Recently, the GSS released the survey results for the year 2012. And a skin color variable has been included. But rather than using the SDA program, available here, I used the GSS cumulative datafile 1972-2012 for SPSS, available here. This allows more complex analyses to be performed than what is possible with the SDA.
Introduction. If color-based discrimination becomes more intense at a later age, when darker-skinned individuals face discrimination in the labor market and thus depressing their economic opportunities at every level, for instance, the colorism hypothesis could have argued that IQ measured at earlier ages would not mediate the IQ-outcome relationship measured at a later age because discrimination would have conditioned later success in life.
Previously, we established that skin color and intelligence are correlated in the NLSY97 sample as predicted by hereditarian theory. Continuing this investigation, we looked into how these variables go together within and between African American families in the same sample. In other words, we wanted to know if lighter-skinned individuals tend to be smarter than their darker-skinned siblings just as the average light-skinned black in the general population is smarter than the average dark-skinned black. Continue reading
The meaning of a Jensen Effect on the Color Effect
Dalliard showed that IQ correlates with color in both the American Black and Hispanic populations (a color effect) and, importantly, that the IQ-color correlations are positively related to a subtest’s general intelligence loading (a Jensen effect). In short, he showed that there was a Jensen effect on the IQ color effect. This is significant for reasons elaborated elsewhere. Generally, if an IQ difference is strongly positively correlated with g – is g(+) – biological causation is implicated; alternatively, if an IQ difference is strongly negatively correlated with g – is g(-) – cultural causation is implicated. Here, “biological causation” refers to psychophysiological influences on mental states that do not act through sensory informational pathways, while “cultural causation” refers to psychophysiological influences on mental states that act through these pathways; as an example of the general biological versus cultural causal schema, with respect to personality, differences induced by pharmacological agents would be classed as “biological causal” while differences induced by psychotherapy would be classed as “cultural causal.” This schema, of course, isn’t perfect – but it has utility and is not infrequently employed in psychology.
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.
(10/18/2014 update: data from two additional studies — Martínez et al. (2007) and Ruiz-Linares et al. (2014) — have been added.)
Over the last decade, scores of large scale admixture-mapping studies have been conducted largely in an attempt to elucidate the origin of ethnic disparities in disease rates and medical outcomes. In the simplest type of such studies, researchers determine if there is a robust association between genotypically defined continental racial ancestry (typically: African, European, and Amerindian) and relevant outcomes in admixed populations. To control for potential confounding effects, measures of educational attainment and other indexes of SES are often included in the analyses. These variables are often treated as environmental indicators, which is odd, since within populations they are found to be under non-trivial genetic influence. For example, based on a recent international meta-analysis of biometric studies involving 51,545 kinship pairs, Branigan, et al. (2013) found that educational attainment had a kinship-based heritability of 0.40, meaning that genes explained 40% of inter-individual educational differences; based on a sample involving 7,959 individuals, Rietveld et al. (2013, table S12) found a GCTA-based heritability, one which takes into account only the effects of population-wide common genetic variants, of 0.22. These results were replicated by Marioni, et al. (2014, table 3), who found a kinship-based heritability of 0.40 and a GCTA-based one of 0.21. When genes explain some of the variance in a trait within groups, they plausibly explain an indefinite portion of the variance between groups. Curious it is, then, that these external outcomes are often assumed to represent environmental influences between groups.
In an earlier article, I have shown that the magnitude of sibling correlations among NLSY-ASVAB subtests correlates with the magnitude of g-loadings, but moderately with the magnitude of black-white IQ gaps in those subtests using Jensen’s method of correlated vectors, a possibly imperfect technique in some instances as explained in my previous article. In another post, it has been seen that US blacks having more (self-reported) white ancestry showed a higher IQ level, and that this effect is not mediated by skin color. Here, I will show that the magnitude of the score advantage for blacks with more white ancestry among subtests correlates with the above mentioned variables.
Some things never change
Nearly 100 years ago George Ferguson tested the racial genetic hypothesis of IQ differences and found the following remarkable results, as reported by Baker (1974):
Just a couple of days ago, the awesome Audacious Epigone pointed out that the GSS (2012) contains a color ratings scale. GSS (2012) gives us the following results:
Charles Murray’s 2005 Commentary article, The Inequality Taboo, expressed the idea that the post genomic era has finally brought us a method to resolve the question of genes, race, and intelligence:
To the extent that genes play a role, IQ will vary by racial admixture. In the past, studies that have attempted to test this hypothesis have had no accurate way to measure the degree of admixture, and the results have been accordingly muddy. The recent advances in using genetic markers solve that problem. Take a large sample of racially diverse people, give them a good IQ test, and then use genetic markers to create a variable that no longer classifies people as ‘white’ or ‘black,’ but along a continuum. Analyze the variation in IQ scores according to that continuum. The results would be close to dispositive.
Murray believed such a project would only project scientific legitimacy if the participating researchers had diverse beliefs about the causes of the black-white IQ gap. But when he attempted to assemble the team, with assurances that he himself would find the funding, only the hereditarian researchers wanted to contribute to the project. So it did not happen.
Would the results of such a study really be close to dispositive? Yes and no. Continue reading
We have shown, amongst other things, that pre-market measures of IQ substantially statistically explain the association between color and outcomes in the US. This implies that the adult color-outcome differences are substantially caused by IQ differences, rather than vice versa. To investigate this issue further, I have taken a longitudinal approach.
As background, it is well established that the year to year correlation for IQ is mediated by general intelligence on the psychometric level and by shared genes and shared environment on the causal levels. The latter two sources contribute to the longitudinal stability of IQ. Unshared environment, on the other hand, does not. This situation is illustrated in the table immediately below. As is shown, the longitudinal stability of IQ scores for both Blacks and Whites is conditioned by genetic and shared environmental effects. For the Add Health sample, in particular, unshared environment contributes only to longitudinal change.
(From: Beaver, et al. (2013). The genetic and environmental architecture to the stability of IQ: Results from two independent samples of kinship pairs. Intelligence, 41(5), 428-438.)
It follows then that if differences are due to shared genes and shared environment, as we propose, then the IQ-color association should be largely on a factor common across ages. Moreover, IQ at earlier ages should explain some of the IQ-color correlations at a latter ages. Overall, the association between color and IQ should have longitudinal stability. It is, of course, not necessary that it does. If, for example, labor marker color discrimination was leading to outcome differences which were, in turn, leading to IQ difference, the pre-market and post-market color-IQ correlations would not be mediated by a common factor. Likewise, if idiosyncratic individual factors such as peer group influences were conditioning the differences, one would not expect longitudinally stable color associated IQ differences (since such idiosyncratic influences don’t condition longitudinally stable IQ differences).