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).

Full text

Submitted: 27th of August 2014
Published: 9th of October 2014
Crime, income, educational attainment and employment
among immigrant groups in Norway and Finland
Emil O. W. Kirkegaard*
Open Differential
Psychology
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).
Keywords:
National IQ, intelligence, group differences, country of origin, Norway, Finland, Denmark, im-
migration, crime, spatial transferability hypothesis, income, employment, educational attainment, general
socioeconomic factor, Islam, method of correlated vectors
1 Introduction
Recent studies show that criminality and other socioe-
conomic traits such as educational attainment among
immigrant groups is predictable from their country
of origin[
1
,
2
,
3
,
4
]. This study attempts to replicate
and generalize these findings.
The theoretical impetus for testing country-level pre-
dictors
1
is the spatial transferability hypothesis.[
5
] In
brief, it proposes that:
1.
A country’s performance on a variety of metrics is
due to some degree to the psychological makeup
of its inhabitants;
2.
People retain their psychological attributes to
some degree when they migrate; and hence
3.
The psychological attributes of groups determine
to some degree their relative performance on a
variety of socioeconomic variables, such as crime,
*
Department of Culture and Society, University of Aarhus, E-mail:
emil@emilkirkegaard.dk
1
A note about terminology. ”predictor” is used here to mean the
same as ”independent variable”. No causality is implied, merely
linguistic convenience.
educational attainments, income, and employ-
ment rate, in the countries that receive them.
For instance, when people from a poorer country
move to a wealthier country, they will tend to be
relatively poor in that country as well. This is because
part of the reason the country is poor to begin with
is that the people living there are low (or high) in
the relevant behavioral traits. When they move to a
new country, they will generally still be low in the
relevant behavioral traits, and this will cause them
to be relatively poor in that country as well. This is
of course still allowing for other causes (e.g. culture
or religion people tend to bring with them) as well
as improvements on an absolute scale. Somalis liv-
ing in Denmark are far richer than those who have
stayed behind in Somalia, but they are nonetheless
poorer than ethnic Danes, just as Somalia is poorer as
a whole than Denmark. Note also that the hypothesis
does not specify why these traits tend to be preserved.
Both genetic and non-genetic models are possible.
The most obvious way of testing the spatial trans-
ferability hypothesis involves looking at immigrant
performance on a variety of measures grouped by
country of origin, and then checking how predictable
this performance is from country-level variables such
1

Published: 9th of October 2014 Open Differential Psychology
Figure 1:
Violent crime in Norway and Finland by country
of origin. From [6].
as national IQ and national prevalence of Islam. In
this study I explore a number of datasets in this fash-
ion.
2 Dataset 1: Norway and Finland
Skardhamar et al (2014)[
6
] presented new crime data
for immigrant groups by country and macro-region of
origin for Norway and Finland and compared the two
countries. In doing so they adjusted for both gender
ratio and age structure in the populations. Their main
findings are shown in Figures 1and 2.
These findings indicate that people from the same
areas of origin are similarly disposed to criminal be-
haviour in Norway and Finland.
3 Dataset 2-4: Norway
I took a closer look at Statistisk Sentralbyrå’s (SSB)
2
website for data that could be useful for testing the
spatial transferability hypothesis. To be useful, the
data must concern a variable of considerable social
interest and contain information on immigrant group
performance by country of origin with at least a small
sample (my threshold was ≥10) of countries.
I searched for ”landbakgrunn” (country background)
on the website
3
, limited the results to publicly avail-
2The official statistics bureau of Norway. http://www.ssb.no/
3
I could not find out how the SSB determines the origin country.
Which country does a person who has a Swedish and a Norwe-
gian parent count as? Likewise there is no information about
immigrant generation.
Figure 2:
Property crime (larceny) in Norway and Finland
by country of origin. From [6].
able datasets (”statistikbanken”) and looked through
all 124 results. I identified three useful datasets:
1.
Income after tax, measured as a percent of the
national mean (income index).
4
No information
about age is given. I included all available coun-
tries (N=23). Generally, SSB limits the available
countries to the ones with samples large enough
to give reliable results.
2.
Registered unemployed persons by sex aged 15-
74, as a percent of the working population.
5
As
before I included all available countries (N=120)
and both sexes separately. There is no fine-
grained age information, so the data are not well-
adjusted for age.
3.
Tertiary educational attainment per capita for
persons aged 16 and above in 2013.
6
This table
was in absolute numbers, so I supplemented it
with the population size by country of origin to
calculate a pseudo per capita value.
7
The rea-
son it is a pseudo per capita is that population
sizes were not available by age groups, so I had
to use the entire age group, even though the edu-
cational attainment data concerned only people
aged 16 and above. This introduces error if the
age structures are different between groups. The
data are also not broken down by gender, so there
is possibly gender ratio bias as well. To examine
4
Tabell: 10489: Innvandreres inntekt etter skatt per forbruksen-
het, etter landbakgrunn
5
Tabell: 07117: Registrerte arbeidsledige 15-74 år, etter landbak-
grunn og kjønn. Absolutte tall og i prosent av arbeidsstyrken
6
Tabell: 09623: Innvandrere 16 år og over, etter utdanningsnivå
og landbakgrunn. Absolutte tall
7Tabell: 05184: Innvandrere, etter kjønn og landbakgrunn
2

Published: 9th of October 2014 Open Differential Psychology
effects of including small samples, I used two dif-
ferent versions of this variable. The first includes
all groups with a population
≥
200 (N=118). The
second only includes groups with
≥
1000 to re-
duce sampling error (N=67).
4 Predictive analyses
I did all analyses with R.
8
The primary question was
whether crime was predictable from country-level
variables as previously found. To test this, I used the
following predictors in a correlation analysis:
•
Prevalence of Islam in 2010 (as estimated by the
Pew Research Center).[7]
•
Lynn and Vanhanen’s national IQs with changes
based on the work of Jason Malloy. When a value
is changed, it is noted in the datafile.[8,9]
• Altinok’s educational achievements.[10]
• The World Bank’s GDP per capita (2013).[11]
•
Kirkegaard’s country-level general socioeco-
nomic (S) factor scores.[12]
Table 1shows the correlations of interest. Generally,
all predictors do well when two conditions are satis-
fied:
1.
The sample of countries is large enough to have
significant inter-country variation.
2.
The sample of countries is not so large as to in-
troduce significant sampling error in estimates.
The reason this introduces error is that the more coun-
tries covered in a variable means that the value must
be based on a smaller sample of persons from that
country.
Findings of note include: Violent crime is easier
to predict than property crime, just as in the Dan-
ish dataset.[
1
] The poor predictive ability of Altinok
with the crime and income variables seems to be due
to sampling error (N’s 13-14). The educational at-
tainment variable which includes only large samples
(”Tert. Ed. Att. Big”) has higher correlations than
the one with smaller samples too. This is probably
because the smaller ones introduce sampling error.
Islam is a better predictor of female unemployment
than of male, which may be related to the role of
women in Islam.
8
R is a free, powerful, easy to use programming language de-
signed for data mining and statistics. See
http://www.r-proje
ct.org/.
The intercorrelations between predictors is shown in
Appendix C. IQ, Altinok, logGDP and international
S have high intercorrelations, with a minimum of
.72 and a mean of .84. Islam correlates weakly to
moderately with the others (-.14 to -.43, mean -.29).
4.1 Predictor vector intercorrelations
Are some predictors just generally better at predict-
ing than others, or is there specificity such that while
predictor A may be better at predicting outcome X,
predictor B is better at predicting outcome Y? An ex-
ample of this would be that the prevalence of Islam
predicts crime better than (national) IQ at predicting
crime, while IQ is better at predicting educational
attainment. To investigate this, I correlated the pre-
diction vectors (rows in Table 1) for each predictor
with the vectors of each other predictor. Correlations
at
±
1 indicate that predictor performance is general,
while correlations near
±
0 indicate specificity. Table 2
shows the results.
Surprisingly, even though there are problems with
small sample sizes of the predictive correlations and
the length of the vectors (N=9), the results strongly
suggest that what is well-predicted by one predictor
is also well-predicted by other predictors, no matter
which two were compared. The mean abs. r=.92.
5 A general socioeconomic factor among
immigrant groups in Norway
Similarly to my previous study of immigrant groups
in Denmark[
3
], I wanted to investigate the possibility
of a general socioeconomic factor at the group level
(S factor).[
12
] To do this, I used all the variables con-
cerning Norway except for the educational attainment
with smaller groups to avoid duplicating variables.
5.1 Handling missing values
Factor analytic methods require that there are no miss-
ing values. The easiest and most common way to deal
with this is to limit the data to the subset with com-
plete cases. This, however, produces biased results
if the data are not missing completely at random,
which they rarely are. Furthermore, it heavily re-
duces sample sizes. Lastly, it wastes non-redundant
information and potentially resources spent gather-
ing it. If a case has values for 5 out of 6 chosen vari-
ables, removing the case wastes 5 pieces of useful
information.[
13
,
14
,
15
,
16
] Table 3shows the distri-
bution of missing values.
For the above reasons, I used four methods for han-
dling missing cases:
3

Published: 9th of October 2014 Open Differential Psychology
Table 1: Correlation matrix for country-level predictors and socioeconomic variables.
Table 2: Correlation matrix of predictor vectors. N=9
Var Altinok.cors Islam.cors GDP.cors S.cors
IQ.cors 0.83 -0.99 0.93 0.96
Altinok.cors -0.86 0.9 0.92
Islam.cors -0.92 -0.97
GDP.cors 0.97
Table 3:
The distribution of missing values in the Norwe-
gian dataset.
Number of
missing values
Number of
cases
0 15
1 3
2 8
3 41
4 61
6 141
1. Complete cases only (N=15)
2.
Imputing
9
data in cases with 1 missing value
(N=18)
3.
Imputing data in cases with 2 or fewer missing
values (N=26)
4.
Imputing data in cases with 3 or fewer missing
values (N=67)
Table 4shows descriptive statistics for each dataset.
The imputed datasets are similar to both the full
datasets and the complete cases although there were
changes in both the skew and kurtosis.
KMO tests show that all datasets are suitable to fac-
tor analysis, KMO’s .68-.75. Note that the method of
imputation used is probabilistic, i.e. does not result
in the same imputation every time. Therefore, any re-
searcher who replicates the analyses will find that the
9
I used the
VIM
package 4.00. The irmi() function imputes
values.[
17
] I used the default settings.
http://cran.r-project
.org/web/packages/VIM/index.html.
numbers deviate somewhat from the shown results.
The KMO values were always around these values in
my tests.
5.2 Number of factors to extract
To find out how many factors to extract, I ran nScree()
from the
nFactors
package.
10
For each dataset, all
four tests within that function suggested to extract
only one factor.
5.3 Strength of the general factor
Previous studies show that principal component anal-
ysis tends to overestimate factor loadings when used
on a small number of variables, but that other factor
methods yield very similar results.[
12
,
18
,
19
,
20
] I
used minimum residuals (the default) to extract the
first factor from each dataset.11
Revelle and Wilt[
21
] showed that one cannot solely
rely on the size of the first factor in a normal analysis
as a measure of the strength of the general factor.
They advocate five other methods, of which I have
used four here:
1. Hierarchical omega and its asymptotic value
2.
The amount of variance accounted for by the first
factor in a Schmid-Leiman transformation
10
Version 2.3.3
http://cran.r-project.org/web/packages/nF
actors/index.html.
11
I used the fa() function from
Psych
package.
http://cran
.r-project.org/web/packages/psych/index.htmlVersion:
1.4.8.11
4

Published: 9th of October 2014 Open Differential Psychology
Table 4: Descriptive stats by dataset
Var name Dataset n mean sd min max skew kurtosis
Violent crime Full 26 1.31 0.87 0.2 3.2 0.55 −0.83
Complete cases 15 1.41 0.99 0.2 3.2 0.39 −1.25
Impute 1 18 1.33 0.93 0.2 3.2 0.57 −0.94
Impute 2 26 1.28 0.81 0.2 3.2 0.7−0.27
Impute 3 67 1.23 0.63 0.2 3.2 1.05 1.41
Larceny Full 26 0.77 0.56 0.1 2 0.56 −1.09
Complete cases 15 0.78 0.55 0.2 1.6 0.38 −1.74
Impute 1 18 0.72 0.53 0.1 1.6 0.55 −1.42
Impute 2 26 0.69 0.56 −0.29 1.96 0.62 −0.58
Impute 3 67 0.69 0.34 0.1 1.6 0.71 0.19
Tert. ed. att. Full 67 0.12 0.08 0.01 0.31 0.42 −0.91
Complete cases 15 0.1 0.07 0.01 0.23 0.39 −1.32
Impute 1 18 0.1 0.07 0.01 0.23 0.5−1.12
Impute 2 26 0.09 0.07 0.01 0.24 0.75 −0.63
Impute 3 67 0.12 0.08 0.01 0.31 0.42 −0.91
Unemployment, men Full 120 7.05 4.18 1.38 22.08 1.26 1.69
Complete cases 15 7.4 5.36 2.68 22.08 1.38 1.18
Impute 1 18 7.31 4.89 2.68 22.08 1.57 2.16
Impute 2 26 6.88 4.3 2.66 22.08 1.8 3.72
Impute 3 67 6.81 4.11 1.66 22.08 1.39 2.18
Unemployment, women Full 120 7.5 4.97 1.32 31.82 1.92 5.11
Complete cases 15 8.9 6.2 1.98 22.42 0.83 −0.58
Impute 1 18 8.17 5.93 1.9 22.42 1.03 −0.04
Impute 2 26 7.4 5.25 1.56 22.42 1.3 1.14
Impute 3 67 7.41 5.39 1.32 31.82 1.97 5.23
Income Full 23 79.86 14.58 53.25 108.25 −0.01 −0.92
Complete cases 15 78.78 14.78 53.25 108.25 0.19 −0.78
Impute 1 18 82.4 16.3 53.25 112.29 0.11 −0.95
Impute 2 26 79.09 13.9 53.25 108.25 0.13 −0.75
Impute 3 67 81.71 13.86 31.96 108.25 −0.76 0.95
3. The explained common variance
4.
the squared multiple correlation of regressing
the first factor on the original variables.12
Table 5shows the comparison statistics including data
from the reanalysis of the Danish data presented in
the next section.
The data makes it clear that the S factors at the group-
level among immigrants in Norway and Denmark
are very strong, even compared to the international
S factor and the general factor of cognitive ability (g)
in 5 classic datasets. The imputation of data has little
12
I used the omega() function from
Psych
package to extract the
information.
effect on the measures of general factor strength.
6 Reanalysis of immigrant performance
in Denmark
To better investigate the question of the strength
of the S factor within another country, I repeated
the analyses discussed above on the dataset from
Kirkegaard and Fuerst (2014)[
3
]. Since I used the
same methods on this dataset as I did on the Norwe-
gian ones discussed above, I will keep the description
short.
I analyzed the data with the fa() and omega() functions
just as before. Results are shown in Table 5above.
KMO is .73 in the complete cases dataset and .83 in
the imputed dataset.
5

Published: 9th of October 2014 Open Differential Psychology
Table 5:
Measures of general factor strength. The cognitive and personality data is from Revelle and Wilt (2013)[
21
],
the international S factor data is from Kirkegaard (2014)[
12
], and the Danish comparison data is from a reanalysis of the
datasets from Kirkegaard and Fuerst (2014)[3] presented in the next section.
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 0.60 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
International 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
6.1 Handling missing values
There are a few missing values in the dataset. I used
two methods to deal with this:
1. Complete cases (N=31)
2.
Imputation via the
VIM
package for cases with 4
or fewer missing values (N=70)
Table 6shows the distribution of missing values.
Table 6:
Distribution of missing values in the Danish
dataset.
Number of
missing values
Number of
cases
0 31
1 9
2 23
3 6
4 1
23 1
6.2 Predictor vector intercorrelations
I repeated the analysis from Section 4.1 on the Dan-
ish data. The Danish data allows for a better test of
the general vs. specificity models. This is because
the Danish data has more variables (25 instead of 9;
Appendix Bcontains a list of the variables), and they
include more countries (N’s close to 70), and they
are age controlled. Table 7shows the correlations
between prediction vectors.
The correlations are even closer to unity than they are
in the Norwegian data. The mean abs. r=.97. This
Table 7:
Correlation matrix of predictor vectors in the
Danish data. N=25
Predictors Altinok Islam logGDP S.score
IQ 0.99 -0.96 0.98 0.99
Altinok -0.94 0.98 0.98
Islam -0.94 -0.96
logGDP 0.99
is probably because the error sources are smaller in
these data.
7 Predictive analyses of S scores
I wanted to know how well S factor scores in Norway
are predictable from the country-level predictor vari-
ables. Table 8shows the correlation matrix with the
Danish scores as a comparison.
The results indicate that S factor scores are about
equally predictable by predictor values in the full
Danish and Norwegian datasets. The size of the corre-
lation decreases with the amount of imputation and
increasing sample size. This may be because the im-
putation introduces error or that the correlations are
artificially high due to sampling error. The only dis-
crepancy is the predictive power of the National S
(.54 vs. .72). I don’t have any good guess for why this
is.
8 Method of correlated vectors
Arthur Jensen invented the method of correlated vec-
tors (MCV) in 1983 to find out if g is responsible
for mean differences in measures of intelligence.[
22
,
23
,
24
] Today, the method is mostly used with g
(e.g. [
25
,
26
,
27
,
28
]), and in context to mean differ-
ences, but it can be used for any latent variable and
6

Published: 9th of October 2014 Open Differential Psychology
Table 8:
Correlation matrix of predictor variables and S factor scores in Denmark (with imputed values) and the four
Norwegian datasets with varying amounts of imputation.
Variable DA S imp. NO S complete NO S imp. 1 NO S imp. 2 NO S imp. 3
National IQ 0.54 0.72 0.73 0.66 0.59
Altinok 0.55 0.26 0.31 0.2 0.6
Islam −0.71 −0.79 −0.79 −0.72 −0.71
log(GDP) 0.51 0.35 0.4 0.44 0.55
National S 0.54 0.7 0.71 0.63 0.72
DA S imp. 0.91 0.91 0.79 0.78
NOS complete 1 0.99 0.99
NO S imp. 1 0.99 0.99
S imp. 2 0.99
in other contexts such as prediction of grade point
averages.[
29
] I have previously used it on interna-
tional rankings data and found that the international
S factor was driving the correlations with predictors
such as national IQs.[12]
To apply the method, one correlates the indicator vari-
ables’ (IVs) loading on the latent variable of interest
with their correlations with the criteria variable. If
the general factor is ’driving’ the association and is
positively correlated with the criteria variable, then
the correlation between factor loadings on it and the
effect sizes of the predictor-criterion associations will
be positive. However, if the association is driven by
the variance not attributable to the general factor, the
correlation will generally be negative. And it will
generally be somewhere in between if the association
is driven by a mix of general and non-general factors.
Since the method relies on the IVs of the latent vari-
able, it is susceptible to IV sampling error. There are
three main sources of error:
1.
When the number of IVs is small, the correlation
will be unstable from sample to sample.
2.
If the IVs are unrepresentative of the total popu-
lation of IVs, then the correlation can be biased
either way.
3.
If the variance in IVs’ loadings on the latent
variable is restricted, the correlations will be
smaller.[30]
The N
IV
is quite small for the Norwegian data (6),
but reasonable for the Danish data (25). The standard
deviation of loadings in the Norwegian and Danish
datasets are .83 and .75, respectively, so range re-
striction does not appear to be a problem. The IVs
are reasonably representative of things considered so-
cioeconomically important, especially for the Danish
dataset. Table 9shows the MCV correlations.
Table 9:
Results from method of correlated vectors applied
to the S factor in Norway and Denmark. Note that Islam
prevalence has been reversed because it is negatively related
to the S factor.
Predictor Norway (N=6) Denmark (N=25)
IQ 0.99 0.97
Altinok 0.95 0.95
Islam 0.99 0.99
logGDP 1 0.95
Int. S factor 0.99 0.96
In every case, the result is close to unity. Strong cor-
relations can result from nonlinearity in the data, so
I examined the scatterplots. However, they were all
very linear.
9 Discussion
The simple predictive analyses give results similar
to those found earlier. They serve as a successful
replication and generalization to two new countries.
The analyses of general factor strength show that the
local S factors are generally very strong, surpassing
even the g factor and the international S factor. This is
due in part to the grouped nature of the data as group
correlations tend to go towards
±
1 when there aren’t
sampling errors or a non-linear relationship.[31]
Surprisingly, the analysis of the predictor vectors
show very high near unity intercorrelations. The re-
sults were even stronger in the reanalyzed Danish
data, which makes it probable that the somewhat
lower correlations in the Norwegian data are due to
statistical artifacts. I interpret this as showing that
predictors are very general in their predictive ability.
In accordance with Occam’s Razor, causal theories
of these correlations should be similarly general, not
specific.
7

Published: 9th of October 2014 Open Differential Psychology
Applying the method of correlated vectors showed
that the relationships between the socioeconomic vari-
ables and the predictor variables were driven by the
latent trait (S factor), not the remaining variance (cor-
relations near unity).
Generally the results strongly confirm the spatial
transferability hypothesis.[5]
Limitations include the small sample sizes and the
lack of adjustment for age and sex in some of the
variables. This probably introduces some bias in an
unknown direction. Note however that the Danish
data is age-controlled, and yet the results are very
similar to the Norwegian ones, showing that bias due
to age is unlikely to be a large source of error.
Supplementary material
All datasets and source code are available in the sub-
mission thread at
http://openpsych.net/forum/s
howthread.php?tid=136
and on OSF
https://osf.i
o/emfag/
. Most of the data used in the study can be
found in version 1.5 extra of the Worldwide Mega-
dataset.
The appendix contains a list of S scores by group in
Norway and Denmark.
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A S factor scores by country
Table 10: S factor scores by country.
Name ID S Factor in NO S factor in DK
Afghanistan AFG −1.09 −1.38
Argentina ARG 0.75
Australia AUS 1.03 1.13
Austria AUT 1.02 0.95
Burundi BDI −0.54
Belgium BEL 1.16 1.09
Bulgaria BGR 0.17 0.81
Bosnia and Herzegovina BIH 0.49 −0.91
Brazil BRA −0.34 0.46
Canada CAN 1.03 1.14
Switzerland CHE 1.13 1.12
Chile CHL 0.25 0.28
China CHN 0.61 0.63
Congo Rep. COG −1.07
Colombia COL 0.26
Czech Republic CZE 0.43 0.25
Germany DEU 1.04 0.85
Denmark DNK 1
Algeria DZA −1.52 −0.78
Egypt Arab Rep. EGY −0.24
Eritrea ERI −0.43
Spain ESP 0.52 0.79
Estonia EST 0.19 0.72
Ethiopia ETH −0.16 −0.59
Finland FIN 0.78 0.89
France FRA 0.97 1.1
United Kingdom GBR 1.14 0.85
Ghana GHA 0.03 0.16
Gambia The GMB −0.84
Greece GRC 0.61 0.61
Croatia HRV 0.54 −0.12
Hungary HUN 0.45 0.84
Indonesia IDN 0.33 0.13
India IND 0.63 0.53
Ireland IRL 0.88
Iran Islamic Rep. IRN −0.35 −0.69
Iraq IRQ −2.26 −1.65
Iceland ISL 0.76 0.55
Israel ISR −0.06
Italy ITA 0.86 0.77
Jordan JOR −1.19
Japan JPN 1.02
Kenya KEN −0.24 0.09
Kosovo KSV −0.43
Kuwait KWT −2.62
Lebanon LBN −1.03 −2.03
Continued on next page
10

Published: 9th of October 2014 Open Differential Psychology
Table 10 – continued from previous page
Name ID S Factor in NO S factor in DK
Sri Lanka LKA −0.14 −0.75
Lithuania LTU −0.08 0.9
Latvia LVA 0.06 0.68
Morocco MAR −0.63 −1.03
Macedonia FYR MKD −0.19 −0.44
Myanmar MMR −0.27 −1.81
Nigeria NGA −0.53 0.34
Netherlands NLD 1.11 1.12
Norway NOR 0.84
Nepal NPL 0.75
Pakistan PAK −0.87 −0.68
Peru PER 0.1
Philippines PHL 0.58 0.36
Poland POL −0.02 0.46
Portugal PRT 0.54 0.63
West Bank and Gaza PSE −3.8
Romania ROU 0.31 0.7
Russian Federation RUS −0.44 0.45
Sudan SDN −1.52
Somalia SOM −3.06 −2.05
Serbia SRB 0.46 −1.93
USSR SUN 0.17
Slovak Republic SVK 0.42
Sweden SWE 1.03 0.77
Syrian Arab Republic SYR −1.62 −2
Thailand THA −0.03 −0.23
Tunisia TUN −0.82
Turkey TUR −0.52 −1.42
Tanzania TZA −0.25
Uganda UGA −0.34
Ukraine UKR 0.34 0.69
United States USA 0.97 1.26
Vietnam VNM −0.11 −0.58
Former Yugoslavia2 YU2 −1.61
Former Yugoslavia YUG −1.25
South Africa ZAF 0.73
11

Published: 9th of October 2014 Open Differential Psychology
B List of variables in the Danish dataset
Table 11: List of variables in the Danish dataset.
Variable name
All.crime.age.15.19
All.crime.age.20.29
Income.15.19
Income.20.29
Income.30.39
Income.40.49
Income.50.59
Income.60
Basic.school.15.19
Basic.school.20.29
Basic.school.30.39
Basic.school.40.49
Basic.school.50.59
Basic.school.60plus
Long.tert.edu.20.29
Long.tert.edu.30.39
Long.tert.edu.40.49
Long.tert.edu.50.59
Long.tert.edu.60plus
Social.benefits.16.19
Social.benefits.20.29
Social.benefits.30.39
Social.benefits.40.49
Social.benefits.50.59
Social.benefits.60plus
12

Published: 9th of October 2014 Open Differential Psychology
C Intercorrelations between predictors
Table 12:
The low correlations between Islam and the others is not due to sampling fluctuation. N’s from 116 to 198. The
full correlation matrix can be found in the supplementary material ”correla- tions Norway2014.xlsx”.
Vars Altinok Islam logGDP International S
IQ 0.91 −0.27 0.72 0.86
Altinok −0.43 0.76 0.87
Islam −0.14 −0.33
logGDP 0.9
13