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We just published a new study in OpenPsych:

• Hu, M., Kirkegaard, E. O. W., & Fuerst, J. (2023). Income and Education Disparities Track Genetic Ancestry. OpenPsych, 1(1). https://doi.org/10.26775/OP.2023.09.11

Structural racism has often been invoked to explain observed disparities in social outcomes, such as in educational attainment and income, among different American racial/ethnic groups. Theorists of structural racism typically argue that racial categories are socially constructed and do not correspond with genetic ancestry; additionally, they argue that social outcome differences are a result of discriminatory social norms, policies, and laws that adversely affect members of non-White race/ethnic groups. Since the examples of social norms and policies commonly provided target individuals based on socially-defined race/ethnicity, and not on genetic ancestry, a logical inference is that social disparities will be related to socially-defined race/ethnicity independent of genetically-identified continental ancestry. In order to evaluate this hypothesis, we employ admixture-regression analysis and examine the independent influences of socially-identified race/ethnicity and genetically-defined ancestry on the educational attainment and income of parents, using data from a large sample of US children. Our study focuses on self-identified Whites, Blacks, Hispanics, and East Asians in the United States. Analyses generally show that the association between socially-identified race/ethnicity and outcomes is mediated by genetic ancestry and that non-White race/ethnicity is unrelated to worse outcomes when controlling for genetic ancestry. For example, conditioned on European genetic ancestry, Americans socially-identified as Black and as Hispanic exhibit equivalent or better social outcomes in both education and income as compared to non-Hispanic Whites. These results are seemingly incongruent with the notion that social outcome differences are due to social policy, norms, and practices which adversely affect individuals primarily based on socially-constructed group status

Various egalitarians claim that discrimination, either directly or indirectly via “structural racism” or “systemic racism”, explains various American race/ethnic differences. Chiefly they are concerned with the omnipresent White-Black gaps in education, income, mortality and so on. They rarely bother to test this model, aside from pointing out that gaps themselves exist, which they mostly take as prima facie evidence of discrimination. This inference of course rests on the assumption of nonexistence of other contributing factors, in particular, human capital in the form of average intelligence, patience, or lower mental illness. In this study, we tested their model once again, but with a twist on the approach. The dataset is still the awesome ABCD study:

The Adolescent Brain Cognitive Development (ABCD) study is a joint long-term initiative that includes 21 research sites throughout the US, focused on examining brain development and child health to investigate the psychological and neurobiological foundations of human growth. At baseline, around 11,000 children aged 9-10 years were sampled, using a probability-based sampling approach aimed at establishing a comprehensive and inclusive sample of US children within that age group. In this current investigation, we utilized the ABCD 3.01 baseline data.

In particular, most of the sample has had their genetics measured using microarrays, which then allows for fine-grained ancestry scoring in the same way that 23andme and other consumer genomics companies do it. The argument of the paper is a typical John Fuerst style one:

1. Egalitarians claim that race has little or nothing to do with genetics.
2. So social race — whatever people perceive themselves and others as — cannot in their model be accounted for by genetics.
3. So if discrimination is happening, then social race should be the potent factor, not actual genetic ancestry.
4. Thus, if one pits these against each other in statistical models to explain race gaps in education or income, then according to their model, social race should explain the association (if any) between genetic ancestry and socially valued outcomes.

[not meant as an airtight formal argument, but a sketch]

For those who doubt (2), we provide some relevant quotes:

According to Jones (2002) “race” is only based on a few phenotypic-related genes, not global genetic ancestry, since the few genes that determine skin color, hair texture, and facial features are not informative about other aspects of the genotype at the individual level. Advocates of the structural racism hypothesis frequently emphasize that race/ethnicity categorization “reflects neither biological nor cultural differences” and that race is “often conflated erroneously with biology and ancestry” (Adkins-Jackson et al., 2022, p. 540), that “race is a social construct and is distinct from ethnicity, genetic ancestry or biology” (O’Reilly, 2020, p. 2), that “social races bear little relationship to the reality of human biological diversity” (Smedley & Smedley, 2005, p. 22) and that race “is a social construct with no biological basis and stems from White supremacy” (Haeny et al., 2021, p. 889). While the phrase “race is a social construct” can have a range of meanings, a popular one, given by the American Sociological Association (2003), is that race is “a social invention that changes as political, economic, and historical contexts change”; this social invention is said to be important because “social and economic life is organized, in part, around race as a social construct.”

Since the sample consists of children, they lack sensible measures of income and education, as they were about 10 at the time of measurement. Instead, we utilized parental education and income. Since children’s genetic ancestry is the average of their parents, one can use children’s ancestry as a measure of the average parental ancestry, and use the average parental education and income as the outcome. The models are simple enough. Here’s the results for parental education:

In the first model, we ignore genetics and just use social race and control variables to predict parental educational attainment. We see large effects such that a child being Black (by mother’s report) predicts 0.55 standard deviations lower education level among the parents, being East Asian a 0.22 d higher level and so on. However, in the second model, when genetic ancestry is included, being Black actually predicts slightly higher parental education (0.24 d, p = .01). This could be taken as evidence of affirmative action or general structural racism in favor of Blacks. Here’s the models for income:

Materially the same is found for income.

The problem with the affirmative action interpretation is that the main sample is selected to those with 2 parents reporting their income and education. Since Blacks have extremely high rates of single-parenthood, this means that the Black sample of children with 2-parents in the study is an above average subset. For this reason, robustness tests were made to loosen this restriction. Generally, the restriction is in place because the education and income level of only one parent will not necessarily reflect the average of the parents. This happens if single mothers (or rarely, fathers) are lower social status than the unobserved fathers, which one might expect. The final table shows the results across the various specifications:

In the table, 3 alternative models are presented. First, one could use the social race of the child instead of the reporting parent (usually mother), and this made the results for being Black non-significant for education, but didn’t change the finding for income. Second, one can drop the subsetting to children with both parents reporting education and income, and this made social race less predictive, though Native American still predicted somewhat lower levels of income and education, consistent with a “grew up on a reservation” effect. Third, one can control for state-levels of “racism” as measured by implicit association tests. This didn’t do much, however. No matter which model is chosen, the effects of genetic ancestry are very large and consistent, and the effects of social race minor and fleeting. The values for genetic ancestry indicate the change in education and income associated with going from 0% to 100% of a given ancestry, in comparison with European ancestry. Thus, a slope of -1.25 means that changing from 0% to 100% African ancestry implies a decrease in parental educational attainment by 1.25 standard deviations. Curiously, the effects of Amerindian (Native American) ancestry are consistently larger than African (i.e., more negative), despite these mainly being recent immigrants that could not have been subject to Jim Crow laws and other racist policies of the past. The larger negative slope is probably related to the parents being recent migrants who have not yet attained their ultimate level of social status. Though, against this, one can say that we did control for immigration family status, but perhaps it was too crudely measured.

In any case, the results are squarely within the predictions of human capital models combined with racial hereditarianism. Genetic ancestry, not social race, explains observed gaps in social status.