Calling Out JayMan

JayMan is an HBd blogger obsessed with IQ, heredity and achievement, and probably Richard Lynn's #1 fan. He claims to be Jamaican, which makes him either the most insanely self-hating black person on earth, or a white Nordicist pretending to be black so he can get away with insulting blacks and everyone else who isn't Northwestern European. Either way, he's an idiot.

Recently he "called me out" on his blog re: my stance on environmental factors in global IQ disparities. I had debated him there about a year and a half ago and easily won, and I guess he's been traumatized by it ever since. He links to that post in his new one, and I wondered why he would want to remind people of his defeat, but when I checked it again I noticed that he'd deleted my final reply to try and make himself look like the winner (you can read the unedited discussion here; the deleted comment is "Mar 27 2012 9:57 AM"). Then when I attempted to reply to his latest "challenge" the other day, he refused to approve that comment too. Just thought you'd want to know what a pathetic, dishonest, chickenshit loser he is...if you couldn't already tell.

Anyway, my response to his nonsense was this study showing that poverty leads to a significant drop in IQ points, and this new one which concludes similarly that the wealth of nations determines "national IQs" (not the other way around).

Poverty Lowers IQ

Poverty and all its related concerns require so much mental energy that the poor have less remaining brainpower to devote to other areas of life, according to research based at Princeton University. As a result, people of limited means are more likely to make mistakes and bad decisions that may be amplified by — and perpetuate — their financial woes.

Published in the journal Science, the study presents a unique perspective regarding the causes of persistent poverty. The researchers suggest that being poor may keep a person from concentrating on the very avenues that would lead them out of poverty. A person's cognitive function is diminished by the constant and all-consuming effort of coping with the immediate effects of having little money, such as scrounging to pay bills and cut costs. Thusly, a person is left with fewer "mental resources" to focus on complicated, indirectly related matters such as education, job training and even managing their time.

In a series of experiments, the researchers found that pressing financial concerns had an immediate impact on the ability of low-income individuals to perform on common cognitive and logic tests. On average, a person preoccupied with money problems exhibited a drop in cognitive function similar to a 13-point dip in IQ, or the loss of an entire night's sleep. But when their concerns were benign, low-income individuals performed competently, at a similar level to people who were well off, said corresponding author Jiaying Zhao, who conducted the study as a doctoral student in the lab of co-author Eldar Shafir, Princeton's William Stewart Tod Professor of Psychology and Public Affairs.

[...]

The cognitive effect of poverty the researchers found relates to the more general influence of "scarcity" on cognition, which is the larger focus of Shafir's research group. Scarcity in this case relates to any deficit — be it in money, time, social ties or even calories — that people experience in trying to meet their needs. Scarcity consumes "mental bandwidth" that would otherwise go to other concerns in life, Zhao said.

"These findings fit in with our story of how scarcity captures attention. It consumes your mental bandwidth," Zhao said. "Just asking a poor person to think about hypothetical financial problems reduces mental bandwidth. This is an acute, immediate impact, and has implications for scarcity of resources of any kind."

Morgan Kelly. "Poor concentration: Poverty reduces brainpower needed for navigating other areas of life". News at Princeton, August 29, 2013.

The poor often behave in less capable ways, which can further perpetuate poverty. We hypothesize that poverty directly impedes cognitive function and present two studies that test this hypothesis. First, we experimentally induced thoughts about finances and found that this reduces cognitive performance among poor but not in well-off participants. Second, we examined the cognitive function of farmers over the planting cycle. We found that the same farmer shows diminished cognitive performance before harvest, when poor, as compared with after harvest, when rich. This cannot be explained by differences in time available, nutrition, or work effort. Nor can it be explained with stress: Although farmers do show more stress before harvest, that does not account for diminished cognitive performance. Instead, it appears that poverty itself reduces cognitive capacity. We suggest that this is because poverty-related concerns consume mental resources, leaving less for other tasks. These data provide a previously unexamined perspective and help explain a spectrum of behaviors among the poor. We discuss some implications for poverty policy.

Mani et al. "Poverty Impedes Cognitive Function". Science, 2013.

Related: Parasite prevalence and cognitive development

African IQ and the Flynn Effect

Average IQ in Sub-Saharan Africa is about 20 points lower than average IQ in Europe, and not the 30+ points proposed by Richard Lynn and his gang. Though that may still seem huge and insurmountable, it's actually normal when placed in the proper historical context. As happened in Europe during the 20th century, it's expected that when living conditions improve in Sub-Saharan Africa, IQ levels will increase, narrowing or even eliminating the gap. This process is known as the Flynn Effect.

In the western world, average IQs have shown remarkable gains over the course of the twentieth century (Flynn, 1984, 1987, 2007). These gains have been largest for non-verbal tests once considered relatively impervious to cultural influences, such as the Raven's (Brouwers et al., 2009). For instance, in the Netherlands an unaltered version of the SPM [Raven's Standard Progressive Matrices] was administered to male military draftees from 1952 to 1982. The 1982 cohort scored approximately 20 IQ points higher than the 1952 cohort (Flynn, 1987). In this section, we consider whether a Flynn Effect has occurred among Africans on the Raven's tests.

[...]

Proposed causes of the Flynn Effect include improvements in test-specific skills (Greenfield, 1998; Wicherts et al., 2004), improvements in nutrition (Lynn, 1989, 1990), urbanization (Barber, 2005), improvements in health care (Williams, 1998), a trend towards smaller families (Zajonc & Mullally, 1997), increases in educational attainment (Ceci, 1991), greater environmental complexity (Schooler, 1998), and the working of genotype by environment correlation in the increasing presence of more intelligent others (Dickens & Flynn, 2001). Many of these environmental variables have not undergone the improvement in developing sub-Saharan African countries that they have in the developed world over the last century. This suggests that the Flynn Effect has great potential in sub-Saharan Africa (Wicherts, Borsboom, & Dolan, 2010b).

[...]

Although the implications of our psychometric findings for the potential of the Flynn Effect in sub-Saharan Africa remain unclear, the Raven's tests and other IQ tests have shown robust increases in many populations (Daley et al., 2003; Flynn, 2007). So suppose that there were a well-validated IQ test that showed measurement invariant scores between westerners and Africans. Even then, lower IQs of Africans still would not support Lynn and Vanhanen's (2002, 2006) assertion that countries in sub-Saharan Africa are poorly developed economically because of their low "national IQ". Wicherts, Borsboom, and Dolan (2010b) found that "national IQs" are rather strongly confounded with the developmental status of countries. Given the well-documented Flynn Effect, we know that "national IQs" are subject to change. An average IQ around 80 among Africans may appear to be low, but from a historical perspective this average is not low at all. A representative sample of British adults, who took the SPM in 1948 would have an average IQ of 81 in terms of the British norms of 1992 (J. C. Raven, 1960; J. C. Raven et al., 1996). Using older British norms, the average IQ of Africans would be much closer to 100. This is evident in Figure 2, where we compared SPM scores of Africans to older norms. In this figure, the average IQ of several African samples is near or above 100.

Present-day sub-Saharan Africa is one of the poorest regions in the world and the home to some of the world's most deprived children. The majority of sub-Saharan children are chronically malnourished, not only from lack of food but particularly from food lacking vital elements related to both physical growth and intellectual development. It has been estimated that up to 70 percent of rural children live in absolute poverty and 90 percent suffer severe deprivation (Gordon, Nancy, Pantazis, Pemberton, & Townsend, 2003). A substantial number of sub-Saharan African children are under-educated. According to Garcia, Gillian, and Dunkelberg (2008), only about 12 percent of sub-Sahara African children have attended preschool, and this generally for well less than a year. They note that children who do not attend or have only minimal experience in pre-primary school tend to do less well in primary school than children who have had that experience. Further, it is important that the preschool experience be successful. For example, Jaramillo and Mingat (2008) have shown that children who have a poor experience in preschool and have to repeat a year or part of a year have a high drop-out rate in primary school (r = -0.875). The probability of preschool without repetition and who complete primary school is low but positive (r = 0.209). With or without preschool experience, approximately only fifty-five percent of 10-14 year-olds in sub-Saharan Africa complete primary school.

[...]

Many of the variables that have been proposed as causes of the Flynn Effect (e.g., Barber, 2005; Ceci, 1991; Dickens & Flynn, 2001; Greenfield, 1998; Lynn, 1989; Schooler, 1998; Williams, 1998) have yet to undergo improvement in developing sub-Saharan African countries that they have enjoyed in the developed world over the last century. Because the environmental variables that potentially contribute to enhanced IQ have yet to improve in the countries of sub-Saharan Africa, we regard the Flynn Effect as still in its infancy. There is in fact considerable empirical support that (mal)nutrition (Grantham-McGregor & Baker-Henningham, 2007; Sigman & Whaley, 1998), health (Williams, 1998), sanitation (Boivin et al., 1993), and schooling (Ceci, 1991) have an effect on IQ. The UN have included such variables in the so-called Millennium Goals, i.e., they are targeted for improvement by 2015 (United Nations, 2005). The formulation of the Millennium Goals provides an interesting opportunity to evaluate the effect of these factors on IQ levels in sub-Saharan Africa. There is now a clear indication that the Flynn Effect seems to have come to a halt in developed nations (Flynn, 2007). It is, therefore, reasonable to think that as circumstances in sub-Saharan Africa improve, the IQ gap between western samples and African samples will diminish.

[...]

The vast literature on IQ testing with the Raven's tests in Africa does not support James Watson's pessimism concerning the prospects of Africa. It is true that Africans show lower average IQs as compared to contemporary western norms, although the IQ gap is substantially smaller than Lynn (and Vanhanen) have maintained. More importantly, there is little scientific basis for the assertion that the observed lower IQs of Africans are evidence of lower levels of general intelligence or g. The validity of the Raven's tests among Africans needs to be studied further before these tests can be used to assess Africans' cognitive ability in educational and professional settings. There are several reasons to expect increases in IQ levels among sub-Saharan Africans in the coming decades.

Wicherts et al. "Raven's Test Performance of Sub-Saharan Africans: Average Performance, Psychometric Properties, and the Flynn Effect". Learning and Individual Differences, 2010.

Related: Parasite prevalence and cognitive development

Devastating Criticism of Richard Lynn

Richard Lynn has long been criticized for his controversial studies on intelligence, but this latest series of criticism might just be the final nail in his coffin. Focusing on his much-condemned African IQ studies, it reveals serious flaws in his methodology and calls him out on manipulating and falsifying data, which has wider implications that make his entire body of work (and that of his associates) untrustworthy. It begins with Wicherts et al. 2010:

Although these estimates of national IQ are claimed to be "highly valid" (Rushton, 2003, p. 368) or "credible" (McDaniel, 2008, p. 732) by some authors, the work by Lynn (and Vanhanen) has also drawn criticism (Barnett & Williams, 2004; Ervik, 2003; Hunt & Carlson, 2007; Hunt & Sternberg, 2006; Lane, 1994). One point of critique is that Lynn (and Vanhanen)'s estimate of average IQ among Africans is primarily based on convenience samples, and not on samples carefully selected to be representative of a given, targeted, population (Barnett & Williams, 2004; Hunt & Sternberg, 2006). Unfortunately, in many developing countries, such representative samples are lacking (McDaniel, 2008).

A literature review is necessarily selective. Despite Lynn's objective of providing a "fully comprehensive review of the evidence" (Lynn, 2006, p. 2), a sizeable portion of the relevant literature was not considered in both his own review, and in reviews with Vanhanen. Nowhere in their reviews did Lynn (and Vanhanen) specify the details of their literature search. Our own searches in library databases resulted in additional relevant studies that may be used to estimate national IQ. For instance, Lynn and Vanhanen (2006) accorded a national IQ of 69 to Nigeria on the basis of three samples (Fahrmeier, 1975; Ferron, 1965; Wober, 1969), but they did not consider other relevant published studies that indicated that average IQ in Nigeria is considerably higher than 70 (Maqsud, 1980a,b; Nenty & Dinero, 1981; Okunrotifa, 1976). As Lynn rightly remarked during the 2006 conference of the International Society for Intelligence Research (ISIR), performing a literature review involves making a lot of choices. Nonetheless, an important drawback of Lynn (and Vanhanen)'s reviews of the literature is that they are unsystematic. Unsystematic literature reviews do not adhere to systematic methodology to control for potential biases in the many choices made by the reviewer (Cooper, 1998; Light & Pillemer, 1984). Lynn (and Vanhanen) failed to explicate the inclusion and exclusion criteria they employed in their choice of studies. Such criteria act as a filter, and may thus affect the estimate of national IQ. Lynn (and Vanhanen) excluded data from several sources without providing a rationale. For instance, they used IQ data from Ferron (1965), who provided averages in seven samples of children from Sierra Leone and Nigeria on a little-known IQ test called the Leone. For reasons not given, Lynn (2006) and Lynn and Vanhanen (2006) only used data from the two lowest scoring samples from Nigeria. Most of the remaining samples show higher scores, but those samples were not included in the estimation of the national IQ of Nigeria and Sierra Leone. Likewise, Lynn (and Vanhanen) did not consider several relatively high-scoring African samples from South Africa (Crawford Nutt, 1976; Pons, 1974). It is unfortunate that Lynn (and Vanhanen) did not discuss their exclusion criteria. In some cases (Crawford Nutt, 1976; Pons, 1974), the Raven's Progressive Matrices was administered with additional instruction. Although this instruction is quite similar to an instruction as described in the test manual (Raven, Court, & Raven, 1996), some have argued that this instruction artificially enhances test performance (cf. Rushton & Skuy, 2000). Given the likely differences in opinion on which samples to include or exclude in a review, inclusion and exclusion criteria should be explicated clearly and employed consistently. It is well known that unsystematic literature reviews may lead to biased results (Cooper, 1998; Light & Pillemer, 1984). Another problem is that the computation of statistics in literature reviews is quite error-prone. Indeed Lynn's work contains several errors (Loehlin, 2007).

Lynn responded, attempting to defend his work, and Wicherts et al. fired back immediately with an even stronger rejoinder, repeating their previous criticism of his methodology and flat out accusing him of cherry-picking data that supports his position while ignoring the rest:

In this rejoinder, we criticize Lynn and Meisenberg's (this issue) methods to estimate the average IQ (in terms of British norms after correction of the Flynn Effect) of the Black population of sub-Saharan Africa. We argue that their review of the literature is unsystematic, as it involves the inconsistent use of rules to determine the representativeness and hence selection of samples. Employing independent raters, we determined of each sample whether it was (1) considered representative by the original authors, (2) drawn randomly, (3) based on an explicated stratification scheme, (4) composed of healthy test-takers, and (5) considered by the original authors as normal in terms of Socio-Economic Status (SES). We show that the use of these alternative inclusion criteria would not have affected our results. We found that Lynn and Meisenberg's assessment of the samples' representativeness is not associated with any of the objective sampling characteristics, but rather with the average IQ in the sample. This suggests that Lynn and Meisenberg excluded samples of Africans who average IQs above 75 because they deemed these samples unrepresentative on the basis of the samples' relatively high IQs. We conclude that Lynn and Meisenberg's unsystematic methods are questionable and their results untrustworthy.

Then in a later paper, Wicherts et al. dug even deeper, finding that in addition to picking and choosing, Lynn actively seeks out and uses data that's not reliable or representative:

The samples, considered by Lynn (and Vanhanen), but discarded here, are given in the Appendix. Besides the two samples described above (Klingelhofer, 1967; Zindi, 1994), these are Wober's (1969) sample of factory workers, and Verhaegen's (1956) sample of uneducated adults from a primitive tribe in the then Belgian Congo in the 1950s. Verhaegen indicated that the SPM test format was rather confusing to the test-takers, and that the test did not meet the standards of valid measurement. In Wober's study, the reliability and validity were too low (Wober, 1975). In three of the samples in Table 1, the average IQ is below 70. These are Owen's large sample of Black South African school children tested in the 1980s, the 17 Black South Africans carefully selected for their illiteracy by Sonke (2001), and a group of uneducated Ethiopian Jewish children, who lived isolated from the western world in Ethiopia and immigrated to Israel in the 1980s (Kaniel & Fisherman, 1991). The last two samples cannot be considered to be representative.

[...]

Our review of the literature on the performance of Africans on the Raven's tests showed that the average IQ of Africans on the Raven's tests is lower than the average IQ in western countries. However, the average IQ of Africans is not as low as Lynn (and Vanhanen) and Malloy (2008) maintained. The majority of studies on IQ test performance of Africans not taken into account by Lynn (and Vanhanen) and Malloy showed considerably higher average IQs than the studies that they did review. We judge the reviews of Lynn (and Vanhanen) and Malloy to be unsystematic. These authors missed a large part of the literature on IQ testing in Africa, failed to explicate their inclusion and exclusion criteria, and made downward errors in the conversion of raw scores to IQs (Wicherts, 2007). Lynn (and Vanhanen)'s estimate of average IQ of Africans of around 67 is untenable. Our review indicates that it is about 78 (UK norms) or 80 (US norms). These means are somewhat lower than the means of Africans on other IQ tests, which lie around 82 (Wicherts et al., 2010). These results undermine evolutionary theories of race differences in intelligence of Lynn (2006), Rushton (2000), and Kanazawa (2004) (Wicherts, Borsboom, & Dolan, 2010a; Wicherts et al., 2010b).

Lynn responded to that too, accusing Wicherts et al. of deriving their higher estimate of average African IQ from elite samples, but they once again showed that his lower estimate results from the unsystematic use of samples that are not random or representative.

United States' North-South Achievement Gap

As in Modern Europe, achievement in the USA since its founding has been concentrated in just a few places, which has created a North-South gap that correlates with economic and educational disparities observed today. Nordicists are quick to jump on this sort of thing elsewhere but ignore it in their own backyard, or try to blame it on minorities, like the South's large black population. But Charles Murray, in his book Human Accomplishment, doesn't fail to note the gap, and the fact that it exists within the white population:

The geographic distribution of significant figures from the United States reflects the rapidly changing settlement of the country. The East Coast dominates, inevitably, because hardly anyone lived anywhere else for much of the nation's history. If I could show you a map of America's significant figures in the last half century, it presumably would look much different from the first half of 20C, just because the population shifted so radically westward throughout 20C. With that in mind, the figure below is offered as a summary of the story from the founding to 1950.

The states that are colored represent the origins of 90 percent of the American significant figures. The small dark blue slice running in an arc from Portland, Maine, to the southern tip of New Jersey encompasses the origins of about 50 percent of them. The light blue wedge encompasses another 25 percent, and the gray fills out the remaining 15 percent. Even after factoring in the history of American expansion, the primary concentration along the northeastern coast of the United States and the secondary concentration in the belt stretching to the Mississippi is striking.

An even more striking aspect of the map is the white space covering the American South. Although more lightly populated than the North, the American South had a substantial population throughout American history. In 1850, for example, the white population in the South was 5.6 million, compared to 8.5 million in the Northeast. In 1900, the comparison was 12.1 million to 20.6 million. By 1950, the gap had almost closed — 36.9 million compared to 37.4 million. While it is understandable that the South did not have as many significant figures as the North, the magnitude of the difference goes far beyond population. The northeastern states of New England plus New York, Pennsylvania, and New Jersey had produced 184 significant figures by 1950, while the states that made up the Confederacy during the Civil War had produced 24, a ratio of more than 7:1.

The scatter plots on the following page show the way in which the American significant figures break down over the three half centuries from 1800-1950.

Underperformance of Poor White British Boys

The GCSEs are national achievement exams in the U.K. that are similar to the international PISA tests Richard Lynn uses to "calculate" IQ and advance his theories about racial, ethnic and sex differences in intelligence. They're even referenced in one of his sources, but I wonder if he's aware of how badly poor white boys do on them, even compared to nonwhite students from similar disadvantaged, working-class backgrounds.

Here's a chart showing rates of underperformance on GCSE exams by ethnic group and economic status (free school meal eligibility), followed by excerpts from several related articles run by BBC News over the past few years:

Poor white boys struggle in GCSEs

Tuesday, 15 December 2009

Poor, white, teenage boys in England have slipped further behind other youngsters in their GCSE exams, reveals a breakdown of this year's results.

The official figures show that fewer than one in five who qualified for free meals achieved the benchmark of five good GCSEs including English and maths.

Twice as many white boys from better-off homes, not eligible for free meals, achieved this level of results.

[...]

Girls are still outperforming boys in achieving the GCSE benchmark — 54.5% to 47.3% — but the gap has narrowed this year by 0.9 of a percentage point.

Black pupils have also improved their GCSE results at a rate that is faster than average — rising to 41.5%.

There has also been a narrowing of the difference between poorer pupils and those who are better off — as defined by eligibility for free school meals.

But this still remains a substantial divide — with 54.4% of pupils not eligible reaching the level of five good GCSEs including maths and English, while only 26.9% of free school meal pupils achieved this.

The results show the intersection of different factors — gender, race, English as a second language and poverty.

But the group of white boys who qualify for free meals is unusual for falling further behind.

The gap between these pupils and their classmates who do not receive free meals has widened from 29.8% to 31.6%.

These results echo findings last month from an analysis of primary school results which found that poor white boys had fallen below almost every other category of pupil.

Link

Poor white boys do worst in tests

Thursday, 19 November 2009

White boys from poorer homes now do worse in primary school tests in England than any other main group, latest figures show.

Only 48% of white British boys eligible for free school meals achieved the expected level in English and maths.

The average for all pupils was 71.8% — and that gap, 23.8 percentage points, was up from 23.1 points last year.

Attainment varied between ethnic groups with Chinese, Irish, Indian and mixed white and Asian children doing best.

[...]

Last year 52.5% of white British boys and girls together had the worst performance, though the girls' attainment was 55.4%.

This year, as a group, white British pupils again did worst.

Again it was the boys' performance that was weaker than the girls'. While 55% of girls achieved the expected level they were outperformed by the next nearest group — Pakistani girls (57.4%).

[...]

There are almost 200,000 white British boys, of whom 31,237 (16%) were from homes poor enough to qualify them for free school meals.

In the past, poor black boys have tended to have the weakest performance.

But this year 51.6% of black boys on free meals made the grade and the gap in their attainment compared with the national average, 20.2, was down from 21.8 in 2008.

Attainment was 49.7% among the weakest group of black boys, those from Caribbean backgrounds.

This left the white British boys on free school meals at the bottom. Even worse for the government is the fact that the attainment gap widened.

This has been a persistent problem for ministers.

Research shows that even children from affluent families who started out in the bottom ability group as toddlers overtake those from the poorest backgrounds who started out in the top ability group, by the time they are six or seven.

LEVEL 4 ENGLISH AND MATHS, FREE SCHOOL MEALS

• All pupils: 53.3%
• White British boys: 48%
• Black Caribbean boys: 49.7%
• Asian boys: 58.7%
• White British girls: 55%
• Pakistani girls: 57.4%

Link

Poor white boys still lag behind

Thursday, 11 December 2008

Five out of six poor white boys in England did not meet the government's target of at least five good GCSEs including English and maths this year.

This compares to 25% of black boys and 32% of Asian boys of similar backgrounds, the new figures show.

Only one group performed worse — Gypsy/Romany pupils on free school meals.

Schools minister Jim Knight said the groups where children were doing well often shared a belief in the "value of family and education".

[...]

Liberal Democrat children's spokesman David Laws said: "Over half of poor Chinese boys achieve the five A*-C standard.

"Urgent questions must be asked about why white boys from similarly deprived backgrounds are falling behind."

Link

White working class boys failing

Thursday, 31 January 2008

Government figures show only 15% of white working class boys in England got five good GCSEs including maths and English last year.

Among white boys from more affluent homes — 45% achieved that level of qualification.

Poorer pupils from Indian and Chinese backgrounds fared much better — with 36% and 52% making that grade respectively.

[...]

"To have 85% of white boys from poor families failing to achieve five good GCSEs including English and maths is truly shocking."

Link

Low attainers 'poor white boys'

Friday, 22 June 2007

Most of the persistent low achievers in England's schools are poor and white, and far more are boys than girls, a Joseph Rowntree Foundation study says.

Chinese and Indian pupils are most successful. Afro-Caribbean pupils do no worse than white British from similar economic backgrounds, results suggest.

[...]

The authors, Robert Cassen and Geeta Kingdon, analysed official data, focusing on four measures of low achievement:

• no passes at all in GCSE/GNVQ exams
• no result better than grade D
• no pass in either English or maths GCSE
• not getting five GCSEs including English and maths at any grade

Prof Cassen also visited schools and colleges and interviewed educationists and council officials.

The chief characteristic of low achievers is that they come from disadvantaged backgrounds.

They are more likely to qualify for free school meals, live in areas of high unemployment, and have single parents who themselves have poor qualifications.

Link

White boys 'trailing at school'

Wednesday, 15 November 2006

Boys from white working-class backgrounds are doing worse at school than black teenagers, according to a Conservative Party report.

The document from the party's social justice policy group says only 17% of white male students gained five or more A*-C grade GCSEs.

That compares with 19% for boys of Caribbean origin, Tories suggest.

[...]

It compared the exam performance of boys in receipt of free school meals from different ethnic backgrounds.

It suggests that social issues, such as a lack of parental support, peer pressure and family breakdown are contributing to white working-class teenagers' poor exam results.

But the report adds that black teenage boys are affected by similar factors, yet are performing marginally better at school.

[...]

"The fact that poor children from Chinese and Indian backgrounds, where family structures are strong and learning is highly valued, outscore so dramatically children from homes where these values are often missing, suggests that culture not ethnicity or cash is the key to educational achievement."

Link

Richard Lynn on Italian "IQ"

Controversial psychologist Richard Lynn, who looks at IQ and its correlates, has published a study claiming to show regional (North-South) differences in intelligence within Italy, which he attempts to correlate with achievement and attribute to admixture. The guy's been called just about every name in the book, and he can now add Padanian Nordicist to that list.

Intelligence

Generally speaking, Lynn is not to be trusted. He's been caught numerous times falsifying and manipulating data to fit his conclusions (e.g. here, here, here, here, here and here), and it looks like he's up to his old tricks again.

This time around, he's not even using actual IQ data, but the proxy of scores on reading, math and science tests administered to 15-year-olds (PISA 2006). So he's attempting to quantify innate general intelligence by looking at the academic performance of school kids, a measure that to a large extent involves learned knowledge and other factors. Indeed, while some researchers report a strong correlation between general intelligence and educational attainment, one of Lynn's own sources, Deary et al. (2007), addressing two of his other sources, suggests that caution should be exercised when attempting to equate the two:

There are various possible causes of the cognitive ability-educational achievement association. Bartels et al. (2002b) found a strong genetic correlation between cognitive ability (measured at 5, 7, 10, and 12 years) and educational achievement at age 12. In an overview, Petrill and Wilkerson (2000) concluded that genetics and shared and non-shared environmental factors all influence intelligence and education, with genetics being important in the correlation between them, and non-shared environment being important in discrepancies between intelligence and educational attainments.

Whereas the correlations indicate that around 50% to 60% of the variance in GCSE [General Certificate of Secondary Education] examination points score can be statistically explained by the prior g [general intelligence] factor, by the same token a large proportion of the variance is not accounted for by g. Some of the remaining variance in GCSE scores will be measurement error, but some will be systematic. Thus, non-g factors have a substantial impact on educational attainment. These may include: school attendance and engagement; pupils' personality traits, motivation and effort; the extent of parental support; and the provision of appropriate learning experiences, teaching quality, school ethos, and structure among other possible factors (Petrides, Chamorro-Premuzic, Frederickson, & Furnham, 2005; Strand, 2003).

But Lynn already knows the pitfalls of his approach. Finland had the highest score in Europe on the 2006 PISA tests, and using his method leads to a calculated IQ of 107, yet he reports Finns' IQ as being just 97. Romanians' PISA score is near the very bottom of Europe, leading to an estimate of 85, though their measured IQ is in fact 94 according to Lynn, just three points lower than that of Finns. With discrepancies like that, there's absolutely no reason to trust his calculated IQs of around 100 and 90 for Northern and Southern Italians. Clearly, PISA scores are not a good substitute for IQ.

Then, to try to prove that disparities in intelligence are long-standing, and therefore genetically based, he uses literacy as another (questionable) proxy for IQ. But whereas for other correlates like stature and infant mortality he includes data from the past and present to show that the North-South gap has remained fairly stable, for literacy he only includes data from 1880, when it was extremely large (55% vs. 20%, on average). Obviously, he wants to hide the fact that the gap has been closing steadily since then, and by the 21st century, literacy among Italians under the age of sixty-five was 99.7% in the North and 99% in the South (Istat 2001).

Achievement

Lynn goes on to attempt to correlate his fake IQs with achievement. The primary measure he uses is per capita income, which is double in the North what it is in the South. His source is the Italian Statistical Office, but he should be aware that figures for the South's economic performance are greatly underestimated because the official statistics fail to take into account a large underground economy there, according to Burnett and Vaccara (1999):

But the third factor, somewhat alleviating the second, is the existence of a far vaster private sector than ever shows up in the economic statistics. The size of the lavoro nero sector and the black market in the South clearly exceeds that of any other EU region.... In Calabria, with its dire employment figures, 84 percent of the families own their own home. What such anomalies must mean is that real income in Calabria is far higher than what is "on the books." Many among the vast numbers of officially unemployed are, in fact, partly or fully employed. They are earning no social benefits, but they are earning the daily lire that keep their families afloat. [...] A very large part of the South's hidden labor is made up of entrepreneurs, sometimes also employing black labor, and existing themselves outside official recognition, taxation, protection, control, or counting. A recent analysis concludes that "there exists in several zones of the Mezzogiorno [Southern Italy] a whole fabric of small and very small businesses that escape every census, but that work and make profits, share among themselves a serious level of production, export to other regions [of Italy] and abroad." [...] This massive sector skews all the statistics. It means that the GDP for the Italian South (and for Italy as a whole) is far from accurate. And the unemployment figures do not reflect reality.

Then, with the same goal of establishing a pattern that extends far back in time, he also looks at historical achievement, citing Charles Murray's Human Accomplishment. But here again he plays fast and loose with the data. He adds up Murray's "significant figures" for Italy from 1400-1950 and divides them into "North", "Center" and "South" based on their origins. Almost all of them (187 out of 236) come from the "North". However, he explains that he uses the 42nd and 41st lines of latitude as borders, which are, respectively, just north of Rome and just north of Naples. So more than half of the country is put into the "North" category, while the "Center" gets flattened down and pushed into the territory of the "South" (click here to see what that looks like). This is a shameless and transparent ploy by Lynn to hugely inflate the number of significant figures from the "North" and reduce the number elsewhere in the country.

But perhaps even worse, he ignores the main finding of Murray's book, which is that achievement has been concentrated in just a few places, and Southern Italy is but one of many "low-achievement" areas throughout Europe, along with some of the northernmost regions in Italy:


As you can see — and as Murray points out in his book — the highest ranking region is Tuscany, which would normally be considered part of Central Italy. Ironically, Lynn doesn't even have PISA/IQ data for Tuscany, though its 1880 literacy rate isn't particularly high. So he's basically attributing the unique genius of that region to the brainpower of regions that have not produced the same level of genius.

Admixture

Finally, Lynn offers his "explanation" for the disparities in fake IQ, which, unsurprisingly, turns out to be admixture from the Middle East and North Africa, where (according to him) IQs are in the range of 80-84. As you might expect from someone with an agenda and little knowledge of genetics, he references a lot of old studies that use single or small numbers of loci and don't directly address the question of admixture. One of them has "Neolithic demic diffusion in Europe" in its title, yet he stupidly follows the citation with references to historical groups like Phoenicians and Arabs.

Recent genome-wide studies have been able to detect and quantify admixture like never before. Li et al. (2008), using more than 600,000 autosomal SNPs, identify seven global population clusters, including European, Middle Eastern and Central/South Asian. Contrary to Lynn's claims, it's actually the overachieving Tuscans who have a small amount of non-European admixture and not the underachieving Sardinians:


López Herráez et al. (2009) typed the same samples at close to 1 million SNPs and analyzed them in a Western Eurasian context, identifying a number of subclusters. This time, all of the European samples show some minor admixture. Among the Italians, Tuscany still has the most, and Sardinia has a bit too, but so does Lombardy (Bergamo), which is even farther north:

Conclusion

The bottom line is, we don't know the average IQs for different regions in Italy, which is why Richard Lynn had to resort to making them up. And while Southern Italians are likely to be a few points lower than Northern Italians — as the Irish and Scottish are a few points lower than the English — there's absolutely no reason to believe that North and South would be separated at their extremes by almost a full standard deviation. Lynn certainly hasn't proven anything of the kind with this ridiculous study, nor has he provided any valid explanations for such a disparity.

Updates

Since I wrote this article, a number of studies have been published that refute Lynn and confirm what I've said:

• Cornoldi et al. (2010) and Beraldo (2010)
• Felice and Giugliano (2011)
• Daniele and Malanima (2011)
• Robinson et al. (2011)
• D'Amico et al. (2012)
• Templer (2012)
• Cornoldi et al. (2013)

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Richard Lynn. "In Italy, north-south differences in IQ predict differences in income, education, infant mortality, stature, and literacy". Intelligence, 2010.