{"id":70348,"date":"2015-07-01T15:35:53","date_gmt":"2015-07-01T23:35:53","guid":{"rendered":"http:\/\/www.lukeford.net\/blog\/?p=70348"},"modified":"2015-07-08T08:28:33","modified_gmt":"2015-07-08T16:28:33","slug":"national-intelligence-and-economic-development","status":"publish","type":"post","link":"https:\/\/lukeford.net\/blog\/?p=70348","title":{"rendered":"National Intelligence and Economic Development"},"content":{"rendered":"

Richard Lynn and Tatu Vanhanen write<\/a>:<\/p>\n

Ten years ago we began our research program for the
\ninvestigation of how far differencesin intelligence can explain the
\ndifferences in economic development between nations in our book
\nIQ and the Wealth of Nations(2002). Our starting point was that it
\nhas been established that intelligence is a determinant of earnings
\namong individuals, and hence that this association should also be
\npresent across nations. We searched for studies throughout the
\nworld in which intelligence tests had been administered, and
\nfound useable data for 81 nations. We calculated the results by
\nsetting the IQ in Britain at 100 (standard deviation =15) and the
\nIQs of other nations were expressed on this metric. The results
\nshowed that there are huge differences in the average IQs of
\nnations, ranging from approximately 70 in sub-Saharan Africa, to
\napproximately 100 in most of Europe and the countries colonized
\nby Europeans in the last few centuries(the United States, Canada,
\nAustralia, New Zealand, Argentina, Chile and Uruguay), to
\napproximately 110 in China, Japan, Korea, Singapore and
\nTaiwan. We then showed that national IQs were correlated with
\nper capita income (measured as real GDP, gross national product,
\nper capita) at 0.73 (Lynn and Vanhanen, 2002, p. 89). This
\nshowed that 53 per cent of the variance in per capita in this group
\nof nations is attributable to differences in intelligence. We then
\nused the measured IQ of the 81 nations to estimate the IQs for a
\nfurther 104 nations that were ethnically similar to those for which
\nwe had measured IQs. For example, we assumed that the IQ in
\nLuxembourg would be the same as in the Netherlands and
\nBelgium. This gave us IQs for all 185 nations in the world with
\npopulations over 50,000. We showed that for these 185 nations,
\nIQs were correlated with per capita income (measured as real
\nGross Domestic Product, per capita) at 0.62. This is lower than the
\ncorrelation for 81 nations, probably because there was some error
\nin the estimated IQs. Nevertheless, the correlation is highly
\nsignificant and shows that 38 per cent of the variance in per capita
\nincome in the nations of the world is attributable to differences in
\nintelligence. To establish the validity of these national IQs, we
\nshowed that they are correlated at 0.88 with national scores on
\ntests of mathematics and at 0.87 with national scores on tests of
\nscience.
\nIn 2006 we published further evidence for this theory in our
\nbook JQ and Global Inequality. In this we presented measured IQs
\nfor an additional 32 nations, bringing the total number of nations
\nfor which we had measured IQs to 113. We showed that these
\nwere correlated with per capita income (measured as real GNI,
\ngross national income) at 0.68 (Lynn and Vanhanen, 2006, p.
\n102). Following the method in our first study, we used the
\nmeasured IQ of the 113 nations to estimate the IQs for an
\nadditional 79 nations that were ethnically similar to those for
\nwhich we had measured IQs. This gave us a total of 192 nations,
\ncomprising all the nations in the world with populations over
\n40,000. We found a correlation of 0.68 between national IQ and
\nper capita income in the 113 nations for which they had measured
\nIQs, and a correlation of0.60 between national IQ and per capita
\nincome in the 192 nations. Once again, the correlation for the 113
\nnations’ measured IQs is a little higher than for the larger 192
\nnation data set, and probably for the same reason that measured
\nnational IQs are more valid than estimated national IQs.
\nIn our 2006 book we extended the analysis beyond
\neconomic development and showed that national IQs explain
\nsubstantial percentages of the variance in national differences a
\nnumber of other phenomena including literacy, life expectancy,
\nand the presence of democratic institutions…<\/p>\n

…national IQs are highly correlated with national scores in tests of mathematics
\nand science, as shown in detail in Chapter 3, as well as with a
\nnumber of other economic and social variables, as documented
\nthroughout this book. If our IQs were meaningless, they would
\nnot be highly correlated with a wide range of economic and
\nsocial phenomena.<\/p>\n

Country Measured Final IQ
\nAfghanistan – – – – – – (75)
\nAlbania – – 385.5 78.7 82 2 82
\nAlgeria – – 403.6 81.5 84.2 2 84.2
\nquality Final IQ
\nAndorra – – – – – – (97)
\nAngola – – – – – – (71)
\nAntigua\/
\nBarbuda
\n– – – – – – (74)
\nArgentina 96 10 407.6 82.1 84.7 4 92.8
\nArmenia 92 3 485.1 94.1 94.1 4 93.2
\nAustralia 98 12 534.3 101.7 100 16 99.2
\nAustria 99.5 4 523.7 100.1 98.7 10 99
\nAzerbaijan – – 409 82.3 84.9 4 84.9
\nBahamas – – – – – – (84)
\nBahrain 81 2 437.1 86.7 88.3 4 85.9
\nBangladesh 81 4 – – – – 81
\nBarbados 80 3 – – – – 80
\nBelarus – – – – – – (95)
\nBelgium 99 8 530.1 101.1 99.5 14 99.3
\nBelize – – 342.5 72.1 76.8 1 76.8
\nBenin – – – – – – (71)
\nBermuda 90 4 – – – – 90
\nBhutan – – – – – – (78)
\nBolivia 87 6 – – – – 87
\nBosnia 94 4 465.5 91.1 91.7 2 83.2
\nBotswana 71 2 367.7 76.0 79.9 4 76.9
\nBrazil 87 13 396.1 80.4 83.3 8 85.6
\nBrunei – – – – – – (89)
\nBulgaria 92.5 6 481.9 93.6 93.7 12 93.3
\nBurkina
\nFaso
\n– – – – – – (70)
\nBurundi – – – – – – (72)
\nCambodia – – – – – – (92)
\nCameroon 64 2 – – – – 64
\nCanada 100 9 538.8 102.4 100.6 16 100.4
\nCape Verde – – – – – – (76)
\nCentral
\nAfrican
\nRep. 64 5 – – – – 64
\nChad – – – – – – (66)
\nChile 91 10 437.9 86.8 88.4 8 89.8
\nChina 105.5 16 601.7 112.1 108.2 2 105.8
\nTibet 92 2 – – – – 92
\nColombia 83.5 7 391.8 79.7 82.8 8 83.1
\nComoros – – – – – – (77)
\nCongo
\n(Brazzaville) 73 8 – – – – 73
\nCongo
\n(Zaire) 68 13 – – – 68
\nCook
\nIslands 89 2 – – – – 89
\nCosta Rica 86 2 – – – – 86
\nCote
\nd’Ivoire
\n71 2 – – – – 71
\nCroatia 99 7 499.1 96.3 95.8 4 97.8
\nCuba 85 2 – – – – 85
\nCyprus – – 466.2 91.2 91.8 8 91.8
\nCzechRep. 98 7 528.2 100.8 99.3 14 98.9
\nDenmark 98 5 507.8 97.6 96.8 10 97.2
\nDjibouti – – – – – – (75)
\nDominica 67 5 – – – – 67
\nDominican
\nRepublic
\n82 6 – – – – 82
\nEast Timor – – – – – – (85)
\nEcuador 88 5 – – – – 88
\nEgypt 81 5 409.4 82.4 84.9 4 82.7
\nEl Salvador – – 352.4 73.6 78 2 78
\nEquatorial
\nGuinea
\n– – – – – – (69)
\nEritrea 75.5 4 – – – – 75.5
\nEstonia 99 7 539.3 102.5 100.6 6 99.7
\nEthiopia 68.5 9 – – – – 68.5
\nFiji 85 3 – – – – 85
\nFinland 97 5 557.8 105.4 102.9 10 100.9
\nFrance 98 10 518.7 99.3 98.1 10 98.1
\nGabon – – – – – – (69)
\nGambia 62 6 – – – – 62
\nGeorgia – – 424.1 84.7 86.7 2 86.7
\nGermany 99 17 520.4 99.6 98.3 10 98.8
\nGhana 70 10 277.5 62.0 69 4 69.7
\nGreece 92 10 487.4 94.5 94.4 10 93.2
\nGreenland – – – – – – 91
\nGrenada – – – – – – (74)
\nGuatemala 79 3 – – – – 79
\nGuinea 66.5 6 – – – – 66.5
\nGuinea- Bissau
\n– – – – – – (69)
\nGuyana – – – – – – (81)
\nHaiti – – – – – – (67)
\nHonduras 81 6 – – – – 81
\nHong Kong 108 16 559.7 105.6 103.1 14 105.7
\nHungary 96.5 8 525.2 100.3 98 16 98.1
\nIceland 101 4 514.7 98.7 97.6 10 98.6
\nIndia 82 21 419.4 84.0 86.1 1 82.2
\nIndonesia 87 8 409.7 82.5 85 12 85.8
\nIran 83.5 9 434.7 86.3 88 8 85.6
\nIraq 87 5 – – – – 87
\nIreland 92.5 18 526.6 100.5 99.1 10 94.9
\nIsrael 95 14 485.3 94.1 94.1 12 94.6
\nItaly 97 14 495.8 95.8 95.4 16 96.1
\nJamaica 71 11 – – – – 71
\nJapan 105 25 558.8 105.5 103 16 104.2
\nJordan 84 8 441.6 87.4 88.8 10 86.7
\nKazakhstan – – 410 82.5 85 2 85
\nKenya 74 12 370.2 76.3 80.2 1 74.5
\nKiribati – – – – – – (85)
\nKorea:
\nNorth – – – – – – (104.6)
\nKorea:
\nSouth 106 9 565.7 106.6 103.8 16 104.6
\nKuwait 86.5 9 398.9 80.8 83.7 4 85.6
\nKyrgyzstan – – 325.4 69.4 74.8 4 74.8
\nLaos 89 2 – – – – 89
\nLatvia – – 500.3 96.5 95.9 14 95.9
\nLebanon 82 4 428 85.3 87.2 4 84.6
\nLesotho – – 257.3 58.9 66.5 1 66.5
\nLiberia – – – – – – (68)
\nLibya 85 8 – – – – 85
\nLiechtenstein – – 536.2 102.0 100.3 8 100.3
\nLithuania 92 7 498.5 96.4 95.7 12 94.3
\nLuxembourg – – 492.9 95.3 95 8 95
\nMacao – – 533.6 101.6 99.9 6 99.9
\nMacedonia – – 455.7 89.6 90.5 4 90.5
\nMadagascar 82 2 – – – – 82
\nMalawi 60 3 204.9 50.8 60.2 1 60.1
\nMalaysia 88.5 8 500.7 96.5 96 6 91.7
\nMaldives – – – – – – (81)
\nMali 69.5 8 – – – – 69.5
\nMalta 97 2 480.7 93.4 93.5 2 95.3
\nMariana
\nIslands 81 2 – – – – 81
\nMarshall
\nIslands 84 3 – – – – 84
\nMauritania – – – – – – (74)
\nMauritius 89 5 395.5 80.3 83.3 1 88
\nMexico 88 8 431.2 85.8 87.6 8 87.8
\nMicronesia – – – – – – (84)
\nMoldova – – 468.1 91.5 92 4 92
\nMongolia 100 6 – – – – 100
\nMontenegro – – 417.7 83.7 85.9 4 85.9
\nMorocco 84 9 369.4 76.2 80.1 6 82.4
\nMozambique 64 2 327.2 69.7 75 2 69.5
\nMyanmar\/
\nBurma
\n– – – – – – (85)
\nNamibia 72 2 262.3 59.7 67.1 1 70.4
\nNepal 78 4 – – – – 78
\nNetherlands 100 10 540.7 102.7 100.8 12 100.4
\nNetherlands
\nAntilles 87 2 – – – – 87
\nNew
\nCaledonia
\n85 2 – – – – 85
\nNew
\nZealand
\n99 1 523.7 100.1 98.7 14 98.9
\nNicaragua – – – – – – (84)
\nNiger – – – – – – (70)
\nNigeria 71 13 302.6 65.9 72 4 71.2
\nNorway 100 2 507.3 97.5 96.8 14 97.2
\nOman 84.5 8 406.8 82.0 84.6 2 84.5
\nPakistan 84 8 – – – – 84
\nPalestine 86 4 393.3 79.9 83 4 84.5
\nPanama – – 369 75.2 80 2 80
\nPapua N.G. 82.5 4 428.6 85.4 87.2 1 83.4
\nParaguay 84 6 – – – – 84
\nPeru 85 9 372 76.6 80.4 2 84.2
\nPhilippines 90 7 363.5 75.3 79.4 4 86.1
\nPoland 95 13 516 98.9 97.8 8 96.1
\nPortugal 94.5 6 487 94.4 94.3 10 94.4
\nPuerto Rico 83.5 8 – – – – 83.5
\nQatar 83 6 345.9 – 77.2 6 80.1
\nRomania 91 6 460 90.2 91 12 91
\nRussia 96.5 6 506.5 97.4 96.7 16 96.6
\nRwanda 76 2 – – – – 76
\nSt Helena – – – – – – (86)
\nSt Kitts &
\nNevis – – – – – – (74)
\nSt Lucia 62 2 – – – – 62
\nSt Vincent 71 2 – – – – 71
\nSamoa
\n(Western) 88 5 – – – – 88
\nSao Tome
\n& Principe – – – – – – (67)
\nSaudi
\nArabia
\n79 8 376.3 77.3 80.9 4 79.6
\nSenegal 70.5 5 – – – – 70.5
\nSerbia &
\nMontenegro 88.5\/93 4 459.6 90.2 91 10 90.3\/92
\nSeychelles – – 405 81.7 84.4 1 84.4
\nSierra Leone 64 3 – – – – 64
\nSingapore 108.5 5 586.8 109.8 106.4 10 107.1
\nSlovakia 98 8 517.1 99.1 97.9 12 98
\nSlovenia 96 11 526 100.4 99 12 97.6
\nSolomon
\nIslands – – – – – – (83)
\nSomalia – – – – – – (72)
\nSouth
\nAfrica 72 16 291.4 64.2 70.7 6 71.6
\nSpain 97 11 503 96.9 96.2 14 96.6
\nSri Lanka 79 2 – – – – 79
\nSudan 77.5 19 – – – – 77.5
\nSuriname 89 4 – – – – 89
\nSwaziland – – 330.7 70.2 75.4 2 75.4
\nSweden 99 8 521.1 99.7 98.4 14 98.6
\nSwitzerland 101 6 531.6 101.3 99.7 10 100.2
\nSyria 80.5 7 427 85.1 87.1 2 82
\nTaiwan 105 19 565.3 106.5 103.8 10 104.6
\nTajikistan – – – – – – (80)
\nTanzania 72.5 9 349.8 73.2 77.7 1 73
\nZanzibar – – 293.7 64.5 70.9 1 70.9
\nThailand 88 8 460.7 90.3 91.1 12 89.9
\nTogo – – – – – – (70)
\nTonga 86 2 – – – – 86
\nTrinidad &
\nTobago – – 421.7 84.3 86.4 2 86.4
\nTunisia 84 4 417.7 83.7 85.9 12 85.4
\nTurkey 88.5 9 453.7 89.3 90.3 10 89.4
\nTurkmenistan – – – – – – (80)
\nUganda 72 9 275.8 61.7 68.8 1 71.7
\nUkraine 95 2 481.7 93.6 93.7 2 94.3
\nUnited Arab
\nEmirates 83 6 477.5 92.9 93.2 4 87.1
\nUnited
\nKingdom 100 7 523.2 100.0 98.7 14 99.1
\nEngland – – 524.3 102.2 98.8 8 98.8
\nScotland – – 502.3 96.8 96.2 6 96.2
\nUSA 98 10 510.6 98.1 97.2 16 97.5
\nUruguay 96 2 441.3 87.3 88.8 6 90.6
\nUzbekistan – – – – – – (80)
\nVanuatu – – – – – – (84)
\nVenezuela 84 6 374.9 77.1 80.8 1 83.5
\nVietnam 94 3 – – – – 94
\nYemen 83 6 247.8 57.4 65.4 1 80.5
\nZambia 75 7 259.6 59.2 66.8 1 74
\nZimbabwe 71.5 4 310.6 67.1 73 3 72.1<\/p>\n

The comparison of countries with large positive and negative
\nresiduals has disclosed that particular local circumstances are
\nconnected with nearly all large outliers and that they may explain a
\nsignificant part of the large deviations from the regression line. It
\nis important to note that the focusis on particular local factors and
\nthat their impact is restricted to limited groups of countries. They
\nare not universal factors which could be used to explain the
\nvariation in per capita income in all countries of the world.
\n(1) The significance of the economic system (market
\neconomy versus socialist command economy) seems to be
\nlimited to the group of countries with high national IQ (90 and
\nover). In the market economies (nearly always connected with a
\ndemocratic political system), the level of per capita income has
\nrisen much higher than expected on the basis of the regression
\nequation, and in the socialist economic systems (and former
\nsocialist systems) at the same level of national IQ, the level of per
\ncapita income tendsto be much lower than expected.
\n(2) The contrast between the Caribbean tourist islands with
\nlarge positive residuals and a group of Oceanian island states
\nwithout important tourist industries and with large negative
\nresiduals illustrates the significance of foreign investments and
\ntechnologies as well as of geographical factors. Because the
\nCaribbean islands are relatively close to potential tourists in the
\nNorth America and Europe, they have attracted extensive foreign
\ninvestments in tourism, whereas remote Oceanian island states
\nhave not been attractive places for extensive foreign investments
\nin tourist industries. This difference may explain why the
\nCaribbean tourist islands have been economically more successful
\nthan the Oceanian island states, although national IQ is for most
\nCaribbean island countries lower than for Oceanian island
\ncountries.
\n(3) The contrast between Asian and African countries with
\nsignificant oil industries and their neighboring countries without
\nsignificant oil and gas resources illustrates the potential
\nimportance of natural resources. Countries with oil or other
\nsignificant natural resources have attracted foreign investments
\nand technologies from countries of higher national IQs, which
\nhas raised the level of per capita income much higher (in some
\ncases many times higher) than expected on the basis of the
\nregression equation, whereas in the countries without attractive
\nnatural resources it has remained at the expected level or, in some
\ncases, it has been lower than expected on the basis of national IQ.
\nCountries like Bahrain, Brunei, Equatorial Guinea, Kuwait,
\nQatar, Saudi Arabia and the United Arab Emirates with extremely
\nlarge positive residuals are dominated by oil industries.
\n(4) The contrast between the countries ravaged by ethnic
\ncivil wars or other wars and with large negative residuals and the
\ncountries which have been able to maintain internal peace
\nillustrates the negative impact of violent strife on economic
\ndevelopment. Wars and civil wars have hampered economic
\ndevelopment and caused the emergence of large negative
\nresiduals in several cases. So this is one of the exceptional local
\nfactors that affects the level of per capita income independently
\nfrom national IQ.
\n(5) To some extent, geographical factors may hamper
\neconomic development independently from national IQ. This
\nconcerns especially isolated landlocked states. Laos, Moldova and
\nMongolia are such countries in the group of large negative
\noutliers. The actual level of per capita income is in all of them
\nmuch lower than expected on the basis of national IQ. It can be
\ninferred that not only the former socialist system but also their
\ngeographical isolation has hampered economic development in
\nthese countries. However, in some cases favorable geographical
\nlocation may have furthered economic development. This
\nconcerns especiallyLuxembourg and Switzerland, which have
\nbenefitted from their proximity to France and Germany.
\nIt is important to note that the impact of exceptional factors
\ndiscussed above is limited to particular groups of countries and
\nthat it is difficult to measure their impact by empirical evidence.
\nLarge positive and negative outliers indicate that national IQ is not
\nthe only factor affecting the variation in per capita income, but it
\nmay be the only systematic causal factor that is relevant across all
\ncultural and geographical boundaries. The level of per capita
\nincome tends to be higher in countries with high national IQ than
\nin countries with low national IQ. Depending on the sample of
\ncountries and of the type of correlation, national IQ explains from
\n35 to 62 per cent of the variation in PPP-GNI-08. Because a part
\nof the variation may be due to measurement errors and accidental
\nfactors, it is not necessary to pay attention to relatively small
\ndeviations from the regression line.
\nSome other indicators of socioeconomic development are
\nmoderately or strongly related to the level of per capita income,
\nbut because their causal relations may be reciprocal and because
\nthey tend to be as strongly related to national IQ as the indicators
\nof per capita income, their ability to explain the variation in per
\ncapita income is quite limited. For example, adult literacy rate (see
\nChapter 3) is moderately correlated with PPP-GNI-08 (0.482,
\nN=196), but when national IQ and Literacy-08 are used together
\nto explain variation in PPP-GNI-08, the multiple correlation
\n(0.608) is only slightly higher than the simple correlation
\nbetween national IQ and PPP-GNI-08 (0.592). In other words,
\nLiteracy-08 raises the explained part of variation in PPP-GNI-08
\nonly by two percentage points independently from national IQ. <\/p>\n

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.<\/p>\n

The relationship between national IQ and rates of
\nunemployment has not been examined hitherto and is considered
\nin this section. At the individual, within-country level, several
\nstudies have shown a robust association between low intelligence
\nand unemployment. Toppen (1971) reported a sample of the
\nunemployed in the United States had an average IQ of 81, more
\nthan a standard deviation (15 IQ points) below the U.S. mean IQ
\nof approximately 100. Lynn, Hampson and Magee (1984)
\nreported that a sample of the unemployed in Northern Ireland had
\nan average IQ of 92, again below the national mean. Herrnstein
\nand Murray (1994) reported that in a sample in the United States,
\n14 per cent of those with IQs below 74 had been unemployed for
\none month or longer during the preceding year, and the
\npercentages of the unemployed declined in successively higher IQ
\ngroupsto 4 per cent among those with IQs above 126. Thus, lowIQ
\nindividuals make up a disproportionate share of unemployed.
\nMroz and Savage (2006), using the National Longitudinal Survey
\nof Youth, found that lower IQ predicted higher probability of
\nunemployment within the last year, higher average weeks of
\nunemployment, and higher probability of job change, even after
\ncontrolling for years of education, ethnicity, parental education,
\nwhether the person’s childhood home received periodicals, and a
\nrich variety of additional covariates. Thus, both the rate of job
\ndestruction and the length of job search are higher for workers
\nwith lower IQ. <\/p>\n

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…<\/p>\n

The principal explanation for the association between low IQ
\nand high rates of unemployment among individuals within
\ncountries is that those with low IQs normally perform poorly at
\nschool and do not acquire educational credentials. Employers
\ntypically select employees on the basis of educational
\nqualifications and are reluctant to employ those without
\neducational qualifications. If those with low IQs do secure jobs,
\nthey typically perform poorly, since numerous studies have
\nshown that intelligence is positively related to the efficiency of
\nperformance. This has been reported in the United States
\n(Ghiselli, 1966; Hunter and Hunter, 1984; Schmidt and Hunter,
\n1998) and in Europe (Salgado, Anderson, Moscoso, et al.,
\n2003). When those with low IQs perform poorly in employment,
\nthey are typically dismissed. They acquire a poor work history,
\nand this makes employers reluctant to employ them. The principal
\nexplanation for the association between low IQ and high rates of
\nunemployment across countries is likely that the population of
\nlow IQ countries are not able to produce goods and services so
\nefficiently for sale international markets, as compared with the
\npopulations of high IQ countries.<\/p>\n

The analysis of economic conditions measured by some
\nindicators of per capita income, poverty, and income inequality
\nshows that national IQ explains nearly half or at least more than
\nany other available variable of the global variation in these
\nindicators. This relationship has been present at least since
\n1500. This suggests that human possibilities to equalize
\neconomic conditions seem to be quite limited.
\nIt has been difficult to equalize per capita income between
\ncountries whose national IQs differ significantly from each other.
\nIt would be much easier to equalize per capita income between
\ncountries whose national IQs are approximately at the same level.
\nHowever, some geographical or other local factors may be
\nenough to maintain economic differences even in these groups of
\ncountries with more or less equal IQs. It is remarkable that factors
\nthat are related to large deviations from the regression line seem
\nto be exceptional local factors. It was not possible to find any
\nuniversal environmental factor that could explain a significant part
\nof the global variation in per capita income independently from
\nnational IQ. <\/p>\n

National IQ explains more than one third of the global
\nvariation in per capita income, but more than 60 percent remains
\nunexplained. National IQ explains approximately half of the
\nglobal variation in poverty measures, but poverty is also related
\nto the level of per capita income and literacy. Because the
\nmeasures of poverty are strongly related to national IQ, it is
\nreasonable to expect that significant global differences in the
\nlevel of poverty will continue indefinitely. Human efforts can be
\nincreased to diminish global differences in the level of poverty,
\nbut the possibilities of reducing these disparities are limited. The
\ncontinual struggle for scarce resources maintains global
\ndifferences in poverty, and high IQ nations tend to be more
\nsuccessful in this struggle than low IQ nations.<\/p>\n

National IQ does not explain more than 22 percent of the
\nvariation in Gini index and Highest 20% variables, but it may be
\nmore than what any other measurable factor could explain. The
\ndifferences in income inequality seem to be significantly related to
\nsome regional and cultural factors and to the racial homogeneity of
\npopulations. Latin America is a region of exceptionally high level of
\neconomic inequality, which may be partly due to the racial
\nheterogeneity of Latin American populations. The same factor
\nappears also in other parts of the world. European and most African
\ncountries, in which income inequality tends to be much lower, are
\nracially relatively homogeneous. It can be anticipated that economic
\ninequalities within countries will continue indefinitely. The impacts
\nof national IQ and regional and racial factors on economic inequality
\nare unlikely to disappear, although it is certainly possible to reduce
\ninequality in particular countries by appropriate social and
\ninstitutionalreforms.
\nOur point is that evolved human diversity, which we have
\nmeasured by national IQ, is a permanent factor behind global
\neconomic inequalities. It provides the most powerful theoretical
\nexplanation for many kinds of global inequalities in human
\nconditions and explains their persistence. A more extensive analysis
\nof the impact of other environmental variables would certainly raise
\nthe explained part of variation to some extent, but we have focused
\non the explanatory power of national IQ…<\/p>\n

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

According to the Transparency International’s corruption
\nperceptions index, the extent of corruption varies greatly in the
\nworld. The problem is why corruption varies so much. We
\nhypothesize that the extent of corruption is negatively related to
\nthe level of national IQ because more intelligent nations may have
\nbetter capabilities than less intelligent nations to exclude
\ncorruption from the functions of their political institutions, or at
\nleast to diminish its extent…<\/p>\n

The level of democratization seems to
\nrise systematically with the level of national IQ. The results of
\nthis analysis lead to the conclusion that all countries do not have
\nequal chances to establish and maintain democratic systems.
\nBecause of the constraining impact of national IQ, the level of
\ndemocratization is and will most probably remain significantly
\nlower in countries with low national IQs than in countries with
\nhigh national IQs. It is a consequence of evolved human
\ndiversity. Vanhanen has analyzed extensively the impact of
\nnational IQ on the level and quality of democratization in his book
\nThe Limits of Democratization (Vanhanen, 2009).<\/p>\n

When national IQ crosses the level of 95, the level of corruption decreases steeply, although not in all countries…<\/p>\n

Economically highly developed countries with relatively high
\nnational IQs constitute the largest coherent group of positive
\noutliers (17 countries). Most of them are European and European
\noffshoot democracies, but the group includes also Singapore from
\nEast Asia…<\/p>\n

There are clear differences, both regional and structural,
\nbetween the countries of large positive and large negative outliers.
\nNearly all positive outliers are economically highly developed
\ndemocracies, mostly in Europe, or countries in which foreign
\ntechnologies, investments, and management have a dominant role.
\nOn the other hand, nearly all large negative outliers are socialist or
\nformer socialist countries or economically less developed Asian
\ncountries. One crucial difference between positive and negative
\noutliers concerns their ethnic structures. The populations of
\npositive outliers are ethnically relatively homogeneous, whereas
\nthe populations of many negative outliers are ethnically highly
\nheterogeneous. These structural differences seem to have affected
\nthe level of corruption independently from national IQ.<\/p>\n

The statistical investigations carried out in this chapter show
\nthat global differences in the levels of democratization, women’s
\nrepresentation in parliaments, gender inequality in human
\ndevelopment, and corruption can be traced, to some extent, to
\ndifferences in national IQs, although only slightly in the case of
\nwomen’s representation. The nature of political institutions is in
\nprinciple under human control, but historical and cultural legacies
\nmay support the survival of existing structures and make it
\ndifficult to change them. Our analysis on the impact of national IQ
\non political institutions is based on the assumption that people use
\ntheir intelligence in their attempts to improve the quality of
\npolitical institutions and that, consequently, more intelligent
\nnations are able to construct qualitatively better political
\ninstitutions than less intelligent nations. Therefore, differences in
\nnational IQs are assumed to explain a significant part of the
\nqualitative differences between political institutions and of the
\npersistence of those differences…<\/p>\n

There are a number of studies reporting that intelligence is
\npositively associated with good health among individuals. For
\ninstance, Anstey, Low and Sachdev (2009) have shown that the
\nintelligence is associated with higher levels of physical activity,
\ngreater likelihood of taking vitamins, and reduced likelihood of
\nsmoking, all of which promote good health.
\nSeveral studies have reported that low birth weight is
\nassociated with low IQ in childhood and adolescence, e.g. Bhutta,
\nCleves, Case, Cradock and Anand, 2002; Deary, Whalley and
\nStarr (2009, pp. 193-195).
\nInfant mortality (infant deaths in the first year of life) is
\nassociated with low IQ mothers. This was first shown by Savage
\n(1946) who reported that the mothers of infants who died in their
\nfirst year had below average intelligence. This was confirmed by
\nHerrnstein and Murray (1994, p. 218) who showed that the
\nmothers of infants who had died in their first year had an average
\nIQ of 94, compared with 100 of the mothers of infants who had
\nnot died in their first year. These results are understandable,
\nbecause mothers with low IQs would be less competent in taking
\ncare of the health of their infants. Mothers with higher IQs would
\nbe better at anticipating possible accidents and preventing them
\nhappening, judging whether illnesses are sufficiently serious to
\njustify seeing a physician, and giving medications that are
\nprescribed.
\nSeveral studies have found that intelligence is a determinant of
\nlife expectancy. This was shown first in Australia by O’Toole and
\nStankov (1992) in a study of 2,309 men who were conscripted
\ninto the military and intelligence tested at the age of 18, between
\n1965 and 1971. They were followed up in 1982, when they were
\naged between 22 and 40, and it was found that 523 had died.
\nThese had an IQ 4 points lower than those who remained alive.
\nThe commonest cause of death was accidents of various kinds
\n(389), of which motor vehicle accidents (217) were the most
\nfrequent. It seems probable that the explanation for this
\nassociation is that those with lower IQs make more
\nmisjudgements. Some of these misjudgements result in accidents
\nand some of these are fatal. Gottfredson (2004) has reviewed a
\nnumber of subsequent studies confirming the association of low
\nintelligence with high mortality, and this has also been found in
\nSweden (Hemmingsson, 2009).
\nAn extensive research program in Scotland examining the
\nrelation of IQ measured at the age of 11 to mortality (i.e. age of
\ndeath) has been summarized by Deary, Whalley and Starr (2009,
\npp. 50-52). They confirm that low intelligence predicts high
\nmortality and have found that low intelligence is associated with
\nseveral specific causes of death. Low intelligence is associated
\nwith smoking and death from lung cancer and other smokingrelated
\ncancers, namely mouth, pharynx, esophagus, larynx,
\npancreas and bladder cancers. Low intelligence is also associated
\nwith death from all cardiovascular diseases, coronary heart
\ndisease, stroke, and respiratory disease. They suggest four
\nexplanations for these associations. First, childhood IQ might be a
\nrecord of bodily insults including illness, poor nutrition, and
\ninjuries. Second, childhood IQ might be a marker for genetic
\nbodily system integrity. Third, people with higher IQs may be
\nbetter at avoiding risks and at preserving their health, for instance
\nby eating sensible foods, avoiding smoking, recognizing
\nsymptoms that might be injurious to health, consulting
\nphysicians, and complying with prescribed treatments. This
\ntheory implies that intelligence differences are causal to mortality.
\nFourth, people with higher IQs may tend to work in occupations
\nwhere there is less risk of death…<\/p>\n

We propose that there is a positive feedback loop across
\nnations between good health, IQ, and per capita income.
\nHealthy people work more efficiently than unhealthy workers,
\nso good health promotes high per capita income, good nutrition
\nand health care, and higher intelligence. In the positive feedback
\nloop, high national intelligence promotes high per capita income
\nand good health Nutrition is a basic factor affecting health because it is not
\npossible to live without sufficient nutrition. Many kinds of
\nclimatic, geographical, and other environmental factors affect the
\navailability of appropriate nutrition, but the sufficiency of
\nnutrition depends also on human skills and policies. Therefore we
\nhypothesize that nutrition correlates positively with national IQ. It
\nis interesting to investigate whether national IQ is the best
\nexplanatory factor or are there some other factors which explain as
\nmuch or more of the variation in indicators of nutrition
\nindependently from national IQ. If national IQ remains as the
\ndominant explanatory variable, it will lead to the conclusion that it
\nwould be extremely difficult to equalize nutritional conditions in
\nthe world.
\nIt can be assumed that people live longer in good health
\nconditions than in poor health conditions. Therefore life
\nexpectancy at birth is a good indicator of the general state of
\nhealth conditions in a country. The higher the average life
\nexpectancy of people, the better health conditions are in a country.
\nIf our basic hypothesis on the positive impact of intelligence on
\nhealth conditions is correct, life expectancy should be positively
\ncorrelated with national IQ. It is again interesting to see how much
\nsome other factors, for example per capita income, might be able
\nto explain of the variation in life expectancy independently from
\nnational IQ.
\nThere are several other perspectives from which differences
\nin national health conditions can be evaluated and measured.
\nInfant mortality rate provides one indicator. It can be assumed that
\na low infant mortality rate indicates good health conditions and a
\nhigh infant mortality rate poor health conditions. Further, because
\nintelligence is needed to lower infant mortality rate, it should be
\nnegatively correlated with national IQ.
\nThe prevalence and spreading of HIV depends crucially on
\nhuman choices. Because it is a dangerous disease, it is reasonable
\nto assume that more intelligent nations are better able to prevent
\nits spreading than less intelligent nations. Consequently, national
\nIQ should be negatively correlated with the prevalence of HIV,
\nalthough, of course, there are also other factors affecting the
\nspreading and avoidance of HIV. We are not able to take into
\naccount or even to know all important factors, but we explore to
\nwhat extent the prevalence of HIV is related to national IQ.
\nThere are also other diseases whose prevalence depends
\nmore or less on human choices and health policies and which,
\nconsequently, should be negatively related to national IQ.
\nTuberculosis is one of such diseases. It can be assumed that
\nmore intelligent nations have been better able to control
\ntuberculosis than less intelligent nations, although the extent of
\ntuberculosis may depend also on per capita income and on some
\nother relevant environmental variables.
\nOur purpose is to test our basic hypothesis on the
\nrelationship between national IQ and national health conditions
\nby using several separate measures of health conditions because
\nthe use of different indicators may produce more reliable results
\nthan reliance on only one or two variables…<\/p>\n

Life expectancy at birth can be regarded to be an
\nultimate measure of health conditions. The better health conditions
\nare in a country, the longer people live. According to our
\nhypothesis, the correlation between national IQ and Life-08 should
\nbe strongly positive. In fact, the correlation is 0.759 in the total
\ngroup of 197 countries and 0.821 in the group of countries with
\nmore than one million inhabitants. These are among the highest
\ncorrelations between national IQ and various measures of human
\nconditions…<\/p>\n

Prevalence of HIV (HIV-07) is slightly related to national IQ
\n(18%) and even less to the three environmental variables (8%)
\nindependently from national IQ. The geographical concentration of
\nHIV to sub-Saharan Africa and especially to the countries of
\nsouthern Africa explains partly the low
\ncorrelation between national IQ and HIV prevalence. The
\nconcentration of HIV in sub-Saharan Africa and in the Caribbean
\ncountries inhabited by black Africans seems to be principally due to
\nsome cultural and other exceptional local factors, although national
\nIQ explains a part of the global variation in HIV prevalence.
\nIncidence of tuberculosis (Tuber-08) is significantly related to
\nnational IQ (32%) but only slightly to the three environmental
\nvariables (3%) independently from national IQ. The unexplained
\npart of variation (65%) is probably due to various local factors but
\nalso to possible errors of measurement.<\/p>\n

It has been well established in a number of countries that the
\nmore intelligent people have been having fewer children than the
\nless intelligent. This negative association between intelligence
\nand fertility was observed in the nineteenth century by Francis
\nGalton in his Hereditary Genius (1869). He contended that in the
\nearly stages of civilization what he called “the more able and
\nenterprising men” were the most likely to have children, but in
\nolder civilizations, like that of Britain, various factors operated to
\nreduce the number of children of these and to increase the number
\nof children of the less able and less enterprising. He suggested
\nthat the most important of these factors was that able and
\nenterprising young men tended not to marry, or only to marry
\nlate in life, because marriage and children would impede their
\ncareers. The effect of this was that
\nthere is a steady check in an old civilization upon the
\nfertility of the abler classes: the improvident and un- ambitious are those who chiefly keep up the breed.
\nSo the race gradually deteriorates, becoming in each
\nsuccessive generation less fit for a high civilization
\n(Galton, 1869\/1962, p. 414)
\nGalton was remarkably perceptive in noting the negative
\nassociation between intelligence and fertility as early as 1869.
\nThis negative association has become known as dysgenic fertility
\nand has been extensively investigated in the United States.
\nAll the studies summarized in Table 7.1 show that dysgenic
\nfertility for intelligence has been present in the United States
\nduring the twentieth century. All the studies show that there has
\nbeen greater dysgenic fertility for intelligence in women than
\namong men. Probably the explanation for this is that children
\nimpose a greater cost on the careers and life style of intelligent
\nand well-educated women than on those of intelligent and well- educated men, and also that women have a shorter period of
\nchildbearing years. It is women who have to bear most of the
\nburden of childbearing and childrearing and who therefore have
\nstronger incentives to limit their number of children or to remain
\nchildless. At the other end of the intelligence spectrum, low IQ
\nwomen tend to have higher fertility because they are inefficient
\nusers of contraception and there are always plenty of men willing
\nto have sex with them. Low IQ men, on the other hand, tend not
\nto have such high fertility because many of them are unattractive
\nto females and lack the social and cognitive skills required to
\nsecure sexual partners.
\nA second factor accounting for the greater dysgenic fertility
\nof women is probably their shorter span of childbearing years.
\nMany intelligent women undergo prolonged education and devote
\nthemselves to their careersin their twenties and into their thirties,
\nintending to postpone childbearing during the years when less
\nintelligent women are having children. By the time childless,
\nhigh- IQ, career women are in their thirties, significant numbers
\nof them discover that they have waited too long to find suitable
\npartners with whom to have children, or that they have become
\ninfertile. Older intelligent men who delay marriage and children
\nuntil their late thirties or forties are less likely to become infertile
\nand can find young wives more easily than older women can find
\nyoung husbands. It has been shown by Meisenberg and Kaul
\n(2010, p. 177) that the lower fertility of intelligent women is not
\ndue to a lack of desire for children.
\nAll the studies show that there has been greater dysgenic
\nfertility for intelligence in American blacks than among whites.
\nDysgenic fertility for intelligence is particularly high among black
\nwomen. Probably the main reason for this is that intelligent and
\nwell educated black women find it hard to find suitable men with
\nwhom to have children. Many black men do not make attractive
\nhusbands because they do not do so well in employment as black
\nwomen, and a significant number of black men find white wives.
\nFor instance, in 1990 6.3 per cent of black men under the age of
\nthirty were married to a white women, but only 2.5 per cent of
\nblack women were married to a white man (Heaton and Albrecht,
\n1996). It seems probable that the continuing disadvantaged
\nposition of blacks in the United States in regard to educational
\nattainment and employment is to some significant extent due to
\nthe greater deterioration of their genotypic intelligence.
\nThe negative association between intelligence and fertility
\nthat has been present in the United States throughout the twentieth
\ncentury and into the twenty-first century implies that the
\ngenotypic intelligence must have declined (the genotypic
\nintelligence is the genetic component of intelligence). This
\ndecline has been compensated for by an increase of phenotypic
\n(measured) intelligence (Flynn, 2007). Meisenberg (2010) has
\ncalculated the magnitude of the decline of genotypic intelligence.
\nHe assumes a narrow heritabily of intelligence of 0.5 and on this
\nbasis calculates a decline of genotypic intelligence of 0.8 IQ
\npoints a generation and 2.9 IQ points a century. He calculated that
\nthe effect of this would be that the proportion of highly gifted
\npeople with IQs of 130 and above would decline by 11.5% in one
\ngeneration and 37.7% in a century. Meisenberg and Kaul (2010)
\nestimate that when the increase of the numbers of blacks and
\nHispanics as a proportion of the population is taken into account,
\ngenotypic intelligence in the United States will decline by
\napproximately 1.2 IQ points a generation…<\/p>\n

Just as the negative correlation between intelligence and
\nfertility within countries implies that genotypic intelligence is
\ndeclining, so the negative correlation between intelligence and fertility across countries implies that the genotypic intelligence of
\nthe whole world is declining. The rate of this decline has been
\ncalculated by Meisenberg (2009) who calculates that the
\ncorrelation between national IQs and TFR (Total Fertility Rate),
\naveraged for the years 2000-2005 is -0.83.<\/p>\n

There is a large amount of evidence showing that crime is
\nassociated with low intelligence. In a review of these studies,
\nWilson and Herrnstein (1985, p. 159) wrote that “For four
\ndecades, large bodies of evidence have consistently shown about
\na ten IQ point gap between the average offender and the average
\nnon-offender in Great Britain and the United States”. This
\nconclusion has subsequently been confirmed by Ellis and Walsh
\n(2003) in a summary of more than a hundred studies from all
\nover the world. The influence of socio-economic status and
\nfamily environment on crime has been controlled in a Danish
\nstudy of pairs of brothers that has shown that the brother with a
\ncriminal record scored an average of 15 IQ points lower than the
\nlaw-abiding sibling (Kandel, Mednick and Kirkegaard-Sorensen,
\n1988).<\/p>\n

Several explanations have been proposed to explain the low
\naverage intelligence of criminals. Wilson and Herrnstein (1985,
\npp. 167-171) suggest that low intelligence is associated with
\n“present- orientation”, i.e. a propensity to seek immediate
\ngratification without regard to the possibility of future
\npunishment; that those with low IQs typically have a weak moral
\nsense and poor moral reasoning ability; typically do poorly at
\nschool, so they become alienated and seek status by joining
\ncriminal gangs; and are typically in low paid jobs or are
\nunemployed, so they have less to lose by crime and obtaining a
\ncriminal record.
\nThe association of low intelligence with crime among
\nindividuals suggests that the same association should be present
\namong populations. The first study showing that this is so was
\npublished by Maller (1933a, 1933b) in an analysis of average IQs
\nand crime rates in 310 districts of New York City. He found that
\nthe correlation between the average IQ of ten year olds and the
\nrates of juvenile delinquency was -0.57. The relation between
\nintelligence and crime among populations has also been
\ninvestigated by Bartels, Ryan, Urban and Glass (2010) in a study
\nof the IQs of American states and crime rates. They report that
\ncrime rates are higher in states with lower IQ and that these
\nnegative correlations are higher for violent crime (-0.58) than for
\nnon-violent crime, including motor- vehicle theft and other theft (- 0.29).<\/p>\n

It has been shown by Kanazawa (2010) that liberalism is
\nassociated with intelligence. He reported that those who
\nidentified themselves as “very liberal” had a childhood IQ of
\n106.4, while those who identified themselves as “very
\nconservative” had a childhood IQ of 94.8…<\/p>\n

Row 4 gives a positive correlation of 0.59 between national
\nIQ and the speed of life as the speed of service at post offices,
\nwalking speed and the accuracy of clocks. The positive
\ncorrelation suggests that the populations of IQ countries are more
\nenergetic and alert.<\/p>\n

Row 5 shows a negative correlation of -0.22 between
\nnational IQ and war measured as participation, intensity and
\ndestructive effects of war in the years 1960-2000, including civil
\nwars. The negative correlation shows that high IQ countries have
\nless engagement in war. The correlation is low but statistically
\nsignificant. Possibly the explanation for this negative correlation is
\nthat high IQ countries are more likely to be democratic, and
\ndemocracies are less likely to engage in war.<\/p>\n

Row 6 shows a correlation of 0.70 between national IQ and
\nlow time preference in 10 Asian countries. Time preference was
\nmeasured by responses to the question “Would you prefer $3400
\nthis month or $3800 next month?” Choosing the second option
\nindicates low time preference or in psychological terms, present- orientation, delay discounting and a capacity to delay gratification.
\nIt has been shown in a meta-analysis of 24 studies that a low time
\npreference (a capacity to delay gratification) is correlated with IQ at
\n0.23 (Shamosh and Gray, 2008)<\/p>\n

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

Further evidence for a negative correlation between
\nintelligence and religious belief is the decline in religious belief
\nduring adolescence and into adulthood as cognitive ability
\nincreases. This has been found in the United States for the age
\nrange of 12-18 year olds by Kuhlen and Arnold (1944) who
\nreported that among 12 year olds 94 per cent endorsed the
\nstatement “I believe there is a God”, while among 18 year olds
\nthis had fallen to 78 per cent. Similarly, in England Francis
\n(1989) has found a decline in religious belief over the age range
\n5-16 years. Religious belief was measured by a scale consisting of
\nquestions like “God means a lot for me” and “I think that people
\nwho pray are stupid”, etc. The results were that among 5-6 year
\nolds 87.9 per cent of boys and 96.0 per cent of girls held religious
\nbelief, but at the age of 15-16, these percentages had fallen to
\n55.7 of boys and 70.4 of girls.
\nFinally, in several economically developed countries there
\nhas been a decline of religious belief during the course of the last
\n150 or so years, while at the same time the intelligence of the
\npopulation has increased. For instance, in England self-reported
\nweekly attendance at church services reported in census returns
\ndeclined from 40 per cent of the population in 1850, to 35 per
\ncent in 1900, to 20 per cent in 1950, and to 10 per cent in 1990
\n(Giddens, 1997, p. 460). Church of England Easter week
\ncommunicants declined from 9 per cent of the population in 1900
\nto 5 per cent in 1970 (Argyle and Beit-Hallahmi, 1975). The
\nattendance of children at Sunday schools declined from 30 per
\ncent of the child population in 1900 to 13 per cent in 1960
\n(Goldman, 1965). In Gallup Polls 72 per cent of the population
\nstated in 1950 that they believed in God (Argyle, 1958), but by
\n2004 this had fallen to 58.5 per cent (Zuckerman, 2006).
\nThere has also been some decline of religious belief during
\nthe course of the last century in the United States. Hoge (1974)
\nhas reviewed several surveys that have found a decline of
\nreligious belief in college students. For instance, students at Bryn
\nMawr were asked whether they believed in a God who answered
\nprayers. Positive responses were given by 42 per cent of students
\nin 1894, 31 per cent in 1933, and 19 per cent in 1968. Students
\nenrolling at the University of Michigan were invited to provide a
\n“religious preference”. In 1896, 86 per cent of students did so; in
\n1930 this had dropped to 70 per cent, and in 1968 it had dropped
\nto 44 per cent. At Harvard, Radcliffe, Williams and Los Angles
\nCity College the percentages of students who believed in God,
\nprayed daily or fairly frequently, and attended church about once
\na week all declined from 1946 to 1966. Heath (1969) has also
\nreported a decline in belief in God among college students from
\n79 per cent in 1948 to 58 per cent in 1968. Among the general
\npopulation, Gallup Polls have found that 95.5 per cent stated that
\nthey believed in God in 1948 (Argyle, 1958), but by 2004 this
\nhad fallen to 89.5 per cent (Zuckerman, 2006)…<\/p>\n

In our previous work we have proposed the theory that
\npopulation differences in IQ evolved in response to the cognitive
\ndemandsin cold winters (Lynn, 2006). To summarize this theory,
\nthe human species (Homo sapiens ) evolved around 150,000
\nyears ago in equatorial East Africa (Relethford, 1988). Around
\n100,000 years ago groups of Homo sapiens began to migrate
\nfrom equatorial Africa and settled in North Africa and in
\nsouthwest Asia. By 60-40,000 years ago they were established
\nthroughout Asia, the Indonesian archipelago and Australia. By
\nabout 35,000 years ago they had settled in Europe, and
\nsubsequently they colonized the Americas and the Pacific islands
\n(Foley, 1987; Mellars and Stringer, 1999; Cavalli-Sforza, 2000).
\nWhen these peoples settled in the temperate and colder
\nlatitudes of North Africa, Asia and Europe, they encountered the
\nproblem of survival during the winter and spring. This was a
\nproblem because the first humans that evolved in equatorial East
\nAfrica subsisted largely on plant foods, of which numerous
\nspecies were available throughout the year (Lee, 1968; Tooby
\nand de Vore, 1989). In temperate and cold environments plant
\nfoods are not available for a number of months in the winter and
\nspring. Thus, “plant foods are often available only during short
\nseasons” (Gamble, 1993, p. 117) and compared to warmer
\nenvironments there would have been fewer edible plant species,
\nand a concomitant requirement for increased reliance on
\nanimals… and the obvious problem of keeping warm, including
\nthe likely necessity of controlling and even making fire. In
\neffect, these northern temperate environments “pushed the
\nenvelope” of Homo’s adaptation (Wynn, 2002, p. 400).
\nThese peoplesthat migrated into North Africa, Asia, Europe
\nand the Americas needed to hunt large animals for food, and to
\nmake clothes, shelters and fires to keep warm. These problems
\nwould have exerted selection pressure for enhanced intelligence.
\nThe colder the winters, the stronger this selection pressure would
\nhave been and the higher the intelligence that evolved. These
\npeoples evolved larger brain size to accommodate greater
\nintelligence. A review of the association between brain size and
\nintelligence in humans has shown that they are correlated at 0.40
\n(Vernon, Wickett, Bazana and Stelmack, 2000). There is
\ntherefore an association across the races for the severity of the
\nwinter temperatures to which they were exposed, brain size and
\nIQs.<\/p>\n

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

These results showing larger brain sizes in populations that
\nevolved in colder environments have been confirmed by Ash and
\nGallup (2007) in an analysis of a sample of 109 fossilized
\nhominid skulls. They found that approximately 22% of the
\nvariance in cranial capacity (brain size) could be accounted for by
\nvariation in equatorial distance such that cranial capacity was
\nlarger with greater distance from the equator. They also found that
\ncranial capacities were highly correlated with paleo-climatic
\nchanges in temperature, as indexed by oxygen isotope data and
\nsea-surface temperature, and that 52% of the variance in the
\ncranial capacity could be accounted by the temperature variation at
\n100 ka intervals. Further support for these results has been
\nreported by Bailey and Geary (2009). They examined 175 skulls
\ndated between 1.9 million years ago and 10,000 years ago and
\nreported a correlation of -0.41 between their size (cubic capacity)
\nand the temperature of their locations, showing greater brain size
\nin lower temperature environments, and a correlation of -0.61
\nbetween their size (cubic capacity) and latitude, showing larger
\nbrain size in latitudes more distant from the equator. This study
\nshows that larger brain size (conferring greater intelligence)
\nevolved before 10,000 years ago in the peoples inhabiting colder
\nenvironments.
\nA more recent study providing additional confirmation for
\nthese results has been published by Pearce and Dunbar (2011).
\nThey measured the brain size of 55 skulls from twelve
\npopulationsfrom around the world and found that brain size was
\ncorrelated with distance from the equator at 0.82.
\nBrain size is the determinant of intelligence at a magnitude of
\napproximately 0.40. The research on this issue has been reviewed
\nby Vernon, Wickett, Bazana and Stelmack (2000), who report 54
\nstudies that used an external measure of head size. All of these
\nreported a positive relationship and the overall correlation was
\n0.18. They also report 11 studies of normal populations that
\nmeasured brain size by CT (computerized axial tomography) and
\nMRI (magnetic resonance imaging), which give a more accurate
\nmeasure of brain size, and for which there was a correlation of
\n0.40. Vernon et al. conclude that the most reasonable interpretation
\nof the correlation is that brain size is a determinant of intelligence.
\nLarger brains have more neurons and this gives them greater
\nprocessing capacity. A further study published subsequent to this
\nreview found a correlation for 40 subjects between brain size
\nmeasured by MRI and intelligence of 0.44 (Thompson, Cannon,
\nNarr, et al., 2001). It has been shown that the association between
\nbrain volume and intelligence is of genetic origin (Posthuma, De
\nCeus, Baar\u00e9, et al., 2002).
\nIt has now become widely accepted that this evidence for
\nrace differences in intelligence and brain size indicates that these
\nrace differences have a genetic basis. As Hunt (2011, p. 434) has
\nrecently written “the 100% environmental hypothesis cannot be
\nmaintained”<\/p>\n","protected":false},"excerpt":{"rendered":"

Richard Lynn and Tatu Vanhanen write: Ten years ago we began our research program for the investigation of how far differencesin intelligence can explain the differences in economic development between nations in our book IQ and the Wealth of Nations(2002). … Continue reading →<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[29582],"tags":[],"class_list":["post-70348","post","type-post","status-publish","format-standard","hentry","category-iq"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/lukeford.net\/blog\/index.php?rest_route=\/wp\/v2\/posts\/70348","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lukeford.net\/blog\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lukeford.net\/blog\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lukeford.net\/blog\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/lukeford.net\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=70348"}],"version-history":[{"count":20,"href":"https:\/\/lukeford.net\/blog\/index.php?rest_route=\/wp\/v2\/posts\/70348\/revisions"}],"predecessor-version":[{"id":70745,"href":"https:\/\/lukeford.net\/blog\/index.php?rest_route=\/wp\/v2\/posts\/70348\/revisions\/70745"}],"wp:attachment":[{"href":"https:\/\/lukeford.net\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=70348"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lukeford.net\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=70348"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lukeford.net\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=70348"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}