by Richard Lynn, University of Ulster, Coleraine, Northern Ireland
and Tatu Vanhanen, University of Helsinki, Finland

Summary :
National IQs assessed by the Progressive Matrices were calculated for 60 nations and examined in relation to per capita incomes in the late 1990s and to post World War Two rates of economic growth. It was found that national IQs are correlated at 0.757 with real GDP (Gross Domestic Product) per capita 1998 and 0.706 with per capita GNP (Gross National Product) 1998; and at 0.605 with the growth of per capita GDP 1950-90 and 0.643 with growth of per capita GNP 1976-98. The results are interpreted in terms of a causal model in which population IQs are the major determinant of the wealth and poverty of nations in the contemporary world.


INTRODUCTION


The causes of the inequalities in income and wealth between nations have been discussed for some two and a half centuries. In 1748 Montesquieu published De l’Esprit des Lois in which he proposed that temperate climates were more favorable to economic development than tropical climates. In 1776 this problem was discussed by Adam Smith in his Wealth of Nations, in which he proposed that the skills of the population are the principal factor responsible for national differences in incomes and wealth.

Since these early attempts to analyse this problem, numerous other theories have been advanced. These theories fall into four principal categories. First, climatic theories are still proposed. Their leading exponent in recent times is Kamarck (1976) who argues that tropical climates are unfavorable for economic development because the heat and humidity reduce the efficiency of working capacities, impair the productivity of the land and provide a favorable environment for debilitating diseases. This explains the difference between what is sometimes called “the rich north” with its temperate climate and “the poor south” with its predominantly tropical climate.

Diamond (1998) presents similar arguments on the crucial significance of climatic and geographical factors.

The second major contemporary explanation is “dependency theory”. This proposes that the economically developed capitalist nations are responsible for the poverty of the underdeveloped nations because they dominate the world economy, force the rest of the world into economic dependency, and pay low prices for Third World agricultural products and natural resources. Some of the leading exponents of this theory are Frank (1969, 1996), dos Santos (1993, 1996), Wallerstein (1998) and Valenzuela and Valenzuela (1998); see also Seligson and Passˇ-Smith (1998).

Third, there is the neoliberal theory. This proposes that the major factor responsible for national differences in economic development consists of the presence of free markets as opposed to command, socialist and communist economies. Bates (1993) and Weede (1993) are leading recent exponents of this theory.

Fourth, there are a variety of psychological theories which argue for the importance of differences in attitudes, values and motivations. The first major theory of this kind was Weber’s (1904) theory that the Protestant work ethic explained the more rapid economic development of northern Europe as compared with the Catholic south from the sixteenth century onwards. Later theorists in this tradition include McClelland (1976) who advanced the similar concept of achievement motivation. Several economists, while not endorsing the theories of Weber or McClelland, are sympathetic to this kind of explanation and propose what are generally termed “cultural” factors as major contributors to national differences in economic development. Landes writes of the importance of culture “in the sense of inner values and attitudes that guide a population” (1998, p. 516). Many economists have taken eclectic positions in which they argue that several of these factors contribute to national differences in incomes and wealth.

We believe it has never been suggested that national differences in intelligence might play some role in national differences in economic development. It is widely assumed that the peoples of all nations have the same average level of intelligence. For instance, Kofi Annan, the United Nations Secretary General, asserted in April2000 that intelligence “is one commodity equally distributed among the world’s people” (Hoyos and Littlejohns, 2000 ). It is known in psychology that this is incorrect and that there are large differences in average levels of intelligence between different nations. Reviews of the literature have shown that in relation to average IQs of 100 in Britain and the United States, the peoples of north east Asia have average IQs of around 105 and the peoples of sub-Saharan Africa have average IQs of around 70 (Lynn, 1991).

In view of these differences, it seems a reasonable hypothesis that national differences in intelligence may be a factor contributing to national differences in wealth. This is a promising hypothesis for two reasons. First, it is well established that intelligence is a determinant of earnings among individuals; and second, several studies have shown that the intelligence of groups is related to their average earnings. The earlier American research literature, up to 1970, on the relationship of intelligence to earnings among individuals was summarized by Jencks (1972) who concluded that the best estimate was expressed by a correlation of .35. Later studies have confirmed this conclusion. Brown and Reynolds (1995) examined the relation between IQ measured in early adulthood and earnings approximately 12 years later for samples of 24,819 whites and 4,008 blacks and reported correlations of .327 and .126, respectively.

Hunter and Hunter (1984) report correlations between .25 and .60 for different types of occupations. Murray (1998) has examined the National Longitudinal Study of Youth sample for the relation between IQ measured in adolescence and income in the late twenties to mid-thirties and found a correlation of .37. Most students of this question have concluded that IQ is a cause of income because IQs are established quite early in childhood and predict incomes achieved in adulthood (Duncan, Featherman and Duncan, 1972; Jensen, 1998). It is estimated by Li (1975) that childhood IQ is correlated .83 with adult IQ. The relation between childhood IQ and adult income is present when parental socio-economic status is controlled (Duncan, Featherman and Duncan, 1972; Jencks, 1979).

The positive association between IQ and income among individuals led to the expectation that there would be positive associations between the average IQs of groups and their average earnings. We believe that the existence of such an association was first reported by Davenport and Remmers (1950) in a study in which the population units were the states of the United States. They obtained IQ scores from tests administered in 1943 to more than 300,000 young men in high schools and colleges as part of selection for placement in training programs for the armed services. The test was composed of verbal, mathematical and scientific items and was described as “a combination of a group intelligence test and a general educational achievement test” (p. 110). They calculated the average score for each state, examined this in relation to the state’s per capita income and found a correlation of .81.

The positive relationship between the average IQs of groups and their average incomes has also been found in studies carried out in Europe. A study of the British Isles examined the relation between average IQs in thirteen regions obtained in the1940s and 1950s and per capita incomes in 1965. The average IQs fell within the relatively narrow range between 102.1 in London and 96.0 in Ireland. The correlation between average IQs and incomes was .73 (Lynn, 1979). A similar study for France examined the relation between average IQs in 90 “departments” (regions) obtained from testing approximately 257,000 young men conscripted into the armed services in the mid-1950s and per capita incomes in 1974. The correlation between IQs and earnings was .61 (Lynn, 1980). The same relationship has been found in Spain in a study in which average IQs for 48 regions were calculated from approximately 130,000 military conscripts for the mid-1960s. The correlation between these and average regional incomes was .65 (Lynn, 1981). In view of these relationships it seems a promising hypothesis that a positive relationship would be present between the average IQs of the populations of nations and their average earnings. It is this hypothesis that we are now about to investigate.


METHOD


This study presents data for 60 countries for national IQs, per capita incomes in 1998, and economic growth 1950-1998 and examines their relationships by the statistical techniques of correlation and regression analyses.

National IQs

National IQs have been calculated from normative data obtained in 60 countries for the Colored and Standard Progressive Matrices. The reasons for using these data are that the Progressive Matrices is the most widely used test in cross-cultural research, is non-verbal and hence is likely to yield more valid cross cultural data than verbal tests which require translation, is among the best measures of g, and the rate of secular increase is well established. The data have been obtained from the bibliographies of Progressive Matrices studies compiled by Court (1980) and Court and Raven (1995), from the data given by Raven in a series of manuals and research supplements for the Progressive Matrices, and from the Raven archive.

The Standard Progressive Matrices was constructed in Britain in the 1930s and was first published in 1938 with norms for 6-15 year olds and adults. This was followed by the publication in 1947 of the Colored Progressive Matrices, a simpler test suitable for 5-11 year olds. The Standard Progressive Matrices was renormed for 6 to 15 year olds in Britain 1979. A norm table is provided by Raven (1981) giving percentile equivalents of raw scores for half year age groups. The procedure for calculating the IQ of a country in which norms have been obtained for the Standard Progressive Matrices is to read off the raw scores of the50th percentile from the norm table and obtain the British 1979 percentile. This is then converted to the British IQ equivalent using a conversion table. The raw score of the 50th percentile is the median IQ rather than the mean. Several studies have provided mean raw scores in addition to the medians and these show that means and medians are virtually identical.

In most countries in which Progressive Matrices data have been collected norms have been given for a number of age groups. IQs are calculated for each of these and averaged to give a single national IQ. This IQ is then adjusted for the secular rise of the IQ which has been 2 IQ points per decade for the Standard Progressive Matrices in Britain over the period 1938-1979 (Lynn and Hampson, 1986). All national IQs are therefore expressed in relation to a British IQ of 100.

Norms for the Standard Progressive were collected for adults for Britain in 1992 and for the United States for 1993. The norm table for the United States provided by Raven, Court and Raven (1996) gives the most detailed information consisting of the percentile equivalents of raw scores. Less information is provided for the British standardization which gives only the raw score equivalents of the5th, 10th, 25th, 50th, 75th, 90th and 95 th percentiles. The British medians have been converted to American IQs by the use of the American norm table. The result of this calculation is that the British IQ is 102 on the American norms. Data for adults from other countries are converted to American IQs and then adjusted to British IQs by the subtraction of 2 IQ points.

There are no norms giving detailed percentiles for the Colored Progressive Matrices for Britain, the United States or elsewhere. To deal with data for the Colored Progressive Matrices, raw scores are converted to those of the Standard Progressive Matrices using the conversion table provided by Raven, Court and Raven (1995) and the IQs calculated in the way set out above.

In a few instances median raw scores fall below the 1st percentile of the British and American norm tables. The 1st percentile is equivalent to an IQ of 65. In these cases the countries are assigned an IQ of 64. For a number of countries Progressive Matrices data have been collected for two or more samples. These have been averaged to provide a single mean given to the nearest whole number.
The IQ for South Africa has been calculated as follows. The study by Owen (1992) gives the following IQs for the four racial groups. Whites: 94; blacks: 66; coloureds: 82; Indians: 83. The percentages of the four groups in the population are whites: 14%; blacks: 75%; coloreds: 9%; Indians: 2% (Ramsay, 1999, p. 158). Weighting the IQs of the four groups by their percentages in the population gives an IQ for South Africa of 72. The IQ of Singapore has been calculated in the same way by weighting the IQs of the ethnic groups (Malays, Chinese and Indians in Singapore) by their numbers in the population. The data on national IQs are shown in Appendix 1 which gives the IQ, the sample size, the test used (Colored or Standard Progressive Matrices) and the reference. For some countries there are two or more studies of the national IQ. These have been averaged to give mean IQs for these countries.

Because the concept of national IQ is new, it will be useful to examine its reliability and validity. To examine its reliability we have taken the sixteen countries for which there are two or more measures of IQ and calculated the correlation between the two measures. For the countries for which there are more than two measures (Brazil, Hong Kong, India and Mexico) we have used the two extreme values. The correlation between the two measures of national IQ is 0.937. This establishes that the measure of national IQ has high reliability.

To examine the validity of the national IQs we have examined their relation with national measures of educational attainment. This follows the long established methodology of the validation of intelligence tests among individuals by showing that they are positively correlated with test of educational attainment. The measures of education attainment are taken from the second and third international studies of educational achievement in mathematics and science. These data are shown in Table 1 for the countries for which we have IQ measures. The correlations between educational attainment and IQ are shown in the bottom two rows of the table.

Five of the six correlations are statistically significant and establish the validity of the measures of national IQ.


National Wealth and Rates of Economic Growth


National wealth is measured by per capita national income. Strictly speaking, national wealth and national per capita income are different concepts because national wealth consists of the value of capital stock, whereas income is income, so we use the term national wealth in the general sense in which people speak of rich countries and poor countries. We use two alternative measures of national income: per capita GNP in US dollars and real GDP per capita in US dollars. The second measure is calculated on the basis of the purchasing power parity of the country’s currency. It is intended “to make more accurate international comparisons of GDP and its components than those based on official exchange rates, which can be subject to considerable fluctuation” (Human Development Report, 1997, p. 239). For some countries data on per capita GNP and real GDP per capita can differ considerably from each other.

The basic difference between GNP and GDP is that GDP comprises the total output of goods and services for final use produced by an economy by both residents and non-residents within the geographical boundaries of a nation, whereas GNP comprises GDP plus income from abroad, which is the income residents receive from abroad, less similar payments made to non-residents who contribute to the domestic economy. The difference between GNP and GDP is relatively small for most countries - much smaller than difference between GNP and real GDP - but in some cases it can be quite substantial (see Gardner, 1998, pp. 22-23; Human Development Report 1999, p. 254; World Development Report 1999/2000, p. 274).

Most data on per capita GNP are taken from the World Bank’s World Development Report 1999/2000 and all data on real GDP per capita from the United Nations Development Program’s (UNDP) Human Development Report 2000. Sources of supplementary data are given at the foot of Appendix 2. Data for per capita GNP and real GDP per capita used in this paper are for the year 1998. These are the latest data available to us at the time of writing. These data for per capita incomes are shown in Appendix 2 for the same countries as in Appendix 1. However, in Appendix 2 the United Kingdom replaces Britain in Appendix 1.

Economic growth rates are measured as percentage increases in per capita GNP and per capita GDP. Consistent national differences in economic growth rates over many decades are responsible for contemporary national differences in GNP and GDP. Our hypothesis that national differences in IQ are a cause of contemporary national differences in GNP and GDP entails the prediction that national IQs should be positively correlated with long term rates of economic growth. We present two tests of this prediction. First, we examine the correlation between national IQs and economic growth rates of per capita GDP over the period1950-1990 using the per capita GDP data given by Maddison ( 1995) for 54 of the countries in our sample. Second, we examine the correlation between national IQs and economic growth rates of per capita GNP over the period1976-1998 using per capita GNP data given in the World Bank’s World Development Reports. From these data we have calculated the percentage changes of per capita GDP over the period1950-90 and per capita GNP over the period 1976-98.


RESULTS

We examine first the correlations between national IQs and the two measures of national per capita income. These are presented in Table 2. It shows that the two measures of per capita national income are highly intercorrelated (.945). It also shows that the correlations between national IQs and the two measures of per capita national income are strongly positive as hypothesized. The national IQs are correlated .706 with per capita GNP and .757 with per capita real GDP. Both correlations are statistically significant at p<.001. We examine next the relation between national IQs and rates of economic growth. The correlation between national IQs and economic growth rates of GDP per capita over the period1950-1990 is . 605 (N=54, p<.001). The correlation between national IQs and economic growth rates of per capita GNP over the period 1976-1998 is .643 (N=56, p<.001).

It has been suggested by a referee that the mean IQs of sub-Saharan African countries are so low that they cannot be valid and that they spuriously inflate the correlations between the national IQs and the measures of per capita income and economic growth. We believe that we have to some degree met this point by showing in Table 1 that attainment in mathematics in Nigeria and South Africa is well below that in the rest of the world and that this goes some way to establishing the validity of the IQs for the countries of sub-Saharan Africa. Nevertheless to meet this point more fully we have excluded the 15 African countries and rerun the calculations.

The results are that the correlation of IQ and per capita GNP 1998 falls from .706 to .625; the correlation of IQ and real GDP per capita falls from .757 to .586; the correlation of IQ and economic growth per capita GDP 1950-90 falls from .605 to .600; and the correlation of IQ and economic growth per capita GNP 1976-98 falls from .643 to .513. Thus the exclusion of the 15 African countries reduces the correlations to some degree, as would be expected with the reduction of variance in the reduced sample, but all four correlations remain substantial and statistically significant at p<.001. We are forced to conclude that the exclusion of the 15 countries of sub-Saharan Africa makes no significant difference to the associations between national IQs and economic growth.

It has been pointed out that correlation analysis does not establish causality because of the fact that correlations merely measure covariation. Let us conseder what causality presupposes. Manheim and Rich (1986: 21-22) say that it is justified to postulate causal relationships only when four conditions are simultaneously met: First, the postulated cause and effect must change together, or covary. Second, the cause must precede the effect. Third, we must be able to identify a causal linkage between the supposed cause and effect. Fourth, the covariance of the cause and effect phenomena must not be due to their simultaneous relationship to some other third factor. We think that the relationship between national IQ and the measures of per capita income and economic growth meets these requirements quite well. First, correlations indicate that the postulated cause and effect change together. Second, because differences in national IQs are partly genetic, they have certainly preceded contemporary differences in economic conditions. Third, the causal linkage between the hypothesized cause and effect will be discussed and explained in the next section. Fourth, it is highly improbable that the observed covariance between cause and effect could be due to any third factor. This last requirement will be discussed in greater detail in the next section. Consequently, we are quite confident that the relationship is causal.

Although the correlations between national IQs and the measures of per capita income are high, there are some countries which have much higher per capita incomes than would be expected from their national IQs and other countries whose national per capita incomes are much lower than expected. To examine these anomalies a regression analysis has been carried out to disclose which countries deviate most from the regression line. This analysis is limited to the regression of real GDP per capita 1998 on IQ. Real GDP per capita 1998 was selected for this analysis because real GDP per capita (purchasing power parity) can be regarded as a more valid measure of living standards than per capita GNP and because the correlation between national IQs and real GDP per capita is stronger than the correlation between national IQs and per capita GNP (see Table 2). The results of regression analysis are given in Table 3.

Table 3 shows how much individual countries deviate from the regression line, which represents the average relationship between national IQs and real GDP per capita in 1998. “Fitted GDP” indicates the predicted value of real GDP per capita in 1998. If the correlation between IQs and Real GDP per capita were perfect, all countries would be at the regression line and all residuals would be zero. Because the correlation (0.757) is not perfect, all countries deviate to some extent from the regression line. The residuals indicate the size and direction of the deviations. Positive residuals indicate that nations have higher real GDP per capita than is predicted on the basis of the average relationship between IQs and real GDP per capita, while negative residuals indicate that their per capita incomes are lower than expected. The sum of “Residual GDP” and “Fitted GDP” is always the same as the actual value of real GDP per capita given in Table 3. There is no natural distinction between countries with large and small deviations. Because one standard error of estimate is 5,583 real GDP per capita dollars in this regression analysis, it is reasonable to regard as highly deviating cases all countries for which positive or negative residuals are larger than 6,000. Positive residuals are large for eight countries: Belgium, Canada, Denmark, Ireland, Qatar, South Africa, Switzerland and the United States. Negative residuals are large for nine countries: China, Iraq, South Korea, the Philippines, Romania, Russia, Slovakia, Thailand and Uruguay. We consider the explanations for these anomalies in the discussion.


DISCUSSION


The hypotheses examined in this study have been that national per capita incomes and rates of economic growth would be positively correlated with national IQs. These hypotheses have been confirmed by strong correlations that are at a high level of statistical significance for both GNP and GDP. If we adopt a one way causal model that national IQs are a determinant of national per capita incomes and rates of economic growth, the results show that national IQ explains 57 percent of the variance of real GDP per capita 1998 and 50 percent of the variance of GNP per capita 1998. National IQ also explains 37 percent of the variance in economic growth of per capita GDP 1950-90 and 41 percent of the variance in economic growth of per capita GNP 1976-98.

There are two reasons why we consider that a causal effect of national IQ on per capita incomes and rates of economic growth is the most reasonable theory to explain the correlations. First, this theory is a corollary of an already established body of theory and data showing that IQ is a determinant of income among individuals, the evidence for which has been reviewed in the introduction. IQs measured in childhood are strong predictors of IQs in adolescence and these are predictors of earnings in adulthood. The most reasonable interpretation of these associations is that IQ is a determinant of earnings. From this it follows that groups with high IQs would have higher average incomes than groups with low IQs because groups are aggregates of individuals. This prediction has already been confirmed in the studies of the positive relationship between IQs and per capita incomes among the American states and among the regions of the British Isles, France and Spain, as noted in the introduction. The positive relation between IQ and income is so well established that it can be designated a law, of which the finding that national IQs are positively related to national per capita incomes is a further instance.

Second, there is a straightforward explanation for the positive association between IQ and incomes at both the individual and population level. The major reason for this association is that people with high IQs can acquire complex skills that command high earnings and that cannot be acquired by those with low IQs. Nations whose populations have high IQs tend to have efficient economies at all levels from top and middle management through skilled and semi-skilled workers. These nations are able to produce competitively goods and services for which there is a strong international demand and for which there is therefore a high value, and that cannot be produced by nations whose populations have low IQs. In addition, nations whose populations have high IQs will have intelligent and efficient personnel in services and public sector employment that contributes indirectly to the strength of the economy such as teachers, doctors, scientists and a variety of public servants responsible for the running of telephones, railroads, electricity supplies and other public utilities. Finally, nations whose populations have high IQs are likely to have intelligent political leaders who manage their economies effectively. Skilled economic management is required to produce the right conditions for economic growth, such as keeping interest rates at the optimum level to produce full employment with minimum inflation, maintaining competition, preventing the growth of monopolies, controlling crime and corruption, and promoting education, literacy and numeracy and vocational training.

While we consider that a causal effect of national intelligence on per capita income and rates of economic growth is the most reasonable model for an explanation of the data, there are two other possible explanations that deserve consideration. The first of these is that there is no direct causal relation between national IQs and per capita incomes and growth rates and the correlation between them is due to some third factor affecting all three. Although this is a theoretical possibility and needs to be mentioned, we do not think it is possible to formulate a plausible theory of this kind.

Second, it might be argued that national per capita incomes are a cause of national differences in IQs. This argument would state that rich nations provide advantageous environments to nurture the intelligence of their children in so far as they are able to provide their children with better nutrition, health care, education and whatever other environmental factors have an impact on intelligence, the nature of which is discussed in Neisser (1998). Intelligence has increased considerably in many nations during the twentieth century and there is little doubt that these increases have been brought about by environmental improvements, which have themselves occurred largely as a result of increases in per capita incomes that have enabled people to give their children better nutrition, health care, education and the like. Such a theory has some plausibility but it cannot explain the totality of the data. Countries like Japan, South Korea, Taiwan and Singapore had high IQs in the1960 s when they had quite low per capita incomes and the same is true of China today. Nevertheless, the model of national differences in IQ as a major determinant of economic growth and per capita incomes should probably be supplemented by the postulation of a small positive feedback in which national per capita income has some impact on the population’s IQ.

Our results are based on a sample of 60 nations out of approximately 185 nations of significant size in the world. We believe that the sample can be regarded as relatively well representative of the totality of nations because all categories of nations are well represented including the economically developed “First World” market economies of North America, Western Europe, Australia and New Zealand; the “Second World” former communist nations of Russia and Eastern Europe; the “Third World” economically developing but impoverished nations of South Asia, sub-Saharan Africa and the Caribbean; and the residual categories of Latin America and East Asia. If the representativeness of our sample is accepted, our results indicate that slightly over half the variance in national per capita income in the contemporary world is attributable to national differences in IQ. However, it should be noted that correlations are somewhat lower in the total group of 185 countries (see Lynn and Vanhanen, 2002). The difference in correlations implies that this sample of 60 nations is probably slightly biased.

The regression analysis suggests that a major additional factor is the economic form of organisation consisting of whether countries have market or socialist economies. The countries that have the largest positive residuals and therefore have higher per capita income than would be predicted from their IQs are Australia, Belgium, Canada, Denmark, France, Ireland, Israel, Qatar, Singapore, South Africa, Switzerland and the United States. With the exception of Qatar and South Africa, all of these are technologically highly developed market economy countries and their higher than predicted per capita incomes can be attributed principally to this form of economic organisation. Qatar’s exceptionally high level of per capita national income is principally due to its oil production industries. South Africa’s much higher than expected level of per capita income should probably be attributed principally to the cognitive skills of its European minority who comprise14 per cent of the population.

The countries that have the largest negative residuals are China, Iraq, South Korea, the Philippines, Romania, Russia, Slovakia, Thailand and Uruguay. Four of these countries (China, Romania, Russia and Slovakia) are present or former socialist countries whose economic development has been hampered by their socialist economic and political systems. After the collapse of the Soviet communist systems in 1991 and the introduction of market economies in these countries and in China, the prospects for rapid economic development for these countries are good, although it takes time to establish effective market economies. Of the remaining five countries with large negative residuals, Iraq’s low level of per capita national income is due principally to the destruction inflicted in 1990 war and the UN sanctions imposed in 1990. South Korea’s Real GDP per capita is also considerably lower than expected on the basis of the country’s exceptionally high level of national IQ (106). The principal explanation for this is probably that South Korea had a very low per capita income at the end of World War Two as a result of military defeat and occupation by the Japanese and that it has not yet had sufficient time to achieve the predicted level of per capita income, although economic growth in South Korea since1950 has been extremely high (see Appendix 2). The Asian economic crisis in 1998 may have increased the negative residuals of the Philippines and Thailand temporarily. Economic growth in Uruguay has been strong since the1970 s, although the country has not yet achieved the per capita income level expected on the basis of its relatively high national IQ.

Thus our general conclusion is that national differences in the wealth and poverty of nations in the contemporary world can be explained first in terms of the intelligence levels of the populations; secondly, to some extent, in terms of whether they operate market or socialist economies; and thirdly by unique circumstances such as the possession of valuable natural resources like oil in the case of Qatar and trade sanctions imposed on Iraq.


Estimation of Missing National IQs

We want to extend the analysis to the further 104 countries with populations of more than 50,000 for which we have not been able to find IQ data. For these 104 countries we have estimated the IQs. Two principles have been adopted for making the estimates of national IQs for those countries for which data are lacking. First, it is assumed that national IQs which are unknown will be closely similar to those in neighboring countries whose IQs are known. It can be seen from the results set out in Table 6.1 that neighboring countries normally have closely similar IQs. Thus, for instance, the IQ in both Germany and the Netherlands is 102; the IQ in Japan is 105 and the IQ in South Korea is 106; the IQ in Argentina and in Uruguay is 96; the IQ in Uganda is 73 and in Kenya 72; and so forth. It is therefore assumed that where national IQs are unknown, they will be closely similar to those in neighboring countries. We have therefore taken the most appropriate neighboring countries and used their IQs to assign IQs to countries whose IQs are unknown. Where there are two or more appropriate neighboring countries, the IQs of these are averaged to obtain an estimated IQ for the country whose IQ is unknown. Thus, for example, to estimate an IQ for Afghanistan, we have averaged the IQs of neighboring India (81) and Iran (84) to give an IQ of 83. Averages with decimal points have been rounded towards 100.

A second principle for the estimation of national IQs has been used for several countries which are racially mixed and for which there is no similar neighboring country. In these cases we have assigned IQs to the racial groups on the basis of the known IQs of these groups in neighboring countries. For example, Cape Verde, the archipelago off the coast of Senegal, has a population which is 1 percent white, 28 percent black and 71 percent mixed black-white (Philip’s, 1996). On the basis of the IQs of these groups in South Africa, it is assumed that the whites have an IQ of 94, the blacks of 66 and the mixed of 82, the IQ of South African coloreds (see Appendix 1). Weighting these figures by the percentages in the population gives an IQ of 78.

The racially mixed population of the Comoros consists of African (black), Arab and Malagasy elements. It is not any longer possible to separate clearly different racial groups. Because the racial composition of the population is comparable with Madagascar’s population, we estimate its national IQ to be 79, the same as in Madagascar. The Malayo-Polynesians and Negroids constitute the principal elements in the racially mixed population of Madagascar. The contribution of each of them may be approximately equal. Therefore, it is reasonable to estimate the national IQ for Madagascar on the basis of the Philippines (86) and Tanzania (72), which gives an IQ of 79 for Madagascar. For Mauritius, the population consists of 68 percent Indians, 27 percent Creole (black-white hybrids), 3 percent Chinese and 1 percent whites. It is assumed that the IQs are 81 for the Indians (as in India), 82 for the Creoles (as for South African coloreds), 100 for the Chinese (as in China) and 94 for the whites (as for the whites in South Africa). Weighting these figures by the percentages in the population gives an IQ of 81.

Table 4 shows these estimated IQs and the comparison countries on which they are based, together with measured IQs. We should emphasize that these data on national IQs are estimates and that they certainly contain errors, but we assume that the margin of error is relatively small in nearly all cases.


Source : Richard Lynn’s website


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