Policy Research Working Paper 9098
Trade and Female Labor Participation
Stylized Facts Using a Global Dataset
Nadia Rocha
Deborah Winkler
Macroeconomics, Trade and Investment Global Practice
December 2019
Policy Research Working Paper 9098
Abstract
Using a cross-section of more than 29,000 manufacturing The female labor share premium is much higher for pro-
firms in 64 developing and emerging countries from the duction workers compared with non-production workers,
World Bank’s Enterprise Surveys, this paper assesses whether implying that women specialize in low-skill production.
trading firms have a female labor share premium relative to In line with these findings, the study finds that the female
non-trading firms. It focuses on four types of trading firms: labor share premium for exporters and global value chain
exporters, importers, global value chain participants, and participants is highest in low-tech sectors. And female
foreign firms. The study finds a female labor share premium ownership and management expand the female labor share
for all four trading types, controlling for firm output, capi- premium for trading firms. Finally, the results suggest that
tal intensity, total factor productivity, and fixed effects. The although average wage rates are lower for firms with higher
findings also hold after controlling for differences in relative female labor shares, this negative correlation is smaller for
wages between men and women and excluding traditional trading firms.
export sectors (apparel and electronics) from the sample.
This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the
World Bank to provide open access to its research and make a contribution to development policy discussions around the
world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may
be contacted at nrocha@worldbank.org.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
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names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
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its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Produced by the Research Support Team
Trade and Female Labor Participation:
Stylized Facts Using a Global Dataset1
Nadia Rocha2 and Deborah Winkler3
Key words: Trade, GVC, women, female labor force participation, labor share, wage gap
JEL codes: F1, F2, F6
1 This paper was prepared as background work for World Bank‐World Trade Organization joint report “Trade and Gender Equality:
Empowering Women in the Global Economy”. The authors thank Ana Fernandes, Caroline Freund, Claire Hollweg and other
participants of the “Trade and Gender Authors’ Workshop” at the World Bank on March 7, 2019 for helpful suggestions. We also
thank the participants of the “Conference on Trade and Gender” at the World Trade Organization on December 6‐7, 2018 for
their comments. We are grateful to Maryla Maliszewska, Israel Osorio‐Rodarte and Huanjun Zhang for sharing sectoral sex‐
disaggregated wage and employment data. We acknowledge financial support from the Trade Umbrella Trust Fund. The findings
of this paper are those of the authors and do not necessarily represent the views of the World Bank, or its member countries.
2 Senior Economist, Macroeconomics, Trade and Investment Global Practice, The World Bank, Washington, DC 20433. Email:
nrocha@worldbank.org.
3 Senior Consultant, Macroeconomics, Trade and Investment Global Practice, The World Bank, Washington, DC 20433. Email:
dwinkler2@worldbank.org.
1. Introduction
The ready‐made garment in sector in Bangladesh showcases how trade has increased female labor
participation over the past 30 years. The sector has expanded on average by 17 percent per year since
1980 and represents over three‐quarters of Bangladesh's export earnings. The sector also offered formal
job opportunities to women and now employs 4 million workers, an estimated 90 percent of them women,
representing a 100‐fold increase in workers within just three decades (Heath and Mobarak 2015).
Similarly, recent evidence from Indonesia suggests that women benefit from trade liberalization. Input
tariff reduction has been linked to higher female labor participation through a relative expansion of
female‐intensive sectors in a region (Kis‐Katos et al. 2018).
Can the positive link between trade and female labor participation in Bangladesh and Indonesia be
confirmed for other developing and emerging countries in general? Using a cross‐section of more than
29,000 manufacturing firms in 64 countries from the World Bank’s Enterprise Surveys, this paper assesses
whether trading firms have a female labor share premium relative to non‐trading firms. It focuses on four
types of trading firms: exporters, importers, global value chain (GVC) participants and foreign firms. We
find a female labor share premium for all four types of trading firms, controlling for firm output, capital
intensity, total factor productivity and fixed effects. The results also hold controlling for differences in
relative wages between men and women, and excluding traditional export sectors (apparel and
electronics) from the sample.
Interestingly, the female labor share premium is much higher for production workers compared to
non‐production workers (i.e. managers, sales, administration), implying that women in our country
sample specialize in low‐skill production. In line with these findings, the female labor share premium for
exporters and GVC participants is highest in low‐tech sectors, suggesting the largest benefits for women
in low‐skill intensive sectors. The paper also analyzes the role of female firm ownership and female top
management for the relationship between trading and the female labor share and finds that both expand
the female labor share premium of trading firms. Finally, additional analysis explores the link between a
firm’s female labor share and average wage rates and in particular the mediating role of the firm’s trading
status. The results suggest that while average wage rates are lower for firms with higher female labor
shares, this negative correlation is smaller for trading firms.
1
The literature has proposed at least four channels through which trade can affect female labor
participation, namely (i) sectoral reallocation of labor, (ii) increased competition, (iii) technological
upgrading, and (iv) changes in discriminatory behavior. While the last three channels can explain
differences across types of firms within a given sector, the literature on sectoral reallocation only captures
differences across (rather than within) sectors and is thus not the focus here.4
First, trading firms face stronger competition in export markets and thus are incentivized to reduce
costs. Hiring more women who earn lower wages relative to men could be part of a firm’s cost
minimization strategy. The theory of competitive (rather than comparative) advantage postulates that
firms compete for export market share on the basis of absolute (rather than relative) costs of production.
Trading stimulates the search for lower cost of labor, as firms compete to reduce absolute unit costs.
Firms might then take advantage of gender inequalities and hire women (considered a cheap and flexible
labor force) to reduce costs. Wages in this theory are determined by the relative bargaining power of
groups of workers. As a result, even if female employment increases, women’s pay might not necessarily
improve. Evidence supporting this channel through which trade can impact female labor participation is
nonexistent.
Second, trading firms adopt new technologies that replace physically‐demanding tasks in reaction to
increased competition which, in turn, could affect men and women in the workplace differently. The effect
depends on the tasks they perform (e.g. blue‐collar or white‐collar) and if these tasks have a
complementary relationship with new technologies. Women with a comparative advantage in non‐
physically demanding tasks become more productive when these new technologies are adopted. For
example, Junh et al. (2013, 2014) show that North American Free Trade Agreement‐induced export entry
by firms that imported computerized equipment (a measure for technological upgrading) led to higher
female employment and wages relative to men in blue‐collar jobs.
Third, trade‐induced competition also changes discriminatory practices. Employers are assumed to
have a taste for discrimination against women for which they “are willing to pay” a price (Becker 1957),
4 The Heckscher‐Ohlin traditional trade model highlights how sectoral reallocation, induced by trade, could potentially affect
female workers. The principle of comparative advantage suggests that in developing countries with relative abundance of low‐
skill labor, trade can increase female labor participation. This is because female workers tend to be concentrated in low value‐
added, low wage and labor‐intensive export industries such as apparel where developing countries have comparative advantage.
Empirical findings confirm that the growth in exports that took place in developing countries during the early 1960s and the mid‐
1980s was positively correlated with increases in female labor participation (e.g., Wood 1991). The sectoral reallocation
mechanism of the impact of trade on gender is also mentioned in other studies such as Aguayo‐Tellez et al. (2013), Busse et al.
(2006), and Sauré et al. (2014).
2
especially in markets characterized by low competition among firms in which monopolistic employers can
afford to pay male workers higher wages than their productivity would suggest. Trade‐induced
competition, however, makes taste‐based discrimination more costly. Oostendorp (2009) studies the role
of globalization on the gender wage gap for a large cross‐section of countries and finds that gender wage
discrimination falls with more economic development and trade. Evidence for a positive role of trade
liberalization in lowering the gender wage gap and increasing female labor demand via reduced
discrimination has also been found for Uruguay (Ben Yahmed 2017) and Colombia (Ederington et al. 2009).
But trading could also re‐enforce female discrimination through at least three channels: (i) Trading
can increase firm profits through better access to inputs and markets or higher productivity and allows
firms to maintain their discriminatory practices (see, e.g., Bernard and Jensen 1999; Ben Yahmed 2017).
(ii) Trade‐induced competition could also reduce women’s bargaining power in the labor market (Darity
and Williams 1985, and Williams 1987), adding further downward pressure on their wages. Support for
this theory has been found in Chinese Taipei, the Republic of Korea, India and China (Berik 2000, Berik et
al. 2004, Menon and Van der Meulen Rodgers 2008, and Chen et al. 2012). (iii) A recent study suggests
that exporting firms demand highly flexible employees (for working peculiar hours, taking late night phone
calls and engaging in international travel arranged at short notice) and therefore discriminate against
women as they are perceived to be less flexible. This theory is confirmed in a study on Norway using
matched employer‐employee data where the gender wage gap is higher in exporting compared to non‐
exporting firms, but only for higher‐skilled jobs (Bøler et al. 2018). It is not clear if this channel also applies
to a developing country context abundant in unskilled workers.
Our paper contributes to the strand of literature linking trade and labor in the following ways: (i) We
modify the cost share functions that have been introduced by Feenstra and Hanson (1996) to assess the
impacts of trade on different worker types. Rather than focusing on high‐ versus low‐skill workers, our
emphasis is on female versus male workers. (ii) We use a rich harmonized micro‐level dataset that covers
a large number of developing and emerging countries, while many studies on trade and female labor
participation in a developing country context are performed at a more aggregate level.5 (iii) Finally, we
also assess the role of female entrepreneurship, specifically female ownership and management, for the
female labor share premium of trading firms.6 Our paper is closest in spirit to Juhn et al. (2013, 2014) who
focus on the relationship between trade liberalization, rather than trading status, and female labor
5 Exceptions include Ederington et al. (2009) for Colombia, Chen et al. (2012) for China, and Juhn et al. (2013, 2014) for Mexico.
6 For an overview of female entrepreneurship and a discussion of different drivers, see, e.g., Carranza et al. (2018).
3
participation and Shepherd (2018) who studies the link between GVC participation and the absolute and
relative demand for female production and non‐production workers.
This paper is organized as follows. In Section 2, we describe the econometric model, data and
measures. We present descriptive statistics of the female labor share in Section 3, focusing on differences
by a firm’s trading status, across sectors and across countries. The regression results of the female labor
share premium are shown in Section 4, also including some robustness checks and assessing the roles of
female ownership and management and sectoral technology intensity. Section 5 concludes.
2. Model and Data Description
2.1 Econometric Model
A variable unit cost function CV is specified as follows7:
CV=CV(Y, wF, wM, k, T)
where Y denotes the output and wF and wM are the exogenous wages for the variable input factors
female labor LF and male labor LM. Capital is considered a quasi‐fixed input factor in the form of capital
intensity k. The technology shifter T=T(tfp, trade) is defined as a function of total factor productivity and
trade.
Using the transcendental logarithmic (translog) form of the variable unit cost function as introduced
by Brown and Christensen (1981) and applying Shephard’s Lemma,8 the following factor demand function
can be derived: ������������ ������ ������������ ������������������ ������������ ������������ ������������ ⁄������������ ������������ ������������������ ������������ ������������������������������ ������������ ������������������������������
SF is the cost share of LF in variable costs CV. Since wF and wM are the only variable costs, CV is
determined by the sum of the products of the variable factor costs with their respective factors, CV = wFLF
+ wMLM = wL, where w designates the average wage per labor input L, regardless of the gender. We refer
to the composite term SF = wFLF / wL as the female wage share. A decrease of SF can reflect both a fall in
LF and/or a fall in wF, which implies a rise in SMand thus an increase in LM and/or in wM.
7 This is a modified version of the unit cost functions by Feenstra and Hanson (1996), Geishecker (2006) and others that
differentiate between skilled and unskilled labor.
8 According to Shephard’s Lemma (1953), factor demand is determined by the first partial derivative of the cost function with
respect to the corresponding factor price, regardless of the kind of production function.
4
Other control variables include output, Y. The coefficient sign of output Y, ß1, is not unambiguously
predictable. An increase in Y normally leads to a higher total wage bill. If the cost increase is equally
distributed between female and male labor, there should be no influence on SF. If the wage bill of male
labor increases more than proportionally, e.g. due to better bargaining power, this results in a higher LM
and/or wM, and SF is expected to fall (ß 1 < 0).
One expects a lower SF and thus a lower cost share of LF in total wages when relative wages for male
labor, wM/wF, as part of CV rise (ß 2 < 0). An increase in the capital intensity, k, will increase SF, if capital is
a substitute for male labor (ß 3 > 0), but reduce SF, if capital is a substitute for female labor (ß 3 < 0). A
higher tfp could increase labor demand in favor of female (γ2 > 0) or male workers (γ2 < 0). The influence
of trade on SHS is not easily predictable, as trade could increase the relative demand for female (γ1 > 0) or
male labor (γ1< 0).
In the absence of gender‐specific wage data at the firm‐level, the model is modified in two ways:
First, the analysis on trade and the female wage share combines the Enterprise Survey dataset with
sectoral wage data by gender, available from Household Surveys. This assumes equal relative wages
across all firms in a sector. Such wage data are available for 30 countries in the Enterprise Surveys,
allowing to estimate the following female wage share equation:
������������ ������������������������ ������ ������������ ������������������������������������������ ������������ ������������ ������������ ⁄������������ ������������
������������ ������������������������������������������ ������������ ������������������������������������������������������ ������������ ������������������������������������������������������
������������������ ������������ ������������ ������������������������������ (1)
Second, the analysis assumes equal wage rates for female and male workers, wF = wM = w. Since SF =
wFLF / wL, the dependent variable becomes the female labor share in total employment, femsh= LF / L.
Similarly, the model no longer controls for relative wages, since we assume ln(wM/wF) = ln1 = 0. While
assuming equal wage rates between female and male workers is a strong assumption, we argue that the
fixed country‐sector effects included in the model partially correct for this. The female labor share
equation takes the following form:
������������������������������ ������������������������ ������ ������������ ������������������������������������������ ������������ ������������������������������������������ ������������ ������������������������������������������������������ ������������ ������������������������������������������������������
������������������ ������������ ������������ ������������������������������ (2)
Equation (2) becomes our baseline specification, allowing us to include all 64 countries in the analysis,
while equation (1) serves as a robustness test. Both models consider four types of trading firms: (1)
5
Exporters are firms with an export share (direct or indirect) of at least 10% of total sales. (2) Importers are
firms with an imported input share of at least 10% of total inputs. (3) GVC participants are firms that are
classified as both exporters and importers. (4) FDI refers to firms with a foreign ownership share of at
least 10%. The trade variables are dummies taking a value of 1 if the firm trades, and 0 if it does not. The
model also controls for country‐sector, subnational region and year fixed effects.
2.2 Data
Our dataset draws on two underlying datasets, published by the World Bank Enterprise Analysis Unit,
namely the Enterprise Surveys Global Database and the Firm‐level TFP Estimates and Factor Ratios. The
Enterprise Surveys Global Database covers 242 surveys in 140 countries over the period 2006 to 2017.
The Enterprise Surveys represent a comprehensive source of firm‐level data in emerging markets and
developing economies. One major advantage of the Enterprise Surveys is that the survey questions are
the same across all countries. Moreover, the Enterprise Surveys represent a stratified random sample of
firms using three levels of stratification: sector, firm size, and region. Sectors are determined based on
the ISIC Rev. 3.1 classification of the main product or service.
The Enterprise Surveys Global Database covers a wide range of indicators on firm characteristics, the
business environment, innovation and technology, and workforce and skills among others. We merged
this dataset with data on firm‐level output, value added, and capital stock obtained from the Firm‐level
TFP Estimates and Factor Ratios dataset. All local currencies have been converted into US dollars and
deflated using a GDP deflator in USD (base year 2009). Exchange rates and GDP deflators have been
obtained from the World Development Indicators.
We apply the following rules to the dataset: (i) We include only the most recent Enterprise Surveys
for each country. (ii) We only cover the years 2010 to 2017, to account for the shock of the global
economic crisis of 2008. (iii) We exclude services firms from the sample, i.e. for which the ISIC Rev. 3
classification of the main product or service is higher than 36 and those that have been classified as
services firm by the Enterprise Surveys, as information on female employment is hardly available. (iv) We
drop countries for which female employment data were spotty9 and firms with missing observations for
9 These include Lao PDR, Madagascar, Malaysia, the Philippines, Turkey, and Thailand.
6
female employment or total employment. (v) Finally, we exclude countries with fewer than 100
manufacturing firms after applying the previous steps.
The procedure above results in more than 29,000 manufacturing firms in 64 countries. The list of
countries, year of the most recent survey and number of firms by country can be found in Appendix 1.
The distribution of firms across ISIC sectors is shown in Appendix 2.
We combine the firm‐level data with Global Employment Sex‐disaggregated Statistics (GESS) at the
sector level from the World Bank Household Surveys and other public resources. GESS uses harmonized
classifications of economic activities and occupation categories. It fills an important information gap in
global sex statistics by providing more detailed accounts on education, employment levels, wages, labor
income, and employment status at very disaggregated economic activity levels and occupation categories
than is usually available. The data are available for 50 countries at the ISIC Rev. 3.1 level, but only 30
countries overlap with the Enterprise Survey data sample. These 30 countries are highlighted bold in the
country list of Appendix 1.
2.3 Measures
This study focuses on two measures of female labor participation:
Female labor share, fem_sh = number of permanent full‐time female employees as % of total
number of permanent full‐time employees. The variable is computed for permanent production
workers, non‐production workers and total workers (sum of both). Non‐production workers
include managers, administration and sales.
Female wage share, SF = total compensation of female employees as % of total compensation of
all employees. Since the Enterprise Survey dataset has only information on average wage rates,10
we approximate female wage rates at the firm level by assuming the same ratio of female‐to‐
average wages in a given sector as in the gender‐disaggregated wage data from the World Bank
Household Surveys. Total female compensation is then obtained by multiplying the female wage
rate with the number of female employees from the Enterprise Surveys.
The analysis examines two measures of trade participation:
10 These are obtained dividing a firm‘s total compensation by its total number of employees.
7
Exporter, exp = 1 if direct plus indirect export share as % of sales >= 10%, and 0 otherwise.
Importer, imp = 1 if share of imported inputs as % of total inputs >= 10%, and 0 otherwise.
We additionally include two measures that describe global firms:
GVC participant, gvc = 1 if both exp = 1 and imp = 1, and 0 otherwise.
Foreign ownership, fdi = 1 if foreign private ownership >= 10% and 0 otherwise.
3. Descriptive Statistics
3.1 Differences by Trading Status
To get a first idea on the relationship between the female labor share and trading/global firms, this
section first describes the average share of female workers in a firm’s total workforce across different
types of trading firms. This does not control for any other factors yet that could influence the female
worker share, as derived from theory (section 2.1). Figure 1 suggests that trading and global firms show a
higher average share of female permanent workers than non‐trading and non‐global firms. Specifically,
the share of women in a firm’s average permanent workforce is 33.2% for exporters versus 24.3% for non‐
exporters. While importers employ on average a similar share of women as exporters (32.8%), the gap
between importers and non‐importers is smaller with non‐importers employing on average 28.1% of
female workers. Note that 45.7% of firms in the sample import, while the share of exporting firms drops
to half (23% ) (see Appendix 3).
Figure 1: Female labor share, by trading status
33.2%
exporting
24.3%
32.8% yes
importing
28.1%
no
36.7%
gvc participant
24.5%
37.8%
fdi firm
26.9%
Note: The female labor shares are averages using firm employment as weights.
8
Shifting the focus to GVC participants and foreign‐owned firms, suggests an even larger difference in
the female labor participation between global and non‐global firms. While less than a quarter of the
workforce of non‐GVC participants is female (which is on par with exporting firms), the average female
labor share increases to 36.7% for GVC participants. Similarly, the share of women in foreign‐owned firms
in the workforce is 37.8%, compared to only 26.9% in domestically‐owned firms. Note that only 14% of all
firms in the sample are considered GVC participants, while the share foreign‐owned firms is only 7.3%
(see Appendix 3). In summary, we hypothesize that the female labor share is higher for trading firms, i.e.
that export or import, and even more so for global firms, i.e. that participate in GVCs and are foreign‐
owned.
Since our dataset allows us to distinguish between production and non‐production workers, we
analyze in a next step whether the hypothesis above differs by worker type. Figure 2 suggests that the
difference between trading (global, resp.) and non‐trading (non‐global, resp.) firms is larger among
production workers (Figure 2, left panel) compared to non‐production workers (Figure 2, right panel). The
difference between exporters and non‐exporters is 11 percentage points among production workers, as
opposed to only 1.3 percentage points among non‐production workers. Similarly, the difference between
importers and non‐importers is around 6 percentage points among production workers, but only 1
percentage point among non‐production workers.
Figure 2: Female labor share, production vs. non‐production workers, by trading status
Production workers Non‐production workers
34.4% 28.5%
exporting exporting
23.3% 27.2%
yes
yes
no
33.9% 29.5%
importing importing no
28.0% 28.5%
38.4% 29.7%
gvc participant gvc participant
23.8% 26.9%
39.9% 31.0%
fdi firm fdi firm
26.9% 27.0%
v
Note: Non‐production workers include managers, administration and sales. The female labor shares are averages using firm
employment as weights.
9
And while the difference in the female labor share is larger between global and non‐global firms
among non‐production workers, the gap is much higher for production workers. The difference between
GVC participants and non‐participants is less than 3 percentage points among non‐production workers,
as opposed to 14.5 percentage points among production workers. Similarly, the difference between
foreign and non‐foreign firms is only 4 percentage points among non‐production workers, as opposed to
13 percentage points among production workers. We therefore hypothesize that the difference in the
female labor share between trading (global, resp.) firms and non‐trading (non‐global, resp.) firms is driven
by production workers.
3.2 Sectoral Differences
This section describes the average female labor share by sector. As can be expected, the share of
female permanent workers exceeds half the workforce (57.7%) in wearing apparel which is known to be
very female‐intensive (Figure 3). This is followed by office, accounting and computing machinery (43.4%),
although this average has to be considered with caution due to the low number of firms in this sector.
Other sectors with a female labor share exceeding 30% are either related to apparel (leather and
footwear; textiles) and electronics (radio, television, and communication equipment; medical, precision
and optical instruments), or tobacco products and food and beverages. Natural‐resources intensive
sectors, like oil, wood and metal products, but also transport equipment, by contrast, tend to show the
lowest female labor share.11
Figure 3: Female labor share, by sector
57.7%
43.4%
37.6%
37.4%
32.3%
31.4%
30.5%
30.5%
25.9%
25.8%
23.6%
22.3%
21.7%
20.8%
20.2%
19.8%
18.3%
17.8%
16.3%
14.6%
13.7%
7.4%
Note: For the sectoral distribution of firms, see Appendix 2. The female labor shares are averages using firm employment as
weights.
11 For the averages by sector overall, and also for trading and global versus non‐trading and global firms, see Appendix 4. The
data for production workers are shown in Appendix 5 and those for non‐production workers in Appendix 6.
10
The previous graph does not differentiate between trading and non‐trading firms. Figure 4 (top panel)
therefore plots the average female labor share of permanent workers for exporters on the x‐axis versus
non‐exporters on the y‐axis by sector. Most sectors are located below the dotted y = x line which suggests
higher female labor shares for exporting compared to non‐exporting firms. The gap between exporters
and non‐exporters is especially high in wearing apparel (ISIC 18) and leather and footwear (ISIC 19), but
also medical, precision and optical instruments (ISIC 33). Interestingly, the female labor share seems to
be lower for exporting firms in office, accounting and computing machinery (ISIC 30) and radio, television,
and communication equipment (ISIC 32).
Figure 4: Female labor share, trade participants vs. non‐participants, by sector
Exporting vs. non‐exporting firms
Female labor share (%), non‐exporting
60%
50% 18
30
40% 16
32
30% 17 15 19
22
24 33
20% 25 36
21 31
26
28 23 20
34
10% 29
27 35
0%
0% 10% 20% 30% 40% 50% 60%
Female labor share (%), exporting
Importing vs. non‐Importing firms
Female labor share (%), non‐importing
60%
50% 18
30
40%
17
15
30% 36 24 16 19
32
31 22
25
20% 26 2034
23 21 33
28
29 35
10% 27
0%
0% 10% 20% 30% 40% 50% 60% 70%
Female labor share (%), importing
Source: The female labor shares are averages using firm employment as weights. Sector numbers relate to ISIC Rev. 3 sectors.
For the sectoral distribution of firms, see Appendix 2.
11
Focusing on importing versus non‐importing firms in Figure 4 (bottom panel) confirms the higher
average female labor share for importers for the majority of sectors although there are more sectors for
which this does not hold. As was the case for exporting, apparel and leather and footwear (ISIC 18 and 19)
and medical, precision and optical instruments (ISIC 33) show a large gap between the female labor share
for importer as opposed to non‐importers, but surprisingly also radio, television, and communication
equipment (ISIC 32). Recall that the latter sector showed a higher female labor share for non‐exporters
compared to exporters (Figure 4). One explanation may be that the products within this sector that are
sold to the local market may show a higher female intensity than products that are exported.
GVC participation shows the strongest relationship with the female labor share across sectors (Figure
5, top panel). Almost all sectors show a larger female labor share among GVC participants relative to non‐
participants, as suggested by the location under the y = x line. Only three sectors are located above the
line, in particular office, accounting and computing machinery (ISIC 30). The same four sectors show the
largest gap between global and non‐global firms: apparel; leather and footwear; radio, television, and
communication equipment; and medical, precision and optical instruments (ISIC 18‐19 and 32‐33).
Finally, the scatterplot in Figure 5 (bottom panel) also tends to suggest a higher female labor share
for FDI firms across sectors. The gap in the female labor share between foreign and domestically‐owned
firms seems to be larger, especially for wearing apparel and leather and footwear (ISIC 18‐19), radio,
television, and communication equipment (ISIC 32), but also furniture and other manufacturing (ISIC 36),
textiles (ISIC 17) and electrical machinery and apparatus (ISIC 31). Overall, the sectoral analysis seems to
confirm the hypothesis that trading/global firms show a larger average female labor share than non‐
trading/non‐global firms.
12
Figure 5: Female labor share, global firms vs. non‐global firms, by sector
Female labor share (%), non‐participant GVC participants vs. non‐participants
60%
50% 18
30
40%
16
30% 15
36 22
24 17 19 32
33
20% 26 212531
20
34
28
29 23
10% 35
27
0%
0% 10% 20% 30% 40% 50% 60% 70%
Female labor share (%), GVC participant
FDI vs. non‐FDI firms
60%
Female labor share (%), non‐FDI
18
50%
30
40% 16
30% 15
33 17 19
32
22 24
20% 35 23 21 25 31 36
26 20
29 2834
10%
27
0%
0% 10% 20% 30% 40% 50% 60% 70%
Female labor share (%), FDI
Source: The female labor shares are averages using firm employment as weights. Sector numbers relate to ISIC Rev. 3
sectors. For the sectoral distribution of firms, see Appendix 2.
3.3 Differences across Countries
This section shifts the focus to the average female labor share by country. The number of firms
surveyed varies drastically across the country sample, ranging from over 6,000 firms in India, to over 1,000
firms in Bangladesh, China, the Arab Republic of Egypt, Indonesia, and the Russian Federation to around
100 in other countries. The findings of the country analysis therefore need to be treated with caution.
They nevertheless allow to get a first idea about the female labor participation across different countries.
The main observation of Figure 6 is that the female labor share ranges from almost two‐thirds in Myanmar
(MMR, 64.7%) to less than 10% in Pakistan (PAK, 8.4%) and Iraq (IRQ, 5%).
13
Figure 6: Female labor share, by country
64.7%
61.0%
70% 58.9%
56.1%
55.5%
55.1%
54.7%
54.3%
54.0%
52.0%
50.4%
50.0%
48.2%
60%
48.2%
47.2%
46.5%
42.9%
41.3%
40.7%
40.5%
40.4%
40.3%
39.2%
39.0%
38.7%
38.6%
50%
37.1%
36.9%
35.9%
34.8%
34.2%
33.9%
32.6%
32.2%
31.9%
31.2%
31.1%
29.8%
40%
28.1%
27.5%
27.0%
26.6%
26.0%
23.4%
23.1%
22.6%
22.4%
22.0%
21.7%
20.9%
20.5%
19.4%
30%
17.0%
16.2%
15.8%
15.6%
14.2%
11.7%
11.7%
11.4%
10.6%
20%
8.4%
5.0%
10%
0%
mng
zwe
mmr
mar
kgz
col
per
kaz
cri
tto
npl
pak
mex
egy
mda
pry
yem
rou
mkd
lva
lka
slv
eth
tun
vnm
hrv
srb
bgd
arm
jam
uzb
nam
gtm
chn
aze
pan
ecu
ken
uga
tza
gha
sen
nga
rus
bih
idn
lbn
arg
zmb
ind
wbg
irq
tjk
blr
ukr
bgr
nic
geo
jor
pol
bol
chl
zar
Note: See Appendix 1 for full country names. The female labor shares are averages using firm employment as weights.
The figure also seems to suggest that the female labor share seems to be highest in Eastern European
and Central Asian (ECA) countries including Moldova, Tajikistan, Mongolia, Belarus, Ukraine, Bulgaria,
Romania, Macedonia, and Latvia, whose female labor shares exceed 50%. These are followed by countries
associated with high trade/GVC participation (e.g., Sri Lanka, Ethiopia, Morocco, Vietnam, Bangladesh)
exceeding female employment shares of 40%.
Since the previous analysis does not differentiate between trading and non‐trading firms, Figure 7
(top panel) plots the average female labor share of permanent workers for exporting firms on the x‐axis
versus non‐exporting firms on the y‐axis by country. As was the case for sectors, most countries are
located below the dotted y = x line, which suggests higher female labor shares for exporting relative to
non‐exporting firms. The aforementioned countries in the ECA region and those with a strong trade
integration show the highest gaps between exporting and non‐exporting firms. A few countries show
higher female labor shares for non‐exporters, in particular Bosnia‐Herzegovina (BHI), Ecuador (ECU) and
the Democratic Republic of Congo (ZAR).
Similarly, the female labor share is higher for importing as opposed to non‐importing firms (Figure 7,
bottom panel) and for GVC participants as opposed to non‐participants (Figure 8, top panel). In both cases,
ECA countries or those with a high trade integration show higher gaps in the female labor shares.
Interestingly, there are more countries for which female labor participation among importers or GVC
participants is lower, which mainly consist of countries in Sub‐Saharan Africa or Latin America (with the
exception of Bosnia‐Herzegovina).
14
Figure 7: Female labor share, trade participants vs. non‐participants, by country
Female labor share (%), non‐exporting Exporting vs. non‐exporting firms
60%
lva
50% bgrukr mng
rus mda
bih tjk mmr
40% kgz srb
col hrv blr
lka
jam idnnic geo mar
rou
ecu chn per
mex arm slv eth
30% gtm aze
pan uzb mkd
tto
polcri nam
zar kaz
bolchl
lbn uga tun
pry
arg
20% tza zwe ken bgd vnm
ngagha
10% indegy sen zmb
wbg jor
irq pak yemnpl
0%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
Female labor share (%), exporting
Importing vs. non‐importing firms
Female labor share (%), non‐importing
60%
bih ukr
50% nic
col lka
bgr
blr mng tjk
hrv rus mda
40% gtm idn mar
pan kgz slv
chn azeper arm
uzb srb
geo tun ethlva rou
30% zar kaz mex jam
uga cri bgd vnm
zwe
lbnchl pol
tto ken nam
20% gha pry
arg ecu mkd
npl tza bol
wbg
nga
10% egy zmb
ind sen jor
irq yem
pak
0%
0% 10% 20% 30% 40% 50% 60% 70%
Female labor share (%), importing
Source: The female labor shares are averages using firm employment as weights. For country abbreviations, see Organization
for Standardization (ISO), https://www.iso.org/obp/ui/#search.
Foreign ownership status, on the other hand, shows a different relationship with the female labor
share, as suggested by Figure 8 (bottom panel). While the majority of countries are still located below the
y = x line, implying a positive association between FDI status and the female labor share in more countries,
the composition of countries looks different compared to expording, importing and GVC participation.
Some of the countries with a strong gap in female labor share among non‐trading firms, such as Sri Lanka,
Morocco, and Mongolia, are now located above the dotted line, i.e. the female labor share appears to be
higher among domestically‐owned firms. While the low share of foreign firms in the sample of 7% could
bias the results, another explanation may be that FDI is less common in female‐intensive sectors like
apparel.
15
Figure 8: Female labor share, global vs. non‐global firms, by country
Female labor share (%), non‐participants GVC participants vs. non‐participants
60%
50% lvabgr ukr mng
bih rus lka mda mmr
tjk
40% col hrv
nic mar
jam kgzsrb
idn geo blr
ecu chn mex
gtm
nam
panper uzbaze slv arm rou eth
mkd
30% cri
tto kaz
pol tun
bol
zar chl
lbn uga bgd vnm
pry zwearg
20% ken
nga gha tza
egy
ind zmb
sen jor
10% wbg npl
irq yem
pak
0%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
Female labor share (%), GVC participants
FDI vs. non‐FDI firms
60%
Female labor share (%), non‐FDI
mng
blr
bgr ukr
50% lka rou tjk
mar lva mda
mmr
rus mkd
kgz eth tun
40% idn geo
srb vnm bgd slv
col arm
jambih hrv
nam
ecu gtmmex per uzb
pan chn
30% nic
ken
poltto kaz
pry chl uga cri
lbn
20% tza argzwe jor
gha bol
egy zmbnga
10% wbg ind zar
sen
yem
pak npl
irq
0%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
Female labor share (%), FDI
Source: The female labor shares are averages using firm employment as weights. For country abbreviations, see Organization
for Standardization (ISO), https://www.iso.org/obp/ui/#search.
4. Regression Results
4.1 Baseline Results
The following section shows the results of the regression analysis following the specification of
equation (1). We include the following additional control variables in all regressions:
Output, lnY = total sales in natural logarithms.
Capital intensity, lnk = capital stock per output in natural logarithms.
Total factor productivity, lntfp = total factor productivity in natural logarithms.
16
The summary statistics are shown in Appendix 3. Table 1 shows the mean differences for the female
labor share between trading and non‐trading firms, while Table 2 focuses on global versus non‐global
firms. Focusing on the control variables, we find consistently that output is positively correlated with the
female labor share, indicating that larger firm size matters positively. By contrast, TFP is negatively
associated with the female labor share, suggesting that women tend to be more employed in less‐
productive firms. The coefficient sign on capital intensity is negative, implying a higher female labor share
in less capital‐intensive firms, but the result is not significant.
The results in Table 1 show that exporting is positively correlated with the female labor share, only
controlling for fixed effects (column 1). Additionally controlling for a firm’s output confirms the positive
relationship, although the coefficient is slightly smaller (column 2). Interestingly, additionally including
capital intensity and TFP as control variables increases the coefficient on exporting (columns 3 and 4).
Interpreting the coefficient in the fully specified model (column 4), the female labor share is 4.4
percentage points higher for exporting firms compared to non‐exporting firms. This is lower than the
difference of 9 percentage points shown in Figure 1 and can be explained by the inclusion of control
variables and fixed country‐sector, subnational region and year effects.
Table 1: Female labor share premium of trading vs. non‐trading firms
Dependent Exporter, exp Importer, imp
variable:
fem_shisrt (1) (2) (3) (4) (5) (6) (7) (8)
lnYisrt 0.0986 0.1665* 0.2401** 0.2987*** 0.3849*** 0.4884***
(0.179) (0.096) (0.025) (0.000) (0.000) (0.000)
lnkisrt ‐0.0757 ‐0.1888 0.0030 ‐0.1225
(0.514) (0.156) (0.981) (0.407)
lntfpisrt ‐1.4452** ‐1.8372***
(0.019) (0.007)
tradeisrt 3.6772*** 3.6145*** 4.3009*** 4.3505*** 1.8163*** 1.7998*** 2.1041*** 1.9359***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
constant 17.8032** 0.6479 2.7873 70.3968*** 28.6015 41.1258 51.8918*** 65.2164***
(0.014) (0.601) (0.746) (0.000) (.) (.) (0.000) (0.000)
Obs. 29,075 24,154 15,935 14,485 24,271 19,701 13,950 12,601
R‐squared 0.53 0.54 0.54 0.54 0.51 0.53 0.53 0.53
Note: p*<0.1, p**<0.05, p***<0.01 (p‐values in parentheses). All regressions include country‐sector, subnational region and year
fixed effects.
Focusing on importing firms, the results suggest a female labor share which is on average 1.9
percentage points higher compared to non‐importing firms, controlling for output, capital intensity, TFP
and the set of fixed effects (column 8). As was the case for exporting, the female labor share premium is
lower than the premium of 4.7 percentage points which was shown in Figure 1. In summary, the findings
suggest that a firm’s export status explains roughly half of the difference in the female labor share (4.4
17
out of 8.9 percentage points), while other factors explain the other half. Similarly, a firm’s import status
explains around 40 percent of the difference in the female labor share (1.9 out of 4.7 percentage points).
Table 2 focuses on the mean differences between global and non‐global firms. The female labor share
premium is 4.0 percentage points for GVC participants compared to non‐participants, wheras the
difference in the female labor share is only 1.5 percentage points on average between foreign‐owned and
domestically‐owned firms. In other words, GVC participation explains roughly a third (4.0 out of 12.2
percentage points) of the difference in the female labor share relative to non‐participants that was shown
in Figure 1, while other factors explain two‐thirds. FDI explains only around 14 percent (1.5 out of 10.9
percentage points).
Table 2: Female labor share premium of global vs. non‐global firms
Dependent GVC participant, gvc Foreign ownership, FDI
variable:
fem_shisrt (1) (2) (3) (4) (5) (6) (7) (8)
lnYisrt 0.1780** 0.2588*** 0.3449*** 0.2878*** 0.4292*** 0.5157***
(0.014) (0.009) (0.001) (0.000) (0.000) (0.000)
lnkisrt ‐0.0565 ‐0.1713 ‐0.0020 ‐0.1138
(0.626) (0.198) (0.987) (0.394)
lntfpisrt ‐1.5388** ‐1.5336**
(0.012) (0.011)
globalisrt 3.7144*** 3.4534*** 4.0120*** 3.9658*** 2.2865*** 1.7237*** 1.4961** 1.5119**
(0.000) (0.000) (0.000) (0.000) (0.000) (0.002) (0.035) (0.044)
constant 17.7215** ‐0.5901 1.6322 68.8774*** 23.3275*** 46.8038*** 51.3708*** 50.4568***
(0.014) (0.636) (0.850) (0.000) (0.000) (0.000) (0.000) (0.000)
Obs. 29,075 24,154 15,935 14,485 29,060 24,143 15,926 14,476
R‐squared 0.52 0.54 0.54 0.54 0.52 0.54 0.54 0.54
Note: p*<0.1, p**<0.05, p***<0.01 (p‐values in parentheses). All regressions include country‐sector, subnational region and year
fixed effects.
4.2 Production versus Non‐Production Workers
In this section, we test the hypothesis (see section 3.1) that the overall findings are driven by
production workers. Figure 9 shows female labor share premium for production and non‐production
workers separately. The latter include managers, administration and sales. The results suggest that the
female labor share premium is much higher for production‐workers compared to non‐production
workers. The female labor share premium is twice as high for production workers when comparing
exporting with non‐exporting firms (5.2 versus 2.7 percentage points) and more than twice as high for
GVC participants relative to non‐participants (4.5 versus 1.9 percentage points). The female labor share
premium between production and non‐production workers is much smaller when comparing importing
with non‐importing firms, and disappears for FDI relative to non‐FDI firms. For the underlying regression
results, see Appendix 4.
18
Figure 9: Female labor share premium (percentage points), trading vs. non‐trading firms, by worker type
6
5
4
3
2
1
0
Exporting vs. non‐exporting Importing vs. non‐importing GVC participants vs. non‐ FDI vs. non‐FDI firms
firms firms participants
Total workers Production workers Non‐production workers
Note: The graph shows mean differences in female labor share between trading and non‐trading firms for total, production and
non‐production workers. All regressions control for output, capital intensity, TFP (all in natural logarithms) as well as country‐
sector, subnational region and year fixed effects. All results shown are significant at the 10% level. For the underlying regression
results, see Appendix 4.
4.3 Robustness Tests
In a next step, Table 3 splits the sample into two periods, namely 2010‐2013 and 2014‐2017, to detect
if the findings hold throughout both periods. Surprisingly, the correlation between exporting and the
female labor share more than twice as high for the earlier period 2010‐2013. While the female labor share
premium is 5.4 percentage points relative to non‐exporting firms for the period 2010‐13, it was only 2.5
percentage points for the period 2014‐17. Moreover, the female labor share premium for importing
versus non‐importing firms, GVC participants versus non‐participants and FDI firms versus non‐FDI firms
is only significant over the period 2010‐2013. While the finding may be related to global GVC trade
slowdown, Appendix 1 shows that the 2014‐17 surveys contain many Latin American, Sub‐Saharan African
and some Middle East and North African countries that are less integrated into GVCs and trade, which
could have driven the results.
In order to rule out that the overall findings are driven by female‐intensive sectors only, we exclude
wearing apparel (ISIC 18) and office, accounting and computing (ISIC 30) in the regressions. Figure 10
shows that the female labor share premium for trading firms becomes somewhat smaller for exporters
and GVC participants, but not for importers and FDI firms. For the underlying regression results, see
Appendix 5 and 6.
One potential omitted variable bias could stem from the lack of gender‐specific wage data in the
Enterprise Surveys. We are able to address this issue by using gender‐specific wage data that are available
19
at the ISIC Rev. 3 level for 30 countries in our dataset. We compute the ratio of male to female wages as
our control measure of relative wages at the sector level in a country. Another important modification to
the econometric model is the change in the dependent variable. Rather than estimating the female labor
share (see equation 2), the regressions in this section estimate the female wage share (see equation 1).
Table 3: Female labor share premium, trading vs. non‐trading firms, 2010‐2013 vs. 2014‐2017
Dependent 2010‐2013 2014‐2017
variable:
fem_shisrt (1) (2) (3) (4) (5) (6) (7) (8)
exp imp gvc fdi exp imp gvc fdi
lnYisrt 0.1107 0.3933*** 0.2095 0.4750*** 0.4244*** 0.6738*** 0.5355*** 0.5661***
(0.455) (0.007) (0.155) (0.001) (0.006) (0.001) (0.000) (0.000)
lnkisrt ‐0.2386 ‐0.1816 ‐0.2114 ‐0.1185 ‐0.1212 ‐0.0064 ‐0.1043 ‐0.0960
(0.198) (0.329) (0.254) (0.524) (0.523) (0.979) (0.583) (0.614)
lntfpisrt ‐1.4652 ‐1.3942 ‐1.5487* ‐1.5008 ‐1.4273* ‐2.5379*** ‐1.5001** ‐1.5296**
(0.122) (0.131) (0.097) (0.103) (0.060) (0.009) (0.048) (0.044)
tradeisrt / 5.4433*** 2.3297*** 5.1306*** 1.7190* 2.4588*** 0.9192 1.3171 1.1118
globalisrt (0.000) (0.000) (0.000) (0.066) (0.001) (0.276) (0.203) (0.378)
constant 72.2688*** 10.8937 70.8940*** 66.9885*** 58.4506*** 0.7596 57.1632*** 55.5684***
(0.000) (0.120) (0.000) (0.000) (0.000) (0.892) (0.000) (0.000)
Obs. 8,540 8,498 8,540 8,531 5,945 4,103 5,945 5,945
R‐squared 0.54 0.54 0.54 0.54 0.51 0.50 0.51 0.51
Note: p*<0.1, p**<0.05, p***<0.01 (p‐values in parentheses). All regressions include country‐sector, subnational region and
year fixed effects.
Figure 10: Female labor share premium (percentage points), trading vs. non‐trading firms excluding apparel and
computing, by worker type
5
4
3
2
1
0
Exporting vs. non‐exporting Importing vs. non‐importing GVC participants vs. non‐ FDI vs. non‐FDI firms
firms firms participants
Total workers Production workers Non‐production workers
Note: The graph shows mean differences in the female labor share between trading and non‐trading firms for total, production
and non‐production workers. All regressions exclude wearing apparel (ISIC 18) and office, accounting and computing (ISIC 30) and
control for output, capital intensity, TFP (all in natural logarithms) as well as country‐sector, subnational region and year fixed
effects. All results shown are significant at the 10% level. For the underlying regression results, see Appendix 5 and 6.
The female wage share premia for trading relative to non‐trading firms are shown in Tables 4 and 5.
The findings suggest that higher relative wages between male and female workers – or a higher gender
wage gap – are associated with a lower female wage share which is in line with expectations (see section
20
2.1). Output is positively correlated with the female wage share, suggesting that more output benefits the
wage bill of female workers more than proportionally. Capital intensity and TFP show negative coefficient
signs, but are uncorrelated with the female wage share.
Table 4: Female wage share premium, trading vs. non‐trading firms
Dependent Exporter, exp Importer, imp
variable:
SFisrt (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
lnYisrt 0.0826 0.1369 0.1887* 0.1897* 0.2387*** 0.2835** 0.3485*** 0.3503***
(0.256) (0.181) (0.078) (0.077) (0.010) (0.016) (0.005) (0.005)
lnkisrt ‐0.0665 ‐0.0708 ‐0.0704 ‐0.0091 ‐0.0215 ‐0.0212
(0.573) (0.593) (0.595) (0.947) (0.892) (0.893)
lntfpisrt ‐0.6686 ‐0.6676 ‐0.9439 ‐0.9484
(0.256) (0.259) (0.185) (0.186)
ln(wM/wF)srt ‐6.6697*** ‐7.3527***
(0.000) (0.000)
tradeisrt 2.3514*** 2.1271*** 2.5869*** 2.5507*** 2.5542*** 1.1885*** 1.0835*** 1.2834*** 1.1058** 1.1052**
(0.000) (0.000) (0.000) (0.000) (0.000) (0.001) (0.007) (0.010) (0.033) (0.033)
constant 2.5657*** 1.5367 0.3800 0.2899 37.4914*** 2.3465*** ‐0.7318 ‐1.3844 ‐1.4560 39.5517***
(0.000) (0.154) (0.793) (0.851) (0.000) (0.001) (0.611) (0.447) (0.450) (0.000)
Obs. 16,481 15,774 10,392 9,668 9,651 2,891 2,707 2,323 2,214 2,210
R‐squared 0.58 0.59 0.57 0.58 0.58 0.54 0.54 0.56 0.56 0.55
Note: p*<0.1, p**<0.05, p***<0.01 (p‐values in parentheses). Regressions include the 30 countries highlighted in bold in
Appendix 1. All regressions include country‐sector, subnational region and year fixed effects.
Table 5: Female wage share premium, global vs. non‐global firms
Dependent GVC participant, gvc Foreign ownership, fdi
variable:
SFisrt (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
lnYisrt 0.1433** 0.2144** 0.2650** 0.2661** 0.1957*** 0.2879*** 0.3447*** 0.3459***
(0.043) (0.033) (0.012) (0.011) (0.005) (0.003) (0.001) (0.001)
lnkisrt ‐0.0498 ‐0.0612 ‐0.0608 ‐0.0251 ‐0.0325 ‐0.0324
(0.673) (0.644) (0.646) (0.831) (0.806) (0.807)
lntfpisrt ‐0.7544 ‐0.7548 ‐0.7347 ‐0.7390
(0.196) (0.198) (0.204) (0.204)
ln(wM/wF)srt ‐7.2412*** ‐7.6126***
(0.000) (0.000)
globalisrt 2.1226*** 1.8750*** 2.1212*** 2.1308*** 2.1364*** 1.8849*** 1.1683* 1.2561 1.0626 1.0638
(0.000) (0.000) (0.000) (0.001) (0.001) (0.004) (0.088) (0.120) (0.198) (0.198)
constant 3.0038*** 1.1113 ‐0.2216 ‐0.2293 40.1618*** 3.0178*** 0.4143 ‐1.1767 ‐1.2822 41.1818***
(0.000) (0.301) (0.878) (0.882) (0.000) (0.000) (0.696) (0.409) (0.399) (0.000)
Obs. 16,481 15,774 10,392 9,668 9,651 16,481 15,774 10,392 9,668 9,651
R‐squared 0.58 0.59 0.57 0.58 0.58 0.58 0.59 0.57 0.58 0.58
Note: p*<0.1, p**<0.05, p***<0.01 (p‐values in parentheses). Regressions include the 30 countries highlighted in bold in
Appendix 1. All regressions include country‐sector, subnational region and year fixed effects.
Exporting firms show a female wage share which is on average 2.5 percentage points higher than non‐
exporting firms in the fully specified model (column 5). Similarly, importing firms show a female wage
share premium of 1.1 percentage points. For GVC participants, the female wage share premium is 2.1
percentage points compared to non‐participants, while the regressions do not find a female wage share
premium for FDI firms relative to non‐FDI firms (Table 5). Note that these patterns hold controlling for the
21
gender wage gap, output, capital intensity, productivity and fixed effects. The summary is shown in Figure
11, both for the full sample and excluding apparel and computing. Interestingly, the wage share premia
are almost identical when the female‐intensive sectors apparel and electronics are excluded from the
sample.
Figure 11: Female wage share premium (percentage points), trading vs. non‐trading firms
3.0
2.5
2.0
1.5
1.0
0.5
0.0
All sectors Excluding apparel and computing
Exporting vs. non‐exporting Importing vs. non‐importing GVC participant vs. non‐participant
Note: The graph shows mean differences between trading and non‐trading firms, using firm’s female share in total wages as
dependent variable. All regressions control for firm output, capital intensity, TFP and relative wages at the sector level (all in
natural logarithms) as well as country‐sector, subnational region and year fixed effects. All results shown are significant at the
10% level.
4.4 Mediating Role of Female Ownership and Management
This section studies if female ownership and management make a difference for the female labor
share premium in trading firms. Analytically, this is assessed by adding an interaction term with the trade
variable to equation (2):
������������������������������ ������������������������ ������ ������������ ������������������������������������������ ������������ ������������������������������������������ ������������ ������������������������������������������������������ ������������ ������������������������������������������������������ ������������������������������������������������������������ ∗ ������������������������������������������������
������������������ ������������ ������������ ������������������������������ (2a)
where firm designates the firm characteristic under consideration.
The total correlation between trade participation and the female labor share is given by the sum of γ2
+ δ*firmisrt. Since firm characteristics are positive (firmisrt > 0), the joint correlation is larger (smaller, resp.)
for δ > 0 (δ < 0, resp.) than γ2. Firm characteristics to be tested include variables on the role of female
management and ownership:
Female ownership share, fem_own = % of the firm owned by females.
Female top manager, fem_man = 1 if the firm’s top manager is female, and 0 otherwise.
22
The preliminary analysis in Appendix 7 suggests that trading firms have a higher probability of being
majority‐female owned relative to non‐trading firms, and to a lesser extent also of having a female top
manager. Female ownership and management could thereore also shape the female labor share
premium. Following equation (2a), Table 6 (columns 1 to 4) shows the results for female ownership share.
The interaction term is positive and significant across all four types of trading. That is, trading firms with
a higher female ownership share show significantly larger female labor shares. For example, a 10
percentage point increase in the female ownership share for exporting firms is associated with an
expansion of the female labor share premium by 0.9 percentage points from 3.8 to 4.7 percentage points,
relative to non‐exporting firms. Both the labor share premium and additional gains from female
ownership are slightly smaller for GVC participants relative to non‐participants.
Table 6: Female labor share premium, trading vs. non‐trading firms, role of female ownership and top manager
Dependent Female ownership share Female manager (dummy)
variable:
fem_shisrt (1) (2) (3) (4) (5) (6) (7) (8)
exp imp gvc fdi exp imp gvc fdi
lnYisrt 0.4654*** 0.7810*** 0.5634*** 0.7512*** 0.2504** 0.5366*** 0.3503*** 0.5342***
(0.000) (0.000) (0.000) (0.000) (0.019) (0.000) (0.001) (0.000)
lnkisrt ‐0.0897 ‐0.0167 ‐0.0781 ‐0.0258 ‐0.1801 ‐0.1187 ‐0.1648 ‐0.1114
(0.521) (0.915) (0.577) (0.854) (0.176) (0.420) (0.216) (0.403)
lntfpisrt ‐1.6845** ‐2.1808*** ‐1.7162*** ‐1.7039*** ‐1.4923** ‐1.8660*** ‐1.5918*** ‐1.5868***
(0.011) (0.004) (0.009) (0.009) (0.016) (0.006) (0.009) (0.009)
expisrt 3.8212*** 3.9387***
(0.000) (0.000)
expisrt* firmisrt 0.0923*** 3.9846***
(0.000) (0.004)
impisrt 0.9449* 1.0590**
(0.063) (0.023)
impisrt* firmisrt 0.1007*** 7.7247***
(0.000) (0.000)
gvcisrt 3.5960*** 3.5711***
(0.000) (0.000)
gvcisrt* firmisrt 0.0673** 3.9926**
(0.018) (0.031)
fdiisrt 0.3374 0.2353
(0.690) (0.758)
fdiisrt* firmisrt 0.1669*** 12.3424***
(0.001) (0.000)
constant 12.5661 ‐13.1416 14.6106* 45.1399*** 70.3351*** 50.3306*** 68.8912*** 95.1718
(0.105) (0.128) (0.060) (0.000) (0.000) (0.000) (0.000) (.)
Obs. 12,590 10,722 12,590 12,583 14,467 12,588 14,467 14,458
R‐squared 0.55 0.54 0.54 0.54 0.54 0.53 0.54 0.54
F-test1) 0.0000 0.0000 0.0000 0.0013 0.0000 0.0000 0.0000 0.0000
Note: p*<0.1, p**<0.05, p***<0.01 (p‐values in parentheses). All regressions include country‐sector, subnational region and year
fixed effects. 1) F‐test of joint significance between trade variable and interaction term.
In Table 6, columns 5 to 8, we look at the role of female management for the female labor share
premium. The positive interaction terms suggest that having a female top manager can expand the female
23
labor share premium dramatically. Exporting firms with a female top manager show a percentage point
premium in the female labor share of 8 percentage points relative to non‐exporting firms, while the
premium for exporting firms with a male top manager is only 4 percentage points. The findings for GVC
participants are similar. Interestingly, the correlation between importing and the female labor share is
eight times higher for importing firms with a female top manager compared to those with a male top
manager. The female labor share premium for FDI firms is only confirmed for FDI firms with a female top
manager (see also Figure 12).
Figure 12: Female labor share premium (percentage points), trading vs. non‐trading firms, mediating role of a
female top manager
13
12
11
10
9
8
7
6
5
4
3
2
1
0
Exporting vs. non‐ Importing vs. non‐ GVC participants vs. FDI vs. non‐FDI firms
exporting firms importing firms non‐participants
Male top manager Female top manager
Note: The graph shows mean differences in the female labor share between trading and non‐trading firms, differentiating
between male and female‐managed firms. All regressions control for firm output, capital intensity, and TFP (all in natural
logarithms) as well as country‐sector, subnational region and year fixed effects. All results shown are significant at the 10% level.
Exporters are firms with an export share (direct or indirect) of at least 10% of total sales. Importers are firms with an imported
input share of at least 10% of total inputs. GVC participants are firms that are classified as both exporter and importers. FDI refers
to firms with a foreign ownership share of at least 10%.
4.5 Mediating Role of Sectoral Technology Intensity
In this section, we assess if the female labor share premium differs by sectoral technology intensity.
Analytically, we can assess the role of a sector’s technology intensity as follows:
������������������������������ ������������������������ ������ ������������ ������������������������������������������ ������������ ������������������������������������������ ������������ ������������������������������������������������������ ������������ ������������������������������������������������������ ������������������������������������������������������������ ∗ ������������������������������������������
������������������ ������������ ������������ ������������������������������ (2b)
where tech designates the sectoral technology intensity under consideration:
Medium‐high or high technology intensity, tech_h = 1 if the sector’s technology intensity is
medium‐high or high, and 0 otherwise;
24
Medium technology intensity, tech_m = 1 if the sector’s technology intensity is medium, and 0
otherwise;
Low technology intensity, tech_l = 1 if the sector’s technology intensity is low, and 0 otherwise.
Appendix 8 shows the classification of ISIC Rev. 3 across the three technology groups. Figure 13 shows
the results for three groups of technology, namely medium‐ to high‐tech, medium‐tech, and low‐tech.
The findings suggest that the female labor share of exporters relative to non‐exporters tends to be larger
in industries characterized by lower technology intensity. Low‐tech industries like food and beverages, or
textiles and clothing show the largest female labor share premia for exporters and GVC participants.
Exporters operating in low‐tech industries have a female labor share that is on average 5.7 percentage
points larger than non‐exporters in the same industries, controlling for other factors that could explain
this difference. The labor share premium for exporters relative to non‐exporters in medium‐tech
industries drops to 2.6 percentage points, while that in medium‐ to high‐tech industries is even lower.
Figure 13: Female labor share premium (percentage points), trading vs. non‐trading firms, mediating role of
sectoral technology intensity
8
7
6
5
4
3
2
1
0
Medium‐ to high‐tech Medium‐tech Low‐tech
Exporting vs. non‐exporting Importing vs. non‐importing
GVC participant vs. non‐participant FDI firm vs. non‐FDI firm
Note: The graph shows mean differences in the female labor share between trading and non‐trading firms, differentiating
between medium‐ to high‐tech, medium‐tech and low‐tech sectors. All regressions control for firm output, capital intensity, and
TFP (all in natural logarithms) as well as country‐sector, subnational region and year fixed effects. All results shown have a joint
significance between trading and the interaction term of at least 10%. Exporters are firms with an export share (direct or indirect)
of at least 10% of total sales. Importers are firms with an imported input share of at least 10% of total inputs. GVC participants
are firms that are classified as both exporter and importers. FDI refers to firms with a foreign ownership share of at least 10%.
For the underlying regression results, see Appendix 9.
The findings for GVC participants are very similar where the female labor share premium relative to
non‐participants is 5.1 percentage points in low‐tech sectors and 1.7 percentage points in medium‐tech
sectors, but slightly larger again in the highest technology category (2.3 percentage points). These findings
25
suggest that the positive relationship between exporting or GVC participation and the female labor share
is strongest for tasks that appear to be low‐skill intensive.
Interestingly, importing firms in medium‐ to high‐tech industries show the highest female labor share
premium relative to non‐importing firms (3 percentage points), followed by low‐tech industries (2
percentage points), whereas FDI firms show a huge female labor share premium in medium‐tech
industries of 7.4 percentage points compared to non‐FDI firms. It appears that while FDI firms overall only
show a moderate female labor share premium of 1.5 percentage points, foreign ownership status in
medium‐tech industries benefits the female labor share much more strongly.
4.6 Additional Analysis
While the previous analysis finds a positive relationship between trading and the female labor and
wage share across manufacturing firms, this section assesses the mediating role of a firm’s trading status
for the link between the firm’s female labor share and average wage rates. The analysis correlates the
female labor share (which now becomes the independent variable) with a firm’s average wage rates (the
dependent variable). A negative correlation suggest that firms with a higher female labor share pay lower
average wage rates which could imply the existence of a gender wage gap.12 Appendix 10 gives futher
details on the underlying model.
The results find that average wage rates are lower for firms with higher female labor shares, but also
suggest that firms that trade, by exporting, importing or integrating into GVCs, show a smaller negative
link than non‐trading firms (Figure 14). For firms involved in FDI, the relationship between female labor
shares and average wages becomes positive. In other words, foreign firms that employ a higher share of
women pay higher average wage rates which could relate to cultural norms adopted in those firms.
Increasing the female labor share by 10 percentage points is associated with average wage rates of foreign
firms being 2.6 percent higher. Interestingly, the correlation between the female labor share and average
wages is even higher for FDI firms that offer training to their employees compared to FDI firms that do
not train their workforce.13
12 While this type of analysis can be performed with firm‐level data only, it cannot control for worker characteristics which might
also explain part of the gender wage gap.
13 These findings are in line with the literature on FDI spillovers, which suggests that foreign investors can help domestic firms
upgrade their technological capabilities directly through sharing of production techniques and product design and assisting with
technology acquisition (Paus and Gallagher, 2008), and indirectly through personnel training, advance payment, leasing of
machinery, provision of inputs, help with quality assurance, and organization of product lines (Lall, 1980; Crespo and Fontoura,
2007; and Javorcik, 2008).
26
Figure 14: Percent change in wages related to a 10 percentage point increase in female labor share, trading vs.
non‐trading firms
5
4
3
2
1
0
‐1
‐2
‐3
Note: The graph shows the results of regression analysis described in Appendix 10 and 11 using the firm’s average wage rate (in
natural logarithms) as dependent variable and the female labor share as independent variable. The model interacts exporter,
importer, GVC participant and FDI dummies with the female labor share variable to assess the mediating role of trading/FDI
status. Exporters are firms with an export share (direct or indirect) of at least 10% of total sales. Importers are firms with an
imported input share of at least 10% of total inputs. GVC participants are firms that are classified as both exporter and importers.
FDI refers to firms with a foreign ownership share of at least 10%. All regressions control for age, firm size, capital intensity, skill
intensity and country‐sector, subnational region and year fixed effects and are at least significant at the 5% level.
5. Conclusions
Using a cross‐section of more than 29,000 manufacturing firms in 64 developing and emerging
countries from the World Bank’s Enterprise Surveys, this paper assessed whether trading firms have a
female labor share premium relative to non‐trading firms. It focused on four types of trading firms:
exporters, importers, GVC participants and FDI firms. The study found a female labor share premium for
all four types of trading firms, controlling for firm output, capital intensity, total factor productivity and
fixed effects. Exporters and GVC participants show the highest female labor share premium compared to
non‐exporting and non‐participants, respectively, underlining the importance of the export channel. The
results also held controlling for differences in relative wages between men and women, and excluding
traditional export sectors (apparel and electronics) from the sample.
Interestingly, the female labor share premium is much higher for production workers compared to
non‐production workers (i.e. managers, sales, administration), implying that women in our country
sample specialize in low‐skill production. In line with these findings, the female labor share premium for
exporters and GVC participants is highest in low‐tech sectors, suggesting the largest benefits for women
in low‐skill intensive sectors. The paper also analyzed the role of female firm ownership and female top
27
management for the relationship between trading and the female labor share and finds that both expand
the female labor share premium of trading firms. Finally, additional analysis explored the link between a
firm’s female labor share and average wage rates and in particular the mediating role of the firm’s trading
status. The results suggest that while average wage rates are lower for firms with higher female labor
shares, this negative correlation is smaller for trading firms.
While this paper explored the relationship between trading and female labor participation from a
variety of angles, i.e. including different types of trading and female labor participation at the firm level,
we identify at least four areas for future research: (i) What is the role of policy in shaping the relationship
between trading and the female labor share? For example, how does country or sub‐national flexibility of
labor regulations shape the findings? (ii) Do patterns hold when more explicit measures of technology or
innovation (beyond TFP) are included? (iii) Do the findings hold when worker characteristics are taken
into account (requiring the use of firm‐level surveys or merging firm‐level data with datasets on worker
characteristics)? (iv) And finally, can a sub‐group of countries with panel data be used for a stronger test
of causality?
28
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30
Appendices
Appendix 1: Number of firms by country
Country Year Firms Country Year Firms
Argentina 2017 626 Mexico 2010 1,125
Armenia 2013 104 Moldova 2013 104
Azerbaijan 2013 113 Mongolia 2013 112
Bangladesh 2013 1,174 Morocco 2013 133
Belarus 2013 114 Myanmar 2016 347
Bolivia 2017 111 Namibia 2014 149
Bosnia and Herzegovina 2013 115 Nepal 2013 240
Bulgaria 2013 101 Nicaragua 2016 107
Chile 2010 773 Nigeria 2014 863
China 2012 1,636 Pakistan 2013 873
Colombia 2010 698 Panama 2010 108
Costa Rica 2010 309 Paraguay 2017 103
Croatia 2013 113 Peru 2017 541
Dem. Rep. Congo 2013 237 Poland 2013 140
Ecuador 2017 100 Romania 2013 169
Egypt, Arab Rep. 2016 1,140 Russian Federation 2012 1,234
El Salvador 2016 394 Senegal 2014 233
Ethiopia 2015 369 Serbia 2013 108
Georgia 2013 101 Sri Lanka 2011 354
Ghana 2013 350 Tajikistan 2013 113
Guatemala 2013 337 Tanzania 2013 325
India 2010 6,807 Trinidad and Tobago 2010 113
Indonesia 2014 1,050 Tunisia 2013 297
Iraq 2015 470 Uganda 2013 355
Jamaica 2011 101 Ukraine 2013 658
Jordan 2010 293 Uzbekistan 2013 124
Kazakhstan 2013 179 Vietnam 2015 420
Kenya 2013 384 West Bank and Gaza 2013 153
Kyrgyz Republic 2013 102 Yemen, Rep. 2013 107
Latvia 2013 106 Zambia 2013 350
Lebanon 2013 215 Zimbabwe 2016 286
North Macedonia 2013 113
Total 29,179
Note: Surveys highlighted in bold are included in the wage share regressions. Surveys from 2014‐2017 are in blue.
31
Appendix 2: Number of firms by sector
ISIC Rev. 3 Sector name No. of firms Percent
15 FOOD PRODUCTS AND BEVERAGES 5,292 18.14
16 TOBACCO PRODUCTS 161 0.55
17 TEXTILES 1,948 6.68
18 WEARING APPAREL; DRESSING AND DYEING OF FUR 2,641 9.05
19 TANNING AND DRESSING OF LEATHER; LUGGAGE, HANDBAGS, SADDLERY, 686 2.35
HARNESS AND FOOTWEAR
20 WOOD AND OF PRODUCTS OF WOOD AND CORK, EXCEPT FURNITURE 826 2.83
21 PAPER AND PAPER PRODUCTS 505 1.73
22 PUBLISHING, PRINTING AND REPRODUCTION OF RECORDED MEDIA 1,116 3.82
23 COKE, REFINED PETROLEUM PRODUCTS AND NUCLEAR FUEL 84 0.29
24 CHEMICALS AND CHEMICAL PRODUCTS 2,163 7.41
25 RUBBER AND PLASTICS PRODUCTS 2,157 7.39
26 OTHER NON‐METALLIC MINERAL PRODUCTS 2,414 8.27
27 BASIC METALS 1,120 3.84
28 FABRICATED METAL PRODUCTS, EXCEPT MACHINERY AND EQUIPMENT 2,526 8.66
29 MACHINERY AND EQUIPMENT N.E.C. 1,773 6.08
30 OFFICE, ACCOUNTING AND COMPUTING MACHINERY 14 0.05
31 ELECTRICAL MACHINERY AND APPARATUS N.E.C. 1,012 3.47
32 RADIO, TELEVISION AND COMMUNICATION EQUIPMENT AND APPARATUS 114 0.39
33 MEDICAL, PRECISION AND OPTICAL INSTRUMENTS, WATCHES AND CLOCKS 261 0.89
34 MOTOR VEHICLES, TRAILERS AND SEMI‐TRAILERS 839 2.88
35 OTHER TRANSPORT EQUIPMENT 100 0.34
36 FURNITURE; MANUFACTURING N.E.C. 1,427 4.89
TOTAL MANUFACTURING 29,179 100
Note: ISIC classification based on most important product of firm.
32
Appendix 3: Summary statistics
Variable Obs Mean Std. Dev. Min Max
Female labor participation
fem_sh_permisrt 29,075 23.370 26.123 0.00 100.00
fem_sh_prodisrt 28,727 21.698 29.211 0.00 100.00
fem_sh_nonpisrt 26,988 28.070 30.502 0.00 100.00
SFisrt 16,481 17.056 21.422 0.00 99.98
lnwageisrt 23,891 7.601 1.321 ‐9.11 14.21
Trade
expisrt 29,179 0.230 0.421 0.00 1.00
impisrt 24,363 0.457 0.498 0.00 1.00
gvcisrt 29,179 0.140 0.347 0.00 1.00
fdiisrt 29,164 0.073 0.259 0.00 1.00
Controls
lnoutpisrt 24,221 13.310 2.233 4.49 21.33
lncapintisrt 15,956 ‐1.506 1.612 ‐16.77 5.81
lntfpisrt 14,499 0.880 0.601 ‐6.00 2.30
M/ F
ln(w w )srt 16,550 5.050 0.569 1.95 7.69
lnageisrt 27,234 2.849 0.713 0.00 5.83
skillshisrt 28,989 73.714 29.803 0.00 100.00
Characteristics
fem_ownisrt 25,931 9.655 24.570 0.00 100.00
fem_manisrt 29,135 0.109 0.312 0.00 1.00
trainisrt 29,006 0.388 0.487 0.00 1.00
tech_hsrt 29,179 0.202 0.402 0.00 1.00
tech_msrt 29,179 0.208 0.406 0.00 1.00
tech_lsrt 29,179 0.590 0.492 0.00 1.00
33
Appendix 4: Female labor share premium, trading vs. non‐trading firms, production vs. non‐production workers
Dependent Female production workers Female non‐production workers
variable: (% of total) (% of total)
fem_shisrt (1) (2) (3) (4) (5) (6) (7) (8)
exp imp gvc fdi exp imp gvc fdi
lnYisrt 0.1858 0.5255*** 0.3265*** 0.5395*** 0.7368*** 0.8669*** 0.8314*** 0.9324***
(0.139) (0.000) (0.009) (0.000) (0.000) (0.000) (0.000) (0.000)
lnkisrt ‐0.1437 ‐0.0247 ‐0.1176 ‐0.0503 0.1905 0.2254 0.2102 0.2440
(0.357) (0.887) (0.452) (0.748) (0.273) (0.233) (0.227) (0.160)
lntfpisrt ‐1.8790*** ‐2.3142*** ‐1.9804*** ‐1.9831*** 0.6524 0.6247 0.5970 0.6011
(0.006) (0.003) (0.004) (0.004) (0.428) (0.486) (0.467) (0.463)
tradeisrt / 5.2185*** 1.7543*** 4.4695*** 1.0084 2.6578*** 1.1439* 1.8841*** 0.2020
globalisrt (0.000) (0.001) (0.000) (0.254) (0.000) (0.065) (0.009) (0.822)
constant 48.3646*** 67.3969*** 69.1348*** 51.1095*** 69.9424*** 66.8840*** 68.5591*** 41.0579***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Obs. 14,344 12,481 14,344 14335 13,661 11,862 13,661 13,652
R‐squared 0.52 0.51 0.51 0.51 0.39 0.38 0.39 0.39
Note: p*<0.1, p**<0.05, p***<0.01 (p‐values in parentheses). All regressions include country‐sector, subnational region and
year fixed effects.
Appendix 5: Female labor share premium, trading vs. non‐trading firms, excluding apparel and computers
Dependent Total permanent workers
variable:
fem_shisrt (1) (2) (3) (4)
exp imp gvc fdi
lnYisrt 0.1480 0.3495*** 0.2468** 0.3592***
(0.171) (0.003) (0.021) (0.001)
lnkisrt ‐0.2730** ‐0.2392 ‐0.2533* ‐0.2155
(0.042) (0.111) (0.060) (0.109)
lntfpisrt ‐0.8251 ‐1.1833* ‐0.9031 ‐0.8737
(0.177) (0.081) (0.137) (0.147)
tradeisrt / 3.7919*** 1.9037*** 3.3342*** 1.8853**
globalisrt (0.000) (0.000) (0.000) (0.016)
constant 49.4215*** 49.2740*** 48.4122*** 52.0073***
(0.000) (0.000) (0.000) (0.000)
Obs. 13,008 11,214 13,008 13,000
R‐squared 0.48 0.47 0.48 0.48
Note: p*<0.1, p**<0.05, p***<0.01 (p‐values in parentheses). All regressions include country‐sector, subnational region and
year fixed effects. Analysis excludes wearing apparel (ISIC 18) and office, accounting and computing machinery (ISIC 30).
34
Appendix 6: Female labor share premium, trading vs. non‐trading firms, production and non‐production
workers, excluding apparel and computers
Dependent Permanent production workers Permanent non‐production workers
variable:
fem_shisrt (1) (2) (3) (4) (5) (6) (7) (8)
exp imp gvc fdi exp imp gvc fdi
lnYisrt 0.0840 0.3829*** 0.2265* 0.3758*** 0.6596*** 0.7481*** 0.7442*** 0.8203***
(0.512) (0.007) (0.073) (0.002) (0.000) (0.000) (0.000) (0.000)
lnkisrt ‐0.2482 ‐0.1647 ‐0.2177 ‐0.1720 0.1721 0.2043 0.1905 0.2166
(0.120) (0.360) (0.173) (0.282) (0.333) (0.292) (0.284) (0.223)
lntfpisrt ‐1.2179* ‐1.5718** ‐1.3030* ‐1.2801* 0.9752 1.0179 0.9271 0.9381
(0.078) (0.042) (0.058) (0.061) (0.237) (0.259) (0.260) (0.253)
tradeisrt / 4.7236*** 1.6429*** 3.7596*** 1.3760 2.3554*** 1.3286** 1.6418** 0.3568
globalisrt (0.000) (0.003) (0.000) (0.145) (0.000) (0.040) (0.031) (0.707)
constant 39.0111*** 14.1549 37.5704*** 36.0309*** ‐7.7307*** ‐8.6779*** ‐8.5908*** 74.1456***
(0.000) (1.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Obs. 12,889 11,114 12,889 12,881 12,310 10,592 12,310 12,302
R‐squared 0.44 0.43 0.43 0.43 0.39 0.39 0.39 0.39
Note: p*<0.1, p**<0.05, p***<0.01 (p‐values in parentheses). All regressions include country‐sector, subnational region and
year fixed effects. Analysis excludes wearing apparel (ISIC 18) and office, accounting and computing machinery (ISIC 30).
Appendix 7: Probability of being majority female‐owned or of having a female top manager, trading vs. non‐
trading firms, probit model
Dependent Majority female‐owned (dummy) Female top manager (dummy)
variable:
(1) (2) (3) (4) (5) (6) (7) (8)
exp imp gvc fdi exp imp gvc fdi
tradeisrt / ‐0.083** ‐0.036 ‐0.123*** ‐0.446*** 0.001 ‐0.013 ‐0.038 ‐0.077*
globalisrt (0.015) (0.274) (0.004) (0.000) (0.984) (0.639) (0.269) (0.085)
constant ‐1.592*** ‐1.808*** ‐1.603*** ‐1.605*** ‐1.679*** ‐1.641*** ‐1.679*** ‐1.679***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Obs. 18,980 14,582 18,980 18,980 25,459 20,485 25,459 25,449
Note: p*<0.1, p**<0.05, p***<0.01 (p‐values in parentheses). All regressions include country‐sector, subnational region and
year fixed effects.
35
Appendix 8: UNIDO Classification of 2‐digit ISIC Sectors by technology intensity
ISIC Rev. 4 sector at 2‐digit level, by technology intensity ISIC Rev. 3
Medium‐high and high technology
Division 20 Chemicals and chemical products 24
Division 21 Pharmaceuticals 24
Division 26 Computer, electronic and optical products 30
Division 27 Electrical equipment 31
Division 28 Machinery and equipment n.e.c. 29
Division 29 Motor vehicles, trailers and semi‐trailers 34
Division 30 Other transport equipment except ships and boats 35
Medium technology
Division 22 Rubber and plastics products 25
Division 23 Other non‐metallic mineral products 26
Division 24 Basic metals 27
Division 32 Other manufacturing except medical and dental instruments 32, 33
Division 33 Repair and installation of machinery and equipment n/a
Low technology
Division 10 Food products 15
Division 11 Beverages 15
Division 12 Tobacco products 16
Division 13 Textiles 17
Division 14 Wearing apparel 18
Division 15 Leather and related products 19
Division 16 Wood and products of wood and cork 20
Division 17 Paper and paper products 21
Division 18 Printing and reproduction of recorded media 22
Division 19 Coke and refined petroleum products 23
Division 25 Fabricated metal products except weapons and ammunition 28
Division 31 Furniture 36
Source: https://stat.unido.org/content/focus/classification‐of‐manufacturing‐sectors‐by‐technological‐intensity‐%2528isic‐
revision‐4%2529;jsessionid=561400724511B33A01F1C32CEA4300FB
36
Appendix 9: Female labor share premium, trading vs. non‐trading firms, role of sectoral technology intensity
Dependent Medium‐ to high‐tech Medium‐tech Low‐tech
variable:
fem_shisrt (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
exp imp gvc fdi exp imp gvc fdi exp imp gvc fdi
lnYisrt 0.2401** 0.4867*** 0.3457*** 0.5145*** 0.2336** 0.4872*** 0.3387*** 0.5138*** 0.2297** 0.4886*** 0.3392*** 0.5171***
(0.025) (0.000) (0.001) (0.000) (0.029) (0.000) (0.001) (0.000) (0.032) (0.000) (0.001) (0.000)
lnkisrt ‐0.1880 ‐0.1213 ‐0.1701 ‐0.1148 ‐0.1926 ‐0.1252 ‐0.1764 ‐0.1226 ‐0.1940 ‐0.1233 ‐0.1753 ‐0.1161
(0.157) (0.412) (0.201) (0.390) (0.148) (0.397) (0.185) (0.359) (0.144) (0.404) (0.188) (0.384)
lntfpisrt ‐1.4247** ‐1.8307*** ‐1.5137** ‐1.5336** ‐1.4478** ‐1.8429*** ‐1.5456** ‐1.5388** ‐1.4271** ‐1.8398*** ‐1.5087** ‐1.5363**
(0.020) (0.007) (0.013) (0.011) (0.019) (0.007) (0.011) (0.011) (0.020) (0.007) (0.014) (0.011)
expisrt 4.9530*** 4.7570*** 2.1834***
(0.000) (0.000) (0.001)
expisrt* techsrt ‐3.1893*** ‐2.1578** 3.4801***
(0.003) (0.042) (0.000)
impisrt 1.7565*** 2.2024*** 1.7326**
(0.001) (0.000) (0.018)
impisrt* techsrt 1.2590 ‐1.5460 0.2968
(0.277) (0.154) (0.743)
gvcisrt 4.3460*** 4.4776*** 1.9834**
(0.000) (0.000) (0.022)
gvcisrt* techsrt ‐2.0807 ‐2.7980** 3.1250***
(0.132) (0.039) (0.006)
fdiisrt 1.2969 2.4705*** ‐0.0978
(0.113) (0.004) (0.933)
fdiisrt* techsrt 1.2152 ‐4.9401*** 2.5589*
(0.541) (0.003) (0.088)
constant 70.3761*** 65.4233*** 68.8400*** 50.4608*** 70.4843*** 64.9612*** 68.9573*** 50.5601*** 70.5153*** 65.1186*** 68.9106*** 50.5018***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Obs. 14,485 12,601 14,485 14,476 14,485 12,601 14,485 14,476 14,485 12,601 14,485 14,476
R‐squared 0.54 0.53 0.54 0.54 0.54 0.53 0.54 0.54 0.54 0.53 0.54 0.54
F‐test1) 0.000 0.001 0.000 0.1122 0.000 0.001 0.000 0.0033 0.000 0.002 0.000 0.0374
Note: p*<0.1, p**<0.05, p***<0.01 (p‐values in parentheses). All regressions include country‐sector, subnational region and year fixed effects. See Appendix 8 for UNIDO
Classification of 2‐digit ISIC Sectors by technology intensity. 1) F‐test of joint significance between trade variable and interaction term.
37
Appendix 10: Econometric analysis on the gender wage gap, model
Drawing on the World Bank Enterprise Surveys, this paper provides firm‐level evidence based on a
cross‐section of roughly 29,000 manufacturing firms across 64 developing and emerging countries
covering the period 2010‐2017. In the absence of matched employer‐employee data which would be able
to test explicitly for the existence and direction of a gender wage gap, we follow the approach by Chen et
al. (2012) who test implicitly how a firm’s employment share correlates with its average wages. A negative
(positive, resp.) relationship between the female labor share and average wages suggests the existence
of a gender wage gap. The role of trade is assessed using interaction terms between the female labor
share and a firm’s trade status.
Following the model by Chen et al. (2012), a firm’s average wage rate, wage, is estimated as the
following function:
������������������������������������ ������ ������������ ������������������������������ ������������ ������������������������������ ������������ ������������������ ������������ ������������������������������������������ ������������ ������������������������������ ������������������������������������ ∗ ������������������������������
The variable age designates the number of years a firm has operated (in natural logarithms) to
measure experience. The next variable is employment, emp (in natural logarithms), to control for firm
size. k denotes capital intensity, measured as the replacement value of machinery in the firm divided by
output (in natural logarithms). skillsh is the share of skilled production workers in total production
workers. femsh is the share of female workers in total employment (see section 2).
More experience (������ 0 , larger firm size (������ 0 and a larger share of skilled production workers
(������ 0 are expected to be positively correlated with average wages. A larger capital intensity can be
positively ������ 0 or negatively ������ 0 associated with the demand for labor and thus average wages,
depending on whether capital and labor are complements or subsitutes. Due to women’s lower skill
intensity, a higher female labor share is assumed to be negatively linked to average wages ������ 0).
The most important element is the interaction term of the female labor share with the trade variable.
Due to their higher productivity and possibly higher labor standards, trading firms are expected to show
a lower wage gap compared to non‐trading firms (������ 0 . The association between the female labor share
and average wages for trading firms is given by the sum of ������ ������ , while the correlation for non‐trading
firms is only given by ������ .
The model considers four types of trading firms: exporters, importers, GVC participants and foreign
firms. The model also controls for country‐sector, subnational region and year fixed effects.
38
Appendix 11: Econometric analysis on the gender wage gap, regression results
Dependent variable: exp imp gvc fdi exp imp gvc fdi
lnwagesrt (1) (2) (3) (4) (5) (6) (7) (8)
lnageisrt 0.0719*** 0.0791*** 0.0723*** 0.0765*** 0.0707*** 0.0778*** 0.0711*** 0.0760***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
lnempisrt 0.0548*** 0.0522*** 0.0553*** 0.0522*** 0.0549*** 0.0491*** 0.0553*** 0.0523***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
lnkisrt ‐0.0867*** ‐0.0816*** ‐0.0867*** ‐0.0867*** ‐0.0871*** ‐0.0822*** ‐0.0872*** ‐0.0873***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
skillshisrt 0.0003 0.0000 0.0003 0.0003 0.0003 0.0000 0.0003 0.0003
(0.345) (0.947) (0.349) (0.394) (0.348) (0.949) (0.348) (0.406)
femshisrt ‐0.0023*** ‐0.0023*** ‐0.0022*** ‐0.0023*** ‐0.0023*** ‐0.0022*** ‐0.0021*** ‐0.0023***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
femshisrt*expisrt 0.0013** 0.0004
(0.027) (0.622)
femshisrt*impisrt 0.0013** 0.0001
(0.019) (0.837)
femshisrt*gvcisrt 0.0014** ‐0.0002
(0.041) (0.810)
femshisrt*fdiisrt 0.0049*** 0.0032**
(0.000) (0.011)
femshisrt*expisrt*trainisrt 0.0017**
(0.047)
femshisrt*impisrt*trainisrt 0.0025***
(0.000)
femshisrt*gvcisrt*trainisrt 0.0028***
(0.006)
femshisrt*fdiisrt*trainisrt 0.0032*
(0.055)
constant 5.7793 7.1339*** 5.7428 3.2136*** 7.0789*** 2.7783*** 7.1297*** 7.4725***
(.) (0.000) (0.996) (0.000) (0.000) (0.000) (0.000) (0.000)
Obs. 14,217 12,256 14,217 14,208 14,176 12,225 14,176 14,167
R‐squared 0.54 0.56 0.54 0.54 0.54 0.56 0.54 0.54
Note: p*<0.1, p**<0.05, p***<0.01 (p‐values in parentheses). All regressions include country‐sector, subnational region and
year fixed effects. Note: See Appendix 10 for the econometric model.
39