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  <fig rect=(446.4570007324219, 53.40504455566406, 503.3330078125, 124.9510498046875)></fig> <sub>256</sub> <sub>R. Lynn / Intelligence 40 (2012) 255</sub> <sub>–</sub> <sub>259</sub>
  <sub>3. The 2009 PISA study</sub>
  <sub>We now present new data showing that IQs are higher in</sub> <sub>the north of Italy than in the south. In the previous study,</sub> <sub>data were presented for 12 Italian regions from the PISA</sub> <sub>(Program for International Student Assessment) 2006 study</sub> <sub>of the reading comprehension, mathematics and science</sub> <sub>performance of 15 year olds, regarded as measures of intelli-</sub> <sub>gence. We are now able to give similar data on the reading</sub> <sub>comprehension, mathematics and science performance of</sub> <sub>15 year olds in 20 Italian regions obtained in the 2009 PISA</sub> <sub>study (</sub> <sub>OECD, 2010</sub> <sub>). These are given in</sub> <sub> Table 1</sub> <sub>. This shows,</sub> <sub>reading from left to right, the latitude of the Italian regions,</sub> <sub>the mean PISA scores for 12 regions for 2006 given in</sub> <sub> Lynn</sub> <sub>(2010a)</sub> <sub>, the mean scores of 15 year olds on reading compre-</sub> <sub>hension, mathematics and science understanding for the 20</sub> <sub>Italian regions obtained in the 2009 PISA study, and the aver-</sub> <sub>ages of the three 2009 PISA scores given because it provides a</sub> <sub>convenient summary of the scores on the three tests.</sub> <sub>The correlations between these variables are given in</sub>
  <sub>Table 2</sub> <sub>. This shows that the high correlation between PISA</sub> <sub>2006 average scores and latitude (.97) previously reported</sub> <sub>for 12 regions is confirmed by the PISA 2009 scores for 20</sub> <sub>regions. The average of the PISA 2009 scores is correlated</sub> <sub>with latitude at .91. The PISA 2009 data make it possible</sub> <sub>to calculate the reliability of the PISA scores from the cor-</sub> <sub>relation between the average 2006 and the average 2009</sub> <sub>scores, for which</sub> <sub> r</sub> <sub>=.89, showing that the PISA data have</sub> <sub>high reliability.</sub>
  <sub>4. The INVALSI survey</sub>
  <sub>scientific community must then judge between competing</sub> <sub>hypotheses. For the present set of problems, it is considered</sub> <sub>that there are no plausible competing hypotheses because the</sub> <sub>alternative proposed by Felice and Giugliano that the regional</sub> <sub>differences in PISA, average incomes and other phenomena</sub> <sub>are attributable to differences in educational resources beg</sub> <sub>the question of why all of these are poorer in the south and</sub> <sub>are highly correlated with latitude, for which they are unable</sub> <sub>to provide an answer.</sub> <sub>(3)</sub> <sub> “</sub> <sub>There is no evidence that the alleged differences in IQ</sub> <sub>are persistent in time and therefore attributable to genetic</sub> <sub>factors.</sub> <sub>”</sub> <sub> They assert that in classical times the Greek colony</sub> <sub>in Sicily produced people like Pythagoras and Archimedes</sub> <sub>and was more advanced than the center and north culturally,</sub> <sub>technologically and probably economically and hence</sub> <sub> “</sub> <sub>if</sub> <sub>there was any relation between race and culture, it goes in</sub> <sub>the opposite direction of the one envisaged by Lynn, at least</sub> <sub>until the low middle ages.</sub> <sub>”</sub> <sub> This criticism misses the point</sub> <sub>that the theory advanced in</sub> <sub> Lynn (2010a)</sub> <sub> is that it was the</sub> <sub>Arab occupation of Sicily and the southern mainland of Italy</sub> <sub>during the eighth to the eleventh centuries that introduced</sub> <sub>North African genes into the population and has been re-</sub> <sub>sponsible for the lower IQs and poorer economic and cultural</sub> <sub>development. Thus, the criticism that regional differences in</sub> <sub>intelligence present in Italy today cannot explain historical</sub> <sub>differences in per capita income and cultural achievements</sub> <sub>in classical times and the first millennium does not invalidate</sub> <sub>the theory that they can do so from the early middle ages up</sub> <sub>contemporary times.</sub> <sub>(4)</sub> <sub> Felice and Giugliano's (2011)</sub> <sub> final point is that the</sub> <sub>greater admixture of North African ancestry in southern</sub> <sub>Italy has no relevance for the lower IQs, earnings, educational</sub> <sub>attainment and other variables. Contrary to this contention,</sub> <sub>an association between the percentage of North African an-</sub> <sub>cestry and lower IQs, earnings, educational attainment and</sub> <sub>other variables is predictable from the lower values of these</sub> <sub>variables throughout North Africa and the Near East docu-</sub> <sub>mented by</sub> <sub> Lynn and Vanhanen (2006)</sub> <sub>.</sub>
  <sub>New data for abilities in math and language comprehen-</sub> <sub>sion of school students in five Italian regions have become</sub> <sub>available in the</sub> <sub> INVALSI (2011)</sub> <sub> survey. This survey consisted</sub> <sub>of tests of math and of knowledge and comprehension in the</sub> <sub>Italian language for five age groups. The survey involved a</sub> <sub>total of 14,400 schools, 112,500 classes and 2,250,000</sub>
  <sub>Table 1</sub> <sub>Descriptive statistics for regional differences in Italy.</sub>
  <sub>Region</sub> <sub>Latitude</sub> <sub>PISA 2006</sub> <sub>PISA 2009</sub> <sub>PISA 2009</sub> <sub>PISA 2009</sub> <sub>PISA 2009</sub> <sub>Blond hair %</sub> <sub>xR1a</sub> <sub>E1b1b</sub>
  <sub>Reading</sub> <sub>Math</sub> <sub>Science</sub> <sub>Average</sub>
  <sub>Friuli VG</sub> <sub>46.00</sub> <sub>522</sub> <sub>513</sub> <sub>510</sub> <sub>524</sub> <sub>516</sub> <sub>17.5</sub> <sub>—</sub> <sub>—</sub> <sub>Trentino</sub> <sub>46.00</sub> <sub>512</sub> <sub>508</sub> <sub>514</sub> <sub>523</sub> <sub>515</sub> <sub>17.5</sub> <sub>67.40</sub> <sub>6.00</sub> <sub>Veneto</sub> <sub>45.50</sub> <sub>515</sub> <sub>505</sub> <sub>508</sub> <sub>518</sub> <sub>510</sub> <sub>12.9</sub> <sub>45.50</sub> <sub>—</sub> <sub>Lombardy</sub> <sub>45.00</sub> <sub>515</sub> <sub>522</sub> <sub>516</sub> <sub>526</sub> <sub>521</sub> <sub>10.6</sub> <sub>61.10</sub> <sub>—</sub> <sub>Valle D'Aosta</sub> <sub>46.00</sub> <sub>—</sub> <sub>514</sub> <sub>502</sub> <sub>521</sub> <sub>512</sub> <sub>22.5</sub> <sub>—</sub> <sub>—</sub> <sub>Piedmont</sub> <sub>45.00</sub> <sub>502</sub> <sub>496</sub> <sub>493</sub> <sub>501</sub> <sub>497</sub> <sub>15.0</sub> <sub>—</sub> <sub>—</sub> <sub>Emilia-Romagna</sub> <sub>44.50</sub> <sub>500</sub> <sub>502</sub> <sub>503</sub> <sub>508</sub> <sub>504</sub> <sub>7.5</sub> <sub>—</sub> <sub>—</sub> <sub>Marche</sub> <sub>43.50</sub> <sub>—</sub> <sub>499</sub> <sub>499</sub> <sub>504</sub> <sub>501</sub> <sub>8.0</sub> <sub>—</sub> <sub>—</sub> <sub>Liguria</sub> <sub>44.50</sub> <sub>481</sub> <sub>491</sub> <sub>491</sub> <sub>498</sub> <sub>493</sub> <sub>7.5</sub> <sub>46.60</sub> <sub>—</sub> <sub>Toscana</sub> <sub>43.50</sub> <sub>—</sub> <sub>493</sub> <sub>493</sub> <sub>500</sub> <sub>495</sub> <sub>4.6</sub> <sub>58.40</sub> <sub>6.50</sub> <sub>Umbria</sub> <sub>43.00</sub> <sub>—</sub> <sub>490</sub> <sub>486</sub> <sub>497</sub> <sub>491</sub> <sub>9.0</sub> <sub>—</sub> <sub>—</sub> <sub>Lazio</sub> <sub>41.50</sub> <sub>—</sub> <sub>481</sub> <sub>473</sub> <sub>482</sub> <sub>479</sub> <sub>3.4</sub> <sub>37.50</sub> <sub>—</sub> <sub>Abruzzo</sub> <sub>41.00</sub> <sub>—</sub> <sub>480</sub> <sub>476</sub> <sub>480</sub> <sub>479</sub> <sub>4.7</sub> <sub>39.90</sub> <sub>—</sub> <sub>Molise</sub> <sub>40.50</sub> <sub>—</sub> <sub>471</sub> <sub>467</sub> <sub>469</sub> <sub>469</sub> <sub>4.3</sub> <sub>—</sub> <sub>—</sub> <sub>Basilicata</sub> <sub>40.00</sub> <sub>447</sub> <sub>447</sub> <sub>473</sub> <sub>474</sub> <sub>471</sub> <sub>4.6</sub> <sub>36.65</sub> <sub>—</sub> <sub>Campania</sub> <sub>40.50</sub> <sub>439</sub> <sub>451</sub> <sub>447</sub> <sub>446</sub> <sub>448</sub> <sub>2.5</sub> <sub>27.50</sub> <sub>20.00</sub> <sub>Puglia</sub> <sub>40.00</sub> <sub>441</sub> <sub>489</sub> <sub>488</sub> <sub>490</sub> <sub>489</sub> <sub>2.6</sub> <sub>39.00</sub> <sub>—</sub> <sub>Sardinia</sub> <sub>40.00</sub> <sub>438</sub> <sub>469</sub> <sub>456</sub> <sub>474</sub> <sub>466</sub> <sub>3.00</sub> <sub>19.00</sub> <sub>9.90</sub> <sub>Calabria</sub> <sub>39.00</sub> <sub>—</sub> <sub>448</sub> <sub>442</sub> <sub>443</sub> <sub>444</sub> <sub>3.70</sub> <sub>32.40</sub> <sub>—</sub> <sub>Sicily</sub> <sub>37.00</sub> <sub>427</sub> <sub>453</sub> <sub>450</sub> <sub>451</sub> <sub>451</sub> <sub>5.00</sub> <sub>23.10</sub> <sub>23.10</sub> <sub>257</sub> <sub>R. Lynn / Intelligence 40 (2012) 255</sub> <sub>–</sub> <sub>259</sub>
  <sub>Table 2</sub> <sub>Correlation matrix and sample sizes for variables shown in</sub> <sub> Table 1</sub> <sub>.</sub>
  <sub>PISA 06</sub> <sub>PISA 09</sub> <sub>PISA 09</sub> <sub>PISA 09</sub> <sub>PISA 09</sub> <sub>Blond Hair</sub> <sub>XR1a</sub> <sub>E1b1b</sub> <sub>Avg</sub> <sub>Rdg</sub> <sub>Math</sub> <sub>Sci</sub> <sub>Avg</sub> <sub>%</sub> <sub>Latitude</sub> <sub>.97</sub> <sup>⁎</sup> <sub>.89</sub> <sup>⁎⁎</sup> <sub>.90</sub> <sup>⁎⁎</sup> <sub>.91</sub> <sup>⁎⁎</sup> <sub>.91</sub> <sup>⁎⁎</sup> <sub>.81</sub> <sup>⁎⁎</sup> <sub>.85</sub> <sup>⁎⁎</sup> <sub>−</sub> <sub>.84</sub> <sup>⁎</sup> <sub>N</sub> <sub>12</sub> <sub>20</sub> <sub>20</sub> <sub>20</sub> <sub>20</sub> <sub>20</sub> <sub>13</sub> <sub>5</sub> <sub>PISA 2006 Average</sub> <sub>—</sub> <sub>.90</sub> <sup>⁎⁎</sup> <sub>.92</sub> <sup>⁎⁎</sup> <sub>.93</sub> <sup>⁎⁎</sup> <sub>.93</sub> <sup>⁎⁎</sup> <sub>.88</sub> <sup>⁎⁎</sup> <sub>.88</sub> <sup>⁎</sup> <sub>−</sub> <sub>.77</sub> <sub>N</sub> <sub>12</sub> <sub>12</sub> <sub>12</sub> <sub>12</sub> <sub>12</sub> <sub>9</sub> <sub>4</sub> <sub>PISA 2009 Reading</sub> <sub>—</sub> <sub>.90</sub> <sup>⁎⁎</sup> <sub>.96</sub> <sup>⁎⁎</sup> <sub>.97</sub> <sup>⁎⁎</sup> <sub>.72</sub> <sup>⁎⁎</sup> <sub>.81</sub> <sup>⁎</sup> <sub>−</sub> <sub>.90</sub> <sup>⁎</sup> <sub>N</sub> <sub>20</sub> <sub>20</sub> <sub>20</sub> <sub>20</sub> <sub>13</sub> <sub>5</sub> <sub>PISA 2009 Math</sub> <sub>.98</sub> <sup>⁎⁎</sup> <sub>.99</sub> <sup>⁎⁎</sup> <sub>.70</sub> <sup>⁎⁎</sup> <sub>.88</sub> <sup>⁎⁎</sup> <sub>−</sub> <sub>.81</sub> <sup>⁎</sup> <sub>N</sub> <sub>20</sub> <sub>20</sub> <sub>20</sub> <sub>13</sub> <sub>5</sub> <sub>PISA 2009 Science</sub> <sub>.99</sub> <sup>⁎⁎</sup> <sub>.76</sub> <sup>⁎⁎</sup> <sub>.84</sub> <sup>⁎⁎</sup> <sub>−</sub> <sub>.91*</sub> <sub>N</sub> <sub>20</sub> <sub>20</sub> <sub>13</sub> <sub>5</sub> <sub>PISA 2009 Average</sub> <sub>.74</sub> <sup>⁎⁎</sup> <sub>.86</sub> <sup>⁎⁎</sup> <sub>−</sub> <sub>.88</sub> <sup>⁎</sup> <sub>N</sub> <sub>20</sub> <sub>13</sub> <sub>5</sub> <sub>Blond Hair %</sub> <sub>.73</sub> <sup>⁎⁎</sup> <sub>−</sub> <sub>.41</sub> <sub>N</sub> <sub>13</sub> <sub>5</sub> <sub>XR1a</sub> <sub>−</sub> <sub>.72</sub> <sub>N</sub> <sub>4</sub>
  <sub>Avg</sub> <sub>Rdg</sub> <sub>Math</sub> <sub>Sci</sub> <sub>Avg</sub> <sub>%</sub>
  <sub>* and ** denote statistical significance at</sub> <sub> p</sub> <sub>b</sub> <sub>0.05 and</sub> <sub> p</sub> <sub>b</sub> <sub>0.01, respectively</sub>
  <sub>students. A statistically representative sample was identified,</sub> <sub>consisting of 3,851 schools, 7,810 classes and 166,199 students</sub> <sub>for assessment in five school years consisting of Primary 2 (age</sub> <sub>7</sub> <sub>–</sub> <sub>8), Primary 5 (age 10</sub> <sub>–</sub> <sub>11), Middle 1 (age 11</sub> <sub>–</sub> <sub>12), Middle 2</sub> <sub>(age 12</sub> <sub>–</sub> <sub>13), and Senior 2 (age 15</sub> <sub>–</sub> <sub>16). For the final year of</sub> <sub>middle school (corresponding to the state exam at the end</sub> <sub>of the first scholastic cycle), a sample of 1,312 schools and</sub> <sub>28,361 students was drawn from a total of 5,969 schools,</sub> <sub>27,646 classes and 586,790 students. Results were presented</sub> <sub>for five Italian regions consisting of the North West (Aosta</sub> <sub>Valley, Liguria, Lombardy, Piedmont), the North East (Emilia-</sub> <sub>Romagna, Friuli-Venezia Giulia, Trentino-Alto Adige/Südtirol,</sub> <sub>Veneto), the Center (Latium, Marche, Tuscany, Umbria), the</sub> <sub>South (Abruzzo, Apulia, Basilicata, Calabria, Campania, Molise)</sub> <sub>and the Islands (Sicily and Sardinia).</sub> <sub>The report presents results as percentages of correct an-</sub> <sub>swers above or below the national average. It does not give</sub> <sub>standard deviations, but it gives confidence intervals from</sub> <sub>which the statistical significance of the differences can be es-</sub> <sub>timated. The results for Italian language ability are given in</sub> <sub>Table 3</sub> <sub>. It will be seen that the average percentages of correct</sub> <sub>answers are generally highest in the northern regions, inter-</sub> <sub>mediate in the central region, lower in the south, and lowest</sub> <sub>in the islands. For 3 of the 5 school years (1st and 2nd year of</sub> <sub>middle school and 2nd year of secondary school), the confi-</sub> <sub>dence intervals of the northern (North East+North West)</sub> <sub>and the southern (South) regions do not overlap, showing</sub> <sub>that the differences are statistically significant (</sub> <sub>p</sub> <sub>b</sub> <sub>0.05).</sub> <sub>The northern students performed better than the southern</sub> <sub>students in the first 2 years the (2nd and 5th years of primary</sub>
  <sub>school) but the differences are not statistically significant.</sub> <sub>The islands (Sicily and Sardinia) obtained the lowest per-</sub> <sub>centage of correct answers. The differences between the</sub> <sub>islands and northern Italy are statistically significant in all</sub> <sub>the 5 school years. The average performance of central Italy</sub> <sub>is lower than that of northern Italy but this difference is sig-</sub> <sub>nificant only for the last year (2nd year of secondary school).</sub> <sub>The results for math ability are given in</sub> <sub> Table 4</sub> <sub> and show</sub> <sub>similar regional differences to those for the Italian language.</sub> <sub>The average percentages of correct answers are highest in</sub> <sub>the North (NW+NE), lower in the Central, still lower in the</sub> <sub>South and lowest in the Islands. The differences between</sub> <sub>northern (NW+NE) and southern Italy are statistically sig-</sub> <sub>nificant for all the 5 school years except the first (2nd prima-</sub> <sub>ry). The differences between northern Italy and the islands</sub> <sub>are statistically significant for the four older classes. Central</sub> <sub>Italy scores significantly lower than northern Italy in the</sub> <sub>last 2 years of school and significantly higher than in south-</sub> <sub>ern Italy and the islands for 3 of the 5 years (1st middle, 3rd</sub> <sub>middle and 2nd secondary). It may be argued, however, that</sub> <sub>statistical significance inferred from interval confidences de-</sub> <sub>rived from such large samples should be treated with caution.</sub> <sub>It will be noted that the regional differences in both lan-</sub> <sub>guage and math ability increase with age. For example, in lan-</sub> <sub>guage ability the regional differences in the youngest children</sub> <sub>(P2) range between 1.6 and</sub> <sub> −</sub> <sub>3.8, a difference of 5.4, while</sub> <sub>the differences in the oldest children (2S) range between 3.6</sub> <sub>and</sub> <sub> −</sub> <sub>4.4, a difference of 8.0. Similarly, in math ability the re-</sub> <sub>gional differences in the youngest children (P2) range between</sub> <sub>0.8 and</sub> <sub> −</sub> <sub>1.0, a difference of 1.8, while the differences in the</sub>
  <sub>Table 3</sub> <sub>Regional differences in Italy in language ability.</sub> <sub>Region</sub> <sub>P2</sub> <sub>P5</sub> <sub>M1</sub> <sub>M2</sub> <sub>S2</sub> <sub>North West</sub> <sub>1.6</sub> <sub>0.5</sub> <sub>2.2</sub> <sub>4.1</sub> <sub>3.6</sub> <sub>North East</sub> <sub>1.0</sub> <sub>0.2</sub> <sub>1.6</sub> <sub>5.2</sub> <sub>3.1</sub> <sub>Central</sub> <sub>1.6</sub> <sub>1.0</sub> <sub>1.8</sub> <sub>2.0</sub> <sub>−</sub> <sub>1.0</sub> <sub>South</sub> <sub>−</sub> <sub>1.4</sub> <sub>−</sub> <sub>0.2</sub> <sub>−</sub> <sub>1.6</sub> <sub>−</sub> <sub>4.9</sub> <sub>−</sub> <sub>1.2</sub> <sub>Islands</sub> <sub>−</sub> <sub>3.8</sub> <sub>−</sub> <sub>2.0</sub> <sub>−</sub> <sub>4.8</sub> <sub>−</sub> <sub>5.0</sub> <sub>−</sub> <sub>4.4</sub>
  <sub>Table 4</sub> <sub>Regional differences in Italy in math ability.</sub> <sub>Region</sub> <sub>P2</sub> <sub>P5</sub> <sub>M1</sub> <sub>M2</sub> <sub>2S</sub> <sub>North West</sub> <sub>0.3</sub> <sub>1.2</sub> <sub>3.2</sub> <sub>4.0</sub> <sub>3.5</sub> <sub>North East</sub> <sub>−</sub> <sub>0.2</sub> <sub>1.4</sub> <sub>4.2</sub> <sub>4.4</sub> <sub>4.3</sub> <sub>Central</sub> <sub>0.8</sub> <sub>0.8</sub> <sub>1.4</sub> <sub>2.2</sub> <sub>−</sub> <sub>1.2</sub> <sub>South</sub> <sub>0.4</sub> <sub>−</sub> <sub>0.8</sub> <sub>−</sub> <sub>3.2</sub> <sub>−</sub> <sub>4.8</sub> <sub>−</sub> <sub>1.8</sub> <sub>Islands</sub> <sub>−</sub> <sub>1.0</sub> <sub>−</sub> <sub>3.0</sub> <sub>−</sub> <sub>6.0</sub> <sub>−</sub> <sub>6.0</sub> <sub>−</sub> <sub>5.4</sub> <sub>oldest children (2S) range between 4.3 and</sub> <sub> −</sub> <sub>5.4, a differ-</sub> <sub>ence of 9.7. These age differences would be predicted from</sub> <sub>the thesis that the regional differences have a genetic basis,</sub> <sub>because the heritability of intelligence increases during</sub> <sub>childhood (</sub> <sub>Plomin, DeFries, &amp; McClearn, 1980, p. 334</sub> <sub>).</sub> <sub>5. North</sub> <sub>–</sub> <sub>south Italian genetic differences</sub> <sub>et al. (2010)</sub> <sub>math and rea</sub> <sub>The data g</sub> <sub>ent of the fre</sub> <sub>try and Nort</sub> <sub>in northern</sub> <sub>the percenta</sub> <sub>quencies of</sub> <sub>258</sub> <sub>R. Lynn / Intelligence 40 (2012) 255</sub> <sub>–</sub> <sub>259</sub>
  <sub>et al. (2010)</sub> <sub>; (7) the newer extensive INVALSI results for</sub> <sub>math and reading abilities reported in this paper.</sub> <sub>The data given in</sub> <sub> Lynn (2010a)</sub> <sub> for the north</sub> <sub>–</sub> <sub>south gradi-</sub> <sub>ent of the frequencies of the percentages of European ances-</sub> <sub>try and North African ancestry in the populations, such that</sub> <sub>in northern regions the frequencies of genetic markers for</sub> <sub>the percentages of European ancestry are higher, while fre-</sub> <sub>quencies of genetic markers for the percentages of North</sub> <sub>African ancestry are lower, are corroboratated by three fur-</sub> <sub>ther data sets. These are, first, the high correlation between</sub> <sub>latidude and the percentage of blonds (.81) as a marker for</sub> <sub>the percentage of northern European ancestry and showing</sub> <sub>greater percentages of blonds in the north of Italy. Second,</sub> <sub>the high correlation between latitude and the frequency of</sub> <sub>the xR1a allele (.85) as another marker for the percentage</sub> <sub>of European ancestry in the populations. Third, the high neg-</sub> <sub>ative correlation between latidude and the frequency of the</sub> <sub>haplogroup E1b1b allele (</sub> <sub>−</sub> <sub>.84) as a marker for the percent-</sub> <sub>age of North African and Middle Eastern ancestry.</sub> <sub>Finally, there are high correlations between the two</sub> <sub>markers for the percentages of central and northern European</sub> <sub>ancestry in the regional populations (the percentage of blonds</sub> <sub>and the frequency of the xR1a allele) and IQs measured</sub> <sub>by PISA 2006 and PISA 2009. For the percentage of blonds</sub> <sub>the correlations are .876 and.730. For the frequency of the</sub> <sub>xR1a allele the correlations are .843 and .855. Conversely,</sub> <sub>there are negative correlations between regional IQs and</sub> <sub>the frequency of the haplogroup E1b1b allele, a marker the</sub> <sub>North African and Middle Eastern ancestry. For PISA 2006</sub> <sub>and PISA 2009, these correlations are</sub> <sub> −</sub> <sub>.773 and</sub> <sub> −</sub> <sub>.876.</sub> <sub>These high correlations provide further support the thesis</sub> <sub>that the percentage of central and northern European ances-</sub> <sub>try in the populations across the Italian regions is a signifi-</sub> <sub>cant determinant of the IQs.</sub>
  <sub>References</sub>
  <sub>Here we present new data corroborating the theory that</sub> <sub>the proportions of North African genes in the Italian regions</sub> <sub>are higher in the south than in the north, while the propor-</sub> <sub>tions of central and northern genes in the Italian regions are</sub> <sub>higher in the north than in the south. These data consist</sub> <sub>first of the percentages of the populations with blond hair, a</sub> <sub>marker for northern European ancestry (</sub> <sub>Coon, 1962</sub> <sub>) and</sub> <sub>given by</sub> <sub> Biasutti (1967)</sub> <sub>. These data are given in column 10</sub> <sub>of</sub> <sub> Table 1</sub> <sub> and the correlations between these and the other</sub> <sub>variables are given in</sub> <sub> Table 2</sub> <sub>.</sub> <sub>The second source of data on the proportion of central</sub> <sub>and northern genes in the Italian regions consists of the fre-</sub> <sub>quency of the haplogroup xR1 allele. The haplogroup xR1a1</sub> <sub>is a marker for European Mesolithic populations, which</sub> <sub>were characterised by high frequencies of this haplogroup,</sub> <sub>considered as representative of the original inhabitants of</sub> <sub>Europe (</sub> <sub>Capelli et al. (2007)</sub> <sub>. Its frequencies are higher in</sub> <sub>Central and Western Europe and lower in southern Europe</sub> <sub>(</sub> <sub>Semino et al. (2000)</sub> <sub>.</sub> <sub>Further data for the proportion of North African ancestry in</sub> <sub>the Italian regions are available in the frequency of the hap-</sub> <sub>logroup E1b1b allele. This is a marker for North African ances-</sub> <sub>try, where it reaches frequencies above 50% and peaks at</sub> <sub>around 82% in Tunisia (</sub> <sub>Zalloua et al., 2008</sub> <sub>). The frequencies</sub> <sub>of the haplogroup xR1 and the E1b1b alleles are taken from</sub> <sub>Capelli et al. (2006)</sub> <sub>,</sub> <sub> Capelli et al. (2007)</sub> <sub>,</sub> <sub> Di Giacomo et al.</sub> <sub>(2003)</sub> <sub>,</sub> <sub> Balaresque et al. (2010)</sub> <sub>,</sub> <sub> Scozzari et al. (2001)</sub> <sub>, and</sub> <sub>Semino et al. (2000)</sub> <sub>. These data are given in columns 11 and</sub> <sub>12 of</sub> <sub> Table 1</sub> <sub> and the correlations between these and the</sub> <sub>other variables are given in</sub> <sub> Table 2</sub> <sub>.</sub>
  <sub>6. Conclusions</sub>
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  <sub>The data reported in this paper provide further evidence</sub> <sub>confirming the thesis that there is a north</sub> <sub>–</sub> <sub>south gradient</sub> <sub>for IQ in Italy and that this is associated with the frequencies</sub> <sub>of genetic markers for the percentages of European and</sub> <sub>North African ancestry in the populations. The thesis that av-</sub> <sub>erage IQs are higher in the northern than in the southern re-</sub> <sub>gions is now supported by seven sets of data. There are (1)</sub> <sub>the studies by</sub> <sub> Peluffo (1962, 1964, 1967)</sub> <sub> using Piagetian</sub> <sub>tests; (2) the results of several studies using the Progressive</sub> <sub>Matrices reported in</sub> <sub> Lynn (2010b)</sub> <sub>; (3) the reading compre-</sub> <sub>hension, mathematics and science performance of 15 year</sub> <sub>olds in the 2006 PISA study for 12 regions, for which the av-</sub> <sub>erage scores are correlated with latitude at .97; (4) the read-</sub> <sub>ing comprehension, mathematics and science performance</sub> <sub>of 15 year olds in the 2009 PISA study for 20 regions, for</sub> <sub>which the average scores are correlated with latitude at</sub> <sub>.93; (5) the MT-Advanced results for math and reading abil-</sub> <sub>ities reported by</sub> <sub> Cornoldi et al. (2010)</sub> <sub>; (6) the INVALSI re-</sub> <sub>sults for math and reading abilities reported by</sub> <sub> Cornoldi</sub> <sub>259</sub> <sub>R. Lynn / Intelligence 40 (2012) 255</sub> <sub>–</sub> <sub>259</sub>
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