Nereid
Staff Emeritus
Science Advisor
Gold Member
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Lynn's own quoted data disproves his conclusion :O
I have done a short study of three of the key webpages of Lynn’s “Intelligence and the Wealth and Poverty of Nations”. Note that Lynn claims “If we adopt a one way causal model that national IQs are a determinant of national per capita incomes […] the results [of his research] show that national IQ explains 57 percent of the variance of real GDP per capita 1998[/color]”
Here are some conclusions from my short study:
1) His study does not present “National IQ”; rather, average IQs from some tests done mostly on children; a study in China comes closest to a national average (ages of subjects “6 to 79”) ... the IQ stated is 98.
2) There are at least two quite obvious systematic trends in the data which Lynn does not appear to have considered. When a crude adjustment is made for these obvious trends, Lynn’s “strong correlation” becomes much weaker (~0.57 to ~0.22), and the standard deviation of “National IQs” dataset drops by ~30%.
3) There is good data in Lynn’s paper to contradict Apollo’s and Nachtwolf’s assertions about the fixed, inherent IQ of races.
4) Lynn seems to have made a very simple mistake – extrapolating the results of individual research work beyond the scope of the work’s validity; or perhaps he simply didn’t address potential sources of systematic error.
This is well illustrated in his own words: “There are two reasons why we consider that a causal effect of national IQ on per capita incomes and rates of economic growth is the most reasonable theory to explain the correlations. First, this theory is a corollary of an already established body of theory and data showing that IQ is a determinant of income among individuals, the evidence for which has been reviewed in the introduction. IQs measured in childhood are strong predictors of IQs in adolescence and these are predictors of earnings in adulthood. The most reasonable interpretation of these associations is that IQ is a determinant of earnings. From this it follows that groups with high IQs would have higher average incomes than groups with low IQs because groups are aggregates of individuals. This prediction has already been confirmed in the studies of the positive relationship between IQs and per capita incomes among the American states and among the regions of the British Isles, France and Spain, as noted in the introduction. The positive relation between IQ and income is so well established that it can be designated a law, of which the finding that national IQs are positively related to national per capita incomes is a further instance.[/color]” Note the leap from studies done in individual countries to the assumption that cross-country comparisons can be made without worrying about possible biases and systematic errors. As I said earlier in this post, there are at least two obvious trends in the data which point to possible sources of systematic error.
If you were already convinced that a nation’s wealth (or poverty) is largely due to how bright or dim the people in the country are, Lynn’s work will be comforting.
If you had doubts about the rigour of the scientific case for significant variation in IQ between economies, you will find plenty of observations on Lynn’s webpages to confirm your doubts.
Some other comments:
a) The five studies in Lynn’s dataset where the subjects’ ages are listed as “Adults” are all from sub-Saharan African counties; 4 of the 5 are among the 8 lowest mean IQs in Lynn’s entire dataset (and two others are Lynn’s own Ethiopian work, and a 1959 study). Curious that Lynn himself implicitly acknowledges that the inclusion of such data will distort the analysis (see point c below), yet he chooses to include all five data points.
b) Lynn states: “While we consider that a causal effect of national intelligence on per capita income and rates of economic growth is the most reasonable model for an explanation of the data, there are two other possible explanations that deserve consideration. The first of these is that there is no direct causal relation between national IQs and per capita incomes and growth rates and the correlation between them is due to some third factor affecting all three. Although this is a theoretical possibility and needs to be mentioned, we do not think it is possible to formulate a plausible theory of this kind.[/color]”
Perhaps PF members and guests could help?
c) There is ample support for the hypothesis “that national per capita incomes are a cause of national differences in IQs” in Lynn’s own data. Yet Lynn writes: “[…] it might be argued that national per capita incomes are a cause of national differences in IQs. This argument would state that rich nations provide advantageous environments to nurture the intelligence of their children in so far as they are able to provide their children with better nutrition, health care, education and whatever other environmental factors have an impact on intelligence, the nature of which is discussed in Neisser (1998). Intelligence has increased considerably in many nations during the twentieth century and there is little doubt that these increases have been brought about by environmental improvements, which have themselves occurred largely as a result of increases in per capita incomes that have enabled people to give their children better nutrition, health care, education and the like. Such a theory has some plausibility but it cannot explain the totality of the data. Countries like Japan, South Korea, Taiwan and Singapore had high IQs in the 1960s when they had quite low per capita incomes and the same is true of China today.[/color]”
As to “Japan, South Korea, Taiwan and Singapore”, guess which researcher did the work to find the “high IQs”? (no prizes for the correct answer)
d) Lynn and Raven – either alone or as lead author - account for just under half the studies Lynn presents; can any PF members give an example of an active area of modern scientific research where just two principals so dominate? The period spans over 50 years.
e) Lynn’s own work stands out quite strongly – he is sole or lead author of 7 of the works reporting the top 10 mean IQs ranked by mean IQ (Japan, South Korea, Hong Kong (3), Singapore, Taiwan), … and of the second lowest (Ethiopia).
f) An elementary statistics question for hitssquad: here are the reported results from two studies, done using the same instrument, on samples purporting to be randomly drawn from the same population:
A: mean 103, sample size 43,825
B: mean 105, sample size 2,496
It is claimed that the difference in the population mean, inferred from these two studies, is not statistically significant. Do you agree? Explain your answer.
[Edit: fixed typo]
I have done a short study of three of the key webpages of Lynn’s “Intelligence and the Wealth and Poverty of Nations”. Note that Lynn claims “If we adopt a one way causal model that national IQs are a determinant of national per capita incomes […] the results [of his research] show that national IQ explains 57 percent of the variance of real GDP per capita 1998[/color]”
Here are some conclusions from my short study:
1) His study does not present “National IQ”; rather, average IQs from some tests done mostly on children; a study in China comes closest to a national average (ages of subjects “6 to 79”) ... the IQ stated is 98.
2) There are at least two quite obvious systematic trends in the data which Lynn does not appear to have considered. When a crude adjustment is made for these obvious trends, Lynn’s “strong correlation” becomes much weaker (~0.57 to ~0.22), and the standard deviation of “National IQs” dataset drops by ~30%.
3) There is good data in Lynn’s paper to contradict Apollo’s and Nachtwolf’s assertions about the fixed, inherent IQ of races.
4) Lynn seems to have made a very simple mistake – extrapolating the results of individual research work beyond the scope of the work’s validity; or perhaps he simply didn’t address potential sources of systematic error.
This is well illustrated in his own words: “There are two reasons why we consider that a causal effect of national IQ on per capita incomes and rates of economic growth is the most reasonable theory to explain the correlations. First, this theory is a corollary of an already established body of theory and data showing that IQ is a determinant of income among individuals, the evidence for which has been reviewed in the introduction. IQs measured in childhood are strong predictors of IQs in adolescence and these are predictors of earnings in adulthood. The most reasonable interpretation of these associations is that IQ is a determinant of earnings. From this it follows that groups with high IQs would have higher average incomes than groups with low IQs because groups are aggregates of individuals. This prediction has already been confirmed in the studies of the positive relationship between IQs and per capita incomes among the American states and among the regions of the British Isles, France and Spain, as noted in the introduction. The positive relation between IQ and income is so well established that it can be designated a law, of which the finding that national IQs are positively related to national per capita incomes is a further instance.[/color]” Note the leap from studies done in individual countries to the assumption that cross-country comparisons can be made without worrying about possible biases and systematic errors. As I said earlier in this post, there are at least two obvious trends in the data which point to possible sources of systematic error.
If you were already convinced that a nation’s wealth (or poverty) is largely due to how bright or dim the people in the country are, Lynn’s work will be comforting.
If you had doubts about the rigour of the scientific case for significant variation in IQ between economies, you will find plenty of observations on Lynn’s webpages to confirm your doubts.
Some other comments:
a) The five studies in Lynn’s dataset where the subjects’ ages are listed as “Adults” are all from sub-Saharan African counties; 4 of the 5 are among the 8 lowest mean IQs in Lynn’s entire dataset (and two others are Lynn’s own Ethiopian work, and a 1959 study). Curious that Lynn himself implicitly acknowledges that the inclusion of such data will distort the analysis (see point c below), yet he chooses to include all five data points.
b) Lynn states: “While we consider that a causal effect of national intelligence on per capita income and rates of economic growth is the most reasonable model for an explanation of the data, there are two other possible explanations that deserve consideration. The first of these is that there is no direct causal relation between national IQs and per capita incomes and growth rates and the correlation between them is due to some third factor affecting all three. Although this is a theoretical possibility and needs to be mentioned, we do not think it is possible to formulate a plausible theory of this kind.[/color]”
Perhaps PF members and guests could help?
c) There is ample support for the hypothesis “that national per capita incomes are a cause of national differences in IQs” in Lynn’s own data. Yet Lynn writes: “[…] it might be argued that national per capita incomes are a cause of national differences in IQs. This argument would state that rich nations provide advantageous environments to nurture the intelligence of their children in so far as they are able to provide their children with better nutrition, health care, education and whatever other environmental factors have an impact on intelligence, the nature of which is discussed in Neisser (1998). Intelligence has increased considerably in many nations during the twentieth century and there is little doubt that these increases have been brought about by environmental improvements, which have themselves occurred largely as a result of increases in per capita incomes that have enabled people to give their children better nutrition, health care, education and the like. Such a theory has some plausibility but it cannot explain the totality of the data. Countries like Japan, South Korea, Taiwan and Singapore had high IQs in the 1960s when they had quite low per capita incomes and the same is true of China today.[/color]”
As to “Japan, South Korea, Taiwan and Singapore”, guess which researcher did the work to find the “high IQs”? (no prizes for the correct answer)
d) Lynn and Raven – either alone or as lead author - account for just under half the studies Lynn presents; can any PF members give an example of an active area of modern scientific research where just two principals so dominate? The period spans over 50 years.
e) Lynn’s own work stands out quite strongly – he is sole or lead author of 7 of the works reporting the top 10 mean IQs ranked by mean IQ (Japan, South Korea, Hong Kong (3), Singapore, Taiwan), … and of the second lowest (Ethiopia).
f) An elementary statistics question for hitssquad: here are the reported results from two studies, done using the same instrument, on samples purporting to be randomly drawn from the same population:
A: mean 103, sample size 43,825
B: mean 105, sample size 2,496
It is claimed that the difference in the population mean, inferred from these two studies, is not statistically significant. Do you agree? Explain your answer.
[Edit: fixed typo]
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