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Solid numbers, missed opportunities: review of The Intelligence of Nations
(2019)
by Richard Lynn and David Becker
Emil O. W. Kirkegaard, independent researcher, Denmark
Invited book review to Evolutionary Behavioral Sciences
The Intelligence of Nations
is the latest iteration of Richard Lynn’s long running
compilation of data on the intelligence of countries. The book has four chapters, the first
of which briefly (6 pages) introduces the reader to the question of why some countries
are richer than others (Smith, 1776). This chapter serves to summarize the three
previous books by Lynn and Vanhanen on the same topic (Lynn & Vanhanen, 2002,
2006, 2012). In his summary, however, Lynn neglects to mention his own 1978 book
chapter (Lynn, 1978) where he first summarized test results for ethnic groups and
countries. In that paper, Lynn did not produce a table of estimates, so I have constructed
one based on his reported studies; the corresponding values from the present book are
reported alongside in Table 1 for comparison.
Table 1 - Estimates of national IQs of select countries, 1978 and 2019
1978 values are based on (Lynn, 1978), using medians to combine studies when
needed. 2019 values are based on Lynn and Becker’s book.
Country
1978 IQ
1978 notes
2019 IQ
United Kingdom
100
99.12
United States
100
European descent
97.43
New Zealand
98.5
European descent
98.57
Australia
95
99.24
Belgium
104
97.49
France
104
96.69
Germany
100
East Germany
100.74
Denmark
100
97.83
Italy
100
Florence
94.23
Spain
87
93.9
Croatia
89
Zagreb
96.19
Greece
89
Thessaloniki
93.56
Iraq
80
89.28
Iran
82
80.01
India
86
76.24
Uganda
84
elite samples
76.42
Jamaica
79
75.08
Tanzania
88
elite samples
74.95
Ghana
75
58.16
South Africa
78
African descent
68.87
Taiwan
100
106.47
Japan
100
106.48
Indonesia
96
Bandung, elite
78.49
The 1978 estimates were often from unrepresentative elite samples, and there were no
adjustments made for the Flynn effect, which was not brought to significant attention until
6 years later by James Flynn (Flynn, 1984). Still, the correlation between the 1978 and
2019 values is r
= .81 (n = 23). This is quite strong, especially given that the early
estimates were based on approximately 30 studies, whereas the latest dataset is based
on 667 studies (version 1.3.1, the one found in the book).
Considering the stability of results over time, the reader might wonder: What’s new in this
book, as compared to the previous works? First and most foremost, Tatu Vanhanen
passed away in 2015, and so Lynn recruited the much younger (born 1983) German
intelligence researcher David Becker to assist him. The previous editions were
sometimes criticized for opaque methods and faulty calculations (e.g., Barnett &
Williams, 2004). To address these criticisms and to remove errors in the calculations, the
authors set out to re-run all calculations for the present book. They also set out to bring
their methodology in line with emerging principles for open science, including open data,
methods, and code (Hesse, 2018; Lindsay, 2017; Paxton & Tullett, 2019). The authors
therefore sought to obtain a copy of every paper that had been used. However, many of
these studies have not yet been obtained (514 accounted for, 70%, with 216 still
missing), and so were excluded from the present edition of the book. This is not so
surprising, given that many of the original articles were published, over the last century,
in extremely obscure, sometimes non-English outlets that no longer exist.
The main take-away from the new set of results is that the values estimated in earlier
works hold up well under increased methodological rigour and recalculation by a new
researcher (Becker did the calculations, Lynn provided copies of the sources). It’s worth
saying something about the new methods employed, which is the topic of the second
chapter. The authors illustrate their methodology by inviting the reader to consider data
for two fictional countries. They provide various characteristics about the fictive studies,
tests used, test administration year, test standardization year, representativeness,
sample size, age span, and so on. Then they introduce the reader to the various ways of
adjusting for distortive effects (including the Flynn effect which differs both by test and by
region), and the various ways of weighting the results by study quality. The process ends
up being somewhat similar to the one used for Cochrane systematic reviews, though the
authors do not seem to take inspiration from any guidelines for meta-analyses (e.g.
PRISMA, Swartz, 2011). Next, the authors discuss methods for computing national IQs
from international scholastic achievement/assessment tests (PISA, TIMSS, etc.), as was
also done by Heiner Rindermann earlier (Rindermann, 2007, 2018). After the process is
explained, tables are presented with the new IQ estimates. However, it’s unlikely that
anyone will be using these tables specifically because, as the authors explain, the
national IQs are now more frequently updated and are available on David Becker’s
website for download in machine-friendly format (http://viewoniq.org/). The current
version is 1.3.2, which contains several small error fixes compared to the values in the
book, and adds a few new studies. This approach brings the database of national IQs up
to par with other widely used datasets, such as the Maddison Project
(https://www.rug.nl/ggdc/historicaldevelopment/maddison/) which provides national
economic measures, and Clio-Infra (https://clio-infra.eu/) which provides historical data of
interest to economic historians.
Of particular interest to the study of group differences in intelligence are the comparisons
with the previous data sets, since so much research has been conducted on these. It has
been frequently claimed that Lynn’s national IQs were biased in favor of European
nations and against African ones (Rindermann, 2013a; Wicherts, Dolan, & van der Maas,
2010). Such bias does not need to result from deliberate actions, but can occur as part of
routine scientific work, which involves making make judgment calls where preconceived
notions might have an effect. In fact, the new IQs correlate at r = .85 with the Lynn and
Vanhannen’s (2012) shown in Figure 1. Since there is a huge overlap in sources, one
might wonder why this value isn’t higher. The reason seems to lie mostly with the use of
expanded norm ranges. Lynn did not previously rely on extrapolated norm ranges to
convert raw scores lying outside the range of standardization samples, whereas the new
calculations do make use of these. As a consequence, samples that would have
previously been given the minimum normed value (usually 60 or 65) are now given very
low values, even into the 40s. These extremely low values, of course, raise serious
questions about the measurement invariance of the tests (i.e. do they measure the same
thing in Germany as they do in Chad?), but it should be noted that some such values can
be expected on sampling error grounds alone. Since these are only found in developing
countries, especially African and Central American (e.g. Guatemala’s national IQ is
estimated to be 48), the use of the new more consistent methodology actually means
that Lynn had been overestimating
some non-European national IQs. In avoiding the use
of outside-norm values, Lynn effectively winsorised the low scoring samples, giving them
the benefit of the doubt. In dealing with these low scores, Lynn and Becker (2019)
recommend that authors apply a winsorization at 60 IQ in the book (p. 201). The
discussion of possible human bias in the prior calculations is surprisingly not given much
attention in the book at all, but Becker elaborated on it in a recent conference talk. He
analyzed relationships between changes in IQ estimates from the prior 2012 manual
calculations to the present mostly automated ones, and on relations to criterion variables
such as ancestry/ethnicity, and well-being. If such relationships are found it can be taken
to indicate a bias whereby the researcher pushes the calculation towards value that
better fit with other data. In Bayesian terms, this would actually be the right thing to do
(i.e. taking the prior into account), but it does open one to criticism if other researchers
have different priors (which of course they often do). Using the published dataset, I
calculated the correlations between European%, African%, HDI 2013, and the change in
scores between estimates. I estimated the ancestry fractions using Putterman’s
migration matrix. There were no relationships to ancestry: r European% = .11 (p =.17), r
African% = -.03 (p = .74). There was a weak relationship to HDI, r = .16 (p = .03),
meaning that lower HDI countries received slightly worse
scores in the new estimates,
opposite of the expected direction of bias. Thus, the criticism of bias against African
nations was contradicted. Furthermore, as can be seen in Figure 1, there is a marked
heteroscedasticity in the regression, i.e. the variance of changes to the scores is much
higher for the countries lower in intelligence and well-being. This of course reflects the
generally poorer data quality in these countries.
[fig. 1]
In reviews of the previous books in the series, much discussion centered on the use of
imputed country scores based on similar neighboring countries (Foster & Frijters, 2013;
Gale, 2013; Strate, 2013). In the new book, the same protocol is applied, but this time
using the border lengths as weights whenever possible (p. 43ff). Actually, data
imputation is not unusual in the least, and in fact should be the norm because the data
are not missing at random; indeed, missing values are concentrated in poorly developed
countries. If one used the national IQs without imputing the missing values, one would
get biased results from the missing observations (restriction of range bias, in particular).
Countries, of course, have spatial positions, and this allows the use of spatial statistics
(Gimond, 2019). Results from such analyses show that there is a very high degree of
(positive) spatial autocorrelation in the data, which in plain language means that country
IQs are highly predictable from neighboring countries (Gelade, 2008; Hassall & Sherratt,
2011). This, of course, also means that one can impute missing data with high accuracy,
justifying Lynn and Becker’s method. Spatial autocorrelation is just one type of
autocorrelation. Autocorrelation features are widely used to impute data in other fields,
for instance, in medicine where within person autocorrelation in time is used to fill in
missing data when observations in longitudinal studies are missing (Bell, Fiero, Horton, &
Hsu, 2014). Furthermore, if one used a multivariate imputation method, relationships to
other variables, such as health, ethnicity and wealth, would also be used to fill in missing
values.
The relative stability of the national IQ estimates across decades of data compilation is
worth remarking on in more detail. Since about 2012, social science and biomedical
sciences have been plagued by open discussions about the lack of replication and
general unreliability of findings, called the replication crisis (Shrout & Rodgers, 2018). It
is generally agreed upon that one of the major causes of the poor reliability of findings is
that studies are too small and underpowered for their purposes. In contrast, as has been
remarked by Steven Pinker, there is little to no replication crisis in (non-candidate gene)
behavioral genetics, and IQ research generally (Pinker, 2015; Plomin, DeFries, Knopik, &
Neiderhiser, 2016). Reviews of statistical (median observed) power by field show why
this is the case. Nuijten, van Assen, Augusteijn, Crompvoets, & Wicherts (2018)
reviewed intelligence research for statistical power and found an overall power of 53%,
which compares favorably to other social science fields (e.g., neuroscience at 21%,
Button et al., 2013; economics, 18%, Ioannidis, Stanley, & Doucouliagos, 2017).
Furthermore, they looked at subfields of intelligence research and found that group
differences had a median power of 62%, the highest reported of any social science field,
and closing in on the minimal requirement of 80% suggested by Cohen decades ago.
The philosopher Neven Sesardic suggested an explanation for why this is so, namely
that because intelligence research and group difference research in particular is disliked
so much by generally left-wing academics (Duarte et al., 2015), the standards of
evidence in peer review have been increased (Sesardić, 2005, sec. 6.4). While this
results in some suppression of published works, it also has the effect of increasing the
average rigor of the published research. As a case in point, the median sample size of
studies in psychology is somewhere between 40 to 120 depending on subfield
(Marszalek, Barber, Kohlhart, & Holmes, 2011), whereas the median sample size in the
current national IQ dataset was 353.
The third chapter (116 pages) of the book relates to the correlations between national
IQs and various other variables. This chapter is structured like the previous books: each
section summarizes findings from studies using the national IQs. These sections do not
represent systematic reviews of studies published, but seem to be the authors’ chosen
examples. The values from the reviewed studies (i.e., the correlations) are also given in
tables. The presentation here is very dry. As other reviewers have noted, this was the
case also for the previous books: X study reported a correlation of A, Y study reported a
correlation of B and so on. There is little to no attempt at describing results from more
causally informative studies, such as time-lagged regressions, path models, or various
econometric designs. As such, it invites the skeptical reader to think that the authors are
simply assuming causation at the aggregate level from the correlations (Barnett &
Williams, 2004). The authors probably regard these as probable inferences based on the
strong evidence from individual level studies (Herrnstein & Murray, 1994; Strenze, 2015;
Trzaskowski et al., 2014), but they make no serious attempt at convincing a skeptical
reader, which is a pity. (For examples of studies which attempt to decompose cause and
effect, see e.g. (Christainsen, 2013; Jones, 2016; Jones & Potrafke, 2014; Jones &
Schneider, 2010; Rindermann, 2018; Wong, 2007).
A problem raised by the authors, but not adequately discussed, concerns the matter of
measurement bias. The omission is odd because the unestablished measurement
invariance of national IQs has been a frequent point of criticism (e.g., Wicherts &
Wilhelm, 2007). There are in fact a small number of studies that have examined
measurement invariance in cognitive ability more broadly on the national level. The first
question to be asked, perhaps, is whether a general factor of intelligence exists at all in
the data from poor non-Western countries. It’s conceivable that this factor could vary by
level of development and perhaps be smaller or absent in poor countries (though this
would be in contradiction of the so called Spearman’s law of diminishing returns (Blum &
Holling, 2017; see also Coyle & Rindermann, 2013)). The question was answered with a
large analysis by Warne & Burningham (2018). These authors found that almost every
dataset analyzed from non-Western countries showed a g-factor similar to that seen in
Western datasets. Further, one might ask whether methods for detecting measurement
invariance find that the tests function similarly across different countries. Research using
the international TIMSS mathematics test data by (Wu, Li, & Zumbo, 2007) analyzed
data from Western developed countries, as well as Northeast Asia (Japan, South Korea,
and Hong Kong). They used state of the art multi-group confirmatory factor analysis, and
found that scores were only comparable within the broad cultures, not across cultures.
That is, one could compare scores for e.g., USA and Australia, as well as Japan and
South Korea, but not e.g. for USA and Japan. Other research has also found that
measurement invariance held for comparisons between the USA and Canada (Bowden,
Saklofske, & Weiss, 2011). The lack of measurement invariance is concerning, and
means that one cannot simply interpret the score difference between nations as being of
the same nature as that between individuals with nations. The matter clearly calls for
further investigation, which can be done using the publicly available data in scholastic
ability datasets (PISA etc.), as well as the various translations of IQ batteries such as the
Wechsler batteries.
The fourth and final chapter discusses the future of national IQs. In fact, the chapter is
about how to increase national IQs, and takes for granted that these are to some extent
causal for the many associations summarized in the third chapter. The authors discuss
five ways: three environmental and two genetic. Nutrition is advocated as an important
cause, and the authors cite a few studies of breastfeeding and vitamin/mineral
supplementation. However, while one can find such studies, there are other equally or
better studies showing no effect. Moreover, the authors do not cite recent studies
examining breastfeeding’s effect on IQ using a sibling control design (which controls for
genetic confounding; Der, Batty, & Deary, 2006), nor a study with a very thorough set of
parental controls (Girard, Doyle, & Tremblay, 2017), both of which show negative effects.
A particularly good study is the randomized controlled trial of about 800 children in Nepal
whose mothers received or did not receive multi-vitamins while pregnant, and which
followed the children until age 12 when they were tested for IQ (Dulal et al., 2018). There
was only a 1 IQ advantage (p = .18) for the intervention group despite the well designed
intervention and the large sample size. Thus, changing nutrition to increase intelligence
in the way the authors propose is probably not as easy as their discussion implies. The
second environmental factor the authors suggest is improving health. While this may be
done on general well-being grounds, does it really improve intelligence? The authors cite
two studies of infectious diseases (Hadidjaja et al., 1998; Jardim-Botelho et al., 2008).
Unfortunately, neither are persuasive. The first is a cross-sectional study without rigorous
controls which is expected to have genetic confounding. The second is a randomized
controlled trial. However, the analysis and reporting is suboptimal, and it’s difficult to
work out what the effect size is; the post-test score means reported for traits of interest
are quite comparable between intervention and control groups. The authors had 6
measures of intelligence, but only 3 of them showed an effect (in ANOVA) despite a
comparatively large sample size of 483. While it is very likely that intelligence levels
could be increased by improving health, these particular studies do not provide strong
evidence for action. For a recent meta-analysis of interventions with somewhat optimistic
conclusions, see (Protzko, 2017). The third proposed method is improved education. Of
course, the relationship between education and intelligence is complicated, and still
unsettled. A recent meta-analysis of studies showed that the method employed to
estimate the causal effect of education on intelligence has a large effect on the estimated
outcome. Specifically, pre-post control studies finding quite small effects, while natural
experiments based on policy changes find, probably unrealistically, large effect sizes
(Ritchie & Tucker-Drob, 2018). The matter is also complicated by the fact that IQ gains
associated with education duration do not appear to be on the general intelligence factor
(g), but rather on the non-g factors (Ritchie, Bates, & Deary, 2015). Considering the
evidence that it’s mainly the g-factor that adds predictive validity to IQ test scores
(Jensen, 1998), it is not clear what improving non-g factor scores would accomplish. The
matter requires more psychometrically sophisticated research to clarify.
The authors also discuss genetic means of improving national intelligence. First, the
authors review of history of research into dysgenics, chiefly in studies reporting negative
relationships between IQ measures and fertility measures. Second, they review
economic policies attempting to encourage smart people to have more children,
especially smart women. They cite some old reviews of policies on maternity leave, and
tax/cash benefits in Western countries, finding small positive effects on fertility. Given
that Western countries vary widely in fertility levels, from near replacement (about 1.8) in
Nordic countries and the United Kingdom to quite low (about 1.4) in neighboring
Germany and southern Europe, it seems likely that one can indeed influence fertility
levels by interventions. Unfortunately, there seems to be a lack of randomized controlled
trials on the topic, so researchers are left with suboptimal research designs. Alternatively,
instead of attempting to get smart people to have more children, one could attempt to get
less bright people have fewer children. The most obvious way to do this, the authors
note, is to scale back welfare policies that enable the practice of single-motherhood. The
authors consider such changes to be doubtful in Western countries due to popular
resistance. Finally, the authors discuss the role of immigration in national IQs. Letting in
immigrants with lower IQs will generally lead to a decline in national IQ whereas letting in
high IQ immigrants will have the opposite effect (Borjas, 2016; Kirkegaard & Tranberg,
2015; Nyborg, 2012; Rindermann & Thompson, 2016; Woodley of Menie,
Peñaherrera-Aguirre, Fernandes, & Figueredo, 2018). They discuss the current political
realities of immigration, which need not concern us here. The final prediction for the
future echoes Lynn’s previous writings (Lynn, 2001): Western civilization is declining for
a variety of reasons, and China will probably emerge as the global superpower sometime
in this century.
All in all, the book is similar to the predecessors in presentation and discussion of
material. On the negative side, there is little to no attempt at using advanced statistical
methods to clarify matters of disputed causality, or even just the relative importance of
predictors. Existing studies on the question are not seriously discussed either, and an
important opportunity is missed. The presentation is quite dry. On the positive side, the
book describes the current state of the art calculations for national IQs, introduces the
reader to the open dataset of national IQs, shows their high replicability, and reviews
their use by tens if not hundreds of other researchers. This last point bears noting
because it shows that, despite criticism, the national IQs subfield has become a very
productive research program (Rindermann, 2013b; Urbach, 1974a, 1974b). In fact, one
might say that national IQs are getting quite popular because various other groups have
begun publishing very similar national cognitive ability estimates (Angrist, Djankov,
Goldberg, & Patrinos, 2019; Coutrot et al., 2018; Lim et al., 2018), even if they call it
something other than “intelligence”, and rarely cite the pioneering efforts of Richard Lynn
and colleagues.
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