Sources for Pakman Video

Sources for a video.

The Concept of Race

 

Sub 1

Remsen (2010)

 

Citation Predictor Grouping Accuracy
Bamshad et al. (2004) Genes C.A. 99%
Allocco et al. (2007) Genes C.A. 95%
Tang et al. (2005) Genes SIRE 99%
Guo et al. (2015) Genes SIRE 99%
Guo et al. (2015) Genes SIRE 99%
Lao et al. (2010) Genes SIRE 87%
Ousley et al. (2009) Craniometrics SIRE 97%
Relethford (2009) Craniometrics C.A. 97%

(Guo et al  had some people who were impossible to correctly bin that I didn’t count)

“No variety exists, whether of color, countenance, or stature, so singular as not to be connected with others of the same kind by such an imperceptible transition, that it is very they are all related, or only differ from each other in degree.” – Blumenbach 1775

“Man descends, by imperceptible degrees, from the most enlightened and polished nations, to people of less genius and industry; from the latter to others more gross, but still subject to kings and laws, and these, again, to savages” – Buffon 1753 (page 186)

“But the most weighty of all the arguments against treating the races of man as distinct species, is that they graduate into each other, independently in many cases, as far as we can judge, of their having intercrossed.” – Darwin 1871 (page 226)

Race and Sports

Races differ in many physical attributes relevant to sports. For instance, a greater proportion of African muscle fibers are “type 2” fast twitch fibers which allow for rapid muscle movement but tire out quickly. The center of gravity is slightly higher among Africans than among Whites. East Asians are shorter on average. Africans have abnormally large limbs relative to their bodies, have higher far free body masses, and are more likely to have sickle cell. Etc.

http://caribbean.scielo.org/pdf/wimj/v55n3/a15v55n3.pdf

https://www.witpress.com/elibrary/dne-volumes/5/3/454

https://www.ncbi.nlm.nih.gov/pubmed/2946652

https://www.physiology.org/doi/full/10.1152/ajpendo.00416.2001

https://www.ncbi.nlm.nih.gov/pubmed/25739558

https://academic.oup.com/ajcn/article/71/6/1392/4729362

https://journals.sagepub.com/doi/abs/10.1177/000992286900800812

 

IQ’s Predictive Validity

 

Work.jpg

(Roth et al., 2005Schmidt and Hunter, 1998)

Strenze 2

(Strenze, 2007)

Zag

Zagorsky (2007)

You see the same thing in other countries. For instance, Hegelund et al. (2018) analyzed data on over one million participants from Denmark and produced the following graph:

Hegelund 2.JPG

Coward and Sackett (1990) analyzed data from 174 studies on the relationship between IQ and job performance. A non-linear trend fit the relation better than a purely linear one only between 5 and 6 percent of the time, roughly what one would expect on the basis of chance alone. Thus, the relationship between IQ and job performance is normally pretty linear, meaning that it doesn’t break down above the midpoint.

Coyle (2015) finds similar results showing that IQ’s relationship to GPA, ACT, and SAT, scores is not significantly increased by altering the model from linear to quadratic (Ns = 706 – 1174).

SAT test scores correlate with IQ about as well as do scores from two different IQ tests. The same is true of the ACT and England’s GSCE. Standardized tests often are basically just IQ tests.

Citation Test Correlation N
Brodnick and Ree (1995) SAT – V 0.8 339
Brodnick and Ree (1995) SAT – M 0.7 339
Brodnick and Ree (1995) ACT 0.87 339
Frey and Detterman (2004) SAT 0.86 917
Frey and Detterman (2004) SAT 0.72 104
Beaujean et al. (2006) SAT 0.58 97
Deary et al. (2007) GCSE 0.81 70000

If we look at research on SAT scores and outcomes ranging from income to educational attainment to scientific achievement, we see that increases at the top of the IQ distribution are most predictive of improvements in outcome. scien

Robertson et al. (2010).

Park 1Park 2

Park et al. (2008)

IQ Explains Racial Income Gaps

 

3

Farkas and Vicknair (1996)

4

Nyborg and Jensen (2001)

(Look at that, IQ once again predicting income past scores of 90!)

13

Kirkergaard (2017)

Mean IQ of Africans

RAce and IQ around the world.png

Heritability of IQ

 

16

Haworth et al. (2009)

17

Briley and Tucker-Drob (2017)

18

Rushton and Jensen (2005)

Fading Environments

50

Protzko (2015)

You see the same thing in shared reading interventions. They have an effect, but it completely goes away after the intervention ends (Noble et al., 2018).

Dahl and Lockner (2008) used longitudinal changes in tax policy to study the effects of increased income on the test scores of children. There were immediate benefits, but no statistically significant relationship was found to exist between a child’s home income increase in the previous year and their current test scores, suggesting that household income effects fade rapidly.

Environments That Don’t Have Effects

Maynard and Murnane (1979) looked at the impact of a guaranteed income experiment on a sample of poor African Americans. While there were benefits for very young children, by middle school enrollment had no significant effect on reading scores and a significant negative effect on GPA equal to .18 SD.

32 - Inc

Similarly, Duncan et al. (2011) analyzed 16 welfare experiments in which poor people were either put into a program or a control group. Some of these programs included cash transfers and thus they were able to study the effect or random increases in income on student achievement. Increased income was shown to have no effect on test scores. 

30 - Inc

The correlation between SES and IQ is mostly genetic:

Citation Age Correlation % Genetic
Krapohl and Plomin (2016) 16 0.5 50%
 Trzaskowski et al. (2014)  7 0.31 94%
 Trzaskowski et al. (2014)  12 0.32 56%
Rowe et al. (1998) Adults 0.34 59%

51 - par52 - par

Beaver et al. (2014)

Within and Between Group Heritability

The heritability of IQ is the proportion of variance in IQ accounted for by genetics. This is an r-squared static.  The square root of the heritability of IQ is therefore correlation between genotypic IQ and actual IQ. This correlation tells you the increase in IQ expected if someone moves from one percentile of the genotypic IQ distribution to the other.

One minus the heritability of IQ is the proportion of IQ explained by the environment. The square root of this is the correlation between the “effective environment” and IQ. This can tell us the expected difference in IQ produced by moving from one percentile of the environment to another.

Now, we can use this information, combined with an empirical estimate of the heritability of IQ, to determine how bad the average environment of African Americans would be, in percentile terms, to produce the 15 point IQ gap observed between blacks and whites. Carrying this out with heritability estimates of 60% to 80% for adulthood IQ, this implies that the average African american would need to have an environment worse than 90% to 98% of white Americans to produce a 15 point gap (Jensen, 1972, p. 172).

This environmental component can be broken into two sources: things that are not the same for people growing up in the same home like their peer group or random life events, and things which are usually the same for people growing up in the same home, such as family income and the number of parents at home. The later sources of various is called the shared environment.

The shared environment explains, at most, 15% of variation in adult IQ. This implies that the average black american would need to have a worse shared environment than 99% of white Americans in order to explain a 15 point gap. If this model holds, it is extremely implausible then that the black-white IQ can could be explained by anything that is shared by people who live within the same home but not by people who live in different homes.

Genetics and Race and IQ

 

21 - Piffer

Piffer (2019)23 - Piffer

Piffer (2019)

It still does so after controlling for variables like malnutrition and the human development index.

22 - Piffer

2930

Lynn and Becker (2019)

National Development

According to the latest data, 53% of variation in national wealth is predictable on the basis of national variation in IQ.

61

Lynn and Becker (2019)

Longitudinally, national increases in IQ are a good prediction of increases in national wealth.

59

Rindermann and Becker (2018)

Importantly, this remains true if you control for the initial wealth of a country. An increase in national IQ best predicts an increase in national wealth ten years after the increase in IQ. At a given point in time, changes in national IQ from ten years ago predict current economic growth better than does national wealth from ten years ago.

60

Rindermann and Becker (2018)

IQ is also an important predictor of economic freedom.

62

Lynn and Becker (2019)

Moreover, the higher a nation’s mean IQ, the more free its economy will tend to be in the future, and this remains true after controlling for inial levels of economic freedom and national wealth.

58

This model also allows us to see that the relationship between current IQ and future wealth and freedom is much greater than the relationship current wealth and freedom and future IQ. Actually, it’s worth noting that the relationship between current wealth and future intelligence is trivial once current intelligence is held constant.

The empirical relation between the history of states in an area and current development has been heavily studied and the results are not consistent with the story David seems to be telling. For one thing, state-history can only explain 23% of current variation in national wealth. This is far less than the proportion of variance accounted for by IQ. Moreover, this relationship is not linear, such that having the longest history of states actually predicted a lower level of current development.

63

Borcan et al. (2018)

Variables like a history of agriculture and the historical prevalence of pastoralism in a country are not very good predictors of national intelligence, especially when compared with variables like national skin color or brain size.

64

Meisenberg and Woodley (2012)

Individualism

 

G alleleMAO-A

Way and Leiberman (2010)

S allele

Chaio and Blinzinsky (2009)

45

Hatemi et al. (2010)

 

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