Girls’ comparative advantage in reading can largely
explain the gender gap in math-related fields
Thomas Bredaa,b,1,2 and Clotilde Nappb,c,2
a
Paris School of Economics, 75014 Paris, France; bCNRS, UMR8545, Paris-Jourdan Sciences-Economiques, 75014 Paris, France; and cUniversité Paris Dauphine,
Paris Sciences et Lettres Research University, 75016 Paris, France
Gender differences in math performance are now small in developed countries and they cannot explain on their own the strong
underrepresentation of women in math-related fields. This latter
result is however no longer true once gender differences in reading
performance are also taken into account. Using individual-level data
on 300,000 15-y-old students in 64 countries, we show that the
difference between a student performance in reading and math is
80% of a standard deviation (SD) larger for girls than boys, a
magnitude considered as very large. When this difference is
controlled for, the gender gap in students’ intentions to pursue
math-intensive studies and careers is reduced by around 75%, while
gender gaps in self-concept in math, declared interest for math or
attitudes toward math entirely disappear. These latter variables are
also much less able to explain the gender gap in intentions to study
math than is students’ difference in performance between math and
reading. These results are in line with choice models in which educational decisions involve intraindividual comparisons of achievement and self-beliefs in different subjects as well as cultural
norms regarding gender. To directly show that intraindividual comparisons of achievement impact students’ intended careers, we use
differences across schools in teaching resources dedicated to math
and reading as exogenous variations of students’ comparative advantage for math. Results confirm that the comparative advantage
in math with respect to reading at the time of making educational
choices plays a key role in the process leading to women’s underrepresentation in math-intensive fields.
|
gender gap math-intensive fields
students’ achievement
| comparative advantage |
and 14 and SI Appendix, Table S1). This has pushed scholars to
look for other explanations, such as discrimination against
women in STEM, or the role of social norms and stereotypes in
shaping educational choices. Evidence of direct discrimination is
limited (3, 14, 15), and many scholars now emphasize the role of
gender differences in preferences, self-concept and attitudes
toward math, as well as the social processes and institutions
possibly shaping these differences (see references in ref. 1).
We revisit the role of abilities and test scores to explain the
gender gap in students’ decision to enroll in math-related fields.
Our examination is motivated by the idea that students are likely
to decide to major in a given field on the basis of their relative
(rather than absolute) ability in that field with respect to other
fields (16–18). This simple theory is backed-up by studies suggesting that students tend to think in terms of “what they are
better at” rather than in terms of “required skills to succeed in a
particular career” (19), and that they are encouraged to do so by
teachers and their environment (20). Research in social psychology also shows that “people think of themselves as either
math persons or verbal persons but not both” (21). Hence, a
student that is good at math but even better at reading may favor
humanities because she perceives herself as a verbal person. This
is despite the fact that her career prospects (which students tend
to be unaware of) may be better after math-related studies.
While in most countries, at the age of making irreversible
educational choices, girls now perform only slightly worse than
boys in math, they however strongly outperform them in reading
(18). This gives girls a comparative advantage for disciplines
related to reading/literature rather than math. Former studies
W
omen are underrepresented in science, technology, engineering, and mathematics (STEM) university majors and
jobs. STEM is however a broad group that includes fields in
which women are not underrepresented, such as life science or
psychology. Scholars have underlined the necessity to focus more
narrowly on the STEM fields which are math intensive, such as
computer science or engineering (1–3), as the underrepresentation
of women in these fields remains large and has not decreased at all
in most developed countries during the two past decades (3–5).
For example, over the period 2004 through 2014, the share of
bachelor’s degrees awarded to women in engineering and computer science in the US has stagnated around 20%, while it has
decreased from 46 to 43% in mathematics and statistics and from
42 to 40% in physical science (6).
This underrepresentation of women in math-intensive fields is
a source of concern for two main reasons. First, it contributes to
gender wage inequality in the labor market as math-intensive
jobs pay more (7–9) and are also subject to a smaller gender
wage gap (10). Second, it represents a loss of talent that can
reduce aggregate productivity (11)—as many talented girls shy
away from math-intensive careers—leading to the shortage of
workers with math-related skills at a time when the demand for
such skills is increasing (12).
Gender differences in math test scores are now very limited in
most countries and can only explain a small fraction of this underrepresentation of women in math-intensive fields (refs. 13
www.pnas.org/cgi/doi/10.1073/pnas.1905779116
Significance
Women remain strongly underrepresented in math-related
fields. This phenomenon is problematic because it contributes
to gender inequalities in the labor market and can reflect a loss
of talent. The current state of the art is that students’ abilities
are not able to explain gender differences in educational and
career choices. Relying on the Programme for International
Student Assessment (PISA) data, we show that female students
who are good at math are much more likely than male students
to be even better in reading. As a consequence, the difference
between 15-y-old students’ math and reading abilities, which is
likely to be determined by earlier socialization processes, can
explain up to 80% of the gender gap in intentions to pursue
math-studies and careers.
Author contributions: T.B. and C.N. designed research, performed research, contributed
new reagents/analytic tools, analyzed data, and wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Published under the PNAS license.
1
To whom correspondence may be addressed. Email: thomas.breda@ens.fr.
2
T.B. and C.N. contributed equally to this work.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1905779116/-/DCSupplemental.
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Edited by Susan T. Fiske, Princeton University, Princeton, NJ, and approved June 10, 2019 (received for review April 4, 2019)
concluded that this relative advantage could not explain gender
differences in STEM choice [e.g., in Sweden in the 1990s (16)
and in the United States in the 2000s (22)]. In contrast, we show
that in 2012, it can explain a very large fraction of the gender gap
in 15-y-old students’ intentions to pursue math-intensive studies
and careers in virtually all developed countries and several
developing countries.
Comparative Advantage and Gender Gap in Intentions to
Pursue Math-Related Studies and Careers
Our main analyses are based on data from the 2012 Program for
International Student Assessment (PISA2012), an every-3-y international assessment of the knowledge and skills of 15-y-old
students in mathematics, reading, and science. PISA2012 takes
place in the 34 mostly developed countries belonging to the
Organization for Economic Co-operation and Development
(OECD) in 2012 and an additional 30 developing countries (see
details on all data and analyses in SI Appendix). PISA2012 is
well-adapted for our purpose for three reasons. First, it allows us
to focus directly on math-intensive fields rather than STEM
fields. Second, it focuses on a critical age, corresponding to the
end of middle school or beginning of high school. In most
countries, the majority of students of that age have not yet
strongly specialized in a specific field (e.g., in the United States,
this PISA assessment occurs before the opportunity to enroll in
Advanced Placement courses such as Calculus BC, Physics C,
and Computer Science), so that gender differences in abilities
are unlikely to capture anterior specialization. However, 15-y-old
students in developed countries are also in the process of
choosing high school courses that will determine their future
major and the gender gap in STEM at universities (23). A final
advantage of PISA is its coverage, as it includes students from
80% of the world economy.
The first column of Table 1 shows that boys outperform girls in
math by about 10% of a SD. This difference is lower than 25% of
a SD in most OECD countries and is not statistically significant
at the 5% level in four of them (SI Appendix, Table S1 completes
Table 1 for all countries). In contrast, girls outperform boys by
about a third of a SD in reading. Together, these observations
suggest that girls have a comparative advantage in reading,
something that appears more strikingly when we look at the
gender gap in the difference between math and reading (MR)
ability (Table 1, column 3). Worldwide, this gap reaches about
80% of a SD, or equivalently 24 percentile ranks of the variable.
The phenomenon occurs in virtually all countries: The gap is
larger than 75% in all OECD countries and larger than 60%
everywhere else except in Singapore. Such magnitudes are
commonly considered as very large by social scientists.
PISA2012 includes questions related to intentions to pursue
math-intensive studies and careers. These intentions are measured through a series of five questions that ask students if they
are willing (i) to study harder in math versus English/reading
courses, (ii) to take additional math versus English/reading courses after school finishes, (iii) to take a math major versus a science
major in college, (iv) to take a maximum number of math versus
science classes, and (v) to pursue a career that involves math
versus science. Our main measure of math intentions is an index
constructed from these five questions (for details, see SI Appendix)
and available for more than 300,000 students. It captures the
desire to do math versus both reading and other sciences. We
complete the analysis with the study of the first two variables that
capture more specifically the arbitrage between math and reading.
Column 4 of Table 1 shows that the gender gap in math intentions amounts to 22% of a SD worldwide (respectively 26%
and 17% among OECD and non-OECD countries). This gap
varies across countries. Two OECD (5 non-OECD) countries
have no gap or even a small gap in favor of girls (e.g., Turkey,
Malaysia or Thailand, see SI Appendix, Table S1). Five OECD
(11 non-OECD) countries have a small-to-medium gap (between
10 and 20% of a SD). Seventeen OECD (13 non-OECD)
countries have large gaps (between 20 and 45% of a SD). Finally, 10 OECD (1 non-OECD) countries have gaps larger than
45% of a SD (e.g., Australia, Germany, Finland).
The gender gap in intentions cannot be explained by differences
in math ability across genders. When one controls for math ability
in a linear regression of these intentions on a gender dummy, the
estimate for the gender dummy is reduced by less than 10%
worldwide and in a majority of the studied countries (column 5 of
Table 1 and SI Appendix, Table S1). Similarly, controlling for
reading ability barely affects the gender gap in intentions.
In contrast, the gender gap in intentions to pursue mathintensive studies and careers disappears almost entirely when
Table 1. Females comparative advantage in reading and the gender gap in intentions to pursue math-intensive
studies and careers
Gender gaps (girls minus boys, as a fraction of
variable SD)
Math
ALL countries
OECD countries
Non-OECD countries
Selected countries (with
United States
United Kingdom
Canada
Germany
France
Finland
Denmark
Brazil
Russia
Reading
Math minus
reading
Intentions to
study math
Share of the gender gap in intentions
to study math explained by
ability in. . .
Math
−0.136
0.351
−0.832
−0.218
0.074
−0.159
0.318
−0.883
−0.258
0.064
−0.11
0.389
−0.775
−0.171
0.087
a gender gap in intentions to study math larger than 0.25 SD)
−0.111
0.276
−0.805
−0.292
0.024
−0.124
0.276
−0.889
−0.250
−0.009
−0.159
0.343
−0.841
−0.485
0.031
−0.186
0.45
−1.238
−0.461
0.016
−0.186
0.323
−0.958
−0.393
0.027
−0.069
0.569
−1.096
−0.494
0.040
−0.202
0.336
−0.967
−0.336
0.046
−0.195
0.379
−0.914
−0.276
0.073
−0.005
0.419
−0.659
−0.398
0.002
Reading
Math minus
reading
−0.036
−0.002
−0.101
0.784
0.810
0.768
0.069
0.146
0.009
0.032
0.02
−0.138
−0.032
−0.002
−0.060
0.881
1.026
0.348
0.516
0.429
0.741
0.27
0.625
0.419
All variables are standardized to have a weighted mean equal to 0 and a weighted SD equal to 1 in each country. Intentions to study
math is an index built from five questions. The total sample includes 301,360 students. See SI Appendix for details and statistical
significance of each estimate.
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Breda and Napp
.4
.4
.2
0
-.2
0
-.4
-.2
-.4
1 2 3 4 5 6 7 8 9 10
Deciles of
math ability
Girls
Boys
1 2 3 4 5 6 7 8 9 10
Deciles of
reading ability
Girls
Boys
1 2 3 4 5 6 7 8 9 10
Deciles of
math minus reading ability
Girls
Boys
Fig. 1. Intentions to pursue math-intensive studies and careers as a function of ability in math, reading, and the comparative advantage in math
versus reading.
Breda and Napp
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tends to be larger among high math achievers (Fig. 1 and SI
Appendix, Table S5).
Turning to the second possible explanation, we observe that
the gender gap in intentions is not reduced among students that
perform above a given threshold in both math and reading (see
SI Appendix, Table S5 for results based on various thresholds). In
contrast, the gender gap in intentions among students that are
better in math than in reading (68% of them boys) or better in
reading than in math (68% of them girls) is more than twice
lower than the average gap.
A possible limitation of the results presented so far is that
declared intentions to study math may not capture well actual
schooling decisions and gender gaps in enrolments. A first
reassuring element is that sex differences in occupational plans
in high school have been found to be a strong predictor of actual
gender differences in STEM majors (26, 27). Moreover, we show
that cross-country variations in gender gaps in intentions to
pursue math-intensive studies and careers measured in PISA are
well correlated with objective country-level measures of sex
segregation by field of study, like (i) the percentage of women
among STEM graduates in tertiary education (ρ = −0.52, see SI
Appendix), (ii) female overrepresentation in humanities (ρ =
0.39), or (iii) the female-to-male ratio in computer science
(ρ = −0.5, see SI Appendix, Table S8).
To discuss more directly the effect of MR on actual schooling
decisions, we use an auxiliary dataset for France. It includes ability
measures in math and reading as well as information on both intentions to study STEM and future enrolment in STEM (see SI
Appendix for details). We find that the correlation between intentions to specialize in STEM in grade 11, declared in grade 10 during
the period January–March (corresponding to the same age as that
of PISA students), and actual enrolment in grade 11 is strong but
not perfect (78%). However, and crucially to us, MR is a good
predictor of both intentions to study STEM and STEM enrolment,
and it reduces the gender gaps in these variables to the same extent
(46% for intentions and 49% for actual enrolment in STEM, which
also corresponds to what we find for France with PISA, see SI
Appendix, Table S6). From these observations and more detailed
analyses presented in the SI Appendix, we conclude that our results
.2
.2
0
-.2
-.4
Intentions to study math (standardized)
.4
one controls for individual-level differences in ability between
math and reading. Column 7 of Table 1 shows that MR can explain 78% of the gender gap in intentions worldwide (95%
confidence interval = [71%,83%], see SI Appendix). The corresponding statistics is 81% for OECD countries only, 88% for the
United States, 52% for Germany, 43% for France, and 157% for
Japan, a country where conditional on MR, girls become more
willing to study in math-intensive fields than boys. Similar results
are obtained when we measure math intentions with the two
questions that capture more specifically the arbitrage between
math and reading (SI Appendix, Table S2).
MR is more strongly associated with students’ intentions to study
math than are math or reading abilities taken in isolation (Fig. 1 and
SI Appendix, Table S3). This association is large and very similar for
boys and girls, implying that the gender gap in intentions is small
and almost constant—only ∼5% of a SD—along the distribution of
MR. In contrast, absolute levels of math or reading abilities leave a
large gender gap in intentions unexplained.
The simple difference MR summarizes relatively well the
relevant information on abilities that is needed to predict intentions to pursue math-intensive studies and careers. We show
in SI Appendix that MR alone captures about 75% of the total
capacity of the distributions of math, reading, and science abilities to predict intentions to pursue math-related studies and
careers. We also show that when we include detailed controls for
students’ abilities in regression models of these intentions, our
results remain qualitatively similar (SI Appendix, Table S4).
Our analyses of the relationship between abilities and intentions invite to nuance two ability-based arguments that are
sometimes advanced to explain the gender gap in enrolment in
STEM: the fact that girls remain underrepresented among high
math achievers, hence less able to pursue math-related studies,
and the fact that they are more often good in both math and
reading, hence less constrained than boys in their choice of
study (24).
An underrepresentation of girls among high math achievers is
indeed observed in most countries (25), but taken in isolation,
this phenomenon is unlikely to be a good explanation for the
gender gap in math-intensive fields. Indeed, this gender gap
on students’ intentions of study are likely to generalize to their
actual course choices in high school and at universities (the latter
being strongly related to the former; ref. 23).
Finally, our analysis of the relationship between abilities
and intentions highlights a “better self-selection” of boys in
math-intensive fields. Indeed, if the relation between MR and math
intentions is similar for boys and girls, the relation between math
ability and math intentions is larger for boys than for girls (Fig. 1
and SI Appendix). Boys take more into account their math ability
when they intend to pursue math studies. This leads to a higher
gender gap in intentions to study math among students performing above the median in math (SI Appendix, Table S5) and
to a larger gender gap in math performance among individuals
who intend to choose math studies over reading (SI Appendix).
These selection patterns are likely to result in an overperformance of boys in math-intensive studies. Similarly, we
show in SI Appendix that girls self-select better in humanities and
are likely to overperform boys in university humanity majors
even more than they do before specialization occurs. These likely
larger gender gaps in performance after specialization, which are
generated by gender differences in the self-selection process
across fields of study, can feed the stereotype that math is not for
girls and humanities not for boys.
Comparative Advantage and Gender Gaps in Math SelfConcept, Interest for Math, and Other Math-Related
Attitudes
Gender differences in math self-concept (i.e., how students
perceive their math ability and their ability to learn math quickly)
is one of the most commonly advanced explanations for the
gender gap in math enrolment (1, 28, 29). A series of questions
in PISA2012 makes it possible to build an index to measure this
concept at the student level (SI Appendix). The gender gap in
math self-concept is indeed large (around 30% of a SD) but
nevertheless three times smaller than the gender gap in MR
(Table 2, column 1 for results worldwide and SI Appendix, Table
S7 for results on a selection of countries/regions). Interestingly,
gender differences in the way students perceive their math ability
are barely reduced when this ability is controlled for in a linear
regression model, while they almost entirely disappear when one
controls for MR (Table 2, columns 2 and 3 and Fig. 2). We then
perform the opposite exercise and show that gender differences
in MR cannot be directly explained by gender differences in
math self-concept (Table 2, column 4). Such results are fully in
line with Marsh’s Internal/External (I/E) Frame of Reference
model (30) according to which people compare their performances across domains (in particular math versus reading) to
reach conclusions about their ability.
Similar results are obtained when we replace math selfconcept by other well-known proximate sources of the gender
gap in math-related fields, such as gender differences in declared
interest for math, instrumental motivation for math, anxiety with
respect to math, willingness to get involved in math-related activities, or having a strong “math environment” (i.e., family
support for doing math and friends being positive about math).
There is a gender gap in the variables that attempt to capture
these concepts (Table 2 and SI Appendix for details), but (i)
these gaps are 3 to 8 times smaller than the gender gap in MR,
(ii) they get close to zero when one conditions on MR (except for
the involvement in math-related activities), and in contrast (iii)
they barely explain the gender gap in MR.
We finally show that the math self-concept and our variables
capturing other possible mechanisms are related to students’
intentions to study math (Table 2, column 5) but account for a
much smaller share of the gender gap in these intentions than
does MR (Table 2, columns 6 and 7 for the gender gap in intentions conditional on each variable separately and together
with MR). MR is not more strongly associated with intentions
than are the other studied variables (Table 2, column 5). This
implies that the larger explanatory power of this variable is
mostly due to the fact that it is subject to a very large gender gap.
Even if all our analyses so far are only descriptive and not
causal, they consistently point to an important role of the comparative advantage of boys in math versus reading for the understanding of women’s underrepresentation in math-intensive
fields. This does not rule out the operation of other (perhaps
earlier-occurring) factors, of course. Math and reading abilities
at 15 y old are likely to be determined by earlier socialization
processes that shape preferences and investment in the different
fields. These processes are themselves likely to be influenced by
countries’ socioeconomic environment and culture (25, 31) or
institutions such as parents and schools, which jointly determine
future abilities, interests, and self-concepts. For example, we
observe that the gender gap in MR at 15 y old is larger in
countries where the stereotype associating math with men is
stronger (SI Appendix, Table S8). We also observe that the
Table 2. Comparing the explanatory power of the comparative advantage with that of other possible determinants of the gender gap
in math-intensive fields
Gender gap in
each variable (fraction of a SD)
Variable
(standardized)
Math minus
reading ability (MR)
Math self-concept
Declared interest
for math
Instrumental
motivation for math
Math anxiety
(opposite of)
Math involvement
Math environment
Intentions to study
math (Standardized)
Conditional
on MR
Gender gap in
MR conditional
on the variable
Association
with each
variable
Gender gap
conditional on
each variable (raw
gap is −0.218 SD)
Gender gap
conditional on
each variable
plus MR
n.a.
n.a.
n.a.
0.215
−0.047
n.a.
−0.270
−0.174
−0.231
−0.160
−0.012
−0.003
−0.780
−0.802
0.372
0.396
−0.132
−0.150
−0.033
−0.046
−0.104
−0.088
0.007
−0.820
0.352
−0.182
−0.049
−0.174
−0.129
−0.020
−0.824
0.228
−0.192
−0.033
−0.293
−0.096
−0.288
−0.101
−0.150
−0.034
−0.789
−0.827
0.227
0.174
−0.155
−0.202
−0.018
−0.041
Absolute
Conditional
on math
ability
−0.832
All variables are standardized to have a weighted mean equal to 0 and a weighted SD equal to 1 in each country. See SI Appendix for details on the
construction of variables and statistical significance of each estimate. n.a., not applicable.
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Breda and Napp
.4
.2
-.4
-.2
0
.2
0
-.2
1 2 3 4 5 6 7 8 9 10
Deciles of
reading ability
Boys
Boys
Girls
Boys
Math self-concept as a function of ability in math, reading, and the comparative advantage in math versus reading.
gender gap in MR at 15 y old is larger in educational systems in
which horizontal stratification by field of study is higher or occurs
earlier, and in which mandatory standardized tests are less frequent
(SI Appendix). These observations and more broadly all our analyses
are entirely consistent with the choice models developed by Eccles
and coworkers in which educational decisions involve intraindividual comparisons of achievement, self-beliefs and motivation
in different subjects, as well as cultural norms, in particular surrounding gender (32, 33). As such, the present paper provides
additional supporting evidence for these models.
Instrumental Variables and Causal Inference
While the codetermination of the variables examined here has to
be kept in mind, it is not contradictory with our hypothesis that
the comparative advantage is an important independent determinant of educational choices, so that exogenous variations in
this advantage (e.g., due to educational policies) can lead students to change their choice of study. We suggest that this is
indeed the case by exploiting differences across schools in the
availability or shortage of resources to learn math. We show, for
example, that in schools that experience a shortage of math
teachers but not of reading teachers, both girls’ and boys’ comparative advantage in math is significantly lower.
The majority of 15-y-old students go to the closest school from
where they live and those who do otherwise might struggle to
observe shortages in some types of teachers or the quality of
math teachers. As a consequence, we assume that quantity and
quality of math teachers in their school is to a certain extent
exogenous to students’ initial intentions to study math. Based on
this assumption, we use these school-level variables as instruments for students’ comparative advantage in math and show
that variations in this comparative advantage that solely arise
from differences of “math resources” across schools do affect
girls’ and boys’ intentions to study math (even more than noninstrumented variations, see SI Appendix, Table S9).
Our approach would fail to show causality if the students with
a large comparative advantage selected into better schools that are
likely to have more math resources. For this reason, we include
controls for school quality and use as instrumental variables resources devoted to math relative to other subjects rather than absolute math resources (which are more directly correlated with
Breda and Napp
Girls
1 2 3 4 5 6 7 8 9 10
Deciles of
math minus reading ability
school quality, see all details in SI Appendix). Finally, we show
that the results also hold on the subsample of schools that
mostly recruit students based on the geographical location as a
self-selection of students in these schools based on their prior
comparative advantage appears less likely.
Policy Implications
The analysis above suggests that external factors influencing
students’ comparative advantage are likely to have consequences
for their educational choices. As a consequence, any educational
policy that could reduce the gender imbalances in comparative
advantage is likely to limit the underrepresentation of women in
math-intensive fields. As the gender gap in reading performance
is much larger than that in math performance, policymakers may
want to focus primarily on the reduction of the former. Systematic tutoring for low reading achievers, who are predominantly males, would be a way, for example, to improve boys’
performance in reading. A limitation of this approach, however,
is that it will lower the gender gap in math-intensive fields mostly
by pushing more boys in humanities, hence reducing the share of
students choosing math. In a context of high and increasing
demand for math-intensive skills, improving boys’ performance
in reading without also improving girls’ performance in math can
therefore be detrimental for the economy and the latter should
also be considered as a valuable option.
The general organization of a country’s educational system can
also play an important role to limit gender imbalances in comparative advantage. As mentioned above, educational systems with
early tracking or specialization are associated with larger gender
gaps in comparative advantage, possibly because stereotypes and
social norms have a stronger influence on choices at younger age.
Delaying the time of making hard-to-reverse educational choices
may therefore limit gender gaps in comparative advantage and
gender segregation across fields.
Another option in terms of policy is to better inform students
regarding the returns to different fields of study, something that is
likely to trigger large effects on educational choices (34). As labor
market opportunities and earnings are significantly higher in mathrelated careers (11), many (mostly female) students who have a
comparative advantage in reading but are nevertheless talented in
math would have better career prospects in math-related fields.
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-.4
Girls
Fig. 2.
.6
.6
.4
.6
.4
.2
0
-.2
Math self-concept (standardized)
-.4
1 2 3 4 5 6 7 8 9 10
Deciles of
math ability
Hence, adequate information campaigns on future career prospects
may be a welfare-improving way (because students can make better
informed choices) of reducing the importance of the comparative
advantage in students’ decision making and, therefore, the gender
gap in enrolment in math-related fields (35). Similarly, interventions
involving teachers or parents targeted at limiting the role of the
comparative advantage in educational choices could also be effective. Of course, these options should complement rather than replace interventions directly aimed at limiting the negative effects of
gender stereotypes.
ACKNOWLEDGMENTS. We warmly thank Francesco Avvisati and Matthias
von Davier for their helpful suggestions regarding the optimal way to deal
with PISA plausible values in the context of this paper, Stephen Ceci for his
support and advice, Elyès Jouini for helpful discussions, Fabrice Riva for
critical reading, and Georgia Thebault for useful research assistance. We
are also grateful to Julien Grenet, Marion Monnet, and Clémentine Van
Effenterre for allowing us the access to their data for France, which are
used in SI Appendix, Table S6. Financial support of the Women and Science Chair (a Dauphine Foundation Chair in partnership with Fondation
L’Oréal, Generali France, La Poste and Talan) is gratefully acknowledged
by both authors.
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