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.

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.

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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).

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Racial Ancestry in the Americas. Part 2: Cognitive Variation between Nations: Parasite Load, Climate, and Ancestry

Following up with a previous analysis, I examined the cognitive variation across the whole of the Americas using a newly constructed data set.  Files can be found here and here, with the latest versions provided on request.  The analysis was restricted to sovereign nations, not e.g., departments such as Martinique or territories such as the Virgin Islands.  Non-sovereign regions were excluded so to avoid an inter-nation x intra-national interaction and because international exam data was not available for these regions.  The following 35 countries were included:  Argentina, Antigua and Barbuda, Bahamas The, Belize, Bolivia, Brazil, Barbados, Chile, Colombia, Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador, Guyana, Grenada, Honduras, Haiti, Jamaica, St. Kitts and Nevis, St. Lucia, Mexico, Nicaragua, Panama, Peru, Paraguay, El Salvador, Suriname, Trinidad and Tobago, Uruguay, United States, St. Vincent, Venezuela RB, and Canada. Eight regression analyses were run, using the following dependent variables:

  • (Skinrefl) Skin reflectance.
  • (AchQ) National Achievement Scores –  this was an updated set provided by Gerhard Meisenberg during October of 2014.
  • (NIQ) National IQ scores – these were based on Richard Lynn’s 2014 (work in progress) results and Jason Malloy’s 2013 to 2014 estimates, with adjustments.
  • (AHQ) 1880 to 1930 birth cohort age heaping scores — this is a measure of education/numeracy.
  • (logSciresearch) Log of scientific researchers from 2005 to 2012.
  • (logGDP) Average of 1990, 2000, and 2010 log of World Bank per capita GDP.
  • (Crimes) Violent Crime rates.
  • (HDI2012) 2012 Human Development Index scores.

The following independents were included:

  • (relativeEu) European Ancestry percent — the percent of European ancestry out of the percent of  European + Amerindian + African ancestry.  (For a discussion of this variable, refer here.)
  • (notUSCanada) Not US or Canada — whether the region was not US or Canada.
  • (logparasiteload) Log Parasite load — the log of the 2004 WHO parasite infections per 100,000 for each country.
  • (logColddemand) Log Cold demand — the log of Van de Vliert’s (2013) cold stress scores.
  • (PopUnder1million)  Population under 1 million — whether the country’s population was under one million.

Simple correlation analysis demonstrated that ancestry, cold weather, and parasite load intercorrelated.  This situation renders difficult the isolation of causal associations.  To illustrate, skin reflectance was set as a dependent with Eu ancestry, cold weather,  parasite load, population under 1 million, and not US and Canada as independents.  The correlation between Eu ancestry and skin reflectance is clearly mostly genetic in origin.  To the extent that the association between ancestry and skin reflectance is mediated by other variables, it is suggested that these variables co-vary with causal effects related to genes (and thus that controlling for them controls for ancestry related causal effects).  Regression results are shown in Table 1, below.  Generally, parasite load and cold weather seem to partially index ancestry effects.  Parasite load is a particularly problematic “environmental factor” because it significantly correlates with STD and HIV rates (at 0.47).  Yet the spread of HIV throughout the Americas, in the ’70s and ’80s, was subsequent to the origin of cognitive ability differences, which, in the form of national age heaping rates, were already present in the 1800s.  Thus, STD and HIV rates and with them parasite load are, to some extent, consequent of cognitive ability differences.

Results will not be discussed in detail.  The data file is made freely available; readers can run the analyses as desired.   Generally, European ancestry was a robust predictor of lower rates of violent crime, scientific activity, and achievement scores, and achievement plus National IQ scores.  (For national IQ alone, in the final model, none of the predictors were significant; this was because the NIQ sample had many missing values.)   In contrast to cognitive ability and the other mentioned indexes, European ancestry was generally not significantly associated with GDP or Human developmental indexes.  The results for National achievement scores are shown in Table 2, below; a regression plot is shown in figure 1.

Table 1.  Regression Results for Skin reflectance

reg1skinrefl

Table 2.  Regression Results for ACHQ2014

ACH2014

Figure 1.  National Achievement Scores by % European Ancestry for Sovereign American Nations

ACH2014AncestryAmer

Educational attainment, income, use of social benefits, crime rate and the general socioeconomic factor among 71 immigrant groups in Denmark

Fuerst, J., Kirkegaard, E. O. (2014). Educational attainment, income, use of social benefits, crime rate and the general socioeconomic factor among 71 immigrant groups in Denmark.

Abstract

We obtained data from Denmark for the largest 71 immigrant groups by country of origin. We show that three important socialeconomic variables are highly predictable from the Islam rate, IQ, GDP and height of the countries of origin. We further show that there is a general immigrant socioeconomic factor and that country of origin national IQs, Islamic rates, and GDP strongly predict immigrant general socioeconomic scores.

Do National IQs Predict U.S. Immigrant Cognitive Ability and Outcomes? An Analysis of the National Longitudinal Survey of Freshman

Apparently so.

Fuerst, J., Kirkegaard, E. O. (2014). Do National IQs Predict U.S. Immigrant Cognitive Ability and Outcomes? An Analysis of the National Longitudinal Survey of Freshman. Open Differential Psychology.

Abstract

We discuss the global hereditarian hypothesis of race differences in g and test it on data from the NLSF. We find that migrants country of origin’s IQ predicts GPA and SAT/ACT.

GED scores by Ethnicity and Nation

I took a gander at the 2010, 2011, and 2012 GED total scores by race and nation (from “GED Testing Statistical Reports”). The sample sizes were small. Unfortunately, the earlier reports, which go back to the ’80s didn’t provide scores for Bermuda, the Virgin Islands, and Jamaica+Cayman+St.Martin; as these scores were what I was particularly curious about, I didn’t include scores from earlier years. The scores aren’t representative, etc., etc. but they, nonetheless, provide a tad of info on e.g., (self-identified) ethnic differences in Bermuda. The score were averaged across the three years mentioned. The d-values presented at the bottom are inter-national. Those presented on the right are intra-national. The differences are roughly consistent with Richard Lynn’s Global Bell Curve position.

GEDRACENATION

Excel.

L&V's (2012) National IQs predict 2011-2012 GRE scores for 114 citizenship groups, 2010 + 2012 TOEFL scores for 157 citizenship groups, PISA scores of migrants from 62 nations of origin across 17 destination nations, 19th century (birth cohort 1820) numeracy rates across 54 nations, and early 20th century (birth cohort 1890) numeracy across 129 nations

MH’s (02/11/2014) Excel File Here.

Previously, we looked at the association between L&V’s (2012) National IQs, GMAT scores, and English Proficiency scores. We extend that analysis here by including 2010-2012 GRE (quantitative, verbal, and total) scores, 2010 + 2012 TOEFL scores, 2003-2009 migrant PISA scores, and national numeracy rates from the 19th and early 20th century.

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Quick Post: L&V's National IQs predict GMAT scores across 173 nations

Introduction

The GMAT is a graduate entrance test used by more than 5,900 business programs offered by more than 2,100 universities worldwide. While the test is given in English, it is designed to be as minimally English dependent as necessary to predict successful completion of Business programs taught in English. Further, the test is carefully scrutinized for item bias. Rudner (2012) explains:

Yes, the GMAT test is administered in English and is designed for programs that teach in English. But the required English skill level is much less than what students will need in the classroom. The exam requires just enough English to allow us to adequately and comprehensively assess Verbal reasoning, Quantitative reasoning and Integrated Reasoning skills….

We carefully review our questions using criteria defining good item construction. We also compute statistics to assess whether our questions are appropriate across culture groups. We constantly update guidelines for our item writers, including a master list of terms and phrases to avoid in order to assure cultural fairness. By using carefully defined and thorough item development and review processes, along with statistical analyses to flag questions with possible cultural bias, we have developed a test that minimizes the impact of culture and language. The GMAT exam is the best objective measure of the likelihood of success in management programs across the globe.

Despite the claimed lack of bias and apparent predictive validity of the test, there is substantial global variance in scores. Rudner (2012) attributes this variance largely to differences in native language spoken and to differences in self-selection.

We decided to explore to what extent global differences could be accounted for differences in National IQ. To do this, we examined the relation between measures of national cognitive ability, English language proficiency, English language usage, and GMAT scores by reported citizenship. We also sought to determine to what extent GMAT scores could be used to index the National IQs for poorly investigated regions such as North Korea, Rwanda, and St. Kitts.

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