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Net fiscal contributions of immigrant groups in Denmark and Finland are highly predictable from country of origin IQ and Muslim%

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The relationships between national IQs, Muslim% in origin countries and estimates of net fiscal contributions to public finances in Denmark (n=32) and Finland (n=11) were examined. The analyses showed that the fiscal estimates were near-perfectly correlated between countries (r = .89 [.56 to .98], n=9), and were well-predicted by national IQs (r’s .89 [.49 to .96] and .69 [.45 to .84]), and Muslim% (r’s -.75 [-.93 to -.27] and -.73 [-.86 to -.51]). Furthermore, general socioeconomic factor scores for Denmark were near-perfectly correlated with the fiscal estimates (r = .86 [.74 to .93]), especially when one outlier (Syria) was excluded (.90 [.80 to .95]). Finally, the monetary returns to higher country of origin IQs were estimated to be 917/470 Euros/person-year for a 1 IQ point increase, and -188/-86 for a 1% increase in Muslim%.
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Submitted: 24th of April 2017
Published: 21st of May 2017
Net fiscal contributions of immigrant groups in Denmark
and Finland are highly predictable from country of origin IQ
and Muslim %
Emil O. W. Kirkegaard*
Open Quantitative
Sociology & Political
The relationships between national IQs, Muslim % in origin countries and estimates of net fiscal contributions to public
finances in Denmark (n=32) and Finland (n=11) were examined. The analyses showed that the fiscal estimates were
near-perfectly correlated between countries (r = .89 [.56 to .98], n=9), and were well-predicted by national IQs (r’s .89 [.49
to .96] and .69 [.45 to .84]), and Muslim % (r’s -.75 [-.93 to -.27] and -.73 [-.86 to -.51]). Furthermore, general socioeconomic
factor scores for Denmark were near-perfectly correlated with the fiscal estimates (r = .86 [.74 to .93]), especially when one
outlier (Syria) was excluded (.90 [.80 to .95]). Finally, the monetary returns to higher country of origin IQs were estimated
to be 917/470 Euros/person-year for a 1 IQ point increase, and -188/-86 for a 1 % increase in Muslim %.
immigration, country of origin, national IQ, IQ, intelligence, cognitive ability, Muslims, Islam,
public finances, inequality
1 Introduction
A number of recent studies have shown that coun-
try of origin characteristics are moderately to very
predictive of immigrant group performance, broadly
speaking (Fuerst & Kirkegaard,2014;Jones & Schnei-
der,2010;Kirkegaard & Becker,2017;Kirkegaard &
Fuerst,2014;Rindermann & Thompson,2014). The
studies have examined a number of socioeconomic
outcomes which can be usefully grouped into four
1) employment/benefits use
2) income/wealth
3) crime
4) educational attainment1
Studies of immigrant performance have often been
limited to just a single type of variable, most com-
monly crime (e.g. Kirkegaard & Becker 2017). The
analyses are dependent on data availability and some
Ulster Institute for Social Research, E-mail:
One might want to split the employment and benefit use out-
comes, but due to most benefits being related to unemployment,
they are very strongly correlated.
data types are harder to find than others. Still, it has
generally been found that two key characteristics of
origin countries predict immigrant performance: na-
tional IQ and Muslim % (the percentage of Muslims
in the population). These can broadly be thought of
as measuring ‘can do’ and ‘will do’ factors, and will
be discussed further below. No formal meta-analysis
yet exists of the predictive validities, but in the stud-
ies with the best quality data, the correlations be-
tween outcomes and the two predictor variables were
usually in the .45 to .70 region (Kirkegaard,2014a;
Kirkegaard & Fuerst,2014).
When variables from multiple categories are present
in a single dataset, they can be factor analyzed. This
has so far invariably revealed a strong general fac-
tor which can be taken to reflect general socioeco-
nomic performance. For this reason, it was named
the S factor by analogy with the g/G factor of cogni-
tive ability (Kirkegaard,2014b;Rindermann,2007).
S emerges not just when the units of analysis are
immigrant groups in a given host country, but also
when they are sovereign nations (Kirkegaard,2014b),
sub-national divisions (states, regions, counties, cen-
sus tracts, city districts etc.; (Carl,2016;Fuerst &
Kirkegaard,2016;Kirkegaard,2016a,b)), first names
(Kirkegaard & Tranberg,2015b), and individuals
Published: 21st of May 2017 Open Quantitative Sociology & Political Science
(Kirkegaard & Fuerst,in print). When a dataset allows
analyses at multiple levels and for multiple groups, it
has been found that the factor structure is very stable
(Kirkegaard,2016a;Kirkegaard & Fuerst,in print).
While the S factor is a useful measure of overall immi-
grant performance, it is not yet known how well it cor-
responds to more traditional economic concepts such
as net fiscal contribution to public finances. Thus, the
purpose of this study was to examine the relationship
between immigrant group fiscal contributions, other
socioeconomic outcomes (S indicators) and country
of origin characteristics.
2 Data
2.1 Fiscal estimates for Denmark
Estimates of the fiscal eects of immigrants usually
group countries into very broad groups that prevent
disaggregation to the country-level. For instance,
Hansen et al. (2015) estimated the net fiscal contribu-
tion to public finances of five broad groups in Den-
Ethnic Danes (etniske danskere)
First generation Western immigrants (vestlige
Later generation Western immigrants (vestlige
First generation non-Western immigrants (ikke-
vestlige indvandrere)
Later generation non-Western immigrants (ikke-
vestlige efterkommere)
Every person with legal residence in Denmark is clas-
sified into a single group. If the person is born in a
foreign country, he is classified as a first generation
immigrant. If the person is born in Denmark, but
neither parent is both a Danish citizen and born in
Denmark, he is classified as a later generation immi-
grant. If at least one parent is both a Danish citizen
and born in Denmark, then he is classified as ethnic
It should be noted that while most categories are im-
mutable, the ‘later generation’ groups are not. If a
person is born in Denmark to parents who are born
in Denmark but lack citizenship, he is classified as a
later generation immigrant. However, if one or both
parents later gains Danish citizenship, the person
will be reclassified to the ethnic Dane category.
search indicates that a substantial number of persons,
This interpretation was confirmed in a personal e-mail from
Dorthe Larsen (22
of Feb. 2017), who is responsible for the
population data at the Danish statistics agency.
Figure 1:
Net fiscal contribution by age (2013 cohort).
Source: Hansen et al. (2015).
perhaps 400k, of non-Danish (genetic) ancestry have
been classified into the ethnic Dane category. This
would constitute about 8 % of the group, so inter-
pretation of this category is increasingly problematic
(Kirkegaard,2017;Kirkegaard & Tranberg,2015a;
Nyborg,2012). In 2014, the Danish statistics agency
published findings about a set of 16.3k third genera-
tion immigrants (børn af efterkommere). 88 % were
classified as ethnic Danes and 90 % had non-Western
ancestors. They were too young to allow reliable anal-
yses of many outcomes, but the data indicated that
they performed no dierently than the second genera-
tion in school grades, secondary education attainment.
There was an uncertain but possible increase in the
female employment rate (Danmarks Statistik,2014).
Hansen et al. (2015) estimated the net fiscal contri-
butions by estimating the total revenue and the total
expenses to the public budget. This was done by es-
timating revenue from sources such as income tax,
sales tax, and interests on stocks, and expenses from
sources such as individual benefits and use of pub-
lic services (e.g. hospitals, education). The net fiscal
contribution was strongly related to age, as shown in
Figure 1.
Thus, between age 20 and 75, the net fiscal contribu-
tion is positive, and negative at all other ages. This re-
flects the fact that young persons do not work (much)
and thus pay few taxes, while costing money due to
education and childcare. The elderly also do not gen-
erally work (much), receive income from the state in
pensions and have large health costs towards the end
of life. The net fiscal contributions of the five groups
according to this model are shown in Table 1.
The above data are not useful for country-level anal-
yses. However, the Danish ministry of finance re-
cently published a report with more refined and de-
tailed estimates (Finansministeriet,2017), including
age-adjusted results for the major origin groups, and
age-unadjusted results for the 32 largest country of
origin groups. They used a more detailed model than
Hansen et al. (2015) by virtue of having more avail-
able data, were better able to individuate income and
expenditures to single persons and hence national
Published: 21st of May 2017 Open Quantitative Sociology & Political Science
Table 1:
Net fiscal contribution to public finances by broad origin group. Denmark, 2013 data. From Hansen et al. (2015).
Not age-adjusted.
Origin group Net contribution per person-year (EUR)
Ethnic Danes -695
First generation Western 2,546
Later generation Western 47
First generation non-Western -2,238
Later generation non-Western -1,070
Table 2:
Net fiscal contribution to public finances by broad origin group, EUR/person per year. Denmark, 2014 data. From
Finansministeriet (2017).3
Group Actual contribution Age-adjusted contribution
Ethnic Danes 1,478 1,478
First generation Western 4,032 134
Later generation Western -9,408 1,344
First generation non-Western -7,526 -12,768
Later generation non-Western -17,204 -6,854
origin groups. They successfully linked 93 % of rev-
enues to single persons, but only 72 % of expenses (p.
18). Table 2shows the main results of their modeling
for the 5 broad groups (values were given in Danish
Kroner were converted to Euros).
Thus we see that the large dierence between the two
Western groups mainly reflects age dierences, while
the relative position of the two non-Western groups
is reversed. The changes reflect the facts that later
generation Westerners are mainly children and youths
and thus still under education, that first generation
non-Westerns are mainly adults in working age with
a low employment rate, and that later generation non-
Westerns are mainly children and youths.
The estimates for the 32 countries of origin were cal-
culated the same way as above, but unfortunately, no
age-adjusted variants were published.
2.2 Fiscal estimates for Finland
The Finnish think tank Suomen Perusta recently pub-
lished a Finnish-language report where they esti-
mated the net fiscal cost for the largest 10 countries
of origin as well as Finland itself (Salminen,2015b).
Along with the report, an English-language summary
was published (Salminen,2015a). The study followed
It should be noted that the large positive contribution of ethnic
Danes is due in part to a change in law that year. If it is adjusted
for, the net contributions are as follows: ED -403, FGW 3,495,
LGW -9,946, FGNW -7,661 and LGNW -17,070. Unfortunately,
no age-adjusted versions of these are given and thus for method
consistency, the law change-unadjusted numbers are given in
the table.
roughly the same method as the Danish studies de-
tailed above, except that all results only concerned
the working age population, defined as ages 20 to 62.
A second report is under preparation and will detail
the life-course contributions of the groups, like the
Danish ministry of finance report. However, it was
not ready in time for this study.
2.3 Other data
The country-level net fiscal contribution estimates
discussed above were used along with data from the
following sources:
S scores for Denmark were copied from
Kirkegaard & Fuerst (2014).
National IQs were copied from Lynn & Vanhanen
National Muslim prevalence rates from Pew Re-
search Center (2011).
A few additions were to the datasets for IQ and Mus-
lim % based on interpolating values from nearby
countries. This method has been validated by pre-
vious research (Lynn & Vanhanen,2012, p. 10) and
works due to the strong spatial autocorrelation for the
variables (Fuerst & Kirkegaard,2016;Gelade,2008;
Hassall & Sherratt,2011).
Published: 21st of May 2017 Open Quantitative Sociology & Political Science
3 Analyses
3.1 Comparison of net fiscal contributions in
Denmark and Finland
Figure 2shows the scatterplot of the net fiscal contri-
butions for the two countries.
Two origin groups did not overlap between the sam-
ples (Finland, Estonia), so there were only 9 cases.
Still, we see a near-perfect relationship. This is de-
spite the fact that the Finnish estimates concern only
the working age population while the Danish num-
bers concern the entire population. Though it may
seem surprising, a recent study of German and Dan-
ish crime rates for immigrant groups found that de-
spite age being a confound in many analyses, adjust-
ing for age does not alter variables’ correlations to
other variables much (Kirkegaard & Becker,2017).
The change usually is one of scale (reduced disper-
sion) but dispersion does not aect correlations as
they are scale-invariant.
In line with the above, it can be noted that the Danish
dispersion of values is much larger: the standard de-
viations are 10,603 and 5,083.
This might reflect the
increased sophistication of the modeling approach,
the increased variation from the age confound, or
plain sampling error.
3.2 Prediction correlations
How well does the predictors predict the net fiscal
contributions? Figures 3-6show the scatterplots.
For Denmark, it is clear that Syria is an outlier. This is
likely because this group consists primarily of recent
refugees. The Finnish data do not seem to have any
consistent outliers: Turkey is an outlier for Muslim %,
but not for IQ, and Iraq is an outlier for IQ, but not
for Muslim %. However, the number of cases is very
small, so any outlier might just be part of a stronger
pattern that would be visible if we had more cases.
3.3 The relationship between S and net fiscal
As discussed in the introduction, an S score is an
overall measure of how well a group does in the so-
ciety. Conceptually, this is very closely related to the
net fiscal contribution of a group. After all, the fiscal
contribution is a complex function of income, employ-
ment, use of benefits, and crime rate. Employment
and income are of course themselves functions of ed-
ucational attainment. Still, there seems to be no theo-
retical reason to expect a perfect relationship between
This is not due to outliers, the median absolute deviations – a
robust alternative – show a 2.3:1 ratio vs. 2.1:1 for standard
Table 3:
Regression model results for both countries. Un-
standardized betas.
Country +1 IQ +1% Muslim
Denmark 917 -188
Finland 470 -86
Mean 694 -137
the two variables. The S factor is a line in multidi-
mensional space that best sums up the co-variation
between the outcome variables, and there is no reason
why this should assign the same weight or importance
to variables that the complex net fiscal contribution
function does. There is no published study that com-
puted S factor scores for immigrant groups in Fin-
land, but there is for Denmark (Kirkegaard,2014a;
Kirkegaard & Fuerst,2014). The S factor scores from
Denmark were based on detailed statistics for income,
benefits use, crime and education broken down by
age group, and thus they represent very reliable S
estimates. Figure 7shows the scatterplot.
The correlation between the variables was very strong
as expected. Some of the lower correlation is due to
Syria, which was a large negative outlier. The data
the S factor scores are based on dates to 2012 which is
before the main waves of refugees/migrants from the
Syrian civil war arrived.
The fiscal data, however,
concern the year 2014, which was the year with the
most refugees/migrants arriving. If we exclude Syria,
the correlation improves to .90 [.80 to .95]. Due to the
near-perfect correlation, S scores might have signifi-
cant utility in economic modeling of origin groups for
which it is hard or impossible to get the data needed
for the complex fiscal models. If one excludes Syria,
the mean prediction error is approximately 4,622 Eu-
ros. 6
3.4 Modeling the fiscal value of the predictors
Because we have real world monetary values as op-
posed to mere factor scores, it is possible to estimate
the worth of a 1 IQ increase in the origin country
as well as a 1 % increase in Muslims (in the origin
country). This can be done for both countries. Table 3
shows the results.
As was also seen in Section 3.1, the dispersion is larger
in the Danish dataset, which makes the slope larger.
Averaging across datasets, a 1 IQ point increase in
the origin country is associated with an increase of
According to
, the num-
ber of persons from Syria arriving in Denmark were: 3,855
(2009), 5,115 (2010), 3,806 (2011), 6,184 (2012), 7,557 (2013),
14,732 (2014), 21,316 (2015), 6,234 (2016).
The formula is:
= s
) (Cohen et al.,2003, p. 39).
Published: 21st of May 2017 Open Quantitative Sociology & Political Science
Figure 2: Net fiscal contribution to public finances in Denmark and Finland.
Figure 3: Origin country IQ and net fiscal contribution in Denmark.
Published: 21st of May 2017 Open Quantitative Sociology & Political Science
Figure 4: Origin country Muslim % and net fiscal contribution in Denmark.
Figure 5: Origin country IQ and net fiscal contribution in Finland.
Published: 21st of May 2017 Open Quantitative Sociology & Political Science
Figure 6: Origin country Muslim % and net fiscal contribution in Finland.
Figure 7: General socioeconomic factor and net fiscal contribution in Denmark.
Published: 21st of May 2017 Open Quantitative Sociology & Political Science
694 Euros/year per person. Similarly, a 1 % increase
in Muslims at the origin country, is associated with
deficit of -137 Euros/year per person.
4 Discussion and conclusion
As has been found in many other studies (Kirkegaard,
2014a;Kirkegaard & Becker,2017;Kirkegaard &
Fuerst,2014), national IQs were good predictors of
immigrant performance at the group level (r’s , 69 and
.84). The proposed causal model for these findings
is a combination of cognitive ability-based meritoc-
racy and spatial transferability of psychological traits:
when bright or dull people move, they mostly remain
equally bright or dull at their new homes, including
when these are in other countries. Because higher cog-
nitive ability cause better socioeconomic outcomes
such as higher education, income, occupational status
and lower crime, group dierences in cognitive ability
– whether these are nationality-related or not – are
reflected in social inequality between them (Gordon,
1997;Gottfredson,1998;Herrnstein & Murray,1994;
Lynn & Vanhanen,2012).
The same previous studies have also found Muslim %
to be a good predictor. The reason for the validity
of Muslim % is less obvious. It is hard to model the
predictor jointly with national IQ because the small
samples of origin countries make regression model
estimates imprecise. A plausible hypothesis is that
countries with more Muslims send more Muslim im-
migrants, and that Muslims immigrants have values
that are disharmonious with those of people living
in Western countries (see e.g. Koopmans 2015). The
disagreements over preferred policies cause signifi-
cant outgroup antipathy resulting in crime against
the native population and reduced willingness to in-
tegrate into the host country’s customs. This causal
path is more speculative due to a relative dearth of
individual-level research on the topic. Unfortunately,
it is dicult to find large survey datasets that sample
sucient numbers of Muslims and measure their cog-
nitive ability, religious beliefs, values, cultural prac-
tices as well as pertinent socioeconomic outcomes
such as crime.
The two predictors are broad ‘can do’ and ‘will do
factors (Gottfredson,1997). The first concerns what
people are able to do; a person with below average
cognitive ability will never become a computer scien-
tist even if he wants to. The second concerns what
people want to do; a person with a very strong desire
to become a computer scientist will never become one
unless he possesses sucient cognitive ability. What
people – and groups of people – actually do in society
is thus a function of what they can do and want to do.
Aside from dierences in the number of Muslims and
the mean cognitive ability of groups, other plausible
causes of social inequality include selection eects,
language barriers, legal barriers (e.g. recognition of
relevant educational degrees), length of stay in the
host country, and lasting environmental eects (e.g.
physical or psychological traumas from war). Unfor-
tunately, little or no data are available for many of
these possible causes.
The monetary estimates of national IQ and Muslim %
should be taken with caution due to the small sam-
ples in terms of origin countries (n’s 32 and 11), host
countries (n = 2) and net fiscal contribution estimates
(n = 1 for each country), as well as the use of zero-
order analyses. The estimates represent a current best
guess in need of further validation.
During review, it was suggested that the author fit a
model with both predictors. However, in the author’s
judgment, this model would be severely underpow-
ered and the results thus uninformative. Readers may
consult the prior study of a larger Danish immigrant
dataset which included multiple regression models
(Kirkegaard & Fuerst,2014).
5 Supplementary material and
Supplementary materials including code, high quality
figures and data can be found at
The peer review thread is located at
Thanks to reviewers: Noah Carl and Gerhard Meisen-
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... The purpose of the present study was to test this general model of social inequality. A number of prior studies have been done on immigration outcomes in Denmark (Kirkegaard, 2013(Kirkegaard, , 2015(Kirkegaard, , 2017(Kirkegaard, , 2019a. However, none of these have included a measure of migration selection, thus there was a need to examine the effects of this covariate. ...
Full-text available
Immigrants to Western countries typically have worse social outcomes than natives, but country of origin immigrant groups differ widely. We studied school performance in Denmark for 116 immigrant groups measured by the grade point average (GPA) of the 9th grade exam at the end of compulsory schooling. General intelligence is a strong causal factor of school outcomes and life outcomes in general for individuals. We accordingly predicted that country of origin average intelligence (national IQ) will predict immigrant group outcomes. We furthermore included as covariates immigrant generation (first vs. second) as well as the Muslim percentage of country of origin. Results show that GPA in Denmark can be predicted by national IQ r = .47 (n = 81), Muslim percentage r = -.40 (n = 81), and educational selectivity of immigrants entering Denmark r = .35 (n = 71). Regression modeling indicated that each predictor is informative when combined. The final model explained 46.3% of the variance with first generation (binary) β = -0.65, βIQ = 0.29, βMuslim = -0.21, and β education selectivity index = 0.27 (all predictors p < .001, n = 97). Our results are in line with existing research on cognitive stratification and immigration.
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A dataset of socioeconomic, demographic and geographic data for US counties (N≈3,100) was created by merging data from several sources. A suitable subset of 28 socioeconomic indicators was chosen for analysis. Factor analysis revealed a clear general socioeconomic factor (S factor) which was stable across extraction methods and different samples of indicators (absolute split-half sampling reliability = .85). Self-identified race/ethnicity (SIRE) population percentages were strongly, but non-linearly, related to cognitive ability and S. In general, the effect of White% and Asian% were positive, while those for Black%, Hispanic% and Amerindian% were negative. The effect was unclear for Other/mixed%. The best model consisted of White%, Black%, Asian% and Amerindian% and explained 41/43% of the variance in cognitive ability/S among counties. SIRE homogeneity had a non-linear relationship to S, both with and without taking into account the effects of SIRE variables. Overall, the effect was slightly negative due to low S, high White% areas. Geospatial (latitude, longitude, and elevation) and climatological (temperature, precipitation) predictors were tested in models. In linear regression, they had little incremental validity. However, there was evidence of non-linear relationships. When models were fitted that allowed for non-linear effects of the environmental predictors, they were able to add a moderate amount of incremental validity. LASSO regression, however, suggested that much of this predictive validity was due to overfitting. Furthermore, it was difficult to make causal sense of the results. Spatial patterns in the data were examined using multiple methods, all of which indicated strong spatial autocorrelation for cognitive ability, S and SIRE (k nearest spatial neighbor regression [KNSNR] correlations of .62 to .89). Model residuals were also spatially autocorrelated, and for this reason the models were re-fit controlling for spatial autocorrelation using KNSNR-based residuals and spatial local regression. The results indicated that the effects of SIREs were not due to spatially autocorrelated confounds except possibly for Black% which was about 50% weaker in the controlled analyses. Pseudo-multilevel analyses of both the factor structure of S and the SIRE predictive model showed results consistent with the main analyses. Specifically, the factor structure was similar across levels of analysis (states and counties) and within states. Furthermore, the SIRE predictors had similar betas when examined within each state compared to when analyzed across all states. It was tested whether the relationship between SIREs and S was mediated by cognitive ability. Several methods were used to examine this question and the results were mixed, but generally in line with a partial mediation model. Jensen's method (method of correlated vectors) was used to examine whether the observed relationship between cognitive ability and S scores was plausibly due to the latent S factor. This was strongly supported (r = .91, Nindicators=28). Similarly, it was examined whether the relationship between SIREs and S scores was plausibly due to the latent S factor. This did not appear to be the case.
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We conducted novel analyses regarding the association between continental racial ancestry, cognitive ability and socioeconomic outcomes across 6 datasets: states of Mexico, states of the United States, states of Brazil, departments of Colombia, sovereign nations and all units together. We find that European ancestry is consistently and usually strongly positively correlated with cognitive ability and socioeconomic outcomes (mean r for cognitive ability = .708; for socioeconomic well-being = .643) (Sections 3-8). In most cases, including another ancestry component, in addition to European ancestry, did not increase predictive power (Section 9). At the national level, the association between European ancestry and outcomes was robust to controls for natural-environmental factors (Section 10). This was not always the case at the regional level (Section 18). It was found that genetic distance did not have predictive power independent of European ancestry (Section 10). Automatic modeling using best subset selection and lasso regression agreed in most cases that European ancestry was a non-redundant predictor (Section 11). Results were robust across 4 different ways of weighting the analyses (Section 12). It was found that the effect of European ancestry on socioeconomic outcomes was mostly mediated by cognitive ability (Section 13). We failed to find evidence of international colorism or culturalism (i.e., neither skin reflectance nor self-reported race/ethnicity showed incremental predictive ability once genomic ancestry had been taken into account) (Section 14). The association between European ancestry and cognitive outcomes was robust across a number of alternative measures of cognitive ability (Section 15). It was found that the general socioeconomic factor was not structurally different in the American sample as compared to the worldwide sample, thus justifying the use of that measure. Using Jensen's method of correlated vectors, it was found that the association between European ancestry and socioeconomic outcomes was stronger on more S factor loaded outcomes, r = .75 (Section 16). There was some evidence that tourist expenditure helped explain the relatively high socioeconomic performance of Caribbean states (Section 17).
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A dataset of 30 diverse socioeconomic variables was collected covering 32 London boroughs. Factor analysis of the data revealed a general socioeconomic factor. This factor was strongly related to GCSE (General Certificate of Secondary Education) scores (r's .683 to .786) and and had weak to medium sized negative relationships to demographic variables related to immigrants (r's -.295 to -.558). Jensen's method indicated that these relationships were related to the underlying general factor, especially for GCSE (coefficients |.48| to |.69|). In multiple regression, about 60% of the variance in S outcomes could be accounted for using GCSE and one variable related to immigrants.
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We present and analyze data from a dataset of 2358 Danish first names and socioeconomic outcomes not previously made available to the public (“Navnehjulet”, the Name Wheel). We visualize the data and show that there is a general socioeconomic factor with indicator loadings in the expected directions (positive: income, owning your own place; negative: having a criminal conviction, being without a job). This result holds after controlling for age and for each gender alone. It also holds when analyzing the data in age bins. The factor loading of being married depends on analysis method, so it is more difficult to interpret. A pseudofertility is calculated based on the population size for the names for the years 2012 and 2015. This value is negatively correlated with the S factor score r = -.35 [95CI: -.39; -.31], but the relationship seems to be somewhat non-linear and there is an upward trend at the very high end of the S factor. The relationship is strongly driven by relatively uncommon names who have high pseudofertility and low to very low S scores. The n-weighted correlation is -.21 [95CI: -.25; -.17]. This dysgenic pseudofertility was mostly driven by Arabic and African names. All data and R code is freely available.
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We argue that if immigrants have a different mean general intelligence (g) than their host country and if immigrants generally retain their mean level of g, then immigration will increase the standard deviation of g. We further argue that inequality in g is an important cause of social inequality, so increasing it will increase social inequality. We build a demographic model to analyze change in the mean and standard deviation of g over time and apply it to data from Denmark. The simplest model, which assumes no immigrant gains in g, shows that g has fallen due to immigration from 97.1 to 96.4, and that for the same reason standard deviation has increased from 15.04 to 15.40, in the time span 1980 to 2014.
Cross-regional correlations between average IQ and socio-economic development have been reported for many different countries. This paper analyses data on average IQ and a range of socio-economic variables at the local authority level in the UK. Local authorities are administrative bodies in local government; there are over 400 in the UK, and they contain anywhere from tens of thousands to more than a million people. The paper finds that local authority IQ is positively related to indicators of health, socio-economic status and tertiary industrial activity; and is negatively related to indicators of disability, unemployment and single parenthood. A general socio-economic factor is correlated with local authority IQ at r = .56. This correlation increases to r = .65 when correcting for measurement error in the estimates of IQ.
On the basis of an original survey among native Christians and Muslims of Turkish and Moroccan origin in Germany, France, the Netherlands, Belgium, Austria and Sweden, this paper investigates four research questions comparing native Christians to Muslim immigrants: (1) the extent of religious fundamentalism; (2) its socio-economic determinants; (3) whether it can be distinguished from other indicators of religiosity; and (4) its relationship to hostility towards out-groups (homosexuals, Jews, the West, and Muslims). The results indicate that religious fundamentalist attitudes are much more widespread among Sunnite Muslims than among native Christians, even after controlling for the different demographic and socio-economic compositions of these groups. Alevite Muslims from Turkey, by contrast, show low levels of fundamentalism, comparable to Christians. Among both Christians and Muslims, strong religiosity as such is not (among Christians) or only mildly (among Muslims) related to hostility towards out-groups. Fundamentalist believers, however, show very high levels of out-group hostility, especially among Muslims.