Discussion Paper No. 963

LONG-TERM EFFECTOF INTERNATIONAL TRADE
ON THE GENDER WAGE
AND EDUCATIONAL GAPS

Eiji Yamamura

March. 2016

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

Long-Term Effect of International Trade on
the Gender Wage and Educational Gaps

Eiji Yamamura

Abstract. This paper uses cross-country data to examine the long-term effect of trade openness on the gender
gaps in wages, education, political empowerment and health. Key findings are: trade openness since 1970
reduced the gender gaps in wages and educational attainment as of 2011 but did not influence the gaps inpolitical attainment and health status. The effect of trade openness on the gender wage gap remained
observable in later years (1980, 1990 and 2000), although it decreased in degree over time. On the other hand,the effect of trade openness on the gender gap in educational attainment disappeared in 2000. Similar results
were obtained even after controlling for endogenous bias.

Key words: Inter4national trade; Globalization; Gender wage gap; Gender educational gap
JEL classification: F16; F66; J16; I14; I24

1

1. Introduction.

In the present era of globalization, markets have become increasingly open.
Globalization has increased the number of individuals participating in markets, leading tomarkets becoming more competitive. Competitive pressure through international trade
seems to change not only efficiency but also non-market interpersonal relations.
The gender gap is a hot issue in modern society (World Economic Forum 2011;
Wolszczak-Derlacz 2013). For instance, economic researchers have paid considerable
attention to analyzing the nature of the gender wage gap (Weichselbaumer andWinter-Ebmer 2005). According to the seminal work of Becker (1959), increased
competition in the product market reduces discrimination in the long run. If this finding iscorrect, increasing competitive pressure through international trade should reduce
discrimination against women and decrease the gender wage gap. However, arguments
against this hypothesis also exist based on Becker’s theory1. Existing literatures haveattempted to empirically test this hypothesis (Berik, Vand der Meullen Rodgers andZveglich 2004, BVZ; Black and Brainerd 2004; Menon and Vand der Meullen Rodgers
2009)2. Black and Brainerd (2004) used census data from the United States to find that
trade reduces the ability of firms to discriminate against women. Conversely, other works

Various researchers have analyzed the relation between trade liberalization and gender
gaps (e.g., Van Staveren et al. 2007; Fontana 2009).
2 Saure and Zoabi (2014) constructed the model to show that the marginal productivity ofwomen drops more than that of men if trade expands sectors with intensive female
employment. Thus, the gender wage gap widens.
12

using data from Asian countries such as Taiwan and Korea (BVZ 2004) and India (Menon
and Vand der Meullen Rodgers 2009) failed to find that competition from trade decreased
wage discrimination against women. Because these works use data from certain countries,
the estimation results vary according to social and economic conditions in the countries
from which data is obtained. Hence, it is necessary to conduct the examination using
cross-country data to investigate how Becker’s theory generally applied to the gender wage
gap.
Discrimination against women in the labor market decreased with increasing
international trade. Under this condition, women’s marginal productivity is reflected intheir wage levels. Human capital, such as educational attainment, is thought to play a more
important role for women in the labor market as international trade increases. In response tothis, educational attainment for women naturally improved, and consequently the gender
gap in educational attainment decreased. Thus far, the impacts of globalization on education
spending and human capital formation have been examined (e.g., Anderson and Konard
2003 a; 2003 b; Baskaran and Hessami, 2012; Shastry 2012). However, little is known
about how international trade influences the gender gap in educational attainment. It isvaluable to explore the effects of international trade on the gender wage and education gaps,
and simultaneously to consider the mechanism of how competitive pressure associated withtrade changes gender inequality in the labor and education markets. This study analyzes
how international trade influences the gender gaps by considering jointly the decrease indiscrimination against women and the increase in women’s human capital.
If gender gaps do not immediately respond to trade openness, it is useful to consider the3

long-term effect of trade openness on gender gaps. To this end, the effect of trade openness
in 1970 on the gender gaps in 2011 is examined. Furthermore, this paper compares thiseffect with those of trade openness in intervening years (1980, 1990 and 2000) to consider
the time lag in the appearance of any effect. This paper used a cross-country dataset
sourced mainly from the World Bank and the Global Gender Gap Report 2011
(GGGR2011).
The key findings were the following. Trade openness decreased the gender wage gap in2011 regardless of the number of years of trade openness. However, the strength of thiseffect decreased as the number of years of trade openness increased. On the other hand,
trade openness in 1970 decreased the gender gap in educational attainment, which was
robust to alternative specifications. However, the effect on the gender education gap oftrade openness in intervening years (1980, 1990, 2000) was less robust.
The remainder of this paper is organized as follows. Section 2 proposed testable
hypotheses. Section 3 explained the data and empirical method. Section 4 presented theestimation results and their interpretation. The final section provided conclusions.

2. Hypotheses
If the gender wage gap decreases owing to international trade, female students are
predicted to have a greater incentive to learn. Consequently, the cognitive skills of female
students will improve relative to those of male students (Guiso, Monte, Sapienza andZingales 2008; Fryer and Levitt 2010). The higher the scores of female students inachievement tests, the more likely girls are to get scholarships for entrance to higher-level4schools and the greater the inventive of parents to invest in the education of daughtersrelative to sons. Restated, international trade decreases the gender gap in educational
attainment. However, human capital accumulation proceeds slower than improvement ofwage levels because it takes over 20 years for a newborn girl to graduate from tertiary
education. Therefore, it takes many years for the responses of female students to improved
female wage levels to be reflected in female educational attainment. Moreover, parents ofdaughters have a larger incentive to invest in their daughters’ education only after observing
a relative increase in female wages. Hence, a time lag exists between the decreases in thegender wage gap and the gender educational attainment gap.
Based on the above observations, I propose the following testable hypotheses:
Hypothesis 1: Trade openness decreased not only the gender wage gap but also thegender education gap,
Hypothesis 2: The effect of trade on the gender education gap trails its effect on thegender wage gap.

3. Data and method
Data 3.1.
As is presented in Table 1, the cross-country data used in this paper were gathered fromvarious sources. Discrimination against women can exist in various contexts and so theWorld Economic Forum (2011) considered various subcategories of the gender gap to
calculate the Global Gender Gap Index (GGGI). To test the hypotheses proposed in theprevious section, it is useful to use data for subcategories of the gender gap, rather than5

GGGI. In GGGR for 2011, the World Economic Forum (2011) provided not only thegender wage gap but also the gender gaps in educational, political and health situations in
2011. These data are used for estimations. Furthermore, the World Bank provided data on
trade openness in various years. To compare the long-term and medium-term effects oftrade, this paper used trade openness in 1970, 1980, 1990 and 2000. Additionally, data on
GDP per capita in 1970, 1980, 1990 and 2000 were gathered from the Penn World Table.
As for other national characteristics, the degree of democracy, legal origins, religions andlatitude used in La Porta, Lopez de Silanes, Shleifer and Vishni (1999; LLSV) wereobtained from the website of Shleifer. The total sample size was 92 (92, 100 and 109) whenusing trade openness in 1970 (1980, 1990 and 2000). Data on trade openness were notavailable for some countries in the earliest years of opening up, and this is why the sample
size was smaller in the earlier years.
According to the definition in Table 1, the wage gap variables such as WAGE_GAP(EDUC_GAP, POLI_GAP and HEAL_GAP) show the female-male ratios of proxies for the
wage, educational, political and heath situations. Accordingly, the larger the values of thesegender gap variables, the better the situations of women. In Fig. 1 (a)–(d), the vertical axes
are WAGE_GAP, EDUC_GAP, POLI_GAP and HEAL_GAP, respectively. In the figures,the horizontal line is OPEN_1970 (trade openness in 1970). Cursory examination of Fig. 1(a) reveals wide variation of WAGE_GAP, which ranges between 40 % and 90 %.
Furthermore, OPEN_1970 was positively related to WAGE_GAP. This implies that trade
openness decreases the gender wage gap, in line with Becker’s theory. Compared with
WAGE_GAP in Fig. 1 (a), EDUC_GAP and HEAL_GAP were concentrated in the upper6

ranges of the vertical axes in Fig. 1 (b) and (d). In most countries, EDUC_GAP(HEAL_GAP) gap exceeded 80 % (95 %), indicating that these gaps were very small. In
contrast, POLI_GAP was concentrated in the lower range of the vertical axis in Fig. 1 (c).
In most countries, POLI_GAP was below 40 %. Therefore, political gap was generally verylarge. Furthermore, Fig. 1 (b)–(d) did not show that OPEN_1970 was clearly related to
EDUC_GAP, POLI_GAP and HEAL_GAP, respectively. These figures do not control forother variables such as national characteristics. Hence, in section 3, to examine the data
more closely, I conducted regression estimations to control for various factors.

Baseline model 3.2.
To test the hypotheses, this study attempted to examine the effect of trade openness ongender gaps. To this end, the following estimated function of the baseline model was used:
Yi = 0 + 1 OPEN_1970i + 2 Ln(GDP_1970) i + X’Β+εi
Y: WAGE_GAP (EDUC_GAP, POLI_GAP or HEAL_GAP)
X: Vector of variables to control for country characteristics
B: Vector of coefficients of variables to control for country characteristics

where Yi represents the dependent variable (proxy for the gender gap in 2011) for country i.
Regression parameters are represented by . The error term is represented by εi.
Furthermore, disturbances in the equation when WAGE_GAP is a dependent variable may
correlate with disturbances when EDUC_GAP (POLI_GAP or HEAL_GAP) is a dependent
variable. WAGE_GAP, EDUC_GAP, POLI_GAP and HEAL_GAP are thought to be jointly

7

estimated because of correlations between disturbances. In this case, a seemingly unrelated
regression model (SUR) model is preferred (Greene 2008).
The key independent variable for investigating the long-term effect of trade openness
is OPEN_1970. In terms of alternative specifications, using trade openness at 10-year
intervals (OPEN_1980, OPEN_1990 or OPEN_2000), I attempt to check the robustness ofthe effects of OPEN_1970 and its medium-term effects. From Hypothesis 1, the coefficientof OPEN_1970 (OPEN_1980, OPEN_1990 or OPEN_2000) is predicted to be positive
when WAGE_GAP (or EDUC_GAP) is the dependent variable. Furthermore, from
Hypothesis 2, trade openness is more likely to be statistically significant for estimation of
EDUC_GAP in the earlier years of that trade openness. Additionally, the log of GDP per
capita in the comparison year is included to control for degree of economic development.
Political factors are considered critical to the effects of globalization on labor
condition and human capital formation (e.g., Anderson and Konard 2003 a; 2003 b;
Baskaran and Hessami 2012). Furthermore, globalization outcomes differed among theregions to which the studied countries belong (BVZ 2004; Black and Brainerd 2004;
Menon and Vand der Meullen Rodgers 2009; Boler 2015). For instance, the North
American Free Trade Agreement (NAFTA) critically impacted the gender gap (Juhn andUjhelyi 2013; 2014). It is necessary to consider these factors for the sake of generality ofthe estimation results based on the cross-country data. Regarding the control variables
included in X, political, legal, geographical and religious conditions are taken into account.
To reduce the possibility of reverse causality, as described in the definition of Table 1, timevarying variables such as DMOCR and religious variables (CATHO, PROT and MUSL) are8

included in the far distant year. In more democratic countries, the gender gap seems
reduced and so the coefficient of DMOCR is expected to have a positive sign. Legal origin
is an important institutional factor that influences economic and political conditions (LaPorta, Loez-de-Silanes and Shleifer 2008) and so LEGAL_FR, LEGAL_SO, LEGAL_GE
and LEGAL_SC are included to control for it. Roles played by women are determined byreligion and should be taken into account. This is why CATHO, PROT and MUSL are
included. Geography and regional specific factors are taken into account by incorporating
latitude and dummies for region such as ASIA, S_AMERIC and EUROP.

Model using instrumental variables 3.3.
Gender wage and educational gaps may influence international trade relations (Busse andSpielmann 2006). Furthermore, gender gaps may influence GDP per capita. For instance,
an increase in women’s human capital may lead women to become skilled labor, which inturn influences GDP. If such an effect holds, reverse causality leads to endogenous bias in
the estimation results. Trade openness and GDP can be considered the predetermined
exogenous variable because of long time lags between the dependent and independent
variables. However, to more closely examine the effect of openness, exogenous
instrumental variables are used to control for bias based on the GMM 2SLS model (Greene,2008).
In this model, trade openness and log of GDP are treated as endogenous variables. Logof population size in the same year as the trade openness, DMOCR, and legal origin
dummies are used as exogenous instrumental variables that are included in the first stage

9

estimation but not the second stage estimations. Country size is expected to cause scaleeconomy, which in turn influences openness and GDP. However, population size did notseem to influence the gender gap, which suggests the validity of the instrumental variable.
As shown later in Table 2, DEMOCR and legal origin dummies did not exhibit statisticalsignificance in most cases of the baseline model. Furthermore, as shown in Table 4 (a)–(d),exclusion of DEMOCR and legal origin dummies did not change the results of trade
openness. This indicated that these variables are unlikely to be related to the dependent
variables. For robustness check, in alternative specifications of the GMM 2SLS model,
DMOCR is not included in the set of exogenous instrumental variables.

4. Estimation results

Table 2 presented the results of the baseline (SUR) model where results of all control
variables are shown. The results of control variables in Tables 3–6 were no different fromthose in Table 2, and so only the result of trade openness was shown3. Table 3 (a)–(c)showed the results of the SUR model when OPEN_1970 was replaced by OPEN_1980,
OPEN_1990 and OPEN_2000, respectively. Table 4 (a)–(d) reported the results where
DMOCR and legal origin dummies are excluded from the set of independent variables.
Table 5 (a)–(d) reported the estimation results of the GMM 2SLS model where the logs of
population size in the same years as the trade openness, DMOCR and legal origin dummies
were used as exogenous instrumental variables in the first stage. Table 6 (a)–(d) reported

3

Full estimation results of Tables 3 to 6 are available from the author upon request.
10

the estimation results of the GMM 2SLS model where DMOCR was excluded from the setof exogenous instrumental variables in the first stage.
From Table 2 we can see that OPEN_1970 was positive and statistically significant at
the 1 % level in column (1). Column (2) suggested that the coefficient of OPEN_1970 was
positive and statistically significant at the 5 % level. This implied that trade openness in1970 reduced the gender wage and educational gaps in 2011. Trade openness thus had thelong-term effect of improving female wages and educational level relative to men. This isconsistent with Hypothesis 1. Moreover, the absolute value of the coefficient of
OPEN_1970 was 0.06 and 0.02 when the dependent variable was WAGE_GAP and
EDUC_GAP, respectively. This can be interpreted as implying that a 1 % increase in the
trade-GDP ratio (trade/GDP in 1970) leads to increases of 0.06 % and 0.02 % in the genderwage and educational gaps, respectively. Hence, the marginal effect of OPEN_1970 on
WAGE_GAP was three times larger than that on EDUC_GAP. In contrast, OPEN_1970
was negative and not statistically significant in columns (3) and (4). Therefore, trade
openness did not have a long-term effect on POLI_GAP and HEAL_GAP. It follows fromthis that competitive pressure in the market was unlikely to influence women’s situations interms of political empowerment and health.
As for other control variables, GDP_1970 was positive and significant when
EDUC_GAP was the dependent variable, suggesting that the gender educational gap was
smaller in more developed countries in 1970. Developed countries could invest incompulsory basic education for both sexes. Consistent with the prediction, the coefficientof DEMOCR was positive in all columns despite not being statistically significant. The

11

dummies of legal origin showed barely significant results for estimations of WAGE_GAP
and EDUC_GAP.
Now, switching attention to Table 3 (a)–(c), as for the estimation results of WAGE_GAP,
the coefficient of trade openness was positive and significant at the 1 % level. The absolutevalue of its coefficient was 0.06 when OPEN_1980 and OPEN_1990 were the independent
variables, and was 0.05 when OPEN_2000 was the independent variable. Accordingly, theresults of trade openness were persistently similar to those listed in Table 2. With respect tothe estimation results of EDUC_GAP, the variable of trade openness was positive in Table 3(a)–(c). In Table 3 (a), OPEN_1980 showed statistical significance whereas OPEN_1990
and OPEN_2000 did not suggest significance in Table 3 (b) and (c). These results suggested
a long-term effect of trade openness on the gender educational gap, while no medium-term
effect of trade openness was observed. This is in line with Hypothesis 2. When it comes tothe results of POLI_GAP and HEAL_GAP, trade openness did not show statisticalsignificance in Table 3 (a)–(c). Therefore, trade openness did not affect the gender politicaland health gaps at all. In alternative specifications, the results presented in Table 4 (a)–(d)were almost the same as those presented in Tables 2 and 3 (a)–(c). Hence, the exclusion of
DEMOCR and legal origin dummies did not influence the results of trade openness on thegender gaps.
I now turn to the results of GMM 2SLS in Table 5 (a)–(d). First, I used the results of theunder-identification and over-identification tests to check the validity of the GMM 2SLSmodel. A specification error occurs if the instrumental variables are correlated with theerror term. In Table 5 (a)–(d), with the exception of column (3), the test statistics of the12over-identification test were not statistically significant and so did not reject the null
hypothesis that additional instruments were uncorrelated with the error term. Moreover, in
Table 5 (a)–(d), the test statistics of the under-identification test were statisticallysignificant, meaning the null hypothesis that the estimation was under-identified was
rejected and hence the under-identification test was passed. The combined results of theunder-identification and over-identification tests showed that the GMM 2SLS was valid in
Table 5 (a)–(d).
As for WAGE_GAP, in Table 5 (a)–(d), trade openness was positive and statisticallysignificant at the 1 % level. The absolute values of the coefficient were 0.09, 0.08, 0.08 and0.07 in OPEN_1970, OPEN_1980, OPEN_1990 and OPEN_2000. Therefore, the long-term
effect of trade openness exceeded its medium-term effect. With respect to EDUC_GAP, in
Table 5 (a)–(d), trade openness was positive and statistically significant.
For robustness check, turning attention to Table 6 (a)–(d), the results of WAGE_GAP
were almost identical to those in Table 5 (a)–(d). However, the statistical significance of
EDUC_GAP varied among the tables although its coefficient was positive in all of them.
The coefficients of OPEN_1970 and OPEN_1990 were positive and statistically significant,whereas those of OPEN_1980 and OPEN_2000 were not statistically significant. Therefore,
the effect of openness on the gender educational gap was less robust than its effects onother gender gaps.
Considering the results shown in Tables 2–6 jointly suggested: (1) Trade openness
reduced the gender wage gap and its effect was greater in the long term than the medium
term. (2) Trade openness in 1970 reduced the gender educational gap although the effects

13

of the openness in 1980, 1990 and 2000 disappeared in some specifications. Hence, thelong-term effect of trade openness was clearly observed but its medium-term effect was lessclear.
The effect of trade openness on the wage gap decreased in the later part of the study
period. This can be interpreted as implying that the early years of trade openness are more
likely to see an increase in women’s marginal productivity as a result of decreasing thegender gap in educational attainment. Therefore, the longer-term effect of trade openness
on the wage gap increased. That is, the difference in the effect of trade on the wage gap
between years of trade openness reflects a decrease in the gender gap in terms of marginal
productivity.
Overall, the estimation results can be interpreted as follows: accumulation of women’shuman capital through education saw women become skilled labor and so improved
women’s marginal productivity. Restated, decreased educational inequality decreased thegender gap in labor productivity, in turn reducing the gender wage gap. Therefore, thegender wage gap was considered to partly reflect the gender productivity gap. However,besides this indirect effect of trade openness on the wage gap via education, trade openness
also directly affected the wage gap by reducing discrimination against women in the labormarket.
Based on the findings of this paper, I argue that trade liberalization benefits women byincreasing female wages, and I further infer that women should prefer trade liberalization.
However, the existing literature provides evidence that women do not prefer tradeliberalization (Mayda and Rodrik 2005; Blonigen 2011). An interesting paradox exists

14between the findings of this paper and some of the rest of the literature. This raises thequestion of whether women are irrational concerning free trade. This paradox should beconsidered by bridging international and behavioral economics.

5. Conclusion

Globalization is expected to increase global competition and improve market function.
In this situation, then assuming Becker’s theory holds true employment discrimination
against women should decrease in the long run. Existing works that have examined thisquestion have not considered the effect of globalization on other gender gaps such as
educational attainment, political empowerment and health status. This paper thus attempted
to investigate the long-term effect of trade openness on various gender gaps. Long-term
effects of trade openness were observed on the wage and education gaps but not on thepolitical and health gaps. Furthermore, the effect of trade openness on the wage gap was
two or three times larger than its effect on the education gap. The effect of trade openness
on the wage gap was observed regardless of the number of years for which trade openness
had existed, although the effect was greater in early years than later years. On the other
hand, the effect of trade openness on the gender education gap was not robustly observed
for the later years (1980, 1990 and 2000).
Overall, the estimation results can be interpreted as follows: decreased education
inequality decreased the gender gap in labor productivity, which in turn decreased the wage
gap. Therefore, the gender wage gap was considered to reflect the gender productivity gap.

15

However, besides this indirect effect of trade openness on the wage gap through education,
there also existed a direct effect. Thus, trade openness reduced the gender wage gap caused
by gender discrimination. The contribution of this paper is to clarify that market
competition through international trade decreased not only the gender wage gap, but also
the gap in educational attainment.
Because of data limitations, this paper did not scrutinize the indirect channels thoughwhich trade openness reduced the gender wage gap. It is valuable to use individual-level
data to decompose the effect of trade openness into the direct effect through decreased
discrimination against women in the labor market and the indirect effect through a decreasein the gender marginal productivity gap. Besides, globalization accompanies not only trade
openness but also massive population mobility and foreign direct investment. Existing
works tried to shed light on the impact of population mobility on human capital investment(e.g., Anderson and Konard 2003 a; 2003 b). The impact of foreign direct investment on thegender wage gap was investigated (e.g., Neumayer and De Soyasa 2007; Oostendorp 2009).
It is necessary to analyze the influences of international trade, migration and foreign direct
investment jointly. Furthermore, it is interesting to consider how the findings of this paper
related to women’s preferences regarding trade by bridging international economics andbehavioral economics. These issues remain to be addressed in future studies.

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19

90

Egypt
Albania50

WAGE_GAP
60
70

80

Malaysia
Singapore
UgandaGambiaMongolia
Botswana
Zimbabwe
Denmark
Burkina
Nigeria
Tunisia
Faso
ThailandMalawi
ZambiaPhilippines
NewSweden
Zealand
NorwayGhanaBrunei
FinlandSenegal Sri Lanka
Cameroon
Ireland
GuyanaIceland
Belgium
Ethiopia
Mali
VietnamKenya
Oman
Namibia
Tanzania
United Kingdom
Netherlands
Indonesia
Trinidad&TobagoUnited StatesMorocco
Mozambique
South Africa
Syria
Romania
LebanonGreeceAustralia Switzerland
Costa
AlgeriaRica
Jamaica Suriname
India Germany
JordanMadagascar
Argentina
Japan
Colombia Venezuela
Cyprus
Iran
HondurasPeru
EcuadorAngolaTurkeyPakistan
Cote d'Ivoire Bulgaria
Bangladesh
Paraguay
Mexico Portugal
El Salvador Dominican
GuatemalaRepublic
Austria
Bolivia
Spain
Uruguay
NicaraguaIsraelPolandBrazilSouthItaly
Korea
Chile

Bahrain

Luxembourg

Hungary

40

France050

100OPEN_1970

150

FIGURE 1 (a) Relation between trade openness in 1970 and the gender wage gap in 2011

20

200

Malta

100
EDUC_GAP
80

90

UnitedAustralia
FranceUnitedStatesNew
Cuba
Denmark
Zealand
Philippines
Ireland
Kingdom
Iceland
NorwayDominican
Botswana
Republic
CostaNamibia
Rica
Bahamas
Nicaragua
Finland
PolandVenezuela
Honduras
Netherlands
Uruguay
Spain
Chile
SwedenColombia
RomaniaParaguay
Italy
Germany
Trinidad
Mongolia&
TobagoArgentina
GreeceBruneiPortugal
Hungary
Bulgaria
MexicoBelgium
Suriname
BrazilMalaysia
Switzerland
ElEcuador
Salvador
Austria
GuyanaJordanJapan
ThailandJamaica
AlbaniaSouth
AfricaPeru Israel
CyprusLebanonMadagascar
Indonesia
TunisiaSouth Korea
GuatemalaAlgeria
Oman
Singapore
Kenya
Zimbabwe
Sri Lanka
VietnamIranTurkey Uganda
Bangladesh
Syria
Egypt
Ghana
Malawi70

India

Luxembourg
Bahrain

Malta

Tanzania
MoroccoCameroon
ZambiaGambiaSenegalMozambique
Nigeria
AngolaPakistan
Burkina Faso
Cote d'Ivoire

60

Ethiopia
Mali

0

50

100
OPEN_1970

150

200

80

FIGURE 1 (b) Relation between trade openness in 1970 and the gender educational attainment gap in 201160

IcelandFinland

POLI_GAP
40

NorwaySwedenIreland
Sri Lanka
New
ZealandSouth Africa
DenmarkBangladesh
Mozambique
Switzerland
Costa Rica
Germany
Philippines
Belgium
Cuba
India
Argentina
Nicaragua
Chile
UnitedKingdom
AngolaUgandaNetherlands
Austria
Guyana & Tobago
EcuadorTanzania
TrinidadPortugal
Honduras
Australia
UnitedStatesPolandNamibia
GreeceMalawiFrancePeru
PakistanItaly
Venezuela
SenegalIsraelMexicoEthiopia Indonesia
GambiaBulgaria
Tunisia
Uruguay
ElBurkina
Salvador
Colombia
Paraguay
Zimbabwe
Vietnam
Faso
Mali
Dominican
Republic
ZambiaTurkeySouth
Korea
Jamaica
Ghana
Madagascar Singapore
SurinameThailandAlbania
Kenya
Japan Cameroon
MoroccoCoteMalaysia
d'Ivoire
CyprusBotswana
Syria
Romania
JordanBrazilGuatemalaBahamasAlgeria
Mongolia
Hungary Nigeria
Egypt
LebanonOman
IranBrunei

0

20

Spain

0

50

100
OPEN_1970

150

Luxembourg

Bahrain

200

FIGURE 1 (c) Relation between trade openness in 1970 and the gender political empowerment gap in 2011

21

Malta

98
HEAL_GAP
95
96

97

BrazilJapan
MexicoArgentina
FranceUruguay
Hungary
Chile
UgandaElFinland
Salvador
Philippines
Venezuela
Trinidad
LebanonThailandMongolia
GambiaJamaica
GuatemalaCote&
Tobagod'Ivoire
AngolaSri Bulgaria
Lanka
BahamasUnitedStatesRomania
Colombia
GuyanaAustria
Belgium
PolandGermany
Egypt
HondurasSpain
Mali
Nicaragua
EcuadorParaguay
Syria
Turkey
Oman
Costa
Rica
Cuba
Denmark
Portugal
Suriname
Ireland
Australia
Italy
Switzerland Senegal
Ethiopia
South
Korea
Malaysia
Madagascar
SwedenGreece Morocco Dominican
Republic
Jordan Iran
CyprusUnitedKingdomNew
ZealandIsraelNetherlands
NorwayIcelandZambiaBurkina Faso
MalawiSouth
Kenya
AfricaSingapore
Ghana
Namibia
Algeria
Peru Indonesia
Brunei
Tunisia
Tanzania
Cameroon
Mozambique
Nigeria
Pakistan
Bangladesh

Luxembourg

Bahrain

Botswana

94

Zimbabwe Vietnam

93

India
Albania050

100
OPEN_1970

150

200

FIGURE 1 (d) Relation between trade openness in 1970 and the gender health and survival gap in 2011

22

Malta

TABLE 1
Definition and basic statistics of variables
Definition

Mean valueStandard

Sourcedeviation
WAGE_GAPa

EDUC_GAPa

Gender wage gap in 2011

65.3

8.77

GGGR 2011

95.5

7.48

GGGR 2011

17.0

13.2

GGGR 2011

97.1

1.01

GGGR 2011

55.0

39.0

World

(female wage level / male wage level) * 100
Gender education gap in 2011
(female educational attainment / male educational attainment) * 100
POLI_GAPa

Gender political empowerment gap in 2011
(female empowerment level / male empowerment level) * 100

HEAL_GAPa

OPEN_1970b

OPEN_1980b

OPEN_1990b

OPEN_2000b

Gender health and survival gap in 2011
(female health and survival level / male health and survival level) * 100
Trade openness in 1970
Trade in 1970 / GDP in 1970 (%)

Indicators

Trade openness in 1980

60.9

40.6

Trade in 1980 / GDP in 1980 (%)

World

Bank

Indicators

Trade openness in 1990

61.6

43.7

Trade in 1990 / GDP in 1990 (%)

World

Bank

Indicators

Trade openness in 2000

80.1

49.0

Trade in 2000 / GDP in 2000 (%)
GDP_1970

Bank

World Bank
Indicators

GDP per capita in 1970 (US $)

6,574

7,831

Penn and World Table
7.1

GDP_1980

GDP per capita in 1980 (US $)

8,731

23

11,087Penn and World Table7.1
GDP_1990

GDP per capita in 1990 (US $)10,233

10,974Penn and World Table
7.1

GDP_2000

GDP per capita in 2000 (US $)12,055

13,599Penn and World Table
7.1

DEMOCRc

Average degree of democracy within a country between 1970 and 1994. Degree of democracy:

4.11

3.98

1 (Undemocratic) to 10 (Democratic)
LEGAL_FRd

LEGAL_SOdLEGAL_GEdLEGAL_SCd

WebsiteofAndreiofAndreiofAndreiofAndreiofAndrei

Shleifer

French Legal Origin dummy

0.44

0.49

WebsiteShleifer

Socialist Legal Origin dummy

0.18

0.39

WebsiteShleifer

German Legal Origin dummy

0.03

0.18

WebsiteShleifer

Scandinavian Legal Origin dummy

0.03

0.18

WebsiteShleifer

S_AMERICA

South America countries dummy

0.21

0.41

ASIA

Asian countries dummy

0.13

0.34

EUROPE

European countries dummy

0.21

0.41

CATHO

Catholic ratio in 1980 (%)

31.2

36.3

24

PROT

Protestant ratio in 1980 (%)

11.9

21.2

WebsiteofAndreiofAndreiofAndrei

Shleifer
MUSL

Muslim ratio in 1980 (%)

23.7

36.1

WebsiteShleifer

LATITUD

Latitude of capital

0.30

0.19

WebsiteShleifer

NOTES:
a. In The Global Gender Gap Report 2011 (GGGR 2011), (female/male) ratio was reported. This paper uses the original ratio multiplied by 100 for convenience of interpretation.
b. Trade is the sum of exports and imports of goods and services measured as a share of gross domestic product.
c. Website of Shleifer http://scholar.harvard.edu/shleifer/publications/quality-government (accessed on July 2, 2015). Data were sourced from an earlier work (LLSV 1999).

25

TABLE 2
Determinants of gender gaps: Baseline model (SUR model)
(1)

(2)

(3)

(4)

WAGE_GAPEDUC_GAPPOLI_GAPHEAL_GAP

0.06***

0.02**−0.01−0.0001(3.04)(2.07)(−0.62)(−0.07)−1.77

1.65**−4.19***

0.36**(−1.53)(2.10)(−2.82)(2.50)

0.13

0.15

0.15

−0.004(0.45)(0.75)(0.38)(−0.01)−4.17*−2.23−6.37**

0.46*(−1.90)(−1.50)(−2.30)(1.68)

−4.56

2.15

−18.6***

0.30(−1.12)(0.78)(−3.56)(0.60)

−3.11

−3.07

−3.07

0.66

(−0.78)(−1.14)(−0.60)(1.34)

6.54

−4.17

18.4**

0.81(1.07)(−1.00)(2.35)(1.07)

1.72−7.68***

−2.87

0.06

(0.64)(−4.23)(−0.84)(0.19)

−9.13

−0.86

5.14

−0.17

(−0.35)(−0.49)(1.56)(−0.53)

2.13

−2.80

5.31

−0.41

(0.72)(−1.39)(1.39)(−1.11)−0.07**

0.03*

0.13***

0.003(−2.32)(1.74)(3.25)(0.91)

0.01

0.04

0.13

−0.003(0.24)(0.96)(1.48)(−0.44)0.01−0.04*−0.03−0.001(0.20)(−1.98)(−0.87)(−0.36)

−6.69

5.10

21.4**−0.56(−0.82)(0.93)(2.07)(−0.56)

R-Square

0.47

0.63

0.62

0.26

Observations

92

92

92

92

OPEN_1970

Ln(GDP_1970)

DEMOCRLEGAL_FR

LEGAL_SO

LEGAL_GELEGAL_SCS_AFRICAASIAEUROPE

CATHO

PROT

MUSL

LATITUDNOTES: Numbers in parentheses are z-values. ***, ** and * indicate statistical significance at the 1 %, 5 % and
10 % levels, respectively. Constant is included, although its results were not reported.

26

TABLE 3
Determinants of gender gaps using openness in different years (SUR model)
(a)
(1)

(2)

(3)

(4)

WAGE_GAPEDUC_GAPPOLI_GAPHEAL_GAP

0.06***

0.02*

−0.02

0.0002(3.44)(1.65)(−1.03)(0.09)

R-Square

0.48

0.64

0.63

0.28

Observations

92

92

92

92

(1)

(2)

(3)

(4)

WAGE_GAPEDUC_GAPPOLI_GAPHEAL_GAP

0.06***

0.01−0.0070.001(3.69)(1.47)(−0.32)(0.36)

R-Square

0.48

0.61

0.64

0.25

Observations

100

100

100

100

(1)

(2)

(3)

(4)

WAGE_GAPEDUC_GAPPOLI_GAPHEAL_GAP

0.05***

0.006−0.0010.001(3.88)(0.63)(−0.87)(0.58)

R-Square

0.47

0.62

0.62

0.23

Observations

109

109

109

109

OPEN_1980(b)

OPEN_1990(c)

OPEN_2000

NOTES: Logs of GDP in 1980, 1990 and 2000 were included in (a), (b) and (c), respectively. Besides the log of GDP,the set of variables included in the equation is equivalent to those used in Table 3 although estimation results were notreported.
Numbers in parentheses are z-values. *** and * suggest statistical significance at the 1 % and 10 % levels,
respectively.

27

TABLE 4
Determinants of gender gaps excluding the proxy for democracy and legal origin dummies (SUR model)

(a)
(1)

(2)

(3)

(4)

WAGE_GAPEDUC_GAPPOLI_GAPHEAL_GAP

0.06***

0.02**−0.03−0.00004(3.15)(2.22)(−1.27)(−0.19)R-Square

0.42

0.60

0.50

0.21

Observations

95

95

95

95

(1)

(2)

(3)

(4)

WAGE_GAPEDUC_GAPPOLI_GAPHEAL_GAP

0.06***

0.02*

−0.04

0.00002(3.30)(1.67)(−1.62)(0.11)

R-Square

0.43

0.61

0.51

0.21

Observations

95

95

95

95

(1)

(2)

(3)

(4)

WAGE_GAPEDUC_GAPPOLI_GAPHEAL_GAP

0.06***

0.02*

−0.01

0.0001(3.97)(1.68)(−0.72)(0.32)

R-Square

0.44

0.59

0.48

0.20

Observations

100

100

100

100

(1)

(2)

(3)

(4)

WAGE_GAPEDUC_GAPPOLI_GAPHEAL_GAP

0.05***

0.01−0.03*

0.0001(3.81)(1.26)(−1.67)(0.30)

0.42

0.59

0.49

0.19

OPEN_1970(b)

OPEN_1980(c)

OPEN_1990(d)

OPEN_2000

R-Square28

Observations

109

109

109

109

NOTES: Besides DEMOCR and legal origin dummies, Table 4 (a)–(d) included the control variables, which wereequivalent to those in Tables 3 and 4 (a)–(c), respectively. However, estimation results of control variables were notreported.
Numbers in parentheses are z-values. ***, ** and * suggest statistical significance at the 1 %, 5 % and 10 % levels,
respectively.

29

TABLE 5
Determinants of gender gaps (GMM 2SLS model)(a) OPEN_1970 and the log of GDP_1970 are endogenous variables. Legal origin dummies, DEMOCR andpopulation in 1970 are exogenous instrumental variables.
(1)

(2)

(3)

(4)

WAGE_GAPEDUC_GAPPOLI_GAPHEAL_GAP

0.09***

0.04**−0.02

0.0001(3.08)(2.01)(−0.44)(0.16)

21.0

20.1

20.1

20.1

p-value=0.00

p-value=0.00

p-value=0.00

p-value=0.00

4.54

5.97

12.1

5.84

(Hansen J stat)p-value=0.32

p-value=0.20

p-value=0.01

p-value=0.21

Centered R-Square

0.42

0.57

0.45

0.17

Observations

93

93

93

93

OPEN_1970

Under-identification test

Over-identification

test(b) OPEN_1980 and the log of GDP_1980 are endogenous variables. Legal origin dummies, DEMOCR andpopulation in 1980 are exogenous instrumental variables.
(1)

(2)

(3)

(4)

WAGE_GAPEDUC_GAPPOLI_GAPHEAL_GAP

0.08***

0.04*

−0.02

0.0003(2.98)(1.78)(−0.55)(0.69)

16.2

15.4

15.4

15.4

p-value=0.00

p-value=0.00

p-value=0.00

p-value=0.00

5.49

6.34

12.0

5.42

(Hansen J stat)p-value=0.24

p-value=0.17

p-value=0.00

p-value=0.24

Centered R-Square

0.43

0.57

0.45

0.17

Observations

93

93

93

93

OPEN_1980

Under-identification test

Over-identification

test

30

(c) OPEN_1990 and the log of GDP_1990 are endogenous variables. Legal origin dummies, DEMOCR andpopulation in 1990 are exogenous instrumental variables.
(1)

(2)

(3)

(4)

WAGE_GAPEDUC_GAPPOLI_GAPHEAL_GAP

0.08***

0.04**−0.02−0.0001(3.12)(2.23)(−0.40)(−0.35)

17.0

15.0

15.0

15.0

p-value=0.00

p-value=0.01

p-value=0.01

p-value=0.01

6.00

4.42

13.6

4.69

(Hansen J stat)p-value=0.19

p-value=0.35

p-value=0.00

p-value=0.32

Centered R-Square

0.40

0.55

0.38

0.20

Observations

101

101

101

101

OPEN_1990

Under-identification test

Over-identification

test(d) OPEN_2000 and the log of GDP_2000 are endogenous variables. Legal origin dummies, DEMOCR andpopulation in 2000 are exogenous instrumental variables.
(1)

(2)

(3)

(4)

WAGE_GAPEDUC_GAPPOLI_GAPHEAL_GAP

0.07***

0.03*

−0.03

−0.0001(2.92)(1.66)(−0.84)(−0.30)

18.9

16.4

16.4

16.4

p-value=0.00

p-value=0.00

p-value=0.00

p-value=0.00

6.31

4.18

12.2

4.36

(Hansen J stat)p-value=0.17

p-value=0.38

p-value=0.01

p-value=0.35

Centered R-Square

0.42

0.55

0.38

0.19

Observations

110

110

110

110

OPEN_2000

Under-identification test

Over-identification

test

NOTES: The log of population size in the same year of the openness, DEMOCR and legal origin dummies were used
as exogenous instrumental variables in the first stage but not the second stage. Besides these, a set of control variables
were equivalent to those in Table 4 (a)–(d), respectively. However, estimation results of control variables were notreported.
Numbers in parentheses are z-values. ***, ** and * suggest the statistical significance at the 1 %, 5 % and 10 %levels, respectively.

31

TABLE 6
Determinants of gender gaps (GMM 2SLS model)(a) OPEN_1970 and the log of GDP_1970 are endogenous variables. Legal origin dummies and population in 1970
are exogenous instrumental variables.
(1)

(2)

(3)

(4)

WAGE_GAPEDUC_GAPPOLI_GAPHEAL_GAP

0.11***

0.04*

0.01

0.0001(3.40)(1.83)(0.15)(0.18)

10.3

9.46

9.46

9.46

p-value=0.03

p-value=0.05

p-value=0.05

p-value=0.05

3.17

5.96

10.3

5.43

(Hansen J stat)p-value=0.36

p-value=0.11

p-value=0.01

p-value=0.14

Centered R-Square

0.40

0.57

0.47

0.14

Observations

93

93

93

93

OPEN_1970

Under-identification test

Over-identification

test(b) OPEN_1980 and the log of GDP_1980 are endogenous variables. Legal origin dummies and population in 1980
are exogenous instrumental variables.
(1)

(2)

(3)

(4)

WAGE_GAPEDUC_GAPPOLI_GAPHEAL_GAP

0.10***

0.04

−0.01

0.0003(3.24)(1.58)(−0.12)(0.70)

8.58

8.00

8.00

8.00

p-value=0.07

p-value=0.01

p-value=0.01

p-value=0.01

4.75

6.18

10.1

5.13

(Hansen J stat)p-value=0.18

p-value=0.10

p-value=0.01

p-value=0.16

Centered R-Square

0.40

0.58

0.48

0.14

Observations

93

93

93

93

OPEN_1980

Under-identification test

Over-identification

test

32

(c) OPEN_1990 and the log of GDP_1990 are endogenous variables. Legal origin dummies and population in 1990
are exogenous instrumental variables.
(1)

(2)

(3)

(4)

WAGE_GAPEDUC_GAPPOLI_GAPHEAL_GAP

0.08***

0.04**−0.01−0.001(3.24)(2.20)(−0.30)(−0.41)

7.67

5.62

5.62

5.62

p-value=0.10

p-value=0.22

p-value=0.22

p-value=0.22

5.58

4.25

13.5

5.06

(Hansen J stat)p-value=0.13

p-value=0.23

p-value=0.00

p-value=0.16

Centered R-Square

0.42

0.55

0.42

0.21

Observations

101

101

101

101

OPEN_1990

Under-identification test

Over-identification

test(d) OPEN_2000 and the log of GDP_2000 are endogenous variables. Legal origin dummies and population in 2000
are exogenous instrumental variables.
(1)

(2)

(3)

(4)

WAGE_GAPEDUC_GAPPOLI_GAPHEAL_GAP

0.07***

0.03

−0.04

−0.002(2.86)(1.53)(−0.92)(−0.56)

15.0

11.9

11.9

11.9

p-value=0.00

p-value=0.01

p-value=0.01

p-value=0.01

6.21

3.91

12.3

4.61

(Hansen J stat)p-value=0.10

p-value=0.27

p-value=0.00

p-value=0.20

Centered R-Square

0.42

0.55

0.38

0.19

Observations

110

110

110

110

OPEN_2000

Under-identification test

Over-identification

test

NOTES: Besides DEMOCR, exogenous instrumental variables were the same as in Table 5. Besides these, the set ofcontrol variables were equivalent to those in Table 4 (a)–(d), respectively. However, estimation results of control
variables were not reported.
Numbers in parentheses are z-values. ***, ** and * suggest the statistical significance at the 1 %, 5 % and 10 %levels, respectively.

33