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Country of Origin IQ and Muslim Percentage Predict Grade Point Average in School among 116 Immigrant Groups in Denmark

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  • Ulster Institute for Social Research

Abstract and Figures

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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MANKIND QUARTERLY 2021 61:3 599-625
599
Country of Origin IQ and Muslim Percentage Predict Grade
Point Average in School among 116 Immigrant Groups in
Denmark
Emil O. W. Kirkegaard*
Ulster Institute for Social Research, London, UK
Jurij Fedorov
Independent researcher, Denmark
* Corresponding author: the.dfx@gmail.com
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.
Key Words: Denmark, Intelligence, Cognitive ability, Muslim, School
outcomes, Grade point average, Immigrants, Brain drain
Immigration of non-European peoples to Western countries has increased
markedly in recent times, starting around the 1960s with various guest worker
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programs. Immigration, however, has not generally been a success because in
every Western country, immigrants as a whole perform worse than the natives.
This is true whether one looks at school outcomes, crime rates, income, or
educational attainment (Andersson & Jespersen, 2018; Haen Marshall, 1997;
MG, 2016; D. Murray, 2017; Roth, 2010; Sanandaji, 2017; Sarrazin, 2012, 2018).
This failure of immigration “to work as intended” has led to decades long calls for
scientific research into the question. Social scientists have responded in the usual
way, namely by producing 1000s of studies that show that parental traits predict
their children’s outcomes (Harris, 2009; Pinker, 2002; Rowe, 1994), and
attributing causality to parental environmental effects to explain immigrant
outcomes. There is no general attempt to find root causes in order to explain why
some immigrant groups consistently fare poorly, and why others succeed
everywhere they go. Indeed, the failure of the family environment to show strong
causal effects for most outcomes studied renders this general explanation very
implausible from the outset (Kirkegaard, 2018). In the rare cases where one sees
research that looks at the different outcomes of immigrants grouped by their
country of origin, there is usually no detailed attempt at a causal model beyond
noticing that migrants from countries with highly educated people and high GDP
per capita tend to fare well in the destination country (Borjas, 2016). It is clear that
this simply pushes the question one step further back: Why are some countries
so rich and others so poor to begin with? From a scientific perspective, it is not
surprising that well-run countries tend to send people who then run their lives well
in a new country. After all, the best predictor of future success is past success.
All this points to stable causes of social success that are related to the origin, and
thus genetic ancestry of people around the world (Easterly & Levine, 2012;
Fulford et al., 2016; Kirkegaard, 2019c; Kodila-Tedika & Asongu, 2015;
Putterman & Weil, 2010). One team of economists put it this way:
William Easterly and Ross Levine (2009) confirm and expand upon
Putterman and Weil’s finding, showing that a large population of European
ancestry confers a strong advantage in development, using new data on
European settlement during colonization and its historical determinants. They find
that the share of the European population in colonial times has a large and
significant impact on income per capita today, even when eliminating Neo-
European countries
1
and restricting the sample to countries where the European
share is less than 15% that is, in non-settler colonies, with crops and germs
associated with bad institutions. The effect remains high and significant when
1
Countries outside of Europe populated mainly by Europeans such as Australia and
Canada.
KIRKEGAARD, E.O.W. & FEDOROV, J. MIGRANT GPA IN DENMARK
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controlling for the quality of institutions, while it weakens when controlling for
measures of education. (Spolaore & Wacziarg, 2013)
Based on macroeconomic findings like these, Fuerst and Kirkegaard (2016a,
2016b) built a dataset of genetic ancestry of all countries in the Americas, as well
as many subnational units, where genetic data were available, or could plausibly
be estimated based on other variables such as self-reported ancestry, colonial
history, skin color, neighboring regions or countries. The result was a large
dataset of ancestry estimates as well as associated variables for cognitive ability
(or intelligence), country well-being summarized as generalized social status or
general socioeconomic factor S (Kirkegaard, 2014; Pesta et al., 2010), and
numerous covariates such as climatic indicators and tourism counts. Their results
showed that both country and subnational disparities could be predicted well from
European ancestry proportions of the populations, even with the admittedly noisy
estimates that were available. Overall, among the 169 units in the combined
analysis, the correlation of European ancestry was .78 with cognitive ability and
.80 with country well-being. The association could not be “explained away” using
the various climatic covariates. The correlations were also fairly strong for
subnational units within countries, thus were not simply an artifact of country fixed
effects. The average correlation across analyses for European ancestry and
cognitive ability was .71, and for well-being .64.
The various researchers who have studied these ancestry associations
generally attribute them to long-running effects of “human capital” traits. This
diffuse term includes cognitive ability (more specifically general intelligence), but
also good work ethics, creativity, generalized trust, and in effect, anything that is
causal to well-being (so called non-cognitive skills). Fuerst and Kirkegaard’s
findings also supported this general contention, as mediation analyses showed
that about 75% of European ancestry’s effect on well-being was mediated by
cognitive ability. Given this general, human capital-centric and meritocratic view
of human inequality, it is possible to make predictions for the success of immigrant
groups, namely:
1. Immigrant groups from countries with higher human capital should fare
better than those from countries with lower human capital, in particular, cognitive
ability as the most important component of human capital (Christainsen, 2013,
2020; O’Boyle et al., 2011; Rindermann, 2018; Schmidt et al., 2016).
2. Insofar as selection affects the average human capital of the people
leaving a country and entering a specific host country (i.e. non-random migration),
this should be taken into account, and should also predict social success (Connor,
2019; Knudsen, 2019).
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3. These effects should last indefinitely, i.e., should not be limited simply to
first or second generation immigrants, but should be in effect in any later
generation no matter how admixed.
4. This can in later generations be affected by non-random mating
behavior, and evolution that affects the average human capital levels of a
population compared to other populations (differential cognitive dysgenics;
Rindermann et al., 2017; Shockley, 1972).
With regards to immigrant outcomes specifically, (Kirkegaard & de Kuijper,
2020) advocated the following causal model, which we present here in
generalized format in Figure 1.
Figure 1. Generalized immigrant outcomes model.
Specifically, populations vary in their mean trait levels, including human
capital ones. There is some selection of people who choose to emigrate from a
country, and furthermore selection on who chooses and is able to enter a specific
destination country, in this case Denmark. We label these factors together as
“migrant selection”, though they could be further broken down if desired.
Immigrant outcomes in the country of destination are then primarily determined
by these migrant traits, but also by contextual factors. The latter include things
such as duration of stay, language similarity (do both groups speak a common
language?), cultural-religious compatibility (e.g. are both groups Christians?),
hostility of the native population to immigration (e.g. do locals refuse to hire
KIRKEGAARD, E.O.W. & FEDOROV, J. MIGRANT GPA IN DENMARK
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immigrants?), services that ease immigration (e.g. are there welcome centers that
provide immigrants with legal help?) and so on. Immigrant outcomes can also be
further split into a variety of outcomes in society, as well as their causal relations.
For instance, education-related outcomes come before employment-related
outcomes (e.g. status, income), and both come before most health-related
outcomes (e.g. diabetes, mortality).
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, 2015, 2017, 2019a; Kirkegaard & Fuerst, 2014).
However, none of these have included a measure of migration selection, thus
there was a need to examine the effects of this covariate. Second, only one prior
study has examined school grades, and this study had only a small number of
origin groups, n = 19 (Kirkegaard, 2015). Thus there was a need for a more
comprehensive study.
Data
School outcomes
We used public data from the Ministry of Children and Education (Børne og
undervisningsministeriet). The interactive data explorer allows one to get the
average GPA for origin countries.
2
Specifically, we use the mean GPA calculated
from the set of obligatory tests to remove any bias from self-selection into elective
subjects.
3
One can further split these by various other variables such as region of
Denmark, and year of the exam. Because of small sample sizes of many of the
origin groups in the dataset, we set a minimum sample size of 25 for the main
analyses resulting in data for 82 origins out of the 116. If we exclude Denmark
itself, the median sample size was 59 (median absolute deviation = 76) with a
range from 5,677 (Turkey) to 3 (Japan). Our data cover the period 2013-2019
(school years beginning 2012-2018). Because of inflation in the GPA over the
years of study, we regressed out the effect of year on the results before computing
an average for each origin group. Figures 2a-c show maps of the resulting
datasets.
2
It is available at https://uddannelsesstatistik.dk/Pages/Reports/1802.aspx but this link
will likely break in the future as these government websites are notoriously prone to link
rot. Our supplementary materials contain exports of these data that others may inspect
or reuse as desired.
3
The obligatory tests are: spoken Danish, written Danish (reading, spelling, writing),
mathematics (with and without accessories), spoken English, and natural sciences
(combination of physics, chemistry, biology and geography).
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Figures 2a-c. Maps of mean grade point average (GPA) in Denmark. a)
combined generations, b) first generation, c) second generation.
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National intelligence
We used estimates of national intelligence or otherwise unspecified cognitive
ability, or human capital from multiple sources (Angrist et al., 2019; Lynn &
Becker, 2019; Lynn & Vanhanen, 2012; Rindermann, 2018).
4
Our main choice of
dataset is the Lynn & Vanhanen (LV) dataset. While this dataset is the oldest, it
is also the most comprehensive in terms of the underlying data used and covers
the widest range of countries. The Becker (B) dataset is a re-calculation from
sources of the LV dataset, but does not yet cover all the sources used (currently
about 70%). The Rindermann (R) dataset mainly uses results from scholastic
ability studies (it assigns them a weight of 3-to-1 compared to the IQ studies),
while the World Bank (WB) dataset solely uses scholastic achievement studies.
While there are more variant datasets one could use (Altinok et al., 2014; Coutrot
et al., 2018; Lim et al., 2018)
5
, including older versions of Lynn’s datasets (Lynn
& Vanhanen, 2002, 2006), these datasets are all very highly correlated
(Rindermann, 2007) and based mainly on the same underlying data, so there
seemed little point in adding more of them. To avoid losing any cases due to
missing national intelligence, we imputed some missing data: Kosovo, Serbia &
Montenegro, and Yugoslavia were assigned an IQ of 90.3 based on Lynn and
Vanhanen’s estimate for Serbia. We assigned Cayman Islands an IQ of 82 based
on Becker’s geospatial imputation.
Muslim percentage
For Muslim population percentage, we used Pew Research’s 2011 estimates
of the 2010 prevalences (Pew Research Center, 2011). Like with the intelligence
data, we imputed some missing data: Kosovo at 95.6% Muslim, and Serbia &
Montenegro at 3.1% Muslim.
6
Brain drain dataset
We used data from The Institute for Employment Research’s Brain Drain
dataset.
7
This is a public use dataset of the education attainment of persons
4
The Becker dataset can be found in the continuously updated version at
https://viewoniq.org/, while the World Bank dataset can be found at
https://www.worldbank.org/en/publication/human-capital.
5
It should be noted that the Lim et al. dataset is a plagiarized version of the Lynn dataset,
published in The Lancet, a prominent medical journal. The study uses the same IQ
studies as the various Lynn datasets, but does not cite it, or any other IQ researcher.
6
These were based on https://www.cia.gov/library/publications/the-world-
factbook/geos/print_mj.html and https://en.wikipedia.org/wiki/Islam_in_Serbia.
7
It is available at https://www.iab.de/en/daten/iab-brain-drain-data.aspx.
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emigrating to OECD countries in the period 1980-2010. Each person is classified
as having low, medium, or high educational attainment. The numbers are further
broken down by which country they emigrate to. We thus filtered to the persons
who emigrated from anywhere to Denmark. We calculated the fraction of highly
educated as a proportion of the total emigrants. We then regressed this on the
educational attainment of the origin country (based on The United Nations’s
dataset for the Human Development Index) to estimate an educational selectivity
factor. Conceptually, two countries that both send immigrants with 30% “high”
educational attainment, but which have different population prevalences of 30%
and 5%, differ in their emigration selection. The first country sends persons who
are representative of the origin country, while the latter sends persons who are
highly positively selected (their rate of high educational attainment is 6 times the
country average). We thus used the standardized residual of this regression as
our measure of educational selectivity as done in a prior study (Fuerst &
Kirkegaard, 2014). As above, we used a minimum sample size requirement of 25
to avoid excessive sampling error in the estimation of the selectivity index. Figure
3 shows the resulting educational selectivity index.
Figure 3. Map of educational selectivity index of immigrants who entered
Denmark in the period 1980-2010.
The appendix gives the data for the main variables used in this study. The
full statistical output, R analysis code, and complete data can be found in the
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supplementary materials at https://osf.io/2vz8u/. The statistical output may also
be found at https://rpubs.com/EmilOWK/GPA_Denmark_2020 which is merely for
convenience.
Results
To begin with, we computed the correlations among the main variables.
These are shown in Table 1. We see that GPA is related to all predictors, although
the predictors are not very strongly intercorrelated aside from the estimates of
national intelligence. Educational selection, in particular, shows near-zero
correlations with the other predictors yet has substantial predictive validity. Thus,
we would expect these variables to combine near-additively in a regression
model. Figures 4a-c show scatterplots of the main variables by generation. We
then ran regression models to examine how the predictors would combine.
Results are shown in Table 2.
Table 1. Correlation matrix for the main variables. Weighted correlations (square
root of GPA sample size) below the diagonal, unweighted above (no minimum
sample size for GPA). IQlv = Lynn & Vanhanen, r = Rindermann, b = Becker, wb
= World Bank, Muslim = Percentage Muslim, edu select. = educational selection
based on Brain Drain dataset.
GPA
IQlv
IQr
IQwb
Muslim
Edu. select.
GPA
0.55
0.59
0.52
-0.24
0.26
IQlv
0.47
0.98
0.90
-0.36
0.00
IQr
0.53
0.98
0.92
-0.36
0.02
IQb
0.52
0.90
0.90
0.87
-0.43
0.00
Iqwb
0.45
0.84
0.86
-0.39
-0.03
Muslim
-0.40
-0.41
-0.44
-0.51
-0.14
Edu. select.
0.35
-0.04
0.01
-0.09
-0.14
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Figures 4a-c. Scatterplots of GPA in Denmark by predictors, split by immigrant
generation. The regressions are weighted by the square root of the GPA sample
size, which also controls the point size shown.
Table 2. Regression models predicting GPA in Denmark. Weighted by square
root of GPA sample size. * = p < .01, ** = p < .005, *** = p < .001. IQlv = Lynn &
Vanhanen, Muslim = Percentage Muslim, edu selection = educational selectivity
index based on Brain Drain dataset. Note that the models only include countries
with no missing data for each predictor and at least 25 persons used to estimate
the mean GPA.
Models with combined generations (N=71)
Predictor/Model
1
2
3
4
Intercept
-0.20
(0.082)
-0.04
(0.087)
-0.12
(0.086)
-0.04
(0.083)
IQlv
0.41
(0.100)***
0.30
(0.106)*
0.34
(0.099)***
Muslim
-0.29
(0.074)***
-0.20
(0.078)
-0.15
(0.073)
Edu. select.
0.23
(0.068)**
R2adj.
0.188
0.166
0.246
0.349
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Models with foreign born dummies (N=97)
Predictor/Model
1
2
3
4
Intercept
-0.01
(0.087)
0.21
(0.091)
0.15
(0.091)
0.26
(0.085)**
Foreign born
-0.50
(0.139)***
-0.61
(0.138)***
-0.60
(0.133)***
-0.65
(0.120)***
IQlv
(0.084)***
0.25
(0.086)**
0.29
(0.078)***
Muslim
-0.32
(0.060)***
-0.24
(0.064)***
-0.21
(0.058)***
Edu. select.
0.27
(0.056)***
R2adj.
0.241
0.282
0.336
0.463
The model results show that the predictors combine fruitfully as expected.
We show two sets of models, one with 1st and 2nd generations combined and one
where they are split. The latter approach allows us to model the effect of
immigrant generation directly with a dummy variable, whereas in the first we
would have had to model it as a fraction of the population variable. The latter
approach affords us more statistical power and results in a better fitting model
(46.3% vs. 34.9% adjusted R2). The effect size of the generational dummy is
surprisingly large, showing that this variable should not generally be ignored, and
that there are large gains in GPA from being the second generation compared to
the first.
In the results above, we used a minimum sample size requirement of 25 for
estimating the GPA by origin. The purpose of this is to avoid excessive sampling
error in the outcome variable that is not entirely mitigated by our use of regression
weights. One might wonder how this affects the results. Countries that send
smaller or larger numbers of migrants to Denmark are not a random subset of the
world population of countries, so picking a specific threshold might bias results
inadvertently. For this reason, we computed the results for a range of sample size
requirements. The results are shown in Figures 5a,b
In the top plot, we see that the use of the threshold seemed to improve
results, though they remained very stable after 50 as the minimum sample size.
Thus, we don’t find any evidence of selection bias of this quality control decision
for the correlations. In the bottom plot, however, we see that three of the
predictors generally increase in absolute beta magnitude, while national IQ
decreases, and after about minimum n 40 no longer has p < .01. This curious
pattern seems to show that national IQ is correlated with the directional sampling
error in the average GPA estimate which seems unlikely. There is in fact no
KIRKEGAARD, E.O.W. & FEDOROV, J. MIGRANT GPA IN DENMARK
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correlation between national IQ and sample size for GPA (r = .10), though there
is a weak one for educational selectivity, r = -.32).
Figure 5a,b. Results across varying minimum sample sizes. The top plot shows
correlations, and the bottom plot the standardized betas from a combined model.
lv = Lynn & Vanhanen, r = Rindermann, b = Becker, Muslim = Percentage Muslim,
edu_selection = educational selectivity index. Weighed by the square root of GPA
sample size.
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Discussion
We studied immigrant school outcomes in Denmark as measured by their
grade point average (GPA) at the obligatory 9th grade exams. There is a wide
variation in mean GPA between origin groups. We found that this variation could
be partially explained by characteristics of the origin countries. This pattern of
results is predicted by human capital models and meritocratic models of social
inequality as summarized in the introduction. Specifically, a wide variety of
evidence supports the causal importance of general intelligence in explaining and
predicting social outcomes in general whether these relate to education, income,
unemployment, crime, or health (Belsky et al., 2016, 2018; Domingue et al., 2015;
Gordon, 1997; Gottfredson, 1997, 2002; Hegelund et al., 2019; C. Murray, 2002).
We did not have a measure of the average intelligence of the different origin
groups, however, prior research shows that there are large cognitive gaps,
whether these are measured with tests labeled “intelligence tests” or something
else (Kirkegaard, 2013, 2019a; Rindermann & Thompson, 2016). Furthermore, it
is known that there are strong genetic correlations between educational
achievement tests, i.e. tests of academic content taught in school, and
intelligence tests that don’t involve such content (Krapohl et al., 2014).
Phenotypic, construct-level correlations (i.e. without random measurement error)
usually reach around .80 when measured well (Deary et al., 2007; S. B. Kaufman
et al., 2012; Saß et al., 2017; Zaboski et al., 2018).
8
Thus, while we don’t have
intelligence results per se, we do have results from a very strongly related proxy
that is measured at age 15, and thus cannot be caused by factors that only come
into play later in life. The results thus are in line with predictions from human
capital models of global inequality, and the various observed ancestry
associations. A 2007 Swedish study using intelligence scores from the army draft
test examined whether income equality (gaps between natives and second
generation immigrants) could be explained by the gaps in intelligence scores
(Nordin & Rooth, 2007). They found that “for these groups of second generation
8
There are two aspects to being measured well. First, having low random measurement
error (i.e., high reliability), and second, having low construct error, i.e., measuring the
right construct. If we want to measure general intelligence, we need to sample a varied
aspect of mental functioning, we cannot simply use e.g. a matrix test (Jensen & Weng,
1994; Johnson et al., 2008; te Nijenhuis et al., 2019). The same applies to educational
or scholastic achievement. We need to sample more than one school topic, not merely
mathematics, for instance. The use of structural equation modeling or adjustment for
unreliability (correction for attenuation) can remove random measurement error from
the estimate, but not construct invalidity.
KIRKEGAARD, E.O.W. & FEDOROV, J. MIGRANT GPA IN DENMARK
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immigrants the income gap primarily mirrors a skill gap.” The model that included
a family variable could account for 88% of the observed income gaps. This kind
of individual-level study is of critical importance for testing the general model
advanced in this paper, and we thus urge that more such studies are carried out.
If we return to the American context, there are many studies which find that the
black-white income gap is mainly due to the “skills gap” as well (Fryer, 2011;
Herrnstein & Murray, 1994; Nyborg & Jensen, 2001).
Study designs based on group-level data, however, can also be used to
assess this predicted mediation effect. For instance, Sanandaji (2017, Fig. 23)
analyzed OECD countries and showed that the PIAAC (so-called adult skills) gap
between persons born within and outside of the country predicted the
employment rate gap between the same groups across countries, as shown in
Figure 6.
Figure 6. Employment rate gap between persons born inside and outside a given
OECD country, and their cognitive test score gaps on the PIAAC test. Figure
remade from data in the Swedish-language original in Sanandaji (2017), Fig. 23.
We see that the employment gap is highly predictable, r = .80, from the skills
gap, as expected from the human capital model of social inequality. This is
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despite the sampling error inherent in both measures as well as the varied
immigration policies, and circumstances across the 19 countries. This kind of
study has also been done for racial gaps in the USA (black-white and Hispanic-
white). The gaps vary in size between subnational divisions (states, counties
etc.), and so one may ask whether the social well-being gaps (a composite of
education, income, crime rates etc.) are predictable from the gaps in intelligence.
The answer is they very much are: r’s = .62 and.69 for black-white and Hispanic-
white, respectively (Kirkegaard & Fuerst, 2016).
Our study has multiple strengths compared to prior studies in Denmark. First,
our sample size of origin groups 116 before a filtering based on minimum
sample size is much larger than the prior study which had up to 19 groups
(Kirkegaard, 2015). Even in our reduced sample with a minimum sample size
requirement, our sample is about 4 times larger than the prior. Second, we were
able to find GPA by immigrant generation. Decades of research on immigrant
traits and immigrant outcomes show that there is some improvement or
assimilation in the second generation compared to the first (Borjas, 2016; Dunn,
1988; Fuerst, 2014; Rindermann & Thompson, 2016; Robie et al., 2017). Without
a measure of this, there will be a confound of the proportion of a given group that
is second generation. Our results indicate a strong effect of generation on GPA,
in line with prior research. Third, our study included a measure of educational
selectivity, specifically to Denmark from the origin countries studied here. It is
well-known that immigrant selection has a large effect on immigrant outcomes.
However, prior studies on immigrant outcomes using national intelligence and
related variables did not generally attempt to include this covariate, possibly
biasing results (for an exception, see Fuerst & Kirkegaard, 2014). Our addition of
this covariate shows that it has an important role in the models, and yet does not
remove the effects of the other variables. In fact, educational selectivity seems
mostly uncorrelated with the other variables in our dataset (cf. Table 1).
There has been recent criticism of national intelligence estimates, and a
forced retraction of a study which used these (Clark et al., 2020; Gelman, 2020;
Oransky, 2020). While this should be seen in the light of the so-called American
woke cultural revolution (E. Kaufman, 2020), it is nevertheless true that it is
difficult to accurately estimate average national intelligence owing to data
limitations (Kirkegaard, 2019b; Lynn & Becker, 2019; Rindermann, 2013). For
many countries of the world, chiefly in poor regions, there simply are no good
data on measured cognitive ability. One is forced to exclude the countries,
causing modeling bias, or use the imperfect data such as they are. In this study
we used different sets of estimates to explore the impact of specific data source
choice on our results. We found that the differences were fairly small, however,
KIRKEGAARD, E.O.W. & FEDOROV, J. MIGRANT GPA IN DENMARK
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thus indicating the robustness of the results to this researcher degree of freedom
(Wicherts et al., 2016).
There are some limitations to the research. First, this study used group-level
data, not individual-level data. It is possible that this has caused some
aggregation biases in the results, as these have been observed before
(Kirkegaard & Becker, 2017). Unfortunately, it is not possible to access the
individual-level data that we rely upon. These data are only available to persons
with access to research data from Statistics Denmark. Only professors at Danish
universities and researchers from some private organizations may apply for such
data, and the process is lengthy. Second, while we were able to estimate
educational selection of immigrants (and thus intelligence indirectly), we did not
have any data on selectivity for broad cultural fit, chiefly for religion. It is possible
that some countries are sending relatively fewer Muslims or more secular
Muslims (e.g. Iran in the 1980s, or South Africa since the end of apartheid), and
others relatively more Muslims, and this causes some errors in our estimates,
probably underestimating the importance of the Muslim predictor (due to
measurement error). Third, while we employed weighted regressions to mitigate
against the sampling error in our outcome variable (mean GPA), this is only a
partial fix and some underestimation will remain. On this note, the results in Figure
5b about decreasing validity of national IQ at higher sample size requirements
was unexpected, and indeed concerning, and requires replication in other
datasets.
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Appendix
The data for the main variables are given below. Because data are not given
in the data source for very small groups for privacy reasons, the GPA for a given
origin group may differ from that from the first and second generations, or their
weighted average. E.g. for ALB (Albania), the mean GPA is 6.61 for the entire
group, but 6.08 for the second generation. Thus, we may infer that the average
for the missing first generation group is somewhat higher.
Table A1. Main variables used in the study. N refers to the number of persons
that mean GPA is calculated from.
ISO
IQlv
Muslim
Edu.
select.
N
GPA
GPA
1st gen.
GPA
2nd gen.
AFG
75.0
1.00
0.36
1832
6.41
6.15
6.73
ALB
82.0
0.82
0.67
17
6.61
6.08
DZA
84.2
0.98
-0.04
128
6.35
5.17
6.54
USA
97.5
0.01
0.84
115
7.79
7.23
8.06
AGO
71.0
0.01
-0.91
5
6.90
6.18
ARG
92.8
0.03
0.38
20
6.79
9.27
4.37
ARM
93.2
0.00
-0.47
74
6.81
7.41
6.63
AZE
84.9
0.98
-1.50
64
7.01
5.90
7.37
AUS
99.2
0.02
0.06
7
5.38
BGD
81.0
0.90
-0.19
54
7.16
6.89
7.68
BEL
99.3
0.06
0.87
12
7.72
7.28
BTN
78.0
0.01
-1.89
52
3.98
3.98
BIH
93.2
0.42
-0.43
1691
6.36
6.42
6.36
BRA
85.6
0.00
0.96
85
5.85
5.62
6.93
BGR
93.3
0.13
0.60
150
6.47
6.03
7.92
BDI
72.0
0.02
1.07
80
5.56
5.27
5.68
KHM
93.0
0.02
-0.43
33
6.72
7.13
CMR
64.0
0.18
0.37
19
5.02
5.82
4.91
CAN
100.4
0.03
0.14
27
7.06
6.97
6.88
CYM
82.0
0.00
256
7.20
7.54
7.06
CHL
89.8
0.00
-0.01
45
5.79
6.10
5.87
COL
83.1
0.00
0.92
52
5.81
5.50
6.64
CUB
85.0
0.00
0.24
13
6.66
6.70
DNK
97.2
0.04
363055
7.22
COD
68.0
0.01
261
4.48
4.44
5.21
ECU
88.0
0.00
-0.11
6
5.59
6.90
EGY
82.7
0.95
1.03
115
6.18
6.64
5.94
CIV
71.0
0.37
0.48
30
5.83
4.86
6.47
ERI
75.5
0.36
-0.62
79
5.47
4.77
5.95
KIRKEGAARD, E.O.W. & FEDOROV, J. MIGRANT GPA IN DENMARK
623
ISO
IQlv
Muslim
Edu.
select.
N
GPA
GPA
1st gen.
GPA
2nd gen.
EST
99.7
0.00
-1.16
28
7.46
6.72
9.05
ETH
68.5
0.34
0.17
150
6.14
3.97
6.24
PHL
86.1
0.05
-0.94
409
5.76
5.44
6.05
FIN
100.9
0.01
-0.11
47
7.28
6.92
7.42
ARE
87.1
0.76
44
4.93
4.79
FRA
98.1
0.07
0.86
42
7.21
6.06
7.30
GMB
62.0
0.95
-0.94
75
5.23
4.69
5.40
GEO
86.7
0.10
-1.15
13
6.78
6.92
GHA
69.7
0.16
-0.04
136
5.93
5.39
6.21
GRC
93.2
0.05
-0.39
12
7.70
6.71
GIN
66.5
0.84
-0.68
7
5.10
5.21
BLR
95.0
0.00
1.11
31
7.02
7.30
6.36
IND
82.2
0.15
0.12
275
6.95
7.27
6.87
IDN
85.8
0.88
0.50
55
6.34
6.32
IRQ
87.0
0.99
0.10
3675
5.98
5.76
6.06
IRN
85.6
1.00
0.33
1190
6.65
5.70
6.89
IRL
94.9
0.01
-0.12
4
9.18
9.38
ISL
98.6
0.00
0.09
503
6.88
6.83
7.03
ISR
94.6
0.18
-0.06
53
5.89
5.82
5.81
ITA
96.1
0.03
-0.31
59
6.86
6.67
6.97
JPN
104.2
0.00
0.10
3
7.67
JOR
86.7
0.99
-0.80
174
5.64
6.10
5.62
YUG
90.3
0.08
1295
5.84
5.41
5.87
KAZ
85.0
0.56
-0.02
6
8.13
7.41
KEN
74.5
0.07
0.85
63
5.81
5.79
6.08
CHN
105.8
0.02
0.76
483
8.09
7.33
8.41
KSV
90.3
0.96
365
5.65
5.75
5.66
HRV
97.8
0.01
0.50
62
6.26
5.75
6.64
KWT
85.6
0.86
-0.92
238
5.23
4.76
5.24
LVA
95.9
0.00
-2.09
169
6.08
5.83
6.84
LBN
84.6
0.60
-0.94
3199
5.29
5.36
5.29
LBR
68.0
0.13
0.37
6
5.10
2.10
LBY
85.0
0.97
-0.10
47
5.47
5.41
LTU
94.3
0.00
-1.41
306
6.38
5.99
8.04
MKD
90.5
0.35
-1.24
552
5.35
5.11
5.37
MYS
91.7
0.61
0.97
3
9.38
9.38
MAR
82.4
1.00
-1.04
1155
5.95
5.24
5.97
MEX
87.8
0.00
1.27
8
6.15
6.18
MDA
92.0
0.00
1.64
10
8.26
9.50
MNE
85.9
0.18
43
5.56
5.79
MANKIND QUARTERLY 2021 61:3
624
ISO
IQlv
Muslim
Edu.
select.
N
GPA
GPA
1st gen.
GPA
2nd gen.
MMR
85.0
0.04
-0.99
196
5.03
4.96
NLD
100.4
0.06
0.91
394
6.91
6.71
7.03
NPL
78.0
0.04
-1.18
26
6.35
6.24
NGA
71.2
0.48
1.47
57
6.67
6.76
6.96
NOR
97.2
0.03
-0.04
199
6.98
7.05
6.87
AUT
99.0
0.06
-0.81
27
7.58
8.57
7.64
PAK
84.0
0.96
-1.38
1831
5.92
6.04
5.90
PER
84.2
0.00
0.56
22
5.62
6.19
POL
96.1
0.00
-0.41
1280
6.15
5.86
6.92
PRT
94.4
0.01
-0.21
21
6.63
6.09
7.24
QAT
80.1
0.78
-0.81
14
5.90
5.34
COG
73.0
0.02
0.15
53
5.52
5.38
6.44
ROU
91.0
0.00
378
6.27
6.02
6.87
RUS
96.6
0.12
-0.87
327
6.76
6.59
7.17
RWA
76.0
0.02
0.61
55
6.43
6.34
6.30
SAU
79.6
0.97
0.57
23
6.71
6.82
CHE
100.2
0.06
0.31
14
8.29
8.23
9.41
SEN
70.5
0.96
-0.43
6
6.23
7.84
SRB
91.0
0.04
97
5.96
6.12
5.87
SRBM
90.3
0.03
141
6.19
5.63
6.33
SLE
64.0
0.72
-0.09
20
4.53
4.95
SVK
98.0
0.00
-1.28
20
6.74
6.54
SOM
72.0
0.99
2818
5.57
5.05
5.66
ESP
96.6
0.02
-0.02
56
6.15
6.19
4.89
LKA
79.0
0.09
-1.46
1425
6.99
6.48
7.01
SDN
77.5
0.71
-0.06
96
5.28
4.94
6.03
SWE
98.6
0.05
-0.18
225
6.92
6.61
7.07
ZAF
71.6
0.01
17
6.47
6.25
8.34
KOR
104.6
0.00
1.66
91
7.78
8.55
7.67
SYR
82.0
0.93
0.32
1230
5.08
4.58
5.82
TWN
104.6
0.00
3
10.09
TZA
73.0
0.30
1.01
62
5.90
5.81
6.07
THA
93.9
0.06
-1.47
530
4.86
4.55
5.94
CZE
98.9
0.00
-0.38
17
5.28
4.70
TGO
70.0
0.12
-0.36
3
4.11
TUN
85.4
1.00
-0.54
116
6.14
6.65
6.11
TUR
89.4
0.99
-2.26
5677
5.23
4.68
5.26
DEU
98.8
0.05
-0.80
935
7.02
6.67
7.63
UGA
71.7
0.12
-0.29
81
6.15
5.74
6.51
UKR
94.3
0.01
-0.30
190
6.81
6.52
7.43
KIRKEGAARD, E.O.W. & FEDOROV, J. MIGRANT GPA IN DENMARK
625
ISO
IQlv
Muslim
Edu.
select.
N
GPA
GPA
1st gen.
GPA
2nd gen.
HUN
98.1
0.00
-0.19
66
7.20
6.94
8.49
UZB
80.0
0.96
-0.69
3
6.43
VEN
83.5
0.00
1.63
18
6.48
6.12
6.82
VNM
91.4
0.00
-1.41
1749
7.24
5.72
7.34
YEM
80.5
0.99
-0.27
19
5.27
5.27
ZMB
74.0
0.00
0.57
41
5.03
4.92
5.11
ZWE
72.1
0.01
-0.50
3
6.16
6.16
... These real differences give rise to descriptive stereotypes (i.e., subjective perceptions about group differences). Few individuals are able to memorize detailed reports about the groups, so they rely on proxies such as the geographical location of countries or their wealth levels (see Kirkegaard & de Kuijper, 2020;Kirkegaard & Fedorov, 2021). ...
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