Intelligence: A Unifying Construct for the Social Sciences – Lynn & Vanhanen

Intelligence: A Unifying Construct for the Social Sciences

Richard Lynn & Tatu Vanhanen

Here is the description of the variables mentioned in the book (see pp. 347-348) :

PPP-GNI-08. Data on PPP GNI per capita in 2008 (see Chapter 4) were transformed into percentages by calculating the percentage of a country’s per capita income from US 40,000 dollars.

ID-08. The level of democratization (see Chapter 5) is measured by the Index of Democratization in 2008 (ID-08).

Literacy-08. The original data on adult literacy rate (see Chapter 3) are already percentages. (see also pp. 11-12)

Tertiary-09. The original data on gross enrollment ratio in tertiary education (see Chapter 3) are percentages.

Life-08. The original data on life expectancy at birth in 2008 (see Chapter 6) are years and they vary from 43 (Djibouti) to 83 (Andorra and Japan).

IMR-08. The original data on infant mortality rate per 1,000 live births (IMR-08) given in Chapter 7 were first transformed into percentages by calculating the percentage of IMR-08 from 120.

Chapter 2

The Measurement of the Intelligence of Nations

4. Reliability of National IQs

Several critics of the national IQs given in our previous studies have asserted that the IQs obtained in different studies from the same countries are inconsistent and therefore that the IQ figures have poor reliability. For instance, Astrid Ervik (2003, p. 408) wrote that there are “large disparities in test scores for the same country” and “the authors fail to estabalish the reliability of intelligence (IQ) test scores”. A similar criticism has been made by Susan Barnett and Wendy Williams (2004): “When more than one sample is used to estimate a national IQ, it is unsettling how great the variability often is between samples from the same country”.

The reliability of a psychometric test means the extent to which the score it provides can be replicated in a further study. The reliability of a test is best assessed by making two measurements of an individual or set of individuals and examining the extent to which the two measurements give the same results. Where the two measurements are made on a set of individuals the correlation between the two scores gives a measure of the degree to which they are consistent and is called the reliability coefficient.

In our IQ and the Wealth of Nations we examined the reliability of the measures by taking 45 countries in which the intelligence of the population has been measured in two or more investigations. This is the same procedure that is used to examine the reliability of tests given to sets of individuals. We reported that the correlation between two measures of national IQs is 0.94, showing that the measures give high consistent results and have high reliability. This reliability coeffcient is closely similar to that of tests of the intelligence of individuals, which typically lies in the range between 0.85 and 0.90 (Mackintosh, 1998, p. 56). In our IQ and Global Inequality we examined the consistency of the IQs for 65 countries for which there were two or more scores. The correlation between the two extreme IQs (i.e. the highest and lowest) was 0.93 and is highly statistically significant. This method underestimates the true reliability because it uses the two extreme values. As an alternative method we excluded the two extreme scores and used the next lowest and highest scores. There were 13 countries for which we had five or more IQ scores (China, Congo-Zaire, Germany, Hong Kong, India, Israel, Jamaica, Japan, Kenya, Morocco, South Africa-blacks, South Africa-Indians, and Taiwan). Using this method, the correlation between the two scores was 0.95. These figures established that the national IQs used in our earlier work had high reliability.

To estimate the reliability of the national IQs obtained from intelligence tests and used in present study, we have adopted the following procedure. In the list of national IQs given in Appendix 1, there are 88 countries for which there are two or more IQs. To calculate the reliability coeffcient we have taken the last two studies for each country (in the case of South Africa, blacks, colored and Indians separately, N=90). The correlation between these is 0.876 and represents the reliability coefficient.

To select two IQs for each country from which to calculate the reliability coefficient, the rules adopted are as follows. Where there are two studies, use both; with three studies, use the first and third; with four studies, use second and forth; with five studies, use second and fourth; with six studies, use second and fourth; with seven studies, use third and fifth; with eight studies, use second and fourth; with nine studies, use third and sixth; with ten studies, use third and sixth; with eleven studies, use fourth and eight; with twelve studies, use fourth and eight; with 23 studies, use eight and sixteenth; with 25 studies, use ninth and seventeenth. The correlation between the two studies obtained in this way was was 0.85 and represents the reliability coefficient. An estimate of the reliability of national Final IQs used in present study can be obtained from the correlation between the national IQs obtained from intelligence tests and the school achievement scores treated as alternative measure of national intelligence. The correlation between these is 0.907 for the 87 countries having both measures.

5. Validity of National IQs

Critics have also asserted that our national IQs lack validity. For instance, Ervik (2003) has written that we fail to establish the cross-cultural comparability (i.e. validity) of intelligence and Barnett and Wlliams (2004) who argues that the tests are not valid measures of the intelligence of peoples in many economically developing nations. More recently, Hunt (2011, p. 439) has written that “Lynn and Vanhanen disregard any question about the validity of various intelligence tests across different countries and cultures”.

Contrary to these assertions, we have gone to considerable trouble to demonstarate that our national IQs are valid. The validity of an intelligence test is the extent to which it measures what it purports to measure and is established by showing that it is highly correlated with other measures of cognitive ability. Foremost among these is educational attainment. As noted in section 2 above, at the level of individuals, intelligence and educational attainment are typically correlated at between 0.5 and 0.8. We have demonstrated that our national IQs are valid by showing that this association is also present at the national level. In our first book, we showed that our national IQs are correlated with national scores on mathematics at 0.881 and with national scores on science at 0.868 (Lynn and Vanhanen, 2002, p. 71). In our second book, we showed that our national IQs are correlated with national scores on mathematics scores obtained by 15 year old school students in PISA 2000 at 0.876 and with national scores on science obtained in PISA 2000 at 0.833 (Lynn and Vanhanen, 2006, p. 69). We have confirmed these high correlations in subsequent studies with larger data sets and shown the correlations between the results of national IQ tests and scholastic assessments are in the vicinity of 0.9 (Lynn and Mikk, 2007; Lynn, Meisenberg, Mikk and Williams, 2007). These results have been confirmed by Rindermann (2007). In a later study of 108 nations, we have shown that national scores aggregated from the PISA and TIMSS studies are perfectly correlated with national IQs (r=1.0) (Lynn and Meisenberg, 2010).

To examine further the association between national IQs and school achievement scores, the correlation between these (given in Table 2.1) is 0.907 for the 87 countries having both measures, as noted in Section 4. This confirms our numerous previous studies showing that national IQs and school achievement scores are measures of the same latent construct of cognitive ability of intelligence. This justifies the combination of the school achievement scores with IQ scores to form the Final IQs given in the right hand column of Table 2.1.

The validity of intelligence tests is not only demonstrated by a high correlation between IQs and educational achievement. The validity of the tests can also be established by showing that they are correlated with other phenomena that IQs partly determine such as earnings, life expectancy, and (negatively) crime and religious belief. The results of numerous studies showing correlations of this kind are summerized in Chapter 3 and show beyond dispute that our national IQs have high validity.

Chapter 3

Educational and Cognitive Attainment

8. Researchers in R&D

We have one variable, researchers in research and development per million people in 1990-2003 (R&D), which measures the application of education and intelligence to research work. It is hypothesized that this variable is positively correlated with national IQ. Unfortunately data on R&D are available only from 97 countries, and countries with low national IQs (below 80) are underrepresented in the sample.

The Pearson correlation between national IQ and R&D is 0.666 (N=97) and Spearman rank correlation considerably higher (0.828). Empirical evidence supports the hypothesis strongly. However, national IQ does not need to be the only factor which explains variation in the R&D variable. It can be assumed that per capita income, democratization, and the level of tertiary education are able to raise the explained part of variation in R&D independently from national IQ. When national IQ, PPP-GNI-08, ID-08, and Tertiary-9 are used to explain variation in R&D, the multiple correlation rises to 0.795 (N=96) and the explained part of variation to 63 per cent, which is 19 percentage points more than national IQ explains (44%). National IQ remains as the dominant explanatory factor, but the three environmental variables raise the explained part of variation significantly. The results of the regression analysis of R&D on national IQ given in Figure 3.2 clarify the relationship between the two variables at the level of single countries.

Figure 3.2 shows that the relationship between national IQ and R&D is extremely curvilinear. The value of R&D remains low for almost all countries below the national IQ level of 90 and also for some countries above this national IQ level, but it has risen steeply in most countries above this IQ level. This is an interesting finding. It seems to imply that a national IQ level of 90 is needed to extend research activities significantly. It is worthwhile to compare the groups of the most extremely deviating countries. Let us use R&D residual ±1300 (one standard deviation is 1261) to separate large outliers from the less deviating countries.

The group of large positive outliers includes the following 11 countries: Denmark, Finland, Georgia, Guinea, Iceland, Japan, Luxembourg, Norway, St Lucia, Sweden and the United States. Large positive residuals for Guinea and St Lucia are due to the fact that because of the linear regression equation, their predicted R&D values are negative. The other nine countries are real positive outliers. Eight of them are European and European offshoot countries (Australia, Denmark, Finland, Iceland, Luxembourg, Norway, Sweden and the United States), and Japan is the first East Asian country which adopted modern science and technology that evolved in Europe.

The group of large negative outliers (residual -1300 or higher) includes only six countries: China, Hong Kong, Malaysia, Malta, Moldova and Mongolia. This group does not include any sub- Saharan African country. It would be technically impossible because the predicted values of R&D are negative for all countries below the national IQ level of 77 (see Figure 3.2). The linear regression line does not take into account the curvilinearity of the actual relationship. The national IQs of all large negative outliers are above 90, and three of these countries are socialist or former socialist countries. Malta is a small European island country, and Moldova is one of the poorest European countries. Modern science and research incubated and evolved in Europe, and some centuries later started to spread to other parts of the world, but it has been a slow process. Large negative residuals imply that these countries and especially China have great human potential to increase the number of researchers in R&D.

Chapter 4

Economics: Per Capita Income, Poverty, Inequality

8. National IQ and Per Capita Income 2008

Correlations between national IQ and PPP-GNI-08 confirm previous studies on the positive relationship between national IQ and per capita income. All correlations are moderate or strong, and the explained part of variation varies from 35 to 62 per cent. Spearman rank correlations are clearly stronger than Pearson correlations because rank orders decrease the impact of extremely deviating cases. It is interesting to note that correlations in the group of large countries (population over one million) are higher than in the total group of countries. It is obvious that the group of small countries includes more highly deviating cases than the category of more populous countries. Correlations in the group of countries with measured national IQs are only slightly higher than in the total group of countries. Regression analysis is used to show how well the average relationship between national IQ and PPP-GNI-08 applies to single countries and which countries deviate most from the regression line and contradict the hypothesis (Table 4.8).

National IQ explains statistically 35 per cent of the variation in PPP-GNI-08 in the total group of 197 countries, which means that 65 per cent of the global variation is due to other factors. In the group of 153 countries of more than one million inhabitants, the explained part of variation in PPP- GNI-08 rises to 48 per cent and on the basis of Spearman rank correlation to 50 per cent. Those other factors may include differences in natural resources; geographical factors; the variation in the impact of foreign investments, technologies, and management; differences in the nature of economic systems, as well as many kinds of other local and temporary factors. It would be difficult to quantify and to get global statistical data for any of those other factors. We focus on the impact of national IQ, which explains probably more of the global variation of PPP-GNI-08 than any other explanatory factor, but we can try to find out what those other factors might be in particular cases. From this perspective, it is useful to pay attention to the most deviating countries. They can be roughly separated from less deviating ones by classifying the countries with residuals larger than ±12,000 into the category of large outliers (one standard deviation is 12,017). It is reasonable to assume that large outliers disclose some impact of other explanatory factors more clearly than countries closer to the regression line. It is useful to compare the two opposite categories of the most deviating countries to examine whether there are any systematic differences in the nature of large positive and large negative outliers.

The category of large positive outliers includes the following 24 countries: Andorra, Antigua & Barbuda, Austria, Bahrain, Bermuda, Brunei, Canada, Denmark, Equatorial Guinea, Hong Kong, Ireland, Kuwait, Luxembourg, Macao, the Netherlands, Norway, Qatar, St Lucia, Saudi Arabia, Singapore, Sweden, Switzerland, the United Arab Emirates and the United States.

There are significant differences in the nature of these 24 countries. European and European offshoot countries (11) constitute the largest coherent group within this category. They are economically highly developed democracies and market economies in which the level of per capita income has risen much higher than expected on the basis of their high national IQ values. They constitute a geographically coherent group of countries in Western Europe and North America and they have a long tradition as market economies and democracies. Hong Kong, Macao, and Singapore as economically highly developed East Asian countries belong to the same category. The outlying position of these 14 countries has made the relationship between national IQ and PPP-GNI-08 slightly curvilinear. When national IQ rises above 90, the level of PPP-GNI-08 starts to rise steeply in most countries, although not in all of them.

Antigua & Barbuda, Bermuda and St Lucia are Caribbean countries whose geographical location has favored the development of tourist industries. The growth of tourist industries has been based on extensive foreign investments and management. These factors provide a local explanation for the much higher than expected level of per capita income in the Caribbean tourist countries.

Bahrain, Brunei, Equatorial Guinea, Kuwait, Qatar, Saudi Arabia and the United Arab Emirates are oil exporting countries in which the level of per capita income has risen much higher than expected on the basis of their national IQs. In these countries foreign investments, technologies, and management have had a crucial role in their oil industries and these explain the exceptionally high level of per capita income in these countries. The fact that residuals are negative for most neighboring countries without significant oil resources supports this conclusion. Our interpretation is that the existence of exceptional natural resources combined with western technologies has raised per capita income in these eight countries much higher than expected.

The category of large negative outliers includes the following 11 countries: Armenia, Bosnia & Herzegovina, Cambodia, China, North Korea, Laos, Moldova, Mongolia, St Helena, Ukraine and Vietnam.

Large negative outliers differ from large positive ones in many respects. It is remarkable that nine of these countries are contemporary or former socialist countries (Armenia, Bosnia & Herzegovina, China, North Korea, Laos, Moldova, Mongolia, Ukraine and Vietnam). Residuals are clearly negative also for several other former socialist countries (see Table 4.8). It is obvious that the communist economic and political system has been much less beneficial for economic development than a market economy combined with a democratic political system. However, their high national IQ values and large negative residuals predict a significant future rise of per capita income in all these countries.

Cambodia is an Asian country, which has suffered from serious civil wars. This exceptional local factor has certainly hampered economic development. St Helena is an isolated island country. Its geographical isolation may have hampered economic development.

Figure 4.1 summarizes the results of the regression analysis and indicates that the relationship between variables is positive as hypothesized but to some extent curvilinear. Most of the largest positive outliers are oil producing countries and Caribbean tourist countries, but the group includes also some socio-economically highly developed Western and East Asian countries. The largest negative outliers are socialist and former socialist Asian countries.

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 focus is 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 tends to 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 especially Luxembourg 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.

9. Measures of Poverty

Global variation in the extent of povertywill be measured by three indicators of international poverty criteria, population below $1.25 a day % and population below $2 a day %. Data on these variables are for the period 1993-2008. The extent of poverty is, of course, negatively related to the level of per capita income (see Table 4.6). Our third measure of poverty is UNDP’s Multidimensional Poverty Index (MPI-00-08). It is also negatively correlated with PPP-GNI-08. The level of poverty can be expected to be much lower in wealthy countries than in poor countries, but it is also reasonable to expect that the level of poverty will tend to decrease when the level of national IQ rises because more intelligent people are better able to take care of themselves and to defend their interests than less intelligent people. Therefore, we focus on the explanatory power of national IQ. The results of correlation analyses are given in Table 4.9.

All correlations are negative as hypothesized and relatively strong. They are almost the same in the three groups of countries, and Spearman rank correlations are only slightly stronger than Pearson correlations. National IQ explains 43-61 per cent of the variation in the three measures of poverty. Approximately half or nearly half of the variation seems to be due to other explanatory factors, including measurement errors.

Let us use PPP-GNI-08 and Literacy-08 to illustrate the impact of other explanatory factors. Taken together national IQ, PPP-GNI-08, and Literacy-08 explain 58 per cent of the variation in Below $1.25 a day variable (multiple correlation 0.762) and 70 per cent of the variation in Population below $2 a day (multiple correlation 0.835) in the group of 101 countries. Further, they explain 79 per cent of the variation in MPI-00-08 (multiple correlation 0.889) in the group of 100 countries. This means that PPP-GNI-08 and Literacy variables are able to explain independently from national IQ 14 percentage points of the variation in the first, 20 percentage points of the variation in the second, and 26 percentage points of the third indicator of poverty. It is evident that the level of poverty in single countries depends not only on differences in national IQ but also on some environmental factors. However, national IQ seems to be the dominant explanatory factor.

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. It is true that PPP-GNI-08 and Literacy-08 explain a significant part of the variation in the level of poverty independently from national IQ, although the explanations provided by PPP-GNI-08 and Literacy-08 are for the most part overlapping with the explanation provided by national IQ.

12. Unemployment

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 groups to 4 per cent among those with IQs above 126. Thus, low-IQ 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.

To examine the relationship between national IQ and rates of unemployment, we take the data for national rates of unemployment from the Central Intelligence Agency (CIA) Yearbook (2003, 2008). This gives the official unemployment figure and an estimate for underemployment for a few nations. In these cases we have used the official estimate and disregarded the estimate of underemployment. The general effect of this decision is to reduce the degree of unemployment of mainly low IQ countries and therefore underestimate the true size of the relationship between IQ and unemployment.

The CIA Yearbook figures are not always for a single calendar year. For a number of nations the Yearbook gives the most recent estimate at the time of publication. Some of these are up to five years old. Taking this into account we have defined two periods encompassing a range of dates. The first period is from 1996 to 2002 (93.6% of the unemployment figures are within the range 1999 to 2002). The median year is 2001. The second period is from 2003 to 2009 (92.8% of the unemployment figures are within the range 2005 to 2008). The median year is 2008.

The first period (median year 2001) has unemployment data for 141 nations for which national IQ data exist. The median unemployment figure was 10.2% and the mean 14.3%. The standard deviation was 12.3% and first and third quartiles 5.4% and 18.25% respectively. The second period (median year 2008) has unemployment data for 128 nations for which national IQ data exist. The median unemployment figure was 6.8% and the mean 11.1%. The standard deviation was 13.89% and first and third quartiles 4% and 11.8% respectively. The average of the two periods yielded unemployment data for 107 nations for which national IQ data exist. 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 correlation can be corrected for unreliability of both variables. The reliability of the average unemployment figures taken as the correlation between the unemployment figures in the two periods is 0.81. The reliability of national IQs given in Chapter 2 is 0.91. Corrected for unreliability, the correlation between national IQ and unemployment is -0.76 and 57 per cent of variance in the rate of unemployment across nations is explained by national IQ. Thus the relationship between low IQ and high rates of unemployment that is present among individuals also holds across 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.

Chapter 5

Political Institutions

4. Democratization

The three measures of democracy (ID-08, FH-08, and Polity-08) are strongly intercorrelated as indicated in Table 5.2, but the unexplained part of variation leaves a lot of room for different measurement results in particular cases. The correlations between national IQ and the three measures of democracy are given in Table 5.3.

Table 5.3 shows that the three measures of democracy are only moderately correlated with national IQ in the total group of countries, but correlations are slightly stronger in the two smaller groups of countries. ID-08 is clearly more strongly related to national IQ than FH-08 and Polity-08. Most of the variation in the level of democratization seems to be due to some other factors, not to the level of national IQ. What might those other factors be?

According to Vanhanen’s resource distribution theory of democratization, the level of democratization depends crucially on the distribution of resources used as sanctions in the struggle for power. It must be so because people tend to use all available resources in the continual struggle for power and scarce resources. The Darwinian theory of evolution by natural selection explains why the struggle for existence is inevitable and incessant in nature. As a consequence, where relevant power resources are concentrated in the hands of the few, political power also tends to be concentrated in the hands of the few, and in societies where important power resources are widely distributed, political power tends to become widely distributed. Briefly stated, the concentration of power resources leads to autocracy, and the distribution of power resources among the many leads to democracy (for this theory, see Vanhanen 2003, pp. 25-29; 2009, pp. 27-36). Vanhanen has used his Index of Power Resources (IPR) to measure the distribution of economic and intellectual power resources. IPR is a combination of four basic indicators: (1) tertiary gross enrollment ratio, (2) percentage of adult literacy, (3) family farms, and (4) the estimated degree of decentralization of economic power resources. These variables are defined and described in greater detail and empirical data on them and IPR are given and documented in Vanhanen’s book The Limits of Democratization (2009). Empirical data on IPR cover 172 contemporary countries whose population in 2000 was more than 200,000 inhabitants. Most data on IPR are from the first years of this century. It is interesting to see how much IPR is able to explain of the variation in ID-08, FH-08, and Polity-08 independently from national IQ.

When national IQ and IPR are taken together to explain the variation in ID-08, the multiple correlation rises to 0.801 (N=172) and the explained part of variation to 64 percent. In the same group of 172 countries, the correlation between national IQ and ID-08 is 0.574 and the explained part of variation 33 percent. This means that IPR explains 31 percent of the variation in ID-08 independently from national IQ. Because the correlation between IPR and ID-08 is 0.774 in this group of 172 countries, it means that the impact of national IQ on ID-08 takes place principally through IPR.

In the case of FH-08, the corresponding multiple correlation is 0.728 in the group of 172 countries and the explained part of variation 53 percent. The correlation between national IQ and FH-08 is 0.462 (explained part of variation 21 percent). In other words, IPR explains 32 percent of the variation in FH-08 independently from national IQ. In the case of Polity-08, the corresponding multiple correlation is 0.593 in the group of 157 countries and the explained part of variation 35 percent. Because the correlation between national IQ and Polity-08 is 0.312 (explained part of variation 10.0 percent), IPR explains 25 percent of the variation in Polity-08 independently from national IQ.

In these cases an environmental variable (IPR) is able to explain independently from national IQ as much or more of the variation in the measures of democracy than national IQ. It should be noted that correlations between national IQ and ID-08 and FH-08 are in this group of 172 countries slightly higher than in the total group of countries (Table 5.3).

The level of democratization is indeed very strongly related to the degree of resource distribution, and national IQ is the most important background factor in this relationship. National IQ explains statistically 60 percent of the variation in IPR (correlation 0.774, N=172). The value of IPR tends to rise with the level of national IQ. Because national IQ is an important background factor of democratization via IPR, it is useful to see on the basis of regression analysis how well national IQ explains the variation in ID-08 at the level of single countries and which countries deviate most clearly from the regression line to positive or negative direction in the total group of 188 countries (Table 5.4).

Table 5.4 shows that the actual value of ID-08 deviates in many cases extensively from the predicted value (regression line) to positive or negative direction. In the countries with large positive residuals the level of democratization is much higher than expected on the basis of the regression equation, and in the countries with large negative residuals it is much lower than expected. Figure 5.1 summarizes the results of regression analysis given in Table 5.4. It shows that the relationship between variables is linear but weak. In fact, 74 percent of the variation in ID-08 seems to be due to some other factors. As noted earlier, IPR together with national IQ explains 64 percent of the variation in ID-08 in the group of 172 countries, but it still leaves 36 percent of the variation unexplained. Many large negative outliers are clustered at the national IQ level 80-90. It is a kind of transition level above which most countries are democracies.

Because many countries deviate greatly from the regression line, it can be assumed that some environmental factors affect the level of democratization independently from national IQ, but the problem is what those environmental factors might be. As indicated above, IPR together with national IQ explains 64 percent of the variation in ID-08, but even then 36 percent of the variation remains unexplained. The comparison of large positive and negative outliers provides some hints about the nature of those other factors. Let us define as large outliers countries whose residuals are ±11.0 or higher (one standard deviation is ±10.1).

The group of large positive outliers (residual +11.0 or higher) includes the following 27 countries: Antigua & Barbuda, Austria, Belgium, Cyprus, Denmark, Finland, Germany, Greece, Grenada, Iceland, Italy, Malawi, Malta, Micronesia, the Netherlands, Norway, Panama, Papua New Guinea, St Lucia, Saint Vincent & the Grenadines, the Seychelles, Sri Lanka, Sweden, Switzerland, Trinidad & Tobago, Uruguay and Vanuatu.

It is remarkable that 14 of these 27 positive outliers are economically highly developed European democracies with high national IQs. On the basis of Vanhanen’s Index of Power Resources (IPR), most of these countries are not large positive outliers because their high level of resource distribution (IPR) predicts a high level of democratization (see Vanhanen 2009, pp. 98-111). In other words, their high level of democratization is more or less in balance with IPR. Antigua & Barbuda, Grenada, St Lucia, Saint Vincent & the Grenadines and Trinidad & Tobago are Caribbean tourist countries in which the level of democratization is much higher than expected on the basis of national IQ. In some way their dependence on Western tourism is related to the success of democracy. The Seychelles is a similar small island state at the Indian Ocean depending on tourism. Micronesia’s high level of democratization is in some way related to its close association with the United States. Papua New Guinea’s high level of democratization is principally due to its extremely heterogeneous ethnic structure, which has prevented the emergence of large political parties. Vanuatu’s exceptionally high positive residual (17.4) is due to the party fragmentation in the 2008 parliamentary elections. We do not have any special explanations for higher than expected levels of democracy in Malawi, Panama, Sri Lanka and Uruguay. However, for all of them positive residuals are only slightly above 11.0.

The survival of democratic institutions in Sri Lanka despite its long ethnic civil war is a remarkable achievement. Large positive residuals predict a decrease in the level of ID, but it does not need to happen if there are exceptional local factors that support the survival of a higher than expected level of democratization.

The group of large negative outliers (residual -11.0 or higher) includes the following 31 countries: Andorra, Bahrain, Belarus, Brunei, China, Cuba, Egypt, Eritrea, Fiji, Iran, Iraq, Jordan, Kazakhstan, North Korea, Kuwait, Laos, Libya, Mauritania, Morocco, Myanmar, Oman, Qatar, Saudi Arabia, Singapore, Sudan, Swaziland, Tonga, Tunisia, the United Arab Emirates and Vietnam.

This group of large negative outliers includes only one economically highly developed European democracy (Andorra). Its large negative residual is completely due to the fact that citizens of other countries, who cannot vote in Andorra, constitute more than half of its population. The group includes only one Latin American country (Cuba), no Caribbean country, and four sub-Saharan African countries (Eritrea, Mauritania, Sudan and Swaziland). Nearly all of the large negative outliers are Asian and North African countries whose national IQs vary between 80 and 90. Arab and other Middle Eastern Muslim countries (Bahrain, Egypt, Iran, Iraq, Jordan, Kuwait, Libya, Morocco, Oman, Qatar, Saudi Arabia, Tunisia and the United Arab Emirates) constitute a geographically and culturally coherent group of large negative outliers. Most of them are oil producing countries, and economic power resources are highly concentrated in all of them. Autocratic political systems persist in nearly all of these countries. Brunei as an oil-exporting autocracy belongs to the same category. Socialist or former socialist countries constitute another coherent group (Belarus, China, Cuba, Kazakhstan, North Korea, Laos and Vietnam). The failure of democratization in these countries is related to the legacy of autocratic socialist systems and to the concentration of power resources (IPR). In fact, because of the concentration of power resources, most countries of these two categories are not highly deviating ones on the basis of IPR. The other large negative outliers (Fiji, Myanmar, Singapore and Tonga) are dispersed around the world without any common characteristics. In Fiji military coups have been due to the ethnic strife between the indigenous Fijians and Indian immigrants. Myanmar has been ruled by autocratic military governments since the 1960s. Singapore is exceptionally an economically highly developed country in which democratization has not yet fully succeeded. Tonga is a traditional autocratic monarchy. In principle, large negative residuals predict a significant rise in the level of ID, but, as explained above, exceptional local factors have hampered democratization and supported the survival of autocratic regimes in most of these countries. However, it is important to note that in 2010-11 popular insurgencies broke out in many Arab and Middle Eastern Muslim countries with large negative residuals. Rebellious people demanded democratization.

The clear differences in the nature of large positive and negative outliers indicate the impact of some environmental factors, which explain the success or failure of democracy independently from the level of national IQ. The degree of resource distribution (IPR) seems to be the most important systematic factor which helps to explain the success of democracy in several countries with large positive residuals as well as the failure of democracy in several countries with large negative residuals. However, national IQ remains as an important background factor because of its strong relationship with IPR. It constrains the level of democratization and the quality of democracy significantly. 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).

Chapter 8

Clean Water and Sanitation

Water is one of the most precious natural resources. It is of vital importance to life, but access to water is extremely unequally distributed in the world. UNDP’s Human Development Report 2006 (HDR-06) notes that water “is at the heart of a daily crisis faced by countless millions of the world’s most vulnerable people – a crisis that threatens life and destroys livelihoods on a devastating scale” and that “overcoming the crisis in water and sanitation is one of the great human development challenges of the early 21st century” (p. 1). We agree. The problem is to what extent this crisis is due to absolute shortages of water and to what extent to deficiencies in securing water.

We believe that the peoples with high IQs have used their intelligence to ensure that they have a constant supply of clean water. For instance, the Egyptians had built an extensive system of reservoirs and canals to provide their cities with clean water by the 14th century B.C. In 272 B.C. the Romans built a 32 mile long underground channel, the Anio Vetus, to convey clean water from springs in the Apennines to Rome. In 144 B.C., the Romans constructed the first overhead aqueduct, the Aqua Marcia, to supply Rome with water. This was 60 miles long and much of it was built on arches. By the third century A.D., the Romans had built eleven aqueducts to carry an estimated 200 million gallons of water to Rome every 24 hours. The Romans built aqueducts to supplywater in many of their cities throughout their empire, some of which survive to this day, including those in Tarragona, Segovia, Seville, Smyrna, and at the Pont du Gard in France. These were sophisticated engineering constructions made of stone or brick, held together with cement, which the Romans invented. The fall was set at 1 in 200, to provide a steady continuous flow of water (Rd, 1960, p.160). Those who lived in the country secured a supply of clean water by constructing wells, generally lined with brick. Later, the peoples with high IQs build reservoirs to provide a continuous supply of clean water. Yet today, many third world peoples do not have clean water from reservoirs, aqueducts or even from wells.

1. Introduction

… We do not assume that national IQ is the only factor capable to explain global disparities in access to clean water and sanitation facilities; we only assume that it is probably the most important single and measurable explanatory factor. HDR-06 does not refer to differences in national intelligence or to educational differences between nations. The report emphasizes the significance of political leadership or, rather, its absence, and secondly the importance of poverty as a barrier to progress. Our argument is that the absence of good political leadership is related to national IQ.

2. Variables

Water-08. This dataset published in HDR-10 (Table 7) concerns the percentage of population without access to improved water services in 2008. Data cover 160 countries.

Sanitation-08. These data published in HDR-10 (Table 7) concern the percentage of the population without access to improved sanitation services in 2008 and they cover 160 countries.

3. Clean Water

… Water-08 is moderately or strongly correlated with several environmental variables: PPP-GNI-08 -0.556 (N=165), ID-08 -0.476 (N=164), Literacy-08 -0.746 (N=166), Tertiary-09 -0.634 (N=164), Life-08 -0.777 (N=166), and IMR-08 -0.834 (N=166). Most of these correlations are stronger than the correlation between national IQ and Water-08 (-0.621), but because all these environmental variables are moderately or strongly correlated with national IQ, the problem is how much they can explain of the variation in Water-08 independently from national IQ and to what extent the explanations provided by them are overlapping with the explanation provided by national IQ. When national IQ, PPP-GNI-08, ID-08, Literacy-08, Tertiary-09, Life-08, and IMR-08 are taken together to explain the variation in Water-08, the multiple correlation rises to 0.852 and the explained part of variation to 73 percent, which is 34 percentage points more than national IQ explains (39%). The independent explanatory power of environmental variables is significant, but still slightly less than the explanatory power of national IQ.

… WDI-09 (Table 3.5) includes data on renewable internal freshwater resources per capita in cubic metres in 2007 (Freshwater). It measures internal renewable resources (internal river flows and groundwater from rainfall) in the country. It is noted that these “estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution” (WDI-09, p. 153). It could be assumed that freshwater resources per capita are negatively correlated with Water-08, but in fact there is no correlation between these variables (0.050, N=139). The correlation between national IQ and Freshwater is also in zero (0.014, N=147).

Figure 8.1 shows that the relationship between national IQ and Water-08 is linear as hypothesized, but many highly deviating countries weaken the relationship. In the countries above the regression line, the percentage of people without access to improved water services is higher than expected on the basis of the regression equation, and in the countries below the regression line it is lower than expected. In all countries above the national IQ level of 90, the percentage of the population without access to clean water is zero or near zero, except in Cambodia, China and Mongolia, whereas this percentage varies greatly in the countries below the national IQ level of 85. National IQ is not able to explain the great variation in Water-08 in the group of countries with low national IQs. Most of that variation seems to be due to some environmental and local factors, perhaps also to measurement errors.

Table 8.3 shows the countries which deviate most from the regression line and for which positive or negative residuals are large. An interesting question is whether some systematic differences between large positive and negative outliers could help to explain their deviations from the regression line. Let us regard as large outliers countries whose residuals are ±15 or higher (one standard deviation is 13).

The group of large positive outliers (residuals +15 or higher) includes the following 26 countries: Afghanistan, Angola, Cambodia, Chad, China, Congo, D.R., Eritrea, Ethiopia, Kenya, Laos, Madagascar, Mali, Mauritania, Mongolia, Mozambique, Myanmar, Niger, Nigeria, Papua New Guinea, Sierra Leone, Somalia, Sudan, Tanzania, Timor-Leste, Yemen and Zambia. The percentage of the population without access to clean water is in all these countries much higher than expected on the basis of the regression equation.

It is remarkable that this group does not include any economically highly developed countries, Caribbean tourist countries, Latin American countries, or oil exporting countries. Most of them are poor sub-Saharan African countries (17). China is not really a large positive outlier for the reason that its predicted value of Water-08 is negative -6. The other eight positive outliers are poor Asian and Oceanian countries. Most of them (especially Afghanistan, Cambodia, Myanmar and Timor-Leste) have suffered from serious civil wars, which have hampered socio-economic development.

The group of large negative outliers includes 17 countries: Barbados, Belize, Botswana, the Comoros, Djibouti, Egypt, Gabon, Gambia, Jamaica, Lesotho, Malawi, Namibia, Qatar, St Kitts & Nevis, St Lucia, Sao Tome & Principe and South Africa.

It is significant that several of these countries below the national IQ level of 85 have benefitted from foreign investments, technologies, and management. These are the Caribbean tourist countries (Barbados, Belize, Jamaica, St Kitts & Nevis and St Lucia), oil exporting countries (Gabon and Qatar), as well as Botswana and South Africa, which were previously ruled by their white minorities. The other eight negative outliers are African countries (the Comoros, Djibouti, Egypt, Gambia, Lesotho, Malawi, Namibia and Sao Tome & Principe), which have been able to reduce the percentage of the population without access to clean water much more successfully than most other African countries at the same level of national IQ. Their example implies that it is possible to improve significantly water services in poor African countries.

Some systematic differences in the characteristics of large positive and negative outliers provide partial explanations for their large residuals. Most countries with large negative residuals have benefitted from investments, technologies, and management from countries of higher national IQs, whereas most countries with large positive residuals have received much less such foreign help.

4. Sanitation

… National IQ explains 51 percent of the variation in Sanitation-08 in the total group of 166 countries and 55 percent in the group of countries with more than one million inhabitants, but the unexplained part of variation leaves room for the impact of other explanatory variables. Because the three indicators of sanitation are strongly intercorrelated (see Table 8.1), it is enough to explore the impact of other explanatory variables only in the case of Sanitation-08 in the total group of countries. Sanitation-08 is approximately as strongly related to PPP-GNI-08 (-0.661), ID-08 (-0.470), Literacy-08 (-0.777), Tertiary-09 (-0.673), Life-08 (-0.788), and IMR-08 (0.805) as Water-08, but most of the explanations provided by these variables are overlapping with the explanation provided by national IQ. Multiple regression analysis clarifies their independent explanatory power. When national IQ, PPP-GNI-08, ID-08, Literacy-08, Tertiary-09, Life-08, and IMR-08 are used together to explain the variation in Sanitation-08, the multiple correlation rises to 0.860 (N=164) and the explained part of variation to 74 percent, which is 23 percentage points more than national IQ explains (51%).

Figure 8.2 shows that the relationship between the variables is approximately linear as hypothesized, but many extremely outlying countries are inconsistent with the hypothesis and weaken the overall relationship. Positive residuals indicate that the percentage of the population without access to improved sanitation services is higher than expected on the basis of the regression equation, and negative residuals indicate that the percentage is lower than expected. We can see from Figure 8.2 that national IQ explains much less of the variation in Sanitation-08 in the group of countries below the national IQ level of 90 than in the group of countries above this IQ level. The value of Sanitation-08 is zero or near zero for most countries above the national IQ level of 90. It is again useful to compare the opposite groups of countries with large positive and large negative residuals. Let us use a residual ±25 to separate the most outlying countries from the countries which are closer to the regression line (one standard deviation is 21).

Using this criterion, the group of large positive outliers includes the following 22 countries: Azerbaijan, Benin, Bolivia, Burkina Faso, Cambodia, Chad, China, Eritrea, Ethiopia, Ghana, India, Laos, Madagascar, Mauritania, Mongolia, Mozambique, Nepal, Niger, Pakistan, Papua New Guinea, Tanzania and Togo. For all these countries, the percentage of population without access to improved sanitation services is much higher than expected on the basis of national IQ.

Thirteen of the large positive outliers are the same as in the case of the Water-08 variable (Cambodia, Chad, China, Eritrea, Ethiopia, Laos, Madagascar, Mauritania, Mongolia, Mozambique, Niger, Papua New Guinea and Tanzania), which reflects the strong positive correlation between Water-08 and Sanitation-08 (0.811). For the other nine countries, residuals for Water-08 are slightly positive or negative. Twelve of the large positive outliers are sub-Saharan African countries and nine others are relatively poor Asian and Oceanian countries. Bolivia is the only Latin American country, and the group does not include any European or Caribbean country.

Negative residuals are large for the following 21 countries: Albania, the Bahamas, Barbados, Belize, Egypt, El Salvador, Gambia, Grenada, Jamaica, Kuwait, Kyrgyzstan, Malawi, the Maldives, Qatar, St Kitts & Nevis, South Africa, Sri Lanka, Syria, Tajikistan, Turkmenistan and Uzbekistan. In all these countries, the percentage of the population without access to improved sanitation services is much lower than expected on the basis of their national IQs.

Nine of these countries are the same as large negative outliers on the basis of Water-08 (Barbados, Belize, Egypt, Gambia, Jamaica, Malawi, Qatar, St Kitts & Nevis and South Africa). The other 12 countries are not large outliers on the basis of Water-08. It is characteristic of large negative outliers that national IQ is below 90 in all of them. Eight of them are Caribbean tourist countries or oil producing countries, which reflects the beneficial impact of foreign investments, technologies, and management. Five others are former socialist countries. The rest of the large negative outliers (Egypt, El Salvador, Gambia, Malawi, the Maldives, South Africa, Sri Lanka and Syria) seem to be without any common characteristics.

There are some systematic differences in the characteristics of large positive and negative outliers. Many of the large negative outliers have benefitted from internal peace and intensive foreign investments, technologies and management, whereas ethnic or other civil wars have devastated some of the countries with large positive residuals, or they are overpopulated compared to the available means of livelihood. The Caribbean tourist countries constitute a coherent core region of large negative outliers, whereas sub-Saharan African countries, at about the same level of national IQ, constitute the main region of large positive outliers.

5. Conclusion

… National IQ explains from 32 to 62 percent of the variation in Water-08 variable and from 41 to 60 percent of the variation in Sanitation-08 in various groups of countries.

… The results show that correlations between Freshwater and Water-08 and Sanitation-08 are close to zero. So the results of empirical analysis support the HDR-06 argument that the crisis in water is not principally related to the scarcity of freshwater resources.

… The summary shows that national IQ is the dominant explanatory factor, although several environmental variables have some explanatory power independently from national IQ. The unexplained part of variation is relatively small. The results of our empirical analyses imply that differences in national IQs are to a significant extent behind the “political processes and institutions that disadvantage the poor.” However, the fact that some low IQ countries have already been able to provide satisfactory water and sanitation services indicates that a low national IQ does not constitute an insurmountable obstacle to provide water and sanitation services to all people, but it is important to note that many of the successful countries have benefitted from significant foreign investments, technologies, and management, whereas several of the least successful countries have been devastated by ethnic and other civil wars. On the basis of these findings, it is reasonable to expect that significant inequalities in water and sanitation services will continue in the world, although it is certainly possible to improve access to clean water and sanitation services in all countries.

Chapter 12

Indexes of Human Conditions

2. Index of Human Conditions (IHC)

For the purposes of this study, we have constructed a slightly different composite Index of Human Conditions (IHC). It combines seven variables measuring the prosperity and wellbeing of nations from different perspectives: (1) PPP GNI per capita 2008, (2) Index of Democratization 2008 (ID-08), (3) Corruption Perceptions Index 2009 (CPI-09), (4) adult literacy rate (Literacy-08), (5) tertiary enrolment ratio (Tertiary), (6) life expectancy at birth 2008 (Life-08), and (7) infant mortality rate per 1,000 live births (IMR-08). These seven variables were selected because they measure national wellbeing and human conditions from clearly different perspectives – from the perspectives of wealth, democracy, education, and health – and because statistical data on these variables are available from nearly all countries of the world.

… Table 12.1 shows that most of the seven components of IHC are only moderately correlated with each other, which means that they measure human conditions from clearly different perspectives.

3. Correlation Analysis

The results of statistical analyses indicate that differences in all kinds of human conditions are significantly related to differences in national IQs. Therefore it is reasonable to assume that different composite indexes of human wellbeing, prosperity, and human conditions should also be positively related to national IQ no matter what indicators are used in those indexes. This central hypothesis will be tested by correlating national IQ with our Index of Human Conditions (IHC), the Human Development Index (HDI-10), the Legatum Prosperity Index, and Newsweek’s ranking list of the best countries in the world 2010.

… Let us first examine the correlations between national IQ and the seven components of IHC given in Table 12.2. All correlations are moderate or strong. … In the Pearson correlations, the explained part of variation in the components of IHC varies from 26 to 60 per cent. Most Spearman rank-order correlations are clearly stronger.

Let us next see how strongly our composite Index of Human Conditions (IHC) and the three other indexes of human development, prosperity, and wellbeing are correlated with each other (Table 12.3). As noted earlier, the four indexes are based on quite different measures of human conditions. Table 12.3 illustrates their intercorrelations.

… The covariation of indexes varies from 82 per cent in the case of HDI-10 and Legatum-10 to 92 per cent in the case of IHC and Newsweek-10.

… The composite index IHC is clearly more strongly related to national IQ than any of its seven components (cf. Table 12.2), which implies that IHC may indicate better the national variation in human conditions than any of its components.

4. Regression of IHC on National IQ

National IQ explains 65 percent of the variation in the Index of Human Conditions (IHC) in the total group of countries and 73 percent in the smaller sample of countries with more than one million inhabitants, but correlations do not tell how well the average relationship applies to single countries.

We can see from Figure 12.1 that the relationship between national IQ and IHC is strong and approximately linear, although most IHC values start to rise more than expected on the basis of the regression equation above the national IQ level of 90. Several highly deviating countries weaken the overall correlation. Some of the largest positive and negative outliers are named in the figure. It is easy to note that there are significant differences in the nature of large positive and negative outliers. Small Caribbean tourist countries and socioeconomically highly developed Western democracies seem to dominate in the category of large positive outliers, whereas socialist and former socialist countries and some countries ravaged by civil wars dominate in the group of large negative outliers. The examination of the nature of the most deviating countries may provide hints about factors which have been related to the level of IHC independently from national IQ. These preliminary observations on the nature of the most deviating countries refer to the impact of exceptional local, historical, and geographical factors, which are largely independent from national IQ. We will discuss the nature and impact of these factors in greater detail on the basis of the detailed results of this regression analysis reported in Table 12.5.

Residuals given in Table 12.5 show how well the average relationship between national IQ and IHC applies to single countries. Small residuals indicate that the actual level of IHC does not differ much from the level predicted on the basis of the regression equation. We do not need to pay particular attention to the countries with small residuals because their deviations from the regression line may be due to measurement errors and various accidental and local factors, whereas it is justifiable to examine in greater detail the countries for which residuals are large. These countries are exceptions to the hypothesis, and it would be useful to find out what factors might explain their deviations and whether there are some systematic differences between large positive and negative outliers. Let us use a residual ±12.0 (one standard deviation is 11.1) to separate the most extremely deviating countries from the countries closer to the regression line. Large residuals imply a significant impact of other factors on the level of IHC. Consequently, the examination of countries with large positive and negative residuals may provide hints about the nature of other causal factors.

Countries with large positive residuals

Positive residuals are large (+12.0 or higher) for 26 countries: Antigua & Barbuda, Australia, Barbados, Belgium, Denmark, Dominica, Finland, Germany, Greece, Grenada, Ireland, Luxembourg, Malawi, Namibia, the Netherlands, Norway, Panama, Puerto Rico, Qatar, St Kitts & Nevis, St Lucia, St Vincent & the Grenadines, Sao Tome & Principe, Sweden, Switzerland and the United States. Do these countries have some common characteristics which might explain their large positive residuals? It is easy to note that they do not constitute a random sample of the 191 countries. This group of 26 large positive outliers includes clearly different types of countries.

Half of these countries (13) are European and European offshoot highly developed market economies and democracies. Eleven of them constitute a geographically coherent group of Northern and Western European countries. Because of their high level of national IQ, IHC values are expected to be high for these countries, but they are much higher than expected on the basis of the regression equation. An explanation for their large positive residuals may be that Western Europe with European offshoot countries constitute the core region of scientific and technological inventions and development. These local and historical factors together with a long established market economy and democracy may be enough to explain large positive residuals in these 13 countries. Positive residuals are significant (8.0 or higher) also for Austria, Canada, Iceland, Italy, New Zealand, Slovenia, Spain and the United Kingdom. The question is on the impact of exceptional local factors. Many other market economies and democracies in other parts of the world do not have large positive residuals. Israel should be added to this group of positive outliers because of its population’s historical connections with Europe and North America.

Eight small Caribbean tourist countries (Antigua & Barbuda, Barbados, Dominica, Grenada, Puerto Rico, St Kitts & Nevis, St Lucia and St Vincent & the Grenadines) constitute another geographically coherent group of large positive outliers. The level of national IQ is low in these countries, but they have been able to raise the level of IHC much higher than expected on the basis of the regression equation. Their geographical position has favored foreign investments in tourist industries and services. As a consequence, these countries are socioeconomically much more developed and wealthier than sub-Saharan African countries at about the same level of national IQ. Thus the explanation for their outlying position seems to be the exceptional success of tourist industries in these countries, which has been supported by extensive foreign investments, technologies and management as emphasized in connection with several other variables in previous chapters. The question again is of an exceptional local factor or a combination of a favorable geographical position and foreign investments in tourism. In the case of Puerto Rico, its connection with the United States has supported socioeconomic development.

Qatar is an oil exporting country, which has benefitted from its natural resources and from extensive foreign investments in its oil industries. Residuals are clearly positive also for several other oil exporting countries (Bahrain, Gabon, Kuwait, Oman, Saudi Arabia and the United Arab Emirates). It is common for them that because of their oil and gas reserves, foreign companies of high IQ countries have supported the establishment of oil and gas industries, which has raised per capita income and furthered socioeconomic development in these countries. It is important to note that the much higher than expected level of IHC in these countries, as well as in the Caribbean tourist countries, is principally due to investments and technologies from countries of higher national IQs.

The four other positive outliers (Malawi, Namibia, Panama and Sao Tome & Principe) are more problematic cases. Malawi’s large positive residual is partly due to the fact that its national IQ (60) is exceptionally low. Namibia’s socioeconomic development may have benefitted from the contributions of its significant white minority. In Panama, the Canal is an exceptional local factor that has benefitted socioeconomic development.

The examination of large positive outliers leads to the conclusion that three exceptional local factors – the combination of market economy and democracy in Western Europe and European offshoot countries, tourism in the Caribbean countries, and exploitation of oil reserves in oil- producing countries – seem to explain the much higher than expected level of IHC in nearly all of these countries. It should be noted that these are exceptional local factors and that the two latter factors have been heavily dependent on the investments and technologies provided by high IQ countries. Therefore it is not reasonable to expect any significant decline of IHC values in these countries.

Countries with large negative residuals

Negative residuals are large (-12.0 or higher) for 21 countries: Afghanistan, Bangladesh, Cambodia, China, Djibouti, Iraq, Kiribati, North Korea, Laos, Madagascar, Mongolia, Myanmar, Pakistan, Papua New Guinea, the Philippines, Somalia, Sudan, Timor-Leste, Uzbekistan, Vietnam and Yemen. The nature of large negative outliers differs markedly from the nature of large positive outliers. The group does not include any economically highly developed Western European country, nor any Caribbean tourist country or Latin American country, and of oil-producing countries it includes only Iraq. It is possible to separate three different groups of large negative outliers. Six of these countries are contemporary socialist countries (China, North Korea, Laos and Vietnam) or former socialist countries (Mongolia and Uzbekistan). It is obvious that the Communist heritage, the combination of command economy and autocracy, has hampered socioeconomic development and kept the level of IHC much lower than expected on the basis of their national IQs (see Figure 12.1). The same observation has already been made in several previous chapters. It should be noted that the question concerns an exceptional local and historical factor. However, the impact of the Communist heritage will certainly weaken in the future as a consequence of market economy reforms and democratization, which means that we can expect a decline of negative residuals in at least some of these countries. Human potential for socioeconomic development is enormous especially in China and North Korea. In fact, residuals are already positive or only slightly negative for most former socialist countries.

It is characteristic for 11 other large negative outliers that they have suffered from serious ethnic conflicts and/or civil wars. Eight of them are Asian and Oceanian countries (Afghanistan, Cambodia, Iraq, Myanmar, Pakistan, the Philippines, Timor-Leste and Yemen) and three others are sub-Saharan African countries (Djibouti, Somalia and Sudan). This is also a local factor limited to particular countries and a factor which does not need to remain permanent. We can expect negative residuals to decline in countries which are able to establish ethnic peace because it would make possible a normal socioeconomic development.

Bangladesh, Kiribati, Madagascar, and Papua New Guinea are separate cases without any common characteristics. Bangladesh is an extremely poor and overcrowded South Asian country. Kiribati and Papua New Guinea are geographically isolated Oceanian countries. Madagascar’s large negative residual is due to the fact that national IQ is for Madagascar (82) much higher than for other sub-Saharan African countries.

It has been possible to separate two exceptional local factors – the Communist heritage and serious ethnic conflict and/or civil war – which seem to have hampered socioeconomic development and reduced the level of IHC in most of these countries. They are quite different from the factors which have supported socioeconomic development and which are related to large positive residuals.

Moderate outliers

The rest of the 191 countries (144) deviate less than ±12.0 IHC index points from the regression line. Small deviations from the regression line may be due to measurement errors or accidental factors, and it is not necessary to seek any additional explanations for them, but it is reasonable to ask whether moderate deviations are related to more or less similar factors as large positive and negative outliers. For this purpose we define as “moderate deviations” countries whose residuals vary from ±8.0 to ±11.9. It is interesting to see whether the characteristics of moderately positive and negative outliers differ from each other as systematically as the characteristics of large positive and negative outliers.

Positive residuals are moderate (from 8.0 to 11.9) for the following 20 countries (see Table 12.5): Austria, the Bahamas, Canada, Cyprus, Gabon, Iceland, Israel, Italy, Lesotho, Montenegro, New Zealand, Saudi Arabia, the Seychelles, Slovenia, South Africa, Spain, the United Arab Emirates, the United Kingdom,Uruguay and Venezuela.

The characteristics of 17 of these 20 countries are similar as for the large positive outliers discussed above. Austria, Canada, Cyprus, Iceland, Italy, New Zealand, Slovenia, Spain and the United Kingdom are European and European offshoot market economies and democracies. Israel and Uruguay are similar countries. The Bahamas and the Seychelles are tourist countries, and Gabon, Saudi Arabia, the United Arab Emirates and Venezuela are oil exporting countries. The other three countries (Lesotho, Montenegro and South Africa) do not have any common characteristics which could explain their moderate positive residuals.

Negative residuals are moderate (from -8.0 to -11.9) for the following 18 countries: Angola, Armenia, Bolivia, Burkina Faso, Burundi, Chad, the Comoros, Eritrea, Guinea-Bissau, Mali, Mauritania, Moldova, Morocco, Niger, Rwanda, Samoa, Tonga, and Zambia.

Most of these countries have similar characteristics as the large negative outliers discussed above. Armenia and Moldova are former socialist countries. Angola, Burundi, Chad, Comoros, Eritrea, Mali, Mauritania and Rwanda have suffered from serious ethnic conflicts and/or civil wars. Samoa and Tonga are isolated Pacific island states. It may be significant that 13 of these 18 moderate negative outliers are sub-Saharan African countries.

6. Discussion

… One significant finding of this analysis is that most of the large positive and negative deviations seem to be due to some exceptional local and historical factors, which are relevant only for some particular countries or groups of countries. Until now it has not been possible to find any universal and measureable factor which could explain a significant part of the variation in IHC independently from national IQ. This leads to the conclusion that we should expect the continuation of large global disparities in human conditions because their causal roots lie to a significant extent in evolved human diversity measured by national IQ and in exceptional local and historical factors which it is not easy to change.

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