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.