OSIPP Discussion Paper: DP-2016-E-002

Does trade liberalization help to reduce gender inequality?
A cross-country panel data analysis of wage gap*

February 18, 2016

Nozomi Kimura†
Master’s student, Osaka School of International Public Policy, Osaka University

Keywords: Trade and gender, wage gap, trade openness
JEL codes: F16, J16
Abstract: This paper examines the relationship between trade openness and the genderwage gap using the wage data divided into six sectors and three different skill levels(high-, medium- and low-skill) in 19 developed countries from 1995 to 2005. We apply
static and dynamic panel data models to investigate whether greater trade openness has
affected the gender wage gap. The results from the fixed effects model indicate that trade
openness decreases the wage gap between male and female workers in medium- andlow-skill jobs, while the relationship between trade openness and the wage gap isinsignificant in high-skill jobs. When the two-step difference generalized method of
moments (GMM) is employed, trade openness is found to reduce the wage gap in
medium-skill jobs, but its effect on the wage gap is insignificant in high- and low-skill
jobs.

*

I would like to thank Professors Hiro Lee, Miki Kohara and Tsunehiro Otsuki for invaluableadvice and helpful comments.

†

Corresponding author. Osaka School of International Public Policy, Osaka University, 1-31
Machikaneyama, Toyonaka, Osaka 560-0043, Japan. Email: nozomi.k.120524@gmail.com

Table of contents
1.

Introduction .................................................................................................................. 1

2.

Literature Review ........................................................................................................ 2
2.1.

Cross-country and panel data studies ............................................................ 2

2.2

Micro-data studies ........................................................................................... 5

3.

Data and Methodology ................................................................................................ 6

4.

Results ........................................................................................................................ 11
4.1. Results of static panel analysis ...................................................................... 11
4.2. Results of dynamic panel analysis ................................................................ 14

5.

Conclusion .................................................................................................................. 15

Reference ............................................................................................................................... 17
Figures and Tables ................................................................................................................ 19

1.

Introduction
The gender discrimination is one of the prominent issues that can be seen in the labormarket. It contains employment, wage and opportunity differences between male and female
due to their sex. This inequality can be observed even in developed countries. World
Economic Forum (2014) reports that female workers receive 21% less wages than maleworkers in Norway, the country with the lowest gender wage gap among the developed
nations of this survey, while the difference in wages between male and female exceeds over
50% in Italy, the country with the highest gender wage gap. However, when we look at thedifference in the labor force participation rates, the participation ratio of female to maleworkers is over 80% in almost all of the developed nations in 2014.
How can we approach this inequality problem between male and female workers?
Becker (1957, 1971) argues that if employers discriminate against female workers and pay
them less than male workers, then under competitive pressure created by greater trade
openness the demand for underpaid female labor is expected to grow, bidding up their wage
and reducing the wage gap. As shown in Figure 1, trade expansion is observed since the1970s and the trade to GDP ratio has been increasing except for the financial crisis year of
2008. If his assertion is correct, the employment of women should be improved; however, thereal world does not follow it exactly. World Bank (various years) reports the employment topopulation ratio over 15 years old, the female employment rates of the world andlow-medium countries are 25% lower than the male rates and the differences are slightly
widening. However, in the OECD member countries, the female employment rate has
improved a little (See Figures 2-4). Although a large number of women may get theopportunities to be employed in the paid jobs through trade expansion, the gender
discrimination would not be improved if female workers are hired as a substitute of low-costlabor and do not gain equal compensations as males in the same skill level.
1The objective of this paper is to investigate whether greater trade openness has actuallyaffected the gender inequality in wage compensations. This study uses the static and dynamic
panel models to analyze the relationship between trade openness and gender wage gap
between male and female workers for 6 sectors and 3 different skill levels (high-, mediumand low-skill) in 19 developed countries from 1995 to 2005.1
The results show that the gender wage gap is reduced in the medium- and low-skill levels
when trade openness becomes greater. This outcome supports the Becker’s hypothesis that
increased competition reduces the wage gap. In the high-skill level, however, the results are
not statistically significant. This might be because the gender wage gap in high-skilled jobs isrelatively small compared with that in medium- and low-skill jobs.
The contributions of this study are twofold. First, there are relatively small number ofstudies that have been conducted using cross-country and panel data, as most of the previous
research has been undertaken by using micro-data for a single country. Second, this study
provides the evidence that trade openness reduces the gender wage gap for medium- andlow-skill level workers.
The rest of the paper is organized as follows. Section II provides the literature review ofprevious studies. Section III describes the data and the methodology, and Section IV presents
the estimation results. Section V conducts the robustness check that examines the estimation
results obtained in the Section IV. Section VI provides concluding remarks.

2.

Literature Review
The relationship between trade expansion and gender inequality was first examined byBecker (1957). Since this study was published, the world has undergone extensive tradeliberalization through several rounds of multilateral trade negotiations and numerous free
1

Canada’s data is available from 1995 to 2004.

2

trade agreements, and the shares of exports and imports in GDP have increased considerablyin emerging and developing countries. So far several studies have conducted to examine
whether one of these agreements or structural changes actually affect countries’ genderinequality in the labor markets. We divide these previous studies into two types, thecross-country and panel data studies and research based on micro-data.

2.1. Cross-country and panel data studies
Wolszczak-Derlacz (2013) conducts an empirical study on the effects of domestic andforeign competition on male/female wage differentials for 12 manufacturing sectors of 18
OECD countries from 1970 to 2005. She estimates the gender wage gap by the generalized
method of moments (GMM), where the endogenous variable is instrumented by its lags. She
provides two findings. First, she shows that an increase in sector concentration is associatedwith gender wage gap growth. Secondly, both import and export penetrations are associatedwith a reduction of the high-skill gender wage gap growth in concentrated industries.
However, there is the opposite effect against medium- and low-skill workers in that the trade
penetration widens the growth of gender wage gap.
Saure and Zoabi (2014) challenge the previous studies relied on Stolper-Samuelsonbased intuition that female labor force participation rises whenever trade expands those
sectors that use female labor intensively. They use bilateral trade data of the United Statesand Mexico during the period of 1990/1991 to 2006/2007 that distinguishes between female
and male intensive sectors. The results of their cross-state regressions suggest that bilateral
trade with Mexico increases the gender wage gap and reduce female labor force participationin the United States.
Seguino (2000) investigates the source of different trends in gender wage differentials
through analysis of the effects of macro-level processes and policies in South Korea and3

Taiwan from 1981 to 1992. She finds that physical capital mobility has contributed to a wider
gender earnings gap in Taiwan. This result comes from women’s greater concentration inindustries where capital is mobile. However, the effect of capital mobility in Korea isdifferent from Taiwan, which may be due to the dissimilar character of outward FDI.
Busse and Spielmann (2006) explore the international linkage between inequality andtrade flows of a sample of 29 developed and developing countries in 2000. Their focus is oncomparative advantage in labor-intensive manufactured goods. They find that gender wage
inequality is positively associated with comparative advantage in labor-intensive goods, forexample, countries with a larger gender wage gap have higher exports of these goods. They
also find that gender inequality in labor force activity rates and educational attainment rates
are negatively linked with comparative advantage in labor-intensive commodities.
Kucera and Milberg (2000) reject the “Wood asymmetry”, using disaggregated industry
level data of 22 manufacturing for ten OECD member countries during 1978 to 1995. Wood
(1991) asymmetry is that trade between developed and developing countries correspondswith an increased female intensity of employment in developing countries and has no
noticeable negative symmetric effect on the female intensity of employment in the
trade-goods sector of developed countries. The authors’ finding is that in most of thecountries (in particular Australia, Canada, Japan, the Netherlands, and the United States),
trade expansion with non-OECD member countries results in employment declines that
disproportionately affected women. In most continental European countries in their sample(France, Germany, and Italy), there are little or no gender bias in the decline in employment
associated with the expansion of non-OECD trade. They attribute this result to the relative
importance of domestic factors related both to female employment directly and to demandchanges occurring as part of the long-term process of economic growth.

42.2. Micro-data studies
Juhn et al. (2013) examine the effect of trade liberalization on gender employment
inequality in Mexico through participating in the NAFTA using the firm-level data of 1992
and 2001. In their analysis, they divide labor force into three categories, white-collar,
blue-collar and all to measure the growth of female-male labor ratio. They find that firms
experiencing larger declines in export tariffs are more likely to hire blue-collar women and to
pay them higher wages. However, there is no similar effect in white-collar occupations,where the relative importance of physically demanding skills is less likely to have changed.
These improvements in blue-collar women’s labor market outcomes are driven by firms
newly entering the export markets who upgraded their technology towards new computerized
production machinery.
Chen et al. (2013) investigate the link between globalization and gender inequality in the
Chinese labor market in 2004. They measure the female employment share and the genderwage gap of domestic and foreign firms in China using the first national economic census
conducted by the National Bureau of Statistics of China. Their single year econometricestimation finds that foreign and exporting firms employ more female workers than domestic
non-exporters. However, they also show that foreign and exporting firms have larger genderwage and productivity gaps than domestic non-exporters and suggest that it mainly reflects
the difference in gender productivity.
Black and Brainerd (2004) test the Becker’s hypothesis by examining the impact of
globalization on gender wage discrimination in manufacturing industries in the United States
between 1976 and 1993. They measure the change in the residual gender wage gap by using
the industry concentration ratio, the import share change and price-cost margin as explanatory
variables in their industry-level regressions. Their finding is that while trade increases wage
inequality by reducing the relative wages of less-skilled workers modestly, at the same time it5

appears to benefit for women by reducing the ability of firms to discriminate.
Chamarbagwala (2006) investigates widening skill wage gap and narrowing gender wage
differentials from 1983 to 2000 that coincides with the economic liberalization in India byusing the individual-level data from the Employment and Unemployment Schedule of the
National Sample Survey Organization. She applies the nonparametric analysis that provides aframework for decomposing the extent to which relative supply and demand changes
contributed to the relative wage changes in India. Her finding is that relative demand shifts
contributed to relative wage shifts and that increases in the demand for skilled labor were
mostly caused by skill upgrading within industries. In assessing the contribution of external
sector reforms to demand for skilled labor, she also finds that international trade-in
manufactures benefited skilled men but hurts skilled women, whereas outsourcing of services
generated a demand for both female and male college graduates.

3.

Data and Methodology
In this study, we use the labor and trade data that are matched at the industry level. To

construct the wage and the sector concentration variables, we solely use the EU KLEMS
database that contains the capital and the labor data inputs for 30 developed countries from1970 to 2012.2 The database is separated into each sex, three kinds of skill levels (high-,
medium- and low-skills) and three age groups (15-29, 30-49 and over 50 years-old). It is also
divided into 72 industries depended on the European NACE revision 1 classification that isvery close to the International Standard Classification (ISIC) Rev. 3 (EU KLEMS, 2007). The
skill levels are split based on the educational attainments. According to EU KLEMS (2007),the high-skill level is equivalent to a worker with bachelor’s or higher degree, the medium-

2

The EU KLEMS data are available at http://www.euklems.net/. An overview and methods are provided
in Timmer et al. (2007) and O’Mahony et al. (2008).

6

skill level is associated with high (secondary) school, junior college or vocational school
graduate level, and the low-skill level is associated with educational attainment below themedium-skill level. Since the EU KLEMS database is constructed by data of each country’s
national accounts, the method of separating the skill level varies on a yearly basis. The labor
compensation and the hours of workers are also split into the same way as above. To create
the gender wage gap variable, we utilize the hours and labor compensations of workers ineach skill level and gender category for 19 developed countries from 1995 to 2005, andcalculated as follows. The list of countries is shown in the Table 1. First, we calculate thehourly wages for each gender and each skill level,
,
,
,
,

,
,

,

1

,

where i denotes sector, j indicates country, t is time and S is the skill category of workers.
,

is the value of total labor compensation in a given sector and country, andthe share of labor compensation obtained by female with given skills.
number of hours worked by all persons engaged in a given sector i and

,
,

is

means the total,,,

is the share ofhours worked by females of all ages for a given skills level. Since superscript F indicatesfemale, equation (1) gives the hourly wages for female workers for a given skill level. Similar
computations are made to obtain the hourly wages for male workers.
Regarding the industry sectors, we apply the aggregated sectoral level data that issummarize in Table 2 (see United Nations Statistics Division, 2015). Due to the availabilityof trade value data, the number of sectors that we cover is limited. We follow the genderwage difference calculation of Wolszczak-Derlacz’s (2013), which can be expressed asfollows:
w ,

ln

,

,

,
,

ln

2

where i denotes sector, j is country, t is time and S is the skill category of workers. Figures7

5-7 plot the gender wage differentials between male and female workers of the manufacturingsector by each skill and country. In these figures, each point shows the wage gap in the
manufacturing industry.3 The x-axis is the time periods and the y-axis indicates the size ofthe gender wage gap. When the point marks zero at the y-axis, it means that there is no wage
difference between male and female. If the points is positive, it means that male workers earnhigher wages than female workers. If the point is negative, the opposite holds. The country
abbreviation are provided in Table 1.
Comparing Figures 5, 6 and 7, the gender wage gaps are relatively large in the low-skill
level and relatively small in the high-skill level. In the high-skill level, there is almost nowage difference between male and female workers in Austria and Italy, whereas the wage
gaps are relatively large in Czech Republic (until 2001), Spain, Finland, Germany, Japan,
Korea, Poland and Slovakia (Figure 5). Eight countries, including Australia and Czech
Republic, experience diminishing gender wage gap and three countries, namely Japan, theUnited States and Spain, do not have significant changes from 1995 to 2005.
In the medium-skill level, again there is almost no gender wage gap in Austria and Italy,
whereas the wage gaps are relatively large in Czech Republic (until 2001), United Kingdom,
Germany, Japan and Korea (Figure 6). Seven countries, such as Czech Republic and GreatBritain, experienced declining gender wage gap but the rate of change is not so large in themedium-skill level. Most of the countries show that the gender inequality situation has not
improved and male workers earn 20-60% higher than female workers, with the exception ofAustria and Italy.
In the low-skill level, Austria is the only country where there is almost no gender wage
gap. The wage gaps are relatively low in Denmark, Finland, Hungary, the Netherlands andSlovenia, whereas they are relatively high in Japan and Korea (Figure 7). Most of these gaps
3

The wage gaps in other industries in 19 countries for the period 1995-2005 are available from the author.

8

relatively constant over time except for Czech Republic and Italy. In Czech Republic, thegender wage gap improved by more than 20% during the 1995-2005 period. In contrast, it
increased by about 30% in Italy. In Italy, the gender wage gaps in high- and medium-skill
jobs are close to zero, they are considerable in low-skill jobs.
The notable difference compared to other previous studies is that we use trade openness
as an explanatory variable. When we examine the relationship between export and wage gap
differences, the export of crude oil is zero in Japan. However, workers engaged for thecommodity and the labor compensation actually exist since Japanese firms import and refine
petroleum. Therefore, we consider using either import or export value as an explanatory
variable may not be able to fully account for the relationship between trade and gender wage
differentials. Wolszczak-Derlacz (2013) provides me a good insight in that point that
including both imports and exports in the same equation may cause multicollinearity. To
avoid this problem and to take into consideration the trade balance within the country, we
decide to introduce the trade openness index. Following Giovanni and Levchenko (2009) andYanikkaya (2003), trade openness, one of the most basic measures of trade intensity, is theratio of exports plus imports to output for each sector. Then the equation of trade openness isderived by
Trade ,
where,

Exports ,Imports ,GO ,3is the gross output at current basis price in a given sector i in country j. The

OECD (2011) and the World Bank also employ this formula as a trade openness index thattakes the simple ratio of total trade to GDP. However, while the value of,always between zero and one, the value of,,,will becan become significantly greater than

one when domestic production of product i is very small and a large percentage of thiscommodity is imported, such as crude oil and natural gas in Japan. Thus, a better9measurement of trade openness is
,

,

,
,

,

4

where total demand = demand for domestically produced output + import value, and total
output = output supplied to the domestic market + exports. In equilibrium, demand fordomestically produced output = output supplied to the domestic market (i.e. demand = supply).
When domestic output is zero and the country completely relies on imports, the value of,,

becomes one. Thus,,

defined by equation (4) is the sumof the import penetration ratio and the export penetration ratio, which appears to be a better
index than the one given by equation (3). To obtain the export and import values, we use the
UN Comtrade of the World Integrated Trade Solution (WITS) in the ISIC Rev. 3. The gross
output data is reported in EU KLEMS (2007). Demand for domestically produced output,
which is also equal to output supplied to the domestic market, is computed as the difference
between gross output and exports. Total demand is the sum of demand for domestically
produced output and imports. Trade openness in 1995 and 2005 for six industries in 19
countries are summarized in Table 6. From this table, we can observe that trade openness inthe mining and manufacturing sectors increased in the large majority of the countries during
1995-2005. The values of the mining sector in Belgium, Hungary and Slovak Republic andthe manufacturing sector in Belgium exceed 100%. The reason why trade openness exceeds
100% is because those countries highly rely on imports of mining products. Regarding the
manufacturing sector of Belgium, the values of import, export and gross output are almost
same and in that case, trade openness is close to 100%. In public utilities, business activities
and other service sectors, the values of trade openness are either zero or very small. In thoseindustries, the import and export values are considerably smaller than gross output, which isrelated with the characteristic of services trade.

10We also include the sector concentration as an explanatory variable that is derived fromthe following equation.
,

where

,

,

,

5

,

is the gross value added at current basis prices and,refers to labor

compensation. This calculation is based on Aghion et al. (2008) that the sector concentration
is driven from the difference between the value added (VA) and the labor compensation(LAB) as a proportion of the gross output (GO). This variable can measure how the genderwage gap is affected by the market concentration of a specific sector.
Finally, we apply the fixed effects model, and which can be defined as follows.
,

,

where

,

,

6

is fixed effects that is different from each skill, S, sector, j and country i.

time-varying error that changes over time and affects
unobserved effect,is, . In the fixed effects model, theis correlated with explanatory variables in any time periods. The

motive we employ the fixed effects model is there should be unobserved effect that cannot be
explained by independent variables such as geographic features, peoples’ attitude against
trade and so on.

4.

Results

4.1. Results of static panel analysis
The summary statistics and the estimation results are provided in Tables 4-6. The featureof this estimation is that we can measure the gender wage gap of the high-, medium- andlow-skill levels separately.
From the first to third columns of Table 6, the coefficients of trade openness are negativeand statistically significant at the 1% level in the medium- and low-skill levels. These results

11suggest that greater trade openness reduces the gender wage gap of medium- and low-skill
workers. This result is consistent with the Becker’s hypothesis that trade expansion promotes
to decrease gender discrimination. On the other hand, the relationship between trade openness
and the wage gap is insignificant in the high-skill level.
As for the sector concentration, the estimated coefficients are not statistically significant
even at the 10% level in any skill levels. This suggests that sector concentration does notaffect gender wage gap, which is contrary to Wolszczak-Derlacz’s study revealing the sector
concentration increases the gender wage gap for all skill levels.
In Table 6, it also reports the results of the random effects model that the unobserved
effect ofon equation (6) is uncorrelated with each explanatory variable;
Cov

, ,

Cov

, ,

0
0

From the estimation results of the random effects model, the coefficients of trade openness inthe medium- and low-skill levels are also negative and significant at the 1% level and thisresult is consistent with the fixed effects model. The estimated coefficients of trade openness
in the medium-skill level in both models are similar. In contrast to the results of the fixed
effects model, the estimated coefficient on trade openness in the high-skill level is significant
at the 10% level in the random effects model. However, in the Wald test that examines
whether all the coefficients in the model are different from zero, it shows the value ofchi-squared is 0.2081, which is not significant even at the 10% level. Therefore, I do not
adopt the high-skill level result. Regarding the sectoral concentration, all of the coefficientsare insignificant. We conduct the Hausman test that the null hypothesis is the preferred modelis the random effects model to the fixed effects model. The values of chi-squared are 0.0124
and 0.000 in the medium- and the low-skill levels, respectively. Therefore we can reject thenull hypothesis at the 1% significance level, which indicates that the preferred model is the12fixed effects model.
The result that greater trade openness reduces the gender wage gaps of medium- andlow-skill workers is consistent with Becker’s hypothesis. Nevertheless, we still wonder
whether this study is able to explain his theory accurately. The degree of trade openness has
been increasing as it is mentioned in the previous section and Table 6, and the countries withincreasing trade openness may have been experiencing greater competitive pressure. To
survive and prosper under the competitive environment, firms may be more willing to hire
female workers while maintaining the share of each skill level of workers.
According to the results, trade openness does not affect the gender wage gap of high-skill
workers. This might be explained by differences in the gender wage gap and labor mobility
across skill levels. First, as mentioned earlier, the gender wage gap (in percent deviations) issmaller for high-skill jobs, compared with medium- and low-skill jobs. Secondly, mediumand low-skill labor might be more mobile than high-skill labor. Medium- and low-skill
workers seldom obtain powerful authorities in their tasks such as taking managerial position
unless they start their own business. Therefore, they are likely to be flexible to move to otherjobs when the condition of the new job is better than the current one. By contrast, high-skill
workers are required to have specific skills and are generally offered their positions based on
their education and experience. Thus, it might be more difficult to change their jobs
frequently compared to medium- and low-skill workers. Juhn et al. (2013) show similar
results as this study in that firms experiencing tariff reductions pay higher wages forblue-collar female workers, but not for white-collar female workers. They explain that this isbecause of technology upgrading in exporting firms, which enables blue-collar female
workers to fill the gap of requirements with male workers. Conversely, they suggest that therequirements in white-collar works are unlikely to change. Unfortunately, we do not include
the variables on labor mobility or technological change to confirm Juhn et al.’s (2013)13

finding.

4.2. Results of dynamic panel analysis
In this section we examine whether the estimation results obtained using the fixed effects
model is consistent with results using a different specification. We use two-step difference
generalized method of moments (GMM) in which the lags of explanatory variables are used
as instrument variables. The objective for using dynamic panel estimation is that it enables us
to observe the linkage with the past, since the present gender wage gap is assumed to havebeen influenced from the past. The GMM is one of the useful methodologies to estimate
dynamic panel and it is not necessary to specify the distribution of error terms. In particular,“two-step GMM” is adopted since Windmejier (2005) finds that it performs better than
one-step GMM in estimating coefficients, with lower bias and standard errors.
The estimation model can be defined as follows.
∆w ,

α ,

∆w ,

β ∆Trade openness ,

β ∆

,

ε ,

7

where ∆w , is the first difference in the gender wage gap defined in the equation (2).
∆Trade openness , is the first difference in trade openness in equation (4), and ∆,isthe first difference in the sector concentration in equation (5). α , is time invariant effectthat is different from each skill, sector and country. We follow the idea of Wolszczak-Derlacz
that the estimated coefficient of the lagged wage differential is an indicator of the conditional
convergence; if it is negative, a gender wage gap approaches its steady state.
Table 7 shows the estimation results for robustness check. From the first row for trade
openness, the estimated coefficient is only significant in the medium-skill level. Regarding
the medium-skill level, the coefficient is negative and significant at the 10% level, thereby
suggesting that trade openness reduces the gender wage gap for that skill level. We confirm
the Arellano-Bond test for AR (1) in the first difference is rejected at the 1% level for the null14

hypothesis is that there is autocorrelation in ε , . We also check the Hansen test cannot berejected even at the 10% level for the null hypothesis that the lagged instruments as a groupare exogenous. Therefore we can accept the null hypothesis. In the Sargan test, it can berejected at the 1% level. However, Roodman (2009) discusses that the Hansen statistic of a
two-step estimate is theoretically superior over-identification test when the errors are
non-spherical and the Sargan statistic is not consistent. Contrary to the trade openness
coefficient of medium-skill, the column 1 and 3 of Table 7 indicates that it does not have asignificant relationship with gender wage gap in the high- and low-skill level.
The estimated coefficients of sector concentration are not statistically significant even at
the 10 % level and this result is consistent with the fixed effects model.
The coefficients of the lagged gender wage gap are positive for all skill levels andsignificant at the 1% level. According to Wolszczak-Derlacz’s (2013), the estimatedcoefficient of the lagged wage differentials is an indicator of the conditional convergence. If
it is negative, a gender wage gap approaches its steady state. However, since it is positive inour results, the gender wage gap has not reached its steady state yet.

5.

Conclusion
We have examined how trade openness affects the gender wage inequality associatedwith three different skill levels of labor in six industries. The results of the fixed effects
model suggest that greater trade openness reduces the gender wage gap in medium- andlow-skill jobs. The robustness of our estimation results for medium-skill jobs is confirmed
using the two-step GMM. Thus, the Becker’s hypothesis that increasing trade openness
induces competitive pressure for employers and helps to reduce the gender wage gap is
supported to some extent in this study. The causal relationship between trade and the genderwage gap in high-skill jobs is not found. Although previous studies apply imports, exports or

15

tariff rates as an explanatory variables, using either import or export values as an explanatory
variable cannot fully capture the relationship with wage inequality. Furthermore, the tariff
rates do not accurately measure the extent of a country’s trade barriers, because there arenontariff barriers (NTBs), such as quantitative restrictions, technical barriers, safeguard
measures and anti-dumping duties. Thus, even when tariff rates fall, it does not necessarily
imply that the magnitude of trade barriers has decreased because the extent of NTBs might
have increased at the same time. One of the contributions of this study is that a new index of
trade openness, one that is superior to the conventional index, is introduced and used as an
explanatory variable.
There are several points that need to be considered for future analysis. First, from the low
value of R-squared (R2) in the estimation using the fixed effects model, the independent
variables may not fully explain the dependent variable. We suspect that there might be othervariables that explain the gender wage gap, such as productivity and the number of years ofschooling. Secondly, due to the data availability, only developed countries are covered in thisstudy. However, since the emerging countries such as China and other East Asian countries
have experienced a significant increase in economic openness, we should try to include thosecountries to capture the trend of gender wage inequality in a broader perspective.
A policy implication from this study is straightforward: the government should promotetrade liberalization and at the same time tackle the gender wage inequality issue using more
direct domestic policies. Reducing inequality of wages between male and female workers
will help to achieve one of the UN millennium development goals, i.e., promoting genderequality and empowerment of women.

16References
Aghion, P., M. Brauny and J. W. Fedderkez (2008), “Competition and Productivity Growth inSouth Africa.” Economics of Transition, 16(4), 741-768.
Black, S. and E. Brainerd (2004), “Importing Equality? The Impact of Globalization on
Gender Discrimination.” Industrial and Labor Relations Review, 57, 540-559.
Becker, G. (1957), The Economics of Discrimination. Chicago: University of Chicago Press.
Becker, G. (1971), The Economics of Discrimination, 2nd edition. Chicago: University of
Chicago Press.
Busse, M. and C. Spielmann (2006), “Gender Inequality and Trade.” Review of International
Economics, 14(3), 362-379.
Chamarbagwala, R. (2006), “Economic Liberalization and Wage Inequality in India.” World
Development, 34(12), 1997-2015.
Chen, G., Y. Ge, H. Lai, and C. Wan (2013), “Globalization and Gender Wage Inequality in
China.” World Development, 44(C), 256-266.
EU KLEMS (2007), EU KLEMS Growth and Productivity Accounts, Version 1.0, PART I:
Methodology. http://www.euklems.net/
Giovanni, J. and A. A. Levchenko (2009), “Trade Openness and Volatility.” Review of
Economics and Statistics, 91(2), 315-331.
Juhn, C., G. Ujhelyi, and C. Villegas-Sanchez (2013), “Men, Women, and Machines: How
Trade Impacts Gender Inequality.” Journal of Development Economics, 106, 179-193.
Kucera, D. and W. Milberg (2000), “Gender Segregation and Gender Bias in Manufacturing
Trade Expansion: Revising the ‘Wood Asymmetry’.” World Development, 28(7),1191-1210.
OECD (2011), OECD Science, Technology and Industry Scoreboard 2011. Paris: OECD.
O’Mahony, M., C. Castaldi, B. Los, E. Bartelsman, Y. Maimaiti, and F. Peng (2008),“EUKLEMS-Linked Data: Sources and Methods.” University of Birmingham.
http://www.euklems.net/data/linked/euklems_linkeddata_sourcesandmethods_220708.pdf

17

Roodman, D. (2009), “How to do xtabond2: An introduction to difference and system GMM
in Stata.” The Stata Journal, 9(1), 86-136.
Saure, P and H. Zoabi (2014), International Trade, the Gender Wage Gap and Female Labor
Force Participation. Journal of Development Economics, 111, 17-33.
Seguino, S. (2000), “The Effects of Structural Change and Economic Liberalization on
Gender Wage Differentials in South Korea and Taiwan.” Cambridge Journal of
Economics, 24, 437-459.
Timmer, M. P., M. O’Mahony., and B. van Ark (2007), “EU KLEMS Growth andProductivity Accounts: An Overview.” http://www.euklems.net/data/overview_07i.pdf
United Nations Statistics Division (2015).
http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=2
Windmeijer, F. (2005) A finite sample correction for the variance of linear efficient two-step
GMM estimator. Journal of Econometrics 126: 25-51
Wolszczak-Derlacz, J. (2013), “Mind the Gender Wage Gap – The Impact of Trade andCompetition on Sectoral Wage Difference.” The World Economy, 36(4), 437-464.
World Integrated Trade Solution (WITS),
https://wits.worldbank.org/WITS/WITS/Restricted/Login.aspx
Wood, A. (1991), “North-South Trade and Female Labour in Manufacturing: An Asymmetry.”
Journal of Development Studies, 27(2), 168-189.
World Bank (various years), Database. http://databank.worldbank.org/data/home.aspx
World Economic Forum (2014), The Global Gender Gap Report 2014. Geneva: World
Economic Forum.
Yanikkaya, H. (2003), “Trade Openness and Economic Growth: A Cross-Country EmpiricalInvestigation.” Journal of Development Economics, 72(1), 57-89.

18

Figure 1: Trade to GDP ratio (%)

70
60
50
40
30
20
10
0

World

High income

Low & middle income

Note: The ratio is computed as (Exports + Imports)/GDP.
Source: World Bank (various years).
Figure 2: Employment to population ratio in the world (%)

Employment to population ratio over 15 years-old in
the world (%)
80
75
70
65
60
55
50
45

Female (%) (modeled ILO estimate)
Total (%) (modeled ILO estimate)

Source: World Bank (various years).

19

Male (%) (modeled ILO estimate)Figure 3: Employment to population ratio in the OECD member countries (%)

Employment to population ratio, over 15 in theOECD countries
75
70
65
60
55
50
45
40
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013

35

Female (%) (national estimate)

Male (%) (national estimate)

Total (%) (national estimate)

Source: World Bank (various years).

Figure 4: Employment to population ratio in the low and medium income countries (%)

Employment to population ratio over 15 in the low
and medium income countries
85
80
75
70
65
60
55
50
45
40

Female (%) (modeled ILO estimate)
Total (%) (modeled ILO estimate)

Source: World Bank (various years).

20

Male (%) (modeled ILO estimate)Table 1: County list
Country

Abbreviation

Australia

AUS

Austria

AUT

Belgium

BEL

CanadaCAN

Czech Republic

CZE

Denmark

DNK

FinlandFIN

Germany

GER

Hungary

HUN

Italy

ITA

Japan

JPN

Netherlands

NLD

PolandPOL

Slovak Republic

SVK

Slovenia

SVN

South Korea

KOR

Spain

ESP

United Kingdom

GBR

United States

USA

Table 2: Sector description
code

Description of Sectors

AtB

C01-C05: Agriculture, Hunting, Forestry and FishingC

C10-14: Mining and QuarryingD

C15-37: ManufacturingE

C40-41: Electricity gas and water supply

K

C70-74: Real estate, renting and business activities

O

C90-93: Other community, social and personal service activities

21

Figure 5: Gender wage gap between male and female in the high-skill level in the manufacturing sector from 1995 to 2005
AUT

BEL

CAN

CZE

DNK

ESP

FIN

GBR

GER

HUN

ITA

JPN

KOR

NLD

0

.2

.4

.6

0

.2

.4

.6

0

.2

.4

.6

AUS

1995

SVK

SVN

USA

0

.2

.4

.6

POL

1995

2000

2005 1995

2000

2005 1995

2000

Source: Author’s calculation based on data from the EU KLEMS (2008).
22

2005 1995

2000

2005

2000

2005

Figure 6: Gender wage gap between male and female in the medium-skill level in the manufacturing sector from 1995 to 2005
AUT

BEL

CAN

CZE

DNK

ESP

FIN

GBR

GER

HUN

ITA

JPN

KOR

NLD

0

.2

.4

.6

0

.2

.4

.6

0

.2

.4

.6

AUS

1995

SVK

SVN

USA

0

.2

.4

.6

POL

1995

2000

2005 1995

2000

2005 1995

2000

Source: Author’s calculation based on data from the EU KLEMS (2008).
23

2005 1995

2000

2005

2000

2005

Figure 7: Gender wage gap between male and female in the low-skill level in the manufacturing sector from 1995 to 2005
AUT

BEL

CAN

CZE

DNK

ESP

FIN

GBR

GER

HUN

ITA

JPN

KOR

NLD

0 .2 .4 .6 .8

0 .2 .4 .6 .8

0 .2 .4 .6 .8

AUS

1995

SVK

SVN

USA

0 .2 .4 .6 .8

POL

Source:calculation2005
based1995
on data fromthe EU KLEMS(2008)
1995 Author’s
2000
2000
2005 1995

2000

Source: Author’s calculation based on data from the EU KLEMS (2008).
24

2005 1995

2000

2005

2000

2005

Table 3: Trade openness (%)
Agriculture

MiningManufacturing

Public utilities

Business

Other services

activities

1995

2005

1995

2005

1995

2005

1995

2005

1995

2005

1995

2005

AUS

19.4

19.2

33.5

40.8

41.4

48.5

0.0

0.0

0.0

0.0

0.4

0.4

AUT

27.5

30.2

89.0

95.6

83.5

79.5

5.4

30.8

0.1

0.0

1.9

1.0

BEL

94.4

92.8

186.2

193.0

104.9

104.2

0.2

9.9

1.1

0.0

0.6

1.2

CAN

29.1

27.5

47.3

47.5

66.2

65.2

4.0

7.4

0.0

0.0

0.6

0.5

CZE

23.8

28.6

66.5

72.5

61.3

72.1

1.6

5.8

0.1

0.0

4.8

0.2

DNK

28.7

41.5

57.6

50.8

66.4

83.5

3.0

10.7

0.0

0.0

0.6

0.6

ESP

34.9

34.3

81.4

96.1

50.8

51.4

1.0

2.0

0.0

0.0

0.5

0.6

FIN

24.3

21.4

94.2

93.7

66.2

59.0

4.9

8.0

0.0

0.0

0.2

0.1

GBR

23.7

31.0

49.0

66.9

53.6

66.6

1.0

0.9

0.0

0.0

4.7

4.5

GER

43.8

41.8

61.2

94.6

60.9

60.8

2.1

2.2

0.0

0.0

0.8

0.5

HUN

17.2

19.9

80.4

113.0

56.1

79.0

1.8

6.9

0.1

0.0

0.2

0.1

ITA

28.9

25.1

73.3

87.9

48.9

46.6

0.0

3.1

0.0

0.0

0.2

0.5

JPN

12.3

12.4

75.9

92.7

18.5

24.5

0.0

0.0

0.0

0.0

0.2

0.1

KOR

14.0

14.1

82.3

96.1

37.9

41.7

0.0

0.0

0.1

0.2

1.2

0.8

NLD

71.9

64.5

86.8

68.8

95.1

92.9

1.6

3.3

0.0

0.0

0.6

0.6

POL

8.9

14.4

39.4

59.7

41.6

61.1

1.1

2.5

0.1

0.0

0.1

0.1

SVK

19.0

25.0

88.4

112.4

60.1

83.8

1.0

0.0

0.2

0.0

1.8

0.0

SVN

39.0

27.8

70.3

73.3

106.8

82.3

16.3

28.2

0.1

0.0

0.2

0.1

USA

15.4

12.2

27.8

44.1

27.4

35.2

0.3

0.9

0.0

0.0

0.9

1.0

Source: Author’s calculation based on data from WITS and EU KLEMS.
*Canada’s 2005 data is 2004 one since the EU KLEMS does not report Canada’s 2005 data.

25

Table 4: Summary statistics of main variables
Variable

Observations

Mean

Std. Dev.

Min

Max

cou_code

1248

57.67332.905

1

114

Wage gap (High skill)

1248

0.323

0.254

-0.598

4.061

Wage gap (Medium skill)

1248

0.266

0.190

-0.5890.752

Wage gap (Low skill)

1248

0.346

0.312

-0.325

3.483

Sector concentration

1248

0.210

0.236

-1.6860.856

Trade openness

1248

0.286

0.355

0

1.930

Table 5: Summary statistic of subsidiary variables
Variable

Observations

Mean

Std. Dev.

Min

Max

Log(High-skill male wage)

1248

4.531

1.866

2.115

10.123Log(High-skill female wage)

1248

4.208

1.837

-0.0299.611

Log(Medium-male wage)

1248

4.031

1.881

1.311

9.969

Log(Medium-female)

1248

3.766

1.847

1.196

9.588

Log(Low-male wage)

1248

3.753

1.951

0.558

9.882

Log(Low-female wage)

1248

3.407

1.956

-0.0299.508

Imports

1248

33684.44

113632.401409146

Exports

1248

30508.36

98885.18

0921389Gross output

1248

200603.7

581153.4

137.3794867028*The units of Imports, Exports and Gross output are millions of US dollars.

26

Table 6: Estimation results (Dependent variable: Gender wage gap in a given skill level)
Fixed effects model

Random effects model

VARIABLESHigh

MediumLow

High

MediumLow

Trade openness

0.123(0.129)0.0368(0.123)0.280***(0.0457)-0.161***(0.0320)-0.0325(0.0305)
0.319***(0.0113)-0.791***(0.140)0.114(0.133)0.549***(0.0494)

0.0715*(0.0419)0.0361(0.0589)
0.295***(0.0237)-0.111***(0.0270)-0.0271(0.0283)0.303***(0.0201)-0.152***(0.0568)
0.110(0.0771)
0.366***(0.0323)

1,248
0.001
114

1,248
0.023
114

1,248
0.028
114

1,248

1,248

1,248

114

114

114

Concentration
Constant

Observations
R-squared
Number ofcountry-code(Standard errors in parentheses: ***: p<0.01, **: p<0.05, *: p<0.1)

Table 7: Robustness check (Dependent variable: ∆Gender wage gap)
VARIABLES
Trade openness
Concentration,

Observations
Number of country-code
Sargan test for overid
Hansen test for overid
Arellano-Bond test for AR(1)Arellano-Bond test for AR(2)(1)
High-skill(2)Medium-skill(3)
Low-skill

0.722(0.666)-0.315(2.172)0.288***(0.0406)-0.287*(0.169)0.129(0.129)0.759***(0.0973)-0.206(0.238)0.524(0.512)0.590***(0.0413)

1,020
114
0.000
0.761
0.114
0.159

1,020
114
0.002
0.366
0.000
0.487

1,020
114
0.002
0.101
0.095
0.505

(Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1)* Using two-step difference GMM by taking the second lags and introducing wage
differential and concentration as exogenous variables.

27