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New evidence for differences in fluid intelligence between north and south Italy and against school resources as an explanation for the north–south IQ differential

  • Ulster Institute for Social Research

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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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New evidence for differences in uid intelligence between north
and south Italy and against school resources as an explanation
for the northsouth IQ differential
Davide Piffer
, Richard Lynn
Ulster Institute for Social Research, London
University of Ulster, Coleraine, Northern Ireland
article info abstract
Article history:
Received 14 May 2014
Received in revised form 29 June 2014
Accepted 14 July 2014
Available online xxxx
The northsouth 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 northwest and
the south and confirms Lynn'stheory. New data are presented for genetic differences between the
populations of north and south Italy.
© 2014 Elsevier Inc. All rights reserved.
Northsouth difference
PISA Creative Problem Solving test
1. Introduction
It has been proposed by Lynn (2010a) that there is a north
south gradient of intelligence in Italy such that average IQs are
approximately 10 IQ points higher in the north than that in the
south. This conclusion was based largely on the 2006 PISA
(Program for International Student Assessment) data for the
reading comprehension, mathematical and science abilities of
15-year-olds in 52 countries and 12 Italian regions. The
justification for the use of these scores as a proxy for intelligence
has been given by Rindermann (2007, 2008), who has shown
that they are highly correlated with IQs across nations. Lynn
proposed that these northsouth differences in IQs in Italy
explain much of the differences in average incomes, literacy,
education, stature, infant mortality and the numbers of individ-
uals who have achieved eminence in the arts and sciences.
Lynn's thesis has been criticized by Beraldo (2010),
Cornoldi, Belacchi, Giofre, Martini, and Tressoldi (2010),Felice
and Giugliano (2011) and Cornoldi, Giofrè, and Martini (2013)
on the groundsthat the PISA scores for reading comprehension,
mathematics and science are measures of educational attain-
ment determined by differences in teaching quality and
educational resources and cannot be used as measures of
intelligence. These critics have been answered by Lynn
(2010b), who gave data for Raven's Progressive Matrices and
an Internet test showing IQs in the north approximately 10
points higher than in the south, and by Lynn (2012a) who
presented data from PISA 2009 and from the INVALSI study of
math and language abilities providing futher evidence for
higher cognitive abilities in the north.
Lynn's thesis has also been criticized by Robinson, Saggino,
and Tommasi (2011) on the grounds that tests reading and
math show much smaller northsouth differences and by
D'Amico, Cardaci, Di Nuovo, and Naglieri (2012) that other
cognitive tests show no northsouth differences. In a more
recent paper, Cornoldi et al. (2013) presented data showing
that the northsouth difference in PISA 2009 was smaller than
in PISA 2006, which they attributed to an improvement in
schools in the south, and they also presented data showing that
Intelligence 46 (2014) 246249
Corresponding author.
E-mail address: (D. Piffer).
0160-2896/© 2014 Elsevier Inc. All rights reserved.
Contents lists available at ScienceDirect
northsouth differences in language and math abilities in the
INVALSI study varied among the second, fifth, sixth and tenth
grade school students from 0.03 to 0.33d(SD units) averaging
0.15 equivalent to 2.25 IQ points. Cornoldi et al. (2013, p. 29)
conclude their discussion of the studies of northsouth differ-
ences in Italy in intelligence and educational attainment by
observing that variations in the different studies are so high to
legitimate radically different conclusions, suggesting that further
evidence is needed to reach unquestionable conclusions.
Lynn (2010a) argued further that the northsouth IQ
differences in Italy are attributable in part to immigration
from North Africa and the Middle East in the more southerly
regions in historical times, and that the genetic legacy of this
has been to reduce the IQs of the populations. This hypothesis
has been supported byTempler (2012), whoshowed that there
are significant genetic differences between north and south
Italy with higher percentages of the population with black hair
and eyes in the south indicating greater admixture of North
African and Middle Eastern genes. This hypothesis has been
further supported by Lynn (2012b) in a study showing that
northsouth differences in intelligence are also present in Spain
with the north having higher IQs, educational attainment, per
capita income, literacy, life expectancy and employment and
lower frequencies of alleles of the Near East and North Africa.
In this paper, we present new data that provide a test of
Lynn's (2010a) hypothesis that the northsouth difference in
Italy in PISA scores in reading comprehension, mathematical
and science abilities of 15-year-olds are attributable to
differences in intelligence and the competing hypothesis of
critics that they are attributable to differences in educational
resources, and of Lynn's hypothesis that the northsouth
difference in intelligence is attributable in part to greater
genetic admixture in the south from NearEast and North Africa.
2. Methods
The OECD (2014) has recently published data for 2012 for
the performance of 15-year-old students in the PISA Creative
Problem Solving, a measure of the ability to solve problems in
non-routine situationsdefined as situations that require at
least 30 minutes to find a good solution(p. 26). The solution of
these problems requires the ability to think flexibly and
creatively about how to overcome the barriers that stand in the
way of a solution(p. 26). A ready-made strategyor a
mastery offacts and procedures is not sufficient for the solution
of these problems. The Creative Problem Solving test assesses
students' general reasoning skills, their ability to regulate
problem-solving processes, and their willingness to do so, by
confronting students with problems that do not require expert
knowledge to solve.The test measures the ability to solve
problems in situations that students may encounter outside of
school as part of their everyday experience(e.g., technology
devices, unfamiliar spaces, food or drink) (p. 31) and an
individual's capacity to engage in cognitive processing to
understand and resolve problem situations where a method of
solution is not immediately obvious(p. 32) involving scenarios
related to real life problemsin the four areas described as
technology, non-technology, personal and social. For example, in
a technological problem, the student is given a technological
device and has to figure out how it works and which buttons
have to be pressed to change the volume or change the type of
music (e.g., Describe how you could change the way the MP3
player works so that there is no need to have the bottom
button,”“Find whether each control influences temperature
and humidity by changing the slidersand Use the controls to
set the temperature and humidity to the target levels). In a
non-technological and social problem, the student is given a
map showingtravel time on each section of a road. An example
item is Pepi is at Sakharov and wants to travel to Emerald. He
wants to complete his trip as quickly as possible. What is the
shortest time for his trip?In terms of Cattell's (1971) concepts
of fluid and crystallized intelligence, the Creative Problem
Solving test is a measure of fluid intelligence defined as the
ability to think logically and solve problems in novel situations,
independent of acquired knowledge, while the PISA tests of
reading comprehension, mathematical and science abilities are
measures of crystallized intelligence.
3. Results
The mean scores of student performance on the Creative
Problem Solving test for five Italian macro-regions extracted
from OECD (2014, Table v2.b2, p. 226) are given in Table 1.The
five Italian macro-regions consist of the northeast (Bolzano,
Emilia Romano, Friuli Venezia, Tentino and Veneto), the
northwest (Liguria, Lombardy, Piedmont and Valle d'Aosta),
the center (Marche, Lazio, Tuscany and Umbria), the south
(Abruzzo, Campania, Molise and Puglia) and the South Islands
(Sicily and Sardinia, andcuriouslyBasilicata and Calabria).
Table 1 gives the meanscores, standard errors and standard
deviations for the five Italian macro-regionsin the PISA creative
problem solving test, followed by the scores converted to
Greenwich IQs,calculated with the British mean = 517 and
SD = 96 (the OECD average) equal to an IQ of 100 and SD =
15. The formula for the conversion to Greenwich IQs is thus
[(X517) / 96] × 15 + 100, where Xis the PISA score. The
right-hand column headed PISA-RMS 2006 gives the Green-
wich IQs for approximately the same regions for the PISA
2006 results for reading, mathematics and science calculated
from the data given in Lynn (2010a).
The OECD report states that the number of students tested in
Italy was approximately 1,300 butdoesnotgivethenumbersfor
the five regions, so it is not possible to calculate the statistical
significance of the differences between the regions. Nevertheless,
it will be seen that there are substantial differences of around 8
IQ points between the highest mean scores in the two northern
regions and the lowest scores in the south and south islands.
Table 1
Mean scores, standard errors and standard deviations and Greenwich IQs in
student performancein the Italian regions in the PISAcreative problem solving
test (PISA-CPS 2012) and Greenwich IQs in student performance in the PISA
2006 tests of reading, mathematics and science (PISA-RMS 2006).
Italian regions Mean score
Northeast 527 (6.4) 91 101.56 101.25
Northwest 533 (8.6) 83 102.50 99.00
Center 514 (10.8) 93 99.53
South 474 (8.4) 82 93.28 91.00
South Islands 486 (8.5) 90 95.15 90.30
247D. Piffer, R. Lynn / Intelligence 46 (2014) 246249
Table 2 shows the mean score regional differences for the
low and high (5th and 95th) percentiles.It will be seen that the
northsouth gradient is present at both these percentiles. The
biggest difference among the regions for the 5th percentile is
53 (northwestsouth islands), and for the 95th percentile
(northeastsouth) is 66.
Table 3 shows the relative performance of Italian students
in the five regions compared with students in the other
countries with similar scores in reading, mathematics and
science in the PISA 2012 tests (i.e., the difference between the
actual performanceand the fitted value from a regression using
a second-degree polynomial as regression function) extracted
from OECD (2014, Table B2.v3). It can be seen that in all five
regions, the Italian students score relatively better at the
Creative Problem Solving test than at the reading, mathematics
and science tests.
4. Discussion
There are seven points of interest in this study. First, the
results confirm Lynn's (2010a) thesis that there is a difference of
approximately 10 IQ points in intelligence in Italy between the
north and the south. In his first paper, Lynn (2010a) estimated a
difference of 10.25 IQ points between the northwest and the
south; in his second paper, Lynn (2010b) presented data for an
intelligence test and estimated a difference of 12.7 IQ points
between Friuli-Venezia in the northeast and Sicily in the south,
and in the present paper, we estimate a difference of 9.20 IQ
points between the northwest and the south. Scores for
individual regions were not published, but it is to be expected
that the difference between the highest and the lowest scoring
region is greater than the differences between the macro-
regions, as within each macro-region there are small differences
between individual regions.
Second, the present results provide a test of the competing
hypotheses that the northsouth differences in cognitive
ability in Italy found in the PISA 2006 scores for reading
comprehension, mathematics and science are measures of
educational attainment determined by differences in educa-
tional resources, as argued by Beraldo (2010),Cornoldi et al.
(2010),Felice and Giugliano (2011) and Cornoldi et al. (2013)
and by Lynn's (2010a) thesis in that the PISA scores are
measures of intelligence. The present results showing that the
northsouth difference in cognitive ability is present in the
PISA 2012 Creative Problem Solving test as a measure of fluid
intelligence designed to be independent of the school curric-
ulum and educationally acquired knowledge supports the Lynn
Third, there is now a substantial body of research showing
that the IQ in the north of Italy is approximately 10 points
higher than in the south. This has been reported for the PISA
2006 and PISA 2009 tests of reading comprehension, mathe-
matics and science minimizing knowledge of the curriculum
(Lynn, 2010a, 2012a), Raven's Progressive Matrices and an
Internet test of intelligence (Lynn, 2010b), and the PISA 2012
Creative Problem Solving test of fluid intelligence (this study).
The northsouth differences are smaller on tests of reading and
math cited by Robinson et al. (2011) and in the INVALSI study
in which they average only 0.15d(SD units) (Cornoldi et al.,
2013). Thus, children in the south perform much better on tests
of educational attainment measured in the INVALSI study than
they perform on tests of intelligence. These results suggest that
poor educational resources in the south, including the poorly
trained teachers cited by Cornoldiet al. (2013, p. 30), make only
a minor contribution to the lower performance in cognitive
Fourth, the results given in Table 1 show that the Italian
students scored slightly higher in the PISA 2012 Creative
Problem Solving test than in the PISA 2006 reading compre-
hension, mathematics and science tests. To the extent that the
skills required to solve novel complex problems are less reliant
upon knowledge and cognitive strategies acquired through
formal instruction, this suggests that Italian students do not
perform at their full potential in the reading, mathematics and
science tests, and this may be attributable to poorer teaching
Fifth, the data given in Table 2 show that the northsouth
gradient in PISA scores for creative problem solving ability is
present not only in the means (shown in Table 1)butatthe
high level of ability represented by the 95th percentile and at
the low level of ability represented by the 5th percentile. The
northsouth differences at the lowest and highest percentiles
(5th and 95th) are of similar magnitude, although there is a
tendency for scores at the higher percentiles to show a greater
difference betweennorth and south compared to the difference
at the lowest percentiles (66 vs 53).
Sixth, the results provide data on the theory that selective
migration from the south to the north may have contributed to
the northsouth IQ disparity. There has been massivemigration
from the poor south to the wealthy north, particularly in the
20th century, and several studies have shown that migrants
from poor regions to more affluent regionshave above average
IQs. For instance, Tolnay (1998) and Vigdor (2002) have
reported that in the United States, Blacks who migrated from
the southern states to the northern states had greater
educational attainment (a proxy for intelligence) than those
who remained in the south, and Maxwell (1967) reported that
Table 2
Mean scores in student performance in creative problem solving in the bottom
(5th) and top (95th) percentiles in Italian regions.
Italian regions Bottom
Italy: all 356 649
Northeast 367 665
Northwest 392 661
Center 345 653
South 344 599
South Islands 339 634
Table 3
Relative performance of Italian regions in creative problem solving and in
reading, mathematics and science (PISA 2012).
Regions Actual minus
expected score (SE)
Northeast 4 (4.9)
Northwest 15 (8.4)
Center 11 (7.2)
South 10 (7.5)
South Islands 9 (8.2)
248 D. Piffer, R. Lynn / Intelligence 46 (2014) 246249
emigrants from Scotland had an IQ of 8.1 points higher than
that of the population.
In Italy, migration from the south has been largely to the
metropolitan areas and cities of Milan, Turin and Genoa in the
northwest. The data given in Table 1 show that there was little
difference in IQs between northwest and northeast in the
creative problem solving ability results (102.50 and 101.56,
respectively), while in the PISA 2006 IQs for reading compre-
hension,mathematics and science the northeast scored 2.25 IQ
points higher than the northwest (101.25 and 99.0, respective-
ly). Taking the two results together shows there is virtually no
difference betweenthe IQs in the northwest and northeast.This
suggests that migrants from southern Italy have not affected
the IQ of the population of the northwest. It may be that in Italy,
selective migration from the south to the northwest has
reduced the IQ in the south. A likely scenario is that migrants
from the south to the northwest had an average IQ about the
same as that of the northwest. This would have lowered the IQ
of the populations in the south and left the IQ of northwest
unchanged. This scenario implies that the historic IQ differ-
ences between north and south Italy documented by Lynn
(2010a) have been increased by selective migration.
Seventh, there are new data that bear on the theory proposed
by Lynn (2010a) that genetic admixture with North African and
Middle East peoples in the population of south Italy has been
that admixture with African populations is unlikely as an
explanation because southern Italians have only around 1% of
African genes (compared to near zero for northern Italians)
(Eurogenes K13),butrecentdatahaveconfirmedthatthe
percentage of Middle Eastern and South West Asian genes
inferred from Eurogenes K13 (East Med + Red Sea + West
Asian) is higher in the south (31.82 + 5.12 + 15.02 = 51.96%)
than that in the north (19.58 + 2.78 + 6.9 = 29.26%).
Conversely, the percentage of North European (Western
European + Baltic) genes in the north (31.67 + 11.92 =
43.59) is higher than in the south (16.71 + 5.91 = 22.62%)
(Eurogenes K13).
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ADHD and general intelligence are negatively correlated (within populations) and this correlation is driven by common genetic variants shared between the two phenotypes. This paper analyzes the population frequency patterns of alleles associated with ADHD and intelligence in two samples of 26 and 50 populations (1000 genomes and ALFRED). Factor analysis of allele frequencies was used to estimate the strength of natural selection on the two traits. The two factors, indicating selection for general intelligence and ADHD, show strong negative correlations in both 1000 Genomes and ALFRED samples (r= -0.93 and -0.90, respectively). Alleles with lower p-values would be less likely to be false positives, so the more significant ADHD GWAS hits are expected to be more strongly negatively correlated with the general intelligence SNP and the ADHD SNP factors, which were also found (r=-0.26 and 0.37, respectively). The ADHD factor predicted national IQs also after accounting for a measure of population structure (Fst). Results are interpreted in a framework based on evolutionary convergent selection pressure for higher general intelligence and lower ADHD.
... Inevitably, the study also has a number of weaknesses: the sample was geographically and diagnostically (TBI) restricted. Given recent reports of varying performance on cognitive tests as a function of geographic location McDaniel, 2006;Piffer & Lynn, 2014), future research is needed to establish the generalizability of the findings. Perhaps more Downloaded from by Leddy Library, University of Windsor user on 11 November 2019 importantly, data on relevant patient characteristics such as the exact duration of loss of consciousness or time since injury, pre-injury trauma history, presence of external incentive to underperform, prior psychiatric treatment and substance abuse were not available, even though research suggests that some of these variables are significant predictors of both recovery from TBI (Belanger et al., 2005;Collaborators, 2008;Jennett et al., 1979;McCrea et al., 2009;Sherer et al., 2008) and PVT failures (Bigler, 2015;Bolinger et al., 2014;Donders & Boonstra, 2007;Erdodi, Seke, et al., 2017;Moore & Donders, 2004;Slick et al., 1999). ...
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Objective: This study was designed to evaluate the classification accuracy of a multivariate model of performance validity assessment using embedded validity indicators (EVIs) within the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV). Method: Archival data were collected from 100 adults with traumatic brain injury (TBI) consecutively referred for neuropsychological assessment in a clinical setting. The classification accuracy of previously published individual EVIs nested within the WAIS-IV and a composite measure based on six independent EVIs were evaluated against psychometrically defined non-credible performance. Results: Univariate validity cutoffs based on age-corrected scaled scores on Coding, Symbol Search, Digit Span, Letter-Number-Sequencing, Vocabulary minus Digit Span, and Coding minus Symbol Search were strong predictors of psychometrically defined non-credible responding. Failing ≥3 of these six EVIs at the liberal cutoff improved specificity (.91-.95) over univariate cutoffs (.78-.93). Conversely, failing ≥2 EVIs at the more conservative cutoff increased and stabilized sensitivity (.43-.67) compared to univariate cutoffs (.11-.63) while maintaining consistently high specificity (.93-.95). Conclusions: In addition to being a widely used test of cognitive functioning, the WAIS-IV can also function as a measure of performance validity. Consistent with previous research, combining information from multiple EVIs enhanced the classification accuracy of individual cutoffs and provided more stable parameter estimates. If the current findings are replicated in larger, diagnostically and demographically heterogeneous samples, the WAIS-IV has the potential to become a powerful multivariate model of performance validity assessment. Brief summary: Using a combination of multiple performance validity indicators embedded within the subtests of the Wechsler Adult Intelligence Scale, the credibility of the response set can be established with a high level of confidence. Multivariate models improve classification accuracy over individual tests. Relying on existing test data is a cost-effective approach to performance validity assessment.
... For Ballarino et al. (2014), the social background influences the transition between secondary education levels, and a problematic condition is observed in the South. Human capital also suffers from regional disparities (Piffer and Lynn, 2014) up to the level of universities and the research system (Abramo et al., 2016), although the relative North-South convergence in terms of human capital is one of the greatest post-unification successes (Felice, 2007). ...
The economic recession that followed the 2007 crisis has widened the economic gaps between the wealthiest and the relatively poorer regions in Italy. The Great Recession has changed the importance of local economic strengths and hindered the possibilities of economic recovery, especially in the Mezzogiorno of Italy. We seek the local strengths present in Italian regions in the post‐crisis period by comparing two macro areas to observe strong and weak points for intervention. A first analysis using multivariate adaptive regression splines (MARS) is used to filter the relevant determinants in a large dataset, and a panel data analysis serves to obtain group‐specific results. Some effects of the prolonged recession are confirmed in all regions, while some weaknesses of the South, such as financial markets, play an increasing role in the regional development scenarios.
... Human capital and social capital have therefore affected the process of divergence since the time of unification, showing very different contributions in the local economies and societies of the North and South [36]. More recently, a North-South divide has been found in the social and cooperative behaviors [39] and the educational and cultural backgrounds [40,41] of the two areas. Although the cultural and scholarly uniformity with the North was the greatest success achieved by the South with unification, the current stock of human capital suffers from a quality gap. ...
In the years of the prolonged post-crisis recession, the well-known North-South divide in Italy has significantly worsened. Several structural weaknesses limit both post-crisis recovery and socioeconomic convergence. A greater understanding of the economic contribution of workers’ human capital, which is not fully exploited in Italy, could address the two issues. We analyze the effects of human capital on local economic performance and productivity, along with other socioeconomic variables, controlling for the endogeneity problem. Workers with a better education can promote economic recovery through productivity enhancement in the South, while traditional aspects related to industrialization are significant in the “wealthy North”. However, structural aspects, such as the local financial systems, must be developed to start a path to convergence for the North and the South.
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This study was designed to investigate the potential of extreme scores on the Behavioral Rating Inventory of Executive Function-Adult Self-Report Version (BRIEF-A-SR) to serve as validity indicators. The BRIEF-A-SR was administered to 73 university students and 50 clinically referred adults. In the student sample, symptom validity was operationalized as the outcome on the Inventory of Problems (IOP-29). In the patient sample, performance validity was operationalized as the outcome on a combination of free-standing and embedded indicators. The BRIEF-A-SR had better classification accuracy in the student sample (.13–.56 sensitivity at .88–.95 specificity) compared with the patient sample (.22–.44 sensitivity at .85–.97 specificity). Combining individual cutoffs into a multivariate model improved specificity (.93) and stabilized sensitivity (.33) in the clinical sample. Failing the newly introduced cutoffs (T ≥ 65/T ≥ 80 in the student sample and T ≥ 80/T ≥ 90 in the clinical sample) was associated with failure on performance validity tests and elevations on other symptom inventories. Results provide preliminary support for an alternative method for establishing the credibility of symptom reports both within the BRIEF-A-SR and other inventories. Pending replication by future research, the newly proposed cutoffs could provide a much needed psychometric safeguard against over-diagnosing neuropsychiatric disorders due to undetected symptom exaggeration.
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Purpose – The aim of this paper is to evaluate the kind of evidence and arguments used to support Richard Lynn's increasingly influential doctrine that genetically determined differences in population IQ are the main cause of differences in regional and national levels of socio‐economic development and public health status. Design/methodology/approach – The paper's approach is two‐fold. First, new data on the correlation between regional differences in educational achievement of Italian schoolchildren and regional differences in socio‐economic development are presented in order to test the validity of Lynn's report that there is a progressive North‐to‐South reduction of Italian regional IQ that is highly correlated with a corresponding North‐to‐South reduction in the level of socio‐economic development. Second, a thorough and systematic review of the content of Lynn's article is carried out in order to assess the validity of the data, methods, and arguments normally used to support his socio‐economic doctrine. Findings – Lynn's study uses regional differences in the performance of Italian secondary school children on Organisation for Economic Co‐operation and Development tests of educational achievement to assess regional IQ differences. However, data on Italian regional differences in educational achievement obtained in a much larger INVALSI study of 2,089,829 Italian schoolchildren provide unequivocal evidence that Lynn's educational achievement measure is not a valid index of IQ differences. More generally, the lengthy literature review in Lynn's article reveals uncritical acceptance of reported correlations between any putative index of IQ and socio‐economic variables. Any measure of cognitive performance that is correlated with IQ is considered a measure of IQ, even if there is only a weak correlation. All correlations between such measures and socio‐economic or public health variables are viewed as evidence of direct causal relationships. In all cases, causality is assumed to be in the direction that supports Lynn's doctrine when it would be equally valid to argue that socio‐economic and public health differences cause differences in the performance of IQ tests. In addition to these fundamental logical and statistical errors the present report records numerous other data processing, methodological, and conceptual errors. Originality/value – The value of the present article is that it demonstrates the flawed manner in which data are interpreted and analysed in order to support Lynn's thesis. Left unchallenged, this pernicious doctrine would promote a socially damaging conception of critically important socio‐economic and public health issues that would discourage the adoption of national policies designed to increase levels of socio‐economic development and improve public health status.
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.
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.
Criticisms advanced by Felice and Giugliano (2011) of the thesis that IQs in Italy are higher in the north than in the south are answered and new data confirming the thesis are given from the PISA 2009 study and for math and reading abilities in the recent INVALSI study. New genetic data are given showing higher frequency of blond hair the haplogroup xR1 allele and the haplogroup E1b1b allele as markers for greater percentage northern and central European ancestry in northern Italian regions.
Beraldo (2010) and Cornoldi, Belacchi, Giofre, Martini, and Tressoldi (2010) (CBGMT) have eight criticisms of my paper (Lynn, 2010) claiming that the large north–south differences in per capita income in Italy are attributable to differences in the average levels of intelligence in the populations. CBGMT give results for seven data sets for IQs in the north and south of Italy. All of these show that IQs are higher than in the north than in the south, although the differences are not as great as those I calculated. Other criticisms to the effect that the PISA tests are not measures of intelligence are refuted. The results of two further studies are given that confirm that IQs in the north of Italy are approximately 10 IQ points higher than in south.
During the twentieth century millions of African Americans have migrated from the South to northern cities. Contrasting descriptions of this migration stream have been presented in the literature—some emphasizing the rural origins and lack of schooling of migrants, others claiming that migrants were positively selected from the southern black population. This study uses the newly available Integrated Public Use Microdata Series to compare the educational characteristics of southern migrants with (1) the southern population they left behind and (2) the northern population they joined. Consistent with the expectations of migration theory, and previous evidence for specific time periods, the findings show that between 1880 and 1990 black migrants had significantly higher levels of education than the sedentary southern population and significantly lower levels of education than the northern-born population. Both differentials grew smaller as the century progressed.