Ryan Faulk, like most IQ-ists, believes that the correlation with job performance and IQ somehow is evidence for its validity. He further believes that because self- and peer-ratings correlate with one’s IQ scores that that is further evidence for IQ’s validity.
Well too bad for Faulk, correlations with other tests and other IQ tests lead to circular assumptions. The first problem, as I’ve covered before, is that there is no agreed-upon model or description of IQ/intelligence/’g’ and so therefore we cannot reliably and truthfully state that differences in ‘g’ this supposed ‘mental power’ this ‘strength’ is what causes differences in test scores. Unfortunately for Ryan Faulk and other IQ-ists, again, coming back to our good old friend test construction, it’s no wonder that IQ tests correlate around .5—or so is claimed—with job performance, however IQ test scores correlate at around .5 with school achievement, which is caused by some items containing knowledge that has been learned in school, such as “In what continent is Egypt?” and Who wrote Hamlet?” and “What is the boiling point of water?” As Ken Richardson writes in his 2017 book Genes, Brains, and Human Potential: The Science and Ideology of Intelligence (pg 85):
So it should come as no surprise that performance on them [IQ tests] is associated with school performance. As Robert L. Thorndike and Elizabeth P. Hagen explained in their leading textbook, Educational and Psychological Measurement, “From the very way in which the tests were assembled [such correlation] could hardly be otherwise.”
So, obviously, neither of the two tests determine independently that they measure intelligence, this so-called innate power, and because they’re different versions of the same test there is a moderate correlation between them. This goes back to item analysis and test construction. Is it any wonder, then, why correlations with IQ and achievement increase with age? It’s built into the test! And while Faulk does cite high correlations from one of Schmidt and Hunter’s meta-analyses on the subject, what he doesn’t tell you is that one review found a correlation of .66 between teacher’s assessment and future achievement of their students later in life (higher than the correlation with job performance and IQ) (Hoge and Coladarci, 1989.) They write (pg 303): “The median correlation, 0.66, suggests a moderate to strong correspondence between teacher judgments and student achievement.” This is just like what I quoted the other day in my response to Grey Enlightenment where I quoted Layzer (1972) who wrote:
Admirers of IQ tests usually lay great stress on their predictive power. They marvel that a one-hour test administered to a child at the age of eight can predict with considerable interest whether he will finish college. But as Burt and colleagues have clearly demonstrated, teachers subjective assessments afford even more reliable predictors. This is almost a truism.
So the correlation of .5 between occupation level and IQ is self-fulfilling, which are not independent measures. In regard to the IQ and job performance correlation, which I’ve discussed in the past, studies in the 70s showed much lower correlations, between .2 and .3, which Jensen points out in The g Factor.
The problem with the so-called validity studies carried out by Schmidt and Hunter, as cited by Ryan Faulk, is that they included numerous other tests that were not IQ tests in their analysis like memory tests, reading tests, the SAT, university admission tests, employment selection tests, and a variety of armed forces tests. “Just calling these “general ability tests,” as Schmidt and Hunter do, is like reducing a diversity of serum counts to a “general. blood test” (Richardson, 2017: 87). Of course the problem with using vastly different tests is that they tap into different abilities and sources of individual differences. The correlation between SAT scores and high school grades is .28 whereas the correlation between both the SAT and high school grades and IQ is about .2. So it’s clearly not testing the same “general ability” that’s being tested.
Furthermore, regarding job performance, it’s based on one measure: supervisor ratings. These ratings are highly subjective and extremely biased with age and halo effects seen with height and facial attractiveness being seen to sway judgments on how well one works. Measures of job performance are unreliable—especially from supervisors—due to the assumptions and biases that go into the measure.
Do IQ tests test neural processes? Not really. One of the most-studied variables is reaction time. The quicker they react to a stimulus, supposedly, the higher their IQ is in average as they are quicker to process information, the story goes. Detterman (1987) notes that other factors other than ‘processing speed’ can explain differences in reaction time, including but not limited to, stress, understanding instructions, motivation to do said task, attention, arousal, sensory acuity, confidence, etc. Khodadadi et al (2014) even write “The relationship between reaction time and IQ is too complicated and reveal a significant correlation depends on various variables (e.g. methodology, data analysis, instrument etc.).” Complex cognition in real life is also completely different than the simple questions asked in the Raven (Richardson and Norgate, 2014).
It is easy to look at the puzzles that make up IQ tests and be convinced that they really do test brain power. But then we ignore the brain power thst nearly everyone displays in their everyday lives. Some psychologists have noticed thst people who stumble over formal tests of cognitive can bangle highly complex problems in their real lives all the time. As Michael Eysenck put it in his well-known book Psychology, “There is an apparent contradiction between our ability to deal effectively with out everyday environment and our failure to perform well on many laboratory reasoning tasks.” We can say the same about IQ tests.
Real-life problems combine many more variables that change over time and interact. It seems that the ability to do pretentious problems in a pencil-and-paper (or computer) format, like IQ test items, is itself a learned, if not-so-complex skill. (Richardson, 2017: 95-96)
Finally, Faulk cites studies showing that how intelligent people and their peers rates themselves and others predicted how well they did on IQ tests. This isn’t surprising. Since they correlate with academic achievement at .5 then if one is good academically then they’d have a high test score more often than not. That friends rate friends high and they end up matching scores is no surprise either as people generally group together with other people like themselves and so therefore will have similar achievements. That is not evidence for test validity though!! See Richardson and Norgate (2015) “In scientific method, generally, we accept external, observable differences as a valid measure of an unseen function when we can mechanistically relate differences in one to diffences in the other …” So even Faulk’s attempt to ‘validate’ IQ tests using peer- and self-ratings of ‘intelligence’ (whatever that is) falls on its face since its not a true measure of validity. It’s not construct validity. (EDIT: Psychological constructs are validated ‘by testing whether they relate to measures of other constructs as specified by theory‘ (Strauss and Smith, 2009). This doesn’t exist for IQ therefore IQ isn’t construct valid.)
In sum, Faulk’s article leaves a ton to be desired and doesn’t outright prove that there is validity to IQ tests because, as I’ve shown in the past, validity for IQ is nonexistent, though some have tried (using correlations with job performance as evidence) but Richardson and Norgate (2015) take down those claims and show that the correlation is between .2 and .3, not the .5+ cited by Hunter and Schmidt in their ‘validation studies’. The criteria laid out by Faulk does not prove that there is true construct validity to IQ tests and due to test construction, we see these correlations with educational achievement.
I am aware that there is no theory of individual or group differences in athleticism. People have used that against the arguments against IQ I have used. However, athleticism and IQ are two different things. One is easily observable (have someone, say, run a race vs another and see who’s faster) while the other is not and takes up a lot of time (over an hour to administer a test then you have to, say, use a fMRI scan). On one of these tests we can get a good idea just by looking at the one who won in comparison to the other and assess somatype and use that as a proxy, what can we do for IQ? Just look at head size? The fact of the matter is, just because there is no ‘theory of athleticism’ doesn’t mean that because there is no ‘theory of intelligence differences’ that it doesn’t matter, because it clearly does. Either way, I will articulate a theory of individual and group athletic differences, and meld them into a coherent theory.
Everyone is different, no one is a perfect clone of their parents. This is common knowledge. Each and every individual has different physiology and anatomy, one person may have a certain organ while another does not. One person may have physiological advantages that another does not. These are the how’s and why’s of athletic differences between individuals and groups. Talking about just individuals, individual A may have a more mesomorphic somatype with more fast twitch fibers while individual B may have an endomorphic somatype with more slower twitch fibers. Let’s say these two individuals didn’t know about their advantages/disadvantages. They then race. You look at them and you automatically say “Individual A won because of his longer legs in comparison to individual B”, and you’d be right. When speaking about sports performance these physical differences are noticeable by the naked eye.
Individuals have different somatypes and different physiological variables unevenly distributed within and between populations that infer different capacity for athletic ability. To understand and formulate a theory of individual and group athletic differences you must know some basic anatomy and physiology. Differences in biomechanics and physiology explain individual differences in athletic competition. Now all you need to do is extrapolate the individual who is more athletic (faster 40 yd dash time, say) and find the population with similar phenotype. The argument for individuals carries over to groups, too. Though I have already elucidated on the theory for type II muscle fibers and athletic success in some West African populations (Morrison and Cooper, 2006), and that is good enough for group differences (at least between whites, blacks, and Asians regarding sports in America). The point is that there is no theory for individual differences in athletic ability and one is not needed therefore one is not needed for IQ is clearly wrong. Theories do exist, but I believe they don’t need to be articulated because it truly is obvious.
Why do other groups have differing somatypes and fiber distribution? The answer is, clearly, due to evolution in different environments. The cause for most African running success can be attributed to fast twitch muscle fibers which may have been brought on by malaria-infected mosquitoes which then changed the physiology of the groups affected.
Either way, there are theories of athletic ability. We know what makes someone faster than someone else. Say they take longer strides, deeper breaths, higher Vo2 max, etc and they have the correct morphology, along with the ACTN3 gene and right morphology and we can then compare them to others who did less well and even other groups who don’t exceed as well as the group with the somatype in question and then attempt to formulate a theory from there.
The RR ACTN3 genotype infers an advantage when coupled with the correct morphology (Broos et al, 2016). We know that certain populations excel over others when it comes to sprinting and distance competition, and at least regarding West Africa and its diaspora, there is a good theory for how and why they excel in these competitions. Regarding Kenyans and Ethiopians, it comes down to their low body fat, ecto-meso somatype and the altitude they live and train at. You need to take a system’s view of running and sports success as a whole and not attempt to reduce things down to, say, only muscle fibers or only somatype or pulmonary differences or Vo2 max etc etc because the whole system works together and if you take one variable out, say type II fibers, one part of the system that made it run is now out of the equation and that system will not work as it used to when all cogs were together.
Nevertheless, we don’t need a theory of individual athletic differences but one is extremely easy to articulate. We know how one’s body begins to cope as they are running at maximal speed or as they are hitting their stride on a distance competition. Take someone who’s ecto, and has type I fibers and more body fat compared to someone who’s ecto and has type II fibers and less body fat. How could we articulate how and why the ecto outperforms the endo in a sprint? It’s easy. The ecto somatype is conducive to running success and due to longer limbs can cover more ground and since he has less body fat he will be quicker, too.
This is in stark contrast to IQ. There is no agreed-upon theory and the one model there is is highly flawed. We don’t need a theory of athletic differences but we do need a theory of IQ. The correlates that people attempt to use to say that there is a theory and that they do test something meaningful are nowhere near good enough because it could just show life experiences, for instance the size of different parts of the brain while in the MRI machine (which, even then, has problems; Rutter and Pickles, 2016). There is no theory of individual intelligence differences, to quote Ian Deary “There is no such thing as a theory of human intelligence differences—not in the way that grown-up sciences like physics or chemistry have theories (quote from Richardson, 2012). Well it seems that according to Deary, psychology isn’t a ‘grown-up science’ since there is no agreed-upon theory of individual differences in ‘intelligence’.
One paper people point to is Jung and Haier (2007) who propose the theory ‘P-FIT’—the Parieto-frontal integration theory—where they show that in more 40 percent of voxel-based morphometry that tissue density and white matter integrity correlate substantially with IQ. Though that only means that 60 percent of the time it did not correlate substantially at all, with the same being noted for fMRI and PET. They also note that neuroimaging is ‘correlational by nature‘, so post hoc, ergo propter hoc. This is the problem: we can reliably state how and why people are more athletic then others, but when it comes to IQ/’intelligence’, these differences are fleeting and there is a ton of contradictory evidence, as noted by Jung and Haier (2007).
All in all, we don’t need a theory of athletic differences for both individuals and populations, though one is easily articulated because we actually—and reliably—know how and why individuals are, say, faster than one another and how and why different people succeed in different athletic competitions, the same cannot be said for IQ, the ‘unseen construct’. Attempting to use a nonexistent theory of athletic ability as an analog to no theory for individual differences in IQ does not make sense because they’re two wildly different things, one is an actual measurable thing (with actual reliable physiological/physical differences), while IQ is a reified construct. Explaining these athletic differences between groups and individuals is extremely easy: differing body type along with differing physiology and, of course—and perhaps most importantly—the mind matters way more than one thinks. One can have all of the physical gifts in the world, but if they don’t have the right mindset then they will not succeed (Lippi, Favoloro, and Guidi, 2008).
Combining all of these factors, we get: individuals and groups differ in morphology, physiology and mindsets which then causes differences in sporting competition. When it comes to differences between races, such as the West African diaspora vs the rest of the world, the hypothesis of sickle cell anemia causing a shift to type II fibers is currently the best hypothesis for group differences and explaining why Africans dominate. Though when it comes to individuals, clearly, variation in the aforementioned traits end up causing these differences as no two individuals are the same in regard to body type, physiology and mindset. Of course no two brains are also the same, and I don’t fall prey to simplistic assumptions that ‘everyone is equal’, criticizing IQ tests and their nonexistent, agreed-upon theories doesn’t mean that I believe that ‘everyone is the same’. Athletic ability and IQ, as I’ve shown, are two different things and they do not form an analogous argument.
I’ve had a few discussions with Grey Enlightenment on this blog, regarding construct validity. He has now published a response piece on his blog to the arguments put forth in my article, though unfortunately it’s kind of sophomoric.
He calls himself a ‘race realist’yet echoes the same arguments used by those who oppose such realism.
1) One doesn’t have to believe in racial differences in mental traits to be a race realist as I have argued twice before in my articles You Don’t Need Genes to Delineate Race and Differing Race Concepts and the Existence of Race: Biologically Scientific Definitions of Race. It’s perfectly possible to be a race realist—believe in the reality of race—without believing there are differences in mental traits—‘intelligence’, for instance (whatever that is).
2) That I strongly question the usefulness and utility of IQ due to its construction doesn’t mean that I’m not a race realist.
3) I’ve even put forth an analogous argument on an ‘athletic abilities test’ where I gave a hypothetical argument where a test was constructed that wasn’t a true test of athletic ability and that it was constructed on the basis of who is or is not athletic, per the constructors’ presuppositions. In this hypothetical scenario, am I really denying that athletic differences exist between races and individuals? No. I’d just be pointing out flaws in a shitty test.
Just because I question the usefulness and (nonexistent) validity of IQ doesn’t mean that I’m not a race realist, nor that I believe groups or individuals are ‘the same’ in ‘intelligence’ (whatever that may be; which seems to be a common strawman for those who don’t bow to the alter of IQ).
Blood alcohol concentration is very specific and simple; human intelligence by comparison is not . Intelligence is polygenic (as opposed to just a single compound) and is not as easy to delineate, as, say, the concentration of ethanol in the blood.
It’s irrelevant how ‘simple’ blood alcohol concentration is. The point of bringing it up is that it’s a construct valid measure which is then calibrated against an accepted and theoretical biological model. The additive gene assumption is false, that is, genes being independent of the environment giving ‘positive charges’ as Robert Plomin believes.
He says IQ tests are biased because they require some implicit understanding if social constructs, like what 1+1 equals or how to read a word problem, but how is a test that is as simple as digit recall or pattern recognition possibly a social construct.
What is it that allows individuals to be better than others on digit recall or pattern recognition (what kind of pattern recognition?)? The point of my 1+1 statement is that it is construct valid regarding one’s knowledge of that math problem whereas for the word problem, it was a quoted example showing how if the answer isn’t worded correctly it could be indirectly testing something else.
He’s invoking a postmodernist argument that IQ tests do not measure an innate, intrinsic intelligence, but rather a subjective one that is construct of the test creators and society.
I could do without the buzzword (postmodernist) though he is correct. IQ tests test what their constructors assume is ‘intelligence’ and through item analysis they get the results they want, as I’ve shown previously.
If IQ tests are biased, how is then [sic] that Asians and Jews are able to score better than Whiles [sic] on such tests; surely, they should be at a disadvantage due to implicit biases of a test that is created by Whites.
If I had a dollar for every time I’ve heard this ‘argument’… We can just go back to the test construction argument and we can construct a test that, say, blacks and women score higher than whites and men respectively. How well would that ‘predict’ anything then, if the test constructors had a different set of assumptions?
IQ tests aren’t ‘biased’, as much as lower class people aren’t as prepared to take these tests as people in higher classes (which East Asians and Jews are in). IQ tests score enculturation to the middle class, even the Flynn effect can be explained by the rise in the middle class, lending credence to the aforementioned hypothesis (Richardson, 2002).
Regarding the common objection by the left that IQ tests don’t measures [sic] anything useful or that IQ isn’t correlated with success at life, on a practical level, how else can one explain obvious differences in learning speed, income or educational attainment among otherwise homogeneous groups? Why is it in class some kids learn so much faster than others, and many of these fast-learners go to university and get good-paying jobs, while those who learn slowly tend to not go to college, or if they do, drop out and are either permanently unemployed or stuck in low-paying, low-status jobs? In a family with many siblings, is it not evident that some children are smarter than others (and because it’s a shared environment, environmental differences cannot be blamed).
1) I’m not a leftist.
2) I never stated that IQ tests don’t correlate with success in life. They correlate with success in life since achievement tests and IQ tests are different versions of the same test. This, of course, goes back to our good friend test construction. IQ is correlated with income at .4, meaning 16 percent of the variance is explained by IQ and since you shouldn’t attribute causation to correlations (lest you commit the cum hoc, ergo propter hoc fallacy), we cannot even truthfully say that 16 percent of the variation between individuals is due to IQ.
3) Pupils who do well in school tend to not be high-achieving adults whereas children who were not good pupils ended up having good success in life (see the paper Natural Learning in Higher Education by Armstrong, 2011). Furthermore, the role of test motivation could account for low-paying, low-status jobs (Duckworth et al, 2011; though I disagree with their consulting that IQ tests test ‘intelligence’ [whatever that is] they show good evidence that in low scorers, incentives can raise scores, implying that they weren’t as motivated as the high scorers). Lastly, do individuals within the same family experience the same environment the same or differently?
As teachers can attest, some students are just ‘slow’ and cannot grasp the material despite many repetitions; others learn much more quickly.
This is evidence of the uselessness of IQ tests, for if teachers can accurately predict student success then why should we waste time and money to give a kid some test that supposedly ‘predicts’ his success in life (which as I’ve argued is self-fulfilling)? Richardson (1998: 117) quotes Layzer (1973: 238) who writes:
Admirers of IQ tests usually lay great stress on their predictive power. They marvel that a one-hour test administered to a child at the age of eight can predict with considerable accuracy whether he will finish college. But as Burt and his associates have clearly demonstrated, teachers’ subjective assessments afford even more reliable predictors. This is almost a truism.
Because IQ tests test for the skills that are required for learning, such as short term memory, someone who has a low IQ would find learning difficult and be unable to make correct inferences from existing knowledge.
Right, IQ tests test for skills that are required for learning. Though a lot of IQ test questions are general knowledge questions, so how is that testing anything innate if you’ve first got to learn the material, and if you have not you’ll score lower? Richardson (2002) discusses how people in lower classes are differentially prepared for IQ tests which then affects scores, along with psycho-social factors that do so as well. It’s more complicated than ‘low IQ > X’.
All of these sub-tests are positively correlated due to an underlying factor –called g–that accounts for 40-50% of the variation between IQ scores. This suggests that IQ tests measure a certain factor that every individual is endowed with, rather than just being a haphazard collection of questions that have nothing to do with each other. Race realists’ objection is that g is meaningless, but the literature disagrees “… The practical validity of g as a predictor of educational, economic, and social outcomes is more far-ranging and universal than that of any other known psychological variable. The validity of g is greater the complexity of the task.”
I’ve covered this before. It correlates with the aforementioned variables due to test construction. It’s really that easy. If the test constructors have a different set of presuppositions before the test is constructed then completely different outcomes can be had just by constricting a different test.
Then what about ‘g’? What would one say then? Nevertheless, I’ve heavily criticized ‘g’ and its supposed physiology, and if physiologists did study this ‘variable’ and if it truly did exist, 1) it would not be rank ordered because physiologists don’t rank order traits, 2) they don’t assume normal variations, they don’t estimate heritability and attempt to untangle genes from environment, 3) they don’t assume that normal variation is related to genetic variation (except in rare cases, like down syndrome, for instance), and 4) nor do they assume within the normal range of physiological differences that a higher level is ‘better’ than a lower. My go-to example here is BMR (basal metabolic rate). It has a similar heritability range as IQ (.4 to .8; which is most likely overestimated due to the use of the flawed twin method, just like the heritability of IQ), so is one with a higher BMR somehow ‘better’ than one with a lower BMR? This is what logically follows from assuming that ‘g’ is physiological and all of the assumptions that come along with it. It doesn’t make logical, physiological sense! (Jensen, 1998: 92 further notes that “g tells us little if anything about its contents“.)
All in all, I thank Grey Enlightenment for his response to my article, though it leaves a lot to be desired and if he responds to this article then I hope that it’s much more nuanced. IQ has no construct validity, and as I’ve shown, the attempts at giving it validity are circular, and done by correlating it with other IQ tests and achievement tests. That’s not construct validity.
Almost two years ago, I wrote an article on smoking and race, discussing racial differences in smoking, the brains smoked, and biochemical differences brought on by different physiological differences when smoke is inhaled. In this article, I will look at how smoking can be prevented for all race/ethnies, the contribution of smoking to the black/white difference in mortality, and certain personality traits that may have one more likely to pick up the habit.
Tobacco and poverty are “inextricably linked“, with smoking contributing to more than 10 percent of household income among those in poverty. Tobacco has even been said to be a ‘social justice issue‘ since tobacco use is more prevalent in lower-income communities.
It is true that advertisements are concentrated around certain areas to target certain sociodemographic communities (Seidenberg et al, 2010). They looked at two communities in Boston, Massachusets, one high -income non-minority population, and the other a minority low-income population. They found that the low-income community was more likely to have stores which had larger advertisements, have more stores selling tobacco, promote menthol cigarettes (which low-income people are more likely to smoke, mainly blacks), and finally that advertisements for cigarettes would be more likely to be found 1000 feet away from a school zone in low-income communities compared to high-income communities.
They say that their study shows “evidence that features of tobacco advertising are manipulated to attract youth or racial minority sub-groups, and these features are disproportionately evident in low income, minority communities.” So, according to analysis, at least in the urban area of Dorchester, near Boston, if advertisements were to be lessened near schools, along with fewer overall advertisements, the percentage of minority smokers would decrease. (This same effect of low SES affecting the odds of smoking was also seen in a sample of Argentine children; Linetzky et al, 2012).
Higher SES communities have fewer tobacco advertisements than lower SES communities (Barbeau et al, 2005; Hillier et al, 2015). Big Tobacco (along with Big Food and Big Soda) advertise the most in lower-income communities, which then have deleterious health consequences in those populations, further increasing national health spending per year.
Among Americans, as income increases, smoking decreases:
Nationwide, the Gallup-Healthways Well-Being Index reveals that 21% of Americans say they smoke. As the accompanying graph illustrates, the likelihood of smoking generally increases as annual incomes decrease. One exception to this pattern occurs among those making less than $6,000 per year, an income bracket often skewed because many in that bracket are students. Among those making $6,000 to $11,999 per year, 34% say they smoke, while only 13% in the top two income brackets (those with incomes of at least $90,000 per year) say the same — a 21 percentage-point gap.
The Well-Being Index also confirms distinctions in U.S. smoking rates relating to gender and race. Among respondents, 23% of men and 19% of women say they smoke. Blacks are the most likely to smoke (23%) and Asians are least likely to smoke (12%). Hispanics and whites fall in between, at 17% and 20%, respectively.
Further, according to the CDC, the prevalence of smoking of people with a GED is at 40 percent, the highest amongst any SES group. The fact that tobacco companies attempt to advertise in low-income areas and to women is also well-studied. These factors combined to then cause higher rates of smoking in lower-income populations, and blacks are some of the most affected. There are also a slew of interesting physiological differences between blacks and whites regarding smoking.
Ho and Elo (2013) show that smoking differences between blacks and whites at age 50 accounted for 20 and 40 percent of the gap between 1980 and 2005, but not for women. Without adjusting for SES, smoking explains 20 percent of the excessive risk blacks have regarding all-cause mortality.
A study of 720 black smokers from Los Angeles, California showed that 57 percent only smoked menthols, 15 smoked regulars while 28 percent smoked both menthols and regulars (Unger et al, 2010). One of their main findings was that blacks who smoked menthol cigarettes thought that it was a ‘healthier alternative’ to regular cigarettes. Unger et al (2010: 405) also write:
This cross-sectional study identified correlates of menthol smoking, but it does not prove causality. It is possible that smoking menthol cigarettes causes changes in some of the psychological, attitudinal, social, and cultural variables. For example, people who smoke menthols may form beliefs about the positive medicinal benefits of menthols as a way of reducing their cognitive dissonance about smoking.
Figuring out the causation will be interesting, and I’m sure that advertisements outside of storefronts are causally related. Okuyemi et al (2004) also show that blacks who smoke menthol cigarettes are less likely to quit smoking than blacks who smoke regulars. Younger children were more likely to smoke cigarettes with a “longer rod length” (for instance, Newport 100s over Newport regulars). People smoke menthol cigarettes because they taste better, while menthol also “is a prominent design feature used by cigarette manufacturers to attract and retain new, younger smokers.” (Klausner, 2011). Klausner, though, advocates to ban menthol cigarettes, writing:
This evidence suggests that a ban on menthol in cigarettes would result in fewer people smoking cigarettes. Menthol is a prominent design feature used by cigarette manufacturers to attract and retain new, younger smokers. In addition, not only would some current smokers decide to quit rather than smoke non-mentholated cigarettes, but some young people would not make the transition from experimenting with cigarettes to becoming a confirmed smoker. The FDA should ban menthol in cigarettes which will help lower smoking rates particularly among African Americans and women.
Sterling et al (2016) also agree, but argue to ban little cigars and cigarillos (LCCs) writing “Our data add to the body of scientific evidence that supports the FDA’s ban of all characterising flavours in LCCs.” Numerous studies attest to the availability of menthol cigarettes and LCCs which then contributes to influence different demographics to begin smoking. Hersey et al (2006) also shows menthol cigarettes to be a ‘starter product for youth’, stating one reason that children begin smoking mentholated cigarettes is that they are more addictive than non-menthols. Menthol cigarettes are a ‘starter product’ because they taste better than regular cigarettes and, as shown above, seem like they are more ‘theraputic’ due to their taste and coolness compared to regular cigarettes.
Smokers are more likely to be extroverted, tense, anxious and impulsive, while showing more traits of neuroticism and psychoticism than ex- or non-smokers (Rondina, Gorayeb, and Botelho III, 2007). In a ten-year longitudinal study, Zvolensky et al (2015) showed that people who were more likely to be open to experience and be more neurotic would be more likely to smoke, whereas conscientiousness ‘protected’ against picking up the habit. Neuroticism is one of the most important factors of personality to study regarding the habit of smoking. Munafo, Zetteler, and Clark (2006) show in their meta-analysis on personality factors and smoking that neuroticism and increased extraversion were risk factors for being a smoker. I am aware of one study on the effects of different personality and smoking. Choi et al (2017) write:
The results emerging from this study indicate that neuroticism and conscientiousness are associated with the likelihood of being a current smoker, as well as level of ND. Furthermore, personality traits have a greater influence on smoking status and severity of ND in AAs relative to EAs. These relationships were particularly pronounced among smokers with reporting TTFC of ≤5 min.
… we found that higher neuroticism and lower conscientiousness were associated with higher likelihood of being a current smoker in the AA sample.
So black smokers were more likely to be conscientious, neurotic and open to experience whereas white smokers were more likely to be neurotic and conscientious.
Finally, racial differences in serum cotnine levels are seen, too. Black smokers have higher levels of cotnine than white smokers (Caraballo et al, 1998; Signorello et al, 2010). Perez-Stable et al (2006) show that higher levels of cotnine can be explained by slower clearance of cotnine along with a higher intake of nicotine per cigarette, because blacks take deeper pulls than whites (though they smoke fewer cigarettes than whites, taking deeper pulls off-sets this; Ho and Elo, 2013).
Smoking can be lessened in all populations—no matter the race/ethincity—with the right universal and intervention efforts (Kandel et al, 2004; Kahende et al, 2011). This can be achieved—especially in low-income areas—by lessening and eventually ridding storefronts of these advertisements for menthol cigarettes, which would then decrease the population of smokers in that area because most only smoke menthols. This would then close some of the black-white mortality gap since smoking causes a good amount of it.
Dauphinee et al (2013) even noted how 52 percent of students recognized Camel cigarettes, whereas 36 percent recognized Marlboro and 32 percent recognized Newports. Black students were three times more likely to recognize Newports than Marlboros (because, in my experience, blacks are way more likely to smoke Newports than Marlboros, which whites are more likely to smoke), while this effect held even after controlling for exposure to smoking by parents and peers. This is yet more proof of the ‘menthol effect’ in lower-income communities that partly drives the higher rates of smoking.
In conclusion, it seems that most of the disparity can be pinned down on Big Tobacco advertising mostly in low-income areas where they spend more than 10 percent of their income on cigarettes. Young children are more likely to know what menthol cigarettes are, what they look like and are more likely to know those type of brains of cigarettes, due mainly to how often and how much they are advertised in low-income areas in comparison to high-income areas. Blacks are also more likely than whites to have the personality traits found in smokers, so this, too, contributes to the how and why of black smoking in comparison to whites; they are more susceptible to it due to their personality along with being exposed to more advertisements since they are more likely to live in lower-income areas than whites.
I don’t believe in banning things, but the literature on this suggests that many people only smoke menthols and that if they were ever banned, most would just quit smoking. I don’t think that we need to go as far as banning menthol cigarettes—or cigarettes in general—we just need to educate people better and, of course, reel in Big Tobaccos reach in lower-income communities. Smoking also began to decline the same year that Joe Camel was ‘voluntarily’ discontinued by its parent company (Pampel and Aguilar, 2008), and so, that is good evidence that at least banning or reforming laws in low-income areas would change the number of smokers in a low-income area, and, along with it, close at least a small part of the black-white mortality gap.
The microbiome is the number and types of different microorganisms and viruses in the human body. Racial differences are seen everywhere, most notably in the phenotype and morphology. Though, of course, there are unseen racial differences that then effect bodily processes of different races and ethnic groups. The microbiome is one such difference, which is highly heritable (Goodrich et al, 2014; Beaumont et al, 2016; Hall, Tolonen, and Xavier, 2017) (though they use the highly flawed twin method, so heritabilities are most likely substantially lower). They also show that certain genetic variants predispose individuals to microbial dysbiosis. However, diet, antibiotics and birth mode can also influence the diversity of microbiota in your biome (Conlon and Bird, 2015; Bokulich et al, 2017; Singh et al, 2017) and so while the heritability of the microbiome is important (which is probably inflated due to the twin method), diet can and does change the diversity of the biome.
It used to be thought that our bodies contained 90 percent bacteria and only 10 percent human cells (Collen, 2014), however that has been recently debunked and the ratio is 1.3 to 1, human to microbe (Sender, Fuchs, and Milo, 2016). (Collen’s book is still an outstanding introduction to this subject despite the title of her book being incorrect.) Though the 10:1 microbe/human cell dogma is debunked, in no way does that lessen the importance of the microbiome regarding health, disease and longevity.
Lloyd-Price, Abu-Ali, and Huttenhower (2016) review definitions for the ‘healthy human microbiome’ writing “several population-scale studies have documented the ranges and diversity of both taxonomic compositions and functional potentials normally observed in the microbiomes of healthy populations, along with possible driving factors such as geography, diet, and lifestyle.” Studies comparing the biomes of North and South America, Europe and Africa, Korea and Japan, and urban and rural communities in Russia and China have identified numerous different associations that are related to differences in the microbiome between continents that include (but are not limited to) diet, genetics, lifestyle, geography, and early life exposures though none of these factors have been shown to be directly causal regarding geographic microbiome diversity.
Gupta, Paul, and Dutta (2017) question the case of a universal definition of a ‘healthy microbiome’ since it varies by geographic ancestry. Of course, ancestry and geographic location influence culture which influences diet which influences microbiome diversity between populations. This, of course, makes sense. why have a universal healthy microbiome with a reference man that doesn’t reflect the diversity of both the individual and group differences in the microbiome? This will better help different populations with different microbiomes lose weight and better manage diseases in certain populations.
The microbiome of athletes also differs, too. Athletes had enhanced microbiome diversity when compared to non-athletes (Clarke et al, 2016). In a further follow-up study, it was found that microbial diversity correlated with both protein consumption and creatine kinase levels in the body (Clarke et al, 2017) are proxies for exercise, and since they’re all associations, causality remains to be untangled. Nevertheless, these papers are good evidence that both lifestyle and diet leads to changes in the microbiome.
Fortenberry (2013: 165) notes that American racial and ethnic classifications are “social and political in origin and represent little meaningful biologic basis of between-group racial/ ethnic diversity“. It is also known that eating habits, differing lifestyles and metabolic levels also influence the diversity of the microbiome in the three ‘races’* studied (Chen et al, 2016), while deep sequencing of oral microbiota has the ability to classify “African Americans with a 100% sensitivity and 74% specificity and Caucasians with a 50% sensitivity and 91% specificity” (Mason et al, 2014). The infant microbiome, furthermore, is influenced by maternal diet and breastfeeding as well as the infant’s diet (Stearns et al, 2017). This is why differences in race/ethnicity call into question the term of ‘healthy human microbiota’ (Gupta, Paul, and Dutta, 2017). These differences in the microbiome also lead to increased risk for colorectal cancer in black Americans (Goyal et al, 2016; Kinross, 2017).
Further, the healthy vagina “contains one of the most remarkably structured microbial ecosystems, with at least five reproducible community types, or “community state types” (Lloyd-Price, Abu-Ali, and Huttenhower 2016). The diversity of the microbiome in the vagina also varies by race. It was found that 80 percent of Asian women and 90 percent of white women harbored a microbiota species named Lactobacillus, whereas only about 60 percent of ‘Hispanics’ and blacks harbored this species. The pH level, too, varied by race with blacks and ‘Hispanics’ averaging 4.7 and 5.0 and Asians and whites averaging 4.4 and 4.2. So, clearly, since Asians and whites have similar vaginal pH levels, then it is no surprise that they have similar levels of vaginal Lactobacillus, whereas blacks and ‘Hispanics’, with similar pH levels have similar vaginal levels of Lactobacillus.
White subjects also have more diverse species of microbiota than non-white subjects while also having a different microbiota structure (Chen et al, 2015). Caucasian ethnicity/race was also shown to have a lower overall microbiome diversity, but higher Bacteroidetes scores, while white babes also had lower scores of Proteobacteria than black Americans (Sordillo et al, 2017). This comes down to both diet and genetic factors (though causation remains to be untangled).
Differences in the skin microbiome also exist between the US population and South Americans (Blaser et al, 2013). They showed that Venezuelan Indians had a significantly different skin biome when compared to US populations from Colorado and New York, having more Propionibacterium than US residents. Regarding the skin microbiota in the Chinese, Leung, Wilkins, and Lee (2015) write “skin microbiomes within an individual is more similar than that of different co-habiting individuals, which is in turn more similar than individuals living in different households.” Skin microbiota also becomes similar in cohabitating couples (Ross, Doxey, and Neufeld, 2017) and even cohabitating family members and their dogs (Song et al, 2013; Cusco et al, 2017; Torres et al, 2017).
Differences between the East and West exist regarding chronic liver disease, which may come down to diet which may influence the microbiota and along with it, chronic liver disease. (Nakamoto and Schabl, 2016). The interplay between diet, the microbiome and disease is critical if we want to understand racial/ethnic differentials in disease acquisition/mortality, because the microbiome influences so many diseases (Cho and Blaser, 2012; Guinane and Cotter, 2013; Bull and Plummer, 2014; Shoemark and Allen, 2015; Zhang et al, 2015; Shreiner, Kao, and Young, 2016; Young, 2017).
The human microbiome has been called our ‘second genome’ (Zhu, Wang, and Li, 2010; Grice and Seger, 2012) with others calling it an ‘organ’ (Baquero and Nombela, 2012; Clarke et al, 2014; Brown and Hazen, 2015). This ‘organ’, our ‘second genome’ can also influence gene expression (Masotti, 2012; Maurice, Haiser, and Turnbaugh, 2013; Byrd and Seger, 2015) which could also have implications for racial differences in disease acquisition and mortality. This is why the study of the microbiome is so important; since the microbiome can up- and down-regulate gene expression—effectively, turning genes ‘on’ and ‘off’—then understanding the intricacies that influence the microbiome diversity along with the diet that one consumes will help us better understand racial differences in disease acquisition. Diet is a huge factor not only regarding obesity and diabetes differences within and between populations, but a ‘healthy microbiome’ also staves off obesity. This is important. The fact that the diversity of microbiota in our gut can effectively up- and down-regulate genes shows that we can, in effect, influence some of this ourselves by changing our diets, which would then, theoretically, lower disease acquisition and mortality once certain microbiome/diet/disease associations are untangled and shown to be causative.
Finally, the Hadza have some of the best-studied microbiota, and since they still largely live a hunter-gatherer lifestyle, this is an important look at what the diversity of microbiota may have looked like in our hunter-gatherer ancestors (Samuel et al, 2017). The fact that they noticed such diverse changes in the microbiome—some species effectively disappearing during the dry season and reappearing during the wet season—is good proof that what drives these changes in the diversity of the microbiota in the Hadza are seasonal changes in diet which are driven by the wet and dry seasons.
Gut microbiota may also influence our mood and behavior, and it would be interesting to see which types of microbiota differ between populations and how they would be associated with certain behaviors. The microbes are a part of the unconscious system which regulates behavior, which may have causal effects regarding cognition, behavioral patterns, and social interaction and stress management; this too makes up our ‘collective unconscious’ (Dinan et al, 2015). It is clear that the microbes in our gut influence our behavior, and it even may be possible to ‘shape our second genome’ (Foster, 2013). Endocrine and neurocrine pathways may also be involved in gut-microbiota-to-brain-signaling, which can then alter the composition of the microbiome and along with it behavior (Mayer, Tillisch, and Gupta, 2015). Gut microbiota also plays a role in the acquisition of eating disorders, and identifying the specific microbiotal profiles linked to eating disorders, why it occurs and what happens while the microbiome is out of whack is important in understanding our behavior, because the gut microbiome also influences our behavior to a great degree.
The debate on whether or not racial/ethnic differences in microbiome diversity differs due to ‘nature’ or ‘nurture’ (a false dichotomy in the view of developmental systems theory) remains to be settled (Gupta, Paul, and Dutta, 2017). However, like with all traits/variations in traits, it is due to a complex interaction of the developmental system in question along with how it interacts with its environment. Understanding these complex disease/gene/environment/microbiotal pathways will be a challenge, as will untangling direct causation and what role diet plays regarding the disease/microbiota/dysbiosis factor. As we better understand our ‘second genome’, our ‘other organ’, and individual differences in the genome and how those genomic differences interact with different environments, we will then be able to give better care to both races/ethnies along with individuals. Just like with race and medicine—although there is good correlative data—we should not jump to quick conclusions based on these studies on disease, diet, and microbiotal diversity.
The study of ethnic/racial/geographic/cultural/SES differences in the diversity of the microbiome and how it influences disease, behaviors and gene expression will be interesting to follow in the next couple of years. I think that there will be considerable ‘genetic’ (i.e., differences out of the womb; I am aware that untangling ‘genetic’ and ‘environmental’ in utero factors is hard, next to impossible) differences between populations regarding newborn children, and I am sure that even the microbiota will be found to influence our food choices in the seas of our obesogenic environments. The fact that our microbiota is changeable with diet means that, in effect, we can have small control over certain parts of our gene expression which may then have consequences for future generations of our offspring. Nevertheless, things such as that remain to be uncovered but I bet more interesting things never dreamed of will be found as we look into the hows and whys of both individual and populational differences in the microbiome.
The word ‘construct’ is defined as “an idea or theory containing various conceptual elements, typically one considered to be subjective and not based on empirical evidence.” Whereas the word ‘validity’ is defined as “the quality of being logically or factually sound; soundness or cogency.” Is there construct validity for IQ tests? Are IQ tests tested against an idea or theory containing various conceptual elements? No, they are not.
Cronbach and Meehl (1955) define construct validity, which they state is “involved whenever a test is to be interpreted as a measure of some attribute or quality which is not “operationally defined.”” Though, the construct validity for IQ tests has been fleeting to investigators. Why? Because there is no theory of individual IQ differences to test IQ tests on. It is even stated that “there is no accepted unit of measurement for constructs and even fairly well-known ones, such as IQ, are open to debate.” The ‘fairly well-known ones’ like IQ are ‘open to debate’ because no such validity exists. The only ‘validity’ that exists for IQ tests is correlations with other tests and attempted correlations with job performance, but I will show that that is not construct validity as is classicly defined.
Construct validity can be easily defined as the ability of a test to measure the concept or construct that it is intended to measure. We know two things about IQ tests: 1) they do not test ‘intelligence’ (but they supposedly do a ‘good enough job’ so that it does not matter) and 2) it does not even test the ‘construct’ that it is intended to measure. For example, the math problem ‘1+1’ is construct valid regarding one’s knowledge and application of that math problem. Construct validity can pretty much be summed up as the proof that it is measuring what the test intends…but where is this proof? It is non-existent.
Richardson (1998: 116) writes:
Psychometrists, in the absence of such theoretical description, simply reduce score differences, blindly to the hypothetical construct of ‘natural ability’. The absence of descriptive precision about those constructs has always made validity estimation difficult. Consequently the crucial construct validity is rarely mentioned in test manuals. Instead, test designers have sought other kinds of evidence about the valdity of their tests.
The validity of new tests is sometimes claimed when performances on them correlate with performances on other, previously accepted, and currently used, tests. This is usually called the criterion validity of tests. The Stanford-Binet and the WISC are often used as the ‘standards’ in this respect. Whereas it may be reassuring to know that the new test appears to be measuring the same thing as an old favourite, the assumption here is that (construct) validity has already been demonstrated in the criterion test.
Some may attempt to say that, for instance, biological construct validity for IQ tests may be ‘brain size’, since brain size is correlated with IQ at .4 (meaning 16 percent of the variance in IQ is explained by brain size). However, for this to be true, someone with a larger brain would always have to be ‘more intelligent’ (whatever that means; score higher on an IQ test) than someone with a smaller brain. This is not true, so therefore brain size is not and should not be used as a measure of construct validity. Nisbett et al (2012: 144) address this:
Overall brain size does not plausibly account for differences in aspects of intelligence because all areas of the brain are not equally important for cognitive functioning.
For example, breathalyzer tests are construct valid. There is a .93 correlation (test-retest) between 1 ml/kg bodyweight of ethanol in 20, healthy male subjects. Furthermore, obtaining BAC through gas chromatography of venous blood, the two readings were highly correlated at .94 and .95 (Landauer, 1972). Landauer (1972: 253) writes “the very high accuracy and validity of breath analysis as a correct estimate of the BAL is clearly shown.” Construct validity exists for ad-libitum taste tests of alcohol in the laboratory (Jones et al, 2016).
There is a casual connection between what one breathes into the breathalyzer and his BAC that comes out of the breathalyzer and how much he had to drink. For example, for a male at a bodyweight of 160 pounds, 4 drinks would have him at a BAC of .09, which would make him unfit to drive. (‘One drink’ being 12 oz of beer, 5 oz of wine, or 1.25 oz of 80 proof liquor.) He drinks more, his BAC reading goes up. Someone is more ‘intelligent’ (scores higher on an IQ test), then what? The correlations obtained from so-called ‘more intelligent people’, like glucose consumption, brain evoked potentials, reaction time, nerve conduction velocity, etc have never been shown to determine higher ‘ability’ to score higher on IQ tests. That, too, would not even be construct validation for IQ tests, since there needs to be a measure showing why person A scored higher than person B, which needs to hold one hundred percent of the time.
Another good example of the construct validity of an unseen construct is white blood cell count. White blood cell count was “associated with current smoking status and COPD severity, and a risk factor for poor lung function, and quality of life, especially in non-currently smoking COPD patients. The WBC count can be used, as an easily measurable COPD biomarker” (Koo et al, 2017). In fact, the PRISA II test has white blood cell count in it, which is a construct valid test. Even elevated white blood cell count strongly predicts all-cause and cardiovascular mortality (Johnson et al, 2005). It is also an independent risk factor for coronary artery disease (Twig et al, 2012).
A good example of tests supposedly testing one thing but testing another is found here:
As an example, think about a general knowledge test of basic algebra. If a test is designed to assess knowledge of facts concerning rate, time, distance, and their interrelationship with one another, but test questions are phrased in long and complex reading passages, then perhaps reading skills are inadvertently being measured instead of factual knowledge of basic algebra.
Numerous constructs have validity—but not IQ tests. It is assumed that they test ‘intelligence’ even though an operational definition of intelligence is hard to come by. This is important, as if there cannot be an agreement on what is being tested, how will there be construct validity for said construct in question?
Richardson (2002) writes that Detterman and Sternberg sent out a questionnaire to a group of theorists which was similar to another questionnaire sent out decades earlier to see if there was an agreement on what ‘intelligence’ is. Twenty-five attributes of intelligence were mentioned. Only 3 were mentioned by more than 25 percent of the respondents, with about half mentioning ‘higher level components’, one quarter mentioned ‘executive processes’ while 29 percent mentioned ‘that which is valued by culture’. About one-third of the attributes were mentioned by less than 10 percent of the respondents with 8 percent of them answering that intelligence is ‘the ability to learn’. So if there is hardly any consensus on what IQ tests measure or what ‘intelligence’ is, then construct validity for IQ seems to be very far in the distance, almost unseeable, because we cannot even define the word, nor actually test it with a test that’s not constructed to fit the constructors’ presupposed notions.
Now, explaining the non-existent validity of IQ tests is very simple: IQ tests are purported to measure ‘g’ (whatever that is) and individual differences in test scores supposedly reflect individual differences in ‘g’. However, we cannot say that it is differences in ‘g’ that cause differences in individual test scores since there is no agreed-upon model or description of ‘g’ (Richardson, 2017: 84). Richardson (2017: 84) writes:
In consequence, all claims about the validity of IQ tests have been based on the assumption that other criteria, such as social rank or educational or occupational acheivement, are also, in effect, measures of intelligence. So tests have been constructed to replicate such ranks, as we have seen. Unfortunately, the logic is then reversed to declare that IQ tests must be measures of intelligence, because they predict school acheivement or future occupational level. This is not proper scientific validation so much as a self-fulfilling ordinance.
Construct validity for IQ does not exist (Richardson and Norgate, 2015), unlike construct validity for breathalyzers (Landauer, 1972) or white blood cell count as a disease proxy (Wu et al, 2013; Shah et al, 2017). So, if construct validity is non-existent, then that means that there is no measure for how well IQ tests measure what it’s ‘purported to measure’, i.e., how ‘intelligent’ one is over another because 1) the definition of ‘intelligence’ is ill-defined and 2) IQ tests are not validated against agreed-upon biological models, though some attempts have been made, though the evidence is inconsistent (Richardson and Norgate, 2015). For there to be true validity, evidence cannot be inconsistent; it needs to measure what it purports to measure 100 percent of the time. IQ tests are not calibrated against biological models, but against correlations with other tests that ‘purport’ to measure ‘intelligence’.
(Note: No, I am not saying that everyone is equal in ‘intelligence’ (whatever that is), nor am I stating that everyone has the same exact capacity. As I pointed out last week, just because I point out flaws in tests, it does not mean that I think that people have ‘equal ability’, and my example of an ‘athletic abilities’ test last week is apt to show that pointing out flawed tests does not mean that I deny individual differences in a ‘thing’ (though athletic abilities tests are much better with no assumptions like IQ tests have.))
Most race deniers say that race isn’t real because, as Lewontin (1972) and Rosenberg (2002) state, the within-group variation is larger than the between-group variation. Though, you can circumvent this claim by not even looking at genes/allele frequencies between races, you can show that race is real by looking at morphology, phenotype and geographic ancestry. This is one of Michael Hardimon’s race categories, the minimalist concept of race. This concept does not entail anything that we cannot physically ‘see’ with our eyes (e.g., mental and psychological traits are off the table). Using these concepts laid out by Hardimon can and does prove that race is real and useful without even arguing about any potential mental and psychological differences between human races.
Morphology is one of the most simple tells for racial classification. Just by looking at average morphology between the races we can use attempt to use this data point as a premise in the argument that races exist.
East Asians are shorter with shorter limbs and have an endomorphic somatype. This is due to evolving in cold climate, as a smaller body and less surface area can be warmer much quicker than a larger body. This is a great example of Allen’s rule: that animals in colder climates will be smaller than animals in warmer climates. Using average morphology, of course, can show how the population in question evolved and where they evolved.
Regarding Europeans, they have an endomorphic somatype as well. This, again, is due to where they evolved. Morphology can tell us a lot about the evolution of a species. Though, East Asians and Europeans have similar morphologies due to evolving in similar climates. Like East Asians, Europeans have a wider pelvis in comparison to Africans, so this is yet another morphological variable we can use to show that race exists.
Finally, the largest group is ‘Africans’ who have the largest phenotypic and genetic diversity on earth. Generally, you can say that they’re tall, have long limbs and a short torso, which is due to evolving in the tropics. Furthermore, and perhaps most important, Africans have narrower pelves than East Asians and Europeans. This character is one of the most important regarding the reality of race because it’s one of the most noticeable, and we do notice in when it comes to sports competition because that certain type of morphology is conducive to athletic success. (Also read my recent article on strength and race and my article on somatype and race for more information on morphologic racial differences.)
Morphology is a part of the phenotype too, obviously, but there is a reason why it’s separated. As is true with morphology, different characters evolved due to cultural evolution (whether or not they adopted farming early) or evolution through natural selection, drift and mutation. Though, of course, favorable mutations in a certain environment will be passed on and eventually become a part of the characteristics of the population in question.
East Asians have the epicanthic fold, which probably evolved to protect the eye from the elements and UV rays on the Mongolian steppes. They also have softer features than Europeans and Africans, but this is not due to lower testosterone as is popularly stated. (Amusingly enough, there is a paper that stated that East Asians have Down Syndrome-like qualities due to their epicanthic folds to bring up one reason.) Even then, what some races find attractive or not can show how and why certain facial phenotypes evolved. To quote Gau et al (2018):
Compared with White women, East Asian women prefer a small, delicate and less robust face, lower position of double eyelid, more obtuse nasofrontal angle, rounder nose tip, smaller tip projection and slightly more protruded mandibular profile.
And they conclude:
The average faces are different from the attractive faces, while attractive faces differ according to race. In other words, the average facial and aesthetic criteria are different. We should use the attractive faces of a race to study that races aesthetic criteria.
We can use studies such as this to discern different facial phenotypes, which, again, proves that race exists.
The climate one’s ancestors evolved in dictates nose shape. In areas where it is extremely dry and also has a lot of heat, a larger mucous area is required to moisten inspired (inhaled) air, which is why a more flat and narrow nose is needed.
Zaidi et al (2017) write:
We find that width of the nares is correlated with temperature and absolute humidity, but not with relative humidity. We conclude that some aspects of nose shape may have indeed been driven by local adaptation to climate.
Though climate, of course, isn’t the only reason for differences in nose shape; sexual selection plays a part too, as seen in the above citation on facial preferences in East Asian and European women.
There are also differences in hirsutism between the races. Racial differences exist regarding upper lip hair, along with within-race differences (Javorsky et al, 2014). The self-reported races of African American, East Asian, Asian Indian, and ‘Hispanic’ predicted facial hair differences in women, but not how light their skin was. The women were from Los Angeles, USA; Rome, Italy; Akita, Japan; and London, England. Indian women had more hair than any other race, while European women had the least. Regarding within-race variation, Italian women had more hair on their upper lip than American and British women. Skin lightness was related to hair on the upper lip. (Also read my article The Evolution of Human Skin Variation for more information on racial differences in skin color.)
In 2012, an interesting study was carried out on hair greying on a sample population of a large number of the world’s ethnies titled Greying of the human hair: a worldwide survey, revisiting the ‘50’ rule of thumb. The objective of the study was to test the ’50-50-50′ rule; that at age 50, 50 percent of the population has at least 50 percent of their hair grey. Africans and Asians showed fewer grey hairs than whites who showed the most. The results imply that hair greyness varies by ethnicity/geographic origin, which is perfect for the argument laid out in this article. The global range for people over 50 with 50 percent or more of their hair grey was between 6 and 23 percent, far lower than what was originally hypothesized (Panhard, Lozano, and Loussouarn, 2012). They write on page 870:
With regard to the intensity of hair greying, the lowest values were found among African and Asian groups, especially Thai and Chinese, whereas the highest values were in subjects with the blondest hair (Polish, Scottish, Russian, Danish, CaucasianAustralian and French).
Altogether, these analyses clearly illustrate that the lowest incidences and intensities of grey hair are found in populations of the darkest hair whereas the highest intensities are found in populations with the lightest hair tones.
Actual hair diversity is much more concentrated in Europeans, however (Frost, 2005). (See Peter Frost’s article Why Do Europeans Have So Many Hair and Eye Colors?) It is largely due to sexual selection, with a few climatic factors thrown in. Dark hair, on the other hand, is a dominant trait, which is found all over the world.
Zhuang et al (2010) found significant differences in facial morphology between the races, writing:
African-Americans have statistically shorter, wider, and shallower noses than Caucasians. Hispanic workers have 14 facial features that are significantly larger than Caucasians, while their nose protrusion, height, and head length are significantly shorter. The other ethnic group was composed primarily of Asian subjects and has statistically different dimensions from Caucasians for 16 anthropometric values.
Statistically significant differences in facial anthropometric dimensions (P < 0.05) were noted between males and females, all racial/ethnic groups, and the subjects who were at least 45 years old when compared to workers between 18 and 29 years of age.
Blacks had statistically significant differences in lip and face length when compared to whites (whites had shorted lips than blacks who had longer lips than whites).
Brain size and cranial morphology, too, differs by geographic ancestry which is directly related to the climate where that population evolved (Beals, Smith, and Dodd, 1984). Most every trait that humans have—on average of course—differs by geographic location and the cause of this is evolution in these locations along with being a geographically isolated breeding population.
The final piece to this argument is using where one’s recent ancestors came from. There are five major populations from a few geographic locales: Oceania, the Americas (‘Native Americans), Europe, Africa and East Asia. These geographic locales have peoples that evolved there and underwent different selective pressures due to their environment and their bodies evolved to better suit their environment, and so racial differences in morphology and phenotype occurred so the peoples could survive better in that location. No one part of this argument is more important than any other, though geographic ancestry is the final piece of the puzzle that brings everything together. Because race is correlated with morphology and phenotype, the geographic ancestry dictates what these characteristics look like.
Thus, this is the basic argument:
P1: Differing populations have differing phenotypes, including (but not limited to) facial structure, hair type/color, lip structure, skull size, brain size etc.
P2: Differing populations have differing morphology which, along with this population’s phenotype, evolved in response to climatic demands along with sexual selection.
P3: This population must originate from a distinct geographic location.
C: If all three of the above premises are true, then race—in the minimalist sense—exists and is biologically real.
This argument is extremely simple, and along with the papers cited above in support of the three premises and the ultimate conclusion, it will be extremely hard for race deniers to counter. We can say that P1 is logically sound because geographically isolated populations differ in the above-mentioned criteria. We can say that P2 is logically sound since differing populations have differing morphology (as I have discussed numerous times which leads to racial differences in sporting competition) such as differing trunk lengths, leg lengths, arm lengths and heights which are largely due to evolution in differing climates. We can say that P3 is logically sound because the populations that would satisfy P1 and P2 do come from geographically distinct locations; that is, they have a peculiar ancestry that they only share.
This concept of minimalist race from Michael Hardimon is (his) the racialist concept of race “stripped down to its barest bones” (Hardimon, 2017: 3). The minimalist concept of race, then, does not discuss any differences between populations that cannot be directly discerned with the naked eye. (Note: You can also use the above arguments/data laid out for the populationist concept of race, which, according to Hardimon (2017: 3) is: “A nonracialist (nonessentialist, nonhierarchical) candidate scientific concept that characterizes races as groups of populations belonging to biological lines of descent, distinguished by patterns of phenotypic differences, that trace back to geographically separated and extrinsically reproductively isolated founder populations.)
Minimalist race is biologically sound, grounded in genetics (though I have argued here that you don’t need genetics to define race), and is grounded in biology. Minimalist race is defined as characteristics of the group, not of the individual. Minimalist race are biologically real. Minimalist races exist because, as shown with the data presented in this article, phenotypic and morphologic traits are unevenly distributed throughout the world which then correlates with geographic ancestry. It cannot get any more simpler than that: race exists because differences in phenotype and morphology exist which then corresponds with geographic ancestry.
From Hardimon (2017: 177)\
No sane or logical person would deny the existence of race based on the criteria laid out in this article. We can also make another leap in logic and state that since minimalist races exist and are biologically real then geographic ancestry should be a guide when dealing with medicine and different minimalist races.
It is clear that race exists in the minimal sense; you do not need genes to show that race is real, nor that race has any utility in a medical context. This is important for race deniers to understand: genes are irrelevant when talking about the reality of race; you only need to just use your eyes and you’ll see that certain morphologies and phenotypes are distributed across geographic locations. It is also very easy to get someone to admit that races exist in this minimalist-biological sense. No one denies the existence of Africans, Europeans, ‘Native’ Indians, East Asians and Pacific Islanders. These populations differ in morphology and other physical characters which are unevenly distributed by geographic ancestry, so, therefore: minimialist races exist and are a biological reality.