Category: Uncategorized (Page 1 of 3)

Previous analyses have identified a strong inverse correlation between Indigenous ancestry and both academic achievement and socioeconomic outcomes in Canada. Similar patterns are anticipated in Alaska and Greenland. While evidence supports this trend in Alaska, the situation in Greenland remains unclear.

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In the previous post, we noted that across Canada—and within individual provinces and territories—Métis, First Nations, and Inuit populations tend to underperform relative to non-Indigenous Canadians. To illustrate this, Table 1 presents average PIAAC literacy and numeracy d-values comparing Aboriginal and non-Aboriginal individuals whose mother tongue matches the language of the test (English or French), disaggregated by province and territory where data are available. Higher positive d-values indicate worse performance among Aboriginal groups. While the magnitude of the gaps varies by region, the disparities are consistently present.

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Demographic Change and Cognitive Variation

Canada is rapidly diversifying as a result of relaxed immigration policies. According to the 2021 Census, individuals of European ancestry now constitute approximately 67% of the population, down from 83% two decades earlier. The largest non-European groups include East Asians (9%), South Asians (7%),  Aboriginal peoples (6%), and Black Canadians (4%). The pace of demographic change has outstripped genetic survey estimates. For instance, Ancestry.com still reported over 90% European ancestry in Canada as recently as 2017, whereas the true proportion is now likely under 70%. Despite this shift, geographic variation in socioeconomic and cognitive outcomes continues to correlate strongly with European and Amerindian ancestry proportions. This post provides an overview of Aboriginal demographic distributions and cognitive performance in Canada.

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In Canada, longstanding differences in cognitive ability and academic achievement exist between the European-origin majority and several traditional minority ethnic groups established prior to the mid-20th century. In particular, Aboriginal populations (First Nations, Inuit, and Métis) and the historically rooted Black Nova Scotian population tend to score below the national average on various educational metrics. Meng Hu and I recently published findings based on data from 2013 to 2022 indicating that Black Nova Scotians score, on average, d = –0.46 below the non-Black Nova Scotian mean (Fuerst & Hu, 2024).

Less is known about the performance of more recent immigrant-origin groups, as academic performance data disaggregated by race/ethnicity is generally not collected at the national level in Canada. However, relatively high academic performance among Chinese and other Northeast Asian Canadians has been documented since Peter Sandiford’s research on Vancouver’s Chinese population in the 1920s (Sandiford & Kerr, 1926), with further evidence summarized by Vernon (1982). More recent national and provincial achievement data corroborate these findings (Bacic & Zheng, 2024; Barber et al., 2021). By contrast, average performance among other ethnic groups—such as Asian Indians, Filipinos, and Hispanics—remains less clear. The same applies to the children of Black African immigrants, many of whom come from highly selected backgrounds — and whom are highly educated. While I have previously reported ethnic performance data from the Toronto District School Board, these findings should not be assumed to generalize nationally or even across Ontario.

Recently, I was able to compile a decade’s worth of Canadian Advanced Placement (AP) data, as reported by College Board (years: 2009, 2011–2019) and to convert AP threshold pass rates into d-scores using the method of thresholds. Unfortunately, post-George Floyd, College Board changed its policy and ceased reporting Canadian scores by race/ethnicity; as a result, data after 2019 is not available.

Note, for these analyses, I dropped the three studio art scores along with all of the ‘language & culture’ scores, as Warne (2016) found that these tests had low correlations with PSAT scores. (Both the original and modified datafiles are attached; thus if readers wish they can modify the analyses as desired.) Moreover, I computed d-values with respect to the total mean; since Asians score higher, self-identifying White Canadians scored below average. Additionally, I dropped the American Indian group since there were too few individuals to allow for reliable estimates. While Hispanic/Latino is a “visible minority” group in Canada, the number reporting as Hispanic in the Canadian AP datasets seems excessive. This group may include Iberians in addition to Latin Americans.

Summary results are presented below, with the final row reporting weighted-average ACHQ scores. Unlike in the United States, AP exams are not widely taken in Canada, so these results should be interpreted with caution — they represent a few more data points.

Table 1. Canadian 2009-2019 Advance Placement Results by Self-reported race/ethnicity

For comparison, Warne (2016) reports that, in the United States, the weighted mean AP group differences in 2015 were: d = 0.774 (White–Black), d = 0.484 (White–Hispanic), and d = –0.156 (White–Asian). Thus, relative to the U.S., all non-White groups perform better on AP tests in Canada. For other national-level comparisons using Canadian datasets, see here. As I’ve previously argued, it would be worthwhile to collect more robust data on ethnic differences across a range of countries prior to hypothesizing about causes, rather than relying solely on U.S. data.

The R code and data files can be found here.

References

• Bacic, R., & Zheng, A. (2024). Race and the income‐achievement gap. Economic Inquiry, 62(1), 5–23.
• Barber, M., & Jones, M. E. (2021). Inequalities in test scores between Indigenous and non-Indigenous youth in Canada. Economics of Education Review, 83, 102139.
• Sandiford, P., & Kerr, R. (1926). Intelligence of Chinese and Japanese children. Journal of Educational Psychology, 17(6), 361.
• Vernon, P., (1982). The abilities and achievements of orientals in North America. Academic Press.
• Warne, R. T. (2016). Testing Spearman’s hypothesis with Advanced Placement examination data. Intelligence, 57, 87–95.

The Cayman Islands, a British Overseas Territory, boasts one of the world’s highest standards of living and per capita GDP, largely due to its status as a major tax haven. Its average Human Development Index (HDI) from 2010, 2015, and 2020 was 0.876 (Economic and Statistics Office, 2021), compared to the United Kingdom’s 0.919 over the same period—both rated as very high human development. The 2021 census recorded a population of 68,811, with 53% identified as Caymanian residents, of whom only 62% were born on the island. These demographic shifts, detailed in Figure 1, complicate efforts to estimate ancestry proportions.

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Although my colleagues are international, their research predominantly focuses on differences within the USA, a tendency I find perplexing. Years ago, at an LCI conference, a British colleague asserted that further research on ethnic or racial differences was unnecessary, claiming the matter was settled. Yet, within a year, he contacted me seeking data on the performance of various ethnic groups in the UK. Similarly, a French colleague, whose work almost exclusively examines differences in the USA, argued that such data was unavailable in France. However, after just ten minutes of searching, I located several French government reports detailing the performance of first- and second-generation immigrants by region of origin. Evidently, he didn’t try too hard. In my view, a logical approach, if one were to pursue this topic seriously, would involve first collecting a broad international dataset—potentially requiring effort such as submitting freedom of information requests—then identifying patterns, and finally hypothesizing about possible causes. After all, the USA is not the world, and we should not assume it reflects global trends. The lack of such comprehensive data is why I remain largely agnostic about the existence of worldwide race differences in cognitive ability, and even more so about their causes

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France governs three overseas departments in the Americas—French Guiana, Guadeloupe, and Martinique—along with three overseas American collectivities: Saint Barthélemy (St. Barth), Saint Martin, and Saint Pierre and Miquelon. The populations of these territories display a rich diversity of ancestries, making them an interesting case study.

Saint Pierre and Miquelon, located off the coast of Canada, has a population of approximately 5,819 and is the most European-influenced of France’s overseas territories. Its residents are primarily descendants of French settlers from Normandy, Brittany, and other regions, supplemented by recent migrants from metropolitan France. Saint Barthélemy, a small Caribbean island that separated from Guadeloupe in 2007, has a population of around 10,000. Although France does not officially collect ethnic or racial data, visual observations of children in local primary schools and at festivals suggest the population is roughly 80% of European ancestry (mostly French), 15% of African ancestry, and 5% of other origins. This aligns with historical settlement records, which indicate a majority of French descent.

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Academic papers’ abstracts are sometimes manipulated to display what is seemingly a good result. Alternatively, abstract conclusions are affected by faulty statistical analyses. The large profits in online publishing, especially the open-access (OA) type, are largely (but unfairly) criticized, with endless calls for regulation. In reality, the problem of monopolistic prices and quasi-monopolistic status of the most prestigious online publishers is not due to lack of regulation, but due to a combination of copyright law, OA mandates, and moral hazard driven by subsidies.

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Note: We computed these results based on multiple versions of the ABCD data (v2.01 & v3.01) and with different inclusion/exclusion criteria.  I originally posted a version based on the ABCD 2.01 data filtered for missing admixture, and other scores. However, after looking, I found a version that uses the maximum 3.01 sample with age-corrected NIHTBX scores (N = 11474).  While the scores for the two versions correlate at r = .98, in some cases (e.g., Vietnamese), there is a notable difference.  I have now replaced the original table with the one based on N = 11474 and moved the original table to the end of the post.  For replicability and modifiability, I attached the latest R code which I had in my file.

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In our manuscript, titled “Reply to Warne,” we present average eduPGS and NIH Toolbox composite scores from the ABCD study, categorized by ethnic and religious groups. In our analyses, we used unweighted means instead of sample weighted scores, since we were only interested in the correlation between mean eduPGS and cognitive ability. However, we also computed weighted NIH Toolbox scores, which may be of interest to some readers.

These scores were computed using the survey package for R as recommended by Heeringa and Berglund (2020). These weighted scores, reported below, represent the “neuropsychological performance” scores, measured between 2016 and 2018, of broadly representative samples of 10-year-old American children. (Though, children were excluded, by the ABCD consortium, if they were not fluent in English or if one of their parents were not fluent in either English or Spanish.) The first three columns, after the group labels, display the sample size, means, and standard deviations, respectively. The fourth column presents the scores normalized with the non-Hispanic White mean set to 100.00 and standard deviations set to 15.00. To norm scores, we pooled the standard deviations across all groups (pooled SD = 16.45) and transformed the values using the pooled SD. On a reader request, I added average years of parental education, which I previously outputted, in the fifth column.

The ethnic groups are mutually exclusive, and the specific variables used to code them are provided in the supplementary materials of the manuscript. Classifications are based on the race/ethnicity of the child as reported by the responding parent in conjunction with the nationality and immigrant status of the parents; see the Parent Demographics Survey for specific variables and the second table for definitions. To be clear, some of the definitions do not perfectly overlap with ones commonly used in the social sciences. For example, the classification “USA Blacks” refers to children who were identified as being Black, not being White, not being Hispanic, but also not having an immigrant parent or grandparent. This was done because, when computing the scores, we were interested in mutually exclusive ethnocultural groups.

Bear in mind that the sample sizes are often small and so the corresponding estimates are imprecise and also that the NIH Toolbox battery is fluid-intelligence loaded. For comparison, Sailer, in 2009, reported cognitive abilities of legal American immigrants based on the digit span backwards test. Additionally, in 2015, I summarized scores by immigrant generation and ethnic groups mostly based on scholastic tests.

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How much the 5 personality traits composing the Big Five contribute to social outcomes? Many studies examined the question but only a few also considered IQ. This article will only cover the studies which evaluate the Big Five while controlling for IQ.

A quick summary reads as follows: conscientiousness is associated with better income and health, extraversion inversely predicts delayed rewards, neuroticism negatively predicts health, perhaps none of these traits are related to academic achievement or occupation status and, finally, publication bias is a problem.

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Gregory Connor and I submitted the paper, “Linear and partially linear models of behavioral trait variation using admixture regression,” to MDPI’s Behavioral Sciences. This is a methodological paper explicating & proposing some modifications to the frequently used – across hundreds of papers – admixture regression method. We illustrated this method and our proposed tweaks using the ABCD cohort. This manuscript was peer-reviewed by three reviewers, accepted, proof-edited, paid for, but not published. Breaking with MDPI’s clearly outlined protocol, the editor of Behavioral Sciences – who I am fairly sure has now blacklisted me — sent it to a mysterious and seemingly not particularly acute 4th “reviewer”. This “reviewer” argued that the paper was “racist” and based on an “outdated” method. We were not given a chance to respond. And the opinions of the original three reviewers, whom we patiently replied to and made revisions for, were discarded.

You might wonder whether this 4th “reviewer” caught a serious methodological error – or even a substantive one. Nope. Instead, he argued that admixture regression – frequently used, since the early 2000s by numerous geneticists, genetic epidemiologists, medical researchers, and so on – is an “outdated approach (more of the 19th century)”. He kept repeating that the paper was about an outdated “biological concept” of race, when it concerned the relation between traits, genetic ancestry, and self-identified race/ethnicity. To note, typical MDPI reviews are not this ill-conceived and incoherent.

To let you judge if this post-hoc “review” had any merit, I provided this full comment along with my point-by-point empty-chair reply. Since the paper already passed peer-review and was accepted by MDPI, but not published for obvious political reasons, Greg and I have decided to publish it as a chapter in a forthcoming book. I usually do not publish reviews. However since I do not plan to have this paper peer-reviewed yet again, publishing the post-hoc commentary is warranted. Moreover, I usually do not speculate on motives, but it should be noted that, according to the editor, our post-hoc commenter was a knowledgeable geneticist. That fact, with the implication that the commenter understood the technique and literature, suggests that this was a hit job, with the goal of simply persuading the editor to cancel the paper. On the other hand, the commentary does read as if the “reviewer” was either clueless or was just trying to rationalize moral outrage.

“Peer-review” #4.

R4: Connor and Fuerst (here, C&F) proposed a new test that measure how differences in racial identity affects trait variation. They apply their variable to neuropsychological data collected by the Adolescent Brain Cognitive Development (ABCD) study and report that there exists a genetic component to neuropsychological traits and that there is a variation in the performance between different racial groups.

Empty chair reply: As we clearly explained in the introduction, admixture regression is commonly used in genetic epidemiology. Over the last two decades, hundreds of papers have been published using this technique by hundreds of well published geneticists, genetic epidemiologists, medical researchers and so on. In this paper, we explicate the underlying statistical model and propose some improvements to this frequently used technique.

R4: I found this paper unfounded, misleading, dishonest, and outdated, i.e., racist.

Empty-chair reply: Did you get your 30 pieces of silver for this hit job?

R4: The authors are missing some important advances in the field of population genetics. They used outdated terms (races) and cite no literature to support their racial perception.

Empty chair reply: You clearly did not understand the paper. We explicitly contrasted self-identified race/ethnicity (SIRE) with genetic ancestry. The former is posited as tagging environmental effects while the latter is posited as tagging genetic effects: Thus, we note: “Admixture regression leverages these two data sources, self-identified race or ethnicity (SIRE) and genetically-measured admixture proportions, to decompose trait variation correspondingly.” In line with ASHG (2018) we contrast self-identified race/ethnicity, a social construct, with genetic ancestry, a genetic construct. As ASHG (2018) notes:

Although a person’s genetics influences their phenotypic characteristics, and self-identified race might be influenced by physical appearance, race itself is a social construct. Any attempt to use genetics to rank populations demonstrates a fundamental misunderstanding of genetics. The past decade has seen the emergence of strategies for assessing an individual’s genetic ancestry. Such analyses are providing increasingly accurate ways of helping to define individuals’ ancestral origins and enabling new ways to explore and discuss ancestries that move us beyond blunt definitions of self-identified race. [Emphasis added]

R4: Their assumptions about human races are from the previous century. They consistently imply that their usage of racial categories used in social sciences have genetic merit, that’s racism and, of course, wrong. It is not surprise that they cannot find papers to support their genetic model, because it is unfounded.

Empty chair reply: See above. Also, we cited a plethora of examples of papers using admixture regression in the introduction and conclusion.

R4: The authors model individuals as races + admixture, but the emphasis is on races, as admixture is simply defined as combination of more than 1 race. This is a very ignorant modelling of human populations that ignores the vast literature on the subject. The genetic analyses results are skewed to reproduce their perceived racist model.

Empty chair reply: No. Genetic ancestry is not a combination of more than one SIRE group. And there are literally hundreds of papers which employ admixture regression analysis using the same major ancestry groups we used. The ABCD consortium, itself, even has their own genetic ancestry variables (European, African, Amerindian, and East Asian ancestry). We only recomputed these so to include South Asian ancestry

R4: Throughout the manuscript, the authors omit results (i.e., graphs and code) necessary to evaluate their code.

Empty chair reply: We provided the code in the supplemental files. Either you did not check or the editors did not forward this to you.

R4: 1. Where is the support to: “Many diverse national populations descend demographically from isolated continental groups within a few hundred years.”? where did you get it from? where is the scientific reference? ancient DNA study show that mixture is the norm rather than the exception.

Empty chair reply: Admixture within continental groups obviously doesn’t preclude isolation between them.

R4: 2. “Modern genetic technology can measure with high accuracy the proportion of an individual’s ancestry associated with these continental groups.” – yes, modern tests can predict continental origins with high accuracy, but where is the citation?

Empty chair reply: This is from ASHG’s positional statement on this topic.

R4: 3. “In many culturally diverse nations, most individuals can reliably self-identify as members of one or more racial or ethnic groups.” – nonsense. All self-reports are biased. No serious study uses self report ancestry. Of course, the authors must believe in that, because their entire method rests on this connection, but it is untrue. Unlike this unsupported claim of the authors, there are plenty of papers that prove otherwise :
https://academic.oup.com/aje/article/163/5/486/61161?login=true
Self-reported ancestry may not be a reliable method to reduce the possible impact of population stratification in genetic association studies of outbred populations, such as in the United States.
https://pubmed.ncbi.nlm.nih.gov/8761246/
https://pubmed.ncbi.nlm.nih.gov/10797159/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2350912/
Read: https://www.nature.com/articles/s41408-018-0132-1 to see the differences between self-reported ancestry and genomic ancestry, calculated very accurately.

Empty chair reply: We did not say that SIRE is a reliable index of genetic ancestry – after all, the whole method is based on the contrast between SIRE and genetic ancestry. Rather, we said that SIRE is a reliable index of itself, in the sense that people who identify as a particular SIRE group at one time identify the same way at another. Thus it reliably tracks a cultural-environment.

R4: 4. Poor modeling: How can self-identified people report their % of ancestry? Hardly anyone mixed is 50%:50%.

Empty chair reply: How much did you bother to read beyond the abstract?

R4: 5. “The genotyped DNA samples are carefully decomposed into admixture proportions of geographic ancestry” – no. they are decomposed into a mixture of racial groups that the authors created after forcing the genetic data to show races. Races and admixture are two different concepts.

Empty chair reply: Translation: “The authors computed genetic ancestry in a standard way and entered this in a regression model with SIRE as have so many other researchers. This is bad: Reasons.”

R4: 6. “In most applications of admixture regression, individuals’ racial or ethnic group identities will have statistical relationships with individuals’ genetically identified geographic ancestries” – No! where is the evidence? Why this paper is completely devoid of reference for any fundamental assumption of the model. What does it mean “statistical relationships”?

Empty chair reply: Yes! Self-identified race/ethnicity generally, but imperfectly correlates with genetic ancestry. This just restates ASHG’s (2018) positional statement. But since you don’t even understand the meaning of “statistical relationship” what can one expect?

R4: 7. “The objective of admixture regression is to decompose trait variation into linear components due to genetic ancestries and linear components due to racial/ethnic group related effects” – unlike admixture mapping techniques, which the author misleading cite as a parallel method, their method is not designed to link a loci with a trait, but rather link conditions with races with a biological support to the racial concept.

Empty chair reply: Whew!… admixture regression analysis is not ‘our’ method. And this frequently used method is not “designed” to provide “biological support to the racial concept”: it explicitly takes advantage of social constructive aspects of racial identification in admixed populations. Do you need this point illustrated with a crayon?

R4: 8. “We show that the admixture regression model can be viewed as a statistically feasible simplification of this linear polygenic index model, in which proportional ancestries serve as statistical proxies for ancestry-related genetic differences.” – proportional ancestries serve as statistical proxies for ancestry-related genetic differences? You calculate ancestries from genetics, this statement means nothing. This is a tautology.

Empty chair reply: So now you finally realize that we used genetic ancestry. But, of course you are still wrong, since local ancestry is a subset of global ancestry. The statement reads: in our model, [global] ancestries serve as statistical proxies for [local] ancestry-related genetic differences.

R4: 9. “an assumption of random mating across ancestral populations” – really? where is the reference for this assumption?

Empty chair reply: Unsurprisingly, no other reviewers had a problem interpreting this statement. To spell it out: It is an assumption made by the theoretical model – thus a limitation – not an assumption about the world.

R4: 10. “A key assumption of the admixture regression model is that admixture arises from recent random mating between the previously geographically-isolated ancestral groups.” – of course no reference, because it is untrue. Your key assumption is not supported by reality.

Empty chair reply:… we restate that random mating is a theoretical assumption of the commonly used admixture regression model which may or may not be violated to a practically significant extent in the real world.

R4: 11. “Many individuals self-identify as belonging to two or more racial or ethnic groups” – you of course model those groups as RACES, by the biological definition, i.e., groups that are completely separate from one another and didn’t mix. Again, where is your evidence (from this century)? Surely you realize that the racial groups that you used do not satisfy this condition, south and east Asians are closer to each other than to Africans, but you ignore that. There are relationships between those groups, it’s not a star topology.

Empty chair reply: We explicitly do not model self-identified “racial or ethnic groups” as “groups that are completely separate from one another and didn’t mix”! If they didn’t mix, we wouldn’t have admixture for our admixture regression! Nowhere in this paper do we talk about “biological races”. We talk about “genetic ancestry” and SIRE. Perhaps you could try reading our actual paper…

R4: 12. The author removed 80% of the genetic data. They claim that they follow the instruction of ADMIXTURE, but there are no such instruction or recommendation.

Empty chair reply: 100,000 random SNPs…. 100,000 random SNPs…

R4:

13. They force the genetic data into 5 racial categories to fit their made up racial categories. They never show a single result of the genetic analyses. we don’t see the STRUCTURE analysis, nor the PCA. We don’t see the scripts that they used. They through populations because they are “overly admixed”?? what does it mean? You think that Hispanics are less admixed than Druze? Where is the evidence? Why everything in this manuscript is made up BS?

Empty chair reply: You mean: we use K=5 (European, Amerindian, African, East Asian, & South Asian) instead of the K=4 (European, African, Amerindian, & East Asian) provided by the National Institute of Health for the ABCD dataset… Yes, only “racists” would use these ancestry components.

R4: 14. The authors don’t report their results. Are they afraid? Where are the findings of the model (blacks are poor and uneducated, bla bla). What is the point of this paper if the authors don’t stand behind their results? Why should anyone believe in it?

Empty chair reply: So you missed the part that this was a methodological paper which then illustrated the methodology using the ABCD sample.

15. Where is the null hypothesis?

Empty chair reply: Whew!

R4: 16. I have major ethical concerns due to the extensive use of races, biologically defined. I think that it is wrong and unsupported by the data nor literature.

Empty chair reply: …so, again, we used SIRE vs. genetic ancestry. Which one, exactly, is the “wrong and unsupported” “races, biologically defined”?

R4: Minor comments 1. “It has particular value in the case of complex behavioral traits where reliably identifying genetic loci associated with trait variation is beyond the current reach of science” – so it is not beyond the reach of science?

Empty chair reply: Would you like it to be?

R4: I have a few more comments, but I think that the trend here is pretty obvious. It is an outdated approach (more of the 19th century).

Empty chair reply: Well, maybe you should tell that to the hundreds of research teams that currently use this method.