Discussion Paper No. 956

GENDER WAGE GAPAND ITS EFFECT ON TEST SCORESOF IMMIGRANT STUDENTS

Eiji Yamamura

February 2016

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Gender Wage Gap and its Effect on Test Scores of
Immigrant Students

Eiji Yamamura
Department of Economics, Seinan Gakuin University
6-2-92 Sawara-ku, Nishijin, Fukuoka 814-8511, Japan
Tel: +81-(0)92-823-4543, Fax: +81-(0)92-823-2506, E-mail: yamaei@seinan-gu.ac.jp

Abstract

This paper examines how gender equality influences difference in cognitive
skills between genders. For closer examination of Guiso et al. (2008), restricting thesample to immigrant allows us to reduce the possibility of reverse causality. Key
findings obtained through regression estimation are: (1) decreased gender wage gap
leads to girls exhibiting a reduced incidence of lateness and skipping school comparedwith boys, which in turn improves girls’ test scores in mathematics, science, andreading; (2) the direct effect of the decreased wage gap on test scores exceeds its
indirect effect on performance owing to influencing school attendance. Considering thedirect and indirect effects of the wage gap: each 1% decrease in the wage gap results in
a 0.20%, 0.13% or 0.06% increase in test scores for mathematics, science, and reading,respectively.
Keywords: Gender gap index, Test scores, Mathematics, Immigrants.
JEL classification: D81, O11; Q54; Z13

1

I.

Introduction

The gender wage gap is a critical issue for both academic researchers and policy
makers to consider (World Economic Forum 2011). The gender wage gap is widely
observed and so has been formally studied (Bartolucci 2013). One potential explanation
for the gender wage gap is that it might reflect differences in cognitive skills. In fact, agender gap exists in educational achievement, for example as measured by school testscores (e.g., Dee 2007; Goldin et al. 2008; Cornwell et al. 2013; Stoet and Geary 2013;
Fortin et al., 2015). Graduates with higher test scores in mathematics are observed toattain a higher educational level and earn higher income than others (e.g., Altonji 1995;
Levine and Zimmerman 1995; Rose and Betts 2004; Joensen and Nielsen 2009). Girls’
scores in mathematics are also observed to be lower than those of boys (Guiso et al.,2008; Fryer and Levitt 2010; Stoet and Geary 2013). Relative lack of mathematicalskills among girls is thought to be one of the factors supporting the gender wage gap.
Economic incentives are thought to play an important role in learning and hence inimproving cognitive skills1. Many researchers have attempted to empirically examine
the economic incentives associated with educational achievement (Angrist and Lavy
2009; Kremer et al. 2009; Leuven et al. 2010; Fryer 2011; Bettinger 2012; Angrist et al.
2014). Increasing numbers of researchers have focused on the gender gap inmathematics and its relationship with empirical economics (e.g., Ellison and Swanson
2010; Guiso et al. 2008; Fryer and Levitt 2010; Niederle and Versterlund 2010).
Existing studies used PISA data to consider the effects of social conditions on
the gender gap in mathematics test scores (Guiso et al. 2008; Fryer and Levitt 2010;
Stoet and Geary 2013)2. However, the key variable used in these papers was the Gender
Gap Index (GGI) sourced from the World Economic Forum’s Global Gender Index(WEF-GGI). The GGI comprises various gender gaps, such as economic, politicalempowerment, health status and so on. Therefore, these studies did not scrutinize
whether economic conditions influenced the gender gap in test scores by creating an
Gneezy and Rustichini (2000) pointed out the possibility that extrinsic rewards can crowdout intrinsic motivation.
2 PISA data are widely used for empirical study in the social sciences (e.g., Fuchs andWoessmann 2007; Sprietsma 2010; Gamboa and Waltenberg 2012; Ammermueller 2013;
Hanushek et al. 2013).
12

incentive to learn. From the viewpoint of economics, it is valuable to specificallyinvestigate the economic gender gap that relates to girls’ incentives to study. Adult
females are a role model for female students, and can motivate girls to study andotherwise influence their behavior (Neumark and Gardecki 1998; Bettinger and Long
2005). Female students have a particular incentive to learn and improve their test scores
when academic achievement is positively related to wage level. When motivated by the
expectation of higher female wage levels in future, female students make more effort tolearn during class. Therefore, girls’ test scores vary with adult female wage levels evengiven identical time spent learning at school. Furthermore, an expectation of higher
female wages in future means that female students have a motivation to regularly attend
school and that their parents have a motivation to direct their daughters to attend school.
This, in turn, improves test score.
To explore the channels through which female students can improve their testscores, this paper investigates both the direct effect of gender wage gap on test scoresand the indirect effect it exerts through influencing school attendance. The direct effectcan be considered to involve quality of classroom learning, while the indirect effect canbe considered to involve quantity of learning time at school. Further, the samples ofexisting works have included fewer than 50 countries (Guiso et al. 2008; Fryer andLevitt 2010). However, the number of countries included in PISA 2012 increased and so
62 countries are used for estimation in this study. This improves the accuracy of theestimation results. Existing studies consider the influence of social conditions on testscores in mathematics and reading (Fryer and Levitt 2010; Stoet and Geary 2013). FromPISA 2012 we can obtain not only mathematics and reading test scores, but also science
test scores. So this paper compares the effects of the gender wage gap on test scoresamong three subjects. Females’ higher academic skills is thought to lead to the higher
marginal productivity. If so, Causality between the gender wage gap and the gender gap
in test scores is ambiguous, resulting in estimation bias. For immigrant student, female’s
wage level is exogenously given. To identify the effect of the gender wage gap, oursample is restricted to immigrant students. The key findings of this paper are that
decreased gender wage gap not only improves test scores in mathematics but also inscience and reading. This holds true especially when considering indirect effects on theperformance of female students, such as truancy reducing wages. However, the effect of3

decreased wage gap on the test scores of female students is larger for mathematics thanfor science and mathematics.
This paper organized as follows: section II describes the data, section III
presents the econometric specifications, section IV presents the estimation results andsection V concludes.

II.

Data

Concerning individual level data, I use data from PISA, a triennial worldwide
test of academic achievement among 15 year olds conducted since 2000. The
Organization for Economic Co-operation and Development (OECD) coordinates theimplementation of PISA. The PISA test conducted in 2012 assessed three subjects,
namely mathematics, science knowledge and reading literacy. Over 485,000 students
from 68 countries took the PISA test in 2012. WEF-GGI provided the data on thegender gap in modern society. Especially, this paper focused on the wage gap between
males and females from an economic viewpoint. Following the method of Guiso et al.
(2008) and Fryer and Levitt (2010), PISA 2012 data are matched with WEF-GGI 2011
data to examine the association of the gender gap with academic test score. In previous
works (Guiso et al. 2008; Fryer and Levitt 2010) national average PISA data arematched with WEF-GGI data. In contrast, in this paper, individual level PISA data arematched with country level WEF-GGI data, which allows controlling for individuallevel characteristics of students. As is exhibited in Table A1, among 68 countries, 63 areused for estimations because WEF-GGI data cannot be obtained for five countries. In
comparison with PISA 2003, which provided the data for previous works (Guiso et al.
2008; Fryer and Levitt 2010), the number of countries participating in PISA 2012
increased to 68 (PISA 2012), from 41 previously. Fryer and Levitt (2010) argued that
estimation results changed when the sample was restricted to that of “TIMSS (Trends inInternational Mathematics and Science Study)-PISA overlap”. To check the robustnessof the estimation results, this paper also used subsamples of countries equivalent to the“TIMSS-PISA overlap” samples. Reverse causality seems to exist between the genderwage gap and gender test score gap because well-educated females can find work thatpays high wages. To reduce the possibility of reverse causality, this paper restricted the4sample to students who were migrants from other countries because students wereunlikely to have been the decision-makers regarding immigration and so the genderwage gap of their country of residence was, to a certain extent, exogenously given.
Consequently, the sample size reduced to about 19,000 individuals in the baseline model,
representing 62 countries3.

III.

Econometric Specification

The variables used in the regression estimations are shown in Table 1, whichindicates definitions and basic statistics (mean and standard deviation). The estimatedfunction of the baseline model takes the following form:
Yim = 0 + 1 WAGE_FM m * FEMALE i + 2WAGE_FM m + 3FEMALEi + 4 LN(GDP) m +5LATEim + 6SKIPim + 7AGEim + 8AR_AGEim + 9WEALTHim + 10 H_FATHim +11H_MOTHim + 12H_BROTHim + 13H_SISTEim + X’Β+ uim,

(1)

where Yim represents the dependent variable for individual i and country m. Yim are student’s testscores (MATH, SCIE, and READ), which take the log form for convenience of interpretation.
Regression parameters are represented by . The error term is represented by uim. Furthermore,disturbances in the equation when MATH is a dependent variable may correlate with
disturbances when SCIE (and also READ) is a dependent variable. Hence, MATH, SCIE, andREAD should be jointly estimated because of correlations between disturbances. In this case, aseemingly unrelated regression model (SUR) is preferred. Hence, I used an SUR model for theestimations. X is a vector of other control variables such as parental educational level, andnumber of books in the home. Β is a vector of the coefficients of national and individual
characteristics.
Gender difference among students and gender gap in economic status are captured by including
FEMALE (female dummy) and WAGE_FM (female wage/male wage), respectively. We see
from Table 1 that the mean value of WAGE_FM is 0.65, which implies that the average wage
level of female adults is around 65% that of male adults. The key variable of this paper is,
WAGE_FM * FEMALE (the cross term of WAGE_FM and FEMALE). If female wage increases
relative to male wage, and as a result the test scores of female students increase relative to those
3 The estimation results using the full sample (including non-immigrant students) arealmost the same as those reported in this paper. The results are available from the author
upon request.

5

of male students, then WAGE_FM * FEMALE has a positive sign. To control for the level of
economic development in students’ country of residence, log of per capita GDP is included. To
consider the effect of time spent on study in school on test score, LATE and SKIP areincorporated. Time spent on class is thought to be an input for educational achievement and soto be positively associated with test score. LATE and SKIP are considered to reduce the timespent and their coefficient sign is predicted to be negative.
Even within the group aged 15 years, the ages of individual students differ by months. PISA
2012 provides student age information not only in years but also in months, and so AGE is
included. Table 1 suggested that the mean value of AGE is 15.7 years. This paper restricted thesample to students that had immigrated from other countries and so degree of assimilation is
critical to academic achievement (Akresh and Redstone 2011, Nielsen and Rangvid 2012). The
timing of arrival in the country of residence is thought to reflect academic performance,
especially for reading scores because reading experience increases with the time students havespent living in a given language environment. AR_AGE (age of arrival in the country ofresidence) is included and its expected coefficient sign is negative. As shown in Table 1, themean value of AR_AGE is 6.4, which is almost the starting year of primary school although theeducational system differs with country. For the 2012 data, an average of 9 years had passed
since the arrival of students at their country of residence from their country of birth. Concerning
family characteristics, PISA 2012 provides information about WEALTH (family wealth) and so
is incorporated to control for family economic status. Further, living with family members isobserved to influence academic performance (Bettinger et al. 2014) and so H_FATH, H_MOTH,
H_BROTH, and H_SISTE are included to capture this effect. As for other control variables,
parental educational level and number of books in the home are included to represent family
educational circumstance. Besides the baseline model (1), time spent on an each subject(STU_MATH, STU_SCIE, and STU_READ) can be obtained from PISA 2012 and so is includedin the alternative model despite its sample size being reduced because the sample used formodel (1) has many missing values for STU_MATH, STU_SCIE, and STU_READ.

The previously suggested equation examines the direct effect of gender wage gap on
test scores. Besides, the indirect effects of gender wage gap on test scores are examined,
including through such mechanisms as lateness in coming to school and skipping school
entirely. The method is as follows: in the first step, coefficients of LATE and SKIP are
obtained in the previous estimations. In the second step, to assess the effect of genderwage gap on lateness for school and skipping school, LATE and SKIP are dependent
variables when the same control variables used in equation (1), apart from LATE and6

SKIP, are included. The equation is:
Z im = β0 + β1 WAGE_FM m * FEMALE i + β2WAGE_FM m + β3FEMALEi + β4 LN(GDP) m +β5AGEim + β6AR_AGEim + β7WEALTHim + β8 H_FATHim + β9H_MOTHim + β10H_BROTHim+ β11H_SISTEim + X’C+ eim,(2)

where Zim represents the dependent variable LATE (or SKIP ), which is censored at 1, and so the
Tobit model is appropriate. Further, disturbances in the equation when SKIP is a dependent
variable may correlate with disturbances when LATE is a dependent variable. Both SKIP and
LATE should be jointly estimated because of correlations between disturbances. In this case, abivariate Trobit model is preferred and so is used in this paper. The key variable is the productof WAGE_FM * FEMALE. A higher education leads students to earn higher income (e.g.,

Altonji 1995; Levine and Zimmerman 1995; Rose and Betts 2004; Joensen and Nielsen2009). If this holds, female wages increase relative to male wages, leading girls to regularly
attend school to increase their probability of earning higher wages in the future. Therefore, thecoefficient of WAGE_FM * FEMALE is predicted to have a negative sign in equation (2).

Considering the results of equations (1) and (2) to estimate the indirect effect ofgender wage gap on test scores via such mechanisms as being late for school andskipping school.

IV.

Results

Table 2 indicates the results of the mean difference test of test scores between
boys and girls. Consistent with existing works (Guiso et al. 2008; Fryer and Levitt
2010; Stoet and Geary 2013), mean score for MATH is 11 points higher for boys than
girls, with this difference being statistically significant at the 1% level. In contrast, meanscores for SCIE and READ are higher for girls than boys. With respect to READ, thedifference is 39 points, and is statistically significant at the 1% level, which is in linewith Fryer and Levitt (2010) and Stoet and Geary (2013). However, for SCIE, thedifference is only 2 points, and is statistically significant at the 10% level. Thus thegender difference for SCIE is less clear than for MATH and READ.
Figures 1(a)–1(c) illustrated the relation between gender wage gap and gender
gap in each test score, and are comparable to Figures 2 of Fryer and Levitt (2010).
Similar to Figures 2 of Fryer and Levitt (2010), we observe positive relations between

7the gender wage gap and gender test score gap in Figures 1(a)–1(c). Hence, as genderwage gap decreases, the test scores of girls improve relative to those of boys. However,these figures exclude other factors. For closer examination of the relation, estimation
based on individual-level data should be checked.
Now we turn to the results of regression estimations. Tales 3–5 report theestimation results of the SUR model, where Ln (MATH), Ln(SCIE) and Ln(READ) aredependent variables. In each Table, columns (1), (2) and (3) indicate results when Ln(MATH), Ln(SCIE) and Ln(READ) are dependent variables, respectively. As for key
variables, WAGE_FM * FEMALE shows a positive sign in columns (1) and (2), and is
statistically significant. In contrast, WAGE_FM * FEMALE shows a negative sign in column (2),despite being statistically insignificant. These results imply that as the wage level of females
approaches that of males, the test scores of female students in mathematics and science improve
relative to those of male students. What is more, the absolute value of the coefficient of
WAGE_FM * FEMALE is 0.13 for Ln(MATH) and 0.07 for Ln(SCIE). This can be interpreted assuggesting that a 1% increase in the female-over-male wage ratio increases the mathematics testscores of female students by 0.13% relative to males, and increases the test scores for science by
0.07%. However, such an effect is not observed for the reading test score. Concerning other key
variables, both LATE and SKIP show negative signs and are statistically significant at the 1%
level in columns (1)–(3). Therefore, absence from class reduces test score regardless of subject.
Further, in all columns, the absolute values of LATE and SKIP are 0.01 and 0.02, respectively.
Specifically, the test score of all subjects deceases by 1 % (2 %) when the frequency of beinglate for school (skipping school) deceases by 1 point on the 4 point scale.. That is, quantity oftime spent in class contributes substantially to cognitive skills for all subjects. As for control
variables, the coefficient of AGE is significant and positive sign in all columns. Therefore, as
students mature, their test scores for all subjects improve. After controlling for student age, it isinteresting to observe that AR_AGE is statistically significant at the 1% level in columns (2) and(3) but not in column (1), although the sign of AR_AGE is negative in all columns. Furthermore,
the absolute value of the coefficient of AR_AGE is 0.002 for reading, which is twice its value of0.001 for science. This implies that for each one year decrease in the age of students’ arrival intheir country of residence there occurs a 0.02 (0.01)% increase in test scores for reading(science). Our interpretation of this result is that more experience of learning the language of thehost country in daily life improves students’ linguistic abilities, which are necessary for reading.
Linguistic ability plays a role in understanding science, but is less important in understanding
mathematics. In columns (1)–(3), H_FATH and H_MOTH are significantly positive whereas8H_BROTH and H_SISTE are significantly negative. Living with parents improves cognitive
skills partly because parents take care of their children’s daily life, which in turn improves
academic performance. In contrast, the existence of siblings is considered to reduce educational
quality because average spending on children’s education decreases as the number of children
increases. For robustness check, total minutes of class study on each subject are included as
independent variables and the results are shown in Table 4. We see from Table 4 that thecoefficients of STU_MATH, STU_SCIE, and STU_READ are positive and statisticallysignificant at the 1% level, suggesting that inputs for each subject increase the output (testscore). Concerning other variables, the estimation results of Table 4 are almost identical to thoseof Table 3. Moreover, the estimation results of Table 3 are robust for alternative specifications.
As pointed out by Fryer and Levitt (2010), the influences of females’ social condition on

test scores disappear when PISA data is restricted to countries that overlapped with the
TIMSS data. For further robustness check, Table 5 reports the results based on the“TIMSS-PISA overlap” samples. The upper part of Table 5 shows the results based on a
sub-sample of countries that are equivalent to the “TIMSS-PISA 2003 overlap”
countries of Fryer and Levitt (2010). The number of countries included in PISA 2012
increased relative to PISA 2003, the data source for Fryer and Levitt (2010). Therefore,
the lower part of Table 5 shows the results based on using a sub-sample of countries
equivalent to the “TIMSS-PISA 2012 overlap” countries. In Table 5, a significant
positive sign of WAGE_FM * FEMALE is persistently observed for mathematics, while forscience it is not statistically significant. Turning to LATE and SKIP, both continue to show asignificant negative sign for all subjects. All in all, restricting the analysis to a sub-sample does
not influence the results, with the exception of the effect of WAGE_FM * FEMALE on science
score.

We now turn our attention to the estimation results of Table 6 when LATE and SKIP
are dependent variables. We focus on the results for WAGE_FM * FEMALE, which is the
key variable. Table 6 indicates the significant negative signs for LATE and SKIP.
Further, the absolute values of the coefficients of LATE and SKIP are 1.04 and 1.98,
respectively. This can be interpreted as suggesting that a 10% increase in female-over-malewage ratio results in a 0.198 (0.104) point decrease, on the 4 point scale, in the frequency ofskipping school (being late for school). To check robustness, we conduct the estimation on the
sub-sample used in Table 5, and the results are presented in Table 7. In Table 7, WAGE_FM *FEMALE continues to show a significant negative sign for SKIP, whereas statistical9significance of WAGE_FM * FEMALE is not observed for LATE despite it having a negativesign. The absolute value of the coefficient of WAGE_FM * FEMALE increased to about 2.9 for
SKIP, which is about three times larger than that reported in Table 6. The combined effect of
WAGE_FM * FEMALE on LATE and SKIP is approximately 3.00 for Tables 6 and 7.

Existing works (Guiso et al., 2008; Fryer and Levitt 2010) only consider the direct
effect of female social condition on the gender gap in test scores. Additionally, we
provide estimations of the indirect effect of the gender wage gap on test scores throughits effect on school attendance. Based on the results of Tables 3 and 6, we calculated thedegree of the effects and its results are presented in Table 8. The direct effects of thedecrease in gender wage gap are the coefficients of WAGE_FM * FEMALE indicated inTable 3. The indirect effect of a 1% decrease in the wage gap is the coefficient of LATE(SKIP) multiplied by that of WAGE_FM * FEMALE. The indirect effect of a 1% decrease in
the wage gap through being late for school is 0.02% for all subjects, while the indirect effectthrough skipping school is 0.05% for mathematics and 0.04% for science and reading. What has
been observed thus far reveals a distinct difference in the direct effect of gender wage gap
among the three subjects while the indirect effect is almost the same for all subjects.
Considering the direct and indirect effects together leads to a 1% decrease in the effect of thegender wage gap, resulting in it being 0.20%, 0.13% and 0.06% for mathematics, science andreading. Therefore, the total effect of the decrease in gender wage gap for mathematics is about
1.5 times larger than for science, and three times larger than for reading.
What has been observed thus far implies that the gender wage gap influences quantityof study, which improves test scores in the three subjects equally. However, after controlling forquantity of study, the gender wage gap improves skills in mathematics and sciences, but not inreading. So, the effect of gender wage gap on attitude toward learning during class is thought tovary according to subject.

V.

Conclusions

This paper examines how the gender wage gap influences the test scores offemale versus male students. Using PISA 2012 matched with the gender wage gap
sourced from WEF-GGI 2011, we compare the effect of the wage gap among the areasof mathematics, science and reading. Key findings are that a decrease in the genderwage gap leads to a decrease in truancy by female students, which in turn improves the10

test scores of female students in mathematics, science, and reading. Even after
controlling this channel, decrease in the gender wage gap improves the test scores offemale students in mathematics and science, though not reading. Combining the indirect
and direct effects of the gender wage gap: for female students, each 1% decrease in thewage gap relative to males leads to a 0.20%, 0.13% and 0.06% increase in test scoresfor mathematics, science and reading, respectively.
In comparison with the existing works of Guiso et al. (2008) and Fryer andLevitt (2010), the findings of this paper are more reliable because (1) analysis at theindividual level lets us consider the effect of individual level characteristics, (2) the
sample was restricted to child immigrants from other countries, reducing the possibilityof reverse causality, (3) the countries included in the sample are larger. The findings ofthis paper made it evident that higher female wage level relative to male wage level
incentivizes female students to attend school, resulting in their achieving higher testscores not only for mathematics, but also for science and reading.

Acknowledgement: I will deeply thank Kunio Urakawa for providing
the data and the discussion for this issue.

11

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Table 1. Definitions of variables and basic statistics
Definition

Mean

MATH

Students’ mathematics test scores

471.3

Standard
Deviation
100.3

SCIEN

Students’ science test scores

474.9

101.1

READ

Students’ reading test scores

471.1

102.9

WAGE_FM

Female/male wage rate in respondents’ countries ofresidence

0.65

0.08

GDP

53.1

41.4

0.50

0.50

0.81

1.30

0.40

0.92

STU_MATH

GDP per capita of students’ country of residence (1000
USD)
Takes the value 1 if students are female, and otherwise
is 0
Frequency of being late for school:
1 (None), 2 (Once or twice), 3 (Three or four times), 4(Five or more times)
Frequencies of skipping school:
1 (none),2 (once or twice), 3 (three or four times), 4 (fiveor more times)
Total minutes of class study on mathematics

239.7

94.5

STU_SCIE

Total minutes of class study on science

225.5

144.5

STU_READ

Total minutes of class study on reading

234.1

92.8

AGE

Respondent age

15.7

0.29

AR_AGEAge at which respondents arrived in their current
country of residence
Index of family wealth.

6.4

4.5

-0.16

1.05

H_FATH

Takes the value 1 if students live with their father, andotherwise is 0.

0.85

0.35

H_MOTH

Takes the value 1 if students live with their mother,and otherwise is 0.

0.93

0.22

H_BROTH

Takes the value 1 if students live with their brother,and otherwise is 0.

0.62

0.48

H_SISTE

Takes the value 1 if students live with their sister, andotherwise is 0.

0.58

0.49

FEMALELATE
SKIP

WEALTH

15

Notes: WAGE_FM is gathered from Hausman et al. (2011). GDP is sourced from Penn
World Table 8.1. (http://www.rug.nl/research/ggdc/data/pwt/pwt-8.1.
accessed on
September 20, 2015). Besides WAGE_FM and GDP, data is obtained from PISA 2012.
Apart from STU_READ, STU_SCIE, and STU_MATH, values are calculated based on
the sample used in Table 3. Values for STU_READ, STU_SCIE, and STU_MATH are
calculated based on the sample used in Table 4. The index of family wealth (WEALTH)
is based on the students’ responses regarding whether they had the following at home: aroom of their own, an Internet connection, a dishwasher (treated as a country-specificitem), a DVD player, and three other country-specific items; as well as their responses
regarding the number of cellular phones, televisions, computers, cars and rooms with abath or shower.

16

Table 2. Mean difference of test score between male and female students (sample ofimmigrants’ children).

MATH

Boys

475

Girls

466

t-value
7.95***

SCIEN

473

475

−1.76*

READ

451

490

−34.5***

Notes: * and *** indicate significance at the 10%, 5% and 1% levels, respectively.

17

Table 3. SUR estimation(1)

WAGE_FM*FEMALE
WAGE_FM

Ln(MATH)(2)

Ln(SCIE)(3)

Ln(READ)0.13***(4.30)

0.07**(2.13)−0.01(−0.24)

0.11***(5.86)

0.03(1.48)

0.11***(4.59)−0.11***(−5.46)−0.01***(−7.26)−0.01***(−16.1)−0.04**(−2.27)−0.01***(−5.36)−0.01***(−14.6)0.08***(3.92)−0.01**(−2.00)−0.01***(−14.5)SKIP−0.02***(−13.6)−0.02***(−12.8)−0.02***(−14.5)AGE

0.02***(5.27)

0.01***(3.39)

0.02***(4.62)

AR_AGE−0.0003(−1.08)0.01***(9.03)
0.01***(4.27)−0.001***(−4.53)0.01***(8.10)
0.01***(3.73)−0.002***(−7.15)0.01***(10.5)
0.01**(2.13)

H_MOTH

0.13***(22.0)

0.12***(19.4)

0.12***(20.2)

H_BROTH−0.04***(−14.6)−0.04***(−13.6)−0.03***(−11.2)

H_SISTE−0.03***(−12.0)−0.03***(−10.8)−0.02***(−9.65)R-square

0.27

0.26

0.28

Observations

19155

19155

19155

FEMALE
Ln(GDP)LATEWEALTH
H_FATH

Notes: Father’s educational level, mother’s educational level, and number of books in
the home are included, but not reported. Numbers in parentheses are z-values. *, ** and*** indicate significance at the 10%, 5% and 1% levels, respectively.

18

Table 4. SUR estimation(1)

WAGE_FM*FEMALE
WAGE_FM

Ln(MATH)(2)

Ln(SCIE)(3)

Ln(READ)0.12***(3.32)

0.07*(1.71)−0.01(−0.20)0.09***(3.35)

0.03(1.16)

0.09***(3.21)−0.11***(−4.39)−0.01*(−1.94)−0.01***(−11.3)−0.05**(−2.04)−0.003(−1.06)−0.01***(−10.4)0.07***(2.87)
0.004(1.15)−0.01***(−9.61)SKIP−0.01***(−8.86)−0.01***(−8.04)−0.02***(−9.57)

STU_MATH

0.05***(5.72)FEMALE
Ln(GDP)LATE

STU_SCIE

0.03***(5.60)

STU_READ

0.07***(7.21)

AGE

0.02***(4.73)

0.01***(3.16)

0.02***(3.74)

AR_AGE−0.0009**(−2.43)0.01***(4.89)
0.01***(2.63)−0.001***(−4.58)0.01***(5.50)
0.01**(2.21)−0.002***(−6.35)0.01***(6.74)
0.003(0.68)

H_MOTH

0.10***(13.5)

0.10***(12.4)

0.10***(12.8)

H_BROTH−0.03***(−10.1)−0.03***(−9.30)−0.02***(−7.50)

H_SISTE−0.03***(−9.16)−0.03***(−8.53)−0.02***(−7.20)R-square

0.28

0.27

0.28

Observations

9998

9998

9998

WEALTH
H_FATH

Notes: Father’s educational level, mother’s educational level, and number of books in
the home are included, but not reported. Numbers in parentheses are z-values. *, ** and*** indicate significance at the 10%, 5% and 1% levels, respectively.

19

Table 5. SUR estimation for the sub-sample of Feyer and Levitt (2010)(1)

Sub-sample A
Model of Table 3

WAGE_FM*FEMALE
WAGE_FM(2)

Ln(SCIE)(3)

Ln(READ)R-square

0.13**(2.44)
0.04(1.19)-1.11***(-3.17)-0.02***(-11.2)-0.02***(-8.91)0.17

0.05(0.85)
0.05(1.34)-0.04(-1.06)-0.02***(-9.94)-0.02***(-8.36)0.19−0.07(−1.27)
0.26***(5.90)
0.13***(3.31)-0.02***(-10.8)-0.02***(-8.77)0.20

Observations

5169

5169

5169

WAGE_FM*FEMALE
WAGE_FMR-square

0.16***(3.74)
0.14***(4.47)-1.13***(-4.48)-0.02***(-13.2)-0.02***(-8.77)0.33

0.06(1.30)
0.13***(3.77)-0.04(-1.48)-0.02***(-11.7)-0.02***(-9.20)0.31−0.07(−1.54)0.24***(6.59)
0.12***(3.91)-0.02***(-11.8)-0.02***(-10.4)0.33

Observations

7346

7346

7346

FEMALE
LATE
SKIP

Sub-sample B
Model of Table 3

Ln(MATH)FEMALE
LATE
SKIP

Notes: Sub-sample A consists of countries equivalent to the sample of “TIMSS-PISA
overlap” in Table 7 of Fryer and Levitt (2010). Sub-sample B consists of countries
included in PISA 2012 and overlapped with TIMSS 2003. Control variables used inTable 3 are included, but not reported. Numbers in parentheses are z-values. *, ** and*** indicate significance at the 10%, 5% and 1% levels, respectively.

20

Table 6. Multivariate Tobit estimation
(2)

(1)

WAGE_FM*FEMALE
WAGE_FM

LATE

SKIP

−1.04*(−1.93)−1.98***(−2.90)1.35***(3.42)−5.24***(−10.2)0.49(1.39)−0.61***(−14.8)0.25***(3.14)

1.35***(3.06)−0.33***(−6.33)0.11(1.10)−0.005(−1.08)0.14***(5.81)−0.49***(−7.46)0.02***(3.24)
0.09***(2.98)−0.43***(−5.13)

H_MOTH−0.23**(−2.33)−0.20(−1.59)H_BROTH

0.09*(1.96)

0.13**(2.17)

H_SISTE

0.09*(1.84)

0.14**(2.24)FEMALE
Ln(GDP)
AGE
AR_AGEWEALTH
H_FATH

Wald
statistics

1116

Observations

19155

Notes: Father’s educational level, mother’s educational level, and number of books in
the home are included, but not reported. Numbers in parentheses are z-values. *, ** and*** indicate significance at the 10%, 5% and 1% levels, respectively.

21

Table 7. Multivariate Tobit estimation for the sub-sample of Feyer and Levitt (2010)(1)

Sub-sample A
Model of Table 3

Sub-sample B
Model of Table 3

WAGE_FM*FEMALE

LATE

(2)

SKIP

-0.65
(-0.64)-2.92**(-2.52)

Wald statistics

596

596

Observations

5169

5169

WAGE_FM*FEMALE-1.34(-1.57)-2.97***(-3.02)

Wald statistics

705

705

Observations

7346

7346

Notes: Sub-sample A consists of countries equivalent to the sample of “TIMSS-PISA
overlap” in Table 7 of Fryer and Levitt (2010). Sub-sample B consists of countries
included in PISA 2012 and overlapped with TIMSS 2003. Control variables used inTable 6 are included, but not reported. Numbers in parentheses are z-values. *, ** and*** indicate significance at the 10%, 5% and 1% levels, respectively.

22

Table 8. Decomposition of difference of female-male wage rate (WAGE_FM) on girl’s test score.

MATH

SCIEN
(%)

READ
(%)

Direct effect of a 1 % decrease in WAGE_FM

(%)
0.13

0.07

0

Indirect effect of a 1 % decrease in WAGE_FM through being late for school

0.02

0.02

0.02

Indirect effect of a 1 % decrease in WAGE_FM through skipping school

0.05

0.04

0.04

Total effect of a 1 % decrease in WAGE_FM

0.20

0.13

0.06

Notes: Values are calculated based on results of Table 3 and Table 6.

23

.3

Figure 1 (a). Relation between test scores in mathematics and female-male wage rate.

ln(girl's test score)-ln(boy's test score)
-.1
0
.1
.2

VNM

MYS

PER
JPN JOR

ROU

THA
TUN

URY

FRA

SVN
SWE
QATKAZNLDNOR
RUSIDN
LVA
GRC
QCN
KOR
ISL
ARE
QUA
HRV
ARG
EST
AUS
CAN
QUC
POL
USAGBRFIN
NZL DNKITA ESP MEX
CHL
QUB
IRL
ISR
CHEQRSBEL
AUTPRTLUX
BRA
CZE
BGRCOLLTU CRI
DEU
HUN
TUR

-.2

SVK

.4

.5

.6
.7
Female-to-male ratio of wage

Note: Sample is restricted to sons and daughters of migrants.

24

.8

ALB
SGP

Figure 1 (b). Relation between test scores in science and female-male wage rate.

.2

VNM
MYS

ln(girl's test score)-ln(boy's test score)
-.1
0
.1

ROU
JOR
URY

JPN
PER

TUN
ALB

IDN
SVN
POL
QAT
GRC
LVA
ARESWE
HRV
NOR
RUS
THA
KAZ
ITA
FIN
KOR
BRAISR
NLD
QUA
AUS
CAN
ESP
USA
QCN
IRL
AUT
CHL
NZL
PRT
LTU ESTGBRISLQUB
ARG
DNK
LUX BEL
QUC
MEX
CHE
CRI
CZE
DEU
TUR
BGRCOL
QRS

FRA

SGP

-.2

HUN

SVK

.4

.5

.6
.7
Female-to-male ratio of wage

.8

Note: Sample is restricted to migrants’ sons and daughters.

Figure 1 (c). Relation between test scores in reading and female-male wage rate.
25

ln(girl's test score)-ln(boy's test score)
0
.1
.2
.3

.4

MYS

ROU

PER
URY
BRA
ITA
HRV
ISR
ESP
AUTPRT
KOR
POL
MEX
CHL
CZE

FRA

JPN

JOR

IDN
VNM
SVN

TUN

GRC

SWE
NORTHAQATKAZ
FIN
ISL
RUS
ARE
ARG LVA
EST LUXDNKCANAUSCRI QUAQUCNLDCHE
GBRBEL IRL NZL
QUB
QRS
USA
QCN
COLDEU
BGR
TUR
LTU

HUN

-.1

SVK

.4

.5

.6
.7
Female-to-male ratio of wage

Note: Sample is restricted to migrants’ sons and daughters.

26

.8

SGP
ALB