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An S Factor Analysis on the Provinces of Vietnam: Relationships with Cognitive Ability, Ethnicity, and Latitude
Abstract and Figures
We compiled cognitive, ethnic, and socioeconomic data for the 63 provinces of Vietnam. The cognitive data came from math and reading achievement tests administered to 70,000 fifth-graders in 2001 (World Bank, 2004). Ethnic and socioeconomic data were coded from various official sources (e.g., The General Statistics Office of Vietnam). Analysis of the socioeconomic data revealed a general factor (S) that was robust to variations in extraction method and controls. The average cognitive ability of the provinces correlated .47 with the S factor. The strongest predictor of S, however, was ethnicity. Specifically, the percent of Vietnamese (Kinh) within each province correlated .74 with S. Moreover, this effect was not mediated by cognitive ability. The lack of mediation is inconsistent with results from earlier studies that examined relations between ethnicity, cognitive ability, and socioeconomic outcomes (see, e.g., Fuerst & Kirkegaard, 2016). Also inconsistent with prior studies, although latitude correlated positively with cognitive ability, it did so inversely with the S factor. We discuss several potential hypotheses for why these discrepant effects occurred.
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... It is well-established that cognitive ability varies across geopolitical divisions such as nations, states, and counties (e.g., nations: [1][2] ; Vietnamese provinces: [3] ; U.S. states: [4] ; U.S. counties: [5] ; Argentinian provinces: [6] ). These cognitive ability differences have frequently been quite large. ...
Using a sample of ~3,100 U.S. counties, we tested geoclimatic explanations for why cognitive ability varies across geography. These models posit that geoclimatic factors will strongly predict cognitive ability across geography, even when a variety of common controls appear in the regression equations. Our results generally do not support UV radiation (UVR) based or other geoclimatic models. Specifically, although UVR alone predicted cognitive ability at the U.S. county-level (β = -.33), its validity was markedly reduced in the presence of climatic and demographic covariates (β = -.16), and was reduced even further with a spatial lag (β = -.10). For climate models, average temperature remained a significant predictor in the regression equation containing a spatial lag (β = .35). However, the effect was in the wrong direction relative to typical cold weather hypotheses. Moreover, when we ran the analyses separately by race/ethnicity, no consistent pattern appeared in the models containing the spatial lag. Analyses of gap sizes across counties were also generally inconsistent with predictions from the UVR model. Instead, results seemed to provide support for compositional models.
... For instance, the Ainu people in Japan have somewhat lower average IQ than the ethnic Japanese (Kura, Armstrong & Templer, 2014); Grigoriev & Lynn (2014) have found different average IQs for Kazakhs, Russians, and Uzbeks living in Kazakhstan. In Russia, average CA and per capita GDP of provinces and cities positively correlate with the percentage of ethnic Russians in the population (Lynn, Cheng & Grigoriev, 2017); in Vietnam, the percentage of ethnic Vietnamese (Kinh) within each province correlates with the general socioeconomic factor (S) at .74 (Kirkegaard & Pesta, 2018). Findings such as these are easily understandable given the substantial differences in average CA across nations (for the most recent review thereof, see Lynn & Becker, 2019). ...
That human ancestry predicts average IQ and socioeconomic outcomes is amongst the most thoroughly replicated findings of the social sciences. Since human ethnic and cultural descent is usually represented on national flags, it was hypothesized herein that national flag symbolism and colors would be predictive of a nation's average IQ and socioeconomic development. In order to test this hypothesis, national flag symbols and colors were coded, quantified, and correlated with country IQ and Human Development Index (HDI). Both country-level IQ and HDI are positively associated with Christian symbolism, and negatively associated with symbols representing celestial bodies. The color green predicts lower IQ and HDI, while the color white predicts higher IQ and HDI. The color red predicts higher IQ, but not higher HDI, and the color yellow predicts lower HDI, but not lower IQ. The correlations are generally higher for HDI than IQ. With the exception of the color yellow, the correlations with HDI are significant even when controlling for the correlation between HDI and IQ. The present study suggests national flag symbolism and colors as yet another correlate of average group intelligence.
... factor (S) at .74 (Kirkegaard & Pesta, 2018). Findings such as these are easily understandable given the substantial differences in average CA across nations (for the most recent review thereof, see Lynn & Becker, 2019). ...
That human ancestry predicts average IQ and socioeconomic outcomes is amongst the most thoroughly replicated findings of the social sciences. Since human ethnic and cultural descent is usually represented on national flags, it was hypothesized herein that national flag symbolism and colors would be predictive of a nation's average IQ and socioeconomic development. In order to test this hypothesis, national flag symbols and colors were coded, quantified, and correlated with country IQ and Human Development Index (HDI). Both country-level IQ and HDI are positively associated with Christian symbolism, and negatively associated with symbols representing celestial bodies. The color green predicts lower IQ and HDI, while the color white predicts higher IQ and HDI. The color red predicts higher IQ, but not higher HDI, and the color yellow predicts lower HDI, but not lower IQ. The correlations are generally higher for HDI than IQ. With the exception of the color yellow, the correlations with HDI are significant even when controlling for the correlation between HDI and IQ. The present study suggests national flag symbolism and colors as yet another correlate of average group intelligence.
... Numerous aggregate (i.e., geopolitical) measures of well-being have been derived. Examples include well-being measures for nations across the world (Chaaban, Irani, & Khoury, 2016), the 50 U.S. states (Pesta, McDaniel, & Bertsch, 2010), and the provinces of Vietnam (Kirkegaard & Pesta, 2018). In all published studies to date, cognitive ability correlates strongly with both overall well-being, and its various "subdomains". ...
A dataset of socioeconomic, demographic and geographic data for US counties (N≈3,100) was created by merging data from several sources. A suitable subset of 28 socioeconomic indicators was chosen for analysis. Factor analysis revealed a clear general socioeconomic factor (S factor) which was stable across extraction methods and different samples of indicators (absolute split-half sampling reliability = .85).
Self-identified race/ethnicity (SIRE) population percentages were strongly, but non-linearly, related to cognitive ability and S. In general, the effect of White% and Asian% were positive, while those for Black%, Hispanic% and Amerindian% were negative. The effect was unclear for Other/mixed%. The best model consisted of White%, Black%, Asian% and Amerindian% and explained 41/43% of the variance in cognitive ability/S among counties.
SIRE homogeneity had a non-linear relationship to S, both with and without taking into account the effects of SIRE variables. Overall, the effect was slightly negative due to low S, high White% areas.
Geospatial (latitude, longitude, and elevation) and climatological (temperature, precipitation) predictors were tested in models. In linear regression, they had little incremental validity. However, there was evidence of non-linear relationships. When models were fitted that allowed for non-linear effects of the environmental predictors, they were able to add a moderate amount of incremental validity. LASSO regression, however, suggested that much of this predictive validity was due to overfitting. Furthermore, it was difficult to make causal sense of the results.
Spatial patterns in the data were examined using multiple methods, all of which indicated strong spatial autocorrelation for cognitive ability, S and SIRE (k nearest spatial neighbor regression [KNSNR] correlations of .62 to .89). Model residuals were also spatially autocorrelated, and for this reason the models were re-fit controlling for spatial autocorrelation using KNSNR-based residuals and spatial local regression. The results indicated that the effects of SIREs were not due to spatially autocorrelated confounds except possibly for Black% which was about 50% weaker in the controlled analyses.
Pseudo-multilevel analyses of both the factor structure of S and the SIRE predictive model showed results consistent with the main analyses. Specifically, the factor structure was similar across levels of analysis (states and counties) and within states. Furthermore, the SIRE predictors had similar betas when examined within each state compared to when analyzed across all states.
It was tested whether the relationship between SIREs and S was mediated by cognitive ability. Several methods were used to examine this question and the results were mixed, but generally in line with a partial mediation model.
Jensen's method (method of correlated vectors) was used to examine whether the observed relationship between cognitive ability and S scores was plausibly due to the latent S factor. This was strongly supported (r = .91, Nindicators=28). Similarly, it was examined whether the relationship between SIREs and S scores was plausibly due to the latent S factor. This did not appear to be the case.
Some new methods for factor analyzing socioeconomic data are presented, discussed and illustrated with analyses of new and old datasets. A general socioeconomic factor (S) was found in a dataset of 47 French-speaking Swiss provinces from 1888. It was strongly related (r’s .64 to .70) to cognitive ability as measured by an army examination. Fertility had a strong negative loading (r -.44 to -.67). Results were similar when using rank-transformed data. The S factor of international rankings data was found to have a split-half factor reliability of .93, that of the general factor of personality extracted from 25 OCEAN items .55, and that of the general cognitive ability factor .68 based on 16 items from the International Cognitive Ability Resource.
Analyses of the relationships between cognitive ability, socioeconomic outcomes, and European ancestry were carried out at multiple levels in Argentina: individual (max. n = 5,920), district (n = 437), municipal (n = 299), and provincial (n = 24). Socioeconomic outcomes correlated in expected ways such that there was a general socioeconomic factor (S factor). The structure of this factor replicated across four levels of analysis, with a mean congruence coefficient of .96. Cognitive ability and S were moderately to strongly correlated at the four levels of analyses: individual r=.55 (.44 before disattenuation), district r=.52, municipal r=.66, and provincial r=.88. European biogeographic ancestry (BGA) for the provinces was estimated from 25 genomics papers. These estimates were validated against European ancestry estimated from self-identified race/ethnicity (SIRE; r=.67) and interviewer-rated skin brightness (r=.33). On the provincial level, European BGA correlated strongly with scholastic achievement-based cognitive ability and composite S-factor scores (r's .48 and .54, respectively). These relationships were not due to confounding with latitude or mean temperature when analyzed in multivariate analyses. There were no BGA data for the other levels, so we relied on %White, skin brightness, and SIRE-based ancestry estimates instead, all of which were related to cognitive ability and S at all levels of analysis. At the individual level, skin brightness was related to both cognitive ability and S. Regression analyses showed that SIRE had little detectable predictive validity when skin brightness was included in models. Similarly, the correlations between skin brightness, cognitive ability, and S were also found inside SIRE groups. The results were similar when analyzed within provinces. In general, results were congruent with a familial model of individual and regional outcome differences.
Two datasets of Japanese socioeconomic data for Japanese prefectures (N=47) were obtained and merged. After quality control, there were 44 variables for use in a factor analysis. Indicator sampling reliability analysis revealed poor reliability (54% of the correlations were |r| > .50). Inspection of the factor loadings revealed no clear S factor with many indicators loading in opposite than expected directions.
A cognitive ability measure was constructed from three scholastic ability measures (all loadings > .90). On first analysis, cognitive ability was not strongly related to 'S' factor scores, r = -.19 [CI95: -.45 to .19; N=47]. Jensen's method did not support the interpretation that the relationship is between latent 'S' and cognitive ability (r = -.15; N=44). Cognitive ability was nevertheless related to some socioeconomic indicators in expected ways.
A reviewer suggested controlling for population size or population density. When this was done, a relatively clear S factor emerged. Using the best control method (log population density), indicator sampling reliability was high (93% |r|>.50). The scores were strongly related to cognitive ability r = .67 [CI95: .48 to .80]. Jensen's method supported the interpretation that cognitive ability was related to the S factor (r = .78) and not just to the non-general factor variance.
We conducted novel analyses regarding the association between continental racial ancestry, cognitive ability and socioeconomic outcomes across 6 datasets: states of Mexico, states of the United States, states of Brazil, departments of Colombia, sovereign nations and all units together. We find that European ancestry is consistently and usually strongly positively correlated with cognitive ability and socioeconomic outcomes (mean r for cognitive ability = .708; for socioeconomic well-being = .643) (Sections 3-8). In most cases, including another ancestry component, in addition to European ancestry, did not increase predictive power (Section 9). At the national level, the association between European ancestry and outcomes was robust to controls for natural-environmental factors (Section 10). This was not always the case at the regional level (Section 18). It was found that genetic distance did not have predictive power independent of European ancestry (Section 10). Automatic modeling using best subset selection and lasso regression agreed in most cases that European ancestry was a non-redundant predictor (Section 11). Results were robust across 4 different ways of weighting the analyses (Section 12). It was found that the effect of European ancestry on socioeconomic outcomes was mostly mediated by cognitive ability (Section 13). We failed to find evidence of international colorism or culturalism (i.e., neither skin reflectance nor self-reported race/ethnicity showed incremental predictive ability once genomic ancestry had been taken into account) (Section 14). The association between European ancestry and cognitive outcomes was robust across a number of alternative measures of cognitive ability (Section 15). It was found that the general socioeconomic factor was not structurally different in the American sample as compared to the worldwide sample, thus justifying the use of that measure. Using Jensen's method of correlated vectors, it was found that the association between European ancestry and socioeconomic outcomes was stronger on more S factor loaded outcomes, r = .75 (Section 16). There was some evidence that tourist expenditure helped explain the relatively high socioeconomic performance of Caribbean states (Section 17).
The relationship between IQ and completed fertility among a sample of 3520 Americans from MIDUS II (1960's birth cohorts) is examined using a common factor comprised of eight cognitive ability measures, in order to determine the rate of phenotypic IQ loss due to genetic selection. Negative correlations are present in both the male and female subsamples, and are associated with a predicted loss in heritable g (g.h) of − .262 points per decade, increasing to − 1.072 points when the additive effect of mutation accumulation is considered. The ability–fertility associations showed Jensen effects at the level of the whole sample (.167), and also separately for each sex (.185 and .147 for the females and males respectively). The magnitude of the expected g.h loss in this cohort due to selection is comparable to that derived from a meta-analysis of disattenuated decadal g.h declines from eight US studies (− .44 points per decade; N = 127,389). There is a Flynn effect in the US amounting to gains of 3.6 points per decade, which are concentrated on more environmentally plastic and specialized sources of ability variance (s.e) suggesting co-occurrent socio-ecological specialization with respect to narrower cognitive abilities in the present cohort.
Many studies have examined the correlations between national IQs and various country-level indexes of well-being. The analyses have been unsystematic and not gathered in one single analysis or dataset. In this paper I gather a large sample of country-level indexes and show that there is a strong general socioeconomic factor (S factor) which is highly correlated (.86-.87) with national cognitive ability using either Lynn and Vanhanen's dataset or Altinok's. Furthermore, the method of correlated vectors shows that the correlations between variable loadings on the S factor and cognitive measurements are .99 in both datasets using both cognitive measurements, indicating that it is the S factor that drives the relationship with national cognitive measurements, not the remaining variance.
Differences in intelligence have previously been found to be related to a wide range of inter-individual and international social outcomes. There is evidence indicating that intelligence differences are also related to different regional outcomes within nations. A quantitative and narrative review is provided for twenty-two countries (number of regions in parentheses): Argentina (24 to 437), Brazil (27 to 31), British Isles (12 to 392), to 79), Spain (15 to 48), Switzerland (47), Turkey (12), the USA (30 to 3100), and Vietnam (61). Between regions, intelligence is significantly associated with a wide range of economic, social, and demographic phenomena, including income (r unweighted = .56), educational attainment (r unweighted = .59), health (r unweighted = .49), general socioeconomic status (r unweighted = .55), and negatively with fertility (r unweighted = −.51) and crime (r unweighted = −.20). Proposed causal models for these differences are noted. It is concluded that regional differences in intelligence within nations warrant further focus; methodological concerns that need to be addressed in future research are detailed.
Cross-regional correlations between average IQ and socio-economic development have been reported for many different countries. This paper analyses data on average IQ and a range of socio-economic variables at the local authority level in the UK. Local authorities are administrative bodies in local government; there are over 400 in the UK, and they contain anywhere from tens of thousands to more than a million people. The paper finds that local authority IQ is positively related to indicators of health, socio-economic status and tertiary industrial activity; and is negatively related to indicators of disability, unemployment and single parenthood. A general socio-economic factor is correlated with local authority IQ at r = .56. This correlation increases to r = .65 when correcting for measurement error in the estimates of IQ.