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.

Alaska

According to the 2020 U.S. Census, Alaska’s population of approximately 733,000 is 59% White (alone), 15% American Indian/Native Alaskan (alone), 12% multiracial, 7% Hispanic, and 6% Asian (alone). Given the substantial European ancestry among Hispanics, multiracial individuals, and American Indian/Native Alaskans, the overall proportion of European ancestry in Alaska is estimated at around 75%, comparable to the U.S. national average. Based on our computations, Alaska’s average Academic Achievement Quotient (ACHQ), derived from NAEP and PIAAC test scores, is 99.42, also near the U.S. average.

Academic achievement disparities by racial and ethnic groups in Alaska mirror those in the contiguous United States. NAEP main assessments (2015, 2017, 2019, 2022) for non-English language learners reveal an average math achievement gap of d = 0.93 standard deviations between White and American Indian/Native Alaskan students. IQ testing shows similar disparities. Grigorenko et al. (2004) administered the Cattell Culture Fair Test and Mill Hill Vocabulary Scale to 261 Yup’ik students (grades 9–12) in Alaska. As reported by Lynn (2016), the group scored an equivalent of IQ 86 on the non-verbal test and IQ 77 on the verbal test.

County-level data from the Stanford Education Data Archive indicate strong negative correlations between the percentage of American Indian/Native Alaskan residents and both ACHQ and an S-factor index based on rates of adults without a high school diploma, uninsured individuals, unemployment, SNAP recipients, and those living below 150% of the poverty line. These correlations persist after controlling for the percentage of Asian and Pacific Islander residents. Table 1 summarizes partial correlations between self-identified racial/ethnic (SIRE) percentages and both ACHQ and S-factor scores across 29 Alaskan counties, controlling for Asian and Pacific Islander populations.

Table 1. Partial correlations between SIRE percentages and academic achievement (ACHQ) and S-factor scores across 29 Alaskan counties, controlling for % Asian and Pacific Islander

Greenland

Greenland has a population of approximately 56,000, of which about 89% are reportedly Inuit (Bjerregaard et al., 2002). The remaining 9% consists primarily of Danes, other Europeans, and small groups such as Filipinos (Central Intelligence Agency, 2020). According to a large genetic study, the Greenlandic Inuit population has, on average, about 25% European ancestry. Based on these figures, the estimated country-wide ancestry proportions would be approximately 31% European, 67% Inuit (Amerindian), and 2% from other sources.

Since Greenland is a constituent country of Denmark, its Human Development Index (HDI) is not routinely reported by the UN. However, several estimates have been made for the 2008–2010 period: 0.869 (Hastings, 2009), 0.786 (Avakov et al., 2013), and 0.839 (Andersen et al., 2021). All are lower than Denmark’s HDI average of 0.93 to .95 for 2010. Summarizing the HDI components for 2010, Andersen et al. (2021) reports the following:

Table 2. Human Development Index (HDI) and component scores for Greenland and Nordic countries, as reported by Andersen et al. (2021).

Little cognitive data is available for Greenland. Becker (in View on IQ) provides an estimate of 98.89 (or 98.74 when scaled relative to the U.S. average) based on WISC Block Design scores from a sample of 40 Inuit adolescents in a study by Weihe et al. (2002). Becker excluded WISC Digit Span (SD) scores, which were low with raw scores of 2.8 and 2.3 for DS forwards and backwards, respectively, arguing that mercury exposure may have artificially lowered performance on that subtest. However, the children tested were not atypical, suggesting that if mercury exposure is affecting scores, it likely reflects a broader issue within the Greenlandic Inuit population. Using U.S. CNLSY scores as norms, computed in another post, the children’s estimated scores would be IQ 85 for Digit Span Backwards and IQ 79 for Digit Span Forwards. Averaging Block Design and Digit Span Backwards yields an estimated IQ of 92 (U.S.-normed) for the sample.

Additionally, Kleist et al., (2021) report validation results for an Greendlandic Inuit translation of The Rowland Universal Dementia Assessment Scale (RUDAS) dementia screen. The discussion and figures imply a mean score of around 24, significantly lower than scores found in validation studies of European populations (Nielson et al., 2019; M = 27.3; SD = 2.2).

Regarding the ACHQ, Statistics Greenland reports rates of satisfactory performance on achievement tests in Math, English, Danish, and Greenlandic. These scores are not directly comparable to Danish test scores, but analyzing student performance across Nuuk schools provides valuable insights. We examined results from top-performing schools on Danish tests. At Nuuk Internationale Friskole, a private international school, 90% of students were European, while 95% of students at Ukaliusaq were Inuit. Atuarfik Hans Lynge and Kangillinnguit had more mixed demographics and more admixed students, with approximately 50% and 33% of students, respectively, exhibiting predominantly European features. Pictures of the student bodies of the schools are shown below.

Based on percentages achieving satisfactory scores in years 2010 to 2019 we computed deviations scores relative to the Greenland average. Results are shown in Table 3.

Table 3. Deviation Scores for the Schools with the Highest Danish Language Performance in Greenland (Greenland average as reference)

Students at Nuuk Internationale Friskole scored 0.77 standard deviations (SD) above the Greenland average, calculated by averaging Math scores and the mean of the three language tests (English, Danish, and Greenlandic). Note, the numbers at this school were small with around 150 kids over a decade of data; estimates are not precise. This may represent an upper bound for the Danish-Greenland performance gap, as the school is selective. The extent of this selectivity is unclear, but Europeans in Greenland are often administrators, suggesting a potential cognitive advantage. If we assume these students perform 0.4 SD above the Greenland average due to selection, their residual advantage would be 0.4d. For comparison, selection for the children of U.S. military personnel at Department of Defense schools is estimated to be approximately 0.3d, where one parent is directly selected on a measure of general intelligence.

For the Admixture in Americas analyses, we will use the score of 92, derived from the average of the DS backwards and Block Design scores reported by Weihe et al. (2002). Based on our preliminary analysis of achievement data, the ACHQ is likely not below this value.

It is notable that Greenlanders in Denmark are reported to have higher educational dropout rates, increased levels of poverty, and greater incidence of homelessness (Graven et al., 2023). Additionally, Greenlanders disproportionately fail the Danish Parent Competency Test. According to Human Rights in Denmark, this may be partly due to lower measured cognitive ability scores, noting:

When the municipalities examine the basis for the forced removal of Greenlandic children in Denmark, a number of tests are generally used to measure parenting skills. But according to several sources, these tests are unsuitable because they are not adapted to the target group. Greenlandic parents risk achieving low test scores, so that it is concluded, for example, that they have reduced cognitive abilities without there being actual evidence for it. Such potential misjudgements can have far-reaching consequences for both children and parents, as they can ultimately contribute to the forced removal of a child. As stated in the memo, it is well known that, among other things, intelligence tests prepared and tested in a given context or culture cannot simply be used among other peoples or cultures. The criticism of the measurement tools used in connection with forced removals should therefore be taken seriously. In Denmark, 7 per cent of children born in Greenland and 5 per cent of children with at least one parent born in Greenland are placed outside the home, compared to 1 per cent of other children in Denmark. Testing of parenting skills among Greenlanders in Denmark. (Danish Institute for Human Rights, 2022)

Greenland is another country where more detailed research is needed on the relationship between ancestry and outcomes.

References

Andersen, T. M. (2021). The Greenlandic economy: Structure and prospects. In Greenland’s Economy and Labour Markets (pp. 11-29). Routledge.

Avakov, A. V. (2013). Quality of Life, Balance of Powers, and Nuclear Weapons, 2013: A Statistical Yearbook for Statesmen and Citizens (Vol. 6). Algora Publishing.

Bjerregaard, P., & Curtis, T. (2002). Cultural change and mental health in Greenland: the association of childhood conditions, language, and urbanization with mental health and suicidal thoughts among the Inuit of Greenland. Social Science & Medicine,

Central Intelligence Agency. (2020). The World Factbook. CIA.gov. Archived January 9, 2021. Retrieved October 3, 2020, from https://www.cia.gov/the-world-factbook/

Graven, V., Abrahams, M. B., & Pedersen, T. (2023). Total pain and social suffering: marginalised Greenlanders’ end-of-life in Denmark. Frontiers in Sociology, 8, 1161021.

Grigorenko, E. L., Meier, E., Lipka, J., Mohatt, G., Yanez, E., & Sternberg, R. J. (2004). Academic and practical intelligence: A case study of the
Yup’ik in Alaska. Learning and Individual Differences, 14(4), 183-207.

Hastings, D. A. (2009). Filling gaps in human development index: findings for Asia and the Pacific.

Kleist, I., Noahsen, P., Gredal, O., Riis, J., & Andersen, S. (2021). Diagnosing dementia in the Arctic: translating tools and developing and validating an algorithm for assessment of impaired cognitive function in Greenland Inuit. International Journal of Circumpolar Health, 80(1), 1948247.

Lynn, R. (2006). Race differences in intelligence. Whitefish, MT: Washington Summit Publishers.

Moltke, I., Fumagalli, M., Korneliussen, T. S., Crawford, J. E., Bjerregaard, P., Jørgensen, M. E., … & Albrechtsen, A. (2015). Uncovering the genetic history of the present-day Greenlandic population. The American Journal of Human Genetics, 96(1), 54–69.

Nielsen, T. R., Segers, K., Vanderaspoilden, V., Bekkhus-Wetterberg, P., Bjørkløf, G. H., Beinhoff, U., … & Waldemar, G. (2019). Validation of the Rowland Universal Dementia Assessment Scale (RUDAS) in a multicultural sample across five Western European countries: diagnostic accuracy and normative data. International Psychogeriatrics, 31(2), 287-296.

Weihe, P., Hansen, J. C., Murata, K., Debes, F., Jørgensen, P. J., Steuerwald, U., … & Grandjean, P. (2002). Neurobehavioral performance of Inuit children with increased prenatal exposure to methylmercury. International Journal of Circumpolar Health, 61(1), 41-49.

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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