Africans
1,267. (See also: Rushton, 1990). Based on a recent study of 699 Nigerians of different ethnicities, Odokuma, et al. (2010) found a mean cranial volume of 1271.

Odokuma, et al. (2010) found a mean cranial volume of 1271; they conclude the following:

The findings in this study are similar to previous studies (Morton, 1839) where the mean cranial volume of the skulls of whites was 1,425 cm³, while that of the Blacks was 1,278 cm³. Based on the measurement of 144 skulls of Native Americans, Morton (1839) reported a figure of 1,344 cm³. Gould (1981) and Rushton (1995) have also showed very similar figures. Tribe had a significant effect on cranial volume at 0.05 levels of significance. Intercultural comparisons demonstrated significant variation as reported by Howells (1989), Froment (1992) and Lahr (1996). While the Ibo’s had an average cranial capacity of 1273.39 cm3 , that of the Urhobo’s was 1255.89 cm3. The Edo’s was 1310.08 cm3. This may be attributable to a . common ancestral origin of the Ibo and Urhobo people or inter marriages which are very common between these cultures with interchange of physical characteristics over the years since these people have been cordial neighbours

There is general acceptance of these findings in the physical anthropology literature. For example, in Chapter 5, Regional Variation and Evolution, of their textbook, Human Lineage, Cartmill and Smith (2009), acknowledge these differences, stating:

They conclude*, as do some others, that the differences are unrelated to IQ, but agree that the differences are genetic, in part. As others argue that the population differences are wholly environmental (e.g., Brody, 2003), it’s important to look at populations that share a common environment.

Cranial capacity differences between racial groups in the US

In the US, it seems that there is indeed a small average difference in cranial circumference between racial groups but more data is needed. (see: Rushton and Ankney, 1999).

Brain mass differences between racial groups in the US

Jensen (1998) summarizes the brain mass findings from the Case-Western Reserve (1980) study (N= 811 W, 450 B). An age matched and height adjusted B-W differences of ~100g (~.78SD) was found, which is commensurate with the findings of Bean (1906), Mall (1909), Pearl (1934), and Vint (1934) as described in Rushton and Ankney (2009). Holloway (2002) found a B-W difference of 63 grams (N = 1,391 W; 615 Black). Similar findings have been found based in imaging studies (see 5). In their study, Isamah, et al. (2010) found that African Americans have 1 SD less total cerebrum volume than European Americans.

Overall, it looks like there’s some consistency among the findings. The results have been numerously replicated, as summarized by Lynn (2006).

Next there must be plausible accounts for selective pressure, relating population differences, brain size, and intelligence. Bailey and Geary (2010) found the following correlations based on the location of 175 archeological sites dating from 1.9 million to 10 thousand years ago.

Cranial capacity positively correlates with latitude and population density both which are positively intercorrelated. It negatively correlates with parasite load and mean temperature.

This conforms with the major evolutionary-hereditarian explanations: Population Density (1), Paleoclimate (2), Geographic Novelty (3), and Disease Burden (4). Ash and Gordon, in Ash and Gordon (2007), give a nice discussion of this perspective:

More recent studies on ASPM and MCPH1, as summarized by Montgomery (2010), bolster this case.

From an environmental standpoint, one would either have to argue that the between population differences in brain size don’t cause between difference in intelligence (a la Cartmill and Smith, 2009) or that the between differences in brain size, while related to differences in cognition, ultimately have an environmental origin. The former requires one to argue that different populations are wired differently, say in the manner of males and females, so that genetic between population differences in size don’t entail between population differences in intelligence; this isn’t supported by within sex studies on brain functioning and structure; and the consistently positive size/IQ correlation within clinal populations (e.g., Sudanese, Guatemalans, African Americans, Asian Indians, Turks, Chileans, etc.) strongly argues against this (see: Rushton and Ankney, 2009). The latter is probably true to some extent; a complete environmental explanation, though, seems very implausible, for several reasons:

1) It requires one to discount the historical evidence which shows a consistent pattern of differences over time and which strongly suggests an adaptive origin to the differences.

2) As the racial population differences in cranial capacity are intercorrelated with numerous other musculoskeletal trait differences, arguing that the cranial capacity differences have a wholly environmental origin require one to maintain that the whole matrix of musculoskeletal trait differences likewise has an environmental origin. Which is implausible. As Rushton and Rushton (2001) note: “[R]ace differences in brain size are correlated with 37 musculoskeletal variables shown in standard evolutionary textbooks to change systematically with increments in brain size. The 37 variables include cranial traits (such as jaw size and shape, tooth size and shape, muscle attachment sites, and orbital bone indentations), and postcranial traits (such as pelvic width, thighbone curvature, and knee joint surface area). Across the three populations, the ‘‘ecological correlations’’ [Jensen, A. R. (1998). The g factor. Westport, CT: Praeger] between brain size and the 37 morphological traits averaged a remarkable r = .94; r = .94. If the races did not differ in brain size, these correlations could not have been found.” See also Rushton and Rushton (2004)

3) Classic studies of geological differences and Cephalic Indexes (CI) contradict a purely environmental hypothesis for differences. For example, Herskovits (1930), found the following association between genealogy an CI:

4) It has been found that population craniometric differences align with genetic differences. As Hubbe et al. (2009) note:

On the other hand, several studies have demonstrated a geographic structure in modern human craniometric diversity on a global level (e.g., Howells, 1973, 1989; Hanihara, 1996). Craniometric data have been found to follow a common geographic pattern with genetic markers, including both classical and microsatellite DNA markers (Relethford, 1994, 2004a, 2009; Manica et al., 2007; Betti et al., 2009). These findings have been interpreted as resulting from an isolation-by-distance model of evolutionary diversification. Furthermore, population relationships inferred from cranial morphology (as reflected both by traditional linear measurements and by 3D geometric morphometric data) have been shown to match those inferred from genetic data (Roseman, 2004; Harvati and Weaver, 2006a,b; Smith, 2009). Taken together, these results suggest that human cranial morphology preserves a relatively strong population history signal, in addition to a climatic and possibly also dietary/masticatory signal (e.g., Relethford, 2004a).

One effectively has to argue that the correlation between craniometric differences and genetic differences is spurious to maintain an environmental case.

Overall, given the reasons above, it seems probable that some of the differences in cranial capacity/brain volume have an evolutionary genetic origin. And, given the genetic correlations between brain volume and IQ, this seems to support a Brain size argument for genotypic IQ differences.

Notes

*Cartmill and Smith (2009) cite Deacon (1997). In the cited chapter, “The Size of intelligence: A gross misunderstanding,” Deacon, in fact, does not discuss intrahuman variation in intelligence or brain size. Rather, he argues, as the title of the chapters suggests, that bigger brains did not make humans smarter than other primates. (Cf. Reader (2011), “The evolution of primate general and cultural intelligence; Table 4. The relationship between primate general intelligence and brain volume).

In their discussion, Cartmill and Smith (2009( not only misrepresent Deacon’s chapter but, having brought up the issue, neglect to inform the readers about the copious amount of evidence that has accumulated internationally since the ’90s which establishes that bigger human brains are, indeed, smarter (McDaniel, 2005; Rushton and Ankney, 2009). Moreover, they cite Beals et al.’s thermoregulatory explanation for human variation without making mention that this climatic explanation is complementary with a size-intelligence one (e.g., Ash and Gordon 2007).

(1) Cochran, G., & Harpending, H. (2009). The 10,000-year explosion: How civilization
accelerated human evolution.

(2) Kanazawa, 2008. Temperature and evolutionary novelty as forces behind the evolution of general intelligence. Intelligence

Lynn, 1991. The evolution of race differences in intelligence

Templer and Arikawa, 2006. Temperature, skin color, per capita income, and IQ: An international perspective.

(3) Kanazawa, S. (2004b). General intelligence as a domain-specific adaptation.

(4) Eppig, Fincher, and Thornhil, 2010. Parasite prevalence and the worldwide distribution of cognitive ability

We also propose a complementary hypothesis that may explain some of the effects of infectious disease on intelligence. As we mentioned, it is possible that a conditional developmental pathway exists that invests more energy into the immune system at the expense of brain development. In an environment where there has consistently been a high metabolic cost associated with parasitic infection, selection would not favour the maintenance of a phenotypically plastic trait. That is, the conditional strategy of allocating more energy into brain development during periods of health would be lost, evolutionarily, if periods of health were rare. Peoples living in areas of consistently high prevalence of infectious disease over evolutionary time thus may possess adaptations that favour high obligatory investment in immune function at the expense of other metabolically expensive traits such as intelligence. Data do not currently exist on temporal variation of the severity of infectious disease across the world over human history. For genetically distinct adaptations in intelligence to exist based on this principle, parasite levels must be quite consistent over evolutionary time.

(5) Holloway (2008: The Human Brain Evolving: A Personal Retrospective) has an interesting discussion on the brain controversy:

In the late 1970s and early 1980s, I collected autopsy data from the Pathology department at Columbia’s College of Physicians and Surgeons (now CUMS). I was interested in age, sex, and ethnic effects on brain size changes through time as might be found in cross-sectional data. Roughly 2000 cases were collected, without personal identifications, and all cases of brain pathology were culled out of the data set. The results, unpublished, were roughly the same as found in the Ho et al. (1980, 1981) work on a sample from Milwaukee, which indicated that African American brains were statistically significantly lower in weight than were European American brains, that is, of course referring to the mean values. Ho et al. (1980) concluded that cultural effects were the reason behind the difference. Interestingly, Lieberman (2005) in his review of Rushton’s (2000, 2002) claims regarding ethnic (racial) differences in brain sizes and behaviors ignored this work by Ho et al. Needless to say, Tobias’s oft-cited paper on brain weight collecting methods (Tobias 1970) was cited to claim that autopsy data on brain weights are useless. Unfortunately, however problematic such data are, one tends to forget that autopsies are not done discriminately. Once the body is on the morgue slab, the autopsy is conducted in exactly the same fashion irrespective of the cadaver’s race, and thus comparisons of such data collected by the same anatomist or medical examiner are surely valid, depending on which variables are being compared. Comparing data collected by different examiners may of course be difficult, and perhaps statistical metaanalyses would be in order. To my knowledge, none exists.

Simply put, this research area remains an intensely political and near-suicidal enterprise. (Indeed, one colleague suggested I should incinerate the data; another suggested this kind of study had led to his relatives perishing in the Nazi concentration camps.) The continuing gap in African American and European-descent test scores on various cognitive tests (particularly IQ) throughout the United States and the world (Lynn & Vanhanen 2006) is a source of tremendous concern and acrimonious debate. Indeed, Jon Marks claimed he “outed” me as a “racist” (Marks 2000; see Holloway 2000 for reply) in his biological section of the American Anthropologist Newsletter because I had the temerity to defend Arthur Jensen against Loring Brace’s assertion that Jensen was a bigot. I had read much of this literature (e.g., Jensen 1998) including Jensen’s infamous 1969 piece in the Harvard Law Review and did not find him a racist. I remain appalled at our discipline, which regards him as such and which invented the appellation “Jensenism” to tar and feather him. I remain interested in the possibility that different populations have variation both in their brains and their behavior, but the issue is so politically incorrect that one cannot even approach such a study with anything but trepidation. (For example, the Annual Review article by Freedman & DeBoer 1979 was declared by sociocultural students at Columbia as racist and therefore not to be read!) If one disbelieves there are populational differences in the weight and/or structure of the brain, one should examine the papers by Klekamp and his colleagues, particularly regarding the finding that the primary visual striate cortex of Australian aborigines is significantly larger than in brains from people of European descent (Klekamp et al. 1994). This paper is, to my knowledge, the only paper published since the 1930s that demonstrates a real difference in brain morphology between modern populations (the last compilation of some of these earlier studies on brain morphology differences between different populations can be found in C.J. Connolly’s 1950 book, External Morphology of the Primate Brain, which is a sort of bible for most people working in paleoneurology. See also Kochetkova 1978.) Of course, there is Gould’s Mismeasure of Man, another bible of sorts, which should be read along with Michael’s (1988) Current Anthropology paper, which found that Morton’s rankings were correct and which Gould ignored in his later editions of the same book. There is certainly no evidence that Paul Broca used his elbow on the scales when measuring brains of peoples of European descent! Additional autopsy data sets await my attention, including some 5000 cases from Hong Kong, collected by my colleague Philip Beh, and ∼7500 cases from Singapore, the latter of multiple ethnicities. I hope to get to these data sets when I retire.

References

Ash and Gordon, 2007. Paleoclimatic Variation and Brain Expansion during Human Evolution

Bailey and Geary, 2010. Hominid Brain Evolution: Testing Climatic, Ecological, and Social Competition Models

Beals, et al., 1984. Brain Size, Cranial Morphology, Climate, and Time Machines

Brody, 2003. Jensen’s genetic interpretation of racial differences in intelligence: Critical evaluation

Gignac et al., 2003. Factors influencing the relationship between brain size and intelligence. In: Nyborg H, Ed. The scientific study of general intelligence: Tribute to Arthur R. Jensen.

Herskovits, 1930. The anthropometry of the American Negro

Hubbe et al., 2009. Climate signatures in the morphological differentiation of worldwide modern human populations

Hunt and Carlson, 2007. Considerations relating to the study of group differences in intelligence

Hwang, et al. 1995. Study on the adult Korean cranial capacity

Isamah, et al., 2010. Variability in Frontotemporal Brain Structure: The Importance of Recruitment of African Americans in Neuroscience Research

Jensen, 1998. The G-Factor

Kanazawa, 2008. Temperature and evolutionary novelty as forces behind the evolution of general intelligence.

Lynn, 2006. Race differences in intelligence.

McDaniels, 2003. Big-brained people are smarter: A meta-analysis of the relationship
between in vivo brain volume and intelligence.

Mekel-Bobrov, 2007. The ongoing adaptive evolution of ASPM and Microcephalin is not explained by increased intelligence and Microcephalin is not explained by increased intelligence

Montgomery, 2010. Brain Evolution: Microcephaly Genes Weigh In.

Peper et al., 2007. Genetic influences on human brain structure: A review of brain imaging studies in twins

Pietschnig, Zeiler, and Voracek, Unpublished. Of valid concerns and invalid effects: Meta-analyzing associations of in-vivo brain volume and IQ.

Posthuma et al., 2002. The association between brain volume and intelligence is of genetic origin.

Odokuma, Igbigbi, Akpuaka, and Esigbenu, 2010. Craniometric patterns of three Nigerian ethnic groups

Rushton, 1990. Race, brain size and intelligence: A rejoinder to Cain and Vanderwolf

Rushton and Ankney, 1999. Size matters: a review and new analyses of racial di􏰀erences in cranial capacity and intelligence that refute Kamin and Omari

Rushton and Rusthon, 2001. Brain size, IQ, and racial-group differences: Evidence from musculoskeletal traits

Rushton and Rusthon, 2004. Progressive changes in brain size and musculo-skeletal traits in seven hominoid populations

Rushton and Ankney, 2009. Whole Brain Size and General Mental Ability: A Review

Rusthon and Jensen, 2010. Race and IQ: A Theory-Based Review of the Research in Richard Nisbett’s Intelligence and How to Get It

Shockely, 1972. Dysgenics, Geneticity, Raceology: A Chalenge to the Intelectual Responsibility of Educators

Smith and Beals, 1990. Cultural correlates with cranial capacity