Last updated: 4/18/2018

I was asked to meta-analyze a century (1914-2014) of IQ/Academic achievement and racial admixture (genealogy, gestalt racial appearance, and color) research. There is a lot out there, especially when one takes into account MA & PhD dissertations. To this end I am posting $20 (negotiable) bounties for each of the following dissertations (to be paid in bitcoin):

…

–Snider, J. G. (1953). A Comparative Study of the Intelligence and Aptitudes of Whites and Nezperce Indians (Doctoral dissertation, University of Idaho).

–Zimmerman, H. E. (1934). The Indian’s Ability to Learn Mathematics According to Degree of Indian Blood. MA, Kansas State Teacher’s College, Pittsburg.

–Rich, B. E. (1936). A Study of the Relation Between Degree of Indian Blood and Specific Tribe and the Intelligence and Scholastic Marks of the American Indian (Doctoral dissertation, University of Kansas, Education).

–Jonasson, I. (1937). The Comparative Intellectual Abilities of Full and Mixed Blood Indians. MS, University of North Dakota.

–Rainey, C. D. (1932). A study of the Salem Indian High School, comparing the cultural background, the intelligence scores, and the per cents of white blood, and the classroom grades (Doctoral dissertation, Willamette University).

–Comeau, P. J. (1978). A Comparative Study of Academic Achievement of Full-blood and Mixed-blood Indian Students (Doctoral dissertation, University of North Dakota).

–Ross, D. D. (1962). A comparative intergroup study of the academic achievement and attendance patterns between the full-blood type and the mixed-blood type Oglala Sioux Indian student in the secondary department of Oglala Community School, Pine Ridge, South Dakota (Doctoral dissertation, Chadron State College).

…

The full dissertations are not needed, but just a copy (or photo) of the relevant tables and/or correlation matrices along with the following sample characteristics: country of sample, first order administrative unit of sample (e.g., North Carolina), group type (e.g., school, college, random stratified), ethnic group, age range, sample size, description of the ancestry index, cognitive tests used, statistical methods for comparing admixed groups (e.g., means & SD, correlations, Chi-square)

This should be an easy job if you are at one of the schools at which there is a copy of the dissertation. If interested email at j122177@hotmail.com. I will update this list as I go along.

There are a couple of new, well designed, obtainable, surveys out — with ancestry, MRI, and cognitive data – which should allow for the (dis)confirmation of certain conjectures of ill repute:

–Neurodevelopmental Genomics: Trajectories of Complex Phenotypes (age 8-21, N ~ 10,000)
–The Brain Genomics Superstruct Project (age 18-35, N ~ 1,500)

For example, Greg Cochran likes to go on about how major ancestry groups often differ in crude brain morphology, and how these differences probably explain a significant chunk (> 20%) of bio-ancestry related differences in CA. I doubt it. But if he agrees to specify the analytic strategy, I will try to get the data and run the analyses.

I did look through the PING survey (age 3-21, N ~ 1,500) – which might not be very informative owing to the age structure. Going by this, Greg seems to be more or less correct about some of the endo differences and probably about their origins. As an example, Figure 1 & 2 show the B/W diffs for intracranial and total brain volume by age. (AAs are picked out for illustration since they are the largest non-White ethnic group, showing the biggest deviation from Whites.) And Figure 3 shows the relation between brain volume and ancestry in the self-identified AA group; the results were basically the same for intracranial volume, etc. — and so not shown.

Yet, as seen in Table 1 &  2, CA was more or less uncorrelated with these particular endophenotypes (r = 0.07-0.08); unsurprisingly, CA explained virtually no endo differences, and vice versa. Yet, CA was strongly (negatively) associated with both African and Amerindian ancestry – and also, though to a lesser degree, with Oceanian.

Perhaps there is a more sound way to run the numbers? Or a better way to take into account age? Dunno, it’s not my position to defend.

Results below.
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Thought criminal extraordinaire, Steve Sailer commented recently on Foreign Policy’s article, “Brazil’s New Problem With Blackness.” Money quotes:

These policies didn’t eliminate race, but they did affect how it came to be classified. The marker of race drifted away from a binary consideration of a person’s ancestry and became increasingly based on one’s appearance.
…
What ultimately binds these definitions together is an awareness that the less “black” a person looks, the better — better for securing jobs, better for social mobility. The widespread acceptance of multiracial identities in Brazil coexists with steep racial inequality — a contradiction that the sociologist Edward E. Telles has called “the enigma of Brazilian race relations.”
…
Eleven experts comprised the panel, among them UFPel administrators, anthropologists, and leaders in the wider black community of Pelotas. They received strict guidelines from the Public Prosecutors Office: “Phenotypical characteristics are what should be taken into account,” read the instructions. “Arguments concerning the race of one’s ancestors are therefore irrelevant.”

And this:

“In Aug. 2016, after it had become clear that the law left room for fraud, the government ordered all departments to install verification committees. But it failed to provide the agencies with any guidance.

The Department of Education in Para, Brazil’s blackest state, attempted to fulfill the decree with a checklist, which leaked to the press. Among the criteria to be scored: Is the job candidate’s nose short, wide and flat? How thick are their lips? Are their gums sufficiently purple? What about their lower jaw? Does it protrude forward? Candidates were to be awarded points per item, like “hair type” and “skull shape” …

But black activists say such measures are unavoidable.

When you allow your national policies to be guided by sociological theories, like those of Telles, you are bound to run into this type of mess.

Below are regression results, based on the Pelotas Birth Cohort (n = ~ 2850), for genetic racial ancestry, interviewer and interviewee-reported color (corr), and three SES indicators. In this 1982 birth cohort, independent of European ancestry, it can be seen that there is no consistent negative association between interviewer rated “black” appearance and outcomes. That is, in Brazil, the average race of one’s ancestors is more relevant than stereotypical race-associated phenotypic characteristics. (Note: the sample sizes for “Yellow” and “Indigenous” were small, so those estimates are fairly unreliable; also, neither an East Asian nor Amerindian ancestry component was included).

(Source: F. Hartwig, personal communication, March 4, 2016; full results)

So, if one is interested in addressing historic race-related inequalities, it would be more efficient and just — since (dis)advantages are mostly being passed along lines of descent — to positively discriminate according to objectively determinable biogeographic ancestry, not subjectively assessed stereotypical racial appearance. And it’s hard to see how requiring 23andMe reports would be more intrusive than having a 12 member panel examine applicants for nose width, lip thickness, craniofacial morphology, etc. to see if they are sufficiently African-looking.

Of course, this isn’t going to happen any time soon, since the conclusion that ancestry with respect to major racial or descent groups is relevant to social outcome needs to be evaded, even at the expense of good science and quality social policy.

Kirkegaard, E.O.W., Wang, M., & Fuerst, J. (2017). Biogeographic Ancestry and Socioeconomic Outcomes in the Americas: A Meta-Analysis. The mankind quarterly, 573(3):398-427

It took a particularly long time to publish, owing to the shenanigans we ran into. For example, the editorial board of Frontiers in Genetics reversed their decision (September 12, 2016; affirmed: October 12, 2016) two-three months after deciding to accept with “moderate revision” (July 5, 2016) and mid-review on the grounds that a request from a reviewer “was not satisfactorily met.” What specific request did we brazenly question?

Reviewer 1, round 1: “The discussion of cognitive ability differences across SIREs feels out of place and innappropriate. This paper makes no attempt whatsoever to investigate cognitive abilities, and this discussion should be removed.”

Reply to reviewer 1: “Following the advice of another reviewer [who approved the paper] we added a diagram (Figure 8) to clarify the relevant discussion. Since that reviewer asked for a model and since cognitive ability seems like a plausible pathways to us, we feel that it would be intellectually dishonest on our part to not include the variable. The reason for the present reviewers objection is not clear to us. We do not investigate colorism, yet no objection is made regarding our mentioning of this as a potential mediator of the BGA x SES associations…”

They should have let it slide, because now we feel obliged to prove the point. And prove it again and again, if needs be.

According to one popular version of the race narrative, natural scientific concepts of race were conceptualized, in the 18^th to early 20^th century, such to imply discrete categories; and human “races,” were, accordingly, imaged to have significant discontinuities until post-World War II anthropologists, such as Frank Livingstone (1962), came to realize that human variation was relatively continuous, a fact which, the story goes, demonstrated that human “races,” as traditionally understood, did not exist — instead, only “populations” do. Anyone sufficiently familiar with actual 18^th to early 20^th century discourses on the matter, would find this tale outlandish. They would recall, for example, Wallace’s (1864) account, in “The origin of human races and the antiquity of man deduced from the theory of natural selection,” of the competing pre-evolutionary views about human variation:
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Science is replete with fake, whiggish histories peddled to bolster new paradigms. Biology is no exception. Two examples are the The Essentialism Story (Winsor, 2006; Richards, 2010; Wilkins, 2013) and The Classic View/The Mutationism Myth (Stoltzfus, 2010; Stoltzfus and Cable, 2014). According to the former, early biologists were inexplicably caught in the thrall of Platonic-Aristotelian typological essentialism, which resulted in the failure to recognize the significance of individual variation and which consequently retarded the recognition of evolution. According to the second, early geneticists were caught in the grip of saltationism, which resulted in the rejection of natural selection and held back for decades the synthesis between Mendelian principles and evolutionary theory. As expected, in these tellings, the actual historical views are often barely recognizable.

The most prominent, yet least discussed, example of pseudohistory of science has to be what should be called The Race Narrative. The Race Narrative is meta-myth, comprised of several related tales, which typically involve some permutation of: “”Race” never described a classification which had a proper place in natural history or a classification which, given how it was historically understood, was applicable to humans, but rather was a political construct imposed to oppress certain human groups, which was then back rationalized by natural historians, who read reality through the political ideology of their times.” Often The Essentialism Story and, to a lesser extent, The Mutationism Myth are incorporated into this Narrative.

To give just a few examples:
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After some deliberation, I determined that the locution “race” is inessential. As such, I now disavow the arguably controversial view that this is necessary when it comes to understanding biological variation, human and otherwise…. At the same time, it has become evident that the concept which I refer to as “lineage population,” which I hitherto called “natural division,” a concept which corresponds with Darwin’s “communities of descent,” as understood in On the origin of species by means of natural selection, or the preservation of favoured races is indispensible. I clarify that concept and discuss it in relation to other systematic ones.

Lineage population: A concept needed by an observer of nature? Preprint.

Abstract: The genealogy-based classificatory programs of Kant and Darwin are briefly discussed for context. It is detailed how in biology there is no unambiguous term to reference infraspecific-level descent-based divisions. The term lineage population is introduced and defined for analytic purposes: a lineage population is one of a set of divisions of intrafertile organisms into which members are arranged by propinquity of descent. It is argued that the lineage population concept avoids the ambiguities associated with related biological and anthropological concepts and polysemes such as population, ethnicity, and race. Other terms and concepts, such as form, cline, cluster, geographic population, breeding population, genetic population, breed, species, subspecies, ancestry, geographic ancestry, biogeographic ancestry, ancestral population, ancestry population, natural division, and population lineage, are discussed in relation to this concept. It is concluded that the lineage population concept is a useful analytic tool which picks out, in line with the Kantian/Darwinian perspective, an interesting class of biological variation.

Jason Malloy and I have individually collected a large number of papers and research reports from countries around the world detailing ethnic and racial differences. I have sent some of the stuff to Richard Lynn, lost a number of reports due to hard drive failures, and simply haven’t got around for various reasons (time, health, other priorities, etc.) to posting on the remainder. In response to an article by Chanda Chrisala, James Thompson recently suggested that it would be informative to look at ethnic differences in other American countries. As such, I will comment on a few studies from Canada and Brazil. Regarding Canada, there seems to be no published detailed ethnic data for the nation as a whole — though many reports discuss the Aboriginal/overall Canada gap. The country has a number of national longitudinal surveys which most likely contain the relevant variables, but as far as I am aware no has looked into the issue. Nonetheless, since the 1980s the Toronto public schools have published research reports which decompose math and reading pass rates by linguistic, ethnic, and racial background.

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I originally intended on including and briefly discussing these values in my “Ethnic/Race Differences in Aptitude” paper since therein I touched upon differences in Asian American subgroup performance (e.g., Table 15 and Table 17). Alas, I ran out of both space and my reviewers’ patience. Since the general topic continues to arise, I thought I might mention them, though. The 1996 and 2000 National Postsecondary Student Aid Studies (NPSAS 1996/2000), which were representative of the university populations at the respective times, contained both an “Asian origin” variable and a composite SAT score one, thus allowing for some investigation of subgroup variability. In expressing the differences, I used citizen/U.S. born White values as a reference for the SAT scores. Standardized differences were computed using the total group standard deviations, since population specific ones were unavailable. NA means that the sample sizes did not meet NCESDataLab’s cutoff for reportability. And negative values mean that the groups in question performed better than U.S. born/citizen Whites. As the confidence intervals — not shown below — were large for all of the Asian subgroups, results should be interpreted with caution. It’s notable that there were large U.S. born/non-U.S. born effects for both East and South Asians. The scores were for college students, so this might represent a foreign student effect (as opposed to a generation 1/generation 2+ immigrant one).

Used the total group standard deviation
Source: https://nces.ed.gov/surveys/npsas/