Does Offshoring Raise Female Employment in a Developing Country?
Hyejoon Im * & Hisamitsu Saito †May 11, 2021

Abstract
We investigate the effects of offshoring on female employment in a developing country as therecipient. We use unique information on outsourcing revenues from the establishment-level
dataset of Indonesia. After correcting for endogeneity of offshoring, we find large positiveeffects on female employment: a 10% increase in offshoring revenues in a plant raises its shareof female employment by 0.8–0.9 percentage points. These positive effects occur in production
occupations, but not in non-production ones. Further, these effects are more pronounced inindustries with a large unskilled workforce, low-technology industries, or light industries.

Keywords: offshoring, gender, employment, globalization, FDI
JEL codes: F14, F16, J16*†

Yeungnam University. Email: hi6w@yu.ac.kr
Hokkaido University. Email: saitoh@econ.hokudai.ac.jp1

1.

Introduction

Female participation in the labor market is important in both developed and developing
countries, especially in the latter. It affects not only macroeconomic performances, such as
economic growth and poverty rate of a country, but also microeconomic decisions ofindividuals or households, such as education, marriage, fertility, and child development.
Globalization, such as trade liberalization and offshoring, greatly influences female labor
participation in developing countries. This study considers the linkage between the two.
Specifically, we investigate the effects of offshoring on female employment in a developing
country as the recipient. We refer to offshoring as international outsourcing where a firm hires
another firm in a foreign country, whether the latter is an inside or outside firm, to perform amanufacturing task.
There are a few theoretical ideas on the relationship between offshoring and gender outcomein employment. Routine tasks are easier to automate and more easily offshored to be performedby unskilled labor with low wages in developing countries (Autor et al., 2003; Ebenstein et al.,2014). Some examples of such routine tasks include assembling parts in TVs and printers,
attaching pieces to metal or wooden articles, and sewing buttons and decorative trimmings toclothing. 1 It is often argued that women have a higher tolerance for repetitive tasks, greater
agility, and lower skill and wages (Standing, 1999; Fontana, 2003; Atkin, 2009). Thus, asdeveloped countries offshore routine tasks, the demand for female workers relative to maleworkers should rise in the host countries.
The next idea is related to the brain vs. brawn hypothesis that women have a lower level of1

Refer to Autor et al. (2003) for a formal definition of routine tasks.
2physical skill relative to men. 2 Offshoring often entails the adoption of new technology in thehost firms, such as automated or computerized production processes, at the request of firms inthe source countries that provide offshoring tasks. The new technology lowers the need forphysical skill at which men have an advantage and leads to an increase in female employment.
In other words, the new technology brought in by offshoring is complementary to female labor.
An opposing but related theory is also possible. Offshoring in the host firms involves imported
intermediated inputs embodying high-level technology or the transfer of such technology,
which requires skilled workers to operate. In this case, offshoring results in so called skillbiased technological change in the host firms. 3 Given that men are more educated than womenin developing countries, including Indonesia, offshoring is likely to increase male employment
but decrease female employment.
In summary, the above theories suggest that the effects of offshoring on female employment in
the host firms are ambiguous. The current study provides an empirical investigation regarding
this issue. We use the establishment-level dataset of Indonesia, which is novel in that it records
outsourcing revenues of plants. After correcting for endogeneity of offshoring with an
instrument based on an external demand shock, we find large positive effects of offshoring on
female employment: a 10% increase in offshoring revenues of a plant raises its female
employment share by 0.8–0.9 percentage points. The positive effects occur in production
occupations, but not in non-production ones. Furthermore, these effects are more pronounced
in industries with a large unskilled (or low-educated) workforce, low-technology industries, or2Juhn et al. (2014) originally proposed this idea in the context of trade and gender composition.
This idea is related to Acemoglu (2003), who argues that increased trade induces skill-biased technologicalchange in both developed and developing countries, causing the skill premium to increase in both groups.

33light industries.
Some authors examine the gender component in response to trade liberalization or offshoring. 4
Black and Brainerd (2004) and Ederington et al. (2010) test Becker’s (1957) famous hypothesisthat employers who discriminate against a particular group (e.g., female or black workers) aredisadvantaged and driven out of business when competition increases. They consider anincrease in foreign competition after trade liberalization and examine its effect on the genderwage gap or female employment.
Juhn et al. (2014) test the brain vs. brawn hypothesis in the context of trade liberalization. Using
Mexican establishment-level data, they show that an increase in export opportunities due to theNorth American Free Trade Agreement leads to new technology (e.g., a computerized
production system), which complements female labor by reducing the need for physical skill.
Gaddis and Pieters (2017) investigate the effect of Brazil’s trade liberalization on genderdifferences in local labor markets. They find no evidence that female employment and labor
participation increase relative to men in response to trade liberalization. Oishi and Wei (2018)use the same dataset as ours and find that tariff cuts are associated with a reduction in the shareof female employment in Indonesia.
Although many studies examine the effects of offshoring, they usually focus on the effects fromthe perspective of the source country, not the host country. 5 Further, they seldom consider
gender outcomes in conjunction with offshoring. One notable exception is Peri and Poole

4

Fontana (2003) provides a comprehensive review of early studies on the gender effects of tradeliberalization.
5
For instance, Ebenstein et al. (2014) consider the wage effects of offshoring in the U.S., and Hummels etal. (2014) examine those in Denmark.
4

(2012), who examine the effect of a firm’s exports (a proxy for offshoring) on its female
employment and relative demand for productive tasks (e.g., cognitive tasks) in the host country,
Brazil.
Foreign direct investment (FDI), particularly vertical FDI, is a kind of offshoring activity inthat it is international outsourcing, where tasks are performed by a foreign-affiliatedsubsidiary. 6 Ouedraogo and Marlet (2018) review the literature on the relationship between
FDI and gender outcomes and report that the effects of FDI on the gender wage gap are unclear.
Instead, the authors use two composite gender indices for a panel of 95 developing countries
and find that FDI inflows are positively associated with gender development (i.e., women are
better off) and negatively associated with gender inequality (i.e., decreasing gender gaps).
Helble and Takeda (2020) consider the effects of FDI on the Cambodian labor market and findno evidence that FDI reduces the gender wage gap or increases the probability of women
working in the formal sector.
In light of the aforementioned studies, our main contribution to the literature is that we examine
the effects of offshoring on female employment from the perspective of the host country. In
doing so, we use a more direct measure of offshoring activities of the host plants. Many studies
employ a rather indirect measure of offshoring. For instance, Feenstra and Hansen (1996a, b)
measure offshoring using the share of imported materials in total non-energy input purchases.
Ebenstein et al. (2014) use employment of foreign affiliates of U.S. multinational enterprises(MNEs) as a measure of offshoring.

6

Since we can identify whether a plant is a foreign affiliate of multinational enterprises in the data, we also
examine the effects of FDI on female employment. See subsection 4.3.
5

The rest of this paper is organized as follows. Section 2 describes the data and variables. Section
3 explains empirical methodology, and section 4 discusses results and provides robustness
checks. Finally, concluding remarks are presented in section 5.

2.

Data

The primary data source is the Annual Survey of Medium and Large Manufacturing
Establishment published by Statistics Indonesia (Badan Pusat Statistik or BPS) for 2001 to
2012. Its microdata are available for plants with 20 or more employees. This dataset contains
production and cost information at the plant level, including the total value of production, therevenue from manufacturing services, the number of workers in production and non-production
occupations by gender, and the book values of fixed capital assets, materials, electricity, andenergy inputs. The dataset also reports the plant’s industry classification for its main product,export and import status, and the share of foreign capital. The industry is defined based on thethree-digit International Standard Industrial Classification (ISIC) Revision 3.
The revenue from manufacturing services measures that from performing tasks outsourced byother firms, which is our main area of interest. According to the survey, “manufacturingservices are industrial activities that serve others. In these activities, the materials are suppliedby others while the processor is processing and receiving payments for these activities ascompensation.” However, the revenue from manufacturing services in the survey includes thatfrom activities outsourced by both domestic and foreign firms.
To disentangle offshoring activities, international outsourcing, we use an interaction term
6

between the plant’s revenue from manufacturing services and a dummy variable indicating theplant’s involvement in international trade. When Indonesian plants perform tasks outsourced
by their partner firms in foreign countries, they often import some inputs from their foreign
partners and/or export the finished products to them. To reflect this, we consider two types ofdummy variables. The first takes the value of one if the plant imports materials and exports its
products, while the second takes the value of one if the plant exports its products whether it
imports materials or not. The interaction term with the former (the latter) is supposed tomeasure the revenue from narrowly (broadly) defined offshored activities. 7 Of the total
observations in the sample, plants that engage in outsourcing activities account for 17.6%,
among which those engaging in the narrowly (broadly) defined offshoring hold 6.2% (19.3%).
The most and the least offshoring industries based on the broad definition of offshoring and theshare of female workers therein are presented in Table 1. A casual inspection suggests thatindustries with the highest intensity of offshoring have a higher female share compared to thosewith the lowest intensity of offshoring. Note that the four industries with the least offshoring
have zero revenues from outsourcing.
A plant’s value-added is obtained by subtracting intermediate consumption, such as materials,
electricity, and energy inputs, from its total revenue. The value-added obtained and the revenue
from manufacturing services are deflated by the wholesale price index (2000 = 100). Nonproduction workers are those engaged in non-manual work, such as factory supervision,
administration, logistics, and research and development. The initial capital stock is proxied by
fixed tangible assets deflated by the price index for gross fixed capital formation in Indonesia’s

7

It should be noted that we cannot rule out cases where the plant imports materials that are not used for theoffshored activities or where it exports goods that are unrelated with the offshored activities.
7System of National Accounts. Capital stock in the following periods is constructed using theperpetual inventory method, assuming a depreciation rate of 9% (Brandt et al., 2012). We
define MNEs or FDI as firms whose foreign capital share is greater than 20%. 8 We construct
instrument variables (IVs) based on world import demand, the data of which are obtained fromthe United Nations Comtrade Database. 9
To determine whether the effect of offshoring on female employment differs between industries,
we classify them in a couple of dimensions. First, the survey in 2006 provides detailed data on
workers’ educational attainment, allowing us to compute the share of skilled workers in the
total employment, including both male and female workers, for each plant. Since the share ofworkers with a college degree is very small (only 4%) in the Indonesian manufacturing
workforce, we define skilled (educated) workers as high-school graduates or above (see Table2). We obtain the average share of skilled workers across plants for each industry. Using themedian value, industries are classified into those employing skilled workers intensively andthose not. Second, we classify industries as high- and low-technology according to theclassification of the OECD (2003). However, because this classification is based on R&D
intensities of industries in OECD countries, it does not necessarily reflect the technology levelof Indonesian manufacturing. Third, following Oishi and Wie (2018), we classify industries
into light and heavy. 10

8According to Blalock and Gertler (2009), the samples of foreign-affiliated firms obtained under thisdefinition are mostly equivalent to those doing business under the foreign capital investment licenses inIndonesia.
9
We use the concordance table between Harmonized System (HS) codes and Indonesian product codes
provided by BPS to construct the instrument variables.
10
Light industries include food and beverages, textiles, wooden commodities, paper and printing, furniture,and recycling. Heavy industries include chemicals, petroleum and rubber, metal products, machinery, andtransportation.
8

Table 3 shows summary statistics. Female workers account for 41% of total employment or
production occupation in average Indonesian plants. By contrast, the share of female workers
in non-production occupations is six percentage points smaller than the former two types ofshares. As for the export and import status of plants, about 20% of plants export products while
only 5% engage in both exporting products and importing materials.
Figure 1 shows the average share of female workers along with the logarithm values of theoutsourcing revenues of plants in the narrowly and broadly defined offshoring over the sample
period. The average share of female workers increased before the global financial crisis in
2008–2009 and recovered to the pre-crisis level in 2011. The outsourcing revenues of narrowly
defined offshored plants are smaller than those of broadly defined ones, but both series exhibit
similar fluctuations. The figure shows that, except for 2005–2008, the female share and theoutsourcing revenues tend to move in a similar fashion. We investigate the relationship between
them more formally in the following section.

3.

Empirical Strategy

We consider the following econometric model.

𝑓𝑓𝑓𝑓𝑓𝑓_𝑒𝑒𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖𝑖𝑖 = α + β1 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑖𝑖𝑖𝑖 + β2 𝑑𝑑_𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖 + β3 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑖𝑖𝑖𝑖 × 𝑑𝑑_𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖 +𝑿𝑿′𝒊𝒊𝒊𝒊 𝛃𝛃 + 𝜃𝜃𝑗𝑗𝑗𝑗 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖

9(1)

where i is an index for a plant, j is an index for an industry, and t is an index for time or year. 11
The dependent variable is the share of female employment of plant i at time t. 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑖𝑖𝑖𝑖 is thelogarithm of outsourcing revenues of plant i at time t. 𝑑𝑑_𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖 is a dummy variable of plantsthat engage in international trade, for which we consider two types: plants that import materials

and export products at the same time (𝑑𝑑_𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡1𝑖𝑖𝑖𝑖 ) and those that export products (𝑑𝑑_𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡2𝑖𝑖𝑖𝑖 ).
𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑖𝑖𝑖𝑖 × 𝑑𝑑_𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖 is the interaction term between outsourcing revenues and one of the twodummy variables. 𝑿𝑿′𝒊𝒊𝒊𝒊 is a row vector of control variables for plant characteristics such as

value-added, the ratio of capital to value-added, and a dummy variable for MNEs or FDI. 𝜃𝜃𝑗𝑗𝑗𝑗

is the fixed effects for an industry-time pair and controls for any industry-time specific shocks,
such as industry-wide technology shocks for a given year. Note that the industry fixed effects𝜃𝜃𝑗𝑗 and the time fixed effects 𝜃𝜃𝑡𝑡 are subsumed into 𝜃𝜃𝑗𝑗𝑗𝑗 . 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖 is a random variable.

The interaction term is the variable of interest and indicates offshoring activities of plants.
Depending on the trade status of plants, we have two definitions of offshoring. If plants thatengage in outsourcing tasks conduct both importing and exporting, the interaction term
( 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑖𝑖𝑖𝑖 × 𝑑𝑑_𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡1𝑖𝑖𝑖𝑖 ) indicates offshoring in a narrow sense. If plants that engage inoutsourcing tasks conduct exporting, the interaction term (𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑖𝑖𝑖𝑖 × 𝑑𝑑_𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡2𝑖𝑖𝑖𝑖 ) indicatesoffshoring in a broad sense. If offshoring increases the demand for female labor, we should
expect positive coefficients for β3 in both cases. Note that, given that we use an industry-time

fixed effect in equation (1), we are essentially comparing plants within a given industry-timepair to identify β3 . In other words, we identify the effects of offshoring through variations

11

When a plant produces multiple products, the industry classification is based on its main product.
10

across plants for a given industry-time pair. 12
Offshoring activities can be endogenous. Plants that employ many female workers may havemore offshored tasks. It is also possible that unobserved technology change is correlated withboth a plant’s decision to upgrade technology for attracting offshoring and to change thedemand for female labor. On the one hand, as noted in the introduction, plants that performoffshored tasks adopt a new automated production system that lowers the need for physicalskill and increases female employment. On the other hand, the adoption of new technology
may increase the demand for skilled workers to work with it. Given that skilled labor isdisproportionately male in the Indonesian workforce, 13 the new technology is likely toincrease the relative demand for male workers. To address the potential endogeneity ofoffshoring, we consider an instrumental variable strategy. A valid instrument should becorrelated with offshoring activities of plants, but should not be directly associated with theirtechnology shocks or female employment. As an instrument, we consider the weighted averageof world import demand, with the weight being plant j’s initial production share of product k:
𝑰𝑰𝑰𝑰𝒗𝒗𝒗𝒗𝒗𝒗
𝒊𝒊𝒊𝒊 ≡ � 𝝎𝝎𝒊𝒊𝒊𝒊𝒕𝒕𝟎𝟎 𝑾𝑾𝑾𝑾𝑾𝑾𝒌𝒌𝒌𝒌

(2)

𝒌𝒌

where 𝑊𝑊𝑊𝑊𝑊𝑊𝑘𝑘𝑘𝑘 is the value of world imports of product k at time t, less imports from Indonesia,and 𝜔𝜔𝑗𝑗𝑗𝑗𝑡𝑡0 is the share of product k in the total production of plant i at the initial year t0. We

assume that the instrument is positively correlated with plant i’s revenue from outsourcing. 14
12

When we use the plant fixed effects instead of the industry-time fixed effects, the results are largely
insignificant. This is due to the lack of variations within plants during the sample period.
13
Male workers with a high school diploma or above comprised 29% of the Indonesian manufacturing
workforce in 2006, while female counterparts made up 20%. See Table 2.
14Here, an implicit assumption is that all products manufactured by plant j are subject to outsourcing.
11This instrument is a modification of Hummel et al.’s (2014) instruments. The idea is that thefluctuations in offshoring activities over the sample period are driven by external demand
shocks to Indonesian manufacturing plants. Our instrument captures such shocks and, thus,
helps identify the causal effects of offshoring. We use a plant’s share of product k in the initial
year to reduce simultaneity bias, because female employment and offshoring may be
determined contemporaneously. The novelty of the instrument variable is that it varies in termsof plant and time. We instrument the interaction of plant i’s outsourcing revenue and its tradestatus with the interaction of the instrument and plant i’s trade status.
Alternatively, we use the share of world import demand for product k in world total importdemand as an instrument:
𝑰𝑰𝑰𝑰𝒔𝒔𝒔𝒔𝒊𝒊𝒊𝒊 ≡ � 𝝎𝝎𝒊𝒊𝒊𝒊𝒕𝒕𝟎𝟎
𝒌𝒌

𝑾𝑾𝑾𝑾𝑾𝑾𝒌𝒌𝒌𝒌
𝑾𝑾𝑾𝑾𝑾𝑾𝒕𝒕(3)

where 𝑊𝑊𝑊𝑊𝑊𝑊𝑡𝑡 is the total value of world imports at time t, less imports from Indonesia. We use
each of these two instruments (equations (2) and (3)) in two-stage least squares (TSLS)

estimations.
The effects of offshoring on female labor can differ depending on industries, because offshoring
activities are likely to be concentrated in particular industries, as evidenced in Table 1. First,
the effects are expected to be more prominent in industries with a large unskilled (low-educated)
workforce, because tasks under offshoring are likely to be routine and unskilled production
activities. Second, they are likely to be stronger in industries with low technology where
production tasks are relatively simple and routine and, thus, are easy to be offshored. In thesame vein, the effects of offshoring are expected to be more pronounced in light industries (e.g.,12food and beverages and textiles) than in heavy industries. In fact, the above discussions arelargely consistent with traditional trade theory. Indonesia, developing countries in general, isabundantly endowed with unskilled workers and specializes in industries with unskilledworkforce, low-technology industries, or light industries. Thus, the Heckscher–Ohlin theory
implies that offshoring activities like international trade are likely to concentrate on suchindustries. Therefore, it is worthwhile examining if the effects of offshoring are particularly
stronger for these industries.

4.

Results

4.1. Effects on Female Employment
The estimation results of equation (1) are reported in Table 4. The first two columns show
ordinary least squares (OLS) results. The coefficients of the interaction terms are positive andstatistically significant (columns (1) and (2) in Table 4). Thus, whether offshoring is narrowly
or broadly defined, as a plant performs more offshoring tasks, its share of female employment
increases.
As for control variables, when a plant’s value-added or the ratio of capital to value-added
increases, its share of female labor decreases. The share of female labor is higher for affiliates
of MNEs. For plants that do not engage in international trade, an increase in outsourcing
decreases the share of female workers. Among plants without outsourcing revenues, those thatengage in both importing and exporting ( 𝑑𝑑_𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡1𝑖𝑖𝑖𝑖 ) and those that conduct exporting13

(𝑑𝑑_𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡2𝑖𝑖𝑖𝑖 ) have higher shares of female workers. 15
To address the endogeneity of offshoring, we consider instrument variables. Columns (3) to (6)in Table 4 show the instrument estimation results. The results stay the same whether we use
world import demand for product k (columns (3) and (5)) or its share in world total importdemand (columns (4) and (6)) as an instrument. In terms of the economic magnitude of thecoefficients, a 10% increase in offshoring revenues raises the female employment share by 0.8–
0.9 percentage points. Our findings contrast those of Peri and Poole (2012), who use a firm’s
intensity of exports as a proxy for offshoring and find that it has no statistical relationship withthe share of female workers in Brazilian firms.
It is noteworthy that the coefficients of offshoring in TSLS are much larger than those in OLS.
In other words, the effects of offshoring are underestimated in OLS, but, after correcting for
endogeneity, the causal effects of offshoring on female employment are considerably large.
Note that this result is not due to a weak instrument problem, as our instruments are relatively
strong. Stock and Yogo (2002) suggest that to avoid a weak instrument problem, the first-stage
F statistic should be above 10. The F statistics at the first stage are slightly less than 10 for thevolume of world import demand (equation (2)) and are more than 10 for its share (equation(3)). In TSLS, the results for all control variables qualitatively stay the same as those in OLS,
except for the dummy variables for the trade status of a plant, whose coefficients are negativein TSLS.
Because tasks that developed countries usually offshore are production ones rather than non-15

Oishi and Wie (2018) find similar results: The share of female labor is higher for exporting plants thanfor non-exporting plants.
14

production ones (Ebenstein et al., 2014), we expect that the previous positive effects occur inthe former rather than in the latter. This prediction is tested in Table 5. In OLS, the coefficientsof the interactions are indeed significantly positive only in production occupation (columns (1)to (4) in Table 5). When we use the share of world import demand as an instrument, the results
remain the same (columns (5) to (8) in Table 5). 16 Again, the coefficients of the offshoringterms in TSLS are much larger than those in OLS. In summary, the affirmative effects ofoffshoring on female employment are concentrated in production occupation.

4.2. Heterogeneous Effects over Industries
We examine whether the effects of offshoring on female employment vary depending on
industries. As previously discussed, jobs that are subject to offshoring are production-related,
so we concentrate on the effects in production occupations in the following analyses. 17First, we classify industries based on education attainment of employees of plants into two
subsamples: industries that employ unskilled (less than high-school diploma) workers
intensively and industries that employ skilled workers intensively. We only report the results
for the former group, as those for the latter group are insignificant in all estimations. 18 Table
6 and Table 7 show the results of OLS and TSLS, respectively. For comparison, columns (1)and (5) in these tables reproduce the results for the full sample in Table 5. In OLS, regardless

16

Using the volume of world import demand as an instrument gives similar results, which are available
upon request.
17
As expected, we find that the heterogeneous effects of offshoring are all insignificant for non-production
occupations. These results are available upon request.
18
This is also true for the other cases: The results for high-technology or heavy industries are insignificant.
These results are available upon request.
15

of which offshoring measures are used, the coefficients of the interaction terms for industries
with many unskilled workers do not differ from those for the full sample (columns (2) and (6)in Table 6). In contrast, when using the instrument, the coefficients for industries with a large
unskilled workforce are much bigger than those in the full sample (columns (2) and (6) in Table
7). This result suggests that the effects of offshoring on female production workers are more
pronounced in unskilled-labor intensive industries.
Second, we use the OECD classification of the level of technology of industries. Columns (3)and (7) in Table 6 and Table 7 show the results for low-technology industries. Again, thecoefficients of the interactions are similar between the full sample and the subsample of lowtechnology industries in OLS. However, in TSLS, they are substantially larger than in the fullsample. Therefore, the effects of offshoring on female production workers are stronger for lowtechnology industries.
Lastly, the results for light industries are presented in columns (4) and (8) in Table 6 and Table
7. In the instrument estimation, the coefficients of the interactions for light industries are muchlarger than those in the full sample, suggesting that the effects of offshoring on female
production workers are more prominent for light industries.
In summary, the effects of offshoring on female production workers are more pronounced inindustries with large unskilled workforce, low-technology industries, or light industries. One
may wonder if these results are really caused by offshoring or simply driven by the fact thatthe majority of Indonesian plants belong to these industries 19 and that they tend to employ

19

In the sample, 86% of plants belong to industries employing unskilled workers intensively, 92% belong
to low-technology industries, and 73% belong to light industries.
16many female workers and have offshoring activities. This is essentially an endogeneity concern,
which we address using the instrumental variable strategy. We reiterate that our instrument isbased on a demand-driven shock, which is external to the Indonesian economic or industrial
environment. Thus, the instrument allows us to identify the causal effects of offshoring. In fact,
this issue can be understood by comparing the OLS and TSLS results. As previously noted, the
OLS estimates of the coefficients of the interaction terms are quite similar between the fullsample and the three industry subsamples, whereas the IV estimates differ greatly between
them. In other words, after correcting for endogeneity with an instrument, we can identify theindustry heterogeneities in the effects of offshoring on female workers.

4.3. Robustness Checks
In this study, offshoring covers international outsourcing, regardless of whether outsourced
tasked are performed by an outside firm or affiliated firm in foreign countries. The latter isinternational production by MNEs or FDI. Since we can identify plants that are owned byforeign firms in the data, we consider FDI as an alternative measure of offshoring andinvestigate its effects on female employment. FDI is categorized as either horizontal or verticalinvestment. Vertical FDI involves a fragmentation of production tasks across countries, whilehorizontal FDI is mainly motivated by serving a foreign market. Thus, the former is more
relevant to offshoring activities than the latter. Since FDI in our data includes both types of
FDI, we distinguish vertical type by creating the interaction term between FDI and the dummy
variable for the trade status of a plant in analogy to our offshoring measures.
The results for all occupations and production and non-production occupations are reported in17

Table 8. The effects of FDI on female workers are not significant for FDI plants engaging inboth importing and exporting (the first three columns in Table 8). However, the effects are
significant for FDI plants engaging in exporting (the last three columns in Table 8). As in theprevious findings based on our offshoring measures, the effects are significant in production
occupations, but not in non-production ones (columns (5) and (6) in Table 8). Remarkably, thecomparison of the coefficients of FDI and those of FDIⅹd_trade2 in columns (5) and (6) inTable 8 reveals that we have different female employment effects over the types of FDI. The
former indicates the effects for horizontal FDI because this type does not involve any exports,
while the latter indicates the effects for vertical FDI. Horizontal FDI increases female
employment in non-production occupations, such as supervision and administration, whereas
vertical FDI raises that in production occupations. Overall, when we constrain offshoring
activities to vertical international production by MNEs, the results are similar to those whenusing our offshoring measures.
As another robustness check, we use relative employment of women to men as a dependent
variable. Table 9 shows the TSLS results with the world import share as an instrument variable,
which are qualitatively similar to those when using the female employment share. We find thatboth the narrowly and broadly defined offshoring raise the ratio of female to male workers,
although the level of significance is marginal for the narrowly defined offshoring. Again, theseeffects prevail only in production occupations. In terms of magnitudes, a 1% increase in
offshoring revenues for the broadly defined offshoring plants raises their ratio of female tomale labor by 0.8 percentage points in all occupations and 1.9 percentage points in production
occupations, respectively.

18

Offshoring may increase productivity of a plant, which, in turn, affects a plant’s size, such as
value-added and the ratio of capital to value-added (Hummels et al., 2014). Thus, thesevariables can be outcome variables. As a robustness check, we run regressions excluding them.
We find that the results are very robust to this exclusion (see Table 10). It is noteworthy thatthe first-stage F statistics in TSLS estimations are larger than before, suggesting that our
instrument variables become stronger when excluding these potentially endogenous variables(columns (3)–(6) in Table 10).

5. Conclusions
We empirically investigate the effects of international outsourcing or offshoring on female
employment in a developing country as the recipient. We use the plant-level panel data ofIndonesia, which is novel in that they include the information on outsourcing revenues of plants.
We use an instrumental variable, which is based on an external demand shock, to address theendogeneity of offshoring and find that offshoring considerably increases the female
employment: a 10% increase in the offshoring revenues of a plant raises its share of female
employment by 0.8–0.9 percentage points. These effects occur primarily in production
occupations, but not in non-production jobs. When we constrain offshoring activities to verticalproduction of foreign affiliates of MNEs, the results are qualitatively similar. Furthermore, wefind that the positive effects of offshoring are more prominent in industries with a large
unskilled workforce, low-technology industries, or light industries. Our findings suggest thatwhen developed countries offshore routine tasks to developing countries, more women in the19

recipient country join in the labor force because they have a comparative advantage in suchtasks relative to men. Furthermore, the findings suggest that the new technology introduced byoffshoring tends to complement rather than substitute female labor.
It is certainly good news that offshoring enhances female labor participation and, perhaps, theirwelfare in a developing country. However, to fully appraise the effects of offshoring on female
workers, we have to consider what type of jobs it offers to female workers. Jobs brought byforeign investment or offshoring could be low-paying, irregular, or informal jobs (Standing,
1999; Fontana, 2003). We leave the investigation of the quality of offshoring jobs that female
labor takes up for future research.

20

References
Acemoglu, D. (2003). Patterns of skill premia. The Review of Economic Studies, 70(2): 199230.
Atkin, D. (2009). Working for the Future: Female Factory Work and Child Health in Mexico.
Mimeo.
Autor, D., F. Levy, and R. Murnane (2003). The Skill Content of Recent Technological
Change: An Empirical Exploration. The Quarterly Journal of Economics, 118(4): 1279–1333.
Badan Pusat Statistik (Statistics Indonesia), various years, the Annual Survey of Medium andLarge Manufacturing Establishment.
Becker, G. (1957). The Economics of Discrimination, Second Edition. Chicago: University of
Chicago Press.
Black S. and E. Brainerd (2004). Importing Equality? The Impact of Globalization on Gender
Discrimination. ILR Review, 57(4): 540-559.
Blalock, G. and P. Gertler (2009). How Firm Capabilities Affect Who Benefits from Foreign
Technology. Journal of Development Economics, 90: 192–199.
Brandt, L., J. Biesebroeck, and Y. Zhang (2012). Creative Accounting or Creative
Destruction? Firm-Level Productivity Growth in Chinese Manufacturing. Journal of
Development Economics, 97: 339–351.
Ebenstein, A., A. Harrison, M. McMillan, and S. Phillips (2014). Estimating the Impact ofTrade and Offshoring on American Workers Using the Current Population Surveys. Review of
Economics and Statistics, 96(4): 581–595.
Ederington, J., J. Minier, and K. Troske (2009). Where the Girls are: Trade and Labor Market
Segregation in Colombia. IZA Discussion Paper No. 4131.
Feenstra, R. and G. Hanson (1996a). Foreign Investment, Outsourcing and Relative Wages.
Political Economy of Trade Policy: Papers in Honor of Jagdish Bhagwati, Edited by R.
Feenstra, G. Grossman, and D. Irwin.
Feenstra, R. and G. Hanson (1996b). Globalization, Outsourcing, and Wage Inequality.
American Economic Review, 240-245.
Fontana, M. (2003). The Gender Effects of Trade Liberalisation in Developing Countries: a
Review of the Literature. Discussion Papers 101. University of Sussex.
Gaddis, I. and J. Pieters (2017). The Gendered Labor Market Impacts of Trade Liberalization:
Evidence from Brazil. Journal of Human Resources, 52(2): 457-490.
21

Helble, M. and A. Takeda (2020). Do Women Benefit from FDI? FDI and Labor Market
Outcomes in Cambodia. Working paper No. 1093. Asian Development Bank Institute.
Hummels, D., R. Jørgensen, J. Munch, and C. Xiang (2014). The Wage Effects of Offshoring:
Evidence from Danish Matched Worker-Firm Data. American Economic Review, 104: 1597–
1629.
Juhn, C., G. Ujhelyi, and C. Villegas-Sanchez (2014). Men, Women, and Machines: How
Trade Impacts Gender Inequality. Journal of Development Economics, 106: 179-193
Kasahara, H., Y. Liang, and J. Rodrigue (2016). Does importing intermediates increase thedemand for skilled workers? Plant-level evidence from Indonesia. Journal of International
Economics, 102: 242-261
OECD (2003). OECD Science, Technology and Industry Scoreboard, Paris: OECD
Publishing.
Oishi, Y. and D. Wie (2018). Importing Inequality: Trade Liberalization, Technology, and
Women’s Employment. GRIPS Discussion Paper 18-16. National Graduate Institute for
Policy Studies.
Ouedraogo, R. and E. Marlet (2018). Foreign Direct Investment and Women Empowerment:
New Evidence on Developing Countries. IMF Working Papers 2018/025. International
Monetary Fund.
Peri, G. and J. Poole (2012). Offshoring and Firm-Level Task Composition: Is There a
Gender Component? Mimeo.
Standing, G. (1999). Global feminization through flexible labor: A theme revisited. World
Development, 27(3): 583-602.
Stock, J. and M. Yogo (2002). Testing for Weak Instruments in Linear IV Regression, NBER
Working Papers 284. National Bureau of Economic Research.

22

Figure 1. Offshoring Revenues and the Average Share of Female Workers for 2001–2012

Notes: Narrowly defined offshored plants are those that engage in outsourcing tasks and conduct both
importing and exporting. Broadly defined offshored plants are those that engage in outsourcing tasks
and conduct exporting.
Source: Authors’ calculation based on BPS, Annual Survey of Medium and Large Manufacturing
Establishment, various years.

23

Table 1. Most and Least Offshoring Industries
Panel A: Top 10 Most Offshoring Industries
Rank
1
2
3
4
5
6
7
8
9
10

Industries
Manufacture of electronic valves and tubes and other electronic components
Rubber and from rubber-product industry
Cement, lime and plaster, as well as cement and lime product industry
Manufacture of textile apparel (except fur apparel)
Manufacture of paper, paper goods, and the like
Manufacture of products of wood, rattan, bamboo and the like
Spinning, weaving, and end textile processing
Processing and preserving meat, fish, fruits, vegetables, oils and fats
Manufacture of tobacco product
Manufacture of other metal goods and metal-goods production services

Female
share
0.59
0.24
0.12
0.60
0.32
0.33
0.48
0.44
0.70
0.25

Panel B: Top 10 Least Offshoring Industries
Rank
10
9
8
7
6
5
4
3
2
1

Industries
Recycling of metal goods
Recycling of non-metal goods
Foundry industry
Manufacture of watches/clock, bell, and the like
Manufacture of coal productIndustry of four-wheeled or more motor vehicles assembling
Kapok/cotton industry
Manufacture of fur apparelReproduction recording media, film and video
Manufacture of aircraft and its equipment and aircraft reparation

Female
share
0.31
0.42
0.10
0.49
0.17
0.07
0.76
0.49
0.32
0.30

Notes: Offshoring is based on the broad definition. The least four offshoring industries (the industries in italics)
have zero revenues from outsourcing.

24

Table 2. Education Attainment of Indonesian Manufacturing Workers in 2006
Educational background
Male%
Female%Total%
Less than primary
13,193,074 24 19,217,894 34 32,410,968 29
Primary
17,870,772 32 18,263,995 32 36,134,767 32
Junior high8,659,083 16 7,434,578 13 16,093,661 14High13,567,054 24 9,779,335 17 23,346,389 21
University
2,568,103
5 1,614,107
3
4,182,210
4
Total
55,858,086 100 56,309,909 100 112,167,995 100
Notes: Age of workers is equal to or above 25.
Source: BPS, Annual Survey of Medium and Large Manufacturing Establishment of 2006.

25

Table 3. Summary Statistics
Variable
Mean
Std. Dev.
Share of female workers in total employment
0.414
0.299
Share of female workers in production occupation
0.411
0.328
Share of female workers in non-production occupation
0.355
0.276
Ratio of female to male workers in total employment
2.147
5.767
Ratio of female to male workers in production occupation
2.401
9.597
Ratio of female to male workers in non-production occupation
0.746
1.444
Log of value-added in 1,000 rupiahs (2000=100)
13.114
1.964
Log of capital to value-added ratio
0.221
1.554
Log of revenue from manufacturing services in 1,000 rupiahs (200
2.138
4.761
0=100)
Dummy variable
FDI: one if a plant is a foreign affiliate of MNEs; zero otherwise
0.065
0.246
d_trade1: one if a plant exports products and imports materials; ze
0.054
0.225
ro otherwised_trade2: one if a plant exports products; zero otherwise
0.188
0.391
Source: BPS, Annual Survey of Medium and Large Manufacturing Establishment, various years.

26

Table 4. Effects on Female Employment in All Occupations
Variablesln(value-added)ln(capital/value-added)
FDIln(outsourcing)d_trade1ln(outsourcing)ⅹd_trade1
d_trade2(1)-0.0258***(0.000964)-0.0228***(0.000924)
0.0351***(0.00609)-0.00100***(0.000269)
0.0848***(0.00613)
0.00309***(0.000694)

ln(outsourcing)ⅹd_trade2
Observations
R-squared
IndustryⅹYear FE
Instrument
Kleibergen-Paap F

173,376
0.310
Yes

(2)
-0.0278***(0.000959)-0.0231***(0.000920)
0.0333***(0.00610)-0.00136***(0.000288)

0.0766***(0.00358)
0.00271***(0.000463)
173,376
0.314
Yes

(3)
-0.0175***(0.00554)-0.0182***(0.00176)
0.0385***(0.00983)-0.0349**(0.0171)-0.153*(0.0803)0.0893***(0.0292)(4)-0.0219***(0.00603)-0.0188***(0.00191)
0.0141(0.0104)-0.0465**(0.0196)

165,222-0.106***(0.0333)
0.0905***(0.0158)
165,222

Yes
WID
8.282

Yes
WID
7.342

(5)
-0.0228***(0.00484)-0.0197***(0.00157)
0.0323***(0.00912)-0.0181(0.0148)-0.136*(0.0703)0.0849***(0.0256)(6)-0.0267***(0.00518)-0.0201***(0.00169)
0.0102(0.00981)-0.0302*(0.0167)

165,222-0.0915***(0.0294)
0.0848***(0.0139)
165,222Yes
WID share
11.07

Yes
WID share
10.08

Notes: The dependent variable is the share of female employment in the total employment of a plant.
The instrument is either world import demand for product k (columns (3) and (4)) or its share in world
total import demand (columns (5) and (6)). All specifications include a pair of three-digit industry andyear fixed effects. Robust standard errors clustered at plant level are in parentheses. *, **, and ***indicate significance at 10%, 5%, and 1%, respectively.

27

Table 5. Effects on Female Employment in Production and Non-Production Occupations(1)

Variablesln(value-added)ln(capital/value-added)
FDIln(outsourcing)d_trade1ln(outsourcing)ⅹd_trade1
d_trade2ln(outsourcing)ⅹd_trade2
Observations
R-squared
IndustryⅹYear FE
Instrument
Kleibergen-Paap F

(2)
(3)
(4)
NonNonProduction
Production
Production
Production-0.0264*** -0.0123*** -0.0287*** -0.0131***(0.00106)(0.000764)(0.00105)(0.000767)-0.0246*** -0.00350*** -0.0250*** -0.00364***(0.00101)(0.000825)(0.00101)(0.000826)
0.0315***0.0145***0.0291***0.0122***(0.00680)(0.00427)(0.00680)(0.00424)-0.00134*** 0.000462* -0.00170***0.000420(0.000295)(0.000236)(0.000315)(0.000257)
0.0948***0.0165***(0.00684)(0.00443)
0.00345***
2.47e-06(0.000763)(0.000528)
0.0872***0.0215***(0.00397)(0.00321)
0.00291***
0.000257(0.000514)(0.000401)
173,339
145,656
173,339
145,656
0.308
0.073
0.313
0.074
Yes
Yes
Yes
Yes

(5)
Production-0.0230***(0.00524)-0.0213***(0.00171)0.0283***(0.00997)-0.0198(0.0160)-0.134*(0.0740)0.0880***(0.0269)(6)
NonProduction-0.0307***(0.00626)-0.00708***(0.00180)-0.00874(0.0102)
0.0554***(0.0193)0.0181(0.0603)
0.00586(0.0215)

165,198

138,967Yes

WID shareYes

WID share(7)
Production-0.0272***(0.00561)-0.0218***(0.00184)
0.00466(0.0107)-0.0330*(0.0181)(8)
NonProduction-0.0324***(0.00667)-0.00761***(0.00191)-0.00949(0.00978)0.0600***(0.0223)-0.0906***(0.0317)0.0897***(0.0150)
165,198

0.0460(0.0321)-0.00536(0.0142)
138,967Yes

WID shareYes

WID share

11.08
6.680
10.09
5.808
Notes: In odd (even) columns, the dependent variable is the share of female employment in the production (non-production) occupation of a plant. The
instrument is the share of world import demand for product k in world total import demand. All specifications include a pair of three-digit industry and year
fixed effects. Robust standard errors clustered at plant level are in parentheses. *, **, and *** indicate significance at 10%, 5%, and 1%, respectively.
28

Table 6. Heterogeneous Effects on Female Employment in Production Occupation: OLS
(1)

(2)
(3)
(4)
(5)
(6)
(7)
(8)
UnskilledLowUnskilledLowworker
technology
Lightworkertechnology
Light
Variables
Full sample
intensiveindustries
industries
Full sample
intensiveindustries
industriesln(value-added)-0.0264*** -0.0271*** -0.0274*** -0.0286*** -0.0287*** -0.0297*** -0.0299*** -0.0315***(0.00106)(0.00118)(0.00112)(0.00128)(0.00105)(0.00117)(0.00111)(0.00127)ln(capital/value-added)-0.0246*** -0.0261*** -0.0255*** -0.0291*** -0.0250*** -0.0266*** -0.0259*** -0.0299***(0.00101)(0.00113)(0.00107)(0.00124)(0.00101)(0.00112)(0.00106)(0.00123)
FDI
0.0315***0.0309***0.0300***0.0413***0.0291***0.0294***0.0279***0.0378***(0.00680)(0.00827)(0.00770)(0.00964)(0.00680)(0.00833)(0.00775)(0.00963)
ln(outsourcing)-0.00134*** -0.00168*** -0.00158*** -0.00150*** -0.00170*** -0.00202*** -0.00188*** -0.00176***(0.000295)(0.000349)(0.000321)(0.000377)(0.000315)(0.000378)(0.000344)(0.000407)
d_trade1
0.0948***0.112***
0.105***
0.120***(0.00684)(0.00829)(0.00774)(0.00944)ln(outsourcing)ⅹd_trade1
0.00345*** 0.00328*** 0.00314*** 0.00330***(0.000763)(0.000971)(0.000891)(0.00108)d_trade2
0.0872***
0.0950***0.0919***0.106***(0.00397)(0.00432)(0.00416)(0.00470)ln(outsourcing)ⅹd_trade2
0.00291*** 0.00267*** 0.00254*** 0.00251***(0.000514)(0.000597)(0.000562)(0.000648)
Observations
173,339
149,625
159,530
125,646
173,339
149,625
159,530
125,646
R-squared
0.308
0.272
0.290
0.236
0.313
0.278
0.295
0.244
IndustryⅹYear FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Notes: This table reports OLS results. The dependent variable is the share of female employment in the production occupation of a plant. Columns (1) and(5) reproduce the results for the full sample in Table 5. Columns (2) and (6) present the results for industries that employ unskilled workers intensively.
Columns (3) and (7) present the results for low-technology industries, while columns (4) and (8) present those for light industries. All specifications include
a pair of three-digit industry and year fixed effects. Robust standard errors clustered at plant level are in parentheses. *, **, and *** indicate significance at
10%, 5%, and 1%, respectively.
29

Table 7. Heterogeneous Effects on Female Employment in Production Occupation: TSLS
(1)

(2)
Unskilledworker
Full sample intensive-0.0230*** -0.0257***(0.00524)(0.00477)-0.0213*** -0.0226***(0.00171)(0.00197)
0.0283***0.0169(0.00997)(0.0158)-0.0198-0.0184(0.0160)(0.0149)-0.134*-0.312***(0.0740)(0.113)0.0880***0.161***(0.0269)(0.0410)(3)Lowtechnology
industries-0.0243***(0.00530)-0.0217***(0.00196)
0.0200(0.0144)-0.0221(0.0164)-0.305***(0.109)0.154***(0.0394)

(4)

(6)
(7)
(8)
UnskilledLowLight
workertechnology
Light
Variables
industries Full sample intensive
industries
industriesln(value-added)-0.0194*** -0.0272*** -0.0290*** -0.0270*** -0.0227***(0.00529)(0.00561)(0.00452)(0.00515)(0.00515)ln(capital/value-added)-0.0226*** -0.0218*** -0.0223*** -0.0216*** -0.0228***(0.00244)(0.00184)(0.00203)(0.00205)(0.00256)
FDI
0.0453***0.00466-0.00591-0.00382
0.00942(0.0174)(0.0107)(0.0142)(0.0131)(0.0152)ln(outsourcing)-0.0463**-0.0330*-0.0415*** -0.0450*** -0.0760***(0.0182)(0.0181)(0.0156)(0.0174)(0.0200)
d_trade1-0.399***(0.128)ln(outsourcing)ⅹd_trade1
0.187***(0.0441)d_trade2-0.0906*** -0.121***-0.127***-0.151***(0.0317)(0.0304)(0.0310)(0.0357)ln(outsourcing)ⅹd_trade2
0.0897***0.114***
0.112***
0.131***(0.0150)(0.0152)(0.0149)(0.0172)
Observations
165,198
142,730
152,173
119,174
165,198
142,730
152,173
119,174
IndustryⅹYear FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Kleibergen-Paap F
11.08
18.12
14.01
13.79
10.09
18.93
14.42
14.91
Notes: This table reports TSLS results where the instrument is the share of world import demand for product k in world total import demand. The dependent
variable is the share of female employment in the production occupation of a plant. Columns (1) and (5) reproduce the results for the full sample in Table 5.
Columns (2) and (6) present the results for industries that employ unskilled workers intensively. Columns (3) and (7) present the results for low-technology
industries, while columns (4) and (8) present those for light industries. All specifications include a pair of three-digit industry and year fixed effects. Robust
standard errors clustered at plant level are in parentheses. *, **, and *** indicate significance at 10%, 5%, and 1%, respectively.
30(5)

Table 8. Robustness Check: FDI as an Alternative Measure of Offshoring

Variablesln(value-added)ln(capital/value-added)
FDI
d_trade1
FDIⅹd_trade1
d_trade2(1)
All
occupations-0.0259***(0.000963)-0.0228***(0.000924)
0.0309***(0.00663)
0.0893***(0.00699)
0.0137(0.0109)(2)
Production-0.0266***(0.00105)-0.0247***(0.00101)
0.0260***(0.00741)
0.0990***(0.00774)
0.0179(0.0121)(3)
NonProduction-0.0122***(0.000763)-0.00350***(0.000825)
0.0163***(0.00482)
0.0181***(0.00512)-0.00564(0.00774)

173,376
0.310
Yes

173,339
0.308
Yes

145,656
0.073
Yes

FDIⅹd_trade2
Observations
R-squared
IndustryⅹYear FE(4)All
occupations-0.0278***(0.000957)-0.0232***(0.000920)
0.0181**(0.00721)(5)
Production-0.0288***(0.00105)-0.0251***(0.00101)
0.00952(0.00818)(6)
NonProduction-0.0130***(0.000765)-0.00363***(0.000826)
0.0117**(0.00564)

0.0801***(0.00355)
0.0267***(0.00862)
173,376
0.314
Yes

0.0904***(0.00394)
0.0341***(0.00972)
173,339
0.313
Yes

0.0218***(0.00324)
0.00131(0.00659)
145,656
0.074
Yes

Notes: This table reports OLS results. The dependent variable is the share of female employment in all
occupations (columns (1) and (4)), production occupations (columns (2) and (5)), or non-production
occupations of a plant (columns (3) and (6)). All specifications include a pair of three-digit industry andyear fixed effects. Robust standard errors clustered at plant level are in parentheses. *, **, and ***indicate significance at 10%, 5%, and 1%, respectively.

31

Table 9. Robustness Check: Ratio of Female to Male Workers as the Dependent Variable

Variablesln(value-added)ln(capital/value-added)
FDIln(outsourcing)d_trade1ln(outsourcing)ⅹd_trade1
d_trade2(1)
All
occupations-0.242***(0.0862)-0.364***(0.0276)
0.364**(0.148)0.108(0.250)-2.243(1.606)
1.154*(0.596)(2)
Production-0.127(0.157)-0.375***(0.0342)0.576*(0.302)0.295(0.408)-5.444(4.055)2.518*(1.519)(3)
NonProduction-0.0585**(0.0266)-0.0228***(0.00662)
0.0299(0.0480)0.140*(0.0799)-0.220(0.324)0.141(0.120)163,921Yes
10.96

161,848Yes
10.99

131,051Yes
6.687

ln(outsourcing)ⅹd_trade2
Observations
IndustryⅹYear FE
Kleibergen-Paap F(4)All
occupations-0.284***(0.0947)-0.374***(0.0303)
0.205(0.179)0.0705(0.273)(5)
Production-0.196(0.191)-0.393***(0.0454)0.251(0.421)0.177(0.454)(6)
NonProduction-0.0671**(0.0281)-0.0248***(0.00699)-0.00245(0.0516)0.133(0.0859)-1.161**(0.533)0.833***(0.266)
163,921Yes
9.952-3.191***(1.186)
1.869***(0.619)161,848
Yes
10.04-0.0654(0.141)
0.107(0.0670)
131,051
Yes
5.945

Notes: This table reports TSLS results where the instrument is the share of world import demand for
product k in world total import demand. The dependent variable is the ratio of female to male workers
of a plant in all occupations (columns (1) and (4)), production occupations (columns (2) and (5)), or
non-production occupations (columns (3) and (6)). All specifications include a pair of three-digit
industry and year fixed effects. Robust standard errors clustered at plant level are in parentheses. *, **,and *** indicate significance at 10%, 5%, and 1%, respectively.

32

Table 10. Robustness Check: Excluding Plant Size Variables
VariablesFDIln(outsourcing)d_trade1ln(outsourcing)ⅹd_trade1
d_trade2ln(outsourcing)ⅹd_trade2
Observations
R-squared
IndustryⅹYear FE
Instrument
Kleibergen-Paap F(1)(2)-0.00412-0.0113**(0.00575)(0.00573)-0.00186*** -0.00231***(0.000255)(0.000273)
0.0372***(0.00591)
0.00339***(0.000674)
0.0418***(0.00348)
0.00315***(0.000448)
188,099
188,099
0.278
0.281
Yes
Yes

(3)
0.0226(0.0140)-0.0492***(0.0141)-0.201***(0.0752)
0.0977***(0.0272)(4)-0.0117(0.0146)-0.0609***(0.0160)(5)0.0112(0.0125)-0.0359***(0.0121)-0.207***(0.0690)0.0982***(0.0248)(6)-0.0223*(0.0132)-0.0486***(0.0137)

178,268-0.138***(0.0285)
0.0951***(0.0139)
178,268

178,268-0.141***(0.0268)
0.0950***(0.0129)
178,268

Yes
WID
11.21

Yes
WID
10.87

Yes
WID share
17.14

Yes
WID share
16.03

Notes: The dependent variable is the share of female employment in the total employment of a plant.
The instrument is either world import demand for product k (columns (3) and (4)) or its share in world
total import demand (columns (5) and (6)). All specifications include a pair of three-digit industry andyear fixed effects. Robust standard errors clustered at plant level are in parentheses. *, **, and ***indicate significance at 10%, 5%, and 1%, respectively.

33