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S and G in Italian regions: Re-analysis of Lynn’s data and new data

  • Ulster Institute for Social Research

Abstract and Figures

I analyze the S factor in Italian states by reanalyzing data published by Lynn (2010) as well as new data compiled from the Italian statistics agency (7 and 10 socioeconomic variables, respectively). The S factors from the datasets are highly correlated (.92) and both are strongly correlated with a G factor from PISA scores (.93 and .88).
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The Winnower
Published 2015-03-05
S and G in Italian regions: Re-
analysis of Lynn's data and new data
Emil O. W. Kirkegaard1
The S factor in Italian regions was examined by reanalyzing data published by Lynn (Lynn, 2010) as
well as new data compiled from the Italian statistics agency (7 and 10 socioeconomic variables,
respectively). The S factors from the datasets were highly correlated (.92) and both are strongly
correlated with a G factor from PISA scores (.93 and .88).
Key words: Italy, regions, social inequality, S factor, general socioeconomic factor, IQ, intelligence,
cognitive ability, PISA, cognitive sociology
1. Introduction
One can study a given human trait at many levels. Probably the most common is the individual level.
The next-most common the inter-national, and the least common perhaps the intra-national. This last
one can be done at various level too, e.g. state, region, commune, and city. These divisions usually vary
by country.
The study of general intelligence (GI) at these higher levels has been called the ecology of intelligence
by Richard Lynn (Lynn, 1979, 1980) and the sociology of intelligence by Linda Gottfredson
(Gottfredson, 1998). Lynn has since published a number of papers on the regions of Italy (Lynn, 2010,
2012; Piffer & Lynn, 2014). The present study re-analyses some of this data. After this a new, larger,
more diverse dataset is presented and analyzed.
2. Lynn’s 2010 data
True to his style, Lynn (2010) contains the raw data used for his analysis. This is fortunate because it
means anyone can re-analyze them. His paper contains the following variables:
1 University of Aarhus, Denmark. Email:
Page 1 of 10.
1. 3x PISA subtests: reading, math, science
2. An average of these PISA scores
3. An IQ derived from the average
4. Stature for 1855, 1910, 1927 and 1980
5. Per capita income for 1970 and 2003
6. Infant mortality for 1955 and 1999
7. Literacy 1880
8. Years of education 1951, 1971 and 2001
9. Latitude.
These data are given for 12 Italian regions.
Lynn himself merely did correlational analysis and discussed the results. The data however can be
usefully factor analyzed to extract a G (from the three PISA subtests) and an S factor (from all the
socioeconomic variables).
Lynn’s choice of variables is quite odd. They are not all from the same years, presumably because he
picked them from various other papers instead of going to the Italian statistics website to fetch some
himself. This opens the question of how to analyze them. Three approaches were used: 1) factor
analyzed the old data alone, 2) factor analyzed the new data alone, 3) factor analyzed all the data. The
two factor analyses of the limited datasets did not reveal anything interesting not shown in the full
analysis, so only the results from the full analysis are shown (Figure 1).
There were no surprises to be seen.
The loadings for the G factor with the PISA subtests were all .99. The scatter plot for G and S is shown
in Figure .
Page 2 of 10.
Figure 1: S factor loadings in Lynn's dataset.
The method of correlated vectors was then applied, as shown in Figure .
There was a moderate positive relationship. But given the lack of diversity and small size of the
indicators, not much can be concluded from this.
Page 3 of 10.
Figure 2: Scatter plot of G and S for Lynn's dataset.
Figure 3: Method of correlated vectors applied to the G x S in Lynn's dataset.
3. New data
Being dissatisfied with the data Lynn reported, I decided to collect more data. The PISA 2012 results
have PISA scores for more regions than before which allows for an analysis with more cases. This also
means that one can use more variables in the factor analysis. The new PISA data has 22 regions, so one
can use about 11 variables (Zhao, 2009). However, due to some missing data, only 21 regions were
available for analysis (Südtirol had some missing data). So I decided to use 10 variables.
To get data for the analysis, the same approach as was used in a previous publication on the S factor in
US was used (Kirkegaard, 2015). Were were selected and downloaded from the Italian statistics
agency, IStat ( As mentioned earlier, for MCV to work well, one needs a large,
diverse selection of variables, so that there is diversity in their S loadings (not just direction of loading).
The following 10 variables were used:
1. Political participation index, 9 years
2. Percent with normal weight, 9 years
3. Percent smokers, 10 years
4. Intentional homicide rate, 4 years
5. Total crime rate, 4 years
6. Unemployment, 10 years
7. Life expectancy males, 10 years
8. Total fertility rate, 10 years
9. Interpersonal trust index, 5 years
10.No savings percent, 10 years
It was attempted to fetch approximately the last 10 years of data for each variable, which were then
For cognitive data, the regional scores for reading, mathematics and science was PISA2012 were used
(OECD, 2014, Annex B2).
3.1. Factor analysis
Factor analysis was carried as before. The loadings are shown in Figure 4.
Page 4 of 10.
We see two odd results. Total crime (TC) rate has a slight positive loading (.16) while intentional
homicide rate has a strong negative loading (-.72). Lynn (1979) reported a similar finding. He
explained it as being due to urbanization, which increases population density which increases crime
rates (more opportunities, more interpersonal conflicts). An alternative hypothesis is that the total crime
rate is being increased by immigrants who live mostly in the north. Perhaps one can get crime rates for
natives only to test this. A third hypothesis is that it has to do with differences in the legal system, for
instance, prosecutor practice in determining which actions to pull into the legal system.
The second odd finding is that fertility has a positive loading. Generally, it has been found that fertility
has a slight negative correlation with GI and s factor indicators at the individual level (Lynn, 2011). It
has also been found that internationally, GI has a strong negative relationship, -.5 to -.7 depending on
measure, to fertility (Lynn & Harvey, 2008; Shatz, 2008). I have also previously reported a group-level
correlation of about -.50 among Danish immigrant groups (Kirkegaard, 2014). However, if one
examines European countries only, one sees that fertility is relatively ‘high’ (a bit below 2) in the
northern countries (Nordic countries, UK), and low in the southern and eastern countries. This means
that the correlation of fertility between countries in Europe and IQ (e.g. PISA) is positive. Perhaps this
has some relevance to the current finding. One hypothesis is that immigrants are pulling the fertility up
in the northern regions.
There is little to report from the factor analysis of PISA results. All loadings were between .98 and .99.
Figure 5 shows the correlation between G and S scores.
Page 5 of 10.
Figure 4: S factor loadings for the new dataset.
Figure 6 shows the method of correlated vectors applied this relationship.
Page 6 of 10.
Figure 5: Scatter plot of G and S in the new dataset.
As before, the relationship was positive. It was much stronger this time, however, perhaps due to the
more varied selection of variables.
3.2. Cross-dataset stability
Finally, the G and S scores from the two datasets were compared to examine cross-dataset stability.
For one case, Lynn’s dataset had data for a merged region. To make the datasets comparable, the same
regions were merged in the new dataset. The scatter plots are shown in Figures 7-8.
Page 7 of 10.
Figure 6: Method of correlated vectors applied to the G x S relationship in the new
These revealed very high cross-dataset agreement.
4. Discussion
The results for the regional G and S in Italian regions are especially strong. They rival even the
international S factor in their correlation with the G estimates. Italy really is a very divided country.
Stability across datasets was strong, so Lynn’s odd choice of data was not inflating the results.
The method of correlated vectors gave stronger results in the dataset with more and more diverse
indicator variables for S, as would be expected if the correlation was artificially low in the first dataset
due to restriction of range in the S loadings.
Supplementary material
Page 8 of 10.
Figure 7: Cross-dataset correlation of G.
Figure 8: Cross-dataset correlation of S.
All project files (R source code, data files, plots) are available at
Thanks to Davide Piffer for catching an error and for help in matching the regions up from the two
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... A number of my own recent papers have reanalyzed data reported by Lynn, as well as additional data I collected. These cover Italy, India, United States, and China (Kirkegaard, 2015c(Kirkegaard, , 2015b(Kirkegaard, , 2015a(Kirkegaard, , 2015d. This paper reanalyzes Lynn's 1979 paper. ...
... Altho not reported in the paper, Kirkegaard (2014) found that the loading of 2 crime variables on the S factor in Norway among country of origin groups was -.63 and -.86 (larceny and violent crime; calculated using the supplementary material using the fully imputed dataset). Kirkegaard (2015c) found S loadings of .16 and -.72 of total crime and intentional homicide variables in Italy. Among US states, Kirkegaard (2015a) found S loadings of -.61 and -.71 for murder rate and prison rate. ...
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I reanalyze data reported by Richard Lynn in a 1979 paper concerning IQ and socioeconomic variables in 12 regions of the United Kingdom as well as Ireland. I find a substantial S factor across regions (66% of variance with MinRes extraction). I produce a new best estimate of the G scores of regions. The correlation of this with the S scores is .79. The MCV with reversal correlation is .47.
... Many recent studies have examined within-country regional correlates of (general) cognitive ability (also known as (general) intelligence, general mental ability, g),. This has been done for the British Isles (Lynn, 1979;Kirkegaard, 2015g), France (Lynn, 1980), Italy (Lynn, 2010;Kirkegaard, 2015e), Spain (Lynn, 2012), Portugal (Almeida, Lemos, & Lynn, 2011), India (Kirkegaard, 2015d;Lynn & Yadav, 2015), China (Kirkegaard, 2015f;Lynn & Cheng, 2013), Japan (Kura, 2013), the US (Kirkegaard, 2015b;McDaniel, 2006;Templer & Rushton, 2011), Mexico (Kirkegaard, 2015a) and Turkey (Lynn, Sakar, & Cheng, 2015). This paper examines data for Brazil. ...
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... In a number of recent articles (Kirkegaard, 2015c(Kirkegaard, , 2015b(Kirkegaard, , 2015a(Kirkegaard, , 2015d, I have analyzed within-country regional data to examine the general socioeconomic factor, if it exists in the dataset (for the origin of the term, see e.g. Kirkegaard (2014)). ...
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