Luke Ford

Richard Lynn and Tatu Vanhanen write:

Ten years ago we began our research program for the
investigation of how far differencesin intelligence can explain the
differences in economic development between nations in our book
IQ and the Wealth of Nations(2002). Our starting point was that it
has been established that intelligence is a determinant of earnings
among individuals, and hence that this association should also be
present across nations. We searched for studies throughout the
world in which intelligence tests had been administered, and
found useable data for 81 nations. We calculated the results by
setting the IQ in Britain at 100 (standard deviation =15) and the
IQs of other nations were expressed on this metric. The results
showed that there are huge differences in the average IQs of
nations, ranging from approximately 70 in sub-Saharan Africa, to
approximately 100 in most of Europe and the countries colonized
by Europeans in the last few centuries(the United States, Canada,
Australia, New Zealand, Argentina, Chile and Uruguay), to
approximately 110 in China, Japan, Korea, Singapore and
Taiwan. We then showed that national IQs were correlated with
per capita income (measured as real GDP, gross national product,
per capita) at 0.73 (Lynn and Vanhanen, 2002, p. 89). This
showed that 53 per cent of the variance in per capita in this group
of nations is attributable to differences in intelligence. We then
used the measured IQ of the 81 nations to estimate the IQs for a
further 104 nations that were ethnically similar to those for which
we had measured IQs. For example, we assumed that the IQ in
Luxembourg would be the same as in the Netherlands and
Belgium. This gave us IQs for all 185 nations in the world with
populations over 50,000. We showed that for these 185 nations,
IQs were correlated with per capita income (measured as real
Gross Domestic Product, per capita) at 0.62. This is lower than the
correlation for 81 nations, probably because there was some error
in the estimated IQs. Nevertheless, the correlation is highly
significant and shows that 38 per cent of the variance in per capita
income in the nations of the world is attributable to differences in
intelligence. To establish the validity of these national IQs, we
showed that they are correlated at 0.88 with national scores on
tests of mathematics and at 0.87 with national scores on tests of
science.
In 2006 we published further evidence for this theory in our
book JQ and Global Inequality. In this we presented measured IQs
for an additional 32 nations, bringing the total number of nations
for which we had measured IQs to 113. We showed that these
were correlated with per capita income (measured as real GNI,
gross national income) at 0.68 (Lynn and Vanhanen, 2006, p.
102). Following the method in our first study, we used the
measured IQ of the 113 nations to estimate the IQs for an
additional 79 nations that were ethnically similar to those for
which we had measured IQs. This gave us a total of 192 nations,
comprising all the nations in the world with populations over
40,000. We found a correlation of 0.68 between national IQ and
per capita income in the 113 nations for which they had measured
IQs, and a correlation of0.60 between national IQ and per capita
income in the 192 nations. Once again, the correlation for the 113
nations’ measured IQs is a little higher than for the larger 192
nation data set, and probably for the same reason that measured
national IQs are more valid than estimated national IQs.
In our 2006 book we extended the analysis beyond
economic development and showed that national IQs explain
substantial percentages of the variance in national differences a
number of other phenomena including literacy, life expectancy,
and the presence of democratic institutions…

…national IQs are highly correlated with national scores in tests of mathematics
and science, as shown in detail in Chapter 3, as well as with a
number of other economic and social variables, as documented
throughout this book. If our IQs were meaningless, they would
not be highly correlated with a wide range of economic and
social phenomena.

Country Measured Final IQ
Afghanistan – – – – – – (75)
Albania – – 385.5 78.7 82 2 82
Algeria – – 403.6 81.5 84.2 2 84.2
quality Final IQ
Andorra – – – – – – (97)
Angola – – – – – – (71)
Antigua/
Barbuda
– – – – – – (74)
Argentina 96 10 407.6 82.1 84.7 4 92.8
Armenia 92 3 485.1 94.1 94.1 4 93.2
Australia 98 12 534.3 101.7 100 16 99.2
Austria 99.5 4 523.7 100.1 98.7 10 99
Azerbaijan – – 409 82.3 84.9 4 84.9
Bahamas – – – – – – (84)
Bahrain 81 2 437.1 86.7 88.3 4 85.9
Bangladesh 81 4 – – – – 81
Barbados 80 3 – – – – 80
Belarus – – – – – – (95)
Belgium 99 8 530.1 101.1 99.5 14 99.3
Belize – – 342.5 72.1 76.8 1 76.8
Benin – – – – – – (71)
Bermuda 90 4 – – – – 90
Bhutan – – – – – – (78)
Bolivia 87 6 – – – – 87
Bosnia 94 4 465.5 91.1 91.7 2 83.2
Botswana 71 2 367.7 76.0 79.9 4 76.9
Brazil 87 13 396.1 80.4 83.3 8 85.6
Brunei – – – – – – (89)
Bulgaria 92.5 6 481.9 93.6 93.7 12 93.3
Burkina
Faso
– – – – – – (70)
Burundi – – – – – – (72)
Cambodia – – – – – – (92)
Cameroon 64 2 – – – – 64
Canada 100 9 538.8 102.4 100.6 16 100.4
Cape Verde – – – – – – (76)
Central
African
Rep. 64 5 – – – – 64
Chad – – – – – – (66)
Chile 91 10 437.9 86.8 88.4 8 89.8
China 105.5 16 601.7 112.1 108.2 2 105.8
Tibet 92 2 – – – – 92
Colombia 83.5 7 391.8 79.7 82.8 8 83.1
Comoros – – – – – – (77)
Congo
(Brazzaville) 73 8 – – – – 73
Congo
(Zaire) 68 13 – – – 68
Cook
Islands 89 2 – – – – 89
Costa Rica 86 2 – – – – 86
Cote
d’Ivoire
71 2 – – – – 71
Croatia 99 7 499.1 96.3 95.8 4 97.8
Cuba 85 2 – – – – 85
Cyprus – – 466.2 91.2 91.8 8 91.8
CzechRep. 98 7 528.2 100.8 99.3 14 98.9
Denmark 98 5 507.8 97.6 96.8 10 97.2
Djibouti – – – – – – (75)
Dominica 67 5 – – – – 67
Dominican
Republic
82 6 – – – – 82
East Timor – – – – – – (85)
Ecuador 88 5 – – – – 88
Egypt 81 5 409.4 82.4 84.9 4 82.7
El Salvador – – 352.4 73.6 78 2 78
Equatorial
Guinea
– – – – – – (69)
Eritrea 75.5 4 – – – – 75.5
Estonia 99 7 539.3 102.5 100.6 6 99.7
Ethiopia 68.5 9 – – – – 68.5
Fiji 85 3 – – – – 85
Finland 97 5 557.8 105.4 102.9 10 100.9
France 98 10 518.7 99.3 98.1 10 98.1
Gabon – – – – – – (69)
Gambia 62 6 – – – – 62
Georgia – – 424.1 84.7 86.7 2 86.7
Germany 99 17 520.4 99.6 98.3 10 98.8
Ghana 70 10 277.5 62.0 69 4 69.7
Greece 92 10 487.4 94.5 94.4 10 93.2
Greenland – – – – – – 91
Grenada – – – – – – (74)
Guatemala 79 3 – – – – 79
Guinea 66.5 6 – – – – 66.5
Guinea- Bissau
– – – – – – (69)
Guyana – – – – – – (81)
Haiti – – – – – – (67)
Honduras 81 6 – – – – 81
Hong Kong 108 16 559.7 105.6 103.1 14 105.7
Hungary 96.5 8 525.2 100.3 98 16 98.1
Iceland 101 4 514.7 98.7 97.6 10 98.6
India 82 21 419.4 84.0 86.1 1 82.2
Indonesia 87 8 409.7 82.5 85 12 85.8
Iran 83.5 9 434.7 86.3 88 8 85.6
Iraq 87 5 – – – – 87
Ireland 92.5 18 526.6 100.5 99.1 10 94.9
Israel 95 14 485.3 94.1 94.1 12 94.6
Italy 97 14 495.8 95.8 95.4 16 96.1
Jamaica 71 11 – – – – 71
Japan 105 25 558.8 105.5 103 16 104.2
Jordan 84 8 441.6 87.4 88.8 10 86.7
Kazakhstan – – 410 82.5 85 2 85
Kenya 74 12 370.2 76.3 80.2 1 74.5
Kiribati – – – – – – (85)
Korea:
North – – – – – – (104.6)
Korea:
South 106 9 565.7 106.6 103.8 16 104.6
Kuwait 86.5 9 398.9 80.8 83.7 4 85.6
Kyrgyzstan – – 325.4 69.4 74.8 4 74.8
Laos 89 2 – – – – 89
Latvia – – 500.3 96.5 95.9 14 95.9
Lebanon 82 4 428 85.3 87.2 4 84.6
Lesotho – – 257.3 58.9 66.5 1 66.5
Liberia – – – – – – (68)
Libya 85 8 – – – – 85
Liechtenstein – – 536.2 102.0 100.3 8 100.3
Lithuania 92 7 498.5 96.4 95.7 12 94.3
Luxembourg – – 492.9 95.3 95 8 95
Macao – – 533.6 101.6 99.9 6 99.9
Macedonia – – 455.7 89.6 90.5 4 90.5
Madagascar 82 2 – – – – 82
Malawi 60 3 204.9 50.8 60.2 1 60.1
Malaysia 88.5 8 500.7 96.5 96 6 91.7
Maldives – – – – – – (81)
Mali 69.5 8 – – – – 69.5
Malta 97 2 480.7 93.4 93.5 2 95.3
Mariana
Islands 81 2 – – – – 81
Marshall
Islands 84 3 – – – – 84
Mauritania – – – – – – (74)
Mauritius 89 5 395.5 80.3 83.3 1 88
Mexico 88 8 431.2 85.8 87.6 8 87.8
Micronesia – – – – – – (84)
Moldova – – 468.1 91.5 92 4 92
Mongolia 100 6 – – – – 100
Montenegro – – 417.7 83.7 85.9 4 85.9
Morocco 84 9 369.4 76.2 80.1 6 82.4
Mozambique 64 2 327.2 69.7 75 2 69.5
Myanmar/
Burma
– – – – – – (85)
Namibia 72 2 262.3 59.7 67.1 1 70.4
Nepal 78 4 – – – – 78
Netherlands 100 10 540.7 102.7 100.8 12 100.4
Netherlands
Antilles 87 2 – – – – 87
New
Caledonia
85 2 – – – – 85
New
Zealand
99 1 523.7 100.1 98.7 14 98.9
Nicaragua – – – – – – (84)
Niger – – – – – – (70)
Nigeria 71 13 302.6 65.9 72 4 71.2
Norway 100 2 507.3 97.5 96.8 14 97.2
Oman 84.5 8 406.8 82.0 84.6 2 84.5
Pakistan 84 8 – – – – 84
Palestine 86 4 393.3 79.9 83 4 84.5
Panama – – 369 75.2 80 2 80
Papua N.G. 82.5 4 428.6 85.4 87.2 1 83.4
Paraguay 84 6 – – – – 84
Peru 85 9 372 76.6 80.4 2 84.2
Philippines 90 7 363.5 75.3 79.4 4 86.1
Poland 95 13 516 98.9 97.8 8 96.1
Portugal 94.5 6 487 94.4 94.3 10 94.4
Puerto Rico 83.5 8 – – – – 83.5
Qatar 83 6 345.9 – 77.2 6 80.1
Romania 91 6 460 90.2 91 12 91
Russia 96.5 6 506.5 97.4 96.7 16 96.6
Rwanda 76 2 – – – – 76
St Helena – – – – – – (86)
St Kitts &
Nevis – – – – – – (74)
St Lucia 62 2 – – – – 62
St Vincent 71 2 – – – – 71
Samoa
(Western) 88 5 – – – – 88
Sao Tome
& Principe – – – – – – (67)
Saudi
Arabia
79 8 376.3 77.3 80.9 4 79.6
Senegal 70.5 5 – – – – 70.5
Serbia &
Montenegro 88.5/93 4 459.6 90.2 91 10 90.3/92
Seychelles – – 405 81.7 84.4 1 84.4
Sierra Leone 64 3 – – – – 64
Singapore 108.5 5 586.8 109.8 106.4 10 107.1
Slovakia 98 8 517.1 99.1 97.9 12 98
Slovenia 96 11 526 100.4 99 12 97.6
Solomon
Islands – – – – – – (83)
Somalia – – – – – – (72)
South
Africa 72 16 291.4 64.2 70.7 6 71.6
Spain 97 11 503 96.9 96.2 14 96.6
Sri Lanka 79 2 – – – – 79
Sudan 77.5 19 – – – – 77.5
Suriname 89 4 – – – – 89
Swaziland – – 330.7 70.2 75.4 2 75.4
Sweden 99 8 521.1 99.7 98.4 14 98.6
Switzerland 101 6 531.6 101.3 99.7 10 100.2
Syria 80.5 7 427 85.1 87.1 2 82
Taiwan 105 19 565.3 106.5 103.8 10 104.6
Tajikistan – – – – – – (80)
Tanzania 72.5 9 349.8 73.2 77.7 1 73
Zanzibar – – 293.7 64.5 70.9 1 70.9
Thailand 88 8 460.7 90.3 91.1 12 89.9
Togo – – – – – – (70)
Tonga 86 2 – – – – 86
Trinidad &
Tobago – – 421.7 84.3 86.4 2 86.4
Tunisia 84 4 417.7 83.7 85.9 12 85.4
Turkey 88.5 9 453.7 89.3 90.3 10 89.4
Turkmenistan – – – – – – (80)
Uganda 72 9 275.8 61.7 68.8 1 71.7
Ukraine 95 2 481.7 93.6 93.7 2 94.3
United Arab
Emirates 83 6 477.5 92.9 93.2 4 87.1
United
Kingdom 100 7 523.2 100.0 98.7 14 99.1
England – – 524.3 102.2 98.8 8 98.8
Scotland – – 502.3 96.8 96.2 6 96.2
USA 98 10 510.6 98.1 97.2 16 97.5
Uruguay 96 2 441.3 87.3 88.8 6 90.6
Uzbekistan – – – – – – (80)
Vanuatu – – – – – – (84)
Venezuela 84 6 374.9 77.1 80.8 1 83.5
Vietnam 94 3 – – – – 94
Yemen 83 6 247.8 57.4 65.4 1 80.5
Zambia 75 7 259.6 59.2 66.8 1 74
Zimbabwe 71.5 4 310.6 67.1 73 3 72.1

The comparison of countries with large positive and negative
residuals has disclosed that particular local circumstances are
connected with nearly all large outliers and that they may explain a
significant part of the large deviations from the regression line. It
is important to note that the focusis on particular local factors and
that their impact is restricted to limited groups of countries. They
are not universal factors which could be used to explain the
variation in per capita income in all countries of the world.
(1) The significance of the economic system (market
economy versus socialist command economy) seems to be
limited to the group of countries with high national IQ (90 and
over). In the market economies (nearly always connected with a
democratic political system), the level of per capita income has
risen much higher than expected on the basis of the regression
equation, and in the socialist economic systems (and former
socialist systems) at the same level of national IQ, the level of per
capita income tendsto be much lower than expected.
(2) The contrast between the Caribbean tourist islands with
large positive residuals and a group of Oceanian island states
without important tourist industries and with large negative
residuals illustrates the significance of foreign investments and
technologies as well as of geographical factors. Because the
Caribbean islands are relatively close to potential tourists in the
North America and Europe, they have attracted extensive foreign
investments in tourism, whereas remote Oceanian island states
have not been attractive places for extensive foreign investments
in tourist industries. This difference may explain why the
Caribbean tourist islands have been economically more successful
than the Oceanian island states, although national IQ is for most
Caribbean island countries lower than for Oceanian island
countries.
(3) The contrast between Asian and African countries with
significant oil industries and their neighboring countries without
significant oil and gas resources illustrates the potential
importance of natural resources. Countries with oil or other
significant natural resources have attracted foreign investments
and technologies from countries of higher national IQs, which
has raised the level of per capita income much higher (in some
cases many times higher) than expected on the basis of the
regression equation, whereas in the countries without attractive
natural resources it has remained at the expected level or, in some
cases, it has been lower than expected on the basis of national IQ.
Countries like Bahrain, Brunei, Equatorial Guinea, Kuwait,
Qatar, Saudi Arabia and the United Arab Emirates with extremely
large positive residuals are dominated by oil industries.
(4) The contrast between the countries ravaged by ethnic
civil wars or other wars and with large negative residuals and the
countries which have been able to maintain internal peace
illustrates the negative impact of violent strife on economic
development. Wars and civil wars have hampered economic
development and caused the emergence of large negative
residuals in several cases. So this is one of the exceptional local
factors that affects the level of per capita income independently
from national IQ.
(5) To some extent, geographical factors may hamper
economic development independently from national IQ. This
concerns especially isolated landlocked states. Laos, Moldova and
Mongolia are such countries in the group of large negative
outliers. The actual level of per capita income is in all of them
much lower than expected on the basis of national IQ. It can be
inferred that not only the former socialist system but also their
geographical isolation has hampered economic development in
these countries. However, in some cases favorable geographical
location may have furthered economic development. This
concerns especiallyLuxembourg and Switzerland, which have
benefitted from their proximity to France and Germany.
It is important to note that the impact of exceptional factors
discussed above is limited to particular groups of countries and
that it is difficult to measure their impact by empirical evidence.
Large positive and negative outliers indicate that national IQ is not
the only factor affecting the variation in per capita income, but it
may be the only systematic causal factor that is relevant across all
cultural and geographical boundaries. The level of per capita
income tends to be higher in countries with high national IQ than
in countries with low national IQ. Depending on the sample of
countries and of the type of correlation, national IQ explains from
35 to 62 per cent of the variation in PPP-GNI-08. Because a part
of the variation may be due to measurement errors and accidental
factors, it is not necessary to pay attention to relatively small
deviations from the regression line.
Some other indicators of socioeconomic development are
moderately or strongly related to the level of per capita income,
but because their causal relations may be reciprocal and because
they tend to be as strongly related to national IQ as the indicators
of per capita income, their ability to explain the variation in per
capita income is quite limited. For example, adult literacy rate (see
Chapter 3) is moderately correlated with PPP-GNI-08 (0.482,
N=196), but when national IQ and Literacy-08 are used together
to explain variation in PPP-GNI-08, the multiple correlation
(0.608) is only slightly higher than the simple correlation
between national IQ and PPP-GNI-08 (0.592). In other words,
Literacy-08 raises the explained part of variation in PPP-GNI-08
only by two percentage points independently from national IQ.

We come to the conclusion that national IQ explains more of the level of poverty than any other explanatory factor. The level of poverty tends to decrease when the level of national IQ rises.

The relationship between national IQ and rates of
unemployment has not been examined hitherto and is considered
in this section. At the individual, within-country level, several
studies have shown a robust association between low intelligence
and unemployment. Toppen (1971) reported a sample of the
unemployed in the United States had an average IQ of 81, more
than a standard deviation (15 IQ points) below the U.S. mean IQ
of approximately 100. Lynn, Hampson and Magee (1984)
reported that a sample of the unemployed in Northern Ireland had
an average IQ of 92, again below the national mean. Herrnstein
and Murray (1994) reported that in a sample in the United States,
14 per cent of those with IQs below 74 had been unemployed for
one month or longer during the preceding year, and the
percentages of the unemployed declined in successively higher IQ
groupsto 4 per cent among those with IQs above 126. Thus, lowIQ
individuals make up a disproportionate share of unemployed.
Mroz and Savage (2006), using the National Longitudinal Survey
of Youth, found that lower IQ predicted higher probability of
unemployment within the last year, higher average weeks of
unemployment, and higher probability of job change, even after
controlling for years of education, ethnicity, parental education,
whether the person’s childhood home received periodicals, and a
rich variety of additional covariates. Thus, both the rate of job
destruction and the length of job search are higher for workers
with lower IQ.

The correlation between the unemployment estimate based on this equation and national IQ is -0.66 (107 nations) and therefore national IQ explains 43.5% of the variance in unemployment. The negative correlations show that unemployment is lower in high IQ nations…

The principal explanation for the association between low IQ
and high rates of unemployment among individuals within
countries is that those with low IQs normally perform poorly at
school and do not acquire educational credentials. Employers
typically select employees on the basis of educational
qualifications and are reluctant to employ those without
educational qualifications. If those with low IQs do secure jobs,
they typically perform poorly, since numerous studies have
shown that intelligence is positively related to the efficiency of
performance. This has been reported in the United States
(Ghiselli, 1966; Hunter and Hunter, 1984; Schmidt and Hunter,
1998) and in Europe (Salgado, Anderson, Moscoso, et al.,
2003). When those with low IQs perform poorly in employment,
they are typically dismissed. They acquire a poor work history,
and this makes employers reluctant to employ them. The principal
explanation for the association between low IQ and high rates of
unemployment across countries is likely that the population of
low IQ countries are not able to produce goods and services so
efficiently for sale international markets, as compared with the
populations of high IQ countries.

The analysis of economic conditions measured by some
indicators of per capita income, poverty, and income inequality
shows that national IQ explains nearly half or at least more than
any other available variable of the global variation in these
indicators. This relationship has been present at least since
1500. This suggests that human possibilities to equalize
economic conditions seem to be quite limited.
It has been difficult to equalize per capita income between
countries whose national IQs differ significantly from each other.
It would be much easier to equalize per capita income between
countries whose national IQs are approximately at the same level.
However, some geographical or other local factors may be
enough to maintain economic differences even in these groups of
countries with more or less equal IQs. It is remarkable that factors
that are related to large deviations from the regression line seem
to be exceptional local factors. It was not possible to find any
universal environmental factor that could explain a significant part
of the global variation in per capita income independently from
national IQ.

National IQ explains more than one third of the global
variation in per capita income, but more than 60 percent remains
unexplained. National IQ explains approximately half of the
global variation in poverty measures, but poverty is also related
to the level of per capita income and literacy. Because the
measures of poverty are strongly related to national IQ, it is
reasonable to expect that significant global differences in the
level of poverty will continue indefinitely. Human efforts can be
increased to diminish global differences in the level of poverty,
but the possibilities of reducing these disparities are limited. The
continual struggle for scarce resources maintains global
differences in poverty, and high IQ nations tend to be more
successful in this struggle than low IQ nations.

National IQ does not explain more than 22 percent of the
variation in Gini index and Highest 20% variables, but it may be
more than what any other measurable factor could explain. The
differences in income inequality seem to be significantly related to
some regional and cultural factors and to the racial homogeneity of
populations. Latin America is a region of exceptionally high level of
economic inequality, which may be partly due to the racial
heterogeneity of Latin American populations. The same factor
appears also in other parts of the world. European and most African
countries, in which income inequality tends to be much lower, are
racially relatively homogeneous. It can be anticipated that economic
inequalities within countries will continue indefinitely. The impacts
of national IQ and regional and racial factors on economic inequality
are unlikely to disappear, although it is certainly possible to reduce
inequality in particular countries by appropriate social and
institutionalreforms.
Our point is that evolved human diversity, which we have
measured by national IQ, is a permanent factor behind global
economic inequalities. It provides the most powerful theoretical
explanation for many kinds of global inequalities in human
conditions and explains their persistence. A more extensive analysis
of the impact of other environmental variables would certainly raise
the explained part of variation to some extent, but we have focused
on the explanatory power of national IQ…

Nations can be ordered on a scale of political freedom in which
free societies are characterized by an absence of corruption,
democracy, efficient bureaucracies, property rights, and the rule of
law. We have argued that populations require a certain level of
intelligence to sustain a free and democratic society because
“people in countries with low national IQs are not as able to
organize themselves, to take part in national politics, and to
defend their rights against those in power as people in countries
with higher national IQs” (Vanhanen, 2009, p. 270).

According to the Transparency International’s corruption
perceptions index, the extent of corruption varies greatly in the
world. The problem is why corruption varies so much. We
hypothesize that the extent of corruption is negatively related to
the level of national IQ because more intelligent nations may have
better capabilities than less intelligent nations to exclude
corruption from the functions of their political institutions, or at
least to diminish its extent…

The level of democratization seems to
rise systematically with the level of national IQ. The results of
this analysis lead to the conclusion that all countries do not have
equal chances to establish and maintain democratic systems.
Because of the constraining impact of national IQ, the level of
democratization is and will most probably remain significantly
lower in countries with low national IQs than in countries with
high national IQs. It is a consequence of evolved human
diversity. Vanhanen has analyzed extensively the impact of
national IQ on the level and quality of democratization in his book
The Limits of Democratization (Vanhanen, 2009).

When national IQ crosses the level of 95, the level of corruption decreases steeply, although not in all countries…

Economically highly developed countries with relatively high
national IQs constitute the largest coherent group of positive
outliers (17 countries). Most of them are European and European
offshoot democracies, but the group includes also Singapore from
East Asia…

There are clear differences, both regional and structural,
between the countries of large positive and large negative outliers.
Nearly all positive outliers are economically highly developed
democracies, mostly in Europe, or countries in which foreign
technologies, investments, and management have a dominant role.
On the other hand, nearly all large negative outliers are socialist or
former socialist countries or economically less developed Asian
countries. One crucial difference between positive and negative
outliers concerns their ethnic structures. The populations of
positive outliers are ethnically relatively homogeneous, whereas
the populations of many negative outliers are ethnically highly
heterogeneous. These structural differences seem to have affected
the level of corruption independently from national IQ.

The statistical investigations carried out in this chapter show
that global differences in the levels of democratization, women’s
representation in parliaments, gender inequality in human
development, and corruption can be traced, to some extent, to
differences in national IQs, although only slightly in the case of
women’s representation. The nature of political institutions is in
principle under human control, but historical and cultural legacies
may support the survival of existing structures and make it
difficult to change them. Our analysis on the impact of national IQ
on political institutions is based on the assumption that people use
their intelligence in their attempts to improve the quality of
political institutions and that, consequently, more intelligent
nations are able to construct qualitatively better political
institutions than less intelligent nations. Therefore, differences in
national IQs are assumed to explain a significant part of the
qualitative differences between political institutions and of the
persistence of those differences…

There are a number of studies reporting that intelligence is
positively associated with good health among individuals. For
instance, Anstey, Low and Sachdev (2009) have shown that the
intelligence is associated with higher levels of physical activity,
greater likelihood of taking vitamins, and reduced likelihood of
smoking, all of which promote good health.
Several studies have reported that low birth weight is
associated with low IQ in childhood and adolescence, e.g. Bhutta,
Cleves, Case, Cradock and Anand, 2002; Deary, Whalley and
Starr (2009, pp. 193-195).
Infant mortality (infant deaths in the first year of life) is
associated with low IQ mothers. This was first shown by Savage
(1946) who reported that the mothers of infants who died in their
first year had below average intelligence. This was confirmed by
Herrnstein and Murray (1994, p. 218) who showed that the
mothers of infants who had died in their first year had an average
IQ of 94, compared with 100 of the mothers of infants who had
not died in their first year. These results are understandable,
because mothers with low IQs would be less competent in taking
care of the health of their infants. Mothers with higher IQs would
be better at anticipating possible accidents and preventing them
happening, judging whether illnesses are sufficiently serious to
justify seeing a physician, and giving medications that are
prescribed.
Several studies have found that intelligence is a determinant of
life expectancy. This was shown first in Australia by O’Toole and
Stankov (1992) in a study of 2,309 men who were conscripted
into the military and intelligence tested at the age of 18, between
1965 and 1971. They were followed up in 1982, when they were
aged between 22 and 40, and it was found that 523 had died.
These had an IQ 4 points lower than those who remained alive.
The commonest cause of death was accidents of various kinds
(389), of which motor vehicle accidents (217) were the most
frequent. It seems probable that the explanation for this
association is that those with lower IQs make more
misjudgements. Some of these misjudgements result in accidents
and some of these are fatal. Gottfredson (2004) has reviewed a
number of subsequent studies confirming the association of low
intelligence with high mortality, and this has also been found in
Sweden (Hemmingsson, 2009).
An extensive research program in Scotland examining the
relation of IQ measured at the age of 11 to mortality (i.e. age of
death) has been summarized by Deary, Whalley and Starr (2009,
pp. 50-52). They confirm that low intelligence predicts high
mortality and have found that low intelligence is associated with
several specific causes of death. Low intelligence is associated
with smoking and death from lung cancer and other smokingrelated
cancers, namely mouth, pharynx, esophagus, larynx,
pancreas and bladder cancers. Low intelligence is also associated
with death from all cardiovascular diseases, coronary heart
disease, stroke, and respiratory disease. They suggest four
explanations for these associations. First, childhood IQ might be a
record of bodily insults including illness, poor nutrition, and
injuries. Second, childhood IQ might be a marker for genetic
bodily system integrity. Third, people with higher IQs may be
better at avoiding risks and at preserving their health, for instance
by eating sensible foods, avoiding smoking, recognizing
symptoms that might be injurious to health, consulting
physicians, and complying with prescribed treatments. This
theory implies that intelligence differences are causal to mortality.
Fourth, people with higher IQs may tend to work in occupations
where there is less risk of death…

We propose that there is a positive feedback loop across
nations between good health, IQ, and per capita income.
Healthy people work more efficiently than unhealthy workers,
so good health promotes high per capita income, good nutrition
and health care, and higher intelligence. In the positive feedback
loop, high national intelligence promotes high per capita income
and good health Nutrition is a basic factor affecting health because it is not
possible to live without sufficient nutrition. Many kinds of
climatic, geographical, and other environmental factors affect the
availability of appropriate nutrition, but the sufficiency of
nutrition depends also on human skills and policies. Therefore we
hypothesize that nutrition correlates positively with national IQ. It
is interesting to investigate whether national IQ is the best
explanatory factor or are there some other factors which explain as
much or more of the variation in indicators of nutrition
independently from national IQ. If national IQ remains as the
dominant explanatory variable, it will lead to the conclusion that it
would be extremely difficult to equalize nutritional conditions in
the world.
It can be assumed that people live longer in good health
conditions than in poor health conditions. Therefore life
expectancy at birth is a good indicator of the general state of
health conditions in a country. The higher the average life
expectancy of people, the better health conditions are in a country.
If our basic hypothesis on the positive impact of intelligence on
health conditions is correct, life expectancy should be positively
correlated with national IQ. It is again interesting to see how much
some other factors, for example per capita income, might be able
to explain of the variation in life expectancy independently from
national IQ.
There are several other perspectives from which differences
in national health conditions can be evaluated and measured.
Infant mortality rate provides one indicator. It can be assumed that
a low infant mortality rate indicates good health conditions and a
high infant mortality rate poor health conditions. Further, because
intelligence is needed to lower infant mortality rate, it should be
negatively correlated with national IQ.
The prevalence and spreading of HIV depends crucially on
human choices. Because it is a dangerous disease, it is reasonable
to assume that more intelligent nations are better able to prevent
its spreading than less intelligent nations. Consequently, national
IQ should be negatively correlated with the prevalence of HIV,
although, of course, there are also other factors affecting the
spreading and avoidance of HIV. We are not able to take into
account or even to know all important factors, but we explore to
what extent the prevalence of HIV is related to national IQ.
There are also other diseases whose prevalence depends
more or less on human choices and health policies and which,
consequently, should be negatively related to national IQ.
Tuberculosis is one of such diseases. It can be assumed that
more intelligent nations have been better able to control
tuberculosis than less intelligent nations, although the extent of
tuberculosis may depend also on per capita income and on some
other relevant environmental variables.
Our purpose is to test our basic hypothesis on the
relationship between national IQ and national health conditions
by using several separate measures of health conditions because
the use of different indicators may produce more reliable results
than reliance on only one or two variables…

Life expectancy at birth can be regarded to be an
ultimate measure of health conditions. The better health conditions
are in a country, the longer people live. According to our
hypothesis, the correlation between national IQ and Life-08 should
be strongly positive. In fact, the correlation is 0.759 in the total
group of 197 countries and 0.821 in the group of countries with
more than one million inhabitants. These are among the highest
correlations between national IQ and various measures of human
conditions…

Prevalence of HIV (HIV-07) is slightly related to national IQ
(18%) and even less to the three environmental variables (8%)
independently from national IQ. The geographical concentration of
HIV to sub-Saharan Africa and especially to the countries of
southern Africa explains partly the low
correlation between national IQ and HIV prevalence. The
concentration of HIV in sub-Saharan Africa and in the Caribbean
countries inhabited by black Africans seems to be principally due to
some cultural and other exceptional local factors, although national
IQ explains a part of the global variation in HIV prevalence.
Incidence of tuberculosis (Tuber-08) is significantly related to
national IQ (32%) but only slightly to the three environmental
variables (3%) independently from national IQ. The unexplained
part of variation (65%) is probably due to various local factors but
also to possible errors of measurement.

It has been well established in a number of countries that the
more intelligent people have been having fewer children than the
less intelligent. This negative association between intelligence
and fertility was observed in the nineteenth century by Francis
Galton in his Hereditary Genius (1869). He contended that in the
early stages of civilization what he called “the more able and
enterprising men” were the most likely to have children, but in
older civilizations, like that of Britain, various factors operated to
reduce the number of children of these and to increase the number
of children of the less able and less enterprising. He suggested
that the most important of these factors was that able and
enterprising young men tended not to marry, or only to marry
late in life, because marriage and children would impede their
careers. The effect of this was that
there is a steady check in an old civilization upon the
fertility of the abler classes: the improvident and un- ambitious are those who chiefly keep up the breed.
So the race gradually deteriorates, becoming in each
successive generation less fit for a high civilization
(Galton, 1869/1962, p. 414)
Galton was remarkably perceptive in noting the negative
association between intelligence and fertility as early as 1869.
This negative association has become known as dysgenic fertility
and has been extensively investigated in the United States.
All the studies summarized in Table 7.1 show that dysgenic
fertility for intelligence has been present in the United States
during the twentieth century. All the studies show that there has
been greater dysgenic fertility for intelligence in women than
among men. Probably the explanation for this is that children
impose a greater cost on the careers and life style of intelligent
and well-educated women than on those of intelligent and well- educated men, and also that women have a shorter period of
childbearing years. It is women who have to bear most of the
burden of childbearing and childrearing and who therefore have
stronger incentives to limit their number of children or to remain
childless. At the other end of the intelligence spectrum, low IQ
women tend to have higher fertility because they are inefficient
users of contraception and there are always plenty of men willing
to have sex with them. Low IQ men, on the other hand, tend not
to have such high fertility because many of them are unattractive
to females and lack the social and cognitive skills required to
secure sexual partners.
A second factor accounting for the greater dysgenic fertility
of women is probably their shorter span of childbearing years.
Many intelligent women undergo prolonged education and devote
themselves to their careersin their twenties and into their thirties,
intending to postpone childbearing during the years when less
intelligent women are having children. By the time childless,
high- IQ, career women are in their thirties, significant numbers
of them discover that they have waited too long to find suitable
partners with whom to have children, or that they have become
infertile. Older intelligent men who delay marriage and children
until their late thirties or forties are less likely to become infertile
and can find young wives more easily than older women can find
young husbands. It has been shown by Meisenberg and Kaul
(2010, p. 177) that the lower fertility of intelligent women is not
due to a lack of desire for children.
All the studies show that there has been greater dysgenic
fertility for intelligence in American blacks than among whites.
Dysgenic fertility for intelligence is particularly high among black
women. Probably the main reason for this is that intelligent and
well educated black women find it hard to find suitable men with
whom to have children. Many black men do not make attractive
husbands because they do not do so well in employment as black
women, and a significant number of black men find white wives.
For instance, in 1990 6.3 per cent of black men under the age of
thirty were married to a white women, but only 2.5 per cent of
black women were married to a white man (Heaton and Albrecht,
1996). It seems probable that the continuing disadvantaged
position of blacks in the United States in regard to educational
attainment and employment is to some significant extent due to
the greater deterioration of their genotypic intelligence.
The negative association between intelligence and fertility
that has been present in the United States throughout the twentieth
century and into the twenty-first century implies that the
genotypic intelligence must have declined (the genotypic
intelligence is the genetic component of intelligence). This
decline has been compensated for by an increase of phenotypic
(measured) intelligence (Flynn, 2007). Meisenberg (2010) has
calculated the magnitude of the decline of genotypic intelligence.
He assumes a narrow heritabily of intelligence of 0.5 and on this
basis calculates a decline of genotypic intelligence of 0.8 IQ
points a generation and 2.9 IQ points a century. He calculated that
the effect of this would be that the proportion of highly gifted
people with IQs of 130 and above would decline by 11.5% in one
generation and 37.7% in a century. Meisenberg and Kaul (2010)
estimate that when the increase of the numbers of blacks and
Hispanics as a proportion of the population is taken into account,
genotypic intelligence in the United States will decline by
approximately 1.2 IQ points a generation…

Just as the negative correlation between intelligence and
fertility within countries implies that genotypic intelligence is
declining, so the negative correlation between intelligence and fertility across countries implies that the genotypic intelligence of
the whole world is declining. The rate of this decline has been
calculated by Meisenberg (2009) who calculates that the
correlation between national IQs and TFR (Total Fertility Rate),
averaged for the years 2000-2005 is -0.83.

There is a large amount of evidence showing that crime is
associated with low intelligence. In a review of these studies,
Wilson and Herrnstein (1985, p. 159) wrote that “For four
decades, large bodies of evidence have consistently shown about
a ten IQ point gap between the average offender and the average
non-offender in Great Britain and the United States”. This
conclusion has subsequently been confirmed by Ellis and Walsh
(2003) in a summary of more than a hundred studies from all
over the world. The influence of socio-economic status and
family environment on crime has been controlled in a Danish
study of pairs of brothers that has shown that the brother with a
criminal record scored an average of 15 IQ points lower than the
law-abiding sibling (Kandel, Mednick and Kirkegaard-Sorensen,
1988).

Several explanations have been proposed to explain the low
average intelligence of criminals. Wilson and Herrnstein (1985,
pp. 167-171) suggest that low intelligence is associated with
“present- orientation”, i.e. a propensity to seek immediate
gratification without regard to the possibility of future
punishment; that those with low IQs typically have a weak moral
sense and poor moral reasoning ability; typically do poorly at
school, so they become alienated and seek status by joining
criminal gangs; and are typically in low paid jobs or are
unemployed, so they have less to lose by crime and obtaining a
criminal record.
The association of low intelligence with crime among
individuals suggests that the same association should be present
among populations. The first study showing that this is so was
published by Maller (1933a, 1933b) in an analysis of average IQs
and crime rates in 310 districts of New York City. He found that
the correlation between the average IQ of ten year olds and the
rates of juvenile delinquency was -0.57. The relation between
intelligence and crime among populations has also been
investigated by Bartels, Ryan, Urban and Glass (2010) in a study
of the IQs of American states and crime rates. They report that
crime rates are higher in states with lower IQ and that these
negative correlations are higher for violent crime (-0.58) than for
non-violent crime, including motor- vehicle theft and other theft (- 0.29).

It has been shown by Kanazawa (2010) that liberalism is
associated with intelligence. He reported that those who
identified themselves as “very liberal” had a childhood IQ of
106.4, while those who identified themselves as “very
conservative” had a childhood IQ of 94.8…

Row 4 gives a positive correlation of 0.59 between national
IQ and the speed of life as the speed of service at post offices,
walking speed and the accuracy of clocks. The positive
correlation suggests that the populations of IQ countries are more
energetic and alert.

Row 5 shows a negative correlation of -0.22 between
national IQ and war measured as participation, intensity and
destructive effects of war in the years 1960-2000, including civil
wars. The negative correlation shows that high IQ countries have
less engagement in war. The correlation is low but statistically
significant. Possibly the explanation for this negative correlation is
that high IQ countries are more likely to be democratic, and
democracies are less likely to engage in war.

Row 6 shows a correlation of 0.70 between national IQ and
low time preference in 10 Asian countries. Time preference was
measured by responses to the question “Would you prefer $3400
this month or $3800 next month?” Choosing the second option
indicates low time preference or in psychological terms, present- orientation, delay discounting and a capacity to delay gratification.
It has been shown in a meta-analysis of 24 studies that a low time
preference (a capacity to delay gratification) is correlated with IQ at
0.23 (Shamosh and Gray, 2008)

…Consistent with Frazer’s analysis, it has been found in a
number of studies of individuals within nations that there is a
negative relationship between intelligence and religious belief.
This negative relationship was first reported in the United States
in the 1920s by Howells (1928) and Sinclair (1928), who both
reported studies showing negative correlations between
intelligence and religious belief among college students of -0.27
to -0.36 (using different measures of religious belief). A number
of subsequent studies confirmed these early results, and a review
of 43 of these studies by Bell (2002) found that all but four found
a negative correlation…

Further evidence for a negative correlation between
intelligence and religious belief is the decline in religious belief
during adolescence and into adulthood as cognitive ability
increases. This has been found in the United States for the age
range of 12-18 year olds by Kuhlen and Arnold (1944) who
reported that among 12 year olds 94 per cent endorsed the
statement “I believe there is a God”, while among 18 year olds
this had fallen to 78 per cent. Similarly, in England Francis
(1989) has found a decline in religious belief over the age range
5-16 years. Religious belief was measured by a scale consisting of
questions like “God means a lot for me” and “I think that people
who pray are stupid”, etc. The results were that among 5-6 year
olds 87.9 per cent of boys and 96.0 per cent of girls held religious
belief, but at the age of 15-16, these percentages had fallen to
55.7 of boys and 70.4 of girls.
Finally, in several economically developed countries there
has been a decline of religious belief during the course of the last
150 or so years, while at the same time the intelligence of the
population has increased. For instance, in England self-reported
weekly attendance at church services reported in census returns
declined from 40 per cent of the population in 1850, to 35 per
cent in 1900, to 20 per cent in 1950, and to 10 per cent in 1990
(Giddens, 1997, p. 460). Church of England Easter week
communicants declined from 9 per cent of the population in 1900
to 5 per cent in 1970 (Argyle and Beit-Hallahmi, 1975). The
attendance of children at Sunday schools declined from 30 per
cent of the child population in 1900 to 13 per cent in 1960
(Goldman, 1965). In Gallup Polls 72 per cent of the population
stated in 1950 that they believed in God (Argyle, 1958), but by
2004 this had fallen to 58.5 per cent (Zuckerman, 2006).
There has also been some decline of religious belief during
the course of the last century in the United States. Hoge (1974)
has reviewed several surveys that have found a decline of
religious belief in college students. For instance, students at Bryn
Mawr were asked whether they believed in a God who answered
prayers. Positive responses were given by 42 per cent of students
in 1894, 31 per cent in 1933, and 19 per cent in 1968. Students
enrolling at the University of Michigan were invited to provide a
“religious preference”. In 1896, 86 per cent of students did so; in
1930 this had dropped to 70 per cent, and in 1968 it had dropped
to 44 per cent. At Harvard, Radcliffe, Williams and Los Angles
City College the percentages of students who believed in God,
prayed daily or fairly frequently, and attended church about once
a week all declined from 1946 to 1966. Heath (1969) has also
reported a decline in belief in God among college students from
79 per cent in 1948 to 58 per cent in 1968. Among the general
population, Gallup Polls have found that 95.5 per cent stated that
they believed in God in 1948 (Argyle, 1958), but by 2004 this
had fallen to 89.5 per cent (Zuckerman, 2006)…

In our previous work we have proposed the theory that
population differences in IQ evolved in response to the cognitive
demandsin cold winters (Lynn, 2006). To summarize this theory,
the human species (Homo sapiens ) evolved around 150,000
years ago in equatorial East Africa (Relethford, 1988). Around
100,000 years ago groups of Homo sapiens began to migrate
from equatorial Africa and settled in North Africa and in
southwest Asia. By 60-40,000 years ago they were established
throughout Asia, the Indonesian archipelago and Australia. By
about 35,000 years ago they had settled in Europe, and
subsequently they colonized the Americas and the Pacific islands
(Foley, 1987; Mellars and Stringer, 1999; Cavalli-Sforza, 2000).
When these peoples settled in the temperate and colder
latitudes of North Africa, Asia and Europe, they encountered the
problem of survival during the winter and spring. This was a
problem because the first humans that evolved in equatorial East
Africa subsisted largely on plant foods, of which numerous
species were available throughout the year (Lee, 1968; Tooby
and de Vore, 1989). In temperate and cold environments plant
foods are not available for a number of months in the winter and
spring. Thus, “plant foods are often available only during short
seasons” (Gamble, 1993, p. 117) and compared to warmer
environments there would have been fewer edible plant species,
and a concomitant requirement for increased reliance on
animals… and the obvious problem of keeping warm, including
the likely necessity of controlling and even making fire. In
effect, these northern temperate environments “pushed the
envelope” of Homo’s adaptation (Wynn, 2002, p. 400).
These peoplesthat migrated into North Africa, Asia, Europe
and the Americas needed to hunt large animals for food, and to
make clothes, shelters and fires to keep warm. These problems
would have exerted selection pressure for enhanced intelligence.
The colder the winters, the stronger this selection pressure would
have been and the higher the intelligence that evolved. These
peoples evolved larger brain size to accommodate greater
intelligence. A review of the association between brain size and
intelligence in humans has shown that they are correlated at 0.40
(Vernon, Wickett, Bazana and Stelmack, 2000). There is
therefore an association across the races for the severity of the
winter temperatures to which they were exposed, brain size and
IQs.

It is apparent that there is a general correspondence between the coldest winter monthly temperatures, brain sizes and IQs. The Northeast Asians were exposed to the lowest winter temperatures, have the largest brain sizes and the highest IQs, followed by the Europeans, Native Americans, and North Africans and South Asians.

These results showing larger brain sizes in populations that
evolved in colder environments have been confirmed by Ash and
Gallup (2007) in an analysis of a sample of 109 fossilized
hominid skulls. They found that approximately 22% of the
variance in cranial capacity (brain size) could be accounted for by
variation in equatorial distance such that cranial capacity was
larger with greater distance from the equator. They also found that
cranial capacities were highly correlated with paleo-climatic
changes in temperature, as indexed by oxygen isotope data and
sea-surface temperature, and that 52% of the variance in the
cranial capacity could be accounted by the temperature variation at
100 ka intervals. Further support for these results has been
reported by Bailey and Geary (2009). They examined 175 skulls
dated between 1.9 million years ago and 10,000 years ago and
reported a correlation of -0.41 between their size (cubic capacity)
and the temperature of their locations, showing greater brain size
in lower temperature environments, and a correlation of -0.61
between their size (cubic capacity) and latitude, showing larger
brain size in latitudes more distant from the equator. This study
shows that larger brain size (conferring greater intelligence)
evolved before 10,000 years ago in the peoples inhabiting colder
environments.
A more recent study providing additional confirmation for
these results has been published by Pearce and Dunbar (2011).
They measured the brain size of 55 skulls from twelve
populationsfrom around the world and found that brain size was
correlated with distance from the equator at 0.82.
Brain size is the determinant of intelligence at a magnitude of
approximately 0.40. The research on this issue has been reviewed
by Vernon, Wickett, Bazana and Stelmack (2000), who report 54
studies that used an external measure of head size. All of these
reported a positive relationship and the overall correlation was
0.18. They also report 11 studies of normal populations that
measured brain size by CT (computerized axial tomography) and
MRI (magnetic resonance imaging), which give a more accurate
measure of brain size, and for which there was a correlation of
0.40. Vernon et al. conclude that the most reasonable interpretation
of the correlation is that brain size is a determinant of intelligence.
Larger brains have more neurons and this gives them greater
processing capacity. A further study published subsequent to this
review found a correlation for 40 subjects between brain size
measured by MRI and intelligence of 0.44 (Thompson, Cannon,
Narr, et al., 2001). It has been shown that the association between
brain volume and intelligence is of genetic origin (Posthuma, De
Ceus, Baaré, et al., 2002).
It has now become widely accepted that this evidence for
race differences in intelligence and brain size indicates that these
race differences have a genetic basis. As Hunt (2011, p. 434) has
recently written “the 100% environmental hypothesis cannot be
maintained”