Review: The Roma: A Balkan Underclass (Jelena Cvorovic)

www.goodreads.com/book/show/23621169-the-roma

Richard Lynn is so nice to periodically send me books for free. He is working on establishing his publisher, of course, and so needs media coverage.

In this case, he sent me a new book on the Roma by Jelena Cvorovic who was also present at the London conference on intelligence in the spring 2014. She has previously published a number of papers on the Roma from her field studies. Of most interest to differential psychologists (such as me), is that they obtain very low scores on g tests not generally seen outside SS Africa. In the book, she reviews much of the literature on the Roma, covering their history, migration in Europe, religious beliefs and other strange cultural beliefs. For instance, did you know that many Roma consider themselves ‘Egyptians’? Very odd! Her review also covers the more traditional stuff like medical problems, sociological conditions, crime rates and the like. Generally, they do very poorly, probably only on par with the very worst performing immigrant groups in Scandinavia (Somalia, Lebanese, Syrians and similar). Perhaps they are part of the reason why people from Serbia do so poorly in Denmark. Perhaps they are mostly Roma? There are no records of more specific ethnicities in Denmark for immigrant groups to my knowledge. Similar puzzles concern immigrants coded as “stateless” which are presumably mostly from Palestine, immigrants from Israel (perhaps mostly Muslims?) and reversely immigrants from South Africa (perhaps mostly Europeans?).

Another interesting part of the book concerns the next last chapter covering the Roma kings. I had never heard of these, but apparently there are or were a few very rich Romas. They built elaborate castles for their money which one can now see in various places in Eastern Europe. After they lost their income (which was due to black market trading during communism and similar activities), they seem to have reverted to the normal Roma pattern of unemployment, fast life style, crime and state benefits. This provides another illustration of the idea that if a group of persons for some reason acquire wealth, it will not generally boost their g or other capabilities, and their wealth will go away again once the particular circumstance that gave rise to it disappears. Other examples of this pattern are the story of Nauru and people who get rich from sports but are not very clever (e.g. African American athletes such as Mike Tyson). Oil States have also not seen any massive increase in g due to their oil riches nor are people who win lotteries known to suddenly acquire higher g. Clearly, there cannot be a strong causal link from income to g.

In general, this book was better than expected and definitely worth a read for those interesting in psychologically informed history.

Meisenberg’s new book chapter on intelligence, economics and other stuff

G.M. IQ & Economic growth

I noted down some comments while reading it.

In Table 1, Dominican birth cohort is reversed.

 

“0.70 and 0.80 in world-wide country samples. Figure 1 gives an impression of

this relationship.”

 

Figure 1 shows regional IQs, not GDP relationships.

“We still depend on these descriptive methods of quantitative genetics because

only a small proportion of individual variation in general intelligence and

school achievement can be explained by known genetic polymorphisms (e.g.,

Piffer, 2013a,b; Rietveld et al, 2013).”

 

We don’t. Modern BG studies can confirm A^2 estimates directly from the genes.

E.g.:

Davies, G., Tenesa, A., Payton, A., Yang, J., Harris, S. E., Liewald, D., … & Deary, I. J. (2011). Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Molecular psychiatry, 16(10), 996-1005.

Marioni, R. E., Davies, G., Hayward, C., Liewald, D., Kerr, S. M., Campbell, A., … & Deary, I. J. (2014). Molecular genetic contributions to socioeconomic status and intelligence. Intelligence, 44, 26-32.

Results are fairly low tho, in the 20’s, presumably due to non-additive heritability and rarer genes.

 

“Even in modern societies, the heritability of

intelligence tends to be higher for children from higher socioeconomic status

(SES) families (Turkheimer et al, 2003; cf. Nagoshi and Johnson, 2005; van

der Sluis et al, 2008). Where this is observed, most likely environmental

conditions are of similar high quality for most high-SES children but are more

variable for low-SES children. “

 

Or maybe not. There are also big studies that don’t find this interaction effect. en.wikipedia.org/wiki/Heritability_of_IQ#Heritability_and_socioeconomic_status

 

“Schooling has

only a marginal effect on growth when intelligence is included, consistent with

earlier results by Weede & Kämpf (2002) and Ram (2007).”

In the regression model of all countries, schooling has a larger beta than IQ does (.158 and .125). But these appear to be unstandardized values, so they are not readily comparable.

“Also, earlier studies that took account of

earnings and cognitive test scores of migrants in the host country or IQs in

wealthy oil countries have concluded that there is a substantial causal effect of

IQ on earnings and productivity (Christainsen, 2013; Jones & Schneider,

2010)”

 

National IQs were also found to predict migrant income, as well as most other socioeconomic traits, in Denmark and Norway (and Finland and the Netherland).

Kirkegaard, E. O. W. (2014). Crime, income, educational attainment and employment among immigrant groups in Norway and Finland. Open Differential Psychology.

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

 

 

Figures 3 A-C are of too low quality.

 

 

“Allocation of capital resources has been an

element of classical growth theory (Solow, 1956). Human capital theory

emphasizes that individuals with higher intelligence tend to have lower

impulsivity and lower time preference (Shamosh & Gray, 2008). This is

predicted to lead to higher savings rates and greater resource allocation to

investment relative to consumption in countries with higher average

intelligence.”

 

Time preference data for 45 countries are given by:

Wang, M., Rieger, M. O., & Hens, T. (2011). How time preferences differ: evidence from 45 countries.

They are in the megadataset from version 1.7f

Correlations among some variables of interest:

r
             SlowTimePref Income.in.DK Income.in.NO   IQ lgGDP
SlowTimePref         1.00         0.45         0.48 0.57  0.64
Income.in.DK         0.45         1.00         0.89 0.55  0.59
Income.in.NO         0.48         0.89         1.00 0.65  0.66
IQ                   0.57         0.55         0.65 1.00  0.72
lgGDP                0.64         0.59         0.66 0.72  1.00

n
             SlowTimePref Income.in.DK Income.in.NO  IQ lgGDP
SlowTimePref          273           32           12  45    40
Income.in.DK           32          273           20  68    58
Income.in.NO           12           20          273  23    20
IQ                     45           68           23 273   169
lgGDP                  40           58           20 169   273

So time prefs predict income in DK and NO only slightly worse than national IQs or lgGDP.

 

 

“Another possible mediator of intelligence effects that is difficult to

measure at the country level is the willingness and ability to cooperate. A

review by Jones (2008) shows that cooperativeness, measured in the Prisoner‟s

dilemma game, is positively related to intelligence. This correlate of

intelligence may explain some of the relationship of intelligence with

governance. Other likely mediators of the intelligence effect include less red

tape and restrictions on economic activities (“economic freedom”), higher

savings and/or investment, and technology adoption in developing countries.”

 

There are data for IQ and trust too. Presumably trust is closely related to willingness to cooperate.

Carl, N. (2014). Does intelligence explain the association between generalized trust and economic development? Intelligence, 47, 83–92. doi:10.1016/j.intell.2014.08.008

 

 

“There is no psychometric evidence for rising intelligence before that time

because IQ tests were introduced only during the first decade of the 20th

century, but literacy rates were rising steadily after the end of the Middle Age

in all European countries for which we have evidence (Mitch, 1992; Stone,

1969), and the number of books printed per capita kept rising (Baten & van

Zanden, 2008).”

 

There’s also age heaping scores which are a crude measure of numeracy. AH scores for 1800 to 1970 are in the megadataset. They have been going up for centuries too just like literacy scores. See:

A’Hearn, B., Baten, J., & Crayen, D. (2009). Quantifying quantitative literacy: Age heaping and the history of human capital. The Journal of Economic History, 69(03), 783–808.

 

 

“Why did this spiral of economic and cognitive growth take off in Europe

rather than somewhere else, and why did it not happen earlier, for example in

classical Athens or the Roman Empire? One part of the answer is that this

process can start only when technologies are already in place to translate rising

economic output into rising intelligence. The minimal requirements are a

writing system that is simple enough to be learned by everyone without undue

effort, and a means to produce and disseminate written materials: paper, and

the printing press. The first requirement had been present in Europe and the

Middle East (but not China) since antiquity, and the second was in place in

Europe from the 15thcentury. The Arabs had learned both paper-making and

printing from the Chinese in the 13thcentury (Carter, 1955), but showed little

interest in books. Their civilization was entering into terminal decline at about

that time (Huff, 1993). “

 

Are there no FLynn effects in China? They still have a difficult writing system.

 

“Most important is that Flynn effect gains have been decelerating in recent

years. Recent losses (anti-Flynn effects) were noted in Britain, Denmark,

Norway and Finland. Results for the Scandinavian countries are based on

comprehensive IQ testing of military conscripts aged 18-19. Evidence for

losses among British teenagers is derived from the Raven test (Flynn, 2009)

and Piagetian tests (Shayer & Ginsburg, 2009). These observations suggest

that for cohorts born after about 1980, the Flynn effect is ending or has ended

in many and perhaps most of the economically most advanced countries.

Messages from the United States are mixed, with some studies reporting

continuing gains (Flynn, 2012) and others no change (Beaujean & Osterlind,

2008).”

 

These are confounded with immigration of low-g migrants however. Maybe the FLynn effect is still there, just being masked by dysgenics + low-g immigration.

 

 

“The unsustainability of this situation is obvious. Estimating that one third

of the present IQ differences between countries can be attributed to genetics,

and adding this to the consequences of dysgenic fertility within countries,

leaves us with a genetic decline of between 1 and 2 IQ points per generation

for the entire world population. This decline is still more than offset by Flynn

effects in less developed countries, and the average IQ of the world‟s

population is still rising. This phase of history will end when today‟s

developing countries reach the end of the Flynn effect. “Peak IQ” can

reasonably be expected in cohorts born around the mid-21stcentury. The

assumptions of the peak IQ prediction are that (1) Flynn effects are limited by

genetic endowments, (2) some countries are approaching their genetic limits

already, and others will fiollow, and (3) today‟s patterns of differential fertility

favoring the less intelligent will persist into the foreseeable future. “

 

It is possible that embryo selection for higher g will kick in and change this.

Shulman, C., & Bostrom, N. (2014). Embryo Selection for Cognitive Enhancement: Curiosity or Game-changer? Global Policy, 5(1), 85–92. doi:10.1111/1758-5899.12123

 

 

“Fertility differentials between countries lead to replacement migration: the

movement of people from high-fertility countries to low-fertility countries,

with gradual replacement of the native populations in the low-fertility

countries (Coleman, 2002). The economic consequences depend on the

quality of the migrants and their descendants. Educational, cognitive and

economic outcomes of migrants are influenced heavily by prevailing

educational, cognitive and economic levels in the country of origin (Carabaña,

2011; Kirkegaard, 2013; Levels & Dronkers, 2008), and by the selectivity of

migration. Brain drain from poor to prosperous countries is extensive already,

for example among scientists (Franzoni, Scellato & Stephan, 2012; Hunter,

Oswald & Charlton, 2009). “

 

There are quite a few more papers on the spatial transferability hypothesis. I have 5 papers on this alone in ODP: openpsych.net/ODP/tag/country-of-origin/

But there’s also yet unpublished data for crime in Netherlands and more crime data for Norway. Papers based off these data are on their way.

 

International general factor of personality? yes, but…

I merged the dataset from Schmitt et al (2007)’s paper about OCEAN traits in 56 countries with the rest of the megadataset. Then i extracted the first factor of the OCEAN means and SDs. These two are nearly uncorrelated (.07). As for factor strength, they are not too bad:

> DF.OCEAN.mean.omega
Omega 
Call: omega(m = DF.OCEAN.mean)
Alpha:                 0.73 
G.6:                   0.74 
Omega Hierarchical:    0.54 
Omega H asymptotic:    0.64 
Omega Total            0.84 

Schmid Leiman Factor loadings greater than  0.2 
                                        g   F1*   F2*   F3*   h2   u2   p2
ExtraversionMeanSchmittEtAl2007      0.44        0.66       0.64 0.36 0.30
AgreeablenessMeanSchmittEtAl2007     0.58  0.56             0.66 0.34 0.51
ConscientiousnessMeanSchmittEtAl2007 0.62  0.52             0.66 0.34 0.58
NeuroticismMeanSchmittEtAl2007      -0.66  0.28  0.36 -0.36 0.76 0.24 0.56
OpennessMeanSchmittEtAl2007          0.23        0.21  0.51 0.38 0.62 0.14

With eigenvalues of:
   g  F1*  F2*  F3* 
1.40 0.69 0.62 0.40 

general/max  2.04   max/min =   1.7
mean percent general =  0.42    with sd =  0.19 and cv of  0.46 
Explained Common Variance of the general factor =  0.45

 

and

> DF.OCEAN.SD.omega
Omega 
Call: omega(m = DF.OCEAN.SD)
Alpha:                 0.79 
G.6:                   0.78 
Omega Hierarchical:    0.72 
Omega H asymptotic:    0.86 
Omega Total            0.84 

Schmid Leiman Factor loadings greater than  0.2 
                                      g   F1*   F2*   F3*   h2   u2   p2
ExtraversionSDSchmittEtAl2007      0.80                   0.64 0.36 0.99
AgreeablenessSDSchmittEtAl2007     0.57        0.47       0.55 0.45 0.59
ConscientiousnessSDSchmittEtAl2007 0.57  0.35             0.48 0.52 0.68
NeuroticismSDSchmittEtAl2007       0.78  0.52             0.87 0.13 0.69
OpennessSDSchmittEtAl2007          0.43        0.24       0.25 0.75 0.74

With eigenvalues of:
   g  F1*  F2*  F3* 
2.08 0.41 0.31 0.00 

general/max  5.09   max/min =   136.11
mean percent general =  0.74    with sd =  0.15 and cv of  0.2 
Explained Common Variance of the general factor =  0.74

 

Compare with values in Table 5 in my just published paper. GFP-mean is clearly weaker than g factor at individual level, GFP-SD is about the same.

Dataset
Var% MR
Var% MR SL Omega h. Omega h. a. ECV R2
NO Complete cases 0.68 0.65 0.87 0.91 0.78 0.98
NO Impute 1 0.66 0.62 0.86 0.9 0.74 0.96
NO Impute 2 0.64 0.6 0.85 0.89 0.75 0.95
NO Impute 3 0.63 0.59 0.82 0.87 0.73 0.99
DK complete cases 0.57 0.51 0.83 0.85 0.68 0.99
DK impute 4 0.55 0.51 0.86 0.88 0.73 0.99
Int. S. Factor 0.43 0.35 0.76 0.77 0.51 0.81
Cognitive data 0.33 0.74 0.79 0.57 0.78
Personality data 0.16 0.37 0.48 0.34 0.41

Then i correlated these with national IQ, S factor and local S factors in Norway and Denmark.

> round(cor(DF.OCEAN.general.scores,use="pairwise.complete.obs"),2)
             GFP.mean GFP.SD S.in.Norway S.in.Denmark Islam S.Int    IQ
GFP.mean         1.00   0.07        0.09        -0.25  0.17 -0.21 -0.58
GFP.SD           0.07   1.00        0.39         0.26 -0.14  0.36  0.24
S.in.Norway      0.09   0.39        1.00         0.78 -0.72  0.73  0.60
S.in.Denmark    -0.25   0.26        0.78         1.00 -0.71  0.54  0.54
Islam            0.17  -0.14       -0.72        -0.71  1.00 -0.33 -0.27
S.Int           -0.21   0.36        0.73         0.54 -0.33  1.00  0.86
IQ              -0.58   0.24        0.60         0.54 -0.27  0.86  1.00

So strangely, the correlation of GFP-mean x national IQ is very negative. It correlates weakly with S factors. Let’s try partialing out national IQ:

> DF.OCEAN.general.scores.no.IQ = partial.r(DF.OCEAN.general.scores,c(1:6),7)
> DF.OCEAN.general.scores.no.IQ
partial correlations 
             GFP.mean GFP.SD S.in.Norway S.in.Denmark Islam S.Int
GFP.mean         1.00   0.26        0.68         0.09  0.02  0.72
GFP.SD           0.26   1.00        0.31         0.16 -0.08  0.32
S.in.Norway      0.68   0.31        1.00         0.67 -0.73  0.53
S.in.Denmark     0.09   0.16        0.67         1.00 -0.70  0.19
Islam            0.02  -0.08       -0.73        -0.70  1.00 -0.21
S.Int            0.72   0.32        0.53         0.19 -0.21  1.00

Even more strange. GFP-mean strongly correlates with 2 S factors, but not the one in Denmark. The Danish data are very good (25 variables) and so are the international data (42-54 variables). And all the S factors correlate strongly before partialing (.78, .73, .54) but mixed after removing IQ (.67, .53, .19). Again Denmark is odd. For GFP-SD, it is similar, but weaker (before: .39, .26, .36; after: .31, .16, .32).

What to make of this? So i emailed some colleagues:

Dear [NAMES]

Do you know if someone have looked at an international general factor of personality? Because I did it just now using a dataset of OCEAN trait scores (big five) from Schmitt et al 2007. There is indeed an international GFP in the data. It correlates negatively with national IQs (-.58). Strangely, partialing out national IQs, it correlates highly with general socioeconomic factors in Norway (.68) and internationally (.72), but not in Denmark (.09). Strange? Thoughts? I can send you the data+code if you like.

Regards,
Emil

One of them had insider info:

Emil,

There is a paper about to appear in Intelligence in which an international GFP has been computed and analyzed.

Best,

[NAME].
So i publish this here quickly so i establish priority and independence.

What about OCEAN traits themselves?

(sorry, tables apparently not easy to make smaller)
All correlations:
E mean E SD A mean A SD C mean C SD N mean N SD O mean O SD Mean SD S.NO S.DK Islam Int.S IQ
E mean 1 0.14 0.2 0.22 0.25 0.23 -0.49 0.17 0.27 0.09 0.23 0.06 -0.19 -0.02 0.09 -0.02
E sd 0.14 1 -0.08 0.47 -0.07 0.48 0.13 0.66 0.3 0.34 0.81 0.45 0.35 -0.35 0.53 0.39
A mean 0.2 -0.08 1 0.15 0.65 0.21 -0.48 0.21 0.26 -0.13 0.11 0.08 -0.26 0.26 -0.25 -0.53
A SD 0.22 0.47 0.15 1 0.23 0.43 0 0.45 0.22 0.35 0.71 0.18 0.23 -0.18 0.12 -0.04
C mean 0.25 -0.07 0.65 0.23 1 0.1 -0.57 0.07 0.2 -0.03 0.07 0.04 -0.19 0.14 -0.19 -0.6
C SD 0.23 0.48 0.21 0.43 0.1 1 0.11 0.62 0.41 0.25 0.78 0.34 -0.03 0.04 0.19 0.04
N mean -0.49 0.13 -0.48 0 -0.57 0.11 1 0.22 -0.09 0.25 0.19 -0.1 0.13 -0.06 0.12 0.38
N SD 0.17 0.66 0.21 0.45 0.07 0.62 0.22 1 0.41 0.28 0.83 0.23 0.19 0 0.24 0.18
O mean 0.27 0.3 0.26 0.22 0.2 0.41 -0.09 0.41 1 0.07 0.4 -0.01 -0.07 0.04 -0.02 -0.06
O sd 0.09 0.34 -0.13 0.35 -0.03 0.25 0.25 0.28 0.07 1 0.56 0.22 0.14 -0.07 0.25 0.37
Mean SD 0.23 0.81 0.11 0.71 0.07 0.78 0.19 0.83 0.4 0.56 1 0.41 0.25 -0.15 0.36 0.25
S.factor.in.Norway 0.06 0.45 0.08 0.18 0.04 0.34 -0.1 0.23 -0.01 0.22 0.41 1 0.78 -0.72 0.73 0.6
S.factor.in.Denmark -0.19 0.35 -0.26 0.23 -0.19 -0.03 0.13 0.19 -0.07 0.14 0.25 0.78 1 -0.71 0.54 0.54
IslamPewResearch2010 -0.02 -0.35 0.26 -0.18 0.14 0.04 -0.06 0 0.04 -0.07 -0.15 -0.72 -0.71 1 -0.33 -0.27
International.S.Factor 0.09 0.53 -0.25 0.12 -0.19 0.19 0.12 0.24 -0.02 0.25 0.36 0.73 0.54 -0.33 1 0.86
LV2012estimatedIQ -0.02 0.39 -0.53 -0.04 -0.6 0.04 0.38 0.18 -0.06 0.37 0.25 0.6 0.54 -0.27 0.86 1
With IQ partialed out:
E mean E sd A mean A SD C mean C SD N mean N SD O mean O SD Mean SD S.NO S.DK Islam Int.S
E mean 1 0.17 0.22 0.22 0.3 0.23 -0.52 0.18 0.27 0.1 0.24 0.09 -0.21 -0.02 0.21
E sd 0.17 1 0.16 0.53 0.22 0.51 -0.02 0.65 0.35 0.23 0.8 0.29 0.18 -0.28 0.42
A mean 0.22 0.16 1 0.15 0.49 0.28 -0.36 0.36 0.27 0.07 0.29 0.58 0.03 0.15 0.48
A SD 0.22 0.53 0.15 1 0.25 0.43 0.02 0.47 0.21 0.4 0.74 0.26 0.3 -0.2 0.3
C mean 0.3 0.22 0.49 0.25 1 0.15 -0.46 0.23 0.21 0.25 0.29 0.63 0.2 -0.02 0.82
C SD 0.23 0.51 0.28 0.43 0.15 1 0.1 0.62 0.41 0.26 0.79 0.39 -0.05 0.06 0.31
N mean -0.52 -0.02 -0.36 0.02 -0.46 0.1 1 0.17 -0.07 0.13 0.11 -0.45 -0.1 0.05 -0.44
N SD 0.18 0.65 0.36 0.47 0.23 0.62 0.17 1 0.43 0.23 0.83 0.16 0.11 0.05 0.18
O mean 0.27 0.35 0.27 0.21 0.21 0.41 -0.07 0.43 1 0.1 0.42 0.03 -0.04 0.03 0.06
O sd 0.1 0.23 0.07 0.4 0.25 0.26 0.13 0.23 0.1 1 0.52 0.01 -0.07 0.03 -0.14
Mean SD 0.24 0.8 0.29 0.74 0.29 0.79 0.11 0.83 0.42 0.52 1 0.33 0.15 -0.09 0.3
S.factor.in.Norway 0.09 0.29 0.58 0.26 0.63 0.39 -0.45 0.16 0.03 0.01 0.33 1 0.67 -0.73 0.53
S.factor.in.Denmark -0.21 0.18 0.03 0.3 0.2 -0.05 -0.1 0.11 -0.04 -0.07 0.15 0.67 1 -0.7 0.19
IslamPewResearch2010 -0.02 -0.28 0.15 -0.2 -0.02 0.06 0.05 0.05 0.03 0.03 -0.09 -0.73 -0.7 1 -0.21
International.S.Factor 0.21 0.42 0.48 0.3 0.82 0.31 -0.44 0.18 0.06 -0.14 0.3 0.53 0.19 -0.21 1
R code (load in the megadataset as DF.mega3 first):
DF.interest = cbind(DF.mega3[2:12],
                    DF.mega3[14],
                    DF.mega3[40],
                    DF.mega3[42],
                    DF.mega3[64],
                    DF.mega3[76])
DF.interest.cor = rcorr(as.matrix(DF.interest))
round(DF.interest.cor$r,2)
write.csv(round(DF.interest.cor$r,2),file="OCEANCors.csv")

#remove IQ
DF.interest.cor.without.IQ = partial.r(DF.interest, c(1:15),16)
write.csv(round(DF.interest.cor.without.IQ,2), file="OCEANCors_no_g.csv")

DF.OCEAN.full = cbind(DF.mega3[2:12])
DF.OCEAN.full.omega = omega(DF.OCEAN.full)
DF.OCEAN.full.mr = fa(DF.OCEAN.full)

DF.OCEAN.mean = cbind(DF.mega3[c(2,4,6,8,10)])
DF.OCEAN.mean.omega = omega(DF.OCEAN.mean)
DF.OCEAN.mean.mr = fa(DF.OCEAN.mean)

DF.OCEAN.SD = cbind(DF.mega3[c(3,5,7,9,11)])
DF.OCEAN.SD.omega = omega(DF.OCEAN.SD)
DF.OCEAN.SD.mr = fa(DF.OCEAN.SD)

DF.OCEAN.general.scores = cbind(DF.OCEAN.mean.mr$scores,DF.OCEAN.SD.mr$scores,
                                DF.mega3[14],DF.mega3[40],DF.mega3[42],DF.mega3[64],DF.mega3[76])
colnames(DF.OCEAN.general.scores) = c("GFP.mean","GFP.SD","S.in.Norway","S.in.Denmark","Islam","S.Int","IQ")
round(cor(DF.OCEAN.general.scores,use="pairwise.complete.obs"),2)
DF.OCEAN.general.scores.no.IQ = partial.r(DF.OCEAN.general.scores,c(1:6),7)

Megadataset is in the OSF repository, version 1.6b.

New paper out: Crime, income, educational attainment and employment among immigrant groups in Norway and Finland

openpsych.net/ODP/2014/10/crime-income-educational-attainment-and-employment-among-immigrant-groups-in-norway-and-finland/

Abstract

I present new predictive analyses for crime, income, educational attainment and employment among immigrant groups in Norway and crime in Finland. Furthermore I show that the Norwegian data contains a strong general socioeconomic factor (S) which is highly predictable from country-level variables (National IQ .59, Islam prevalence -.71, international general socioeconomic factor .72, GDP .55), and correlates highly (.78) with the analogous factor among immigrant groups in Denmark. Analyses of the prediction vectors show very high correlations (generally ±.9) between predictors which means that the same variables are relatively well or weakly predicted no matter which predictor is used. Using the method of correlated vectors shows that it is the underlying S factor that drives the associations between predictors and socioeconomic traits, not the remaining variance (all correlations near unity).

All data and source files are at the OSF repository: osf.io/emfag/

Review: Dataclysm: Who We Are (When We Think No One’s Looking) (Christian Rudder)

www.goodreads.com/book/show/21480734-dataclysm

gen.lib.rus.ec/book/index.php?md5=9d2c0744b6bcce6ec9e67625125244a8

This good is based on the popular but discontinued OKTrends blog, but now apparently active again becus of the book release. There is some more info in the book than can be found on the blog, but overall there is much more on the blog. The book is short (300 pp) and written in non-academic style with no statistical jargon. Read it if u think big data about humans is interesting. The author is generally negative about it, so if u are skeptical about it, u may like this book.

New paper out: The international general socioeconomic factor: Factor analyzing international rankings

openpsych.net/ODP/2014/09/the-international-general-socioeconomic-factor-factor-analyzing-international-rankings/

Abstract
Many studies have examined the correlations between national IQs and various country-level indexes of well-being. The analyses have been unsystematic and not gathered in one single analysis or dataset. In this paper I gather a large sample of country-level indexes and show that there is a strong general socioeconomic factor (S factor) which is highly correlated (.86-.87) with national cognitive ability using either Lynn and Vanhanen’s dataset or Altinok’s. Furthermore, the method of correlated vectors shows that the correlations between variable loadings on the S factor and cognitive measurements are .99 in both datasets using both cognitive measurements, indicating that it is the S factor that drives the relationship with national cognitive measurements, not the remaining variance.

This one took a while to do. Had to learn a lot of programming (R), do lots of analyses, 50 days in peer review. Perhaps my most important paper so far.

 

Comment on CPGGrey’s new video on the future of automatization

Posted on reddit.

 

This is your best film yet, and that says something.

For automatization for clinical decisions, it has been known for decades that simple algorithms are better than humans. This has so far not been put to much practice, but it will eventually. See review article: Grove, W. M., Zald, D. H., Lebow, B. S., Snitz, B. E., & Nelson, C. (2000). Clinical versus mechanical prediction: a meta-analysis.[1] Psychological assessment, 12(1), 19.

There is only one temporary solution for this problem. It is to make humans smarter. I say temporary because these new smarter humans will quickly make robots even smarter and so they can replace even the new smarter humans.

How to make humans more intelligent? The only effective way to do that is to use applied human genetics aka. eugenics. This is because general intelligence (g-factor) is about 80% heritable in adults (and pretty much everything else is also moderately to highly heritable). There are two things we must do: 1) Find the genes for g. This effort is underway and we have found a few SNPs so far.[1-2] It is estimated that there are about 1k-10k genes for g. 2) Find out how to apply this genetic knowledge in practice to make both existing humans and the new ones smarter. The first effective technology for this is embryo selection[2] . Perhaps CRISPR[3] can work for existing humans.

  1. Rietveld, C.A., Medland, S.E., Derringer, J., Yang, K., Esko, T., et al. (2013). GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340: 1467-1471.
  2. Ward, M.E., McMahon, G., St Pourcain, B., Evans, D.M., Rietveld, C.A., et al. (2014) Genetic Variation Associated with Differential Educational Attainment in Adults Has Anticipated Associations with School Performance in Children. PLoS ONE 9(7): e100248. doi:10.1371/journal.pone.0100248

A troublesome inheritance (Nicholas Wade)

This book is very popsci and can be read in 1 day for any reasonably fast reader. It doesnt contain much new information to anyone who has read a few books on the topic. As can be seen below, it has a lot of nonsense/errors since clearly the author is not used to this area of science. It is not recommended except as a light introduction to people with political problems with these facts.

gen.lib.rus.ec/book/index.php?md5=7a48b9a42d89294ca1ade9f76e26a63c

www.goodreads.com/book/show/18667960-a-troublesome-inheritance?from_search=true

 

But  a  drawback  o f  the  system  is  its  occasional  drift  toward
extreme  conservatism.  Researchers  get  attached  to  the  view  of their
field  they  grew  up  with  and,  as  they  grow  older,  they  may  gain  the
influence  to thwart change.  For  50  years  after it was  first proposed,
leading geophysicists  strenuously resisted the idea that the continents
have  drifted  across  the  face  of  the  globe.  “Knowledge  advances,
funeral  by funeral,”  the economist Paul  Samuelson  once  observed.

 

Wrong quote origin. en.wikiquote.org/wiki/Max_Planck

>A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.

 

Academics, who are obsessed with intelligence, fear the discovery
of  a  gene  that  will  prove  one  major  race  is  more  intelligent  than
another.  But  that  is  unlikely  to  happen  anytime  soon.  Although
intelligence has a genetic basis, no genetic variants that enhance intel­
ligence  have  yet  been  found.  The  reason,  almost  certainly,  is  that
there  are  a  great  many  such  genes,  each  of  which  has  too  small  an
effect  to  be  detectable  with  present  methods.8  If  researchers  should
one  day  find  a  gene  that  enhances  intelligence  in  East  Asians,  say,
they can  hardly argue on that  basis that East Asians are more  intelli­
gent than other races, because hundreds of similar genes remain to be
discovered  in  Europeans  and  Africans.
Even  if  all  the  intelligence-enhancing  variants  in  each  race  had
been identified, no one would try to compute intelligence on the basis
of genetic  information:  it would  be  far easier  just to  apply  an  intelli­
gence test.  But IQ  tests already  exist, for what  they may  be  worth.

 

We have found a number of SNPs already. And we have already begun counting them in racial groups. See e.g.: openpsych.net/OBG/2014/05/opposite-selection-pressures-on-stature-and-intelligence-across-human-populations/

 

 

It s social behavior that is of relevance for understanding pivotal—
and otherwise imperfectly explained— events in history and econom­
ics.  Although  the  emotional  and  intellectual  differences  between  the
world’s peoples  as  individuals are slight enough,  even a  small  shift in
social  behavior  can  generate  a  very  different  kind  of society.  Tribal
societies, for instance, are organized on the basis of kinship and differ
from  modern  states  chiefly  in  that  people’s  radius  of trust  does  not
extend too far beyond the family and tribe.  But in this small variation
is  rooted  the  vast  difference  in  political  and  economic  structures
between tribal and modern societies. Variations in another genetically
based behavior, the readiness to punish those who violate social rules,
may explain why  some societies  are  more conformist than others.

 

See: www.goodreads.com/book/show/3026168-the-expanding-circle

 

 

The  lure  of  Galton’s  eugenics  was  his  belief  that  society  would
be  better  off  if  the  intellectually  eminent  could  be  encouraged  to
have  more  children.  W hat  scholar  could  disagree  with  that?  More
of  a  good  thing  must  surely  be  better.  In  fact  it  is  far  from  certain
that  this  would  be  a  desirable  outcome.  Intellectuals  as  a  class  are
notoriously  prone  to  fine-sounding  theoretical  schemes  that  lead
to  catastrophe,  such  as  Social  Darwinism,  Marxism  or  indeed
eugenics.
By  analogy  with  animal  breeding,  people  could  no  doubt  be
bred,  if it were ethically acceptable, so  as to  enhance  specific desired
traits.  But  it  is  impossible  to  know  what  traits would  benefit  society
as a whole. The eugenics program, however reasonable it might seem,
was  basically incoherent.

 

Obviously wrong.

 

 

The  principal  organizer  of  the  new  eugenics  movement  was
Charles  Davenport.  He  earned  a  doctorate  in  biology  from  Harvard
and  taught  zoology  at  Harvard,  the  University  of  Chicago,  and  the
Brooklyn  Institute  of  Arts  and  Sciences  Biological  Laboratory  at
Cold  Spring  Harbor  on  Long  Island.  Davenport’s  views  on  eugenics
were  motivated  by  disdain  for  races  other  than  his  own:  “Can  we
build a  wall high  enough around this country so as to keep  out these
cheaper  races,  or will  it  be  a  feeble  dam  .  .  .  leaving it to  our  descen­
dants to abandon  the country to the  blacks,  browns  and  yellows and
seek  an  asylum in New  Zealand?”  he wrote.9

 

Well, about that… In this century europeans will be <50% in the US. I wonder if the sociologists will then stop talking about minority, as if that somehow makes a difference.

 

 

One  of  the  most  dramatic  experiments  on  the  genetic  control  of
aggression was performed by the Soviet scientist Dmitriy Belyaev. From
the same population of Siberian gray rats he developed two strains, one
highly sociable  and  the  other  brimming with  aggression.  For  the tame
rats, the parents of each generation were chosen simply by the criterion
of how well they tolerated  human presence.  For the  ferocious  rats, the
criterion  was  how adversely they reacted  to people.  After many gener­
ations of breeding,  the  first strain was  now so tame that when visitors
entered  the  room  where  the  rats  were  caged,  the  animals  would  press
their  snouts  through  the  bars to  be  petted.  The  other  strain  could  not
have  been  more  different.  The  rats  would  hurl  themselves  screaming
toward  the  intruder,  thudding  ferociously  against  the  bars  of  their
cage.12

 

Didnt know this one. The ref is:

N icholas  Wade,  “N ice  R a ts,  N asty  R a ts:  Maybe  I t ’s  All  in  the  G en es,”
N ew  York  Tim es, Ju ly  2 5 ,  2 0 0 6 ,  www.nytimes.com/2006/07/25/health/
25 ra ts.h tm l?p a g ew a n ted = a ll& _ r=0  (accessed  Sept.  2 5 ,  2 0 1 3 )

 

 

Rodents and humans use many of the same genes and  brain regions
to control  aggression.  Experiments with  mice  have  shown that a  large
number of genes are involved in the trait, and the same is certainly true
of  people.  Comparisons  of  identical  twins  raised  together  and  sepa­
rately  show  that  aggression  is  heritable.  Genes  account  for  between
3 7%  and 72%  of the heritability, the variation  of the trait in a  popula­
tion, according to various studies.  But very few of the genes that under­
lie  aggression  have  yet  been  identified,  in  part  because  when  many
genes control  a  behavior,  each  has  so  small  an  effect  that  it  is  hard  to
detect.  Most  research  has  focused  on  genes  that  promote  aggression
rather than those at the other end of the  behavioral  spectrum.

 

This sentence is nonsensical.

 

 

Standing  in  sharp  contrast  to  the  economists’  working  assumption
that  people  the  world  over  are  interchangeable  units  is  the  idea  that
national  disparities  in  wealth  arise  from  differences  in  intelligence.
The possibility should  not be  dismissed  out of hand:  where  individu­
als are concerned,  IQ  scores do correlate,  on average,  with economic
success, so  it is not unreasonable to inquire if the same  might  be true
of countries.

 

Marked sentence is nonsensical.

 

 

Turning to economic indicators, they find that national  IQ scores
have an extremely high correlation  (83%)  with economic growth  per
capita  and  also  associate  strongly  with  the  rate  of economic  growth
between  1950  and  1 9 9 0  (64%  correlation).44

 

More conceptual confusion.

 

 

And  indeed  with  Lynn  and  Vanhanen’s correlations,  it  is  hard to
know  which  way  the  arrow  of  causality  may  be  pointing,  whether
higher  IQ  makes  a  nation  wealthier  or  whether  a  wealthier  nation
enables  its  citizens  to  do  better  on  IQ  tests.  The  writer  Roy  Unz  has
pointed out from  Lynn and Vanhanen’s own data examples  in  which
IQ  scores  increase  10  or more points  in  a generation  when  a  popula­
tion  becomes  richer,  showing  clearly  that  wealth  can  raise  IQ
scores  significantly.  East  German  children  averaged  90  in  1 9 6 7  but
99  in  1984.  In  West  Germany,  which  has  essentially the  same  popu­
lation,  averages  range  from  99  to  107.  This  17  point  range  in  the
German  population,  from  90  to  107,  was  evidently  caused  by  the
alleviation  of poverty,  not genetics.

 

Ron Unz, the cherry picker. conservativetimes.org/?p=11790

 

 

East  Asia  is  a  vast counterexample to the  Lynn/Vanhanen  thesis.
The  populations  of China, Japan  and Korea  have consistently  higher
IQs  than  those  of Europe  and  the  United  States,  but  their  societies,
despite  their  many  virtues,  are  not  obviously  more  successful  than
those of Europe and  its outposts. Intelligence can’t hurt, but it doesn’t
seem  a  clear  arbiter  of  a  population’s  economic  success.  W hat  is  it
then  that determines  the  wealth  or poverty of nations?

 

No. But it does disprove the claim that IQs are just GDPs. The oil states have low IQs and had that both before and after they got rich on oil, and will have in the future when they run out of oil again. Money cannot buy u intelligence (yet).

 

 

From  about  9 0 0  a d   to  1700  a d ,  Ashkenazim  were  concentrated
in  a  few  professions,  notably  moneylending  and  later  ta x  farming
(give  the prince  his  money  up  front,  then  extract the  taxes  due  from
his  subjects).  Because  of  the  strong  heritability  of  intelligence,  the
Utah team calculates that 20 generations, a mere 5 0 0 years, would be
sufficient for Ashkenazim to have developed an  extra  16 points of IQ
above that of Europeans. The Utah team assumes that the heritability
of  intelligence  is  0 .8 ,  meaning  that  8 0 %  of the  variance,  the  spread
between high and low values in a population, is due to genetics. If the
parents of each generation have an  IQ of just  1  point above the mean,
then  average  IQ  increases  by  0 .8 %  per  generation.  If  the  average
human  generation  time  in  the  Middle Ages was  2 5  years,  then  in  20
human  generations,  or  5 0 0  years,  Ashkenazi  IQ  would  increase  by
2 0  x  0.8  =  16  IQ  points.

 

More conceptual confusion. One cannot use % on IQs becus IQs are not ratio scale and hence division makes no sense. en.wikipedia.org/wiki/Levels_of_measurement#Comparison

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

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

openpsych.net/ODP/2014/05/educational-attainment-income-use-of-social-benefits-crime-rate-and-the-general-socioeconomic-factor-among-71-immmigrant-groups-in-denmark/

The IQ Controversy, the Media and Public Policy (1988, Snyderman, Rothman)

www.goodreads.com/book/show/2404700.The_IQ_Controversy_the_Media_and_Public_Policy

The I. Q. Controversy The Media and Public Policy Stanley Rothman 323p_0887381510

 

I read this becus i want to do a follow-up study like this. Both analyzing media output and doing another expert survey.

 

 

I had been thinking about using PCA on political questions to see any obvious underlying structure. Basically, I want to do OKC questions style. Gather lots of questions, have lots of ppl answer them. Do PCA, see what results are.

Political perspective was assessed in two ways. First, respondents stated their agreement or disagreement with a series of six political statements. The statements dealing with U.S. economic exploitation, the fairness of the private enterprise system, affirmative action, the desirability of socialism, alienation caused by the structure of society, and the propriety of extramarital sexual relations. Responses to these statements were discovered, in a previous investigation incorporating many more such statements, to load highly on a factor representing overall political perspective.6o Agreement was assessed on a 4- point scale, where I was “Strongly agree” and 4 was “Strongly disagree.” For four of the six statements, the mean response is approximately at indifference. Respondents are somewhat more likely to disagree that “The United States would be better off if it moved toward socialism” and that “The structure of our society causes most people to feel alienated.” The second measure of political perspective asked experts to indicate their global political perspective on a 7-point scale, where I was “Very liberal” and 7 was “Very conservative.” Mean self-assessment on this scale is 3.19 (s.d.: 1.28, r.r.:95.6%), putting this expert population slightly to the left of center.

Factor analysis of responses to the six statements and the global rating reveal that all questions, with the exception of the statement about extramarital affairs, load highly on a single factor (i.e., are highly correlated). The five statements and the global rating were therefore normalized and combined to form a political perspective supervariable. It is this variable that is used as a measure of overall political perspective. Note that the liberal position on the five included statements (e.9., belief in socialism, affirmative action, economic exploitation) can all be characterized as placing a higher value on equality of outcome than on economic efficiency.

This tactic has been used before, even if only on a limited set of political opinions.

-

While few would argue that intelligence and aptitude test scores do nor affect self-esteem and motivation, the magnitude of this influence is difficult to measure. There have been many reports of significant positive correlations between test scores and self-concept, motivation, or expectancy, but causality remains ambiguous.rs rhe evidence seems to indicate, however, that the influence of test scores on these affective variables is probably not large. (Causation in the opposite direction may not be very significant either, as the correlation may reflect the influence of a third variable, students’actual level ofability and success in school.) Brim and his associates found that high school students tended to greatly overestimate their own intelligence, as measured by test scores. This was particularly true of students with low scores. Fifty percent of students thought their scores were too low relative to their actual level of ability, while 45 percent thought their scores were accurate. only 7 percent ofthe students reported lowering their self-estimates of intelligence as a result of their test scores, while 24 percent raised their estimates.16

Dunning Kruger, but much earlier.

Reference 16 is: Orville G. Brim, Jr., ‘American Attitudes Towards Intelligence Tests,” American Psychologrsl 20 (1965):125-130; Brim et al. 17. Goslin, p. 133