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Edited by Susan T. 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. 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.

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. These 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.

Women 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. This underrepresentation of women in math-intensive fields is a source of concern for two main reasons. 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 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.

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 31415and 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.

Hence, a student that is good at math Women want sex Breda 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 Former studies concluded that this relative advantage could not explain gender differences in STEM choice [e.

Our main analyses are based on data from the Program for International Student Assessment PISAan everyy international assessment of the knowledge and skills of y-old students in mathematics, reading, and science. PISA 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.

However, 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 In Women want sex Breda, 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 1column 3. Such magnitudes are commonly considered as very large by social scientists.

Females comparative advantage in reading and the gender gap in intentions to pursue math-intensive studies and careers. PISA includes questions related to intentions to pursue math-intensive studies and careers. Our main measure of math intentions is an index constructed from these five questions for details, see SI Appendix and available for more thanstudents. 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.

This gap varies across countries. The gender gap in intentions cannot be explained by differences in math ability across genders. Similarly, controlling for reading ability barely affects the gender gap in intentions. In contrast, the gender gap in intentions to pursue math-intensive studies and careers disappears almost entirely when one controls for individual-level differences in ability between math and reading. Similar are obtained when we measure math intentions with the two Women want sex Breda that capture more specifically the arbitrage between math and reading SI AppendixTable S2.

In contrast, absolute levels of math or reading abilities leave a large gender gap in intentions unexplained. Intentions to pursue math-intensive studies and careers as a function of ability in math, reading, and the comparative advantage in math versus reading.

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. 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 An underrepresentation of girls among high math achievers is indeed observed in most countries 25but taken in isolation, this phenomenon is unlikely to be a good explanation for the gender gap in math-intensive fields.

Indeed, this gender gap tends to be larger among high math achievers Fig. 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 AppendixTable S5 for based on various thresholds.

A possible limitation of the 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 To discuss more directly the effect of MR on actual schooling decisions, we use an auxiliary dataset for France.

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. Boys take more into their math ability when they intend to pursue math studies.

This le to a higher gender gap in intentions to study math among students performing above the median in math SI AppendixTable 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.

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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.

Gender differences in math self-concept i.

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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 2columns 2 and 3 and Fig. We then perform the opposite exercise and show that gender differences in MR cannot be directly Women want sex Breda by gender differences in math self-concept Table 2column 4.

Comparing the explanatory power of the comparative advantage with that of other possible determinants of the gender gap in math-intensive fields. Math self-concept as a function of ability in math, reading, and the comparative advantage in math versus reading. There is a gender gap in the variables that attempt to capture these concepts Table 2 and SI Appendix for detailsbut 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 activitiesand in contrast iii they barely explain the gender gap in MR.

MR is not more strongly associated with intentions than are the other studied variables Table 2column 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.

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.

For example, we observe that the gender gap in MR at 15 y old is larger in countries where the stereotype associating math with Women want sex Breda is stronger SI AppendixTable S8. We also observe that the 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 As such, the present paper provides additional supporting evidence for these models. 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.

We suggest that this is indeed the case by exploiting differences across schools in the availability or shortage of resources to learn math. The majority of 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 Women want sex Breda of math teachers.

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 school quality, see all details in SI Appendix.

Finally, we show that the 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.

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. 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.

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 As labor market opportunities and earnings are ificantly higher in math-related careers 11many 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.

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. Author contributions: T. This article contains supporting information online at www.

Published under the PNAS. We do not capture any address. Thomas Breda. ificance Women remain strongly underrepresented in math-related fields. Abstract 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. Comparative Advantage and Gender Gap in Intentions to Pursue Math-Related Studies and Careers Our main analyses are based on data from the Program for International Student Assessment PISAan everyy international assessment of the knowledge and skills of y-old students in mathematics, reading, and science.

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View this table: View inline View popup. Table 1. Table 2. 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.

: thomas. The authors declare no conflict of interest. KahnD. LeslieA. CimpianM. MeyerE. FreelandExpectations of brilliance underlie gender distributions across academic disciplines. Science— BredaM.

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