Page 2 of 12

The Genealogy of Differences in the Americas

The first two of our admixture in the Americas papers have been published at Mankind Quarterly. To note, as I am skeptical of a behavioral genetic model, we advanced a genealogical one with an unspecified mode of inter-generational transmission. Similar models have been adopted in the economic literature (for example: Putterman and Weil, 2010; Spolaore & Wacziarg, 2015). For open access, we uploaded our papers to Research Gate. For the sake of transparency, the 18 supplementary files, the R syntax and the other data files have been made publicly available at Open Science Frame. The six commentaries are locked behind a paywall, but we covered most of the criticisms in our reply paper. If you can get a hold of them, though, they are well worth the reading. The conclusion of the reply paper sums up our general position:

We were pleased with the caliber of the comments. While incisive, none of them have inclined us to alter our conclusion concerning the R~CA-S hypothesis. But what now? First, more data. Specifically, indices of national cognitive ability need to be refined and more regional data needs to be located. In searching for this, it would be helpful to collaborate with researchers who are more familiar with Latin American datasets. Second, it would be worthwhile to further investigate a discriminatory model of individual differences using kinship designs and also to further investigate geographic models of regional differences, for example, using individual-level longitudinal data (to see if relocation to higher absolute latitude or colder regions has a positive effect on individual-level outcomes). Our models, in aggregate, are consistent with the view that contemporaneous cold weather and/or latitude is causally associated with positive outcomes, but an accurate assessment of the magnitude of these effects necessitates taking into account intergenerational factors. More generally, proponents of genealogical, discriminatory and geographic models have a mutual interest in building and making accessible databases that allow for the testing of these competing and probably co-occurring models.

As part of the reply we wrote another paper which focuses on the U.S. and will be published in the summer edition. Three related projects are also in the works.

….

Fuerst, J., & Kirkegaard, E. O. W. (2016). Admixture in the Americas: Regional and national differences. Mankind Quarterly.

Ibarra, L. (2016). Statistics vs Scientific Explanation. Mankind Quarterly.

Flores-Mendoza, C., & Da Silva, J. A. (2016). Great effort, interesting results, but not everything is what it seems. Caution is required. Mankind Quarterly.

de Baca, T., Figueredo, A. J., & Garcia, R. A. (2016). Commentary on Fuerst and Kirkegaard: Some groups have all the luck, some groups have all the pain, some groups get all the breaks. Mankind Quarterly.

Christainsen, G. (2016). Admixture in the Americas: Social Differences as a Reflection of Human Biodiversity. Mankind Quarterly.

León, F. R. (2016). Race vis-à-vis Latitude: Their Influence on Intelligence, Infectious Diseases, and Income. Mankind Quarterly.

Pesta, B. (2016). Does IQ Cause Race Differences in Well-being? Mankind Quarterly.

Fuerst, J., & Kirkegaard, E. O. W. (2016). The Genealogy of Differences in the Americas. Mankind Quarterly.

Equal Environments Assumption and Sex Differences

In the classic twin study design, identical (MZ) twin pairs are compared to fraternal (DZ) twin pairs so as to estimate the relative contributions of heredity and environment to individual differences. The classic twin design depends on the equal environments assumption (EEA) according to which the shared environment of MZ twins is not more similar than that of DZ twins.

The claim that the EEA is an unrealistic assumption which is routinely violated in reality is perhaps the most common criticism of the classic twin design. Violations of the EEA generally bias estimates of the effect of heredity upwards and those of the environment downwards. For this reason, there have been a number of studies where the assumption has been put to test with research questions such as:

  • Are twin pairs who are misinformed about their actual zygosity as similar as pairs who know their real zygosity?
  • Are twin pairs with objectively more similar environments more similar phenotypically?
  • Are the results of twin studies consistent with the results of other kinds of behavioral genetic designs, such as adoption studies?

This research has indicated that the EEA is generally valid and that even when it’s violated, the effect on parameter estimates is small (Barnes et al., 2014; Felson, 2014).

I think sex differences offer an underappreciated way of further evaluating the EEA. Half of DZ pairs are same-sex (male-male or female-female) and half are opposite-sex (male-female), whereas MZ pairs are, of course, all same-sex. Differences in twin correlations across these sex categories are informative about the EEA because if the shared environment differs by zygosity, you would expect it to differ by sex, too. Continue reading

Human Varieties on Twitter

Here.

IQ and Permanent Income: Sizing Up the “IQ Paradox”

In his recent book Hive Mind economist Garett Jones argues that the direct effect of IQ on personal income is modest, and that most of the benefits of higher IQ flow from various spillover effects that make societies more productive, boosting everyone’s income. This, he says, explains the “IQ paradox” whereby IQ differences appear to explain a lot more of the economic differences between nations than within them.

Jones does not say in his book what he thinks the exact effect of IQ on personal income is, but on Twitter he has asserted that “Fans of g would do well to look at the labor lit: 1 IQ point predicts just 0.5% to 1.2% higher wages.” He has also said that, in terms of standardized effect sizes, IQ accounts for only about 10% of variance in personal income (a correlation of ~0.32).

While I don’t doubt Jones’s overall thesis that the effect of IQ on productivity is broader than its effect on personal productivity or income, I think he understates the importance of IQ in explaining income differences between individuals. I analyzed a large American population sample and found a substantially larger effect of IQ on permanent income than previous investigations. It appears that the literature Jones refers to has failed to pay sufficient attention to various measurement issues. Continue reading

The Evolutionary Default Hypothesis and Negative HBD

Jayman (2016) argues:

There is no reason to suspect that human groups that have been separated for tens of thousands of years in vastly different environments would be the same in all their cognitive and behavioral qualities. In fact, a priori we should expect them not to be, since such equivalence after so many generations of separate evolution is nigh impossible.

We can quantify the expectation.

When it comes to quantitative genetic trait differences between populations, the evolutionary default expectation is that differences will be commensurate with the degree of drift (not to be equated with neutral mutations). For diploids, the formula is:

VA G,B = 2FST*VA, C
where,

VA G,B is the genetic variance between groups
VA, C is the additive genetic variance in a common ancestral population
2FST is 2 times the fixation index with respect to low mutation rate biallelic polymorphs of the type that underlie the traits in question (see: Edelaar and Björklund, 2011) Continue reading

Measured Proficiency of Ethnic Groups in Canada

Jason Malloy and I have individually collected a large number of papers and research reports from countries around the world detailing ethnic and racial differences. I have sent some of the stuff to Richard Lynn, lost a number of reports due to hard drive failures, and simply haven’t got around for various reasons (time, health, other priorities, etc.) to posting on the remainder. In response to an article by Chanda Chrisala, James Thompson recently suggested that it would be informative to look at ethnic differences in other American countries. As such, I will comment on a few studies from Canada and Brazil. Regarding Canada, there seems to be no published detailed ethnic data for the nation as a whole — though many reports discuss the Aboriginal/overall Canada gap. The country has a number of national longitudinal surveys which most likely contain the relevant variables, but as far as I am aware no has looked into the issue. Nonetheless, since the 1980s the Toronto public schools have published research reports which decompose math and reading pass rates by linguistic, ethnic, and racial background.

Continue reading

The Measured Proficiency of Somali Americans

The discussion of the performance of African immigrants led by Chanda Chisala has been of unusually poor quality. As such, I thought that I might write a brief tutorial post on how to locate data and estimate differences in hopes that this will inspire better research practices and more rigorous debate. I will also elaborate on the Jensenist position and its predictions, as Chanda, and apparently many others, do not seem to have a good grasp of it at least in its quantified form.

Continue reading

Heritability of Racial and Ethnic Pride, Preference, and Prejudice

A while back, in “People in the Future Will Not Look Like Brazilians”, Razib suggested that the great amalgamation will stall because those who are inclined to out mix will do so, taking with them their xenophilic dispositions. The suggestion prompted a commenter to question whether there was any evidence that preferences for (racial) endogamy had, as seemingly presumed by Razib’s argument, a non-trivial genetic component. Apparently, there has been very little genetically informed research on this or closely related topics. Nonetheless, I was able to locate eight studies based on five independent samples which provided heritability estimates for some measures of national, ethnic, or racial pride, preference, or prejudice. The study results are summarized in the table below.

Continue reading

Asian American Subgroup SAT Performance

I originally intended on including and briefly discussing these values in my “Ethnic/Race Differences in Aptitude” paper since therein I touched upon differences in Asian American subgroup performance (e.g., Table 15 and Table 17). Alas, I ran out of both space and my reviewers’ patience. Since the general topic continues to arise, I thought I might mention them, though. The 1996 and 2000 National Postsecondary Student Aid Studies (NPSAS 1996/2000), which were representative of the university populations at the respective times, contained both an “Asian origin” variable and a composite SAT score one, thus allowing for some investigation of subgroup variability. In expressing the differences, I used citizen/U.S. born White values as a reference for the SAT scores. Standardized differences were computed using the total group standard deviations, since population specific ones were unavailable. NA means that the sample sizes did not meet NCESDataLab’s cutoff for reportability. And negative values mean that the groups in question performed better than U.S. born/citizen Whites. As the confidence intervals — not shown below — were large for all of the Asian subgroups, results should be interpreted with caution. It’s notable that there were large U.S. born/non-U.S. born effects for both East and South Asians. The scores were for college students, so this might represent a foreign student effect (as opposed to a generation 1/generation 2+ immigrant one).

NPSAS 1996 and 2000              
1996       2000      
Nationality non-Citizen Citizen All Nationality Not US Born US BORN All
Chinese 0.01 -0.66 -0.44 Chinese -0.28 -0.64 -0.46
Korean -0.38 -0.63 -0.54 Korean -0.12 -0.82 -0.37
Japanese NA NA -0.79 Japanese NA -0.20 -0.06
               
Filipino NA -0.17 -0.13 Filipino NA 0.03 0.12
Vietnamese 0.86 -0.18 0.31 Vietnamese 0.61 NA 0.39
               
Asian Indian 0.47 -0.96 -0.43 Asian Indian 0.22 -0.88 -0.24
               
               
Asian/PI (total) 0.29 -0.37 -0.19 Asian/PI (total) 0.10 -0.41 -0.12
               
White 0.08 Reference 0.00 White -0.03 Reference 0.03
               
Black 0.84 0.87 0.87 Black 0.74 1.00 0.96

Used the total group standard deviation
Source: https://nces.ed.gov/surveys/npsas/

Using Surnames to Assess Ethnic Aptitude

Attempts to assess population aptitude from elite achievement go back to at least Galton. In Hereditary Genius, Galton used an estimate of the number of eminent persons produced by various ethnic and racial groups to quantify the differences between the means of these groups. Since his time, variants and refinements of this genre of analysis have become frequent. In “The Racial Origin of Successful Americans (1914)” Frederick Woods attempted to estimate ethnic achievement by counting and classifying the number of ethnic surnames in Marquis’ “Who’s Who” list. Lauren Ashe (1915) improved on the strategy by determining the representation of ethnic names in “Who’s Who” relative to that found in various U.S. city populations. In the 1960s, Nathaniel Weyl developed a variant of the “Who’s Who” surname method, one which relied on rare surnames, and in the 1980s he applied the method to National Merit Scholarship (NMS) lists (1), which record those high school seniors who obtained the top scores on College Board’s Preliminary SAT/National Merit Scholarship Qualifying Test (PSAT/NMSQT).

Continue reading

« Older posts Newer posts »

© 2017

Theme by Anders NorenUp ↑