CAES Working Paper Series

Is There a Linear Relationship between the Gender Gap in
Test Scores and Gender Equality?

Keiko TAMADA
Faculty of Economics
Fukuoka University

WP-2024-004

Center for Advanced Economic Study
Fukuoka University(CAES)
8-19-1 Nanakuma, Jonan-ku, Fukuoka,
JAPAN 814-0180

Is There a Linear Relationship between the Gender Gap in Test Scoresand Gender Equality?
Keiko TAMADA†
Faculty of Economics
Fukuoka University
ktamada@adm.fukuoka-u.ac.jp

Abstract
The relationship between the gender gap in academic achievement and gender equality
has been investigated for a long time, and the results obtained are mixed. This study
examines the nonlinear relationship between the gender gap in academic achievementand detailed gender equality measures. To the best of my knowledge, no study has focusedon the nonlinear relationship and used detailed gender equality measures. I employed datafrom the Programme for International Student Assessment from 2006 to 2018, whichmeasures 15-year-old students’ skills, and the Gender Gap Index and its subindexvariables to measure gender equality. I found that the gender gap in academic
achievement has a U-shaped relationship with the Gender Gap Index, the female-to-malelabor force participation ratio, female-to-male professional and technical workers ratio,
and the proportion of women at the ministerial level compared to men. The female-tomale gross tertiary enrollment ratio has a positive relationship with the gender gap inmath achievement. These results demonstrate that the functional form and detailed genderequality measures are important when analyzing the relationship between the gender gap
in academic achievement and gender equality measures.
Keywords: Gender equality, Gender gap, Test scoresThis work was supported by JSPS KAKENHI Grant Number 21K01536. I am gratefulto Brindusa Anghel, Nuria Rodoriguez-Planas, and Anna Sanz-de-Galdeano for providing
me with programs to replicate their results.
†

1. Introduction
This study examined the relationship between the gender gap in math and reading andgender equality measures. The underrepresentation of women in high-paying science,technology, mathematics, and engineering (STEM) jobs is partly related to the genderwage gap (Huacong et al., 2022). Many studies attempted to explain the gender gap in
STEM fields with biological reasons, but the results were mixed (Ceci et al. 2009).
Furthermore, Schleicher (2019) pointed out that the gender gap in math test scores is
closing. These results suggest that the gender gap in STEM fields may be due to reasons
other than biological differences. Therefore, many studies have been conducted to
identify the factors that correlate with the gender gap in academic achievement. Amongthese studies, some studies investigated the effects of gender equality on the gender gap
in academic achievement (Guiso et al. 2008; Else-Quest et al. 2010; Fryer and Levitt,
2010; Dickerson et al., 2015; Nollenberger et al. 2016; Rodríguez-Planas andNollenberger 2018; Gevrek et al. 2020). Despite the wide range of existing literature on
this subject, the results are not robust. This study offers new insights into the relationshipbetween the gender gap in academic achievement and gender equality, using detailed
gender equality measures and a quadratic functional form.
The gender stratification hypothesis (Baker and Jones, 1993) proposes that genderdifferences in educational, economic, and occupational opportunities can affect numerous
socialization processes that form math achievement and attitudes. Based on thishypothesis, the relationship between the gender gap in test scores and gender equality has
been investigated in previous studies; the results obtained were mixed. For instance,
Guiso et al. (2008), Nollenberger et al. (2016), Rodríguez-Planas and Nollenberger (2018),
and Gevrek et al. (2020) used gender equality measures, such as the Gender Gap Index(GGI) conducted by the World Economic Forum (WEF), and found that greater genderequality decreases the gender gap in math. On the other hand, Fryer and Levitt (2010)
found no relationship between gender equality and the gender gap in test scores.
Most previous studies have used the Programme for International Student Assessment(PISA) and the Trends in International Mathematics and Science Studies (TIMSS) tomeasure the gender gap in academic achievement, as they can make reasonable
comparisons of achievement across countries. When investigating this relationship using
PISA and GGI, most results indicate that greater gender equality leads to a narrower
gender gap in math (Guiso et al. 2008; Else-Quest et al. 2010; Fryer and Levitt 2010;2

Nollenberger et al. 2016; Rodríguez-Planas and Nollenberger 2018). However, thesubindex that comprise the GGI and other gender equality measures are not always
positively associated with the gender gap in PISA test scores. Guiso et al. (2008) foundthat the female-to-male labor force participation ratio and political empowerment
indicators, such as the proportion of women’s seats in parliament compared to that of men,positively correlate with the gender gap in math and reading. However, there is no
relationship between the cultural values of women’s social advancement and the gender
gap in math and reading. Else-Quest et al. (2010) found that the gender gap in academic
achievement in PISA correlates with the ratio of economic activity, women’s share ofresearch positions, and women’s share of parliamentary seats, however, it does notcorrelate with women’s share of higher labor market positions. Using an epidemiologicalapproach that uses second-generation children’s information to identify the effects ofgender social norms on the gender gap in test scores, Nollenberger et al. (2016) andRodríguez-Planas and Nollenberger (2018) found that ancestry countries’ GGI, politicalempowerment, and economic opportunity subindexes have positive effects on secondgeneration girls’ math, reading, and science test scores relative to that of secondgeneration boys; ancestry countries’ educational attainment and health and survival
subindexes have no effect on the gender gap of second-generation immigrants. Unlike theprevious studies described above, Gevrek et al. (2020) and Anghel et al. (2020) found thatthe gender gap in PISA did not correlate with the GGI. By decomposing the gender gap
in math into explained and unexplained parts, Gevrek et al. (2020) found that theunexplained parts of the gender gap in math are not significantly associated with the GGI.
Anghel et al. (2020) investigated the nonlinear effect of Gross Domestic Product (GDP)per capita on the gender gap in test scores and found a positive association between thegender gap in academic achievement and the GGI and its subindexes in lower-income
countries; a correlation between the two was not found in higher-income countries.
When the TIMSS is used to measure the gender gap in math, a relationship between
the gender gap in math and the GGI is not found (Else-Quest et al., 2010; Fryer and Levitt2010). Else-Quest et al. (2010) found that the gender gap in math on the TIMSS does notcorrelate with the GGI and other composite gender equality indicators, and that it
negatively correlates with the female-to-male enrollment ratios of primary, secondary,and tertiary schools. Using the 2003 PISA and TIMSS, Fryer and Levitt (2010) did not
find a relationship between the gender gap in math in the TIMSS and GGI, although a3

positive relationship was found when they limited their analysis to the countries thatappear in the PISA.1 These results suggest that the characteristics of countries covered
by TIMSS differ from those covered by PISA. They pointed out that all secondary
schooling is segregated by gender in some TIMSS countries, whereas mixed-genderclassrooms prevail in most PISA countries. In countries where single-sex education is
widespread, girls tend to outperform boys in math, whereas gender equality tends to below. Thus, the results of the analysis of the relationship between the gender gap inacademic achievement and gender equality may vary depending on the country ofcoverage.
In addition to PISA and TIMSS, Dickerson et al. (2015) used microdata covering 19
sub-Saharan African countries and found no relationship between the gender gap in mathand school quality, within-school gender discrimination regarding access to schooling
inputs, gender differences in family social socioeconomic status and the schoolingenvironment at home, and the GGI. However, there is a negative relationship between theeconomic participation and opportunity subindex and the gender gap in math, although
the number of countries is too small to be estimated precisely.
Previous studies have mainly focused on the relationship between the gender gap inacademic achievement and the GGI. However, the GGI and its subindexes are intended
to focus on women’s empowerment (WEF, 2018). Hence, women and men are consideredequal when the GGI and its subindexes are calculated, even though women outperformmen, except in the health and survival subindex. Furthermore, the variances of someindicators that constitute the GGI are minute, therefore the coefficients of the genderequality measures are not estimated precisely. Ignoring instances where women surpassmen may bias the coefficients of gender equality measures.
I investigate the nonlinear relationship between the gender gap in math and reading
and gender equality measures using not only the GGI but also its subindex-specific genderequality measures to clarify the areas of gender equality that are associated with thegender gap in academic achievement. By employing subindex-specific gender equality
measures, I can more accurately clarify which areas of gender equality are associated witha gender gap in academic achievement. Furthermore, I focus not only on the linear

They pointed out that their results are imprecisely estimated owing to a small sample
size.

1

4

relationship, but also on the nonlinear relationship between the gender gap in academic
achievement and gender equality. Figure 1 presents the relationship between the gender
gap in math scores and GGI. The graph shows that there is a U-shaped relationship,
whereas the slope of the linear relationship is moderate. Most previous studies assume
that greater gender equality decreases the gender gap, even though there are countries
where girls outperform boys in terms of test scores, while gender equality is low. Thus, it
is unclear whether the gender gap decreases when girls outperform boys. To the best of
my knowledge, no study has focused on the nonlinear relationship between the gender
gap in academic achievement and gender equality.
The remainder of this paper is organized as follows. Section 2 introduces the data,
section 3 explains the empirical model, section 4 presents the estimation results, andsection 5 concludes the paper.
2. Data
2-1. PISA Data
The PISA is a study conducted by the Organization for Economic Cooperation andDevelopment (OECD) to assess relevant skills and competencies in math, reading, andscience skills for 15-year-old students, and has been implemented in a three-year cycle
since 2000. In the survey, students, parents, school principals, and teachers are requested
to complete background questionnaires. The codes allow students’ test scores andbackground data to be linked to information regarding the schools attended by students.
PISA uses plausible variables to measure student performance, which is calculated using
the item response theory. A scale is constructed with a mean of 500 and standard deviationof 100 among the participating OECD countries.
I pooled data from the PISA conducted in 2006, 2009, 2012, 2015, and 2018,2 which
covered 57, 75, 66, 72, and 79 countries and economies, respectively. Scores were
measured using five plausible values for PISA 2006, 2009, and 2012 and 10 plausible
values for PISA 2015 and 2018. To measure students’ skills, plausible values were used
to calculate their test performance. I calculated the gender gap in academic achievementby country by running a linear regression of each of the plausible values on a constantand a female dummy variable. I estimated one regression for each set of plausible values
2

I excluded the PISA conducted in 2003 because the GGI was first introduced in 2006.
5and reported the arithmetic averages of these estimates.3 I then applied Fay’s Balanced
Repeated Replicated (BRR) methodology to estimate standard errors, considering thestratified two-stage sample design of PISA.
2-2. Gender equality measures
The GGI is an indicator of gender-based disparities in access to resources andopportunities, as measured by the WEF. The GGI comprises four fundamental
subindexes: economic participation and opportunity, educational attainment, health andsurvival, and political empowerment. As shown in Table 1, these subindexes arecomposed of 14 variables. The GGI is calculated as follows (WEF 2018). 4 First, allsubindex-specific variables used to calculate the GGI are converted to female-to-maleratios. Second, all ratios, except the sex ratio at birth and the female-to-male healthy life
expectancy ratio, are truncated at one, which means that women and men are consideredequal, even when women outperform men. For the sex ratio at birth, the equality
benchmark is set at 0.944, and for healthy life expectancy, the equality benchmark is set
at 1.06. Third, the weighted average of the indicators within each subindex is calculatedusing truncated ratios to calculate the subindex scores. A simple average of each subindex
is used to calculate the GGI value. The GGI value ranges from 0 (imparity) to 1 (parity),
which means that women cannot surpass men; however, girls outperform boys in theliteracy and enrollment rates in many countries. This outperformance of girls may affect
the gender gap in academic achievement; thus, using raw variables is important to clarify
the relationship between the gender gap in academic achievement and gender equality
measures. The data of subindex-specific variables are taken from WEF from 2006-2018.5
2-3. Descriptive statistics
Figure 2 illustrates the gender gap in math scores by country.6 The upper panel shows
the gender gap in OECD countries and the lower panel shows that in non-OECD countries.
Boys tend to outperform girls in most OECD countries, whereas girls tend to outperform
In total, there are 78 countries. In 2006, the U.S. did not participate in the assessmentof reading skills.
4This explanation is based on WEF (2018).
5
The descriptive statistics by country are presented in supplement Table S1.
6Country names are shown in supplement Table S1.
3

6

boys in Southeast Asia, Northern Europe, and the Middle East. Figure 3 illustrates thegender gap in reading scores, with girls outperforming boys in all countries. The gender
gap in reading scores tends to be large in Northern Europe and the Middle East, but issmall in some Latin American countries. The variances in the gender gap are not verysmall across countries, and it does not tend to be small over time in most countries.
Figure 4 illustrates graphically the relationship in the raw PISA data between genderequality measures and the gender gap in math.7 The gender gap in math appears to havea quadratic relationship with the GGI, female-to-male labor force participation ratio,
wage equality between women and men for similar work, female-to-male estimatedearned income ratio, proportion of women legislators, senior officials, and managers
compared to men, female-to-male professional and technical workers ratio, proportion of
women’s seats in parliament compared to that of men, proportion of women at theministerial level compared to men, and the number of years with a female head of statecompared to men. Girls tend to outperform boys in math in countries with high and low
gender equality and in countries with moderate gender inequality, boys tend to outperformgirls in math. The turning points of each graph differ according to the gender equality
measure. The gender gap seems to be positively related to the female-to-men net
secondary enrollment ratio and the number of years with a female head of state compared
to men. As mentioned previously, the variances in the female-to-male literacy ratio,
female-to-male net primary enrollment ratio, female-to-male net secondary enrollment
ratio, the sex ratio at birth, and the female-to-male healthy life expectancy ratio are minute,therefore the gender gap in math does not appear to be associated with these genderequality measures.
3. Econometric model
Based on the findings of previous studies, I estimate the following model as a benchmark.
Ysct = α0 + α1Ect +α2ln(GDPct) + α3fmratiosct + yearγ + usct(1)

where Ysct is the gender gap in subject s (math or reading) in country c in year t. Ect is the

The relationships between the gender gap in reading and gender equality measures areshown in supplement Figure S1.

77

gender equality measure, ln(GDPct) is the log of real GDP per capita in PPP,8 fmratiosct
is the female-to-male ratio among the PISA test takers in year t, year is year dummies,
and usct is an error term.
The interpretation of the gender gap (α1) in math scores coefficient is complicatedbecause there are some countries in which girls outperform boys. For example, when thecoefficient is positive, the gender gap narrows, as the degree of gender inequality
decreases when boys outperform girls. On the other hand, the gender gap widens whengirls outperform boys, as the degree of gender inequality increases. Thus, a positive
correlation between the gender gap in math and gender equality does not imply that thegreater the gender equality, the smaller the gender gap in test scores. Furthermore, as
Fryer and Levitt (2010) point out, when countries where single-sex classrooms or schools
are prevalent are included (mainly Muslim majority countries), gender equality may not
be associated with the gender gap in academic achievement. In those countries, girls
outperform boys in academic achievement, and gender equality in terms of the economic
and political subindexes is low. In Nothern European countries, girls outperform boys inacademic achievement, and men and women are relatively equally represented in termsof the economic and political subindexes. To avoid these issues, I estimate the following
model with quadratics.
Ysct = β0 + β1 Ect + β2 Ect２ + β3ln(GDPct) + β4fmratiosct + yearγ + usct.

(2)

Wooldridge (2010) pointed out that when variances of the key variables are small, thefixed effects method can produce imprecise estimates. My preferred specification in thepanel data analysis contains only year effects because most explanatory variables arecharacterized by little variation within countries across time.
It should be noted that these empirical analyses are plagued by an endogeneity
problem owing to omitted variable bias; I do not claim causality. My analyses allowed
me to clarify the association between the gender gap in test scores and gender equality.
8

I use a log of GDP per capita to control each country’s economic condition. The data

was taken from the Penn World Table, version 10.0 as available at www.ggdc.net/pwt(Feenstra et al. 2015).

8

4. Results
4-1. Replicating the results of Anghel et al. (2020)
I begin by replicating the results of Anghel et al. (2020) using the 2006–2018 PISA as abenchmark for comparison using the same countries as those used by Anghel et al.
(2020).9 Table 2 presents the results of this replication.10 Columns (1) and (2) show that
there is a positive and statistically significant association between the gender gap in mathand the GGI for OECD countries. Column (3) shows that there is a negative andstatistically insignificant association once country-specific time-invariant idiosyncrasies
are controlled. Columns (4) and (5) show the negative and statistically insignificant
associations for non-OECD countries. Column (6) shows a positive and statisticallyinsignificant association, corroborating Anghel et al.’s (2020) findings. However, theresults in Columns (3) and (6) may not be precisely estimated because the GGI does nothave sufficient variation within countries.11
4-2. Results of the linear relationship
Table 3 presents the results of the linear relationships between the gender gap in math andgender equality measures. There is a negative and statistically insignificant association
between the GGI and the gender gap in math. For the economic participation andopportunity subindex, wage equality between women and men for similar work is positiveand statistically significant with the gender gap. The female-to-male labor force
participation ratio, the female-to-male estimated earned income ratio, and the proportionof female legislators, senior officials, and managers compared to men are negative andstatistically insignificant with the gender gap in math. The female-to-male professional
and technical workers ratio is positive and statistically insignificant with the gender gap.

The replication is conducted for math only because Anghel et al. (2020) mainly focus
on math achievement.
10
I replicate Panels B and C of Table A2, A3, and A4 of Anghel et al. (2020) using the
2006–2018 PISA sample of all students. Using the 2003–2015 PISA, Anghel et al. (2020)
limited the sample of students above the median of the socioeconomic status for theirmain analyses, to avoid attrition bias owing to potential differential drop-out rates
between boys and girls in each country.
11
In this estimation, year effects and country-specific time-invariant idiosyncrasies arecontrolled as Anghel et al. (2020).
99For the educational attainment subindex, the female-to-male literacy, net primaryenrollment, and net secondary enrollment ratios are statistically insignificant with thegender gap, whereas the female-to-male gross tertiary enrollment ratio is positive andstatistically significant with the gender gap. For the health and survival subindex, the sex
ratio at birth and the female-to-male healthy life expectancy ratio are statisticallyinsignificant with the gender gap. For the political empowerment subindex, all variables
are statistically insignificant with the gender gap.
Table 4 presents the results of the linear relationship between the gender gap inreading and gender equality measures. The GGI is negative and statistically insignificant
with the gender gap. For the economic participation and opportunity subindex, wageequality between women and men for similar work is positive and statistically significant
with the gender gap. The other variables are statistically insignificant with the gender gap.
For the educational attainment subindex, the female-to-male gross tertiary enrollment
ratio is positive and statistically significant with the gender gap. For the health andsurvival and political empowerment subindexes, the coefficients of all variables are
statistically insignificant with the gender gap.
4-3. The results of the quadratic relationship
Table 5 presents the results of the quadratic relationship between the gender gap in mathand gender equality measures. The turning points for each result are also shown in Table
5. The coefficient of the primary term of the GGI is negative and statistically significant,and that of the quadratic term of the GGI is positive and statistically significant, meaningthat the greater the gender equality, the narrower the gender gap in math when the GGIreaches the turning point (0.774), and that the greater the gender equality, the wider thegender gap in math when the GGI deviates from the turning point.
For the economic participation and opportunity subindex, the coefficients of the
primary term of the female-to-male labor force participation ratio, the female-to-maleestimated earned income ratio, and the female-to-male professional and technical workers
ratio are negative and statistically significant, and those of the quadratic term are positiveand statistically significant. The results suggest that the gender gap closes when thosevariables are increased from a lower initial rate; a value above the turning points indicatesa positive effect. The turning points for the female labor force participation over malevalues and female estimated earned income over male values are less than one, while that10

of the female professional and technical workers over male values is over one. The
coefficients of the other variables are statistically insignificant.
For the educational attainment subindex, the coefficient of the primary term of thefemale-to-male gross tertiary enrollment ratio is positive and statistically significant,whereas that of the quadratic term is negative and statistically insignificant. The results
suggest that there is a positive linear relationship between the gender gap and the femaleto-male gross tertiary enrollment ratio. The coefficients of other variables are statisticallyinsignificant.
For the health and survival subindex, the coefficient of the primary term of the femaleto-male healthy life expectancy ratio is negative and statistically significant, while that of
the quadratic term is positive and statistically significant. The turning point is 1.09, which
is close to the equality benchmark (1.06).
For the political empowerment subindex, the coefficients of the primary term of the
proportion of women’s seats in parliament compared to men and the proportion of womenat the ministerial level compared to men are negative and statistically significant, whereas
those of the quadratic term are positive and statistically significant. These results suggest
a U-shaped relationship between the gender gap and the proportion of women’s seats inparliament compared to men and the proportion of women at the ministerial levelcompared to men. These results suggest that at the low value of these two variables, thehigher gender equality measures close the gender gap. When the gender equality
measures have a value over the turning points, the effect becomes positive. The turning
points are below one, thus before girls and boys are equal, the gender gap decreases. The
coefficients of other variables are statistically insignificant.
Table 6 presents the results of the quadratic relationship between the gender gap inreading and gender equality measures. The coefficient of the primary term of the GGI is
negative and statistically significant, and that of the quadratic term of the GGI is positiveand statistically significant. For the economic participation and opportunity subindex, thecoefficients of the primary term of the female-to-male labor force participation ratio andfemale-to-male professional and technical workers ratio are negative and statisticallysignificant and those of the quadratic term are positive and statistically significant. The
coefficient of the quadratic term of the female-to-male estimated earned income ratio is
positive and statistically significant. For the educational attainment subindex, thecoefficient of the primary term of the female-to-male gross tertiary enrollment ratio is11

positive and statistically significant, whereas that of the quadratic term is negative andstatistically significant. The result suggests that the gender gap and female-to-male grosstertiary enrollment ratio have a positive relationship since the turning point is too big(6.661). For the health and survival subindex, there is no statistically significant
coefficient. For the political empowerment subindex, the coefficient of the primary termof the proportion of women at ministerial levels compared to men are negative andstatistically significant, and that of the quadratic term is positive and statisticallysignificant.
The results of the relationship between the gender gap in math and the gender equality
measures differ by gender equality measures. Thus, focusing only on the GGI does notclarify which areas of gender equality affect the gender gap in academic achievement.
Furthermore, I found a U-shaped relationship between the gender gap in math and reading,and the GGI, the female-to-male labor force participation ratio, female-to-maleprofessional and technical workers ratio, and the proportion of women at the ministerial
level compared to men. Thus the functional forms are also important to investigate therelationship between the gender gap in academic achievement and the gender equality
measures. There is a positive relationship between the gender gap and the female-to-malegross tertiary enrollment ratio. For wage equality between women and men for similar
work, the coefficients are no longer statistically significant when a quadratic term is added
unlike the result in Column (3) of Table 3.
4-4. The results of quadratic relationships using truncated variables
In the previous section, I used raw subindex-specific variables, but most were truncatedto one to calculate the GGI. I analyzed the association between the gender gap inacademic achievement and truncated gender equality measures to clarify the extent towhich truncation affected the results.12 The results of the relationship between the gender
gap in math and truncated gender equality measures are presented in Table 7. The signand significance of the coefficients are the same as those in Table 5, except for the female-

The values of the GGI, female-to-male labor force participation ratio, wage equality
between women and men for similar work, and the estimated female-to-male earned
income ratio does not exceed one in targeted countries. The raw values of the sex ratio
at birth and the female-to-male healthy life expectancy ratio are used to calculate the
GGI.

12

12

to-male gross tertiary enrollment ratio. The relationships between the gender gap, and thefemale-to-male professional and technical workers ratio, and the proportion of women at
the ministerial level compared to men are U-shaped, but the turning point is smaller thanthat in column (6) of Table 5. The relationship between the gender gap and female-tomale gross tertiary enrollment ratio is U-shaped. Without outliers, the parabolic
relationship disappears and a U-shaped relationship emerges.
The results for the relationship between the gender gap in reading and gender equality
measures are presented in Table 8. The signs of the coefficients of the female-to-malegross tertiary enrollment ratio contrast with those in
Table 6, although both coefficients are still statistically significant. The relationships
between the gender gap and the female-to-male professional and technical workers ratio,
the proportion of women at ministerial levels compared to men, and the female-to-malegross tertiary enrollment ratio are U-shaped. The relationship between the gender gap andthe female-to-male net primary enrollment ratio is parabolic.
The results in Table 7 and Table 8 indicate that there are differences between the
results obtained using raw values and those obtained using truncated values. Thus, using
raw variables is important for investigating the relationship between the gender gap inacademic achievement and gender equality measures. Some studies have investigated therelationship between the gender gap in academic achievement and the gender gap using
the GGI’s subindexes, but the results may be biased because the values of the variables
are truncated.
4-5. The quadratic relationship between the gender gap in academic achievement
and gender equality measures excluding Muslim majority countries
As shown in Figure 2, in most Muslim majority countries, girls outperform boys in mathand gender equality is low. Thus, I estimated Eq. (2) excluding countries where the ratioof Muslims is over 40%.13 The results of the gender gap in math are presented in Table
9. The GGI has a U-shaped relationship with the gender gap in math. This may be because,in countries where the GGI is around the turning point, the performance of girls in mathtends to be much lower than boys. For the economic participation and opportunity
subindex, the coefficients of all variables are statistically insignificant. For the13

This data was obtained from the World Population Review. The data are for 2010.
13

educational attainment subindex, the female-to-male gross tertiary enrollment ratio has apositive relationship with the gender gap. For the health and survival subindex, thecoefficients of the gender equality measures are statistically insignificant though thefemale-to-male healthy life expectancy ratio has a U-shaped relationship with the gender
gap when Muslim majority countries are included. For the political empowerment
subindex, the proportion of women’s seats in parliament compared to that of men has apositive and statistically significant relationship with the gender gap. The proportion of
women at ministerial levels compared to men has a U-shaped relationship with the gender
gap as the results with Muslim majority countries.
The results of the gender gap in reading are presented in Table 10. The GGI has a Ushaped relationship with the gender gap in reading. For the economic participation andopportunity subindex, wage equality between women and men for similar work has apositive relationship with the gender gap. For the educational attainment subindex, thefemale-to-male gross tertiary enrollment ratio has a positive relationship with the gender
gap. For the health and survival subindex, the female-to-male healthy life expectancy
ratio has a parabolic relationship with the gender gap. For the political empowerment
subindex, the proportion of women at the ministerial level compared to men has a positive
relationship with the gender gap, whereas there is a U-shaped relationship when Muslim
majority countries are included.
The results excluding Muslim majority countries suggest that the results of therelationship between the gender gap in academic achievement and gender equality
measures vary depending on which countries are included in the analysis. Girls
outperform boys even though gender equaliity in economic and political areas in Muslim
majority countries is low. Fryer and Levitt (2010) point out that girls tend to outperformboys in math in countries where all secondary schooling is segregated by gender, andgender-segregated education is shown in Muslim majority countries. Thus, further
investigation of the relationship between the gender gap in academic achievement andgender-segregated education is needed.14
5. Conclusion

PISA’s school survey asks the number of students by gender to schools, but many
schools do not answer the question.

14

14

I investigated the nonlinear relationship between the gender gap in academic achievement
and more detailed gender equality measures. First, I found a U-shaped relationshipbetween the gender gap in math and reading and the GGI. Some previous studies havefailed to find a statistically significant relationship between the gender gap in math andthe GGI and its subindexes because they only focused on investigating its linear
relationship. Second, I also found that investigating the relationship between the gender
gap in academic achievement and the GGI and its subindexes is insufficient because mostvariables comprising the GGI or its subindexes do not consider instances where women
may outperform men. I clarified the areas of gender equality that are associated with thegender gap in academic achievement using the raw values of the variables comprising the
GGI. The results show that there are U-shaped relationships between the gender gap inacademic achievement and the female-to-male labor force participation ratio, the femaleto-male professional and technical workers ratio, and the proportion of women at theministerial level compared to men. The female-to-male gross tertiary enrollment ratio has
a positive relationship with the gender gap in academic achievement. These results
suggest the importance of using detailed gender equality variables to examine the gender
gap in academic achievement.
It should be noted that these results should not be interpreted as causal relationships.
Social norms or cultural variables that may affect the gender gap in academic achievement
were not included in the estimation, which may have introduced bias in the estimates.
Thus, further research is needed to determine which areas of gender equality affect thegender gap in academic achievement in a causal sense. Furthermore, it is necessary toanalyze the relationship between the gender gap in academic achievement and gendersegregated education.
Supplementary materials
Supplementary materials associated with this article can be found online athttps://fukuoka-u.box.com/s/9aof103zrg2i27a088y80yfbh27s4z6w
References
Anghel, B., Rodríguez-Planas, N., Sanz-de-Galdeano, A., 2020. Is the math gender gap
associated with gender equality? Only in low-income countries. Econ. Educ. Rev.
79, 102064. https://doi.org/10.1016/j.econedurev.2020.102064
15

Baker, D. P., & Jones, D. P. 1993. Creating gender equality: Cross-national gender
stratification and mathematical performance. Soci. Educ, 66(2), 91–
103.
Ceci, S., Williams, W., Barnett, S., 2009. Women’s Underrepresentation in Science:
Sociocultural and Biological Considerations. Psyc. Bul. 135, 218-261.
Dickerson, A., McIntosh, S., Valente, C., 2015. Do the maths: An analysis of the gender
gap in mathematics in Africa. Econ. Educ. Rev. 46, 1–22.
https://doi.org/10.1016/j.econedurev.2015.02.005
Else-Quest, N.M., Hyde, J.S., Linn, M.C., 2010. Cross-National Patterns of GenderDifferences in Mathematics: A Meta-Analysis. Psychol. Bull. 136, 103–127.
https://doi.org/10.1037/a0018053
Feenstra, R. C., Inklaar, R. and Timmer, M. P. 2015, The Next Generation of the Penn
World Table" Am. Econ. Rev.

105(10), 3150-3182.

www.ggdc.net/pwt

Fryer, B.R.G., Levitt, S.D., 2010. An Empirical Analysis of the Gender Gap in
Mathematics Author ( s ): Roland G . Fryer , Jr . and Steven D . Levitt Source :Am. Econ. J. : Applied Economics , April 2010 , 2 (2) ( April Published by :American Economic Association

2, 210–240.

Gevrek, Z.E., Gevrek, D., Neumeier, C., 2020. Explaining the gender gaps inmathematics achievement and attitudes: The role of societal gender equality. Econ.
Educ. Rev. 76, 101978. https://doi.org/10.1016/j.econedurev.2020.101978
Guiso, L., Monte, F., Sapienza, P., Zingales, L., 2008. Culture, gender, and math.
Science (80-. ). 320, 1164–1165. https://doi.org/10.1126/science.1154094
Huacong, L., Fernandez, F., Dutz, G., 2022. Educational attainment, use of numeracy atwork, and gender wage gaps: Evidence from 12 middle-income countries.
Int. J. of Educ. Devel. 92, 102625.
Nollenberger, N., Rodríguez-planas, N., Sevilla, A., 2016. The Math Gender Gap : The
Role of Culture. Am. Econ. Rev. 106, 0–5.
Rodríguez-Planas, N., Nollenberger, N., 2018. Let the girls learn! It is not only aboutmath … it’s about gender social norms. Econ. Educ. Rev. 62, 230–253.
https://doi.org/10.1016/j.econedurev.2017.11.006
Schleicher, A., 2019. PISA 2018: Insights and Interpretations. OECD.
World Economic Forum, 2018. The Global Gender Gap Report 2018. WEF.

16

Table 1 Subindexes and variables of the GGISubindex

Variable

Economic Participation and Opportunity Female-to-male labor force participation ratio
Wage equality between women and men for similar work (survey data, normalized on a 0-to-1 scale)
Female-to-male estimated earned income ratio
The proportion of female legislators, senior officials and managers compared to males
Female-to-male professional and technical workers ratio
Educational Attainment

Female-to-male literacy ratio
Female-to-male net primary enrollment ratio
Female-to-male net secondary enrollment ratio
Female-to-male gross tertiary enrollment ratio

Health and Survival

Sex ratio at birth (converted to female-over-male ratio)
Female-to-male healthy life expectancy ratio

Political Empowerment

The proportion of women’s seats in parliament compared to men
The proportion of women at ministerial level compared to men
Number of years with a female head of state (last 50 years) compared to men

Source: WEF (2018)
Table 2 The replication of Anghel et al.’s (2020) results using the 2006–2018 PISA and
GGI
(1)

GGI
Observations
R-squared
Year effect
Country effect(2)(3)
OECD countries
70.672**67.208**-22.758(15.790)(17.487)(23.764)
170
170
170
0.211
0.296
0.745
No
Yes
Yes
No
No
Yes

(4)

(5)
(6)
Non-OECD countries-55.739-76.243
10.480(43.886)(47.033)(50.026)
125
125
125
0.074
0.140
0.828
No
Yes
Yes
No
No
Yes

Notes: Standard errors clustered at the country-level are in parentheses. ** significant at
5%, * significant at 1%. The log of GDP per capita in PPP and female-to-male ratio
among the PISA test takers are controlled.

17

Table 3 The linear relationship between the gender gap in math and gender equality
measures
(1)
GGI

(2)

Subindex
Explanatory variable

GGI

LFP

Wage
equality

Primary term

(3)
(4)
(5)
Economic Participation and Opportunity
Estimatedearnedincome(6)(7)

Legislators,
Professional
seniorand technical Literacy rate
officials andworkers
managers(8)(9)
Educational Attainment
Primary
enrollment
rate

(10)

Secondaryenrollment
rate

Tertiary
enrollment
ratio-12.861-6.40543.558**-0.626-4.2401.581-13.255-43.82110.006

5.953**(24.138)(7.722)(7.345)(7.657)(4.915)(2.652)(25.508)(42.409)(25.580)(0.826)

Number of observations

315

302

302

303

297

295

299

287

292

297

R-squared

0.096

0.095

0.258

0.089

0.097

0.083

0.084

0.097

0.095

0.220

Year effect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

(11)

(12)

(13)

(14)

(15)

Subindex
Explanatory variable
Primary term

Health and Survival
Sex ratio at
birth

Healthy life
expectancy

Political Empowerment
Seats inparliament

Women atministerial
level

Number ofyears with afemale headof state

13.414-14.488-7.023-3.0094.220(41.974)(26.732)(6.204)(3.709)(7.438)

Number of observations

304

304

304

304

304

R-squared

0.086

0.088

0.104

0.093

0.089

Year effect

Yes

Yes

Yes

Yes

Yes

Notes: (1) Columns 1–15 present the estimation results of Eq. (1).
(2) GGI = the Gender Gap Index; LFP = female-to-male labor force participation ratio;
Wage equality = wage equality between women and men for similar work; Estimatedearned income = female-to-male estimated earned income ratio; Legislators, senior
officials and managers = the proportion of female legislators, senior officials andmanagers compared to males; Professional and technical workers = female-to-maleprofessional and technical workers ratio; Literacy rate = female-to-male literacy ratio;
Primary enrollment rate = female-to-male net primary enrollment ratio; Secondary
enrollment rate = female-to-male net secondary enrollment ratio; Tertiary enrollment ratio= female-to-male gross tertiary enrollment ratio; Sex ratio at birth = sex ratio at birth;
Healthy life expectancy = female-to-male healthy life expectancy ratio; Seats inparliament = the proportion of women’s seats in parliament compared to men; Women at
ministerial level = the proportion of women at ministerial level compared to men; Female
head of state = number of years with a female head of state (last 50 years) compared tomen.
(3) Standard errors clustered at the country-level are in parentheses. ** significant at 5%,* significant at 1%.
(4) The log of GDP per capita in PPP, female-to-male ratio among the PISA test takers,
and year effects are controlled.

18

Table 4 The linear relationship between the gender gap in reading and gender equality
measures
(1)
GGI

(2)

Subindex
Explanatory variable

GGI

LFP

Wage
equality

Estimatedearnedincome-9.637(31.681)314
0.148-6.520(11.692)301
0.149

31.650**(11.333)
301
0.192-6.432(10.642)302
0.147

Primary term
Number of observations
R-squared
Year effectSubindex
Explanatory variable
Primary term
Number of observations
R-squared
Year effect

(3)
(4)
(5)
Economic Participation and Opportunity

Yes
Yes
(11)
(12)
Health and Survival

Yes
(13)

(6)

(7)

Legislators,
Professional
seniorand technical Literacy rate
officials andworkers
managers-2.081
4.134
20.204(7.037)(3.827)(29.646)
296
294
298
0.154
0.170
0.151

Yes
Yes
(14)
(15)
Political Empowerment

Sex ratio
at birth

Healthy life
expectancy

Seats inparliament

Women atministeriallevel

92.137(65.868)
303
0.155

9.999
(35.618)303
0.146-7.817(7.625)303
0.158-0.378(4.548)303
0.146

Number ofyears with afemale headof state
2.323(9.583)
303
0.146

Yes

Yes

Yes

Yes

Yes

Yes

Yes

(8)
(9)
Educational Attainment(10)

Primary
enrollment
rate

Secondaryenrollment
rate

Tertiary
enrollment
ratio-29.275(60.234)286
0.138

18.829(30.168)291
0.136

7.403**(1.241)
296
0.262

Yes

Yes

Yes

Notes: Notes (1) – (4) of Table 3 applies.
Table 5 The quadratic relationship between the gender gap in math and gender equality
measures
(1)
GGI

(2)

Subindex
Explanatory variable

GGI

LFP

Wage
equality

Estimatedearnedincome-935.226**(255.272)642.513**(170.392)
315
0.185
0.774

-88.077**(30.251)62.948*(24.846)
302
0.140
0.700

-12.982(86.916)44.459(67.769)
302
0.259
0.146

-63.838*(27.279)
57.520*(22.297)
303
0.134
0.555

Primary term
Squared term
Number of observations
R-squared
Turning point
Year effectSubindex
Explanatory variable
Primary term
Squared term
Number of observations
R-squared
Turning point
Year effect

Yes
Yes
(11)
(12)
Health and Survival

(3)
(4)
(5)
Economic Participation and Opportunity

Yes
(13)

(6)

(7)

(8)
(9)
Educational Attainment

Legislators,
Professional
Primaryseniorand technical Literacy rate enrollment
officials andworkers
rate
managers-17.921-32.262**493.822-1,698.472(16.790)(8.931)(522.097) (2,517.270)
14.89113.688**-275.750
839.609(17.917)(3.402)(292.767) (1,276.693)
297
295
299
287
0.106
0.206
0.092
0.098
0.602
1.178
0.895
1.011

Yes
Yes
(14)
(15)
Political Empowerment

Sex ratio at
birth

Healthy life
expectancy

Seats inparliament

Women atministeriallevel-1,063.748(792.010)
571.515(411.102)
304
0.089
0.931

-2,589.128**(761.803)
1,185.807**(350.646)
304
0.178
1.092

-39.870*(18.148)
40.236*(19.755)
304
0.137
0.495

-30.199**(8.428)24.881**(6.399)
304
0.187
0.607

Number ofyears with afemale headof state-2.190(16.702)12.308(29.937)
304
0.090
0.089

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

(10)

Secondaryenrollment
rate

Tertiary
enrollment
ratio-432.949(285.068)
215.472(142.209)
292
0.117
1.005

11.405**(3.388)-0.757(0.411)
297
0.232
7.533

Yes

Yes

Notes: Columns 1–15 present the estimation results of Eq. (2). Notes (2) – (4) in Table 3
applies to this table.

19

Table 6 The quadratic relationship between the gender gap in reading and gender equality
measures
(1)
GGI

(2)

Subindex
Explanatory variable

GGI

LFP

Wage
equality

Estimatedearnedincome-1,120.832**(377.226)
774.073**(253.418)
314
0.080
0.733

-151.390**(46.404)111.660**(35.759)
301
0.224
0.678

13.001(143.558)
14.663(113.868)301
0.192-0.443-81.472(41.627)
68.280*(33.017)
302
0.181
0.597

Primary term
Squared term
Number of observations
R-squared
Turning point
Year effectSubindex
Explanatory variable
Primary term
Squared term
Number of observations
R-squared
Turning point
Year effect

Yes
Yes
(11)
(12)
Health and Survival

(3)
(4)
(5)
Economic Participation and Opportunity

Yes
(13)

Legislators,
seniorofficials andmanagers-0.878(21.323)-1.317(20.786)
296
0.154
0.333

Yes
Yes
(14)
(15)
Political Empowerment

Sex ratio at
birth

Healthy life
expectancy

Seats inparliament

Women atministerial
level

1,313.232(1,182.803)-647.883(612.473)
303
0.157
1.013

-1,877.404(1,178.819)
869.289(540.723)
303
0.172
1.080

-33.129(21.715)31.003(24.032)
303
0.168
0.534

-26.418*(10.948)
23.829**(7.516)
303
0.192
0.554

Number ofyears with afemale head of
state
9.200
(23.219)-13.199(40.182)
303
0.147
0.349

Yes

Yes

Yes

Yes

Yes

(6)

(7)

(8)
(9)
Educational Attainment

Professional
and technical Literacy rate
workers(10)

Primary
enrollment
rate

Secondaryenrollment
rate

Tertiary
enrollment
ratio-33.244*(14.231)
15.111**(5.319)
294
0.248
1.100

-85.861(701.746)
57.679(394.975)
298
0.151
0.744

228.488(2,751.380)-130.780(1,398.070)
286
0.138
0.874

-297.178(338.328)
153.713(172.258)
291
0.142
0.967

16.120**(3.661)-1.210**(0.450)
296
0.279
6.661

Yes

Yes

Yes

Yes

Yes

Notes: Columns 1–15 present the estimation results of Eq. (2). Notes (2) – (4) in Table 3
applies to this table.
Table 7 The quadratic relationship between the gender gap in math and gender equality
measures using truncated variables(1)
Explanatory variable
Primary term
Squared term
Number of observations
R-squared
Turning point
Year effect

(2)

(3)

(4)

Legislators,
Professional
Primaryseniorand technical Literacy rate enrollment
officials andworkers
rate
managers-25.175-132.016**
180.6133,536.597(14.756)(33.842)(518.958) (5,324.825)
23.82282.752**-93.639-1,845.633(12.398)(24.004)(290.772) (2,735.154)
328
320
155
208
0.117
0.217
0.086
0.100
0.528
0.798
0.964
0.958
Yes
Yes
Yes
Yes

(5)

(6)

(7)

Secondaryenrollment
rate

Tertiary
enrollment
ratio

Women atministeriallevel

195.401(461.729)-111.153(257.020)
291
0.094
0.879
Yes

-258.846**(89.866)
155.957**(54.355)
321
0.088
0.830
Yes

-40.408**(10.140)
37.075**(8.898)
323
0.193
0.545
Yes

Notes: Columns 1-15 present the estimation results of Eq. (2). Notes (2) – (4) in Table 3
applies to this table. The explanatory variables in this table are truncated at one.

20

Table 8 The quadratic relationship between the gender gap in math and gender equality
measures using truncated variables
(1)

(2)

(3)

Legislators,
Professional
seniorand technical Literacy rate
officials andworkers
managers-8.099-217.936**-396.621(21.905)(37.382)(766.101)
7.544
142.829**238.449(18.909)(26.801)(432.067)
327
319
155
0.149
0.322
0.148
0.709
0.763
0.709
Yes
Yes
Yes

Explanatory variable
Primary term
Squared term
Number of observations
R-squared
Turning point
Year effect

(4)

(5)

(6)

(7)

Primary
enrollment
rate

Secondaryenrollment
rate

Tertiary
enrollment
ratio

Women atministerial
level

15,703.529*(6,167.589)-8,088.266*(3,187.246)
207
0.148
0.971
Yes

-91.541(564.142)
58.032(313.776)
290
0.140
0.709
Yes

-466.054**(128.176)
287.045**(79.854)
320
0.180
0.812
Yes

-30.770*(14.325)
27.428*(12.883)
322
0.179
0.561
Yes

Notes: Columns 1–15 present the estimation results of Eq. (2). Notes (2) – (4) in Table 3
applies to this table. The explanatory variables in this table are truncated at one.
Table 9 The quadratic relationship between the gender gap in reading and gender equality
measures excluding Muslim majority countries
(1)
GGI

(2)

Subindex
Explanatory variable

GGI

LFP

Wage
equality

Estimatedearnedincome-833.916**(279.994)
576.219**(183.775)
266
0.527
0.724

-176.086(134.015)
119.192(81.253)
253
0.510
0.739

-103.204(70.398)
100.430(54.971)
253
0.523
0.514

-24.501(33.622)24.997(24.397)
254
0.495
0.490

Year effect

Yes

Yes

Yes

Yes

(13)

Subindex(11)(12)
Health and Survival

Primary term
Squared term
Number of observations
R-squared
Turning point

Explanatory variable
Primary term
Squared term
Number of observations
R-squared
Turning point
Year effect

(3)
(4)
(5)
Economic Participation and Opportunity

(6)

(7)

(8)
(9)
Educational Attainment

Legislators,
Professional
Primaryseniorand technical Literacy rate enrollment
officials andworkers
rate
managers-7.745-2.106-45.660-554.884(15.649)(15.997)(586.023) (2,066.058)
7.038
1.875
48.346279.197(10.907)(5.070)(316.432) (1,049.785)
254
252
299
240
0.485
0.487
0.474
0.504
0.550
0.562
0.472
0.994
Yes

Yes

Yes

Yes

(10)

Secondaryenrollment
rate

Tertiary
enrollment
ratio-113.928(267.870)
60.951(131.041)
244
0.492
0.935

-23.571(14.684)
11.389*(5.556)
251
0.514
1.035

Yes

Yes

(14)
(15)
Political Empowerment

Sex ratio at
birth

Healthy life
expectancy

Seats inparliament

Women atministerial
level-2,776.561(1,718.972)
1,563.015(933.209)
255
0.504
0.888

-458.712(558.414)
212.900(249.805)
255
0.491
1.077

-25.353(14.948)30.328*(15.056)
255
0.506
0.418

-13.385*(6.268)12.691**(4.687)
255
0.517
0.527

Number ofyears with afemale headof state
12.906(15.971)-16.452(28.138)
255
0.494
0.392

Yes

Yes

Yes

Yes

Yes

Notes: Columns 1-15 present the estimation results of Eq. (2). Notes (2) – (4) in Table 3
applies to this table. The explanatory variables in this table are truncated at one.

21

Table 10 The quadratic relationship between the gender gap in reading and genderequality measures excluding Muslim majority countries
(1)
GGI

(2)

Subindex
Explanatory variable

GGI

LFP

Wage
equality

Estimatedearnedincome-1,184.183**(432.926)
811.125**(289.177)
265
0.554
0.730

-231.131(166.685)
156.265(104.343)
252
0.541
0.740

-194.026(98.399)174.571*(78.071)
252
0.554
0.556

-58.836(39.955)
44.398(28.372)
253
0.524
0.663

Year effect

Yes

Yes

Yes

Yes

(13)

Subindex(11)(12)
Health and Survival

Primary term
Squared term
Number of observations
R-squared
Turning point

Explanatory variable
Primary term
Squared term
Number of observations
R-squared
Turning point
Year effect

(3)
(4)
(5)
Economic Participation and Opportunity

(6)

(7)

(8)
(9)
Educational Attainment

Legislators,
Professional
Primaryseniorand technical Literacy rate enrollment
officials andworkers
rate
managers
5.608
13.204-546.910
501.808(22.320)(19.905)(555.148) (2,374.085)-5.194-2.819332.366-259.517(15.077)(6.426)(302.463) (1,209.931)
253
251
298
239
0.524
0.534
0.482
0.523
0.540
2.342
0.823
0.967
Yes

Yes

Yes

Yes

(10)

Secondaryenrollment
rate

Tertiary
enrollment
ratio

208.316(480.806)-98.550(237.819)
243
0.517
1.057

-34.387(20.159)
16.600*(8.124)
250
0.550
1.036

Yes

Yes

(14)
(15)
Political Empowerment

Sex ratio at
birth

Healthy life
expectancy

Seats inparliament

Women atministerial
level-2,691.032(2,771.076)
1,560.842(1,512.980)
254
0.544
0.862

1,675.272*(715.262)-751.292*(322.518)
254
0.541
1.115

-26.066(20.723)28.859(20.696)
254
0.532
0.452

-17.090(9.694)17.043*(6.714)
254
0.552
0.501

Number ofyears with afemale headof state
29.302(21.382)-48.091(37.746)
254
0.538
0.305

Yes

Yes

Yes

Yes

Yes

Notes: Columns 1–15 present the estimation results of Eq. (2). Notes (2) – (4) in Table 3
applies to this table. The explanatory variables in this table are truncated at one.

22

Figure 1 The relationship between gender gap in math score from 2006-2018 and GGI

20
10
0
-10
-20
-30
.6

.7

.8

.9

GGI
Gender gap in math score
Fitted values (linear)

Fitted values (quadratic)

Notes: The gender gap was obtained by running a linear regression using the plausible
values provided by the PISA from 2006–2018 as dependent variables and a femaleindicator and constant term as explanatory variables. The red line illustrates the quadratic
relationship. The green line illustrates the linear relationship.

23

JO
R
Q
A
BG T
R
AR
E
TT
O
M
L
SA T
G U
EO
KG
SV Z
M N
KD
LT
M U
D
A
LV
A
TH
A
AL
B
H
R
V
SR
M B
U
S
IS
R
ES
R T
O
U
KA
R Z
U
S
U
R
M Y
Y
U S
K
M R
N
AR E
G
TU
N
D
ZA
D
O
M
H
KG
BR
N
BI
H
BR
VN A
M
PH
SG L
P
ID
M N
AR
C
H
N
AZ
E
PA
N
BL
R
LB
N
C
H
L
C
R
PE I
R
C
O
L
ZE

ZL

2006

2006

U
N

S

T

2009

2009

H

2012

2012

2006–2018.

24
2015

2015

X
K

EX

M

U
SA
G
BR

N

LD

JP

N

L

2015

BE

2015

IR
L

P

ES

N

2012

D

2012

LU

PR
T

AN

2009

C

2009

KO
R

E

EU

C

2006

D

Q
A
SA T
U
TT
O
PH
ML
L
JO T
BR R
N
TH
M A
K
G D
EO
D
Z
AR A
E
AL
M B
YS
D
O
M
BG
R
KG
Z
LT
U
M
D
A
KA
M Z
A
SGR
P
LV
A
ID
N
BI
H
SV
M N
U
R S
U
VN S
R M
O
M U
AC
AZ
E
ES
C T
H
N
IS
R
BL
R
PA
M N
N
U E
KR
SR
H B
KG
LB
H N
R
U V
R
TU Y
AR N
G
BR
A
LI
PE E
R
C
H
L
C
R
C I
O
L
2006

IT
A

H

AU

AU

FR
A

N

PO
L
TU
R

C

K

SV

IS
L
SW
E

FI
N
G
R
C
N
O
R

N

K

S

H

E

AU

T

IT
A

X

EU
LU

D

JP
N
G
BR

BE
L

C

IR
L
ES
P

PR
T
M
EX

D

AU

ZL

SA
N

U

AN

N

LD
U
C

H

N

FR
A

SV
K

TU
R

PO
L
C
ZE

O
R
G
R
C
KO
R

N

FI
N

IS
L
SW
E

Figure 2 The gender gap in math score by country

The Gender Gap in Math score

10

-10
0

-20

-30

2018

20

10

-10
0

-20

-30

2018

Notes: Countries are ranked according to the average gender gap in math score from2006–2018.

Figure 3 The gender gap in reading score by country

The Gender Gap in Reading Score

60

40

20

0

2018

80

60

40

20

0

2018

Note: Countries are ranked according to the average gender gap in reading score from

Figure 4 The relationship between the gender gap in math and gender equality measures
20

20

20

10

10

10

0

0

0

0

-10

-10

-10

-10

-20

-20

-20

-20

-30

-30

-30

0

.2

.4

.6

.8

1

0

GGI

.2

.4

.6

.8

20
10

-30

1

0

.2

LFP

.4

.6

.8

1

0

Wage equality

20

20

20

10

10

10

0

0

0

0

-10

-10

-10

-10

-20

-20

-20

-20

-30

-30

-30

0

.5

1

1.5

0

Legislators, senior officials and managers

.5

1

1.5

2

.2

.4

.6

.8

1

0

Literacy rate

20

10

10
0

0

-10

-10

-20

-20

-20

-20

-30

.4

.6

.8

1

-30

1.2

0

Secondary enrollment rate

2

4

6

0

.2

.4

.6

.8

1

Seats in parliament
Gender gap in math0.2

Fitted values

.4

.6

.8

1

1.2

Sex ratio at birth

20
10
0
-10
-20
-30

.6

.8

1

-30

8

Tertiary enrollment ratio

20
10
0
-10
-20
-30

.4

10

0
-10

.2

.2

20

0

0

1

Primary enrollment rate

-10
-30

.8

-30

0

20

.6

10

2.5

10

.4

20

Professional and technical workers

20.2

Estimated earned income

0

.2

.4

.6

.8

1

1.2

Healthy life expectancy

20
10
0
-10
-20
-30

0

.5

1

1.5

2

Women at ministerial level
Gender gap in math

0

.2

.4

.6

.8

1

Female head of state

Fitted values

Gender gap in mathFitted values

Notes: Gender equality measures are variables that comprise the GGI. The red lines
curve with the fitted values for the gender gap in math from a linear regression of thegender gap in math on gender equality measures and gender equality measures squared.
Notes (2) in Table 3 applies to this figure.

25