IQ and Global Inequality

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IQ and Global Inequality

IQ and Global Inequality is a controversial 2006 book by Dr. Richard Lynn, Professor Emeritus of Psychology at the University of Ulster, Northern Ireland, and Dr. Tatu Vanhanen, Professor Emeritus of Political Science at the University of Tampere, Tampere, Finland.[1] IQ and Global Inequality is follow-up to their 2002 book IQ and the Wealth of Nations[2], an expansion of the argument that international differences in current economic development are due in part to differences in average national intelligence as measured by average national IQ, and a response to critics. Unlike IQ and the Wealth of Nations, the book was not published by an academic publisher but by Washington Summit Publishers.

Lynn and Vanhanen's research on IQ and economic development has attracted academic attention from several fields, some of it very enthusiastic, some dismissive.[3][4][5][6]

Contents

[edit] Summary

Chapter 1 summarizes theories of economic growth.
Chapter 2 defines and describes intelligence.
Chapter 3 argues that the scientific literature indicates that intelligence is a determinant of incomes and related phenomena among individuals within a number of countries.
Chapter 4 describes the collection and determination of national IQ, presenting calculated IQs for 113 countries and estimated IQs for an additional 79 countries. This represents all countries with population greater than 40,000.
Chapter 5 introduces a new statistic, the quality of human condition index (QHC) and 12 alternative variables that measure human conditions.
Chapter 7 focuses on the relationship between national IQ and QHC, which Lynn and Vanhanen report to be strongly correlated.
Chapter 8 examines the relationship between national IQ and 12 alternative variables, which Lynn and Vanhanen report are also correlated with national IQ.
Chapter 9 discusses the genetic and environmental contributions to differences in national intelligence, and argues that racial composition of the population is a major factor.
Chapter 10 considers the causal relationship between national IQ and important variables related to global inequality.
Chapter 11 discusses and responds to criticisms made to Lynn and Vanhanen's theory by reviewers.
Chapter 12 summarizes the book and discusses policy recommendations.

[edit] National IQ and economic development

[edit] Quality of human conditions index

Richard Lynn's (QHC) index.
     11      15      20      30      40      50      60      70      80      85      89      N/A

The quality of human conditions (QHC) index was computed from five variables.

  1. purchasing power parity Gross National Income (PPP-GNI) per capita 2002
  2. adult literacy rate 2002
  3. gross tertiary enrollment ratio
  4. life expectancy at birth 2002
  5. the level of democratization 2002 (Tatu Vanhanen's Index of Democratization)

Values of the index range from 10.7 (Burkina Faso) to 89 (Norway). Lynn and Vanhanen write that they would have preferred to include a sixth measure, an indicator of income inequality, but that statistical data for that variable was not available for all countries. They write that the QHC index differs significantly from other widely used indexes (such as the Human Development Index) in that QHC also measures democratization. Some of their claims have been received support in a 2007 study by Rindermann.[7]

All countries Estimate IQ
(79 countries)		 Total
(192 countries)
QHC 0.805 0.725 0.791
PPP GNI per capita 2002 0.693 0.342 0.616
Adult literacy rate 2002 0.642 0.655 0.655
Tertiary enrollment ratio 0.746 0.699 0.745
Life expectancy at birth 2002 0.765 0.690 0.750
Index of Democratization 2002 0.569 0.322 0.530
Excluding smallest countries Calculated IQ
(98 countries)		 Estimate IQ
(62 countries)		 Total
(160 countries)
QHC 0.846 0.800 0.839
PPP GNI per capita 2002 0.739 0.266 0.649
Adult literacy rate 2002 0.710 0.746 0.733
Tertiary enrollment ratio 0.778 0.734 0.780
Life expectancy at birth 2002 0.833 0.753 0.817
Index of Democratization 2002 0.598 0.408 0.584

[edit] Other measures of global inequality

The relationship of national IQ to twelve other measures of global inequality were examined.

  1. Human Development Index (HDI)
  2. Gender-related Development Index (GDI)
  3. Economic growth rate (EGR)
  4. Gini index of inequality in income or consumption (Gini)
  5. Population below $2 a day international poverty line (Poverty)
  6. Measures of undernourishment (PUN)
  7. Maternal mortality ratio (MMR) and infant mortality rate (IMR)
  8. Corruption Perceptions Index (CPI)
  9. Economic freedom ratings (EFR)
  10. the Index of Economic Freedom (IEF)
  11. Population pyramids (MU-index)
  12. Human happiness and life-satisfaction.

All twelve measures of global inequality are significantly correlated with the QHC index. According to the book, eleven of the twelve measures are significantly correlated with national IQ. The measures of human happiness and life satisfaction are not significantly correlated with national IQ.
Correlations IQ QHC
HDI 0.776 0.940
GDI 0.849 0.951
EGR 3 0.747 0.840
EGR 4 0.709 0.871
Gini -0.538 -0.464
Poverty -0.653 -0.799
PUN 1 -0.500 -0.648
MMR -0.730 -0.759
IMR -0.771 -0.861
CPI 0.591 0.762
EFR 0.606 0.674
IEF 0.418 0.620
MU-index 0.806 0.902
Happiness 0.029 0.315
Life satisfaction 0.033 0.396

[edit] Latitude and temperature

Correlation Latitude Temperature
Degrees latitude 1 -0.885
Annual mean temperature -0.885 1
National IQ 0.677 -0.632
QHC 0.659 -0.562
PPP GNI per capita 2002 0.528 -0.407
Adult literacy rate 2002 0.482 -0.467
Tertiary enrollment ratio 0.718 -0.649
Life expectancy at birth 2002 0.505 -0.379
Index of Democratization 2002 0.512 -0.460

[edit] National IQ and QHC values

Calculated and estimated national average IQ according to book.
     ≤65      70      75      80      85      90      95      100      ≥105      N/A


Country/Region IQ (2002)[2] IQ (2006)[1] PPP-GNI per capita 2002[1] QHC[1]
 Hong Kong 107 108 27,490 60.8
 Singapore 103 108 23,730 60.7
 North Korea 105* 106* 1,000 38
 South Korea 106 106 16,960 75.4
 Japan 105 105 27,380 71.4
 People's Republic of China 100 105 4,520 39.7
 Taiwan 104 105 23,400 79.4
 Italy 102 102 26,170 78.9
 Iceland 98* 101 29,240 80
 Mongolia 98* 101* 1,710 48.1
 Switzerland 101 101 31,840 82.2
 Austria 102 100 28,910 80.7
 Luxembourg 101* 100* 53,230 76.4
 Netherlands 102 100 28,350 82.8
 Norway 98 100 36,690 89
 Germany 102 99 26,980 78
 Belgium 100 99 28,130 84.1
 Canada 97 99 28,930 77.8
 Estonia 97* 99 11,630 64.5
 Finland 97 99 26,160 85.1
 United Kingdom 100 100 26,580 76.7
 New Zealand 100 99 20,550 76.2
 Poland 99 99 10,450 62.7
 Sweden 101 99 25,820 82.9
 Andorra N/A 98* 19,000 58.7
 Australia 98 98 27,440 82.8
 Czech Republic 97 98 14,920 64.5
 Denmark 98 98 30,600 85.4
 France 98 98 27,040 78.1
 Hungary 99 98 13,070 64.1
 Latvia 97* 98* 9,190 65.5
 Spain 97 98 21,910 75.8
 United States 98 98 36,120 86.6
 Belarus 96* 97* 5,500 57.2
 Malta 95* 97 17,710 66.4
 Russia 96 97 8,080 64.5
 Ukraine 96* 97* 4,800 61.8
 Moldova 95* 96* 1,600 46.2
 Slovakia 96 96 12,590 63.2
 Uruguay 96 96 7,710 64
 Israel 94 95 19,000 75.3
 Portugal 95 95 17,820 67
 Armenia 93* 94* 3,230 50.2
 Georgia 93* 94* 2,270 51.2
 Kazakhstan 93* 94* 5,630 49
 Romania 94 94 6,490 53
 Vietnam 96* 94 2,300 39.5
 Argentina 96 93 10,190 64.7
 Bulgaria 93 93 7,030 59.1
 Greece 92 92 18,770 76.1
 Malaysia 92 92 29,570 78.5
 Ireland 93 92 8,500 52.1
 Brunei 92* 91* 19,210 50.8
 Cambodia 89* 91* 1,970 28.6
 Cyprus 92* 91* 18,650 67.6
 Lithuania 97* 91 10,190 65.4
 Republic of Macedonia 93* 91* 6,420 54.4
 Thailand 91 91 6,890 50.3
 Albania 90* 90* 4,960 51.2
 Bermuda N/A 90 36,000 75.8
 Bosnia and Herzegovina N/A 90* 5,800 51.4
 Chile 93* 90 9,420 59.5
 Croatia 90 90 10,000 61.7
 Kyrgyzstan 87* 90* 1,560 48.1
 Turkey 90 90 6,300 50.2
 Mexico 87 90 12,500 52.9
 Cook Islands N/A 89 5,000 45.7
 Costa Rica 91* 89* 8,650 53.7
 Laos 89* 89 1,660 24.9
 Mauritius 81* 89 10,820 52.2
 Suriname 89 89 6,590 50.6
 Ecuador 80 88 3,340 47.4
 Samoa 87 88 5,570 49.7
 Azerbaijan 87* 87* 3,010 47.2
 Bolivia 85* 87 2,390 49.7
 Brazil 87 87 7,450 51.1
 East Timor N/A 87* 3,940 46.7
 Guyana 84* 87* 3,070 40.2
 Indonesia 89 87 1,600 28.1
 Iraq 87 87 1,027 30.7
 Myanmar 86* 87* 930 42.4
 Tajikistan 87* 87* 1,640 27.5
 Turkmenistan 87* 87* 4,780 41.7
 Uzbekistan 87* 87* 1,640 39.4
 Kuwait 83* 86 17,780 49.9
 Philippines 86 86 4,450 51.6
 Seychelles 81* 86* 18,232 60.6
 Tonga 87 86 6,820 40.5
 Cuba 85 85 5,259 46.2
 Fiji 84 85 5,330 51.9
 Kiribati 84* 85* 800 37.1
 New Caledonia N/A 85 21,960 54.9
 Peru 90 85 4,880 49.2
 Trinidad and Tobago 80* 85* 9,000 52
 Yemen 83* 85 800 24.5
 Afghanistan 83* 84* 700 13.2
 Belize 83* 84* 15,960 56.1
 Colombia 88 84 5,490 44.2
 Federated States of Micronesia 84* 84* 6,150 48.4
 Iran 84 84 6,690 40.2
 Jordan 87* 84 4,180 43.4
 Marshall Islands 84 84 1,600 44.2
 Morocco 85 84 2,000 39.9
 Pakistan 81* 84 3,730 31.7
 Panama 84* 84* 1,960 26.2
 Paraguay 85* 84 6,060 56.6
 Puerto Rico 84 84 4,590 45.2
 Saudi Arabia 83* 84* 15,800 63.6
 Solomon Islands 84* 84* 12,660 44.1
 The Bahamas 78* 84* 1,590 41.5
 United Arab Emirates 83* 84* 24,030 48.8
 Vanuatu 84* 84* 2,850 31.4
 Venezuela 88* 84 5,220 47.4
 Algeria 84* 83* 5,530 39.9
 Bahrain 83* 83* 16,190 49.3
 Libya 84* 83* 7,570 49.3
 Oman 83* 83* 13,000 40.6
 Papua New Guinea 84* 83 2,180 38.4
 Syria 87* 83 5,348 38.9
 Tunisia 84* 83* 6,440 40.6
 Bangladesh 81* 82* 1,720 29.8
 Dominican Republic 84* 82 6,270 46.8
 India 81 82 2,650 36.3
 Lebanon 86 82 4,600 55.8
 Madagascar 79* 82 730 28.6
 Egypt 83 81 3,810 37.3
 Honduras 84* 81 2,540 41.9
 Maldives 81* 81* 4,798 38.5
 Nicaragua 84* 81* 2,350 41.3
 Northern Mariana Islands N/A 81 12,500 51.3
 Barbados 78 80 14,660 60.9
 Bhutan 78* 80* 1,969 24.1
 El Salvador 84* 80* 4,790 42.6
 Guatemala 79 79 4,040 34.6
 Sri Lanka 81* 79 3,510 47.7
 Nepal 78 78 1,370 26.9
 Qatar 78 78 19,844 45.6
 Comoros 79* 77* 1,640 24.6
 Cape Verde 78* 76* 4,920 40.5
 Mauritania 73* 76* 1,790 20.5
 Uganda 73 73 1,360 25.4
 Kenya 72 72 1,010 27.3
 South Africa 72 72 9,810 38.3
 Tanzania 72 72 580 23.2
 Ghana 71 71 2,080 33.7
 Grenada 75* 71* 6,600 45.3
 Jamaica 72 71 3,680 46.5
 Saint Vincent and the Grenadines 75* 71 5,190 48.4
 Sudan 72 71 1,740 24.6
 Zambia 77 71 800 21.8
 Antigua and Barbuda 75* 70* 10,390 53.2
 Benin 69* 70* 1,060 20.5
 Botswana 72* 70* 7,740 29.4
 Namibia 72* 70* 6,880 31.1
 Rwanda 70* 70* 1,260 18.5
 Togo 69* 70* 1,450 26
 Burundi 70* 69* 630 15.2
 Côte d'Ivoire 71* 69* 1,450 18.1
 Malawi 71* 69* 570 24.3
 Mali 68* 69* 840 13.4
 Niger 67* 69* 800 13.5
 Nigeria 67 69 800 27.3
 Angola 69* 68* 1,840 13.7
 Burkina Faso 66* 68* 1,090 10.7
 Chad 72* 68* 1,010 20.4
 Djibouti 68* 68* 2,040 22
 Eritrea 68* 68* 1,040 21.4
 Somalia 68* 68* 500 15.2
 Swaziland 72* 68* 4,730 22.2
 Dominica 75* 67 4,960 48.8
 Guinea 63 67 2,060 22.5
 Guinea-Bissau 63* 67* 680 20.3
 Haiti 72* 67* 1,610 20.4
 Lesotho 72* 67* 2,970 24.3
 Liberia 64* 67* 1,000 21.2
 Saint Kitts and Nevis 75* 67* 10,750 45.5
 São Tomé and Príncipe 59* 67* 1,317 37.9
 Senegal 64* 66* 1,660 20.7
 The Gambia 64* 66* 1,540 21.3
 Zimbabwe 66 66 2,180 25.2
 Republic of the Congo 73 65 630 17.9
 Cameroon 70* 64 1,910 23.1
 Central African Republic 68* 64 1,170 19.1
 Democratic Republic of the Congo 65 64 700 26.9
 Ethiopia 63 64 780 16.7
 Gabon 66* 64* 5,530 32.2
 Mozambique 72* 64 990 18
 Sierra Leone 64 64 500 13.8
 Saint Lucia 75* 62 4,950 51.1
 Equatorial Guinea 59 59 9,100 30.4
"*" Denotes estimated National IQ

PPP-GNI = purchasing power parity gross national income. QHC = is a composite index called quality of human conditions.

[edit] Criticism

Hunt and Wittmann (2008) write of Lynn’s IQ data:

The majority of the data points were based upon convenience rather than representative samples. Some points were not even based on residents of the country. For instance, the “data point for Suriname was based on tests given to Surinamese who had migrated to the Netherlands, and the “data point” for Ethiopia was based on the IQ scores of a highly selected group that had emigrated to Israel and, for cultural and historical reasons was hardly representative of the Ethiopian population. The data point for Mexico was based on a weighted averaging of the results of a study of “Native American and Mestizo children in Southern Mexico” with result of a study of residents of Argentina [8].

Upon reading the original reference, they found that the “data point” that Lynn and Vanhanen used for the lowest IQ estimate, Equatorial Guinea, was actually the mean IQ of a group of Spanish children in a home for the developmentally disabled in Spain. Corrections were applied to adjust for differences in IQ cohorts (the “Flynn” effect) on the assumption that the same correction could be applied internationally, without regard to the cultural or economic development level of the country involved. While there appears to be rather little evidence on cohort effect upon IQ across the developing countries, one study in Kenya (Daley, Whaley, Sigman, Espinosa, & Neumann, 2003) shows a substantially larger cohort effect than is reported for developed countries (p.?) [9]

In Johnson (2009), Wendy Johnson, a frequent critic of Lynn's works, reviews the data and conclusions in Lynn's (2006) book for the journal Intelligence:

"Lynn's data are essentially correct and do reflect the general state of the world," but his causal attributions are not justified because an experiment where "being certain that cultural environments (keep in mind which cultures developed the IQ test) and educational social opportunities are equal across race in infancy and even before, and ensuring that those environments and opportunities remain equal throughout at the very least childhood and adolescence and likely much further in the lifespan...hasn't come close to being run. To emphasize, despite many possible statistical and psychometric quibbles, the data Lynn presents in this book are essentially correct. At the same time, despite Lynn's protestations to the contrary, these data do little or nothing to address the questions of why this is the case...."[10]

Crawford-Nutt (1976) found that African black students enrolled in westernized schools scored higher on progressive matrix tests than did American white students. The study was meant to examine perceptual/cultural differences between groups, and demonstrated that one’s performance on western standardized tests correspond more closely with the quality and style of schooling that one receives more so than other factors [11]. Buj (1981) showed Ghanaian adults to score higher on a supposedly ‘culture fair’ IQ test than did Irish adults; scores were 80 (Ghanaian) and 78 (Irish), respectively [12]. Shuttleworth-Edwards et al. (2004) conducted a study with black South Africans between the ages of 19–30, where highly significant effects for both level and quality of education within groups whose first language was an indigenous black African language, was revealed. Black African first language groups (as well as white English speaking groups) with advantaged education were comparable with the US standardization in IQ test scores (e.g. WAIS-III)[13].

[edit] See also

Book's Publisher

Theories of Race and Intelligence:

Publications of Race and Intelligence:

Theories of other Intelligence links:

[edit] External links

[edit] References

  1. ^ a b c d Richard Lynn and Tatu Vanhanen (2006). IQ and Global Inequality. Washington Summit Publishers: Augusta, GA. ISBN 1593680252
  2. ^ a b Lynn, R. and Vanhanen, T. (2002). IQ and the wealth of nations. Westport, CT: Praeger. ISBN 0-275-97510-X
  3. ^ "Relevance of education and intelligence at the national level for politics: Democracy, rule of law and political liberty" (PDF). http://groups.uni-paderborn.de/rindermann/materialien/PublikationsPDFs/ISIRSF.pdf.  Paper by Heiner Rindermann.
  4. ^ "Intelligence, Human Capital, and Economic Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach". http://ideas.repec.org/p/wpa/wuwpdc/0507005.html.  Paper by Garett Jones and W. Joel Schneider.
  5. ^ Älykkyyden tabu murtuu? Review by J.P. Roos in Sosiologia 3/2007.
  6. ^ Review by J.Philippe Rushton in Personality and Individual Differences, 2006, 41, 983-5.
  7. ^ Rindermann, Heiner: 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 (2007) 667-706 [1]
  8. ^ Hunt, E. & Wittmann, W. (2008). National intelligence and prosperity. Intelligence. Vol. 36, 1, January-February pp. 1-9.
  9. ^ Hunt, E. & Wittmann, W. (2008). National intelligence and prosperity. Intelligence. Vol. 36, 1, January-February pp. 1-9.
  10. ^ Johnson, W. (2004) Intelligence Volume 37, Issue 1, Pages 119-120 (January-February 2009)
  11. ^ Crawford-Nutt. D. (1976). Are black scores on Raven’s Standard Progressive Matrices an artifact of method of test presentation? Psychologia Africana, 16, 201-206
  12. ^ Buj, V. (1981). "Average IQ values in various European countries." Personality and Individual Differences, 2:168-169
  13. ^ Shuttleworth-Edwards A., Kemp R., Rust A., Muirhead J., Hartman N., Radloff S. (2004). Cross-cultural Effects on IQ Test Performance: A Review and Preliminary Normative Indications on WAIS-III Test Performance. Journal of Clinical and Experimental Neuropsychology (Neuropsychology, Developm, Volume 26, Number 7, October 2004 , pp. 903-920(18)
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