Content uploaded by Paola Sapienza
Author content
All content in this area was uploaded by Paola Sapienza on Dec 28, 2013
Content may be subject to copyright.
30 MAY 2008 VOL 320 SCIENCE www.sciencemag.org
1164
EDUCATIONFORUM
The existence (1), degree (2), and origin
(3, 4) of a gender gap (difference
between girls’ and boys’ scores) in
mathematics are highly debated. Biologically
based explanations for the gap rely on evi-
dence that men perform better
in spatial tests, whereas women
do better in verbal recall ones
(1, 5, 6). However, the perform-
ance differences are small, and
their link with math test per-
formance is tenuous (7). By
contrast, social conditioning
and gender-biased environ-
ments can have very large ef-
fects on test performance (8).
To assess the relative
importance of biological and
cultural explanations, we
studied gender differences
in test performance across
countries (9). Cultural inequal-
ities range widely across
countries (10), whereas re-
sults from cognitive tests do
not (6). We used data from
the 2003 Programme for
International Student Assess-
ment (PISA) that reports on
276,165 15-year-old students
from 40 countries who took
identical tests in mathematics
and reading (11, 12). The
tests were designed by the
Organisation for Economic
Co-operation and Develop-
ment (OECD) to be free of
cultural biases. They are sufficiently chal-
lenging that only 0.6% of the U.S. students
tested perform at the 99th percentile of the
world distribution.
Girls’ math scores average 10.5 lower
than those of boys (2% less than the mean
average score for boys), but the results vary
by country (see chart, above): in Turkey,
–22.6, whereas, in Iceland, 14.5. A similar
variation exists in the proportion of girls
over boys who score above 95%, or 99% of
the country-level distribution (fig. S2A).
The gender gap is reversed in reading.
On average, girls have reading scores that
are 32.7 higher than those of boys (6.6%
higher than the mean average score for
boys), in Turkey, 25.1 higher and in Iceland,
61.0 higher (see chart). The effect is even
stronger in the right tail of the distribution.
In spite of the difference in levels, the gender
gap in reading exhibits a variation across
countries similar to the gender gap in math.
Where girls enjoy the strongest advantage in
reading with respect to boys, they exhibit the
smallest disadvantage (sometime even an
advantage) in math. [The correlation between
the average gender gaps in mathematics and
reading across countries is 0.59 (fig. S4)].
To explore the cultural inputs to these
results, we classified countries according to
several measures of gender equality. (i) The
World Economic Forum’s Gender Gap Index
(GGI) (10) reflects economic and political
opportunities, education, and well-being for
women (see chart). (ii) From
the World Values Surveys
(WVSs) (13), we constructed
an index of cultural attitudes
toward women based on the
average level of disagreement
to such statements as: “When
jobs are scarce, men should
have more right to a job than
women.” (iii) The rate of female
economic activity reflects the
percentage of women age 15
and older who supply, or are
available to supply, labor for the
production of goods and serv-
ices. (iv) The political empow-
erment index computed by the
World Economic Forum (8)
measures women’s political
participation, which is less
dependent on math skills than
labor force participation. These
four measures are highly corre-
lated (table S2).
We find a positive correla-
tion between gender equality
and gender gap in mathemat-
ics (fig. S5). If Turkey, a low
gender-equality country (GGI
= 0.59), were characterized by
the degree of gender equality
manifested in Sweden (GGI =
0.81), our statistical model suggests that the
mean score performance in mathematics of
girls relative to boys would increase by 23
points, which would eliminate the Turkish
gender gap in math (see table, p. 1165).
In more gender-equal countries, such as
Norway and Sweden, the math gender gap
disappears. Similar results are obtained
when we use the other indicators of women’s
roles in society. These results are true not
only at the mean level, but also in the tail of
the distribution (table S3). In Iceland, the
ratio of girls to boys who score above the
99th percentile of the country distribution in
math scores is 1.17.
There are many unobserved reasons why
countries may differ in a way that affects the
Analysis of PISA results suggests that the gender
gap in math scores disappears in countries with
a more gender-equal culture.
Culture, Gender, and Math
Luigi Guiso,1* Ferdinando Monte,2* Paola Sapienza,3*† Luigi Zingales4*
DIVERSITY
1European University Institute, Villa San Paolo, Via della
Piazzuola 43, 50133 Florence, Italy. 2Economics
Department, University of Chicago,1126 East 59th Street,
Chicago, IL 60637, USA. 3Kellogg School of Management,
Northwestern University, Evanston, IL 60208, USA.
4Graduate School of Business, University of Chicago, 5807
South Woodlawn Avenue, Chicago, IL60637, USA.
*These authors contributed equally to this work.
†To whom correspondence should be addressed. E-mail:
Paola-Sapienza@northwestern.edu
Women’s emancipation (GGI)
Gender gap, math
Gender gap, reading
70
60
50
40
30
20
10
0
-10
-20
-30
0.8
0.75
0.7
0.65
0.6
0.55
0.5
TUR KOR ITA USA PRT FRA POL NOR SWE ISL
Test score differences between girls and boysGGI index
Math and reading gender gaps. In more gender-equal cultures, the math gender gap dis-
appears and the reading gender gap becomes larger. (Top) Gender gaps in mathematics
(yellow) and reading (gray) are calculated as the difference between the average girls’ score
and the average boys’ score. A subset of countries is shown here (see SOM for complete data
set and calculations). In many countries, on average, girls perform more poorly than boys in
mathematics. In all countries, girls perform better than boys in reading. The gender gap in
mathematics and reading correlates with country measures of gender status within the cul-
ture, one of which measures is the GGI (bottom). Larger values of GGI point to a better aver-
age position of women in society. Besides USA, the countries are abbreviated as their first
three letters, except for PRT, Portugal, and ISL, Iceland.
math gender gap. Without appropriate con-
trols, we run the risk of capturing a spurious
correlation between the unobserved factors
and our measures of gender equality. We reran
our regression at the student level, inserting a
dummy variable for each country, to control
for unobserved heterogeneity (table S4). The
interaction between gender and GGI index
remains statistically significant at the 1% con-
fidence level in a two-tailed ttest, which sug-
gests that the correlation between gender
equality and girls’ math scores is not driven by
unobserved heterogeneity. This interaction
between gender gap and GGI remains signifi-
cant even when we insert an interaction
between gender and log of GDP per capita,
which suggests that the improvement in math
scores is not just related to economic develop-
ment, but to the improvement of the role of
women in society.
To investigate whether the disappearance of
the math gender gap in some countries trans-
lates into an overall improvement of girls or is
simply limited to mathematics scores, we cor-
related reading performance differences with
measures of womens equality (see table, above).
In countries where women are more emanci-
pated, girls’ comparative advantage in reading
widens. Comparing Turkey (GGI = 0.59) and
Sweden (GGI = 0.81), we see an increase in the
mean score performance of girls relative to
boys in reading by 18 points, which almost dou-
bles Turkey’s reading gap in favor of girls.
To verify that these results are not driven by
biological differences across countries, we ana-
lyzed whether they persist in populations that
have a similar or identical evolutionary history.
To assess history, we used a genetic distance
measure (14–17) based on the frequency of
each allele across DNA polymorphisms.
According to this measure, there are 13
European countries with genetic distance equal
to zero and 26 European countries with genetic
distance less than 100 (table S5). When we
restrict the regression of the table (above) to
either one of these two groups, our findings are
substantially unchanged (table S6).
These results suggest that the gender gap
in math, although it historically favors boys,
disappears in more gender-equal societies.
The same cannot be said for how boys score
in mathematics compared with how boys
score in readings. Boys’ scores are always
higher in mathematics than in reading, and
although the difference between boys’ math
and boys’reading scores varies across coun-
tries, it is not correlated with the GGI index
or with any of the other three measures of
gender equality (table S7A). Hence, in coun-
tries with a higher GGI index, girls close the
gender gap by becoming better in both math
and reading, not by closing the math gap
alone. The gender gap in reading, which
favors girls and is apparent in all countries,
thus expands in more gender-equal soci-
eties. Similarly, although the gender gaps in
all math subfields decrease in societies
with more gender equality, the difference
between the gender gap in geometry (where
the boys’ advantage relative to the girls’ is
the biggest) and arithmetic (where the boys’
advantage relative to the girls’ is the small-
est) does not (table S7B).
This evidence suggests that intra-gender
performance differences in reading versus
mathematics and in arithmetic versus geometry
are not eliminated in a more gender-equal cul-
ture. By contrast, girls’ underperformance in
math relative to boys is eliminated in more gen-
der-equal cultures. In more gender-equal soci-
eties, girls perform as well as boys in mathe-
matics and much better than them in reading.
These findings shed some light on recent trends
in girls’ educational achievements in the United
States, where the math gender gap has been
closing over time (2).
References and Notes
1. L. V. Hedges, A. Nowell, Science 269, 41 (1995).
2. C. Goldin, L. F. Katz, I. Kuziemko, J. Econ. Perspect. 20,
133 (2006).
3. C. P. Benbow, J. C. Stanley, Science 210, 1262 (1980).
4. “The science of gender and science: Pinker vs. Spelke, a
debate,” Edge, 10 May 2005, no. 160; www.edge.org/
documents/archive/edge160.html#d.
5. S. Baron-Cohen, The Essential Difference: Men, Women,
and the Extreme Male Brain (Allen Lane, London, 2003).
6. D. Kimura, Sex and Cognition (MIT Press, Cambridge MA,
1999).
7. E. S. Spelke, Am. Psychol. 60, 950 (2005).
8. D. Halpem, J. Wai, A. Saw, in Gender Differences in
Mathematics, A. M. Gallagher and J. C. Kaufman, Eds.
(Cambridge Univ. Press, New York, 2005), pp. 48–72.
9. Materials and methods are available as supporting mate-
rial on Science Online.
10. R. Hausmann, L. D. Tyson, S. Zahidi, The Global Gender
Gap Report (World Economic Forum, Geneva,
Switzerland, 2006).
11. OECD, Programme for International Student Assessment
(PISA), 2nd Assessment (OECD, Paris, 2003).
12. PISA includes originally 41 countries; we drop
Liechtenstein because it contains only 165 observations,
which makes problematic any calculation of the tail of
the distribution. All other countries have at least 639
observations.
13. R. Inglehart et al., “World Values Surveys and European
Values Surveys, 1981–1984, 1990–1993, and
1995–1997” [Computer files; Interuniversity Consortium
for Political and Social Research (ICPSR) version]
(Institute for Social Research, Ann Arbor, MI, 2000), dis-
tributed by ICPSR.
14. This measure was originally computed at the population
level by (15).
15. L. L. Cavalli-Sforza, P. Menozzi, A. Piazza, The History and
Geography of Human Genes (Princeton Univ. Press,
Princeton, NJ, 1996).
16. This measure has been mapped on modern countries by (17).
17. E. Spolaore, R. Wacziarg, “The diffusion of development,”
Centre for Economic Policy Research Discussion Paper
5630 (CEPR, London, 2006).
18. We thank S. Baliga; C Hoxby; seminar participants at
Northwestern University, University of Chicago, and the
National Bureau of Economic Research (NBER); and two
anonymous referees for their helpful comments. The
Initiative on Global Markets at the University of Chicago
provided financial support.
10.1126/science.1154094
Supporting Online Material
www.sciencemag.org/cgi/content/full/320/5880/1164/DC1
www.sciencemag.org SCIENCE VOL 320 30 MAY 2008 1165
EDUCATIONFORUM
Differences in Test Scores Correlated with Indicators of Gender Equality
LHS: Gender difference in math LHS: Gender difference in reading
105.49± 83.56±
26.92** 30.43**
13.21± 16.39±
7.06 8.46
0.45± 0.34±
0.14** 0.15*
29.10± 24.35±
10.05** 10.86*
-6.56± 1.09± -3.12± -4.95± -2.23± 0.52± -0.56± -1.06±
2.40** 2.26 1.93 2.52 2.71 2.71 2.15 2.73
-19.62± -57.16± -2.75± 32.43± -3.02± -16.09± 21.49± 39.03±
20.01 23.27* 17.72 23.72 22.62 27.90 19.80 25.63
0.32 0.15 0.23 0.21 0.20 0.14 0.12 0.15
37 32 39 36 37 32 39 36
Women’s emancipation
(GGI)
Avg. WVS indicators
Female economic
activity rate
Women’s political
empowerment
Log GDP per capita,
2003
Constant
Observations (no.)
R2
Culture affects the gap. More gender-equal cultures are associated with reducing the negative gap in math
and further enlarging the positive gap in reading in favor of women. Test scores are positively correlated with
indicators of gender equality in society (GGI, WVSs, see text). Economic conditions are accounted for by per
capita Gross Domestic Product (GDP). The correlation persists among high achievers on both tests (table S3).
See SOM for details of statistical analysis. The constant is where the regression line intercepts the yaxis, rep-
resenting the amount the dependent y(gender gap) will be when all the independent variables are set to 0.
LHS, left-hand side variable in the least-squares regression analysis. *P< 0.05; **P< 0.01.
