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Iq and socioeconomic development across regions of the UK

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Abstract

Cross-regional correlations between average IQ and socioeconomic development have been documented in many different countries. This paper presents new IQ estimates for the twelve regions of the UK. These are weakly correlated ( r =0.24) with the regional IQs assembled by Lynn (1979). Assuming the two sets of estimates are accurate and comparable, this finding suggests that the relative IQs of different UK regions have changed since the 1950s, most likely due to differentials in the magnitude of the Flynn effect, the selectivity of external migration, the selectivity of internal migration or the strength of the relationship between IQ and fertility. The paper provides evidence for the validity of the regional IQs by showing that IQ estimates for UK nations (England, Scotland, Wales and Northern Ireland) derived from the same data are strongly correlated with national PISA scores ( r =0.99). It finds that regional IQ is positively related to income, longevity and technological accomplishment; and is negatively related to poverty, deprivation and unemployment. A general factor of socioeconomic development is correlated with regional IQ at r =0.72.

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... A reanalysis of (Carl, 2015) revealed that the inclusion of London had a strong effect on the S loading of crime and poverty variables. S factor scores from a dataset without London and redundant variables was strongly related to IQ scores, r = .87. ...
... Introduction Carl (2015) analyzed socioeconomic inequality across 12 regions of the UK. In my reading of his paper, I thought of several analyses that Carl had not done. ...
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... A considerable amount of scholarship has found that there are regional differences in intelligence within nations and that regions with lower average scores on IQ tests have lower levels of socioeconomic development (Lynn, Fuerst, & Kirkegaard, 2018). This has been established in regions of the UK (e.g., Carl, 2016b;Lynn, 1979), France (Lynn, 1980), Italy (e.g. Lynn, 2010), Spain (Lynn, 2012), Portugal (Almeida, Lemos, & Lynn, 2011), Germany (Roivainen, 2012), Finland (Dutton & Lynn, 2014), China (Lynn & Cheng, 2013), Japan (Kura, 2013), the USA (e.g. ...
... Pesta, McDaniel, & Betsch, 2010), Turkey (Lynn, Sakar, & Cheng, 2015), Brazil (Fuerst & Kirkegaard, 2016), Mexico (Fuerst & Kirkegaard, 2016), Egypt , Sudan (Bakhiet & Lynn, 2014), and Russia (Grigoriev, Lapteva, & Lynn, 2016). Carl (2016aCarl ( , 2016b has scrutinized data from local governments areas in Britain and has reported that the average IQ of those who live within a 'local authority' area correlates with a general socioeconomic factor at r = 0.58. It has been shown that IQ is consistently correlated with SES (e.g., Jensen, 1998) and that a country's average IQ predicts socioeconomic differences between nations, although additional factors also manifestly play their part. ...
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We administered the SPM to a sample of 1614 pupils aged between 9 and 18 in 2018 in the Dhofar region of Oman. Our results were compared to a previous administration of the SPM to 5139 pupils aged 9 to 18 in the capital region of Muscat which took place in 2001. We found that the IQ of Muscat in 2001 is substantially higher than the IQ of Dhofar is 17 years later. As there are only a small number of studies on the mean IQ in Oman, we did not apply a Flynn-effect correction. It is found that these regional IQ differences are paralleled by regional differences on many correlates of IQ such as life expectancy and years spent in schooling. We suggest three key factors as likely explaining the difference in IQ: poorer conditions in Dhofar, the association between intelligence and urban migration, and the effects of the Dhofar Rebellion. Other possible explanations are also examined.
... A reanalysis of (Carl, 2015) revealed that the inclusion of London had a strong effect on the S loading of crime and poverty variables. S factor scores from a dataset without London and redundant variables was strongly related to IQ scores, r = .87. ...
... Introduction Carl (2015) analyzed socioeconomic inequality across 12 regions of the UK. In my reading of his paper, I thought of several analyses that Carl had not done. ...
Article
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A reanalysis of (Carl, 2015) revealed that the inclusion of London had a strong effect on the S loading of crime and poverty variables. S factor scores from a dataset without London and redundant variables was strongly related to IQ scores, r = .87. The Jensen coefficient for this relationship was .86.
... Although the first modern intelligence tests were designed for the measurement of individual differences in intellectual competence and capacity (Binet & Simon, 1907, 1908, the study of intergroup differences in intelligence is almost as old as the formal study of intelligence itself. Geographical differences in intelligence have also been described for decades; these have been detailed within countries such as the UK (Carl, 2016;Lynn, 1979), Italy (Lynn, 2010;Templer, 2012), and Egypt, (Dutton et al., 2018) and have also been observed in relation to ecogeographical features such as latitude (Kura, 2013;León & Burga León, 2014). Differences in child performance on intelligence tests have also been reported by urban-rural location, both in developed countries (Alexopoulos, 1997;Wahlquist, 1927) and in developing ones (Castro & Rolleston, 2015, 2018. ...
... Previous studies demonstrating a greater prevalence of micronutrient deficiency and environmental contamination within rural areas may support this interpretation Liu, Ai, et al., 2012a;Liu & Lewis, 2014). Following Carl's (2016) hypothesis that environmental quality can lead to differences in magnitude of the Flynn Effect by region, it is conceivable that an increase in nutritional quality in rural areas coupled by a reduction in harmful contaminants could reduce or even minimize the urban-rural gap over time. This is supported by Lynn's suggestion that pre-natal and early post-natal nutrition is a prime causal factor behind the Flynn Effect (Lynn, 2009). ...
... Studies from Western countries consistently find lower IQ in the countryside than in the cities. It has been found that intelligence predicts migration and that the more intelligent will tend to migrate to these cities in order to improve their lives and practice more skilled occupations (see Carl, 2015). This process of urbanization tends to increase as the country industrializes. ...
... Between 1990 and 2015 women in the workforce (outside of agriculture) increased from 35% to 41% (UNDP United Nations Development Programme, 2015). In general, we would also expect that people that lived in the capital would hold less conservative political attitudes than those living elsewhere (see Carl, 2015) meaning that this change should be particularly pronounced in Khartoum. It may be the case, and for the same reasons, that in other developing countries the magnitude of the Flynn Effect has been stronger among females and becomes less pronounced as the cohorts become younger. ...
Article
Abstract Three recent studies have summarized evidence for Negative Flynn Effects (Dutton et al., 2016; Flynn & Shayer, 2018; Woodley of Menie, Peñaherrera-Aguirre, Fernandes, and Figueredo, 2017), that is secular deceases in IQ scores. To develop this important line of research, as many instances of this effect must be reported and understood as possible. Dutton, Bakhiet, Ziada, Essa, and Blahmar (2017) reported, in Intelligence, a Negative Flynn Effect in Khartoum, where education was voluntary for some cohorts. This study reports an increase in IQ, as assessed by the Colored Progressive Matrices, in Khartoum between 2004 and 2016. The increase in IQ amounted to 8 to 13 points, based on assessments of children between the ages of 6 and 9. Thus, the original negative Flynn Effect reflected schooling not being compulsory for some of the earlier sample. Keywords Colored progressive matrices; Sudan; Intelligence; Flynn effect
... In addition to studies that compared intelligence between countries, the differences in intelligence were studied inside of a nation [8][9][10][11][12]. In this context of comparing different populations' intelligences, the research on this theme and its implications for the social indicators in Brazil's states and the Federal District (FD) of Brasilia are important since the country has an extensive territorial dimension; these states contain areas similar to the areas of several nations. ...
... Lynn et al. [7], using spatial analysis, did not find statistically significant correlations between students' intelligence, estimated by the PISA, and the per capita income in the 26 states and the FD of Brazil. However, positive and statistically significant correlations between per capita income and population intelligence were found by other authors when spatial analysis was not used; in Turkey [8], in the United Kingdom [11] and also in Brazil [7]. ...
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The aim of this study was to investigate the effects of intelligence and income on nutrition in Brazil, by means of large-scale secondary data. The cognitive abilities of students were used as a measure of intelligence. In order to evaluate the nutritional quality of the population, the state hunger and undernutrition index (SHUI) was created. The intelligence explained 34% of the SHUI variation in the country. The development of the population's intelligence influences the decrease in the rates of hunger and undernutrition.
... Putting aside technical difficulties, it is unclear how representative any of these samples are of the South Sudanese. On the one hand, they might be regarded as relatively elite samples, insomuch as migration is predicted by intelligence (see Carl, 2015) and all of them have migrated out of South Sudan. However, it must be emphasised that the largest tribe in South Sudan is the Dinka. ...
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... Although there is variation in gains and the methodology employed to document those score increases, generational IQ test performance changes have been reported for 31 countries located on six continents: Africa (Kenya, South Africa, Sudan), Asia (China, Israel, Japan, Saudi Arabia, South Korea), Europe (Austria, Belgium, Bulgaria, Denmark, Estonia, Finland, France, Germany, Ireland, Netherlands, Norway, Spain, Sweden, Switzerland, Turkey, United Kingdom), North America (Canada, Dominica, United States), South America (Argentina, Brazil), and Oceania (Australia, New Zealand) [13]. In addition to robust findings across countries, IQ score gains have been reported across intelligence test batteries, socioeconomic backgrounds [14], and races [15]. Past research has indicated that nutrition, increased environmental complexity, and genetics are causations of the FE [16][17][18]. ...
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... When such socioeconomic variables are factor analyzed, though, a general socioeconomic factor (S factor) emerges such that, most of the time, desirable outcomes load positively and undesirable outcomes load negatively on it. Previous research has found S factors at the national level (Kirkegaard, 2014b), the state/region/department level (Carl, 2015;Kirkegaard, 2015bKirkegaard, , 2015d and the city district level (Kirkegaard, 2015a). Analyses of national and state level data showed that Human Development Index (HDI) scores correlated strongly with S factor scores at typically >.9. ...
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We conducted novel analyses regarding the association between continental racial ancestry, cognitive ability and socioeconomic outcomes across 6 datasets: states of Mexico, states of the United States, states of Brazil, departments of Colombia, sovereign nations and all units together. We find that European ancestry is consistently and usually strongly positively correlated with cognitive ability and socioeconomic outcomes (mean r for cognitive ability = .708; for socioeconomic well-being = .643) (Sections 3-8). In most cases, including another ancestry component, in addition to European ancestry, did not increase predictive power (Section 9). At the national level, the association between European ancestry and outcomes was robust to controls for natural-environmental factors (Section 10). This was not always the case at the regional level (Section 18). It was found that genetic distance did not have predictive power independent of European ancestry (Section 10). Automatic modeling using best subset selection and lasso regression agreed in most cases that European ancestry was a non-redundant predictor (Section 11). Results were robust across 4 different ways of weighting the analyses (Section 12). It was found that the effect of European ancestry on socioeconomic outcomes was mostly mediated by cognitive ability (Section 13). We failed to find evidence of international colorism or culturalism (i.e., neither skin reflectance nor self-reported race/ethnicity showed incremental predictive ability once genomic ancestry had been taken into account) (Section 14). The association between European ancestry and cognitive outcomes was robust across a number of alternative measures of cognitive ability (Section 15). It was found that the general socioeconomic factor was not structurally different in the American sample as compared to the worldwide sample, thus justifying the use of that measure. Using Jensen's method of correlated vectors, it was found that the association between European ancestry and socioeconomic outcomes was stronger on more S factor loaded outcomes, r = .75 (Section 16). There was some evidence that tourist expenditure helped explain the relatively high socioeconomic performance of Caribbean states (Section 17).
... Desirable (/undesirable) socioeconomic outcomes at the aggregate-level have often been found to correlate positively (/negatively) with estimates of cognitive ability for the same regions, e.g. (Carl, 2015;Kura, 2013;Lynn, 1979Lynn, , 1980Lynn & Cheng, 2013). Because the S factor is an aggregate of such outcomes, it is not surprising that S scores have been found to have strong positive correlations with cognitive ability as well, e.g. ...
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Two datasets of Japanese socioeconomic data for Japanese prefectures (N=47) were obtained and merged. After quality control, there were 44 variables for use in a factor analysis. Indicator sampling reliability analysis revealed poor reliability (54% of the correlations were |r| > .50). Inspection of the factor loadings revealed no clear S factor with many indicators loading in opposite than expected directions. A cognitive ability measure was constructed from three scholastic ability measures (all loadings > .90). On first analysis, cognitive ability was not strongly related to 'S' factor scores, r = -.19 [CI95: -.45 to .19; N=47]. Jensen's method did not support the interpretation that the relationship is between latent 'S' and cognitive ability (r = -.15; N=44). Cognitive ability was nevertheless related to some socioeconomic indicators in expected ways. A reviewer suggested controlling for population size or population density. When this was done, a relatively clear S factor emerged. Using the best control method (log population density), indicator sampling reliability was high (93% |r|>.50). The scores were strongly related to cognitive ability r = .67 [CI95: .48 to .80]. Jensen's method supported the interpretation that cognitive ability was related to the S factor (r = .78) and not just to the non-general factor variance.
... The positive associations between regional differences in IQs within countries and per capita incomes have been reported in a number of studies of other countries including for 13 regions of the British Isles (r = 0.73) (Lynn, 1979), 90 regions of France (r = 0.61) (Lynn, 1980), 12 regions of Italy (r = 0.94) (Lynn, 2010), 19 regions of Italy (r = 0.98) (Templer, 2012), 18 regions of Spain (r = 0.42) (Lynn, 2012), 16 regions of Germany (r = 0.79) (Roivainen, 2012) and 12 regions of the United Kingdom (general factor of economic development: r = 0.72; weekly earnings: r = 0.42) (Carl, 2016). ...
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This study reports the associations between the intelligence of children aged 8–10 years across thirty-one provinces and municipalities of the People's Republic of China and their economic and social correlates. It was found that regional IQs were significantly correlated at the p < 0.001 significant level with the percentage of Han in the population (r = 0.75), GDP per capita (r = 0.73), and years of education (r = 0.76). Results of a multiple regression analysis showed that regional IQs were the only significant predictor of regional differences in the GDP per capita accounting for 56% of the variance.
... Further studies reporting associations of intelligence with per capita income and related economic measures at the level of sub-national administrative units have been conducted in other countries (Table 1). They include 13 regions of the British Isles (Lynn, 1979), 90 regions of France (Lynn, 1980;Montmollin, 1958), 12 regions of Italy (Lynn, 2010), 19 regions of Italy (Piffer & Lynn, 2014;Templer, 2012), 18 regions of Spain (Lynn, 2012), 16 regions of Germany (Roivainen, 2012), 47 prefectures of Japan (Kura, 2013), 12 regions of Turkey (Lynn, Sakar & Cheng, 2015), 31 regions of the People's Republic of China (Lynn & Cheng, 2013), and 12 regions of the United Kingdom (Carl, 2016). Many of these studies also reported significant correlations between regional differences in intelligence and a variety of social phenomena including health, fertility and crime. ...
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In a number of countries, earlier studies have reported significant associations between regional differences in intelligence within countries and economic and social phenomena. Using scores on the Program of International Student Assessment (PISA) tests as indicator of intelligence, we find statistically significant correlations for the 27 states of Brazil between intelligence and nine indicators of socioeconomic development. Spatial analysis indicates that relationships are present both at the level of differences between adjacent states and over long-distance clines. Most of the relationships observed after initial analysis persisted after controlling for spatial autocorrelation. Among the socioeconomic variables, those that describe the standard of living of the less affluent sections of the population tend to correlate most with PISA scores.
... 6 For instance, a case might have high crime rates, high use of social benefits as well as high income and high educational attainment despite the loadings for these being negative and positive respectively. Such patterns are often seen for cases that consist mostly of one large city (Carl, 2015;Kirkegaard, 2015d). I previously called this phenomenon mixedness because the indicators of these cases give a decidedly mixed picture of the case, but it seems more suitable to use the term structural outlier (Kirkegaard, 2015b). ...
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Some new methods for factor analyzing socioeconomic data are presented, discussed and illustrated with analyses of new and old datasets. A general socioeconomic factor (S) was found in a dataset of 47 French-speaking Swiss provinces from 1888. It was strongly related (r’s .64 to .70) to cognitive ability as measured by an army examination. Fertility had a strong negative loading (r -.44 to -.67). Results were similar when using rank-transformed data. The S factor of international rankings data was found to have a split-half factor reliability of .93, that of the general factor of personality extracted from 25 OCEAN items .55, and that of the general cognitive ability factor .68 based on 16 items from the International Cognitive Ability Resource.
... Further studies reporting associations of intelligence with per capita income and related economic measures at the level of sub-national administrative units have been conducted in other countries (Table 1). They include 13 regions of the British Isles (Lynn, 1979), 90 regions of France (Lynn, 1980;Montmollin, 1958), 12 regions of Italy (Lynn, 2010), 19 regions of Italy (Piffer & Lynn, 2014;Templer, 2012), 18 regions of Spain (Lynn, 2012), 16 regions of Germany (Roivainen, 2012), 47 prefectures of Japan (Kura, 2013), 12 regions of Turkey (Lynn, Sakar & Cheng, 2015), 31 regions of the People's Republic of China (Lynn & Cheng, 2013), and 12 regions of the United Kingdom (Carl, 2016). Many of these studies also reported significant correlations between regional differences in intelligence and a variety of social phenomena including health, fertility and crime. ...
Article
Full-text available
In a number of countries, earlier studies have reported significant associations between regional differences in intelligence within countries and economic and social phenomena. Using scores on the Program of International Student Assessment (PISA) tests as indicator of intelligence, we find statistically significant correlations for the 27 states of Brazil between intelligence and nine indicators of socioeconomic development. Spatial analysis indicates that relationships are present both at the level of differences between adjacent states and over long-distance clines. Most of the relationships observed after initial analysis persisted after controlling for spatial autocorrelation. Among the socioeconomic variables, those that describe the standard of living of the less affluent sections of the population tend to correlate most with PISA scores.
... Many studies have found that intelligence predicts migration, at least in peace time (see Carl, 2015). The more intelligent have a longer time preference, are more inclined to plan for the future, are better able to judge how situations will unfold, and are better able to accrue the resources necessary in order to migrate, as evidenced by socioeconomic status being a mediating factor in the association (Jokela, 2014). ...
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The results of two administrations of the SPM to two samples of Damascus school children aged 13 to 18 are compared. It is shown that average SPM scores did not change statistically significantly between 2004 and 2013/14. In light of Flynn Effects in other developing countries, it is suggested that the brain drain caused by the ongoing civil war in Syria – and around Damascus specifically – would substantially explain this finding. In addition, it is shown that in both samples intelligence declines from late adolescence, a phenomenon which has been observed in other Arab countries.
... A set of additional factors used in mathematical models, the accounting of which could more accurately describe the structure of changes in the intelligence, is also important. To create a more complete and systematic view of current research on the Flynn effect, we analyzed a number of papers [7,21,30,31,[37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52] on this topic in accordance with the above attributes and presented them in an overview in Table A1 in Appendix A. ...
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In the present paper, we investigate how the general intelligence quotient (IQ) and its subtests changed for students from Russian University from 1991 to 2013. This study of the effect of such factors as gender, department, and year on the IQ response is carried out using the ANOVA model. Given the unevenness of the initial sample by years and departments, and consequently, heterogeneity of variances when divided by the original natural categories, we decided to aggregate the values of explanatory variables to build an adequate model. The paper proposes and investigates an algorithm for joint discretization and grouping, which uses the procedure of partial screening of solutions. It is an intermediate option between the greedy algorithm and exhaustive search. As a goodness function (an optimality criterion), we investigate 26 intermediate options between the AIC and BIC criteria. The BIC turned out to be the most informative and the most acceptable criterion for interpretation, which penalizes the complexity of the model, due to some decrease in accuracy. The resulting partition of the explanatory variables values into categories is used to interpret the modeling results and to arrive at the final conclusions of the data analysis. As a result, it is revealed that the observed features of the IQ dynamics are caused by changes in the education system and the socio-economic status of the family that occurred in Russia during the period of restructuring the society and intensive development of information technologies.
... Mirroring international results, this was found for 12 of 15 countries in which correlations between latitude and IQ were noted by the original authors: Argentina (Fuerst & Kirkegaard, 2016a,b); Brazil ; Chile (Fuerst & Kirkegaard, 2016b); Colombia (Fuerst & Kirkegaard, 2016b); Italy (Lynn, 2010a); Japan (Kura, 2013); Mexico (Fuerst & Kirkegaard, 2016b); Peru (León & Avilés, 2016), Russia (Lynn, Cheng, & Grigoriev, 2017); Spain (Lynn, 2012b); Turkey ; and the USA (various). A positive association, however, was not found for Germany (Roivainen, 2012), India (Lynn & Yadav, 2015) or the UK (Carl, 2016a). Associations were not reported for China ; Denmark (Teasdale et al., 1988); Finland (Dutton & Lynn, 2014); France (Lynn, 1980); Portugal (Almeida et al., 2011); Switzerland (Kirkegaard, 2016c); or Vietnam (Holsinger, 2007). ...
Article
Differences in intelligence have previously been found to be related to a wide range of inter-individual and international social outcomes. There is evidence indicating that intelligence differences are also related to different regional outcomes within nations. A quantitative and narrative review is provided for twenty-two countries (number of regions in parentheses): Argentina (24 to 437), Brazil (27 to 31), British Isles (12 to 392), to 79), Spain (15 to 48), Switzerland (47), Turkey (12), the USA (30 to 3100), and Vietnam (61). Between regions, intelligence is significantly associated with a wide range of economic, social, and demographic phenomena, including income (r unweighted = .56), educational attainment (r unweighted = .59), health (r unweighted = .49), general socioeconomic status (r unweighted = .55), and negatively with fertility (r unweighted = −.51) and crime (r unweighted = −.20). Proposed causal models for these differences are noted. It is concluded that regional differences in intelligence within nations warrant further focus; methodological concerns that need to be addressed in future research are detailed.
... This has been found in different regions of the UK (e.g. Lynn, 1979;Carl, 2016b), France (Lynn, 1980), Italy (e.g. Lynn, 2010), Spain (Lynn, 2012), Portugal (Almeida et al., 2011), Germany (Roivainen, 2012), Finland (Dutton & Lynn, 2014), China (Lynn & Cheng, 2013), Japan (Kura, 2013), the USA (e.g. ...
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Two administrations of the Coloured Progressive Matrices in Egypt were compared. The first was administered to a large, representative national sample between 2011 and 2013. The second was administered to primary school pupils in villages in Menoufia in northern Egypt in 2017. Adjusting for the Flynn Effect, the IQ of the rural northern Egyptians was shown to be statistically significantly higher than the national average. It is demonstrated that this is consistent with regional socioeconomic differences in Egypt, which strongly imply that northern Egypt has a higher average IQ than southern Egypt.
... Kura reported mean IQ scores for the different regions of Japan and showed a large difference between the highest-scoring region and the lowest-scoring region [37]. Similarly, regional differences in IQ, often correlating in the expected direction with measures such as wealth and educational level, have been reported between, for example, the different regions of the UK [38,39], Spain [40], Germany [41], Turkey [42], and between northern and southern Egypt [43], though they do not test Spearman's hypothesis. ...
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Many groups differ in their mean intelligence score. Spearman’s hypothesis states that the differences are a function of cognitive complexity. There tend to be large differences on subtests of high cognitive complexity and small differences on subtests of low cognitive complexity. Spearman’s hypothesis has been supported by a large number of studies. Can Spearman’s hypothesis be generalized to regions of a country, where these regions differ in mean intelligence? We utilized data from 86 different cognitive tests from all 47 Japanese prefectures and correlated the g loadings of 86 subtests with standardized differences on the same subtests. Spearman’s hypothesis was clearly supported: the biggest differences between the regions were on the tests that were of the greatest complexity, meaning that Spearman’s hypothesis may be generalizable from groups to regions. In Japan, g loadings offer a better explanation of group differences in intelligence than cultural differences. Future research should explore whether Spearman’s hypothesis is also supported for differences between regions of other countries.
... Kura reported mean IQ scores for the different regions of Japan and showed a large difference between the highest-scoring region and the lowest-scoring region [37]. Similarly, regional differences in IQ, often correlating in the expected direction with measures such as wealth and educational level, have been reported between, for example, the different regions of the UK [38,39], Spain [40], Germany [41], Turkey [42], and between northern and southern Egypt [43], though they do not test Spearman's hypothesis. ...
Article
Full-text available
Many groups differ in their mean intelligence score. Spearman’s hypothesis states that the differences are a function of cognitive complexity. There tend to be large differences on subtests of high cognitive complexity and small differences on subtests of low cognitive complexity. Spearman’s hypothesis has been supported by a large number of studies. Can Spearman’s hypothesis be generalized to regions of a country, where these regions differ in mean intelligence? We utilized data from 86 different cognitive tests from all 47 Japanese prefectures and correlated the g loadings of 86 subtests with standardized differences on the same subtests. Spearman’s hypothesis was clearly supported: the biggest differences between the regions were on the tests that were of the greatest complexity, meaning that Spearman’s hypothesis may be generalizable from groups to regions. In Japan, g loadings offer a better explanation of group differences in intelligence than cultural differences. Future research should explore whether Spearman’s hypothesis is also supported for differences between regions of other countries.
... In this paper I was able to give further studies showing that the IQ in the Republic of Ireland is significantly lower than that in Britain and I attributed this to selective migration of the more intelligent from Ireland to Britain, the United States and elsewhere. Some forty years later, these regional IQ differences were confirmed for the United Kingdom (excluding the Irish Republic) by Noah Carl (2016) [25]. The next year I published a paper giving IQs for 79 regions of France showing that the average IQ is highest in Paris and that the regional IQs are highly correlated with per capita incomes, infant mortality and intellectual achievements (Lynn, 1980) [26]. ...
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I first encountered the question of race and intelligence sixty-eight years ago[...]
... This has been demonstrated in the regions of the UK (e.g. Carl, 2015;Lynn, 1979), France (Lynn, 1980), Italy (e.g. Lynn, 2010), Spain (Lynn, 2012), Portugal (Almeida et al., 2011), Germany (Roivainen, 2012), Finland (Dutton & Lynn, 2014), China (Lynn & Cheng, 2013), Japan (Kura, 2013), the USA (e.g. ...
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The purpose of the article is to develop the concept of economic and social development of Russian regions that corresponds to requirements of global crisis management. With the help of methods of correlation and regression analysis, the authors determine the most important factors of economic and social development of modern Russian regions. The objects of the research include regions of Russia of various sizes and various places in ranking of socioeconomic development—for the purpose of representativeness—Moscow and Leningrad Oblasts (leading regions), Kirov and Volgograd Oblasts (medium regions), and Kostroma and Pskov Oblasts (underdeveloped regions). The results of the analysis contradicted the provisions of existing concept of economic and social development of the region. Thus, instead of direct correlation of subsidizing and socioeconomic regional development, we can see reverse and insufficiently strong connection; instead of reverse connection between competition in regional markets and socioeconomic regional development, we see strong direct correlation. The influence of post-industrialization and development of foreign economic relations is insignificant. At the same time, the offered hypothesis on strong and direct influence of other factors on socioeconomic development of a region—investments, interaction of state and business within public–private partnership, level of informatization, and innovative activity—was confirmed. This shows that the use of existing concept could be a reason for high aptitude of Russian regions to economic crises. That is why as an alternative to existing concept of economic and social development of a region, this work offers a new proprietary concept, adapted to modern economic conditions and requirements of global crisis management.
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The relationship between PISA 2012 maths test scores and relative poverty was tested in a sample of 35 Italian and Spanish regions, together with a larger sample that included Australian, Belgian, and Canadian regions. The correlation between mean scores in mathematics, adjusted for students' socioeconomic and cultural backgrounds, and poverty rates is ‐−0.84 for the Italian and Spanish sample, and −0.68 for the complete sample. In the regressions, the effect of relative poverty on mean scores in mathematics is highly significant (p < 0.01), robust to different specifications, and independent from students' backgrounds and regional development levels. It is proposed that disparities in average scores in mathematics across regions depend on the shares of low-performing students which, in turn, depend on the degree of relative poverty within regions. The implications for the thesis according to which, in Italy and Spain, regional disparities in educational achievements reflect genetic differences in the IQ of populations are discussed.
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The north–south difference in Italy in PISA 2006 scores in reading comprehension, mathematical and science abilities of 15-year-olds has been attributed by Lynn (2010a) to a difference of approximately 10 IQ points in intelligence and by critics to differences in educational resources. New evidence for differences between north and south Italy in the PISA 2012 Creative Problem Solving test as a measure of fluid intelligence shows a 9.2 IQ point between the north–west and the south and confirms Lynn's theory. New data are presented for genetic differences between the populations of north and south Italy.
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This paper summarizes the results of 244 correlates of national IQs that have been published from 2002 through 2012 and include educational attainment, cognitive output, educational input, per capita income, economic growth, other economic variables, crime, political institutions, health, fertility, sociological variables, and geographic and climatic variables.
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Can the average intelligence quotient (IQ) of populations be considered the root cause of international development inequalities? Psychologists and some economic studies have proposed the existence of a link between intelligence quotient and economic development. The paper tests this hypothesis, using different measures of economic development for the year 1500. Consistent with Jared Diamond’s (1997) hypothesis, the paper shows how the differences in the timing of agriculture transition and the histories of States, not population IQ differences, predict international development differences before the colonial era. The average IQ of populations appears to be endogenous, related to the diverse stages of nations’modernization, rather than being an exogenous cause of economic development.
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Regional differences in IQ and per capita incomes are presented for five regions of Portugal: North, North Central, Lisbon-Central, Lisbon-Suburb, and South. Regional IQs were calculated from a representative sample of 4548 Portuguese school students from 5th to 12th grades. The average IQ and average incomes are highest in Central Lisbon. The results show a positive association between IQs and average regional incomes, as it has been observed in other countries.
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Human capital" is a key requirement for the establishment and maintenance of effective institutions. It is the ultimate requirement for innovation, efficient use of resources, and economic growth. This contribution describes two measures of cognitive human capital: the average IQ of the population, and the performance of school children on international scholastic assessment tests in mathematics, science, and reading. These two measures are shown to be closely correlated at the country level, and distinct from traditional measures of education. A measure of human capital is described that is derived from IQ and school achievement. Data based on measured IQ and/or school achievement are given for 168 countries and territories, and estimates based on neighboring countries with similar population, culture and economy are provided for 28 additional countries. 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
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Socioeconomic disparity between North and South Italy has been recently explained by Lynn (2010) as the result of a lower intelligence quotient (IQ) of the Southern population. The present article discusses the procedure followed by Lynn, supplementing his data with new information on school assessments and per head regional income. Genetic North–South differences are then discussed on the basis of the most recent literature on the subject. The results do not confirm the suggested IQ-economy causal link.
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Well-being is a construct spanning multiple disciplines including psychology, economics, health, and public policy. In many ways, well-being is a nexus of inter-correlated variables, much like the g nexus. Here, we created an index of well-being for the geographical and political subdivisions of the United States (i.e., states). The measure resulted from hierarchical principal components analyses of state-level data on various hypothesized sub-domains of well-being, including general mental ability, education, economics, religiosity, health, and crime. A single, general component of well-being emerged, explaining between 52 and 85% of the variance in the sub-domains. General mental ability loaded substantially on global state well-being (.83). The relationship between global well-being and other important state-level outcomes was examined next. We conclude by offering parallels between the g nexus and the well-being nexus.
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Working with data from the PISA study (OECD, 2007), Lynn (2010) has argued that individuals from South Italy average an IQ approximately 10 points lower than individuals from North Italy, and has gone on to put forward a series of conclusions on the relationship between average IQ, latitude, average stature, income, etc. The present paper criticizes these conclusions and the robustness of the data from which Lynn (2010) derived the IQ scores. In particular, on the basis of recent Italian studies and our databank, we observe that : 1) school measures should be used for deriving IQ indices only in cases where contextual variables are not crucial: there is evidence that partialling out the role of contextual variables may lead to reduction or even elimination of PISA differences; in particular, schooling effects are shown through different sets of data obtained for younger grades; 2) in the case of South Italy, the PISA data may have exaggerated the differences, since data obtained with tasks similar to the PISA tasks (MT-advanced) show smaller differences; 3) national official data, obtained by INVALSI (2009a) on large numbers of primary school children, support these conclusions, suggesting that schooling may have a critical role; 4) purer measures of IQ obtained during the standardisation of Raven's Progressive Coloured Matrices also show no significant differences in IQ between children from South and North Italy.
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Although a negative relationship between fertility and education has been described consistently in most countries of the world, less is known about the relationship between intelligence and reproductive outcomes. Also the paths through which intelligence influences reproductive outcomes are uncertain. The present study uses the NLSY79 to analyze the relationship of intelligence measured in 1980 with the number of children reported in 2004, when the respondents were between 39 and 47 years old. Intelligence is negatively related to the number of children, with partial correlations (age controlled) of −.156, −.069, −.235 and −.028 for White females, White males, Black females and Black males, respectively. This effect is related mainly to the g-factor. It is mediated in part by education and income, and to a lesser extent by the more “liberal” gender attitudes of more intelligent people. In the absence of migration and with constant environment, genetic selection would reduce the average IQ of the US population by about .8 points per generation.
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Kanazawa (2008), Templer (2008), and Templer and Arikawa (2006) claimed to have found empirical support for evolutionary theories of race differences in intelligence by correlating estimates of national IQ with indicators of reproductive strategies, temperature, and geographic distance from Africa. In this paper we criticize these studies on methodological, climatic, and historical grounds. We show that these studies assume that the Flynn Effect is either nonexistent or invariant with respect to different regions of the world, that there have been no migrations and climatic changes over the course of evolution, and that there have been no trends over the last century in indicators of reproductive strategies (e.g., declines in fertility and infant mortality). In addition, we show that national IQs are strongly confounded with the current developmental status of countries. National IQs correlate with all the variables that have been suggested to have caused the Flynn Effect in the developed world.
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In this rejoinder, we respond to comments by Lynn, Rushton, and Templer on our previous paper in which we criticized the use of national IQs in studies of evolutionary theories of race differences in intelligence. We reiterate that because of the Flynn Effect and psychometric issues, national IQs cannot be taken to reflect populations’ levels of g as fixed since the last ice age. We argue that the socio-cultural achievements of peoples of Mesopotamia and Egypt in 3000 B.C. stand in stark contrast to the current low level of national IQ of peoples of Iraq and Egypt and that these ancient achievements appear to contradict evolutionary accounts of differences in national IQ. We argue that race differences in brain size, even if these were entirely of genetic origin, leave unexplained 91–95% of the black-white IQ gap. We highlight additional problems with hypotheses raised by Rushton and Templer. National IQs cannot be viewed solely in evolutionary terms but should be considered in light of global differences in socio-economic development, the causes of which are unknown.
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Regional differences in IQ are presented for 12 regions of Italy showing that IQs are highest in the north and lowest in the south. Regional IQs obtained in 2006 are highly correlated with average incomes at r = 0.937, and with stature, infant mortality, literacy and education. The lower IQ in southern Italy may be attributable to genetic admixture with populations from the Near East and North Africa.
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Traditional economic theories stress the relevance of political, institutional, geographic, and historical factors for economic growth. In contrast, human-capital theories suggest that peoples' competences, mediated by technological progress, are the deciding factor in a nation's wealth. Using three large-scale assessments, we calculated cognitive-competence sums for the mean and for upper- and lower-level groups for 90 countries and compared the influence of each group's intellectual ability on gross domestic product. In our cross-national analyses, we applied different statistical methods (path analyses, bootstrapping) and measures developed by different research groups to various country samples and historical periods. Our results underscore the decisive relevance of cognitive ability--particularly of an intellectual class with high cognitive ability and accomplishments in science, technology, engineering, and math--for national wealth. Furthermore, this group's cognitive ability predicts the quality of economic and political institutions, which further determines the economic affluence of the nation. Cognitive resources enable the evolution of capitalism and the rise of wealth.
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The ‘Flynn effect' is a surprising finding, identified by James R. Flynn, that IQ test scores have significantly increased from one generation to the next over the past century. Flynn now brings us an exciting new book which aims to make sense of this rise in IQ scores and considers what this tells us about our intelligence, our minds and society. Are We Getting Smarter? features fascinating new material on a variety of topics including the effects of intelligence in the developing world; the impact of rising IQ scores on the death penalty, cognitive ability in old age, and the language abilities of youth culture; as well as controversial topics of race and gender. He ends with the message that assessing IQ goes astray if society is ignored. As IQ scores continue to rise into the 21st century, particularly in the developing world, the ‘Flynn effect' marches on.
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Regional differences in IQ are reported for Finland showing that average IQs are highest in the south, containing the capital city of Helsinki. It is proposed that the selective migration of those with higher IQs to Helsinki has been the major factor responsible for the higher average IQ in the south. Regional IQs are positively correlated with the percentage of the population with tertiary education, mean income, and average male and female life expectancy; and negatively with the percentage of the population with average income less than 60% of the national median, the percentage of unemployment, and the rate of infant mortality.
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IQs are presented for fifteen regions of Spain showing a north-south gradient with IQs highest in the north and lowest in the south. The regional differences in IQ are significantly correlated with educational attainment, per capita income, literacy, employment and life expectancy, and are associated with the percentages of Near Eastern and North African genes in the population.
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This study reports the differences in intelligence across thirty-one regions of the People's Republic of China. It was found that regional IQs were significantly associated with the percentage of Han in the population (r = .59), GDP per capita (r = .42), the percentage of those with higher education (r = 38, p<.05), and non-significantly with years of education (r = .32). The results of the multiple regression showed that both the percentage of Han in the region and the GDP per capita were significant predictors of regional IQs, accounting for 39% of the total variance.
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Regional differences in cognitive ability are presented for 33 states and union territories of India. Ability was positively correlated with GDP per capita, literacy and life expectancy and negatively correlated with infant and child mortality, fertility and the percentage of Muslims. Ability was higher in the south than in the north and in states with a coast line than with those that were landlocked.
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Two dysgenic models of declining general intelligence have been proposed. The first posits that since the Industrial Revolution those with low g have had a reproductive advantage over those with high g. The second posits that relaxed purifying selection against deleterious mutations in modern populations has led to g declining due to mutation accumulation. Here, a meta-analytic estimate of the decline due to selection is computed across nine US and UK studies, revealing a loss of .39 points per decade (combined N = 202,924). By combining findings from a high-precision study of the effects of paternal age on offspring g with a study of paternal age and offspring de novo mutation numbers, it is proposed that, 70 de novo mutations per familial generation should reduce offspring g by 2.94 points, or .84 points per decade. Combining the selection and mutation accumulation losses yields a potential overall dysgenic loss of 1.23 points per decade, with upper and lower bound values ranging from 1.92 to .53 points per decade. This estimate is close to those from studies employing the secular slowing of simple reaction time as a potential indicator of declining g, consistent with predictions that mutation accumulation may play a role in these findings.
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Summary Immigration, immigration policies and education of immigrants alter competence levels. This study analysed their effects using PISA, TIMSS and PIRLS data (1995 to 2012, N=93 nations) for natives' and immigrants' competences, competence gaps and their population proportions. The mean gap is equivalent to 4.71 IQ points. There are large differences across countries in these gaps ranging from around +12 to -10 IQ points. Migrants' proportions grow roughly 4% per decade. The largest immigrant-based 'brain gains' are observed for Arabian oil-based economies, and the largest 'brain losses' for Central Europe. Regarding causes of native-immigrant gaps, language problems do not seem to explain them. However, English-speaking countries show an advantage. Acculturation within one generation and intermarriage usually reduce native-immigrant gaps (≅1 IQ point). National educational quality reduces gaps, especially school enrolment at a young age, the use of tests and school autonomy. A one standard deviation increase in school quality represents a closing of around 1 IQ point in the native-immigrant gap. A new Greenwich IQ estimation based on UK natives' cognitive ability mean is recommended. An analysis of the first adult OECD study PIAAC revealed that larger proportions of immigrants among adults reduce average competence levels and positive Flynn effects. The effects on economic development and suggestions for immigration and educational policy are discussed.
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Both generalized trust and intelligence are correlated with economic development. However, recent research has shown that trust and intelligence are themselves correlated, both across countries and among individuals. Theory suggests that causality runs from intelligence to trust at the individual level, which raises the possibility that the association between trust and development is explained by intelligence. Indeed, intelligence may cause both trust and development. Alternatively, development may lead to higher intelligence, which in turn gives rise to greater trust. Note that intelligence may cause trust not only because individuals with higher intelligence tend to report greater trust, but also because such individuals tend to be more trustworthy. This study analyzes data on trust, intelligence and economic development for 15 Spanish regions, 20 Italian regions, 50 US states, and 107 countries. In all four domains, there is a statistically significant positive relationship between trust and intelligence (r = .74, r = .74, r = .72 and r = .50, respectively). Moreover, partial correlations suggest that intelligence accounts for some or all of the association between trust and development in at least two out of the four domains.
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Socioeconomic status and other socio-demographic factors have been associated with selective residential mobility across rural and urban areas, but the role of psychological characteristics in selective migration has been studied less. The current study used 16-year longitudinal data from the U.S. National Longitudinal Survey of Youth 1979 (NLSY79) to examine whether cognitive ability assessed at age 15–23 predicted subsequent urban/rural migration between ages 15 and 39 (n = 11,481). Higher cognitive ability was associated with selective rural-to-urban migration (12 percentile points higher ability among those moving from rural areas to central cities compared to those staying in rural areas) but also with higher probability of moving away from central cities to suburban and rural areas (4 percentile points higher ability among those moving from central cities to suburban areas compared to those staying in central cities). The mobility patterns associated with cognitive ability were largely but not completely mediated by adult educational attainment and income. The findings suggest that selective migration contributes to differential flow of cognitive ability levels across urban and rural areas in the United States.
Article
Despite the fact that the recently evolved Microcephalin and the related Abnormal Spindle-like Microcaphaly Associated (ASPM) alleles do not appear to be associated with IQ at the individual differences level, the frequencies of Microcephalin have been found to correlate strongly with IQ at the cross-country level. In this study, the association between these two alleles and intelligence is examined using a sample of 59 populations. A bivariate correlation between Microcephalin and population average IQ of r = .790 (p ≤ .01) was found, and a multiple regression analysis in which the Human Development Index, Disability Adjusted Life Years (DALY) lost due to Infectious diseases, DALY Nutritional deficiencies, and Würm glaciation temperature means were included revealed that Microcephalin remained a good predictor of IQ. Path analysis, with both direct and indirect paths from Microcephalin to intelligence, showed good model fit. These multivariate analyses revealed strong and robust associations between DALYs and Microcephalin, indicating that the former partially mediates the association between the latter and IQ. A second smaller correlational analysis involving ten country-level estimates of the frequencies of these two alleles collected from the 1000 genomes database replicated this pattern of results. To account for the findings of this study, we review evidence that these alleles are expressed in the immune system. Microcephalin is strongly associated with DNA repair, which indicates a special role for this allele in the intrinsic anti-viral immune response. Enhanced immune functioning may have advantaged both hunter–gatherer and agrarian societies coping with the heightened disease burden that resulted from population growth and exposure to zoonotic diseases, making it more likely that such growth and concomitant increases in intelligence could occur.
Article
This study estimates the effect of dysgenic trends in Taiwan by exploring the relationships among intelligence, education and fertility. Based on a representative adult sample, education and intelligence were negatively correlated with the number of children born. These correlations were stronger for females. The decline of genotypic intelligence was estimated as 0.82 to 1.33 IQ points per generation for the Taiwanese population.
Article
Flynn has been credited with having discovered the increase in IQs that has been reported in a number of countries during most of the twentieth century and that has come to be known as “the Flynn effect”. This paper documents and discusses a number of reports of increases in IQs that were published from 1936 onwards before the phenomenon was rediscovered by and . These early reports showed that the Flynn effect is fully present in pre-school children, does not increase during the school age years, and is greater for non-verbal abilities than for verbal abilities.
Article
I argue in this comment that Hunt's analysis of the intelligence of nations is smarter than it is wise. It is based on too narrow a conception of intelligence. It also conflates correlation and causation with regard to the relation between IQ and socially defined success, both individual and familial. I suggest that a better approach might be to compare nations as well for their creativity and wisdom. © The Author(s) 2012.
Article
The present study was intended to provide perspective, albeit less than unequivocal, on the research of Lynn (2010) who reported higher IQs in the northern than southern Italian regions. He attributes this to northern Italians having a greater genetic similarity to middle Europeans and southern Italians to Mediterranean people. Higher regional IQ was associated with biological variables more characteristic of middle European than Mediterranean populations (cephalic index, eye color, hair color, multiple sclerosis rates, schizophrenia rates). It was maintained, however, that very confident and definitive inferences regarding genetic regional differences in IQ are not warranted. Social conceptualized variables also correlated significantly with IQ so as to suggest the importance of nutrition and economic developmental status more generally.
Article
Counter-intuitively, sociobiological and evolutionary theories predict a negative relationship between g and reproduction when applied to modern humans. Although existing research has documented this dysgenic trend, the association between g and socio-economic factors presents a confound that has not systematically been addressed in prior research. Based on a sample of 325,252 individuals drawn from the nationally representative Project Talent database, we examined the unique effects of g and socio-economic wealth, assessed in adolescence, on marital and reproductive behavior over the next 11 years. Results show that both g and socio-economic wealth have unique, independent negative effects on marital and reproductive behavior such that individuals of higher intelligence and higher wealth delay marriage and reproductive longer than those of lower intelligence and wealth. The effect of g was slightly stronger than that of wealth, though for both variables much of their influence was mediated by educational attainment. Consistent with sociobiological theory, these dysgenic effects were stronger among females than males.
Article
Lynn and Vanhanen (2012) have convincingly established that national IQs correlate positively with GDP, education, and many other social and economic factors. The direction of causality remains debatable. The present study re-examines data from military psychological assessments of the German federal army that show strong IQ gains of 0.5 IQ point per annum for East German conscripts in the 1990s, after the reunification of the country. An analysis of IQ, GDP, and educational gains in 16 German federal states between 1990 and 1998 shows that IQ gains had a .89 correlation with GDP gains and a .78 correlation with educational gains. The short time frame excludes significant effects of biological or genetic factors on IQ gains. These observations suggest a causal direction from GDP and education to IQ.
Article
Data from the General Social Survey (GSS) collected in the years 1990–1996 are examined for the relationship between fertility and intelligence as measured by vocabulary. The results show that the relation between fertility and intelligence has been consistently negative for successive birth cohorts from to 1900 to 1979, indicating the presence of dysgenic fertility for all of the 20th century studied thus far. The most recent cohort for which fertility can be regarded as complete is that born in the years 1940–1949. In this cohort, the decline of genotypic intelligence arising from the negative association between intelligence and fertility is estimated at .90 IQ points per generation. The decline of genotypic intelligence of Whites is estimated at 0.75 IQ points a generation.
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a b s t r a c t Human intelligence (IQ) correlates with a number of important life outcomes ranging from mortality and morbidity to social rank and status. Noticeably absent from the literature, however, has been a unifying framework from which to examine why IQ should correspond to so many seemingly disparate outcomes. Rushton's Differential-K theory represents a theoretical perspective capable of accounting for the role of IQ in understanding human flourishing. We tested predictions suggested by Differential-K theory by examining the association between IQ at the aggregate level and two life-history variables: parental investment and fertility rates. Our results provide tentative support for some of the predictions of Differ-ential-K theory.
Article
Cognitive ability theory claims that peoples’ competences are decisive for economic wealth. For a large number of countries Lynn and Vanhanen (2002) have published data on mean intelligence levels and compared them to wealth and productivity indicators. The correlation between intelligence and wealth was supported by studies done by different authors using different countries and controls. Based on their pioneering research two research questions were developed: does intelligence lead to wealth or does wealth lead to intelligence or are other determinants involved? If a nation’s intelligence increases wealth, how does intelligence achieve this? To answer them we need longitudinal studies and theoretical attempts, investigating cognitive ability effects at the levels of individuals, institutions and societies and examining factors which lie between intelligence and growth. Two studies, using a cross-lagged panel design or latent variables and measuring economic liberty, shares of intellectual classes and indicators of scientific-technological accomplishment, show that cognitive ability leads to higher wealth and that for this process the achievement of high ability groups is important, stimulating growth through scientific-technological progress and by influencing the quality of economic institutions. In modernity, wealth depends on cognitive resources enabling the evolution of cognitive capitalism.
Article
Data are presented to show that there are differences in mean population IQ in different regions of the British Isles. Mean population IQ is highest in London and South-East England and tends to drop with distance from this region. Mean population IQs are highly correlated with measures of intellectual achievement, per capita income, unemployment, infant mortality and urbanization. The regional differences in mean population IQ appear to be due to historical differences which are measured back to 1751 and to selective migration from the provinces into the London area.
Article
Data are presented for the 90 Departements of France for mean population IQs, earnings, unemployment, intellectual achievement and infant mortality. Most of the variables are significantly associated. Mean population IQs are also significantly correlated with migration since 1801 and it is suggested that internal migration has been an important factor leading to contemporary differences in intelligence.
Article
In this study, we tested the parasite-stress hypothesis for the distribution of intelligence among the USA states: the hypothesis proposes that intelligence emerges from a developmental trade-off between maximizing brain vs. immune function. From this we predicted that among the USA states where infectious disease stress was high, average intelligence would be low and where infectious disease stress was low, average intelligence would be high. As predicted, we found that the correlation between average state IQ and infectious disease stress was − 0.67 (p < 0.0001) across the 50 states. Furthermore, when controlling the effects of wealth and educational variation among states, infectious disease stress was the best predictor of average state IQ.Highlights► We compared rates of infectious disease with average IQ across USA states. ► We controlled for education and wealth. ► Infectious disease was the best predictor of average IQ.
Article
Lynn (2010a, 2010b) argued that individuals from south Italy have a lower IQ than individuals from north Italy, and that these differences in IQ are at the basis of north–south gap in income, education, infant mortality, stature, and literacy. In the present paper, we discuss several theoretical and methodological aspects which we regard as flaws of Lynn's studies. Moreover, we report scores of southern Italian children on Raven's Progressive Matrices and a north–south comparison for the PASS theory of intelligence as measured by the Cognitive Assessment System (Taddei & Naglieri, 2006). Both results reveal similar levels of performance of northern and southern Italian children in fluid intelligence and PASS (Planning, Attention, Simultaneous, and Successive) cognitive abilities.
Article
In his article “In Italy, North–South differences in IQ predict differences in income, education, infant mortality, stature, and literacy,” Richard Lynn claims to have found the reason causing the divergence between the Northern and the Southern regions of Italy. This article identifies the four main hypotheses formulated in his paper and presents significant evidence against each one of them. We claim that the evidence presented by the author is not sufficient to say that the IQ of Southern Italians is lower than the one of Northern Italians; that his analysis does not prove that there is any causal link between what he defines as IQ and any of the variables mentioned; that there is no evidence that the alleged differences in IQ are persistent in time and, therefore, attributable to genetic factors.
Article
The purpose of this study is threefold. First, an estimate of state IQ is derived and its strengths and limitations are considered. To that end, an indicator of downward bias in estimating state IQ is provided. Two preliminary causal models are offered that predict state IQ. These models were found to be highly predictive of state IQ, yielding multiple R's of 0.83 and 0.89. Second, the extent to which state IQ predicts state outcome variables (e.g., gross state product, health, violent crime, and government effectiveness) is estimated. State IQ shows positive correlations with gross state product, health, and government effectiveness and negative correlations with violent crime. These results are consistent with the extent to which IQ predicts outcomes at the level of the individual. Third, a research agenda is provided for improving estimates of state IQ, identifying factors that cause differences in state IQ, and delineating the role of IQ in predicting important variables.