Revista de economía mundial 65, 2023, 115-139

ISSN: 1576-0162

DOI: http://dx.doi.org/10.33776/rem.vi65.7899

The ReducTion of The GendeR Gap ThRouGh Global Value chains:

poliTical commiTmenT oR peRpeTuaTion of GendeR Roles?

La reducción de La brecha de género en cLave de cadenas gLobaLes

de vaLor: ¿apuesta poLítica o perpetuación de Los roLes de género?

Hugo Campos-Romero

Universidade de Santiago de Compostela

hugo.campos.romero@usc.es

Bruno Blanco-Varela

Universidade de Santiago de Compostela

b.blanco.varela@usc.es

Recibido: agosto 2023; aceptado: septiembre 2023

Premio Jose Luís Sampedro, 2023

absTRacT

The relationship between international trade and the gender gap is complex.

On one hand, foreign insertion can generate economic opportunities for

women in the export sector. On the other hand, it can also result in heightened

competition and the relocation of jobs, especially in the context of global

value chains (GVCs). The study on the gender gap through GVCs examines

the mechanisms within production networks that contribute to enhance

women’s economic empowerment and gender equality. The paper analyzes

the relationship between the gender gap, GVCs, technological intensity of

exports, and female empowerment in the EU-27, investigating whether GVCs

could reduce or worsen gender disparities. The aim of this paper is to expand

the gender gap studies incorporating the effects of trade in value added in

the European Union. The study used a panel of data from various sources,

including Eurostat, Trade in Employment (TiM), and the World Bank. The results

suggest that an increase in exports, regardless of the level of technological

intensity, tends to exacerbate the gender gap in export sectors by increasing

the proportion of male workers relative to female workers. However, a higher

female participation rate in medium-low technology sectors could reduce the

gender gap in export sectors. The study identiﬁed obstacles to female insertion

in export markets, including the need to improve education and training for

women in high-demand areas, promote STEM (science, technology, engineering

and mathematics) education and labor participation for women. This paper

makes signiﬁcant contributions to the literature by incorporating global value

GVCs and considering the European context. Additionally, it provides an in-

depth analysis of socioeconomic factors that are crucial for designing policies

aimed at addressing gender inequality.

Keywords: gender gap, global value chain, employment, gender roles,

technology intensity, EU-27.

Resumen

La relación entre el comercio internacional y la brecha de género es

compleja. Por un lado, la inserción extranjera puede generar oportunidades

económicas para las mujeres en el sector exportador. Por otro lado, también

puede dar lugar a una mayor competencia y a la deslocalización de puestos

de trabajo, especialmente en el contexto de las cadenas globales de valor

(CGV). El estudio sobre la brecha de género a través de las CGV examina los

mecanismos dentro de las redes de producción que contribuyen a mejorar

el empoderamiento económico de las mujeres y la igualdad de género. El

trabajo analiza la relación entre la brecha de género, las CGV, la intensidad

tecnológica de las exportaciones y el empoderamiento femenino en la UE-

27, investigando si las CGV pudieran reducir o empeorar las disparidades de

género. El objetivo de este trabajo es ampliar los estudios sobre la brecha

de género incorporando los efectos del comercio en el valor añadido en la

Unión Europea. El estudio está basado en un panel de datos elaborado a

partir de diversas fuentes, entre ellas Eurostat, Trade in Employment (TiM) y el

Banco Mundial. Los resultados sugieren que un aumento en las exportaciones,

independientemente del nivel de intensidad tecnológica, tiende a exacerbar la

brecha de género en los sectores exportadores al aumentar la proporción de

trabajadores masculinos en relación con las trabajadoras. Sin embargo, una

mayor tasa de participación femenina en los sectores de tecnología media-

baja podría reducir la brecha de género en los sectores de exportación. El

estudio identiﬁcó obstáculos para la inserción femenina en los mercados de

exportación, incluida la necesidad de mejorar la educación y la capacitación de

las mujeres en áreas de alta demanda, promover la educación STEM (ciencia,

tecnología, ingeniería y matemáticas) y la participación laboral de las mujeres.

Este documento hace contribuciones signiﬁcativas a la literatura al incorporar

CGV de valor global y considerar el contexto europeo. Además, proporciona

un análisis en profundidad de los factores socioeconómicos que son cruciales

para diseñar políticas destinadas a abordar la desigualdad de género.

Palabras clave: brecha de género, cadena global de valor, empleo, roles de

género, intensidad tecnológica, UE-27.

Clasiﬁcación JEL / JEL Clasiﬁcation: F14, J16, J21, J78.

Revista de economía mundial 65, 2023, 115-139

1. inTRoducTion

Reducing the gender gap can lead to greater economic growth. In turn,

economic growth contributes to the reduction of the gender gap through

increased labor participation, productivity, and human capital. In addition,

international trade, and integration into global value chains (GVCs) can

be articulated as an engine of economic growth through innovation, the

emergence of new business opportunities and improved labor conditions for

the companies that participate in them.

The relationship between international trade and the gender gap is

complex. On the one hand, a greater degree of foreign insertion can create new

economic opportunities for women, especially in the export sector. This can

lead to an increase in their income, a reduction in poverty and an improvement

in their overall economic situation. On the other hand, international trade can

also lead to increased competition and offshoring of jobs, which can affect

women in insecure and less skilled jobs to a greater extent. This is particularly

relevant in a trade context deﬁned to a large extent by GVCs, insofar as they

imply a profound fragmentation of production chains.

The aim of this paper is to analyze the relationship of the gender gap

with global value chains, the technological intensity of exports and female

empowerment in the EU-27. Speciﬁcally, it analyzes how integration in GVCs

affect gender disparities. This paper contributes to the literature on the effects

of international trade on the gender gap. In addition, it brings novelty by

approximating foreign trade from GVCs in relation to the gender gap.

To meet the objective, a panel of data has been designed from Eurostat,

Trade in Employment (TiM, OECD) and the World Bank including export

variables in domestic value added, other economic variables and variables of a

social nature, such as the number of women in management positions, among

others.

The results reveal some of the factors affecting the gender gap in

employment in export sectors embedded in GVCs. The female activity rate, the

number of women in executive positions and the gap between men and women

in the degree of success in tertiary studies (which points to a higher attainment

rate among women) reveal a signiﬁcant effect and reduce the gender gap.

However, an increase in the fertility rate would lead to an increase in the gap.

This effect may be due to traditional care-giving gender roles.

118 Hugo Campos-Romero · Bruno Blanco-Varela

This paper introduces several contributions. Firstly, it expands the existing

literature on the relationship between GVCs and the gender gap in the

European context, which has received reduced attention in previous studies.

In this sense, it should be noted that addressing the effect of GVCs represents

a contribution itself. Secondly, it examines a range of economic and social

variables that provide valuable insights to design public policy measures.

Thirdly, it delves deeper into the gender patterns in the domestic and foreign

labor markets, shedding light on their differences.

The structure of this article starts with this introduction and continues with

the second section which deals with the literature review on gender gap and

the effect of trade and global value chains. Thirdly, the methodology and data

sources used are explained in detail. Fourthly, descriptive variables and the

results of the econometric model are presented. Finally, a discussion section is

carried out, detailing the results and conclusions of the study. This structured

approach has enabled a clear and well-organized presentation of the research

conducted on the gender gap and the impact of trade and global value chains,

allowing for a rigorous evaluation of the topic and providing meaningful insights

for further investigation.

2. liTeRaTuRe ReView

The gender gap in the labor market is a critical global issue that has been

extensively studied by scholars. International trade has been recognized as

one of the factors inﬂuencing this gap, but its impact on gender disparities

is multifaceted and not yet fully understood. On the one hand, greater trade

openness can enhance the working conditions of workers, including women

(Gilles, 2018). On the other hand, participation in international trade may

affect the gender gap in terms of the sexual division of labor and access to

productive assets, leading to signiﬁcant differences in wages, employment

opportunities, and career advancement.

The interest in reducing the gender gap is not only justiﬁed on social but also

on economic grounds. Morais (2017) argues that improving gender equality

would have a positive effect on economic growth and women’s employment. The

positive impacts are due to an increase in productivity and an improvement in

the potential productive capacity of the European economy. While traditionally

viewed as a national issue, globalization and economic integration have made

gender disparities a global concern (Ghosh et al., 2022), underscoring the

need for a comprehensive approach to address these inequalities.

The issue is considered in the classic models that deal with international

trade. However, this methodology is insufﬁcient to explain gender disparity

factors (Papyrakis et al., 2012). For example, the Heckscher-Ohlin (HO) model

does not consider the social factors that inﬂuence women’s role in the labor

market. Instead, it focuses on determining wages as a function of external

demand and the mobility of factors of production. In the same way, the work

of Manning (2003) points to two characteristics of women in the labor market

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ReVisTa de economía mundial 65, 2023, 115-139

that may accentuate the wage gap. Firstly, because they are willing to accept a

lower reservation wage and, secondly, because of their lower labor elasticity, in

both cases due to various socio-cultural factors. Therefore, a broader and more

inclusive approach that considers gender inequalities and other relevant social

variables is necessary for a more complete understanding of international

trade relations.

Black and Brainerd (2004) work builds upon Becker’s (1957) traditional

model, which posits that increased market competition can lead to a reduction

in discrimination against women and minority groups over time. To address

this issue, the authors develop a model that identiﬁes changes in market

competitiveness through imports and ﬁrm concentration at the sectoral level.

Their ﬁndings suggest that wage differentials narrowed more in sectors that

experienced increased competition through foreign trade at the beginning of

the period, compared to ﬁrms that were already operating in a competitive

environment. Sauré and Zoabi (2014) challenge the common assumption that

trade expansion in female-intensive sectors (FIS) leads to an increase in female

labor force participation (FLFP). Instead, the paper argues that FLFP may

decrease if trade expands FIS in capital-abundant economies, resulting in a

widening gender wage gap. This highlights again the importance of considering

the broader economic context when examining the effects of competition on

reducing discrimination.

While greater trade openness generally implies better working conditions

for employees in companies with some degree of external insertion, it does

not necessarily lead to a narrowing of the wage gap. Initially, greater external

insertion could lead to greater facilities for the incorporation of women into

working life (Bussmann, 2009). However, several studies have found that the

wage gap increases in sectors with a higher relative participation in foreign

trade (Chowdhury et al., 2021; Li et al., 2020; Orkoh et al., 2022). Furuta et

al. (2018) and Menon and Rodgers (2009) found that trade openness in India

resulted in an increase in the gender gap, although this effect varied depending

on whether labor-intensive or capital-intensive sectors were considered, with

the gap increasing in the former. Some studies have pointed to fundamental

causes differences in gender pay, such as the productivity differential between

men and women. However, not all studies reach this same conclusion. For

example, Almasifard (2018) and Sepehrivand (2017) indicate that greater

external integration may not only increase the income level of competitive

sectors but also decrease the gender gap. This suggests that the effects of

trade openness on employment and the gender gap may be contingent upon

national policies and the type of external integration (Dluhosch, 2021).

Other studies have explored how the origin of capital can impact the

gender pay gap. Vahter and Masso (2019) analyzed gender pay gaps in foreign

and domestic ﬁrms, with a focus on multinational acquisitions. They utilized

propensity score matching to compare the differences between the two groups

and found a larger wage gap in ﬁrms owned by multinationals. This difference

may be attributed to greater demands for employee commitment from these

120 Hugo Campos-Romero · Bruno Blanco-Varela

companies, consistent with Manning’s (2003) discussion of factors contributing

to wage differentials. Fernández (2020) analyzes the effect of foreign ownership

on female employment. Results show that foreign ownership increases the

proportion of female workers within the company. Foreign acquisition increases

the proportion of skilled women only when the acquired company was not an

exporter before its acquisition, supporting Becker’s (1957) theory of taste-

based discrimination.

Regarding foreign direct investment (FDI), Kodama et al. (2018) shows

how, in the case of Japan, foreign-owned ﬁrms have a higher participation of

women in leadership and management positions. According to the authors,

this contrast between domestic versus foreign-owned domestic ﬁrms has to

do with the culture and social norms imported through FDI. It is worth noting

that family reconciliation is easier in foreign-owned companies, facilitating the

attraction of talent not only among the male audience. In addition, Lee and Shin

(2020) notes that women see FDI as an opportunity insofar as it can entail not

only new jobs for women, but also better working conditions and, ultimately,

greater equality. In this sense, the analysis conducted by Pantelopoulos (2022)

for a set of OECD countries points out that women’s access to the labor market

is a factor of attraction for FDI. Bui et al.(2018) research shows that promoting

gender equality is a crucial factor in attracting foreign direct investment (FDI)

to developing nations.

However, Helble and Takeda’s (2020) research on the manufacturing sector

in Cambodia did not provide clear evidence that FDI inﬂuences the gender gap.

Nonetheless, they acknowledged that FDI can lead to wage improvements and

increased formal employment opportunities. Thus, while competition can play

a role in reducing discrimination, it is not a silver bullet, and policymakers and

researchers must consider a wide range of factors when striving to achieve

greater equality. In contrast, other studies have found that the free trade and

capital ﬂows may not always lead to an increase in gender disparities.

Some literature considers the export orientation to explain the gender

gap (Bonﬁglioli & De Pace, 2021; Busse & Spielmann, 2006; Ozler, 2000).

In the exporting sectors, the sexual division of labor often involves women

being concentrated in lower-paying and lower-status jobs, such as sewing and

assembly work in the clothing and textile industries. This division is a result

of gender-based discrimination and a lack of equal opportunities for women

in these industries. Women are also underrepresented in leadership and

management positions in exporting sectors. The study by Granitoff and Hong

Tiing Tai (2022) highlights the differences that exist between exporting and

domestically traded ﬁrms. The results ﬁnd that exporting ﬁrms have a positive

impact on wages, while being a woman reduces wages.

To address this issue, it is important to promote gender equality and

increase women’s participation in STEM education and careers. This can include

initiatives aimed at breaking down gender-based stereotypes and increasing

girls’ access to educational resources and opportunities in these ﬁelds.

Encouraging women to pursue careers in STEM and promoting a supportive

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and inclusive work environment is also critical in reducing the sexual division of

labor according to technological level.

Related to exports is technological intensity. In this sense, it is worth

noting the interpretative framework implied by the sexual division of labor.

The gender gap is inﬂuenced by several factors, including care work and entry

into the labor market under more precarious working conditions, such as part-

time contracts. According to Morais (2017) the reasons that can reduce the

gender gap are: active participation in the labor market, matching their male

counterparts; reduction of wage gaps; and larger presence in STEM jobs.

The sexual division of labor according to technological level refers to the

way in which tasks and responsibilities within different industries are divided

based on the level of technology used. In many cases, higher-skilled and

higher-paying jobs that utilize advanced technology are dominated by men,

while women are concentrated in lower-skilled and lower-paying positions

that involve manual labor or routine tasks. This division is often a result of

gender-based biases and a lack of equal opportunities for women in STEM

ﬁelds (Donmez, 2020). It should be noted that access to certain sectors may

be determined by access to productive assets by virtue of social stereotypes.

This can be seen when analyzing access to credit by gender (Andrés Alonso et

al., 2019; Wang et al., 2022; Morais, 2017).

Although there has been extensive literature on the inﬂuence of exports,

trade openness, and even FDI ﬂows on the gender gap, the role of GVCs in

this regard remains relatively unexplored. McCarthy et al. (2021) argue

that despite the potential for analysis of governance patterns, the role of

gender roles in GVCs has been largely overlooked. Given the dominance of

men in decision-making positions within GVCs, the authors propose a shift in

methodologies and a more profound analysis of the social relations and gender

roles surrounding these chains.

A handful of studies have delved into the relationship between GVCs

and gender equality. For example, Szymczak and Wolszczak-Derlacz (2022)

examine the impact of GVCs on wages and employment levels, ﬁnding a negative

correlation between these variables. The study suggests that the increasing

competition resulting from external integration under these international

networks puts downward pressure on wages, especially in backward linkages.

Deb (2022) also explores this issue focusing on the gender wage gap. Her

ﬁndings indicate that increased participation in GVCs has led to a reduction in

women’s wages relative to men’s in India. The ﬁndings of Gagliardi et al. (2019)

and Nikulin and Wolszczak-Derlacz (2022) also suggest that GVCs contribute

to greater gender inequalities.

Despite the negative results found by some studies regarding GVC

participation and gender inequality, not all studies show negative effects of

GVC participation. For instance, Said-Allsopp and Tallontire (2015) approached

the issue of female employment in GVCs from a different perspective than that

of employment level and wage gender gaps. Based on a case study conducted

in Kenya, the study concludes that increased female employment in GVCs

122 Hugo Campos-Romero · Bruno Blanco-Varela

can empower women in three ways: by enabling them to adopt different ways

of being, different ways of doing things, and by sharing experiences. These

changes can alter the way women ﬁt into society and their roles within the

family, leading to positive societal transformations.

To summarize, the gender gap is a complex and multifaceted phenomenon

that can be examined from various angles. Although the literature has

primarily focused on wage and employment disparities, there are many other

dimensions to consider (Jehn et al., 2021; Ma et al., 2021; Wolszczak-Derlacz,

2013). Women’s participation in certain sectors, participation in positions of

greater economic relevance and representation or changes in social roles.

For instance, the gender gap can be analyzed in terms of differences in labor

market participation or access to education and healthcare (Yamamura, 2016).

To address this issue, policies and initiatives aimed at promoting gender

equality and empowering women in the workplace are necessary. This includes

equal pay for equal work, access to education and training, and opportunities

for career advancement. Challenging gender-based stereotypes and promoting

diversity and inclusion in the workplace are also important steps in breaking

down the sexual division of labor in the exporting sectors.

3. daTa and meThodoloGy

This section describes the methodology of the study. To achieve the

objective established in the Introduction, data was collected on employment

levels by gender and technological intensity of manufacturing sectors, exports

by technological intensity, GVC, educational level, fertility, and social status of

women. Table A1 in Appendix A deﬁnes the selected variables along with their

sources. The data collected covers the period 2008-2018 and includes all

EU27 countries. Next, some of these indicators are discussed in more detail.

Starting with the employment dimension, the employment gap is deﬁned

as female to male employees ratio. This research differentiates between

total, export, and domestic employment by gender. The ﬁrst two indicators

are obtained from Trade in Employment (TiM OECD, 2021 edition), while the

third can be obtained by difference between the two previous ones. The input-

output methodology for the calculation of the employment variables related to

foreign trade is detailed below. The complete methodology can be consulted

at (Horvát etal., 2020).

For a set of c countries and s sectors, the following basic input-output

entities are deﬁned: Tcsxcs is the intermediate transactions matrix, Ycsxc is the

ﬁnal demand vector and Xcsx1 is the total output vector obtained as the row-

wise sum of T and Y. From these entities, the technical coefﬁcients matrix

Acsxcs can be deﬁned, where each aij=tij/xj represents the fraction of value

incorporated in the production of sector j from sector i (being possible i=j).

Then, the Leontief inverse matrix, Lcsxcs=(I-A)-1, can be obtained, where I is the

identity matrix. Each element Iij of the Leontief inverse represents the total

requirements (both direct and indirect) for a given sector, indicating how much

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the production of that sector will grow with an increase in ﬁnal demand by one

monetary unit.

In order to obtain the number of employees resulting from foreign trade, it

is necessary to calculate the employment intensity. E1xcs is deﬁned as a vector

that collects, for each sector, the number of employees. The employment

intensity, e1xcs, is deﬁned as follows:

(1)

Where X

is a square matrix that diagonalizes the total output vector. To

differentiate between the total employment and the employment linked to

a country’s exports, adjustments need to be made to the Leontief inverse

matrix. The diagonal submatrices of the matrix, with size sxs, represent the

domestic market, whereas the remaining elements depict foreign relations. To

determine the impacts on the domestic employment market, only the diagonal

submatrices’ elements are needed, with all other values null. The resulting

Leontief inverse matrix is called Lcsxcs.

Also, gross exports, EXPbcsx1, can be obtained by making similar

modiﬁcations to T and Y matrices, i.e., making the domestic market elements

null. Thus, the domestic employment resulting from foreign trade, Et

1xcs, can be

obtained as follows:

(2)

After obtaining E and Et, the level of domestic employment, Ed, can be

derived by determining the difference between the two elements. This value

represents the employment generated by domestic transactions. Additionally,

by distinguishing between employment by gender, the intensity and

employment variables deﬁned in expressions (1) and (2) can be recalculated

for each gender. For instance, the number of employed men and women is

represented by Em and Ef respectively, while their employment intensities are

em and ef (e=em+ef). Finally, Et

m and Et

f are used to denote the employment

associated with foreign trade by gender and Ed

m and Ed

f to denote the

employment associated with domestic transactions.

Moving on to foreign trade indicators, the participation and position indexes

within GVCs require special attention. These indicators are conceptually

deﬁned in Table A1. To calculate these indices, one should start by determining

the forward and backward participation ratios in the GVC, known as and

respectively. The forward participation is calculated as the ratio of domestic

value-added exports re-exported by third countries over gross exports, while

the backward participation is determined as the ratio of foreign value-added

exports over gross exports. From these ratios, the participation and position

indexes are usually obtained as follows:

(3)

124 Hugo Campos-Romero · Bruno Blanco-Varela

(4)

In order to meet the established objective, an econometric model was

developed using some of the variables outlined in Table A1. Due to the nature

of the data, a panel data estimation was chosen. Based on Hausman test, a

ﬁxed effects versus random effects estimation is deemed more appropriate.

The employment gap, deﬁned as Et

f /Et

m, is the dependent variable. Values

below unity indicate a higher proportion of male presence in export sectors.

The speciﬁcation of the model is shown below, for countries in years:

(5)

The objective of this model is to study the impact of export specialization

in manufacturing and the type of participation in GVCs on the gender gap in

employment in export sectors. In addition to the variables related to technology

intensity, trade, and GVCs, various control variables are included, such as

the female activity rate, fertility rate, women’s participation in management

positions and national parliaments, and disparities in tertiary education

attainment between men and women. It also incorporates female participation

rates in different sectors of activity grouped by technological intensity. For

example, high-tech sectors may require speciﬁc skills and knowledge that

may not be available to women due to barriers in access to education and

training. Additionally, in some cases, the work culture and social norms may

discourage women from working in certain sectors. Finally, Table 1 shows the

main descriptive statistics of the variables used in the model.

Table 1. descRipTiVe sTaTisTics. n. of obseRVaTions: 297

Variable Mean Std. Dev. Min Max

GAP 0.66 0.10 0.44 0.92

GDP 33308.29 22664.48 6853 123678.7

PI -0.12 0.10 -0.41 0.02

ACT 65.96 7.11 40.4 81

FER 1.56 0.20 1.21 2.06

FPAR 25.76 10.08 8.7 46.99

FMNG 29.63 8.93 0 46.3

EDU 1.38 0.25 0.84 1.86

FHT 0.4 0.14 0 0.96

FMHT 0.22 0.11 0 0.47

FMLT 0.15 0.07 0 0.28

FLT 0.39 0.11 0 0.63

XH 7.29 5.61 0.68 28.39

XMH 19.57 9.9 3.09 45.19

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Variable Mean Std. Dev. Min Max

XML 13.01 4.7 2.73 30.96

XL 18.62 8.54 5.86 50.32

Source: Authors.

4. ResulTs

This section presents the key ﬁndings of the research and their implications

in two parts. The ﬁrst subsection provides descriptive statistics on some of

the most relevant indicators outlined in section 3. Following this analysis, the

second subsection presents the results of the econometric analysis developed

using a panel data model relating the relationship between the employment

gap in foreign trade and various factors, such as the technological intensity

of exports, female empowerment, and comparative education outcomes,

among others.

4.1. descRipTiVe analysis

The term gender gap refers to the differences between men and women

in the labor market, including disparities in salaries and employment rates

(Morais, 2017). Despite progress in protecting women’s rights and promoting

work-life balance, there is still much to be done to achieve gender equality.

As mentioned in the literature review, trade liberalization has been found

to increase the number of women employed and to change the gender

composition of certain sectors in foreign trade. However, this does not always

lead to a decrease in the disparity between genders. Figure 1 shows the ratio of

female to male employment in export sectors across EU27 countries. A value

below unity indicates a greater male presence. This is used as the dependent

variable in the econometric model described in the following subsection.

On average across the EU, the results show that for every 10 men employed

in export sectors, there are only 7 women. In other words, approximately 40%

of the foreign trade workforce involves women. It is worth noting that none of the

countries in the sample have a value above unity. In 2018, the highest female

labor force participation rates in export sectors were observed in Slovenia, Italy,

Ireland, Romania, and Luxembourg, with rates close to 45% of the total. In

contrast, Greece, the Czech Republic, Portugal, Estonia, and others had rates

below 35%. Regarding changes between 2008 and 2018, it can be observed that

female participation remained relatively stable, with a few exceptions. Romania,

Hungary, Cyprus, and Sweden experienced a decrease in female participation

after the ﬁnancial crisis, while Spain’s female participation increased.

Although a gender gap in employment exists in foreign trade sectors,

the situation in the domestic market is often different for many European

countries. When referring to the domestic market, only the level of domestic

employment is considered, excluding from the total the number of employees

in export sectors for each gender. Even in cases where there is still a disparity

126 Hugo Campos-Romero · Bruno Blanco-Varela

in employment between genders, the difference is typically smaller than in

export sectors. Figure 2 displays the ratio of female to male employment for

the domestic markets of the EU27 countries.

In 2018, the European average was at gender parity in 2018, improving

since 2008. Regarding the member countries, 14 out of the 27 had a higher

level of female participation than male employment volume, while a gender

gap continued to exist in the remaining 13. It is worth noting that gender

gaps in the domestic market are generally smaller compared to the foreign

market, except for Italy, Malta, Romania, and Greece, where the differences in

female participation are around 45% or less (still smaller than those revealed

by Figure 1 for these countries). Looking at the evolution of the indicator over

time, it’s worth noting that female participation in domestic employment has

increased in most European countries.

The literature review also examined the impact of technological intensity on

the gender gap. Descriptive analysis of the EU context shows that regardless

of the technological level of each sector, male participation is higher in

industry and manufacturing (see Figure 3). These differences are particularly

pronounced in medium-high and medium-low technology intensity sectors,

where women’s participation is less than 25% and 20%, respectively. However,

these differences are less signiﬁcant in high and low technology sectors, which

traditionally have had a greater female presence, such as the pharmaceutical,

textile, and certain food industries (Blickenstaff, 2005; Wang & Degol, 2017).

On this occasion, no signiﬁcant differences were found in female participation

between the two periods considered. Note that Figure 3 measures female

participation as a percentage of the total number of employees, not as a

percentage of male employees.

fiGuRe 1. GendeR Gap in foReiGn TRade employmenT, eu27 counTRies, 2008 and 2018

Source: Authors based on TiM (OECD, 2021 edition).

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Table A2 in Appendix A presents the percentage of domestic value-added

exports by technological intensity over total manufacturing exports. The results

show that, on average, 47.5% of total European manufacturing exports (both

intra- and extra-zone) are concentrated in sectors of medium-high technology

intensity, which have a low female participation in employment. In contrast,

high-tech exports, which have higher female representation, account for only

12.5% of domestic value-added manufacturing exports. Furthermore, most

female manufacturing jobs are concentrated in low technology-intensive

sectors, representing, on average, 53% in the EU (see Table A3 in Appendix A).

Finally, Figure 4 illustrates the GVC position index as deﬁned in Expression

(4). This index reﬂects the kind of participation of each country in this type of

foreign trade, where a positive value indicates a greater progressive or forward

participation –higher share of domestic value-added reexported by third

countries–, while a negative value indicates a greater regressive or backward

participation in GVCs –higher share of foreign value-added exported–. The

position index is a complex indicator to interpret in terms of a country’s

competitiveness. Various factors must be taken into account, such as the stage

of development and the role of each country in the supply chain, or more

speciﬁcally, each sector of activity in each territory. All member countries

show some degree of backward participation, although there are signiﬁcant

differences among them. Notably, Luxembourg and Malta stand out due to

their singular production structures.

If a country specializes in activities more closely linked to the ﬁnal stages of the

value chain (such as marketing, sales, and after-sales), the position index tends

towards negative values. Conversely, countries that specialize in the exploitation

fiGuRe 2. female shaRe oVeR ToTal employmenT by TechnoloGy inTensiTy, eu27, 2008 and 2018

souRce: auThoRs based on Tim (oecd, 2021 ediTion).

128 Hugo Campos-Romero · Bruno Blanco-Varela

of natural resources and manufacturing, as well as R&D and design, tend to

show positive values (Rodil-Marzábal, 2017). In developed economies, industries

tend to specialize in R&D, design, and the ﬁnal stages of the production chain,

leading low positive or negative values, close to 0. Note that most European

economies show values above -0.1, with a few exceptions. When interpreting the

results for Europe, it is important to consider the region’s strong dependence on

external resources. The GVC participation index (see Table A4 in Appendix A) is a

complementary indicator that measures the chains’ contribution to total foreign

trade. European countries have a high participation rate, averaging over 50%

For example, Italy, France, and Spain stand out, with a share of over 60%. The

country with the lowest share is the Slovak Republic, with 37%.

To understand its impact on gender participation in employment, it’s crucial

to consider the relevance of the productive sector. The productive structure is

conditioned by the sexual division of labor and the type of activities performed

by both genders. If women have lower participation in R&D or manufacturing

activities, their involvement in GVCs may lead to further disadvantages. This

concern is compounded by the low presence of women in manufacturing, as

shown in Figure 3. Furthermore, among European countries, medium-high

technology value-added exports are prominent (see Table A1), precisely one

of the sectors with the lowest female representation in employment.

Several factors inﬂuence the gender gap in employment, including the

technological intensity of different sectors and the importance of considering a

country’s position in GVCs. The GVC position index is mentioned as a complex

indicator reﬂecting a country’s position in foreign trade and competitiveness.

Furthermore, the low participation of women in higher value-added activities,

combined with Europe’s dependence on external resources, could limit

women’s empowerment in GVCs.

fiGuRe 3. female shaRe oVeR ToTal employmenT by TechnoloGy inTensiTy, eu27, 2008 and 2018

Source: Authors based on Eurostat (dataset: LFSA_EGAN22D).

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To provide a more comprehensive understanding of the gender gap

between socio-economic factors in the economies of the EU27 and their impact

on participation in foreign trade, a detailed examination is proposed in the

following section. This analysis will delve into various aspects of the relationship

between gender, socio-economic factors, and foreign trade, including but not

limited to the role of education. By examining these factors in depth, we aim to

provide a more nuanced perspective on the complex interplay between gender

and foreign trade participation in the EU27, and to identify potential avenues

for addressing the gender gap in this context.

fiGuRe 4. posiTion index in GVcs, eu27 counTRies, 2008 and 2018

Source: Authors based on TiVA (OCDE, 2021 edition).

130 Hugo Campos-Romero · Bruno Blanco-Varela

4.2. economeTRic ResulTs

This subsection describes the main results obtained from the estimation

of Expression (5) using a panel data and ﬁxed effects model with the variables

described in Section 3 and Table A1 (see Table 2). The ﬁndings indicate that an

increase in exports, regardless of the level of technological intensity, tends to

worsen the gender gap in export sectors by increasing the proportion of male

workers relative to female workers. This effect is particularly pronounced in the

medium-high technology sectors, which not only have the highest export levels

within the manufacturing industry but also exhibit lower female participation rates.

These ﬁndings are consistent with the research conducted by Chowdhury

etal. (2021) and Granitoff and Hong Tiing Tai (2022), who suggest that an

increase in exports and trade openness contributes to an increase in gender

disparities. Similarly, Li etal. (2020) also supports this claim, albeit with more

nuanced results. Considering ﬁrst that Li’s research focuses on Asian countries,

it suggests that initial increases in trade openness and foreign competition

may lead to higher female labor market participation. However, beyond a

certain threshold of trade openness, the gender gap may widen once again in

favor of men. Additionally, depending on the import substitution effect, these

inequalities could further increase.

The model produces varied results when it comes to the variables that

measure the proportion of female employment in manufacturing sectors by

technological intensity. Only participation in high and medium-low intensity

sectors shows signiﬁcant results with opposite signs. Greater female participation

in high-tech intensity sectors would not help reduce the gender gap, while greater

participation in medium-low technology sectors would reduce the gender gap in

export sectors. These results can be explained by the ﬁndings depicted in Figure

3, where women’s participation in high-tech sectors is relatively high (around

40%) compared to medium-low intensity sectors, where there is still a signiﬁcant

gender gap (less than 20% female representation).

Regarding the position index in GVCs, the results point out in the opposite

direction than technology intensive exports. Interpreting the effects of this index

is complex and requires relying on the results shown in Figure 4. Changes in this

index that result in increased forward participation appear to indicate a reduction

in the gender gap, while the opposite –greater backward participation– would

tend to increase the gap. An increase in forward participation would imply

greater involvement in global markets in the initial and intermediate stages of

the production chain, which could boost activity in sectors with higher female

participation (such as certain manufacturing and high-tech industries).

However, an intensiﬁcation of the regressive position could lead to the

replacement of certain productive tasks by imports. If this substitution occurs in

sectors with a relatively higher female presence, it could lead to an increase in

the number of female workers. Conversely, if it occurs in sectors with a relatively

lower female presence, it could result in greater job losses among the female

population. In this regard, Dluhosch (2021) study suggests that an increase in

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ReVisTa de economía mundial 65, 2023, 115-139

import penetration would result in a wider gender gap, consistent with Li etal.

(2020) ﬁndings. Regarding its link with the position index, a greater quantity of

imports would correspond to a more regressive position within GVCs. Gagliardi

etal. (2019) also points out that a higher progressive position in GVCs would lead

to better salaries, although the distribution between men and women depends

to a large extent on gender parity in each case. This insightful ﬁnding highlights

the necessity of further exploring the impacts of GVCs on gender disparities.

Table 2. esTimaTion ResulTs. dependenT VaRiable: women To men woRkinG in expoRTinG secToRs

souRce: auThoRs fRom Tim

Variable Coefﬁcient Standard error t

GDP -1.31E-06 4.31E-07 -3.04*

PI 0.5189563 0.1202727 4.31*

ACT 0.0003655 0.000623 0.59

FER -0.0584337 1.79E-02 -3.270*

FPAR -0.000897 0.0004 -2.140**

FMNG 0.0024537 0.0006 4.370*

EDU -0.0334915 0.0261 -1.280

FHT -0.0392383 0.0131 -2.990*

FMHT -0.0141325 0.0203 -0.700

FMLT 0.1361949 0.0425 3.200*

FLT 0.0373118 0.0376 0.990

XH -0.0030893 0.0010 -3.050*

XMH -0.0055916 0.0012 -4.580*

XML -0.0025853 0.0012731 -2.03**

XL -0.0018247 0.0011046 -1.65***

cons 1.008535 0.0882868 11.42

Rho 0.97 R2 within 0.27

F-prob 0 R2 between 0.06

Source: Authors based on TiM (OECD, 2021 edition), TiVA (OECD, 2021 edition), World Bank and

Eurostat.

As for the GDP per capita, the econometric results indicate its statistical

signiﬁcance with a negative coefﬁcient, implying that an increase in income

level would exacerbate the gender gap within the export sectors. Nonetheless,

to make a conclusive statement based solely on the evolution of GDP per capita

would be oversimplifying. However, one can infer that further economic growth

in European countries cannot be considered a sufﬁcient factor in reducing the

gender gap. However, multiple studies have agreed that policies targeted at

diminishing gender gaps can foster higher levels of economic growth (Agénor

etal., 2021; Agénor & Canuto, 2015; Dheer etal., 2019).

Regarding the control variables, the analysis indicates that the female activity

rate is not a signiﬁcant factor, while the proportion of women in parliament has

yielded counterintuitive results. Therefore, it is possible that this variable is

inadequate in explaining the employment gap in export sectors. Meanwhile, the

132 Hugo Campos-Romero · Bruno Blanco-Varela

fertility rate and the proportion of women in management positions were both

signiﬁcant at the 1% level, with negative and positive signs, respectively.

The ﬁnding that a higher fertility rate leads to a larger gender gap may

be attributed to established gender roles in society. Despite advances in

work-life balance policies (Goldin & Mitchell, 2017), in addition to the time

women have to devote to pregnancy, they tend to take more time off work or

assume the primary responsibility for caregiving and household duties (Cortés

& Pan, 2020). When examining individual countries, it is important to take into

account the effectiveness of work-life balance policies, as they can facilitate

the simultaneous increase of fertility rates and women’s participation in the

labor market (Vitali & Billari, 2017). The proportion of women in management

positions serves as a proxy variable for the gender gap’s evolution and the

situation with respect to the so-called glass ceiling effect (Bear etal., 2017;

Haveman & Beresford, 2012; Said-Allsopp & Tallontire, 2015). A better value

for this variable reﬂects greater gender parity in society.

Finally, the rho indicator measures the proportion of variance attributable

to individual effects in the panel data model, indicating the extent to which

unobserved heterogeneity factors across countries explain the variance of

the dependent variable. A higher value suggests the existence of substantial

differences among the analyzed countries. This ﬁnding emphasizes the

importance of considering not only the variables included in the model but

also the speciﬁc circumstances of each economy. Additionally, the index

underscores the relevance of employing a panel data and ﬁxed effects model

to examine this matter in the European context. Moreover, this could provide

an explanation for the results obtained from certain variables, such as the lack

of signiﬁcance of the education variable, which may be heavily inﬂuenced by

the speciﬁc demand for human capital in each country, as well as the non-

signiﬁcant impact of the female activity rate. The differences between the R2

within (which explains how the model ﬁts for each country) and the R2 between

(which explains how the model ﬁts to understand the differences between the

different countries) are consistent with the rho index obtained, since these

variables do not capture the differences between the results of each country.

5. conclusions

The gender gap represents a signiﬁcant impediment to the development

of nations, affecting not only developing economies but also persisting

within more developed ones. Therefore, it is crucial to examine the impact of

international relations, particularly participation in GVCs on gender inequality.

On the one hand, increased participation in foreign markets can create new

economic opportunities for women, particularly in the export sector. This can

result in higher incomes, reduced poverty, and overall economic improvement.

However, international trade can also lead to intensiﬁed competition and the

relocation of jobs, affecting women in precarious and low-skilled positions

disproportionately. This is especially pertinent in the current trade landscape,

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ReVisTa de economía mundial 65, 2023, 115-139

heavily deﬁned by GVCs, which entail a signiﬁcant fragmentation of production

processes. Thus, the aim of this paper is to delve into the relationship between

the gender gap, global value chains, technological intensity of exports and

female empowerment in the EU-27. Speciﬁcally, it analyzes how integration in

GVCs affects gender inequalities.

This paper uncovers several signiﬁcant ﬁndings. Firstly, the results indicate

that an increase in exports, regardless of the level of technological intensity,

tends to worsen the gender gap in export sectors by amplifying the proportion of

male workers compared to female workers. This effect is particularly noticeable

in medium-high technology sectors, which not only exhibit the highest export

levels within manufacturing but also display lower female participation rates.

Secondly, we demonstrate the intricate and nuanced nature of the effects

GVCs on gender inequalities. Depending on the type of participation (forward

or backward), the impact on inequalities may vary. Thirdly, a priori, increasing

manufacturing exports contributes to widening the gender gap. This ﬁnding

aligns with the observation of disparities between internal and external labor

markets.

The study revealed various obstacles to women’s integration into export-

driven markets, which justify the development and implementation of policies.

Firstly, enhancing women’s education and training is crucial to improve their

skills and knowledge in high-demand areas of the labor market, which are

vital for future economic growth. Additionally, promoting women’s education

in STEM ﬁelds and ensuring their access to technology training and education

are essential. Secondly, ensuring social protection for women in vulnerable and

precarious sectors, particularly in low-tech export-intensive sectors, is imperative.

This may entail implementing social security policies, combating workplace

discrimination and harassment, and implementing measures to support work-

life balance. Lastly, it is recommended that institutions facilitate women’s entry

into traditionally male-dominated industries by promoting their representation

in decision-making bodies associated with international trade, such as ministries

of commerce, chambers of commerce, and business organizations.

This work arises future lines of research. Firstly, it is crucial to delve deeper

into the effects of global value chains (GVCs) by differentiating between types

of participation and examining their potential impact on gender inequalities.

Secondly, considering the indication of variations among European countries,

it becomes necessary to analyze these variables within groups of countries

sharing similar characteristics.

acknowleGmenTs

This research has been supported by the ICEDE research group, to which

the authors belong, Galician Competitive Research Group ED431C 2022/15

ﬁnanced by Xunta de Galicia and project “REVALEC” REFERENCE PID2022-

141162NB-I00 Financed by MCIN/ AEI / 10.13039/501100011033 / EFRD,

EU.

134 Hugo Campos-Romero · Bruno Blanco-Varela

annex a

tabLe a1. description of variabLes incLuded in the research

Dimension Indicator Description Source

Employment

Employment embodied

in trade Gap in employ-

ment embodied in

trade (GAP)

Gap in domestic

employment

Number of employees linked to export sectors. In-

cludes direct and indirect jobs (see Expression (2))

Ratio between the number of women and men working

in export sectors. If the ratio is less than 1, it indicates

a greater representation of men in exporting workforce

Ratio between the number of women and men working

in non-export sectors. If the ratio is less than 1, it

indicates a greater representation of men in domestic

workforce

TiM

(OECD,

2021

edition)

Women

empowerment/

female social

status

Women in management

(FMNG)

Women in national

parliaments (FPAR)

Fertility rate (FER)

Female activity rate

(ACT)

Proportion of females in total employment in senior

and middle management

Proportion of parliamentary seats in a single or lower

chamber held by women

Number of children that would be born to a woman if

she were to live to the end of her childbearing years

and bear children in accordance with age-speciﬁc fertil-

ity rates of the speciﬁed year

Percentage of active women in relation to the total

comparable population between 15 and 64 years of

age

World Bank

Eurostat

Technology

intensity

High-technology VAD

exports (XH)

Medium-high-technolo-

gy VAD exports (XMH)

Medium-low-technolo-

gy VAD exports (XML)

Low-technology VAD

exports (XL)

Domestic value-added exports of high technological

intensity. Only includes the value generated in the

domestic country

Domestic value-added exports of medium-high techno-

logical intensity. Only includes the value generated in

the domestic country

Domestic value-added exports of medium-low techno-

logical intensity. Only includes the value generated in

the domestic country

Domestic value-added exports of low technological

intensity. Only includes the value generated in the

domestic country

TiVA

(OECD,

2021

edition)

Female em-

ployment by

technological

level

High-technology (FHT)

Medium-high-technolo-

gy (FMHT)

Medium-low-technolo-

gy (FMLT)

Low-technology (FLT)

Ratio of women to total employment in high-technol-

ogy sectors

Ratio of women to total employment in medium-high-

technology sectors

Ratio of women to total employment in medium-low-

technology sectors

Ratio of women to total employment in low-technology

sectors

Eurostat

Other

Gap in tertiary educa-

tion (EDU)

GDP per capita (GDP)

Participation Index

Position Index (PI)

Ratio between males and females in the success rate in

tertiary education (ISCED 5-8)

GDP share over population

The participation index in GVCs reﬂects the degree to

which a country performs in this category of foreign

trade with respect to its total exports. It is measured

as the sum of the backward and forward participation

indexes (see Expression (3))

Measure of a country’s position in GVCs. A score

above 0 indicates a higher relative volume of domestic

value-added exported by third countries while a lower

score indicates a higher relative volume of foreign

value-added exports (see Expression (4))

World Bank

TiVA

(OECD,

2021

edition)

Source: Authors.

Note 1: Eurostat sectoral classiﬁcation has been used to determine the degree of technological intensity.

Note 2: The column “indicator” speciﬁes the abbreviations of the variables employed in the

econometric model.

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Table a2. shaRe of domesTic Value-added expoRTs by TechnoloGy inTensiTy oVeR ToTal manufacTuRinG,

eu27 counTRies, 2018

High-tech Medium-high-tech Medium-low-tech Low-tech

Austria 8.72 41.08 24.97 25.23

Belgium 20.14 32.75 24.89 22.22

Bulgaria 6.47 28.43 32.65 32.45

Croatia 8.49 19.52 28.59 43.41

Cyprus 22.01 12.93 10.82 54.23

Czech Republic 9.48 52.14 21.90 16.49

Denmark 31.68 29.36 11.24 27.72

Estonia 8.10 22.00 18.04 51.86

Finland 10.90 34.40 21.90 32.80

France 12.63 51.05 15.09 21.23

Germany 11.19 61.38 14.73 12.69

Greece 6.48 15.73 43.41 34.38

Hungary 16.39 47.48 19.29 16.84

Ireland 47.64 15.49 12.98 23.90

Italy 6.22 45.02 20.45 28.31

Latvia 6.55 15.36 13.29 64.80

Lithuania 3.54 21.18 19.10 56.18

Luxembourg 2.36 15.57 64.09 17.98

Malta 39.89 9.17 7.72 43.22

Netherlands 11.78 41.26 16.23 30.74

Poland 5.65 35.65 24.63 34.07

Portugal 5.91 27.84 23.31 42.94

Romania 6.98 54.19 13.51 25.32

Slovak Republic 5.44 51.47 27.79 15.30

Slovenia 14.29 37.84 28.44 19.43

Spain 6.28 43.07 22.82 27.82

Sweden 8.99 49.73 19.24 22.04

Source: Authors based on TiVA (OECD, 2021 edition).

Table a3. female employmenT by TechnoloGy inTensiTy oVeR ToTal female manufacTuRinG employmenT

(%), eu27 counTRies, 2018

High-tech Medium-high-tech Medium-low-tech Low-tech

Austria 11.00 24.24 24.30 40.46

Belgium 13.91 21.52 15.09 49.48

Bulgaria 4.23 13.96 14.25 67.57

Croatia 6.68 11.59 8.46 73.28

Cyprus 19.30 - 15.79 64.91

Czechia 7.82 33.83 23.78 34.57

Denmark 26.96 24.04 11.69 37.32

Estonia 8.60 10.93 9.30 71.16

Finland 11.61 23.80 15.58 49.01

France 10.30 20.76 19.90 49.04

136 Hugo Campos-Romero · Bruno Blanco-Varela

Germany 11.51 31.81 17.18 39.50

Greece 7.59 5.77 10.26 76.39

Hungary 15.37 30.29 12.97 41.37

Ireland 40.00 - - 60.00

Italy 6.93 22.98 19.24 50.85

Latvia ----

Lithuania 2.14 10.18 11.36 76.31

Luxembourg ----

Malta 26.79 10.71 8.93 53.57

Netherlands 6.16 17.90 16.08 59.86

Poland 5.95 22.94 18.94 52.16

Portugal 3.80 14.75 13.43 68.03

Romania 3.04 24.39 9.45 63.12

Slovakia 8.86 38.72 20.39 32.02

Slovenia 10.93 33.54 23.60 31.93

Spain 7.87 22.98 16.18 52.97

Sweden 8.99 36.47 19.15 35.39

Source: Authors based on Eurostat (dataset: LFSA_EGAN22D).

Table a4. GVc paRTicipaTion index, eu27 counTRies, 2018

Participation Index in GVC

Austria 53.97 Italy 78.59

Belgium 59 Latvia 55.49

Bulgaria 61.81 Lithuania 53.65

Croatia 48.94 Luxembourg 46.73

Cyprus 54.38 Malta 67.02

Czech Republic 52.19 Netherlands 56.74

Denmark 46.3 Poland 42.45

Estonia 46.25 Portugal 47.68

Finland 46.47 Romania 54.06

France 63.31 Slovak Republic 37

Germany 53.57 Slovenia 48.92

Greece 43.01 Spain 63.01

Hungary 45.54 Sweden 47.67

Ireland 52.23

Source: Authors based on TiVA (OECD, 2021 edition).

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