Intelligence: a measure of human capital in nations.

Meisenberg, Gerhard ; Lynn, Richard

Japan is a rich country, and Nigeria is a poor country. There is no lack of explanations for this discrepancy. Some authors have offered geography as an ultimate explanation for economic disparities between countries and world regions (Diamond, 1997; Hibbs & Olsson, 2004; Nordhaus, 2006). Everything else being equal, countries with greater natural resources and greater proximity to world markets should be richer. Nigeria has more natural resources than Japan and is closer to the old industrial centers of Europe. Therefore Nigeria should be richer than Japan.

History and culture fare not much better than geography as explanations for macroeconomic trends and developmental disparities. For example, the backwardness of African countries today has been blamed on the trans-Atlantic slave trade of the 18th and early 19th centuries (Nunn, 2008). This, however, begs the question of why Europeans enslaved Africans but Africans did not enslave Europeans.

Economic institutions are a more proximal explanation for worldwide economic disparities. For example, the current poverty of formerly rich countries has been blamed on Europeans who "introduced or maintained already-existing extractive institutions to force the local population to work in mines and plantations" (Acemoglu et al, 2002, p. 1279) during the colonial age. When geography is pitted against institutions as explanation for economic disparities in today's world, institutions are the more immediate predictor (Easterly & Levine, 2003; Rodrik et al, 2002).

Institutions are made by people. Therefore the immediate causes of institutional quality and economic outcomes are to be sought in the physical, cognitive or attitudinal traits of the human actors. According to this view, Japan is rich and Nigeria is poor because the Japanese possess more "human capital" than the Nigerians. The importance of human capital at the country level is supported by the observation of large differences in labor productivity between countries (Hall & Jones, 1999).

Human capital includes both cognitive and non-cognitive resources. Value systems have been stressed by many writers, from Max Weber's (1930) "spirit of capitalism" to Gregory Clark's notion that the industrial revolution was triggered not by new incentives, but by people responding differently to incentives that had been in place for ages (Clark, 2007). There is ample evidence for associations of non-cognitive traits with prosperity and economic growth (e.g., Knack & Keefer, 1997; McCauley et al, 1999), but the direction of causality is difficult to ascertain.

In addition, the measurement of non-cognitive traits is fraught with conceptual and psychometric ambiguities. Perhaps for this reason, cognitive traits have received the greater attention, as is evidenced by the inclusion of measures for literacy, school enrolment and related measures in the Human Development Reports of the United Nations, the World Development Indicators of the World Bank, and similar compilations. Primary school enrolment (Sala-iMartin et al, 2004), secondary school enrolment (Mankiw et al, 1992), and the average years of schooling of the adult population (Barro & Lee, 1993) have all been proposed or used as measures of human capital. Many authors consider the introduction of mass education an essential condition for economic growth (e.g., Easterlin, 1981).

However, educational degrees and years spent in school are not directly relevant for economic outcomes because they do not guarantee that children have successfully acquired important cognitive or non-cognitive skills. Attained skills, abilities and knowledge are measured more directly in international school achievement tests that assess skills in curricular subjects such as mathematics, science, and/or reading.

Therefore the results of school achievement tests have been proposed as measures of human capital (Lee & Barro, 1997), and they have been used for the country-level prediction of economic growth (Hanushek & Kimko, 2000; Hanushek & WoBmann, 2007, 2009; Rindermann, 2008a), democracy and rule of law (Rindermann, 2008b) and the spread of AIDS (Rindermann & Meisenberg, 2009). In an extension of this approach, national differences in the high reaches of the ability distribution have been implicated as especially important for economic wealth, patents, democracy, and rule of law (Rindermann et al, 2009).

Intelligence tests ("IQ tests") provide an alternative measure of cognitive skills. Data quality varies widely. In many countries, major intelligence tests have been standardized with representative population samples. In other cases, IQ tests have been applied to convenience samples that may or may not be representative of the general population. The results of these studies have been surveyed in Lynn & Vanhanen (2002, 2006) and Lynn (2006). IQ was found to be a reasonably close correlate of GDP (Lynn & Vanhanen, 2002) and several other economic outcomes (Lynn & Vanhanen, 2006). Also at the level of population groups within countries, higher IQ is related to more education, greater prosperity, less criminal involvement, and reduced fertility (Lynn, 2008a).

IQ and school achievement are closely related. At the individual level within countries, correlations between IQ tests and school achievement tests are typically between 0.5 and 0.7 (Jencks, 1972; Jensen, 1998; Mackintosh, 1998), but can be as high as 0.8 (Deary et al, 2006). At the country level, correlations between the results of IQ tests and scholastic assessments are in the vicinity of 0.9 (Lynn & Mikk, 2007; Lynn et al., 2007; Lynn & Meisenberg, 2010). Therefore the two types of test appear to measure identical or closely related constructs.

The study of cognitive differences between countries is a rapidly developing field. The present paper updates the country-level data for IQ and school achievement. It further investigates the relationship between these two cognitive measures, and compares the correlates of IQ with those of school achievement. We finally integrate both types of data into a composite measure of human capital for 168 countries, and demonstrate some economic, political and cultural correlates of this measure. Thus we provide an overview of the range of country-level traits that appear to be related more closely to intelligence than to per-capita GDP and other conditions.

Methods and Data Sources

1. International school assessments General strategy

The most important international school assessment studies are TIMSS (Trends in International Mathematics and Science Study) and PISA (Program for International Student Assessment). TIMSS assessments of 8th graders in mathematics and science were conducted 1995, 1999, 2003 and 2007, and PISA assessments of 13-year-olds were done 2000, 2003, 2006 and 2009. 74 countries participated in at least one TIMSS assessment, and 18 participated in all four. 65 countries participated in at least one PISA assessment, and 30 participated in all four. 47 countries have data for both TIMSS and PISA, and 92 have data for either TIMSS or PISA or both.

Several other international student assessments have been performed, some dating back to the 1970s. More recently, regional scholastic assessments have been performed in the less developed countries of Latin America and Africa. Together with TIMSS and PISA, these additional sources provide quantitative data for 131 countries.

Because TIMSS and PISA appear to be the most reliable assessments, and because adult intelligence is expected to be more closely related to cognitive ability at age 13 or 14 than at younger ages, we adopted the strategy of calculating the average of PISA and 8th-grade TIMSS scores for those countries participating in at least one assessment. Missing data were extrapolated into this data set from the other assessments, producing a total of 131 countries with information about scholastic achievement.

TIMSS and PISA

TIMSS is organized by the IEA (International Association for the Evaluation of Educational Achievement), and assessments are performed in a 4-year cycle. Tests of mathematics and science are administered in grades 4 and 8, with a larger number of countries participating in grade 8 than in grade 4. The results are publicly available at:

http://timss.bc.edu/timss2003.html, and:

http://nces.ed.gov/timss/tables07.asp. Further information is available in Gonzalez et al (2004), Martin et al (2004, 2008), and Mullis et al (2004, 2008).

PISA is organized by the OECD in a 3-year cycle. Children aged 13 are tested in mathematics, science and reading. The results are available at:

http://www.oecd.org/dataoecd/30/18/39703566.pdf, http://nces.ed.gov/pubs2002/2002116.pdf, and: http://pisacountry.acer.edu.au/.

Both TIMSS and PISA are graded with methods based on item response theory, which models student proficiency as a latent variable. In both assessments the results are published separately for each tested subject and are reported on a 500/100 scale. In TIMSS the mean score of 500 is the average of the countries participating in the first TIMSS assessment in 1995, and in PISA it is the average of the participating OECD countries. The individual-level, within-country standard deviation is about 85 in TIMSS and 95 in PISA.

Within each assessment the scores of the different subjects were highly correlated at the country level, as expected from the results of earlier studies (Rindermann, 2006, 2007). They were averaged separately for each of the four TIMSS and four PISA assessments. Minor trend adjustments were made based on the countries participating in TIMSS 2007 and PISA 2009, respectively. For example, 27 countries participated both in TIMSS 1995 and TIMSS 2007. Mean and standard deviation of these 27 countries in TIMSS 1995 were adjusted to the same mean and standard deviation that these countries had in TIMSS 2007, and all other countries in TIMSS 1995 were adjusted accordingly. The averaged TIMSS scores and the averaged PISA scores were brought to the same mean and standard deviation of 500 [+ or -] 50 for those 47 countries that participated in at least one TIMSS and one PISA assessment. These adjusted scores were averaged based on the number of assessments in which each country participated. Regressions in which the score was predicted by IQ and age at testing (which varied slightly among countries) showed no consistent age-effect in either TIMSS or PISA.

These scores are somewhat biased measures for the average cognitive ability in the country because they measure only the proficiency of children who are still in school in grade 8 (TIMSS) or at age 13 (PISA). Therefore the proportion of children still in school in grade 8 or at age 13 was estimated from the Barro-Lee data set for years of schooling obtained from:

http://www.barrolee.com/data/dataexp.htm. The TIMSS and PISA scores were adjusted assuming that those not in school would score 40 points (about 7 IQ points) lower than those in school.

Other assessments scored with methods of item response theory

Several assessments other than TIMSS and PISA were graded with modern methods of item response theory and published on a 500/100 scale. Those used for the extrapolation of data points missing in the original TIMSS/PISA data set were:

TIMSS 2007, 4h grade included Yemen, which did not participate in any of the PISA and 8th-grade TIMSS assessments.

PIRLS Reading, 2001 was organized by the IEA to assess reading literacy of 4th-graders. 34 countries participated. Data are available at http://nces.ed.gov/surveys/pirls/. This study provides data for Belize.

IAE Reading 1991 assessed reading literacy of 9 and 14 year olds in 30 countries. The results are published in Elley (1992). This assessment provided data for Venezuela at age 9 and 14, and Nigeria and Zimbabwe at age 14.

The raw scores were adjusted for age at testing in those assessments that showed non-trivial age effects. This was followed by adjustment for the approximate proportion in school at the age/grade of testing. To make the scores numerically equivalent to the TIMSS/PISA scale, the mean and standard deviation for each assessment were equalized with those of the TIMSS/PISA score for the countries participating in both kinds of assessment.

Older studies

Some older scholastic assessments are available for which the results were published as "percent correct" scores:

IAEP Mathematics 1990/91 assessed mathematics in 13-yearolds. 19 countries participated, of which Mozambique did not participate in TIMSS or PISA. Results are published in Lapointe (1992).

The Second International Science Study 1983/84 tested children from 23 countries at age 14 and from 17 countries at age 10. The age 10 test provided data for Nigeria, and the age 14 test for Nigeria, Papua New Guinea and Zimbabwe. The results are published in Keeves, 1992.

The Second International Mathematics Study 1981 was organized by the IEA to assess mathematics in 13-year-olds. 17 countries participated, including Nigeria and Swaziland. The raw scores are published in Medrich & Griffith (1992).

The results of these assessments show nonlinear relationships with IQ and TIMSS-PISA score, and therefore nonlinear model fitting was employed after adjustments for age (if applicable) and proportion in school had been made.

Regional assessments

The SACMEQ (Southern and Eastern Africa Consortium for Monitoring Educational Quality) assessments of 2000/01 and 2007 tested mathematics and reading of 6th graders in the countries of South and East Africa. The results are available at http://www.sacmeq.org/indicators.htm and:

http://www.sacmeq.org/downloads/sacmeqIII/WD01_SACMEQ_I II_Results_Pupil_Achievement.pdf. The math-reading average was used for each assessment, and adjustments were made for the proportion of children in school in grade 6. Correlations with IQ were .570 with SACMEQ II (p = .067) and .722 with SACMEQ III (p = .012) for the 11 countries having both measures. SACMEQ provides data for Kenya, Lesotho, Malawi, Mauritius, Mozambique, Namibia, Seychelles, Swaziland, Tanzania, Uganda, Zambia, Zanzibar and Zimbabwe. Results are published on a 500/100 scale.

Only two of the countries in SACMEQ (Botswana, South Africa) participated also in TIMSS, and none in PISA. For these two countries, the SACMEQ scores were 177.1 points (SACMEQ II) and 184.5 points (SACMEQ IIII) higher than the TIMSS/PISA scores (weighted by the number of times they participated in TIMSS). SACMEQ scores for all participating countries were adjusted accordingly, without changing the standard deviation.

The PASEC (Programme d'Analyse des Systemes Educatifs de la CONFEMEN) assessments for Francophone African countries (Conference des Ministres, 2008) include 11 countries, none of whom have participated in TIMSS or PISA. French and mathematics were tested in 5th grade, but only the math scores were used because of large differences between countries in the proportion of children speaking French at home. Only 5 of the countries have an IQ score, and the PASEC-IQ correlation is a non-significant .254. For scaling, the means and standard deviations of the PASEC math scores were brought to the same mean and standard deviation with IQ for the 5 countries having both measures, followed by rescaling from the 100/15 IQ metric to the 500/100 school assessment metric and adjustment for the proportion of children still in school at 5th grade. This study provides data for Benin, Burkina Faso, Burundi, Cameroon, Chad, Comoros, Congo (B), Cote d'Ivoire, Gabon, Madagascar and Senegal.

The 1999 MLA (Monitoring Learning Achievement) assessments of UNESCO/UNICEF assessed reading/writing, mathematics and life skills in 4th grade. Results are reported for 11 African countries in Chinapah et al (2000). 3 of these countries (Botswana, Morocco, Tunisia) had participated in TIMSS and/or PISA. Correlations with IQ were .006 for life skills, .767 for literacy (p = .010) and .639 for numeracy (p = .047) for the 10 countries having both kinds of measure. Consequently, a composite of literacy and numeracy was used as the measure of school achievement. This composite was scaled to the 500/100 metric. For those 9 countries in MLA that also had school achievement data from other sources, the standard deviation of the MLA measure was adjusted to the standard deviation of the other measures. This measure was adjusted for the proportion of children in school in grade 4, and finally adjusted to the TIMSS/PISA mean for the 3 countries with scores from TIMSS and/or PISA. MLA provides data for Madagascar, Malawi, Mali, Mauritius, Niger, Senegal, Uganda and Zambia.

SERCE (Second Regional Comparative and Explanatory Study) was performed in 16 Latin American countries between 2002 and 2008. Children in grades 3 and 6 were tested in mathematics and reading. Results are published on a 500/100 scale (Valdes et al, 2008). The average of the 6th grade mathematics and reading scores was used. This measure was adjusted for the proportion of children in school. The final measure was created by adjusting the SERCE scores to the mean and standard deviation of the TIMSS/PISA scores for the 9 countries having both measures. The SERCE scores correlate at r = .965 (p<.001) with TIMSS-PISA (N = 9) and r = .442 (p = .131) with IQ (N = 13). They provide data for Costa Rica, Cuba, the Dominican Republic, Ecuador, Guatemala, Nicaragua and Paraguay.

Additional sources

The only school achievement data for India are from the First International Science Study in 1970 (Comber & Keeves, 1973), and a recent study with a subset of the 2007 TIMSS study in the states of Rajasthan and Orissa (Das & Zajonc, 2010). The school achievement score of India was averaged from these two sources.

2. IQ

Compilations of national IQs have been published by Lynn (2006) and Lynn & Vanhanen (2002, 2006). The current data set is based on Lynn & Vanhanen (2006), with amendments and additions published in Lynn (2010). Measured IQs are available for 136 countries. The IQ for Northern Ireland is taken from Lynn (1979).

3. Estimates of data quality

The quality of the IQ data was defined based on the number of independent studies available for each country and the total sample size in all studies combined. The following scores were given for total sample size:

1 <200
2 200-500
3 500-999
4 1000-1999
5 2000-4999
6 5000-9999
7 >10,000

The IQ quality score was calculated by adding this score to the number of independent IQ studies available for the country, with the maximum capped at 25.

For school achievement, countries were awarded 2 points for each PISA or 8th-grade TIMSS study in which they participated. Those that did not participate in PISA or 8th-grade TIMSS were awarded 1 point for each of the other assessments in which they participated. The maximum score was 16 for countries participating in all four PISA and all four TIMSS studies.

4. Properties of IQ and school achievement compared.

The correlation between IQ and school achievement is .889 for the 99 countries having both measures, and both have virtually the same correlates (Table 1). However, the relationship between the within-country and between-country standard deviations is different. For IQ, the within-country standard deviation is 15 by definition, at least for Britain. For school achievement, the individual-level standard deviation in TIMSS is set at 100 for those countries that participated in the 1995 assessment, and in PISA it is 100 for the participating OECD countries. Within-country standard deviations in Britain and other advanced nations are approximately 85 in TIMSS and 95 in PISA, and these standard deviations are not changed substantially during the scaling procedure. Therefore school achievement was scaled directly to the IQ metric, assigning a score of 100 to Britain and assuming a within-country standard deviation of 90 for school achievement:

SA absolute = (SchAch - 521.9) x 15/90 + 100

The between-country standard deviation is 38.6% higher for this measure of school achievement (SA absolute in the appendix) than for IQ: 15.18 versus 10.95 (N = 99). The discrepancy is best attributed to the generally low quality of schooling in low-scoring countries, which depresses school achievement to a greater extent than IQ. In this sense, school achievement is more "culturally biased" than IQ.

5. Calculation of a measure of "human capital."

For construction of a combined measure of (cognitive) human capital, school achievement was not scaled directly to the IQ metric. We instead adjusted the international mean and standard deviation for school achievement to those of IQ, based on the 99 countries having both measures (SA relative in the appendix). The resulting scores were averaged with weighting for data quality. For countries having only IQ data or only school achievement data, these scores were used (Human capital in the appendix).

In all there are 101 countries and territories (including England and Scotland in addition to the United Kingdom) whose human capital score is based on school achievement and IQ. For 37 countries it is based on IQ alone, and for 30 on school achievement alone. Scores for 28 additional countries and territories were estimated from the scores of neighboring countries with similar population, culture, and economic development, as described for IQ in Lynn & Vanhanen (2002, 2006). Provincial data were used in two cases: The estimate for Afghanistan was derived from the measured IQ in the Northwest Frontier Province of Pakistan (Ahmad et al., 2008), which is inhabited by ethnic Pashtuns living under conditions similar to Pashtuns in Afghanistan; and the estimate for Bhutan was the average of Nepal, India and Tibet (Lynn, 2008b). Estimates are included in the last column of the appendix as numbers in parentheses.

6. Other country-level measures

lgGDP is the logarithm of gross domestic product adjusted for purchasing power, averaged for the years 1995-2005. Data are from the Penn World Tables (Heston et al., 2009). Missing data were extrapolated into this data set from the World Development Indicators of the World Bank. The logarithmic transformation was used because of the highly skewed nature of GDP worldwide, which approximates to a normal distribution in the logarithmic form.

Education measures length of schooling for adults 25+ years old, based on the Barro-Lee data set for 143 countries (http://www.barrolee.com/data/dataexp.htm). Missing data points were extrapolated from World Bank and United Nations sources.

No Corruption was averaged from Transparency International's Corruption Perception Index for the years 1998-2003 (http://www.transparency.org) and the "no corruption" index from the Governance Indicators of the World Bank for 1996 or earliest available date:

(http://info.worldbank.org/governance/wgi/mc_countries.asp).

Economic Freedom was averaged from the unrotated first factors of maximum-likelihood factor analyses of areas 2-5 of the Fraser Institute's Economic Freedom Index for the periods 1995-2005 (Gwartney et al., 2010), and domains 1, 2, and 5-8 of the Heritage Foundation Index for 1995-2005 (http://www.heritage.org/research/). This measure indexes the extent of business regulation and red tape.

Big Government is calculated from area 1 of the Fraser Institute's Economic Freedom Index for the periods 1995-2005 (size of government), and domains 3 and 4 of the Heritage Foundation Index for 1995-2005 (fiscal freedom and government spending). These measures are factorially and conceptually different from the other components of the Fraser Institute and Heritage Foundation indices for "economic freedom."

Gini index: The primary data source is the World Income Inequality Database (WIID2a) of the United Nations University at www.wider.unu.edu/wiid/wiid.htm, as described in Meisenberg, 2007. Missing data points were extrapolated from the World Bank's World Development Indicators of 2005, the Human Development Report 2005 (United Nations, 2005), and the CIA's World Factbook of 2009.

Political freedom is the average of political rights + civil liberties from Freedom House at:

http://www.freedomhouse.org/research/freeworld, average 1985-2005.

Democracy is defined as Vanhanen's democracy index (average 1985-2004), from the Finnish Social Science Data Archive at:

http://www.fsd.uta.fi/english/data/catalogue/FSD1289/.

Suicide is the average of the standardized male and female suicide rates, average of available years since 1985, reported by the World Health Organization at:

http://www.who.int/mental_health/prevention/suicide/country_rep orts/en/index.html.

Life expectancy is life expectancy at birth for the years 2000-2005, as reported in the 2005 Human Development Report (United Nations, 2005).

Infant mortality is from the 2004 Human Development Report (United Nations, 2004).

TFR is the Total Fertility Rate, averaged for the years 1990 to 2005, from the World Development Indicators of the World Bank.

Religiosity is the average of three measures: (1) the average of four questionnaire items about belief in God and emotional involvement with religion from the World Values Survey, 1981-2008 combined data file v.20090901, 2009, available at www.worldvaluessurvey.org; (2) A question about the importance of religion from the Gallup World Poll:

(https://worldview.gallup.com/signin/login.aspx?ReturnUrl=%2f), accessed February 10, 2011; and (3) reverse scored atheism rate according to Zuckerman (2005). Missing data points were extrapolated from the available measure(s).

Happiness is a question in the World Values survey (Are you happy?).

Life satisfaction is averaged from a question about overall life satisfaction in the World Values Survey, and a question about the best possible life (Cantrill ladder) in the Gallup World Poll, 2006-2009 average. These two measures correlate at r = .830.

Properties of the measures

Table 1 shows the correlations of alternative human capital measures and of log-transformed GDP with a number of country-level variables. A number of observations can be made:

1. All "development indicators," including log-transformed GDP, education, school achievement, intelligence, freedom and democracy, form a positive manifold. However, the correlations between school achievement and IQ are higher than any of the other correlations in the table. This high correlation justifies the averaging of these two cognitive measures into a single measure of (cognitive) human capital, or intelligence.

2. The correlates of IQ and school achievement are very similar. In addition to their high intercorrelation, this observation provides further justification for averaging of these two measures into a combined measure of cognitive human capital, or intelligence.

3. The cognitive measures are nearly as highly correlated with lgGDP as with education (measured as years in school). This shows that the cognitive outcomes are more than the predictable outcomes of prolonged schooling.

4. Economic growth is related more closely to the cognitive measures than to education. This suggests a causal role of intelligence for economic growth, confirming earlier reports about such a relationship (Hanushek & Wossmann, 2007, 2009; Jones & Schneider, 2006; Meisenberg, in press; Weede, 2004; Weede & Kampf, 2002).

5. In addition to economic growth, variables that are related more closely to intelligence than to length of schooling and lgGDP include the Gini index of income inequality, suicide, life expectancy, total fertility rate, and religiosity. Other outcomes, however, including corruption, economic freedom and life satisfaction, are related more closely to education and/or lgGDP than to intelligence.

Tables 2 and 3 show the results of regression models in which various outcomes are predicted by the combined measure of human capital (average of IQ and school achievement, without estimates), schooling, log-transformed GDP, and a dummy variable for countries with communist history. Because small countries are more likely than larger ones to be atypical in many ways, the regressions were limited to countries with a population of more than 250,000.

The results of Tables 2 and 3 confirm that some outcomes are related more closely to intelligence than to schooling or lgGDP. These include economic growth (positive), Gini index (negative), suicide (positive), life expectancy (positive), and religiosity (negative). In this larger sample, the total fertility rate is predicted to similar extents by intelligence, schooling, and lgGDP.

Other variables are related more closely to education or lgGDP than to intelligence. They are likely to correlate with intelligence mainly because intelligence correlates highly with education and lgGDP. Length of schooling (in addition to communist history) appears to be the most important predictor of democracy, economic freedom and political freedom. Outcomes that clearly are related most closely to lgGDP (in addition to communist history) rather than intelligence are corruption and life satisfaction.

Proposed uses of human capital measures

Country-level data on school achievement and IQ can be used for a number of purposes:

1. Studies of educational quality. Although our measure of schooling is about equally related to IQ and school achievement, prolonged, intensified, or more efficient schooling is expected to raise school achievement to a greater extent than it raises IQ. Therefore the difference between school achievement and IQ can be used as a measure for the quality of the educational system. However, these studies need to take account of the fact that the between-country standard deviation, relative to within-country standard deviations, is greater for school achievement than for IQ. Because this difference is best attributed to systematic differences in schooling quality between high-scoring and low-scoring countries, the comparison between school achievement and IQ should be based on school achievement scaled directly to the IQ metric (SA absolute in the appendix).

2. Studies of economic outcomes. Earlier studies have shown that high scores on cognitive measures predict faster economic growth (Hanushek & Kimko, 2000; Hanushek & WoBmann, 2007, 2009; Jones and Schneider 2006; Rindermann, 2008a; Weede 2004; Weede and Kampf, 2002) and lower levels of income inequality (Meisenberg, 2007, 2008, in press). These results are confirmed by the correlations and regression coefficients in Tables 1 and 2. Presumably, high intelligence promotes economic growth because technical skills and "managerial capital" (Bruhn et al., 2010) are required for economic growth. The reasons for the relationship of high intelligence with low income inequality are uncertain. Possibly, high-IQ societies are more efficient at restraining the exploitation of the poor by the rich or at redistributing resources from the rich to the poor. Another possibility is that high average intelligence in the population reduces income inequality by market forces. Where cognitive skills are abundant, the skill premium is expected to be low; and where highly skilled individuals are rare, the skill premium is expected to be high.

3. Studies of cultural traits. Tables 1 and 3 show that high cognitive ability is associated with lower religiosity. Conversely, happiness and life satisfaction rise with rising prosperity, not with rising intelligence, confirming earlier results that had been obtained with less complete data (Meisenberg, 2004). The suicide rate is increased by high intelligence, confirming earlier work (Voracek, 2005). The ways in which cognitive ability interacts with these cultural and psychological traits remain to be determined.

4. Studies of the determinants of human capital. There is no generally accepted theory to explain why, for example, the level of human capital (a.k.a. intelligence) is so much higher in Japan than in Nigeria. Genetic theories have been proposed by some (e.g., Lynn, 2006). These theories have been attacked by others (e.g., Wicherts et al., 2010), but without convincing alternative explanations. Precise knowledge about the current level of intelligence in different countries is required for these investigations.

5. Studies of temporal trends. Measured intelligence has been rising through most of the 20th century in advanced industrialized societies (Flynn, 1987). It seems to be stagnating in advanced societies today (Cotton et al., 2005; Shayer & Ginsburg, 2009; Sundet et al., 2004; Teasdale & Owen, 2008), but is rising in at least some of the less developed countries (Batterjee, in press; Colom et al., 2006; Khaleefa et al., 2009; Meisenberg et al., 2006). At this time, only the periodic scholastic assessment tests, including PISA and TIMSS, are sufficiently accurate to allow close tracking of cognitive skills for a substantial number of countries on a time scale of about one decade. Together with standardization studies of major IQ tests, these can be used to study both the determinants of trends in cognitive ability, and their consequences.

Appendix

Measures of human capital by country:

SchAch, school achievement on the 500/100 scale.

SA absolute, SchAch scaled to a mean of 100 and within-country standard deviation of 15 for the United Kingdom, assuming a within-country standard deviation of 90 for SchAch.

SA relative, SchAch scaled to the IQ metric by equalizing mean and standard deviation with IQ.

SAq and IQq, quality of data for school achievement and IQ.

Human capital, weighted average of SA relative and IQ. Estimates are in parentheses.

 SchAch SA absolute SA relative

Afghanistan
Albania 390.7 78.1 82.9
Algeria 389.9 78.0 82.8
Andorra
Angola
Argentina 400.6 79.8 84.1
Armenia 487.7 94.3 94.5
Australia 532.5 101.8 99.9
Austria 521.0 99.9 98.5
Azerbaijan 406.6 80.8 84.8
Bahamas
Bahrain 432.0 85.0 87.8
Bangladesh
Barbados
Belarus
Belgium 526.9 100.8 99.3
Belize 339.0 69.5 76.7
Benin 264.1 57.0 67.7
Bermuda
Bhutan
Bolivia
Bosnia 464.6 90.4 91.8
Botswana 369.7 74.6 80.4
Brazil 403.4 80.2 84.4
Brunei
Bulgaria 480.4 93.1 93.7
Burkina Faso 292.2 61.7 71.0
Burundi 325.7 67.3 75.1
Cambodia
Cameroon 339.4 69.6 76.7
Canada 538.0 102.7 100.6
Cape Verde
Centr. Afr. R.
Chad 259.3 56.2 67.1
Chile 431.4 84.9 87.8
China 605.6 113.9 108.7
 Hong Kong 559.3 106.2 103.1
 Macau 532.5 101.8 99.9
 Tibet
Colombia 395.5 78.9 83.5
Comoros 288.3 61.1 70.6
Congo (B.) 288.0 61.0 70.5
Congo (K.)
Cook Islands
Costa Rica 444.9 87.2 89.4
Cote d'Ivoire 230.9 51.5 63.7
Croatia 489.5 94.6 94.8
Cuba 500.1 96.4 96.0
Cyprus 462.8 90.2 91.5
Czech Rep. 526.8 100.8 99.2
Denmark 508.5 97.8 97.0
Djibouti
Dominica
Dominican R. 315.4 65.6 73.8
East Timor
Ecuador 349.8 71.3 78.0
Egypt 407.0 80.8 84.8
El Salvador 357.7 72.6 78.9
Equ. Guinea
Eritrea
Estonia 539.0 102.8 100.7
Ethiopia
Fiji
Finland 555.6 105.6 102.7
France 516.1 99.0 98.0
Gabon 338.8 69.5 76.6
Gambia
Georgia 420.5 83.1 86.5
Germany 516.1 99.0 98.0
Ghana 293.1 61.9 71.1
Greece 484.7 93.8 94.2
Guatemala 340.2 69.7 76.8
Guinea
Guinea-Bissao
Guyana
Haiti
Honduras
Hungary 525.2 100.6 99.0
Iceland 511.2 98.2 97.4
India 434.1 85.4 88.1
Indonesia 408.9 81.2 85.1
Iran 433.3 85.2 88.0
Iraq
Ireland 523.2 100.2 98.8
Israel 480.8 93.2 93.7
Italy 492.3 95.1 95.1
Jamaica
Japan 558.7 106.1 103.1
Jordan 438.6 86.1 88.6
Kazakhstan 406.3 80.7 84.7
Kenya 371.5 74.9 80.6
S. Korea 567.4 107.6 104.1
Kuwait 387.8 77.7 82.5
Kyrgyztan 320.4 66.4 74.4
Laos
Latvia 500.3 96.4 96.1
Lebanon 425.8 84.0 87.1
Lesotho 273.2 58.5 68.7
Liberia
Libya
Liechtenstein 536.1 102.4 100.4
Lithuania 497.0 95.8 95.7
Luxembourg 494.0 95.4 95.3
Macedonia 453.7 88.6 90.5
Madagascar 322.3 66.7 74.7
Malawi 232.1 51.7 63.8
Malaysia 499.6 96.3 96.0
Maldives
Mali 270.8 58.2 68.5
Malta 477.7 92.6 93.3
Mariana Isl.
Marshall Isl.
Mauritania
Mauritius 388.1 77.7 82.6
Mexico 428.9 84.5 87.5
Micronesia
Moldova 470.8 91.5 92.5
Mongolia
Montenegro 415.0 82.2 85.8
Morocco 367.6 74.3 80.1
Mozambique 322.9 66.8 74.7
Myanmar
Namibia 272.8 58.5 68.7
Nepal
Netherlands 539.2 102.9 100.7
Neth. Antilles
New Caledonia
New Zealand 528.5 101.1 99.4
Nicaragua 357.2 72.5 78.8
Niger 209.2 47.9 61.1
Nigeria 348.8 71.1 77.8
Norway 505.0 97.2 96.6
Oman 402.1 80.0 84.2
Pakistan
Palestine 394.0 78.7 83.3
Panama 371.0 74.9 80.5
Papua NG 486.6 94.1 94.4
Paraguay 357.0 72.51 78.8
Peru 370.7 74.8 80.5
Philippines 371.0 74.9 80.5
Poland 514.9 98.8 97.8
Portugal 487.0 94.2 94.5
Puerto Rico
Qatar 342.8 70.1 77.1
Romania 455.6 89.0 90.7
Russia 503.9 97.0 96.5
Rwanda
St. Lucia
St. Vincent
W. Samoa
Sao Tome & P.
Saudi Arabia 369.0 74.5 80.3
Senegal 289.3 61.2 70.7
Serbia & M. 457.0 89.2 90.8
Seychelles 384.7 77.1 82.2
Sierra Leone
Singapore 584.4 110.4 106.2
Slovakia 514.4 98.8 97.8
Slovenia 524.2 100.4 98.9
Solomon Isl.
Somalia
S. Africa 289.0 61.2 70.7
Spain 499.5 96.3 96.0
Sri Lanka
Sudan
Suriname
Swaziland 370.9 74.8 80.5
Sweden 519.9 99.7 98.4
Switzerland 515.8 99.0 97.9
Syria 410.9 81.5 85.3
Taiwan 566.1 107.4 104.0
Tajikistan
Tanzania 358.7 72.8 79.0
 Zanzibar 308.1 64.4 73.0
Thailand 459.0 89.5 91.1
Togo
Tonga
Trinidad & T. 422.9 83.5 86.7
Tunisia 409.1 81.2 85.1
Turkey 446.2 87.4 89.5
Turkmenistan
Uganda 305.7 64.0 72.7
Ukraine 480.0 93.0 93.6
Un. Arab Em. 472.6 91.8 92.7
United K. 521.9 100.0 98.7
 England 523.6 100.3 98.9
 Scotland 500.7 96.5 96.1
 N. Ireland
USA 512.4 98.4 97.5
Uruguay 442.2 86.7 89.1
Uzbekistan
Vanuatu
Venezuela 357.3 72.6 78.9
Vietnam
Yemen 239.2 52.9 64.7
Zambia 240.9 53.2 64.9
Zimbabwe 324.1 67.0 74.9

 Human
 SAq IQ IQq Capital

Afghanistan (75.0)
Albania 2 82.9
Algeria 2 82.8
Andorra (97.2)
Angola (69.9)
Argentina 4 96.0 9 92.3
Armenia 4 92.0 3 93.5
Australia 16 98.0 10 99.2
Austria 10 99.5 4 98.8
Azerbaijan 4 84.8
Bahamas (84.0)
Bahrain 4 81.0 2 85.6
Bangladesh 81.0 4 81.0
Barbados 80.0 2 80.0
Belarus (95.1)
Belgium 14 99.0 7 99.2
Belize 1 76.7
Benin 1 67.7
Bermuda 90.0 4 90.0
Bhutan (84.2)
Bolivia 87.0 6 87.0
Bosnia 2 94.0 4 93.3
Botswana 4 72.15 2 77.2
Brazil 8 87.0 13 86.0
Brunei (88.9)
Bulgaria 12 92.5 6 93.3
Burkina Faso 1 71.0
Burundi 1 75.1
Cambodia (92.0)
Cameroon 1 64.0 2 68.2
Canada 16 100.0 6 100.4
Cape Verde (76.0)
Centr. Afr. R. 64.0 5 64.0
Chad 1 67.1
Chile 8 91.0 9 89.5
China 2 105.5 16 105.9
 Hong Kong 14 108.0 16 105.7
 Macau 6 99.9
 Tibet 92.0 2 92.0
Colombia 8 83.5 7 83.5
Comoros 1 70.6
Congo (B.) 1 73.0 7 72.7
Congo (K.) 68.0 8 68.0
Cook Islands 89.0 2 89.0
Costa Rica 1 86.0 89.4
Cote d'Ivoire 1 71.0 2 68.6
Croatia 4 99.0 7 97.5
Cuba 1 85.0 5 86.8
Cyprus 8 91.5
Czech Rep. 14 98.0 7 98.8
Denmark 10 98.0 5 97.4
Djibouti (74.8)
Dominica 73.0 5 73.0
Dominican R. 1 82.0 6 80.8
East Timor (85.4)
Ecuador 1 88.0 5 86.3
Egypt 4 81.0 5 82.7
El Salvador 2 78.9
Equ. Guinea (72.3)
Eritrea 75.5 4 75.5
Estonia 6 99.0 7 99.8
Ethiopia 68.5 8 68.5
Fiji 85.0 3 85.0
Finland 10 97.0 5 100.8
France 10 98.0 9 98.0
Gabon 1 76.6
Gambia 62.0 4 62.0
Georgia 2 86.5
Germany 10 99.0 16 98.6
Ghana 4 70.0 6 70.5
Greece 10 92.0 10 93.1
Guatemala 1 79.0 3 78.5
Guinea 66.5 6 66.5
Guinea-Bissao (66.5)
Guyana (86.0)
Haiti (70.0)
Honduras 81.0 6 81.0
Hungary 16 96.5 8 98.2
Iceland 10 101.0 4 98.4
India 2 82.0 19 82.6
Indonesia 12 87.0 8 85.8
Iran 8 83.5 9 85.6
Iraq 87.0 6 87.0
Ireland 10 92.5 10 95.7
Israel 12 95.0 14 94.4
Italy 16 97.0 13 95.9
Jamaica 71.0 12 71.0
Japan 16 105.0 20 104.1
Jordan 10 84.0 7 86.7
Kazakhstan 2 84.7
Kenya 2 74.0 11 75.0
S. Korea 16 106.0 9 104.8
Kuwait 4 86.5 9 85.3
Kyrgyztan 4 74.4
Laos 89.0 2 89.0
Latvia 14 96.1
Lebanon 4 82.0 4 84.6
Lesotho 2 68.7
Liberia (66.4)
Libya 84.6 8 84.6
Liechtenstein 8 100.4
Lithuania 12 92.0 5 94.6
Luxembourg 8 95.3
Macedonia 4 90.5
Madagascar 2 82.0 2 78.3
Malawi 3 60.0 3 61.9
Malaysia 6 88.5 7 92.0
Maldives 81.0
Mali 1 69.5 8 69.4
Malta 2 97.0 2 95.2
Mariana Isl. 81.0 2 81.0
Marshall Isl. 84.0 3 84.0
Mauritania 74.1
Mauritius 3 89.0 5 86.6
Mexico 8 88.0 8 87.7
Micronesia (84.0)
Moldova 4 92.5
Mongolia 100.0 6 100.0
Montenegro 4 85.8
Morocco 6 84.0 9 82.4
Mozambique 3 64.0 2 70.4
Myanmar (86.7)
Namibia 2 72.0 2 70.4
Nepal 78.0 4 78.0
Netherlands 12 100.0 10 100.4
Neth. Antilles 87.0 2 87.0
New Caledonia 85.0 2 85.0
New Zealand 14 99.0 11 99.3
Nicaragua 1 78.8
Niger 1 61.2
Nigeria 4 71.0 6 73.7
Norway 14 100.0 2 97.0
Oman 2 84.5 7 84.4
Pakistan 84.0 4 84.0
Palestine 4 86.0 4 84.6
Panama 2 80.5
Papua NG 1 82.5 4 84.9
Paraguay 1 84.0 6 83.3
Peru 2 85.0 9 84.2
Philippines 4 90.0 3 84.6
Poland 8 95.0 13 96.1
Portugal 10 94.5 6 94.5
Puerto Rico 83.5 8 83.5
Qatar 6 83.0 6 80.1
Romania 12 91.0 6 90.8
Russia 16 96.5 6 96.5
Rwanda 76.0 2 76.0
St. Lucia 62.0 2 62.0
St. Vincent 71.0 2 71.0
W. Samoa 88.0 5 88.0
Sao Tome & P. (70.0)
Saudi Arabia 4 79.0 6 79.5
Senegal 2 70.5 5 70.6
Serbia & M. 10 88.5 2 90.5
Seychelles 2 82.2
Sierra Leone 64.0 3 64.0
Singapore 10 108.5 5 106.9
Slovakia 12 98.0 4 97.8
Slovenia 12 96.0 7 97.8
Solomon Isl. (84.9)
Somalia (71.8)
S. Africa 6 72.0 14 71.6
Spain 14 97.0 8 96.3
Sri Lanka 79.0 2 79.0
Sudan 77.5 14 77.5
Suriname 89.0 4 89.0
Swaziland 3 80.5
Sweden 14 99.0 8 98.6
Switzerland 10 101.0 6 99.1
Syria 2 80.5 7 81.6
Taiwan 10 105.0 18 104.6
Tajikistan 79.6
Tanzania 2 72.5 8 73.8
 Zanzibar 2 73.0
Thailand 12 88.0 6 90.1
Togo 69.1
Tonga 86.0 2 86.0
Trinidad & T. 2 86.7
Tunisia 12 84.0 2 84.9
Turkey 10 88.5 8 89.1
Turkmenistan (79.6)
Uganda 3 72.0 2 72.4
Ukraine 2 95.0 2 94.3
Un. Arab Em. 4 83.0 6 86.9
United K. 14 100.0 7 99.1
 England 8 101.0 7 99.9
 Scotland 6 97.0 7 96.6
 N. Ireland 96.3
USA 16 98.0 11 97.7
Uruguay 6 96.0 2 90.8
Uzbekistan (79.6)
Vanuatu (84.0)
Venezuela 1 84.0 6 83.3
Vietnam 94.0 3 94.0
Yemen 1 83.0 6 80.4
Zambia 3 75.0 5 71.2
Zimbabwe 4 71.5 4 73.2

References

Acemoglu, Daron, S. Johnson & J.A. Robinson (2002) Reversal of fortune: geography and institutions in the making of the modern world income distribution. Quarterly Journal of Economics 117: 1231-1294.

Ahmad, Riaz, S.J. Khanum, Z. Riaz & R. Lynn (2008) Gender difference in means and variance on the Standard Progressive Matrices in Pakistan. Mankind Quarterly 49: 50-57.

Barro, Robert J. & W. Lee (1993) International comparisons of educational attainment. Journal of Monetary Economics 32: 363-394.

Batterjee, Adel A. (in press) Intelligence and education: the Saudi case. Mankind Quarterly.

Bruhn, Miriam, D. Karlan & A. Schoar (2010) What capital is missing in developing countries? American Economic Review: Papers and Proceedings 100: 629-633.

Chinapah, Vinayagum, E.L.M. H'ddigui, A. Kanjee, W. Falayajo, C.O. Fomba, O. Hamissou, A. Rafalimanana & A. Byamugisha (2000) With Africa for Africa. Towards Quality Education for All. Pretoria: Human Sciences Research Council.

Clark, Gregory (2007) A Farewell to Alms. A Brief Economic History of the World. Princeton: Princeton University Press.

Colom, Roberto, C.E. Flores-Mendoza & F.J. Abad (2006) Generational changes on the Draw-a-Man Test: a comparison of Brazilian urban and rural children tested in 1930, 2002 and 2004. Journal of Biosocial Science 35: 33-39.

Comber, L.C. & J.P. Keeves (1973) Science Education in Nineteen Countries. New York: Wiley.

Conference des Ministres de l'Education des pays ayant le francais en partage (2008) Synthese des Resultats PASEC VII, VIII et IX. Available at: http://www.confemen.org/IMG/pdf/Synthese_PASEC_VII_VIII_IX_f inal.pdf.

Cotton, Sue M., P.M. Kiely, D.P. Crewther, B. Thomson, R. Laycock & S.G. Crewther (2005) A normative and reliability study for the Raven's Coloured Progressive Matrices for primary school aged children from Victoria, Australia. Personality and Individual Differences 39: 647-659.

Das, Jishnu & T. Zajonc (2010) India shining and Bharat drowning: comparing two Indian states to the worldwide distribution in mathematics achievement. Journal of Development Economics 92: 175-187.

Deary, Ian J., S. Strand, P. Smith & C. Fernandes (2006) Intelligence and educational achievement. Intelligence 35: 13-21.

Diamond, Jared (1997) Guns, Germs and Steel: The Fates of Human Societies. New York: Norton.

Easterlin, Richard A. (1981) Why isn't the whole world developed? Journal of Economic History 41: 1-19.

Easterly, William & R. Levine (2003) Tropics, germs, and crops: how endowments influence economic development. Journal of Monetary Economics 50: 3-39.

Elley, Warwick B. (1992) How in the World Do Students Read? IEA Study of Reading Literacy. International Association for the Evaluation of Educational Achievement, Report No. ISBN-92-9121-002-3. Accessed 15 June 2009 at: http://eric.ed.gov/ERICDocs/data/ericdocs2sql/content/storage_01/00 00019b/80/13/07/42.pdf

Flynn, J.R. (1987) Massive IQ gains in 14 nations: what IQ tests really measure. Psychological Bulletin 101: 171-191.

Gonzalez, Eugenio J., J. Galia, A. Arora, E. Erberber & D. Diaconu (2004) Reporting student achievement in mathematics and science. In: M.O Martin, I.V.S. Mullis & S.J. Chrostowski (eds): TIMSS 2003 Technical Report, pp. 274-307. Chestnut Hill (MA): TIMSS & PIRLS International Study Center. Accessed 9 June 2009 at: http://timss.bc.edu/timss2003i/technicalD.html

Gwartney, James D., J.C. Hall & R. Lawson (2010) Economic Freedom of the World: 2010 Annual Report. Vancouver (BC): Fraser Institute. Data retrieved from www.freetheworld.com.

Hall, Robert E. & C.I. Jones (1999) Why do some countries produce so much more output per worker than others? Quarterly Journal of Economics 114: 83-116.

Hanushek, Eric A. & D.D. Kimko (2000) Schooling, labor-force quality, and the growth of nations. American Economic Review 90: 1184-1208.

Hanushek, Eric A. & L. WoBmann (2007) Education quality and economic growth. Working paper available at: http://edpro.stanford.edu/hanushek/admin/pages/files/uploads/Edu_Q uality_Economic_Growth-1.pdf

Hanushek, Eric A. & L. WoBmann (2009) Do better schools lead to more growth? Cognitive skills, economic outcomes, and causation. IZA Discussion Papers No. 4575.

Heston, Alan, R. Summers & B. Aten (2009) Penn World Table Version 6.3. Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania, accessed Dec 15, 2010, at: http://pwt.econ.upenn.edu/php_site/pwt63/pwt63_retrieve.php.

Hibbs, Douglas A. Jr. & O. Olsson (2004) Geography, biogeography, and why some countries are rich and others are poor. Proceedings of the National Academy of Sciences USA 101: 3715-3720.

Jencks, Christopher (1972) Inequality: A Reassessment of the Effects of Family and Schooling in America. New York: Basic Books.

Jensen, Arthur R. (1998) The g Factor: The Science of Mental Ability. Westport (CT): Praeger.

Jones, Garett & W.J. Schneider (2006) Intelligence, human capital, and economic growth: a Bayesian averaging of classical estimates (BACE) approach. Journal of Economic Growth 11: 71-93.

Keeves, John P. (1992) Learning Science in a Changing World. Cross-National Studies of Science Achievement: 1970 to 1984. International Association for the Evaluation of Educational Achievement Report No. ISBN-92-9121001-3. Accessed 14 June 2009 at: http://eric.ed.gov/ERICDocs/data/ericdocs2sql/content_storage_01/00 00019b/80/15/2b/8d.pdf

Khaleefa, Omar, A. Sulman & R. Lynn (2009) An increase of intelligence in Sudan, 1987-2007. Journal of Biosocial Science 41: 279-283.

Knack, Stephen & P. Keefer (1997) Does social capital have an economic payoff? A cross-country investigation. Quarterly Journal of Economics 112: 1251-1288.

Lapointe, Archie E. (1992) Learning Mathematics. Educational Testing Service Report No. ETS22-CAEP-01; ISBN-0-88685-120-3. Princeton (NJ): ETS. Accessed 16 June 2009 at: http://eric.ed.gov/ERICDocs/data/ericdocs2sql/content_storage_01/00 00019b/80/24/21/ac.pdf

Lee, Jong-Wha & R.J. Barro (1997) Schooling quality in a cross-section of countries. NBER Working Papers No. 6198.

Lynn, Richard (1979) The social ecology of intelligence in the British Isles. British Journal of Social and Clinical Psychology 1979:18,1-12.

Lynn, Richard (2006) Race Differences in Intelligence. An Evolutionary Analysis. Augusta (GA): Washington Summit.

Lynn, Richard (2008a) The Global Bell Curve. Race, IQ, and Inequality Worldwide. Augusta (GA): Washington Summit.

Lynn, Richard (2008b) IQ and mathematics ability of Tibetans and Han Chinese. Mankind Quarterly 48: 505-510.

Lynn, Richard (2010) National IQs updated for 41 nations. Mankind Quarterly 50: 275-296.

Lynn, Richard & T. Vanhanen (2002) IQ and the wealth of nations. Westport (CT): Praeger.

Lynn, Richard & T. Vanhanen (2006) IQ and global inequality. Augusta (GA): Washington Summit.

Lynn, Richard & J. Mikk (2007) National differences in intelligence and educational attainment. Intelligence 35: 115-121.

Lynn, Richard, G. Meisenberg, J. Mikk & A. Williams (2007) National IQs predict differences in scholastic achievement in 67 countries. Journal of Biosocial Science 39: 861-874.

Lynn, Richard & G. Meisenberg (2010) National IQs calculated and validated for 108 nations. Intelligence 38: 353-360.

Mackintosh, Nicholas J. (1998) IQ and Human Intelligence. Oxford: Oxford University Press.

Mankiw, N. Gregory, D. Romer & D. Weil (1992) A contribution to the empirics of economic growth. Quarterly Journal of Economics 107: 407-438.

Martin, Michael O., I.V.S. Mullis, E.J. Gonzales & S.J. Chrostowski (2004) TIMSS 2003 International Science Report. Chestnut Hill (MA): IEA. Accessed 23 Jan. 2007 at: http://timss.bc.edu/timss2003i/intl_reports.html

Martin, Michael O., I.V.S. Mullis & P. Foy (2008) TIMSS 2007 Science Report. Chestnut Hill (MA): TIMSS & PIRLS International Study Center. Accessed 9 June 2009 at: http://timss.bc.edu/TIMSS2007/intl_reports.html

McCauley, Clark, V. Ottati & Y.-T. Lee (1999) National differences in economic growth: the role of personality and culture. In: Y.-T. Lee, C.R. McCauley & J.G. Draguns (eds): Personality and Person Perception Across Cultures. Mahwah (NJ): Erlbaum.

Medrich, Elliott A. & J.E. Griffith (1992) International Mathematics and Science Assessment: What Have We Learned? National Center for Education Statistics Research and Development Report. Washington DC: U.S. Department of Education.

Meisenberg, Gerhard (2004) Talent, character, and the dimensions of national culture. Mankind Quarterly 45: 123-168.

Meisenberg, Gerhard (2007) Does multiculturalism promote income inequality? Mankind Quarterly 47: 3-39.

Meisenberg, Gerhard (2008) How does racial diversity raise income inequality? Journal of Social, Political and Economic Studies 33: 3-26.

Meisenberg, Gerhard (in press) National IQ and economic outcomes. Personality and Individual Differences.

Meisenberg, G., E. Lawless, E. Lambert & A. Newton (2006) The social ecology of intelligence on a Caribbean island. Mankind Quarterly 46: 395-433.

Mullis, Ina V.S., M.O. Martin, E.J. Gonzales & S.J. Chrostowski (2004) TIMSS 2003 International Mathematics Report. Chestnut Hill (MA): IEA. Accessed 23 Jan. 2007 at: http://timss.bc.edu/timss2003i/intl_reports.html

Mullis, Ina V.S., M.O. Martin & P. Foy (2008) TIMSS 2007 International Mathematics Report. Chestnut Hill (MA): TIMSS & PIRLS International Study Center. Accessed 9 June 2009 at: http://timss.bc.edu/TIMSS2007/intl_reports.html

Nordhaus, William D. (2006) Geography and macroeconomics: new data and new findings. Proceedings of the National Academy of Sciences USA 103: 3510-3517.

Nunn, Nathan (2008) The long-term effects of Africa's slave trades. Quarterly Journal of Economics 123: 139-176.

Rindermann, Heiner (2006) Was messen internationale Schulleistungsstudien? Schulleistungen, Schulerfahigkeiten, kognitive Fahigkeiten, Wissen oder allgemeine Intelligenz? [What do international school achievement studies measure? School achievement, pupils' abilities, mental abilities, knowledge or general intelligence?] Psychologische Rundschau 57(2), 69-86.

Rindermann, Heiner (2007) The g-factor of international cognitive ability comparisons: the homogeneity of results in PISA, TIMSS, PIRLS and IQ-tests across nations. European Journal of Personality 21: 667-706.

Rindermann, Heiner (2008a) Relevance of education and intelligence at the national level for the economic welfare of people. Intelligence 36: 127-142.

Rindermann, Heiner (2008b) Relevance of education and intelligence for the political development of nations: democracy, rule of law and political liberty. Intelligence 36: 306-322.

Rindermann, Heiner & G. Meisenberg (2009) Relevance of education and intelligence at the national level for health: the case of HIV and AIDS. Intelligence 37: 383-395.

Rindermann, Heiner, M. Sailer & J. Thompson (2009) The impact of smart fractions, cognitive ability of politicians and average competence of peoples on social development. Talent Development & Excellence 1: 3-25.

Rodrik, D., A. Subramanian & F. Trebbi (2002) Institutions rule: the primacy of institutions over geography and integration in economic development. NBER Working Papers 9305. http://www.nber.org/papers/w9305

Sala-i-Martin, Xavier, G. Doppelhofer & R. Miller (2004) Determinants of long-run growth: a Bayesian averaging of classical estimates (BACE) approach. American Economic Review 94: 813-835

Shayer, Michael & D. Ginsburg (2009) Thirty years on--a large anti-Flynn effect? (II): 13-and 14-year olds. Piagetian tests of formal operations norms 1976-2006/7. British Journal of Educational Psychology 79: 409-418.

Sundet, Jon Martin, D.G. Barlaug & T.M. Torjussen (2004) The end of the Flynn effect? A study of secular trends in mean intelligence test scores of Norwegian conscripts during half a century. Intelligence 32: 349-362.

Teasdale, Thomas W. & D.R. Owen (2008) Secular declines in cognitive test scores: a reversal of the Flynn effect. Intelligence 36: 121-126.

United Nations (2004) Human Development Report. Retrieved 15 Oct 2008 at http://hdr.undp.org/en/media/HDR04_complete.pdf

United Nations (2005) Human Development Report. Retrieved 10 Oct 2008 at http://hdr.undp.org/en/media/HDR05_complete.pdf

Valdes, Hector, E. Trevino, C.G. Acevedo, M. Castro, S. Carrillo, R. Costilla, D. Bogoya & C. Pardo (2008) Student Achievement in Latin America and the Caribbean. Santiago (Chile): Regional Bureau for Education in Latin America and the Caribbean OREALC/UNESCO, available at: http://www,llece.org/public/component/option,com_publicaciones/task ,ver/id,6/Itemid,6/lang.en/

Voracek, Martin (2005) National intelligence, suicide rate in the elderly, and a threshold intelligence for suicidality: an ecological study of 48 Eurasian countries. Journal of Biosocial Science 37: 721-740.

Weber, Max (1930) The Protestant ethic and the spirit of capitalism. London: Allen & Unwin.

Weede, Erich (2004) Does human capital strongly affect growth rates? Yes, but only if assessed properly. Comparative Sociology 3: 115-134.

Weede, Erich & S. Kampf (2002) The impact of intelligence and institutional improvements on economic growth. Kyklos 55: 361-380.

Wicherts, Jelte M., D. Borsboom & C.V. Dolan (2010) Why national IQs do not support evolutionary theories of intelligence. Personality and Individual Differences 48: 91-96.

Zuckerman, Phil (2005) Atheism: contemporary rates and patterns. In: M. Martin (ed): The Cambridge Companion to Atheism, pp. 47-67. Cambridge: Cambridge University Press.

Gerhard Meisenberg (1)

Ross University School of Medicine

Richard Lynn (2)

University of Ulster, Coleraine

(1) Email: Gmeisenberg@rossmed.edu.dm

(2) Email: LYNNR540@aol.com

Table 1. Correlations of human capital measures with some other
country-level variables. Human capital (HC) is the composite of IQ
and school achievement. N, number of countries; *, p<0.05; **,
p<0.01; ([.sub.NS]), non-significant. All other correlations are
significant at p<0.001.

 IQ SA

School achievement (SA) 0.889 1
Human capital (HC) 0.981 0.949
Education 0.757 0.747
lgGDP 1995-2005 0.745 0.717
Econ. Growth 1985-2009 0.426 0.467
Corruption 1996-2003 -0.599 -0.576
Econ. Freedom 1995-2005 0.635 0.607
Big Government 1995-2005 0.258 * 0.359
Gini index -0.614 -0.638
Democracy 1985-2005 0.684 0.611
Pol. Freedom 1985-2005 0.557 0.464
Suicide 0.557 0.595
Life Exp. 2000-2005 0.838 0.761
Infant Mortality 2002 -0.834 -0.777
TFR 1995-2005 -0.839 -0.837
Religiosity 1981-2008 -0.779 -0.788
Happiness -0.047 ([sub.NS]) -0.058 ([sub.NS])
Life satisfaction 0.576 0.503

 HC Education

School achievement (SA)
Human capital (HC) 1
Education 0.774 1
lgGDP 1995-2005 0.759 0.711
Econ. Growth 1985-2009 0.451 0.108ns
Corruption 1996-2003 -0.606 -0.582
Econ. Freedom 1995-2005 0.648 0.621
Big Government 1995-2005 0.299 ** 0.378
Gini index -0.646 -0.489
Democracy 1985-2005 0.667 0.675
Pol. Freedom 1985-2005 0.544 0.593
Suicide 0.598 0.498
Life Exp. 2000-2005 0.833 0.648
Infant Mortality 2002 0.838 -0.755
TFR 1995-2005 -0.862 -0.774
Religiosity 1981-2008 -0.797 -0.739
Happiness -0.067 ([sub.NS]) -0.120 ([sub.NS])
Life satisfaction 0.568 0.471

 lgGDP N

School achievement (SA) 99
Human capital (HC) 99
Education 99
lgGDP 1995-2005 1 99
Econ. Growth 1985-2009 0.215 ([sub.NS]) 82
Corruption 1996-2003 -0.751 99
Econ. Freedom 1995-2005 0.761 98
Big Government 1995-2005 0.248 * 98
Gini index -0.468 87
Democracy 1985-2005 0.609 95
Pol. Freedom 1985-2005 0.526 97
Suicide 0.241 * 68
Life Exp. 2000-2005 0.782 98
Infant Mortality 2002 -0.837 99
TFR 1995-2005 -0.754 99
Religiosity 1981-2008 -0.586 97
Happiness 0.214 ([sub.NS]) 73
Life satisfaction 0.753 96

Table 2: Regression models predicting economic and political
outcomes with human capital, schooling, log-transformed GDP,
and history of communist rule. * <.05; **. <.01; ***, <.001.

 Corruption Big
Predictor Growth freedom Economic govt.

Human capital 0.659 *** -0.228 * 0.181 * 0.124
Schooling -0.445 ** -0.239 * 0.405 *** 0.087
lgGDP 0.163 -0.378 *** 0.319 ** 0.076
Communism 0.214 * 0.345 *** -0.376 *** 0.104
N 124 150 145 145
Adj [R.sup.2] 0.324 0.606 0.684 0.068

 Gini Democracy
Predictor index Freedom Political

Human capital -0.566 *** 0.411 *** 0.126
Schooling 0.142 0.608 *** 0.702 ***
lgGDP -0.073 -0.118 -0.060
Communism -0.191 -0.402 *** -0.353 ***
N 129 143 147
Adj [R.sup.2] 0.339 0.625 0.482

Table 3. Regression models predicting quality of
life measures with human capital, schooling,
log-transformed GDP, and history of
communist rule. * <.05; **. <.01; ***,
<.001.

 Life Infant
Predictor Suicide Expectancy mortality TFR

Human capital 0.387 ** 0.471 *** -0.327 *** -0.289 ***
Schooling 0.160 0.042 -0.248 ** -0.238 ***
lgGDP -0.053 0.396 *** -0.383 *** -0.392 ***
Communism 0.298 * -0.007 0.003 -0.218 ***
N 89 147 150 150
Adj. [R.sup.2] 0.345 0.704 0.763 0.823

Predictor Religious Happy Satisfied

Human capital -0.506 *** -0.156 0.186
Schooling -0.265 ** -0.019 0.069
lgGDP -0.012 0.339 0.556 ***
Communism -0.174 ** -0.570 *** -0.255 ***
N 140 93 138
Adj. [R.sup.2] 0.656 0.458 0.601