Race Differences: A Very Brief Review

Abstract

The nature of race differences, and even the mere “existence” of human races, continues to be a major source of controversy and confusion. This brief review summarizes the empirical evidence about race differences and the conceptual issues related to taxonomy, as well as practical implications for medicine and the social sciences. The review shows that human races are distinctive phenotypically and genotypically, the latter with regard to the frequencies of a very large number (millions) of alleles. Distributions of these traits are clinal rather than discrete, and human races are subject to continuous change across evolutionary time.

Full text

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Race Differences: A Very Brief Review
Emil O. W. Kirkegaard
Ulster Institute for Social Research, London, UK
Email: emil@emilkirkegaard.dk
The nature of race differences, and even the mere “existence”
of human races, continues to be a major source of controversy and
confusion. This brief review summarizes the empirical evidence
about race differences and the conceptual issues related to
taxonomy, as well as practical implications for medicine and the
social sciences. The review shows that human races are distinctive
phenotypically and genotypically, the latter with regard to the
frequencies of a very large number (millions) of alleles. Distributions
of these traits are clinal rather than discrete, and human races are
subject to continuous change across evolutionary time.
Key Words: Human races, Skin color, Allele frequencies, Genome-
wide association studies, Admixture, Evolution
Differences between human racial groups are perhaps the most controversial
topic in all of the social sciences, with almost every conceivable fact being
contested by two or more opposing factions. The matter is also scientifically
challenging because a comprehensive account of such differences and their
origins involves findings from a large number of scientific (sub)fields including
evolutionary psychology, differential psychology, psychometrics, sociology,
anthropology, population genetics, genomics, behavioral genetics, history,
archaeology, and almost every interdisciplinary field between these. On top of this
comes the fact that the topic became heavily politicized in Western countries after
World War II. The following account attempts a cautious summary of the current
thrusts of the research, which will unavoidably be seen as unsatisfactory by some.
Semantic, ontological and historical status
To begin with, both the current and historical meaning of the word race is
disputed. The most popular view in the West currently among social scientists is

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that race denotes a concept of discrete/typological populations in an ancient
Greek sense (Platonic/essentialist), and that genomic data shows that such
discrete populations do not exist. Hence human races do not exist in any
biological sense, but only as (at least somewhat) arbitrary social categories (e.g.
James, 2017; Kitcher, 2007; Ousley, Jantz & Freid, 2009; Pigliucci & Kaplan,
2002; Smedley & Smedley, 2005; Sussman, 2014). The contrary minority view is
that race denotes a subspecies, breeding population, genetic cluster, extended
family (or some other biologically-based idea along those lines), and that genomic
data shows that these exist, and have or might have important relationships to
socially valued phenotypic traits among humans (e.g. Andreasen, 2000; Barnes,
2018; Fuerst, 2015; Levin, 1997; Lynn, 2015; Relethford, 2009; Rushton, 2000;
Sarich & Miele, 2004; Sesardic, 2010; Spencer, 2015). In line with standard
terminology in philosophy (Miller, 2016), the first view will be denoted the social
constructivist view (or sometimes, anti-realist), and the second the realist view
(“race realism”). This is not meant to be an endorsement of the realist view as
being more realistic in the everyday sense of the word, but only as a descriptive
term meaning the reality of something is asserted. Therefore, the difference is
essentially semantic rather than substantive.
Fuerst (2015) reviewed 12 surveys of anthropologists, anatomists and
biologists, which asked about agreement with statements such as “There are
biological races in the species Homo sapiens” (see also Lieberman et al., 2004).
Agreement with race realism is lower among researchers in the USA and higher
in East Europe and East Asia, however, there are substantial numbers of experts
with both views in every survey. Furthermore, agreement is higher among
physical as opposed to cultural anthropologists, lower in recent years in the
US/West 1, and higher among biologists and anatomists than anthropologists.
Agreement ranged from 14% to 75% depending on the survey year, exact
question, country, and type of researcher. Partly in response to the above
compilation, another large US survey was carried out which polled about 1900
anthropologists and included many variant question formulations (Wagner et al.,
2017). The patterns of that study replicate those above in that contemporary US
anthropologists mostly are to be found in the anti-realist or social constructivist
1 Working population geneticists in the West have generally avoided the term race since
it fell out of political favor, opting instead for synonyms or closely related terms such as
genetic cluster, population, genetic ancestry and so on (Frost, 2014). In medical
genetics, the currently preferred term is the somewhat unwieldy biogeographical
ancestry (Mersha & Abebe, 2015; Shriver & Kittles, 2004; Tishkoff & Kidd, 2004),
though this is not to say that this term does not also have its detractors (Gannett, 2014).

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camp, but it depends on the specific framing of the question. Similarly, Horowitz
et al. (2019) surveyed 301 US anthropologists about various topics. One question
included was “The social construct of ’race’ has no corresponding biological
reality”, with which 76% of their sample agreed, and 15% disagreed (9% don’t
know/other). All in all, we can say that opinion seems to be moving against the
realist view, but that there is not yet a consensus level of agreement, even among
anthropologists.
There is no non-question-begging way to even write about race differences
since using race as normally done would implicitly appear to assume a realist
position of some sort, while adding scare quotes (‘race’) would indicate the
opposite. This entry does not take a position on the question but uses the normal
writing style for ease of reading. For the matters at stake, the ultimate fate of the
word race is immaterial because the ancestry associations will be there no matter
what we call them, and no matter how well typical racial classification schemes
are congruent with ancestry variation.
Overview of human populations
There is consensus in the field that when human genomic data is analyzed
with methods such as principal components analysis or cluster analysis, certain
non-arbitrary patterns can be seen in the data (J. L. Baker, Rotimi & Shriner, 2017;
Cavalli-Sforza, Menozzi & Piazza, 1994; Reich, 2018a). Specifically, for persons
who don’t belong to ‘recent migrant’ populations, those who are geographically
close tend to go together or cluster (in some sense) in the results. Recent
migration usually refers to peoples that have moved since 1492, in the post-
Columbus period. This date is a somewhat arbitrary but convenient choice since
mass migration to the Americas started at that time. There is a large number of
mathematical approaches to doing such clustering with no agreement on a single
best method (Padhukasahasram, 2014; Yuan et al., 2017). Because of this,
results from multiple methods will be summarized. Figure 1 shows the results of
a principal components analysis on a large genomic dataset from populations
across the world. It is evident there is some patterning in the data related to
geographic location.
Principal components analysis works by constructing a new dimension
(variable) based on the data such that it ‘best explains’ the existing data in the
sense of having the highest possible variance in common with the input variables
(here, variation in genetic loci). This process is usually repeated multiple times,
giving a set of principal components. Hence, the first principal component (PC1)
summarizes the largest possible amount of the data in a single dimension. Of the
remaining variation, PC2 best summarizes that, and so on. In the figure, the two

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first dimensions are shown and the subjects are colored by their geographical
origin (or origin of their remote ancestors). It is evident that similarly colored
persons are usually close to each other. There are some exceptions, however.
Mexicans (MEX) are not well separated from Indians (GIH). They are however far
apart in genetic space if one considers more than the first two dimensions, but
this is difficult to convey in a two-dimensional image.
Figure 1. Principal components analysis of HapMap3 data. Each dot is a person.
Axes are the first two principal components of the SNP (single nucleotide
polymorphism) data. Populations: ASW = African American in USA; CEU =
Central European from Utah, USA; CHB = Chinese from Beijing; CHD = Chinese
from Denver, USA; GIH = Gujarati Indians in Houston, USA; JPT = Japanese
from Tokyo, Japan; LWK = Luhya Africans from Kenya; MEX = Mexicans from
Los Angeles, USA; MKK = Maasai Africans from Kenya; TSI = Italians from
central Italy (Toscani); YRI = Yuruba Africans from Nigeria. Figure reproduced
from Abraham and Inouye (2014).

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An alternative approach is to construct a phylogenetic tree (dendrogram, tree
plot) based on the most likely estimated relationships between the groups in
terms of evolutionary divergence. An example is shown in Figure 2.
Figure 2. Dendrogram based on data from 51 populations in the Human Genome
Diversity Panel. Colors represent the 7 continental clusters. Reproduced from Jun
et al. (2017).
As before, one can clearly see that geographically close populations tend to
‘join up’. Some other relations are more surprising and reflect older migrations
that are now mostly forgotten. For instance, northern Indians are related to
Europeans, and indeed speak related languages from the Indo-European family
(Reich et al., 2009). In general, language relatedness reflects earlier migrations
and thus genetic relatedness as well (J. L. Baker et al., 2017). There are various
exceptions to this general pattern, such as the Hungarians (a central European
population). Their language is related to those of Finns and Estonians (northeast

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European populations), who live about 1,500 kilometers (900 miles) away across
several national borders and bear little genetic resemblance to Hungarians.
Various language isolate populations speak languages (apparently) unrelated to
their neighbors, with the most well-known being Basque (located in north of
Spain).
The degree to which genetic relatedness mirrors geographical distance can
be impressive. Figure 3 shows a scatterplot of the first two principal components
with a map of Europe shown on top.
Figure 3. Map of European populations' genetic distance with a map of Europe.
From Novembre et al. (2008).
The relationship between genetic distance and geographic distance is good
but not perfect, thus indicating some recent population movements or
inaccuracies in the data. More fine-grained differences can also be detected,
including ones inside a single country of relatively homogeneous people. Recent
studies have looked at the relationships between geographic location and genetic
distances in the British Isles (Abdellaoui et al., 2018; Byrne et al., 2018; Kandt,
Cheshire & Longley, 2016; Leslie et al., 2015), Belgium (Van den Eynden et al.,

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2018), France (Karakachoff et al., 2015), and the Nordic countries (Athanasiadis
et al., 2016; Kerminen et al., 2017).
The two approaches to analyze the data used above hail from two different
ways of looking at the genetic data. In the first approach, one is concerned with
continuous distances between persons and groups, and there are no rigid
boundaries. In the other approach, one thinks of the populations more as discrete
units which can be descended from one another. Reality is somewhere in
between these two extremes, which is called the clinal vs. cluster debate of
human genetic variation (Rosenberg et al., 2005). Both sides recognize the fact
that genetic distance between populations correlates strongly with geographic
distance (again, for populations that haven’t migrated ‘recently’). Depending on
theoretical assumptions and definitions, finding certain low (high) levels of clinality
might indicate the absence (reality) of human races. Figure 4 shows a world map
overlaid with relative rates of migration.
Figure 4. Large-scale patterns of population structure in the Old World. Color
coding shows estimated rate of migration with brown indicating 'troughs', i.e.
areas across which there was little human intermixing. Reproduced from Peter
et al. (2018).

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While all human variation is clinal to some degree, some areas have more
migration (and thus gene flow) than others. The areas with particularly low
migration usually correspond to geographical barriers: oceans, mountains and
deserts. With regards to the clines vs. clusters debate, the authors of the study
that produced the above map concluded:
Our rugged migration landscapes suggest a synthesis of the clusters
versus clines paradigms for human structure: By revealing both sharp and
diffuse features that structure human genetic diversity, our results suggest
that more continuous definitions of ancestry in human population genetics
should complement models of discrete populations with admixture.
This might be taken as a reasonable middle position on the clines vs. clusters
debate.
Ancestry estimates and social race
When clustering methods are used to analyze genetic data, the results allow
one to score a given individual on their proportion of genetic ancestry — or
biogeographic ancestry as it is often called in medical genetics (Shriver et al.,
2003) — from each cluster identified in the analysis. Such ancestry (or admixture)
analysis has since become big business (dubbed consumer genomics or
recreational genomics) with multiple competing companies offering ancestry
estimation services based on microarray data obtained from customers (Khan &
Mittelman, 2018). At the beginning of 2018, about 10 million people had been
genotyped this way. Essentially, the customer purchases a small kit (a tube with
liquid), deposits spit into it, mails it to the laboratory for analysis, and then 2-3
weeks later receives a report on a website. Ancestry analysis and presentation is
somewhat of an art, not exact science (Khan, 2017a,b), but provides valuable
information to many people who are curious about their origins. The services can
also identify distant or lost family members (most commonly siblings adopted
away, or unknown half-siblings). The ability to do this has also led to the arrest of
multiple people suspected of serious crimes based on DNA evidence left at the
crime scene that for decades could not be matched to a person but which could
be found by matching to distant relatives (Regalado, 2018; Wilson, 2018).

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Figure 5. Individual ancestry estimates for three US race groups with European,
Amerindian and African ancestry. Reproduced from Bryc et al. (2015) based on
23andme customer data.
Figure 5 shows an example of an ancestry distribution for the United States
from the consumer genomics company 23andme. Each mini-pie chart represents
the distribution of self-reported race/ethnicity for a given combination of
genetically measured ancestry. European (White) Americans are almost entirely
European on average (about 99%) but Latinos and African Americans show
considerable variation, almost every person having some degree of admixture
compared to reference populations (Africans in Africa and Amerindian
populations without interbreeding since the European conquest). The mixed
nature of many human groups, especially in Latin America, and somewhat
imperceptible nature of precise genetic ancestry means that typical social labels
such as White, Black/African American, Mestizo do not map up exactly with
genetic ancestry, and in some cases, not well at all (Ruiz-Linares et al., 2014).
Still, the terms are widely used as rough proxies for genetic ancestry, which can

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be valuable in situations where genetic data is missing, both in medicine
(Bonham, Sellers & Woolford, 2014; Rosenberg et al., 2002) and in other
research (Fulford, Petkov & Schiantarelli, 2016; Putterman & Weil, 2010).
Figure 6. Self-identified race among a sample of American blacks and whites
from Philadelphia. Biracial are those that identified as both black and white.
Figure from Lasker et al. (2019)
Figure 6 shows a sample of Americans and their self-identification as a
function of their genetic ancestry. In this dataset, only persons who self-identified
as White, Black or both were included. One can see that individuals with nearly
100% ancestry from either group have a nearly (but not entirely) 100% chance of
identifying as White or Black. However, for persons of mixed ancestry, the
probabilities were intermediate almost but not entirely in line with their ancestry.
It would make more sense to have the doubly identifying group exactly in the
middle, but instead we see that such persons are somewhat more European
genetically than would be expected with a maximum probability around 60%. This
seems to be a remnant of the so-called one drop rule (or law) that was present in
the USA in past times (Guo et al., 2014); or it simply means that in the US, most
individuals identifying as biracial have a white parent who is nearly 100%
European and a “black” parent with substantial European admixture. Other

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research has shown that a person’s visual characteristics (skin color, nose shape
etc.) and social status also affect how they self-identify above and beyond their
actual genetic ancestry (Ruiz-Linares et al., 2014; Telles & Paschel, 2014).
Physical differences
Physical differences between races are much less controversial than mental
ones, at least, insofar as they relate to traits unrelated to social status or other
valued traits. The most obvious physical trait related to race is skin color. Figure
7 shows a world map of estimated skin color.
Figure 7. Worldwide distribution of human skin color, as estimated by Jablonski
(2004).
There is geographical clustering which is related to the amount of UV
radiation that people in different parts of the world are exposed to. Other visible
traits that strongly covary with skin color and geographic location include
tannability, freckles, hair color, hair texture (straight, curly etc.), eye color, lip and
nose thickness (J. R. Baker, 1974). A variety of less visible physical differences
also exist, and can in many cases be identified from skeletal remains to infer the
likely ancestry/race of the decedent (Albanese & Saunders, 2006; Kennedy,
1995). Detailed cranium measures can also be analyzed with the same methods
used for large genetic datasets and tend to give similar results (Reyes-Centeno,
Ghirotto & Harvati, 2017). Another human trait that shows large race differences
is height (NCD Risk Factor Collaboration, 2016). Human height has increased
considerably during the last 100 years in almost every country while the between
country differences have generally remained large (“A rising tide lifts all boats, but
tall sails remain high”). The tallest people in the world 100 years ago (e.g. Dutch

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and Scandinavians) are also among the tallest people now, but gained about 10
cm. Meanwhile some populations increased in the relative ranking, such as post-
war Japan and South Korea coinciding with rapid economic growth (North
Koreans stayed short, however). This temporal relationship between
development and height has led economic historians to create large databases
of historical data for human height for use in research as a proxy for development,
or measure of health of a population (Baten, 2000, see https://clio-
infra.eu/Indicators/Height.html).
What is common for the traits discussed above is that few researchers
dispute that differences between race groups are the result of genetic differences
(though the secular trend in height is attributed to environmental improvements).
However, for more socially valuable traits, the relative contributions of genetics,
environment and their potential interactions are heavily debated. Since large
health datasets began to be collected, medical researchers have noted that race
groups differ in various disease rates. Many of the rare diseases have relatively
simple genetic causes (single-gene/monogenic/Mendelian disorders), with one or
only a few genes involved (Tibayrenc, 2017). The genetic etiology of race
differences is not disputed for these, probably because most of them are quite
rare (though not sickle cell disease) and the molecular causes are often known
to some degree. Populations that have had recent migration-related bottlenecks
usually have their own collection of special disorders they acquired from the
genetic drift induced by the bottleneck. Ashkenazi Jews, for instance, suffer from
higher than average European rates of Tay-Sachs disease, Gaucher's disease,
and BRCA-related breast cancer among others (Slatkin, 2004). Other populations
with well-known elevated rates for rare single-gene disorders include French
speaking Canadians (Scriver, 2001), Finns (Martin et al., 2018), and Amish
(Mitchell et al., 2015).
Since the advent of large datasets with SNP (single nucleotide
polymorphism, a location in the genome with a variable base) data, it has become
possible to estimate the fractional admixture of people with mixed ancestry for
large samples of people with known phenotypes. This information can then be
related to having a given disease, or the value of some continuous trait (e.g.
height, body mass index). Hundreds if not thousands of such studies now exist
that find many relationships between genetically estimated ancestry and disease
traits, which usually replicate those seen for the corresponding socially defined
racial groups (e.g. African Americans, AA). Such associations are often
interpreted causally, especially when the most plausible environmental causes
were controlled in a regression. For instance:

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The role of genetic predisposition in this disparity is supported by two
admixture mapping studies of AAs which demonstrated that greater
proportion of European ancestry was inversely associated with fibroids in
AA women. (Giri et al., 2017; two other examples: Bidulescu et al., 2014;
Meigs et al., 2014)
It should be mentioned that some researchers advise against such causal
interpretations (Cooper, 2004), on account that the associations with ancestry
might just reflect a relevant but omitted environmental variable. More advanced
methods based on local ancestry analysis exist, but are not yet as widely used as
global ancestry analysis (for a brief introduction, see Shriner, 2013). These
methods have been used to examine both diseases (Tibayrenc, 2017) and
physical traits that are thought to have evolved recently by natural selection
(Chacón-Duque et al., 2018). For instance, Jeong et al. (2014) examined a
population of mixed Tibetan and Han (Chinese) ancestry, and found that those
with more Tibetan ancestry did better in higher altitudes. Furthermore, local
ancestry analysis revealed particular blood-related genes which were much more
distinctive than the rest of the genome in comparisons between Han and
highland-adapted Tibetans, indicating a causal protective effect of these.
More controversial is the topic of race differences in sports (Dutton, 2015;
Epstein, 2014). A commonly noted difference is that West Africans (the ancestors
of most Africans in the New World) tend to do very well at short distances while
East Africans tend to do well at long distance running. Currently, all top 25 records
for the 100 meter dash are held by persons of West African descent (Wikipedia,
2019a), whether born in Africa or to ancestors who emigrated to somewhere. In
contrast, the current top 25 male (and female) record holders for half marathons
(21 km) all are of Kenyan or Ethiopian descent (Wikipedia, 2019b). It seems
difficult to argue that other groups lack an interest in this sport considering the
millions of people in Western countries who enjoy running, including competitively
(Deaner, 2015). It is also hard to argue that these people don’t have the necessary
wealth to pursue training and the requisite nutrition. Yet they are being beaten
consistently by persons who either hail from or grew up in very poor states, and
which are geographically distant. Thus, to many researchers (Dutton, 2015;
Entine, 2016; Epstein, 2014) it seems likely that genetics plays some role in these
differences.
Psychological differences
Psychological differences between race groups are controversial, perhaps
the most controversial topic in all of social science (Horowitz, Haynor & Kickham,

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2018; Hunt & Carlson, 2007; Tabery, 2015; Yee et al., 1993). The literature on the
topic is enormous and goes back to the 1860s with Victorian English polymath
Francis Galton’s pioneering work (Galton, 1869). Although there are many ways
to categorize psychological traits, I will employ a binary division according to
which psychological traits can be roughly divided into cognitive and noncognitive
domains (e.g. as used in Kaestner & Callison, 2011). Cognitive refers to cognitive
ability/intelligence related traits such as working memory, long term recall, 3d
spatial ability, verbal fluency, general intelligence and many more (Carroll, 1993).
Noncognitive refers to everything that isn’t cognitive, which is a very
heterogeneous remainder category that includes personality traits (both broad
and narrow), interests, dispositions, beliefs, and psychiatric diseases. These
various traits are of course often statistically related, including across the binary
classification, and sometimes strongly enough that one might question their
independence. In other cases, the traits themselves admit both cognitive,
noncognitive and mixed conceptions, such as with emotional intelligence
(O’Boyle et al., 2011). However, to attempt a summary, we must allow for some
level of simplification.
Noncognitive differences
Personality
There is broad agreement that personality is multi-dimensional. Several
approaches exist that attempt to distill personality variation to a few latent
dimensions. The most popular of these is the Big Five/Five factor model/OCEAN
approach, which summarizes personality as variation in Openness,
Conscientiousness, Extraversion, Agreeableness, and Neuroticism/Emotional
Stability (McCrae & Costa, 2006). Social group differences, including racial, in
OCEAN traits are difficult to investigate due to implicit group comparisons in the
scales, sometimes called the reference group effect (Heine et al., 2002). Most of
the data about human personality comes from subjects rating themselves on
adjectives or short phrases. These ratings are implicit comparisons to other
people, but which other people exactly? When asked whether one often attends
parties, the reference frame is some kind of typical party-going rate among other
humans in comparison to which one might be above average or not. This problem
becomes especially troublesome when one does personality comparisons across
countries where most people have little or no experience with other groups
(Kajonius & Mac Giolla, 2017; Meisenberg, 2015). Such country-level
comparisons of OCEAN traits find sizable gaps, which depending on the
demographics of the countries, may or may not reflect racial group differences.
The psychometric quality of the measurements is unfortunately low and

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confounded with other traits such as intelligence (Kajonius & Mac Giolla, 2017;
Meisenberg, 2015; Meisenberg & Williams, 2008; Nye & Drasgow, 2011). Heine
et al. (2008) compared national stereotypes (termed national character
perceptions) to measures of conscientiousness from self- and other reported
personality scales, as well as objective data based on e.g. precision of public
clocks and speed of postal workers. They found that the typical personality
measures had negative (self report, mean r’s -.43 and -.19) and null associations
(other report, r = .06) with national stereotypes, but that objective measures had
sizable positive correlations with stereotypes (r = .61). Based on this, one might
conclude that the stereotypes were accurate and the self-report personality data
is problematic.
To reduce the reference group problem, we might instead consider racial
group differences in OCEAN traits within a country. A very large (k = 567) meta-
analysis by Tate and McDaniel (2008) found that gaps between African
Americans and Whites in the United States were small or trivial in size: openness
d = 0.02, conscientiousness d = 0.02, extraversion d = 0.18, agreeableness d =
0.09, and neuroticism d = 0.06 (where positive values mean whites are higher).
Racial group personality differences on other personality inventories have rarely
been reported in large samples or meta-analyses and are thus hard to describe.
These results were replicated by Foldes et al. (2008) who included data from over
700 studies. While Tate and McDaniel (2008) only covered the black-white
comparison, Foldes et al. (2008) covered data from Whites, Blacks, Hispanics,
and Asians (heterogeneous as these groups are). Generally, their findings agree
with the previous study in that they find overall small gaps. The gaps are not
consistent in direction within each trait (e.g. conscientiousness), so that while
whites seemed to be favored on one facet (e.g. dependability, d = 0.05) blacks
where higher on others (e.g. cautiousness, d = -0.16). Results were similar for the
other comparisons. Exceptions related mainly to small samples, as would be
expected by sampling error alone (e.g., Asian-White gap was d = 0.63 for
agreeableness, but the Asian sample for this was only n = 93).
In general, the findings should be viewed with suspicion in the light of existing
stereotypes, which tend to be especially accurate for demographic groups
(Jussim, 2018). The question then boils down to: are the stereotypes quite
incorrect for personality traits, or are we not measuring personality correctly? The
matter requires more research to clarify. It seems unlikely that the existing
approach of collecting more self-report data can clarify matters, so it is
recommended that researchers try other approaches as well as better statistical
methods to clarify measurement invariance (Church et al., 2011; Mõttus, Allik &
Realo, 2010; Schmitt, Golubovich & Leong, 2011).

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Occupational interests
Occupational (job) interest scales such as the Holland Occupational Themes
are used for guidance counseling. These tests attempt to summarize variation in
occupational interests with a few dimensions. RIASEC is the acronym of a
popular 6-factor model (Lubinski, 2000) — Realistic, Investigative, Artistic, Social,
Enterprising, Conventional — though much recent research has used a simpler 2-
dimensional model that distills variation down to a people-things dimension and
a data-ideas dimension (Su, Rounds & Armstrong, 2009; Tay, Su & Rounds,
2011). Studies using occupational interest scales and racial group are rare, but
Schmitt et al. (2011) reported gaps for the usual black and white comparison (in
Cohen’s d, positive values mean whites are higher): R = 0.31, I = 0.28, A = -0.42,
S = -0.51, E = -0.45, C = -0.17. Their design was stronger than usual because
they also used multi-group confirmatory factor analysis to guard against
measurement bias. The same study, however, also examined OCEAN traits and
found only minor differences, the same as in the meta-analysis discussed
previously. These results are in need of replication to reach firm conclusions.
Psychiatric traits
Table 1. Odds ratios of mental disorders by US racial groups, compared to the
White prevalence scaled as 1.00. Table from Coleman et al. (2016), who
calculated them based on large samples. * indicates the odds ratio was not
statistically different (p>.05), all other values differed with p<.001.
Disorder Asian Black Hispanic Mixed
Native Amer.
& Alaska
Native
Hawaiian/Pacific
Islander
Anxiety disorder
0.43
0.65
0.83
0.68
1.09
0.47
Any psychiatric
diagnosis 0.36 0.69 0.72 0.64 1.03 0.47
Bipolar disorder
0.24
0.65
0.44
0.65
1.34
0.33
Depressive disorder
0.32
0.68
0.70
0.66
0.99*
0.46
Schizophrenia
spectrum disorder 0.77 1.98 0.72 0.88* 1.18* 0.67
Other psychosis 0.50 1.13 0.61 0.34 0.80 0.51
Racial differences in rates of psychiatric disorders have long been noted,
though they are hard to estimate accurately. Table 1 shows odds ratios of mental
disorders from major US racial groups. With the exception of Native Americans
who have similar prevalence rates as whites, for most of the disorders in the table,
whites have the highest rate (the odds ratios for others are below 1). This pattern
may be caused by ascertainment bias with whites being more likely to admit

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psychiatric problems, seek help, afford evaluation, or some combination of
factors, or simply by them having the highest genetic liability. The main exception
to this pattern is the higher rate of schizophrenia and other psychosis seen for
blacks, which is a heated topic of debate in the literature (Curtis, 2018).
An increasingly popular view is that psychiatric disorders are mainly a
continuation of normal personality variation. In a slogan, abnormal is normal
(Plomin et al., 2016). Based on this, one might consider race differences in
psychiatric disorders to be measures of race differences in normal personality
variation, which could result from evolutionary trade-offs (Del Giudice, 2018;
Sikela & Quick, 2018). In line with this approach, the MMPI (Minnesota
Multiphasic Personality Inventory), spans both ‘non-diseased’ personality
variation and psychopathology (Sellbom & Ben-Porath, 2005). Evidence going
back to the 1970s indicates that blacks outscore whites on some of the scales of
this battery (Castro et al., 2008). Unfortunately, the scales lack good descriptive
names, so it is not easy to summarize the nature of these findings. The meta-
analysis by Hall et al. (1999) indicates that these gaps are small in size, with
Cohen d’s around 0.20. More controversially, Lynn (2002) reviewed evidence
from studies of psychopathy (broadly speaking) and found that East Asians have
the lowest levels, Europeans intermediate and Africans the highest. His
conclusions have however been contested by others (e.g. DeLisi, 2018).
Generally speaking, aside from a few facts such as the higher rate of depression
among whites compared to blacks and schizophrenia among blacks compared to
whites, there is not much agreement in the field about the relationships between
race and psychiatric disorders.
Cognitive differences
Soon after the start of the 1900s when the first modern cognitive tests were
invented, a large research effort began with the purpose of documenting and
understanding racial gaps in various tested abilities. This was by no means limited
to the study of blacks (vs. whites) in USA, but also covered Aborigines in
Australia, Maori in New Zealand, Indians in South Africa, and so on (Herrnstein &
Murray, 1994; Lynn, 2015; Shuey, 1966). Most of the early studies were very
simple since they were chiefly concerned with detecting whether racial cognitive
gaps existed, and whether these were due to faulty tests or real differences. The
question of measurement bias remains very much a central topic of active
research, though the methods employed have markedly improved from the
earliest studies. Much current research is interested in the question of national
differences in cognitive ability (Jones, 2016; Lynn & Becker, 2019; Rindermann,
2018), which is of course strongly related to the deeper question of racial gaps

KIRKEGAARD, E.O.W. RACE DIFFERENCES: A VERY BRIEF REVIEW
159
due to the varying demographics of countries. Lynn (2015) provides a review of
typical IQ scores for each of 12 major racial groups, shown in Table 2.
Table 2. Mean IQ scores by racial group. All groups measured in their native
habitat (e.g. Africans measured in Africa, not in Western countries). IQ normed to
UK British norms (white British = 100/15). Based on Lynn (2015).
Racial group Brain size (cm3
)
Mean IQ Number of studies
Arctic Peoples 1443 91 18
Northeast Asians
1416
105
75
Europeans 1369 100 162
Native Americans 1366 86 31
South Asians 1293 84 77
North Africans 1293 83 26
Bushmen 1270 55 5
Sub-Saharan Africans
1280
71
143
Australians 1225 62 17
Southeast Asians 1332 87 51
The values given by Lynn cannot be taken as final estimates because many
are based on small, old samples and with unclear levels of test bias. For instance,
it is difficult to accept that the true level of intelligence among Australian
Aborigines is about 60 without strong evidence of measurement invariance. As
far as the author knows, there are no recent, large, advanced measurement
studies for this population, and studies from the early 20th century can hardly be
considered informative about present-day intelligence levels. A particularly
contentious topic is the best estimate of African intelligence, with other
researchers estimating either about 80 or about 75 (Rindermann, 2013; Wicherts,
Dolan & van der Maas, 2010). Still, however, the numbers are reasonably
consistent and quite stable across time and place.
Neither can the values be taken at face value to indicate what one might call
genetic level of intelligence. Both sides in the debate recognize the importance of
environmental variation, especially for the lower scoring groups. Unfortunately, it
is difficult to estimate the relative importance of genetic and environmental factors
since these are usually correlated in practice — countries with good nutrition also
have high intelligence levels, but which causes what? A few modern genetic

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studies have been done on psychological traits and are worth summarizing. Piffer
(2019) found that when looking at 26 quasi-national populations, their mean
polygenic score for educational attainment/intelligence was correlated .80 to .90
with estimates of intelligence. However, it is well known that simple comparisons
of polygenic scores across groups are hard to interpret due to biases in their
construction which is mainly based on genotyping of European-origin individuals
(Berg et al., 2018; Curtis, 2018; Duncan et al., 2018; Kerminen et al., 2018; Sohail
et al., 2019). One can avoid this problem by using ancestry analysis instead, and
there are two such published studies. Kirkegaard et al. (2019) studied ~1400 US
children and youth and found that genetic ancestry predicted IQ scores even
controlling for parental education. Lasker et al. (2019) analyzed data from ~7200
US children and youth, and found that genetic ancestry predicted IQ scores even
when including a genetic score for skin color in the regression. So far, however,
no study using the stronger design of local admixture analysis has been
published, and the aforementioned studies all have limitations that make a
substantial role of genetics plausible but not conclusive. They do, however,
conform to predictions made by hereditarian researchers back in the 1960s (e.g.,
Jensen, 1969).
Finally, it is worth noting that experts have not yet reached any consensus
on this topic with regards to causation. There exist at least four surveys of experts
which asked about causes of racial or national gaps (Friedrichs, 1973;
Rindermann, Becker & Coyle, 2016; Sherwood & Nataupsky, 1968; Snyderman
& Rothman, 1988). All of these found that a sizable minority believes the gaps to
result purely from environmental causes. The average opinion, however, seems
to be that there is some unclear mix of genetics and environmental causes. For
instance, the most recent survey by Rindermann et al. (Becker, 2018;
Rindermann et al., 2016; Rindermann, Becker & Coyle, 2020) was conducted
2013 to 2014 by surveying authors who had published in the journal Intelligence,
the highest impact factor journal in the field. 86 experts answered a question
about the causes of the US black-white intelligence gap. They estimated a genetic
contribution of on average 49% (SD = 31%), with 16% believing environmental
factors to be the sole cause, and 6% believing genetics to be the sole cause. The
large standard deviation of the mean estimate indicates that experts strongly
disagree with one another, and the question remains a topic of ongoing scholarly
debate.
Conclusion
The present review is necessarily quite limited in scope. However, it is hoped
that it has provided a useful summary of the main findings of the many scientific

KIRKEGAARD, E.O.W. RACE DIFFERENCES: A VERY BRIEF REVIEW
161
fields that contribute towards the study of race. Regarding the causes of the many
racial group differences noted above, the present author expects that advances
in genomics will relatively soon (less than 10 years from now, probably sooner)
provide crucial evidence on the relative role of genetics in causing or not causing
such gaps. David Reich, a population geneticist with impeccable credentials,
explained what we might expect in a recent New York Times article (Reich,
2018b):
Recent genetic studies have demonstrated differences across populations
not just in the genetic determinants of simple traits such as skin color, but
also in more complex traits like bodily dimensions and susceptibility to
diseases. For example, we now know that genetic factors help explain why
northern Europeans are taller on average than southern Europeans, why
multiple sclerosis is more common in European-Americans than in
African-Americans, and why the reverse is true for end-stage kidney
disease.
I am worried that well-meaning people who deny the possibility of
substantial biological differences among human populations are digging
themselves into an indefensible position, one that will not survive the
onslaught of science. I am also worried that whatever discoveries are
made — and we truly have no idea yet what they will be — will be cited as
“scientific proof” that racist prejudices and agendas have been correct all
along, and that those well-meaning people will not understand the science
well enough to push back against these claims.
This is why it is important, even urgent, that we develop a candid and
scientifically up-to-date way of discussing any such differences, instead of
sticking our heads in the sand and being caught unprepared when they
are found.
For readers interested in more in-depth reviews about race differences, see (more
realist view: J. R. Baker, 1974; Fuerst, 2015; Jensen, 1998; Lynn, 2015; Rushton,
2000; Rushton & Jensen, 2005; Sarich & Miele, 2004; Wade, 2014; Winegard,
Winegard & Boutwell, 2017; less realist view: Conley & Fletcher, 2017; Evans,
2019; Nisbett, 2009; Nisbett et al., 2012; Sussman, 2014).
Background
This review was originally written with intent to send to the Encyclopedia of
Evolutionary Psychological Science, as I was invited to submit an entry for their
encyclopedia. However, upon completion, the editor, Todd Shackelford, sent me

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an email letting me know that “After further discussion, we have decided to
eliminate this entry. You are now free to send to a different publication.” This
series of events should probably be interpreted in the light of a recent shaming of
Shackelford by a journalist, which happened in between the invitation and the
submission of the entry, which has made him more wary of taking on
“controversial” material (Schulson, 2018; for context, see Carl & Woodley of
Menie, 2019 and Woodley of Menie et al., 2018).
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