Local Labor Market Effects of Offshoring:
Evidence from Matched Foreign Affiliate-Domestic
Parent-Domestic Plant Data in Japan*
Kozo Kiyota†

Kentaro Nakajima‡

Miho Takizawa§

April 28, 2022

Abstract
This study examines the local labor market effects of offshoring on manufacturing activities. We develop novel matched-foreign affiliate-domestic parentdomestic plant data in Japan from 1995 to 2016 to measure the manufacturing employment and offshoring of manufacturing activities at the local level. Our results
indicate that offshoring has positive impact on local manufacturing employment.
We find that a 10 percent increase in foreign manufacturing employment drives a
1 percent increase in local employment. We also find that offshoring exposure hasa significantly positive effect on the employment of non-offshoring firms in thesame local labor market.

Keywords: Offshoring, Foreign direct investment, Local labor market, Import competition, Firm heterogeneity
JEL classification codes: F16, F23, F66, R23, J23, L60* This study was conducted as part of the project “Microeconometric analysis of firm and industry
growth,” undertaken at the Research Institute of Economy, Trade and Industry (RIETI). The empirical analysis uses microdata from the questionnaire in the Census of Manufacture, the Basic Survey ofJapanese Business Structure and Activities, and the Basic Survey on Overseas Business Activities, conducted by the Ministry of Economy, Trade and Industry (METI). We would like to thank Kyoji Fukao
and Masayuki Morikawa for their helpful comments. We also acknowledge the financial support from theJapan Society for the Promotion of Science Grants-in-Aid (grant numbers JP19H00598, JP20K01635).
†
Keio University, RIETI, and TCER. E-mail: kiyota@keio.jp‡
Hitotsubashi University and RIETI. E-mail: nakajima.kentaro@gmail.com§
Corresponding author: Gakushuin University. E-mail: miho.takizawa@gakushuin.ac.jp1

1

Introduction

One of the most controversial aspects of globalization is offshoring, that is, when manufacturing operations and business functions move abroad. While the simple RicardoViner model, which is also known as the specific factors model, suggests that the international factor movement generally enhances welfare of both source and host countries
of offshoring, more general models indicate the ambiguous effect of offshoring: As
a consequence of offshoring, the welfare of source country may fall (e.g., Acemoglu,
Gancia, and Zilibotti, 2015; Egger, Kreickemeier, and Wrona, 2015).
Accordingly, the public tends to dislike offshoring. In the United States, for example, less than two percent of respondents consistently support it (Mansfield and Mutz,
2013, Figure 1), because of its possible negative economic consequences. For instance,
when firms relocate their production abroad, workers may lose their jobs; such negative
impacts go beyond workers and affect the local economy (Frieden, 2019) by their negative effects on workers in auxiliary companies and local suppliers, local income, andproperty values decreases, young people leaving the area, and social services decline(Rickard, 2021). Nonetheless, “there is little agreement among academic economists
regarding the sign of offshoring’s effects on domestic labor market outcomes, let alone
the magnitude” (Kovak, Oldenski, and Sly, 2021, p. 381). As we will see below, thestudies on the local labor market effects of offshoring exposure are still limited.
Based on this background, this study investigates the impact of offshoring exposure
on local labor market in manufacturing sector.1 A key challenge for this type of researchis that one needs to measure manufacturing employment and the offshoring exposureof manufacturing activities at the local level. To address this issue, we focus on Japan,
where detailed confidential firm-, foreign-affiliate- and plant-level data are available
and, similar to many other high-income countries, experiences declining manufacturing
employment and increasing offshoring of manufacturing activity.
1
Following Kovak, Oldenski, and Sly (2021), we describe the potential threats of offshoring for the
local labor market due to offshoring exposure, which differs from the actual offshoring. As discussed in
Section 2, the offshoring exposure each local market faces is measured using predicted values. The same
argument can be applied to import exposure. Section 2 explains this point in detail.

2

Figure 1: Changes in Manufacturing Employment, Share of Foreign Production, andShare of Chinese Imports, 1995–2016

Notes: The share of foreign production is defined as the foreign sales to total (domestic andforeign) sales of manufacturing firms in Japan.
Sources: Manufacturing employment is obtained from the Census of Manufacture (METI);
Share of foreign production is from the Basic Survey on Overseas Business Activities (METI);and the share of Chinese imports is from the JIP 2018 database (RIETI).

Figure 1 presents the changes in manufacturing employment and the share of foreign production to total production from 1995 to 2016 for Japan.2 The figure indicates
that, while manufacturing employment declined from 10.3 million to 7.6 million workers over the analyzed period, the share of Chinese imports to total manufacturing imports increased from 12.5 percent to 33.0 percent. Simultaneously, the share of foreign
production increased from 22.8 percent to 40.9 percent during the period. These observations imply that offshoring, as well as import competition, might lead to a decline in
manufacturing employment.
In this paper, we develop novel matched-foreign affiliate-domestic parent-domestic
plant data in order to estimate the local labor market effects of offshoring exposure. Fig2

The share of foreign production is defined as the foreign sales to the total (domestic and foreign)sales of manufacturing firms in Japan. This definition follows that of the Ministry of Economy, Tradeand Industry’s (METI) Basic Survey on Overseas Business Activities.

3

Figure 2: Conceptual Framework of Our Dataset
UEA X
Country A
1Offshoring firm

UEA Y

a

Prod.

2
Country B
3
b
4

Sales

Non-offshoring firm

UEA Z

Notes: UEA stands for Urban Employment Area, which is local labor market in this study; 1, 2,
3, and 4 indicate plants; a and b indicate firms; Prod. (Production) and Sales indcate production
and sales affiliates locate in country A and country B, respectively. Dashed circle indicates that
it is not included in our analysis. For more detail, see Section 2.2.
Sources: Authors’ representation.

ure 2 presents the conceptual framework of our dataset. As we will discuss in Section
2.2, not firm-level data but plant-level data are needed to examine the local labor market
effects because a number of firms have more than two plants in different locations.3 Besides, we need firm-level data to identify which firms do offshoring because not plants
but firms (or the headquarters) make decision to offshore activities. Furthermore, inorder to identify the activity of foreign affiliates, one needs the foreign affiliate-level
data. The matched-foreign affiliate-domestic parent-domestic plant dataset enables us
to measure both local-level employment and offshoring in a precise manner.
Our motivation comes from two research strands. One of them investigated theeffects of offshoring on domestic employment, presenting mixed results. For example,
Harrison and McMillan (2011) argued that the effects of US offshoring depend on boththe type and location of investment. Wright (2014) estimated that the aggregate effect ofoffshoring leads to a 2.6 percent cumulative increase in aggregate employment. Kiyota
and Maruyama (2017) found that offshoring is associated with increasing demand forhigh-skilled workers but has insignificant effects on the demand for unskilled workers
3

In 2016, for example, for more than one-third of plants in our sample, the location of plants isdifferent from that of the headquarters.

4

in Japan.4
The other strand is composed of studies on local labor markets with rising importcompetition. After the pioneering study of Topalova (2007), who examined the effects
of a national trade shock on the local labor market in India, several studies, such as
Topalova (2010), Autor, Dorn, and Hanson (2013), Kovak (2013), and Hakobyan and
McLaren (2016), examined the local labor market effects of increasing import competition. An important finding of these studies is that the effects are heterogeneous
across regions or metropolitan areas; some regions benefit from increasing imports,
while others do not. That is partly because some workers are immobile across regions
and metropolitan areas. This, in turn, implies that labor markets are local rather thannational. Moreover, Autor, Dorn, Hanson, and Majlesi (2020) found that increasing import competition contributed to the polarization of US politics. Therefore, it is essential
to focus on local labor market effects in analyzing the impacts of globalization.
Both these research strands make significant contributions to the literature. However, the first one ignores the fact that the effect of offshoring exposure could differ
between the regions within a home country. Figure 3 presents the changes in manufacturing employment from 1995 to 2016 by urban employment area (UEA), which is
the Japanese version of the metropolitan statistical areas (MSAs) in the United Statesand is widely used in urban economics studies such as Fujita, Mori, Henderson, andKanemoto (2004) and Hsu and Zhang (2014).5 Green color indicates the large positive change while pink color indicates the large negative change. This figure indicatesthe spatially uneven pace of the declining employment in manufacturing. While some
UEAs indicate significant declines, others indicate increases. Similar to the local labormarket effect of import competition, the effect of offshoring exposure could be heterogeneous within a country.
By contrast, the second research stream ignored another important channel of glob4

Additionally, several studies have examined the effects of foreign direct investment (FDI) on domestic employment in Japan. Generally, they found either positive or insignificant effects. For more details,
see Yamashita and Fukao (2010), Hayakawa, Matsuura, Motohashi, and Obashi (2013), and Kambayashi
and Kiyota (2015).
5
A more detailed description of the data is provided in Section 2.

5

Figure 3: Changes in Manufacturing Employment between 1995 and 2016, by Urban
Employment Area

Notes: Some UEAs are omitted to facilitate visualization.
Sources: Manufacturing employment is obtained from the Census of Manufacture (METI);UEAs are from Kanemoto and Tokuoka (2002).

6

alization: offshoring. As confirmed by Figure 1, both import competition and offshoring have been on the rise during this period. Therefore, the effects of offshoringshould be considered when analyzing the impact of globalization on domestic employment. In this context, our study is also related to Ebenstein, Harrison, McMillan, andPhillips (2014), who examined the effects of trade and offshoring on wages and occupations in the United States at the country level, finding substantial wage effects ofoffshoring to low-wage countries. This study contributes to the literature by also considering Chinese imports and offshoring.
We focus on offshoring by Japanese multinational firms. An advantage of using Japanese data is that we can develop matched- foreign affiliate-domestic parentdomestic plant data. As we will see in Section 2.2, this enables us to construct employment and offshoring datasets at the local level. Our sample period is 1995–2016, when
the Japanese economy experienced a decline in manufacturing employment and an increase in offshoring. As explained in Section 2, we define the local labor market as thelevel of the UEA. UEAs are a more plausible definition than jurisdictional areas, suchas cities, because the labor market sometimes extends beyond jurisdictional boundaries,
especially in urban areas with good public transportation.
To the best of our knowledge, only a few studies have examined the effects of offshoring exposure and outward FDI on local employment.6 Federico and Minerva (2008)
examined the effects of Italy’s outward FDI on local employment growth between 1996
and 2001 for 12 manufacturing industries and 103 administrative provinces.7 Theirmain finding was that FDI leads to faster local employment growth than the national
industry average. Kovak, Oldenski, and Sly (2021) studied the effects of offshoring exposure by US multinationals on local employment from 1987 to 2007. In their study, thelocal labor market is defined as a metropolitan area, while local offshoring is measured
by the weighted average of industry-level foreign affiliate employment in all industries.
6

For inward FDI, some studies investigated local market effects, such as Axarloglou and Pournarakis(2007) and Figlio and Blonigen (2000). Jofre-Monseny, Sánchez-Vidal, and Viladecans-Marsal (2017)
investigated the effects of big plant closures, which relocated abroad, on local employment in Spain.
However, offshoring does not necessarily lead to plant closure.
7
However, there is no explanation of how local-level FDI is measured in their paper.

7

They found that a metro area whose industries experienced, on average, a 10 percent
increase in affiliate employment exhibited a 0.17 percent increase in metro-area employment.
Our study is closely related to, but also different from these studies in several respects. First, we focus on the relationship between domestic and foreign manufacturing activities to investigate the impacts on the offshoring of manufacturing activities.
Second, our study considers the effects of Chinese import competition because it has asignificant effect on local employment in various countries, including Japan (Taniguchi,
2018; Kiyota, Maruyama, and Taniguchi, 2021). In other words, we examine the local
labor market for offshoring exposure, explicitly controlling for the effects of Chinese
import competition. Third, we focus on Japan, and because our study is the first to
examine the local labor market effect of offshoring in Japan, it adds another national
perspective to the available evidence.
Our results indicate that while Chinese import competition negatively affects local
manufacturing employment, offshoring exposure contributes to mitigating such negative effects. This result is robust even when we employ an alternative measure of offshoring exposure; we exclude the Tokyo Metropolitan Area and conduct a sub-sample
analysis. We also find that offshoring exposure has a significantly positive effect on theemployment of non-offshoring firms in the same UEA. In sum, our results indicate that
offshoring positively impacts local employment.
A limitation of this study is that plant-level data in Japan report employment only at
the aggregate level. The impact of Chinese import competition and offshoring exposure
could differ between the skills of workers and/or between tasks. Another limitationis that data on technology-related variables, such as information and communication
technology, are unavailable at the plant level. Thus, we could not consider the effects oftechnology shocks at the UEA level. Owing to data limitations, these issues are beyond
the scope of our study.
The remainder of this paper is organized as follows. The following section presents
the analytical framework. Section 3 presents our estimation results. Section 4 presents

8the results of the robustness check, and section 5, the extensions of our research. A
summary of our findings and their implications are presented in the final section.

2

Methodology and Data

2.1

Methodology

Our approach builds upon the analysis of Autor, Dorn, and Hanson (2013), who examined the effects of Chinese import competition on the local labor market in the UnitedStates. We extend this framework to consider the offshoring effects. In this study, wedefine the local labor market for each UEA.
Let Lj,t be manufacturing employment in UEA j in year t. Let Offshoringj,t andImportsj,t be offshoring from UEA j in Japan to low-wage countries in year t and imports from China to UEA j, respectively. We refer to firms that engage in offshoring as
offshoring firms and those that do not as non-offshoring firms. The regression equation
is as follows:∆Lj,t = αt + β∆Offshoringj,t + γ∆Importsj,t + εj,t ,(1)

where ∆Lj,t is the change in manufacturing employment between the initial year (i.e.,t = 0) and year t, while ∆Offshoringj,t and ∆Importsj,t are the changes in the exposures
to the offshoring of manufacturing activity and Chinese import competition between theinitial year (i.e., t = 0) and year t, respectively. εj,t is the error term. This specification
allows us to examine the effects of offshoring exposure while also controlling for theeffects of Chinese import competition. Note that time-invariant UEA-specific effects,
such as the initial level of offshoring, are eliminated in equation 1 because we take thedifferences for both the left- and right-hand-side variables.
There are two remarks regarding this equation. First, manufacturing employment
Lj,t includes both, the employment of offshoring and non-offshoring firms. Therefore,
we examine the impact of offshoring on the overall local labor market. We expect a9significantly negative β if offshoring negatively affects UEA-level employment.
Second, our main focus is offshoring. In this study, the Chinese import competition is a control variable. While the imports of final goods may negatively affect
employment, those of intermediate goods could have positive effects, as pointed out
by Taniguchi (2018), Wang, Wei, Yu, and Zhu (2018), and Kiyota, Taniguchi, andMaruyama (2021). However, to distinguish between the imports of final goods andthose of intermediate inputs, we need input–output table data from 1995 to 2016, which
are not available. In this study, we thus use total imports, although we acknowledge thedifference between the imports of final goods and those of intermediate inputs.8
Similarly, we can expand the analysis by using imports from China for low-wagecountries, including China. This is an interesting topic as, since 2001, China hasrecorded the largest share of Japanese manufacturing imports. The share of Chinese
imports increased from 12.5 percent in 1995 to 33.0 percent in 2016. Additionally, themain focus of this study is offshoring. Therefore, we follow Autor, Dorn, and Hanson(2013) and do not pursue this issue here.
There are several issues in estimating equation (1). The first is the availability of data
on offshoring and import exposure. In Japan, neither offshoring nor import exposure isavailable at the UEA level. Following Autor, Dorn, and Hanson (2013), we construct
the UEA-level offshoring exposure as follows:∆Offshoringj,t =X Lij,0 ∆Offshoringi,t
Li,0

i

Lj,0

,

(2)

where i indicates the industry of the foreign affiliate, Lij,0 /Li,0 is the share of industry
i’s employment in UEA j in industry i’s total employment in Japan in the initial year
(i.e., t = 0), and ∆Offshoringi,t is the change in industry i’s offshoring from the initial
year (i.e., t = 0) to year t.
In our main analysis, we measure offshoring by the employment of manufacturing foreign affiliates. Note that even if a parent firm is a manufacturing one, the main
8
Even if input–output table data are available between 1995 and 2016, the distinction between theimports of final goods and those of intermediate inputs mean that the regression equation has additional
endogenous variable, which brings another technical issue.

10

activity of its foreign affiliates may not be manufacturing. For example, if a manufacturing firm establishes a sales branch in a foreign country, the foreign affiliate’s main
activity is classified as services, rather than manufacturing. This implies that if we aggregate all foreign affiliates, we overestimate their manufacturing activity. Indeed, in
our sample period, more than 10 percent of the labor in foreign affiliates works for nonmanufacturing industries, although their parent firms are manufacturing companies.9
Accordingly, we measure the offshoring industry by the industry of foreign affiliates
rather than that of domestic parent firms.10 A detailed description of the data is provided in Section 2.2.
Similarly, the UEA-level import exposure is constructed as follows:∆Importsj,t =X Lij,0 ∆Importsi,t
Li,0

i

Lj,0

,

(3)

where i indicates the industry, Lij,0 /Li,0 is the share of industry i’s employment in
UEA j in industry i’s total employment in Japan in the initial year (i.e., t = 0), and∆Importsi,t is the change in imports from China in industry i from the initial year (i.e.,t = 0) to year t, obtained from the JIP 2018 database (RIETI).
The second issue concerns possible endogeneity. In general, the local labor market
conditions can affect the offshoring decisions of a firm that operates in that local market.
For example, if the local labor market becomes too tight for a manufacturing firm to secure regional employment, the firm may decide to go abroad to continue its production
activity. Another example is that if productive firms agglomerate in a particular region,
they become productive through productivity spillovers, which leads to the decision to
offshore.11 It is also possible that some unobservable local factors affect local labormarket conditions and firms’ decisions to conduct offshoring. A similar argument canbe applied to import variables.
9

For more details, see Table A1.
One may ask whether we can classify foreign affiliates by horizontal or vertical FDI. However, there
is no consensus on how horizontal and vertical FDI can be measured. For details, see Braconier, Norbäck,and Urban (2005).
11
For example, Helpman, Melitz, and Yeaple (2004) and Kimura and Kiyota (2006) found that large
and productive firms are more likely to engage in offshoring in the United States and Japan, respectively.
1011

To address this issue, we employ an instrumental variable method, in which we use
a Bartik-type shift-share variable (Bartik, 1991) as an instrument. One of the key ideasof the Bartik-type instrument is that it reflects overall offshoring from Japan or imports
from China while excluding the UEA. This is because, if we consider the UEA, it could
directly affect its employment, which in turn would violate the exclusion restriction.12
For this reason, we refer to offshoring and import exposures rather than offshoring andimports.
In the first stage, we estimate the following equations:13

JP∆Offshoringj,t = ζt + φ1 ∆OffshoringJP
j,t + φ2 ∆Importsj,t + uj,t(4)

JP
∆Importsj,t = ξt + ψ1 ∆OffshoringJP
j,t + ψ2 ∆Importsj,t + vj,t ,

(5)

and

JP
where ∆OffshoringJP
j,t and ∆Importsj,t are the Bartik instruments and are respectively

defined as follows:∆OffshoringJPj,t =X Lij,0i

Li,0∆Offshoringi,−j,t(6)∆Importsi,−j,t ,

(7)

and
∆ImportsJPj,t =X Lij,0iLi,0where Lij,0 /Li,0 is the share of industry i’s employment in UEA j in industry i’s total
employment in Japan in the initial year (i.e., t = 0); ∆Offshoringi,−j,t is the change inindustry i’s offshoring from the initial year (i.e., t = 0) to year t, excluding offshoringfrom UEA j; and ∆Importsi,−j,t is the change in industry i’s imports from China between the initial year (i.e., t = 0) and year t, excluding the imports of UEA j, uj,t andvj,t .14 The next section explains the data used to estimate these equations.
12

For details about the Bartik instrument, see Goldsmith-Pinkham, Sorkin, and Swift (2020).
See Woodridge (2021, Chapter 15-3d) for the estimation of multiple endogenous explanatory variables.
14
Several studies utilize the Bartik instrument to address endogeneity. See Glaeser, Gyourko, and Saks
(2006), Saiz (2010), and Diamond (2016).
13

12

2.2

Data

2.2.1

Definition of the local labor market

In this study, we define the local labor market as an UEA. UEAs comprise a set ofmunicipalities and an intermediate spatial scale between prefectures and municipalities
in Japan. There are 47 prefectures and approximately 1,700 municipalities in Japan.
While some studies, such as Taniguchi (2018), defined the local labor market as theprefecture level, it is common to commute from one prefecture to another in an urban
area in Japan. For example, many people working in central Tokyo commute fromneighboring prefectures of Chiba, Kanagawa, and Saitama. By contrast, in many rural
areas, the prefecture is too large to represent the local market. Therefore, prefectures
do not necessarily represent the local markets as geographical units. The municipalities
are too small to cover the local labor market, as many workers can easily commute fromone municipality to another in rural and urban areas.
This study considers the 224 urban UEAs proposed by Kanemoto and Tokuoka(2002). UEAs are regional units comparable to the MSAs in the United States. The
UEAs consist of core and surrounding suburban municipalities, from which more than
10 percent of the workers commute to core cities. The UEAs can be classified by thesize of the core city and are divided into two areas. The first are the metropolitan employment areas, whose central cities have densely inhabited district (DID) populations
exceeding 50,000. The other are micropolitan employment areas, with a DID population between 10,000 and 50,000. Data were obtained from the UEA website maintainedby the Center for Spatial Information Science, University of Tokyo (https://www.csis.utokyo.ac.jp/UEA/index_e.htm). Among the 224 UEAs, 213 had manufacturing employment over the study period.15
15
One may be concerned that the UEAs do not cover some of the rural areas. For the period between
1995 and 2016, our dataset covers 81.7 percent of the manufacturing employment in the the Census of
Manufacture. Therefore, we can conclude that UEAs cover most manufacturing employment in Japan.

13

2.2.2

Data sources

To measure employment and offshoring at the local level, we need information on theparent firm, domestic plants, and foreign affiliates. Figure 2 presents the conceptual
framework of our dataset. This figure indicates that firm a owns domestic plants 1 and 2
in UEA X and UEA Y , respectively. In addition, firm a has foreign affiliates in country
A and country B. However, the foreign affiliate in country A specializes in production
activities while the other foreign affiliate in country B specializes in sales activities.
Because our focus is on the manufacturing activity at both firm and plant levels, we
focus on production affiliates in country A. Firm a is classified as an offshoring firm
because it does not have any foreign production affiliates. In contrast, firm b owns
domestic plants 3 and 4 in UEA Y and UEA Z, respectively, Because firm b does nothave any foreign production affiliates, it is classified as a non-offshoring firm.
There are three remarks. First of all, in order to examine the local labor market effects, we need plant-level data rather than firm-level data. In general, the location of thefirm is defined by the location of the headquarters. For example, suppose that the firm
has headquarters in Tokyo while having plants in both Tokyo and another metropolitan area. If we use the firm-level data, the employment in another metropolitan area isrecorded as the employment of Tokyo because the location of the firm is defined by thelocation of the headquarters.
Second, in addition, we need firm-level data to identify which firms do offshoring
because the decision to offshore activities is determined not at the plant level but at thefirm level. This in turn means that we need to match plant-level data with firm-level
data. Finally, one needs foreign affiliate-level data to identify the activity of foreign
affiliates. Note that the foreign affiliates of manufacturing firms engage in not only
manufacturing activities (i.e., production) but also non-manufacturing activities (e.g.,sales, financing). To estimate the relationship between domestic and foreign production
activities, we must exclude non-production activities from foreign activities. This study
thus focuses only on the manufacturing activities of foreign affiliates owned by Japanese

14manufacturing firms, which we refer to as offshoring.16 In other words, this study
excludes the effects of foreign affiliates’ non-manufacturing activities from the analysis.
In a nutshell, it is essential to match domestic firms with their domestic plants and with
their foreign affiliates in order to analyze the local labor market effect of offshoring.
For the parent firm-level information, we utilize the confidential firm-level databaseof Kigyou Katsudou Kihon Chousa Houkokusyo (the Basic Survey of Japanese Business
Structure and Activities: BSJBSA), prepared annually by the Research and Statistics
Department, METI (1995–2016). The survey is compulsory for firms in the manufacturing, wholesale trade, and retail trade industries having more than 50 employees andcapital exceeding 30 million yen.
As foreign affiliate-level data, we utilize the confidential foreign affiliate-level databaseof Kaigai Jigyou Katsudou Kihon Chousa Houkokusyo (the Basic Survey on Overseas
Business Activities: BSOBA). This survey is conducted annually by the Research andStatistics Department of the METI. The BSOBA covers all firms with foreign affiliates,
except for the insurance/finance and real estate industries. The definition of a foreign
affiliate in the survey is a company abroad where the Japanese parent firm has morethan a 10 percent share of investment or is a subsidiary in which other foreign affiliates
have above 50 percent equity share.
For plant-level data, we use the confidential plant-level database of Kogyo Toukei
Hyo (the Census of Manufacture: CM) prepared by the Research and Statistics Department, METI. CM is conducted annually, covering all Japanese manufacturing plants
with more than four workers.
We construct matched-foreign affiliate-domestic parent-domestic plant data from1995 to 2016 using the concordance developed by the Research Institute of Economy,
Trade and Industry (RIETI).17 That allowed us to capture employment and offshoring at
the local level. Without our dataset, manufacturing employment and offshoring cannot
be measured at the local level. Moreover, the use of our dataset enables us to distin16

We share the same spirit as Alfaro and Charlton (2009) that estimates the share of vertical FDI, using
the detailed industry information of multinational subsidiaries.
17
Because of the availability of concordances, the data cannot be extended before or after this period,
as of the day of writing this paper.

15

guish the employment of plants that belong to offshoring firms from those belonging to
non-offshoring firms, which is essential in analyzing the spillover effects of offshoring(Section 5).
Because we utilize matched-foreign-affiliate-domestic parent-domestic plant data,
we limit our sample to domestic plants and foreign affiliates that belong to firms withat least 50 employees and capital exceeding 30 million yen. In other words, as longas domestic plants and foreign affiliates belong to these firms, they are included in our
analysis, even when the size of the plant is small (e.g., a plant with four workers). As
mentioned in footnote 15, our dataset covers 81.7 percent of domestic manufacturing
employment, even when we focus on firms with at least 50 employees and capital exceeding 30 million yen.
For imports from China, we utilize import data from the Japan Industrial Productivity (JIP) Database 2018 by the RIETI. Because the industry classifications used in thesedata are different, we aggregate some industries. The total number of industries is 44. In
sum, our sample consists of 213 UEAs (j = 1, ..., 213) and 44 industries (i = 1, ..., 44)
over 22 years (t = 1995, ..., 2016).

2.3

Measurement of local level employment, offshoring, and imports

Industry i’s employment in the UEA j in year t, Lij,t , is computed by aggregating
the employment of plants at the UEA and industry levels. Then, local employment isP
obtained by aggregating employment at the UEA level, Lj,t = i Lij,t . As mentioned
in Section 2.1, offshoring and imports are not available at the UEA level. We construct
UEA-level offshoring and imports based on equations (2) and (3), respectively.
Table 1 presents the summary statistics for the changes in employment, offshoring,and imports from China at the UEA-level between 1995 and 2016. Table 1 indicates
that, although the median employment growth is −8.5 percent, its mean is 14.4 percent, implying that a few UEAs had large manufacturing employment growth.18 As per
18

At the industry level, domestic employment declined in all manufacturing industries except for motor

16

Table 1: Summary Statistics∆L∆Offshoring ∆Imports
Mean 14.398
8.934
40.700SD
97.52131.75498.476p5-46.838
0.251
2.973
p50
-8.4592.414
18.393p95
167.530
21.950118.772
N
213
213
213
Notes: ∆L indicates manufacturing employment growth from 1995 to 2016. ∆Imports and∆Offshoring are based on equations (2) and (3), respectively. SD, p5, p50, and p95 indicate
standard deviation, the 5 percentile, median, and 95 percentile, respectively. N means the number of observations.
Source: For the data sources, see Section 2.2.

Figure 3, both offshoring and imports from China show an increasing trend.
Figures 4 and 5 present the changes in offshoring and the imports from China from1995 to 2016, by UEA, respectively. Green color indicates the large positive change
while pink color indicates the small positive change. These figures indicates that bothoffshoring exposure and Chinese import competition are heterogeneous within a country. These figures also show similar patterns with each other. This, in turn, implies thatthe descriptive statistics cannot distinguish between the effects of offshoring exposureand those of the Chinese import competition. We now turn to the regression analysis.

3

Main Results

Table 2 presents the estimation results of equation (1). As mentioned in Section 2, thelocal labor market is defined as UEAs, while offshoring is measured by the employmentof manufacturing foreign affiliates. Column (1) presents the results of the ordinary leastsquares (OLS) estimations. Column (2) is our main specification, where both offshoringand import exposures are endogenous variables. Column (3) presents the estimation results when offshoring exposure is treated as an exogenous variable, while column (4)presents the results when import exposure is treated as an exogenous variable for reference. For the first-stage results, we report Shea’s adjusted partial R2 because equation
vehicles, motor vehicle parts and components, and other transportation equipment (See Figure 6).

17Figure 4: Changes in Offshoring between 1995 and 2016, by Urban Employment Area

Notes: Some UEAs are omitted to facilitate visualization.
Sources: Offshoring is from the Basic Survey on Overseas Business Activities (METI); Manufacturing employment is obtained from the Census of Manufacture (METI); UEAs are from
Kanemoto and Tokuoka (2002).

18

Figure 5: Changes in Imports from China between 1995 and 2016, by Urban Employment Area

Notes: Some UEAs are omitted to facilitate visualization.
Sources: Chinese imports are from the JIP 2018 database (RIETI); Manufacturing employment
is obtained from the Census of Manufacture (METI); UEAs are from Kanemoto and Tokuoka(2002).

19

Table 2: Main Results∆Offshoring∆Imports(1)
OLS
0.25109[0.28109]-0.07952[0.07087]
213
UEA

(2)
IV
0.09851***[0.02950]-0.06828***[0.00782]
213
UEA

N
Local market
Industry
Parent
MFG
MFG
Foreign affiliates
MFG
MFG
Offshoring
EMPL
EMPL
First stage: Shea’s adjusted partial R2
OffshoringNA
0.920
Imports
NA
0.894

(3)
IV
0.11572**[0.05271]-0.07192***[0.01303]
213
UEA

(4)
IV
0.06943[0.12927]-0.06132**[0.03093]
213
UEA

MFG
MFG
EMPL

MFG
MFG
EMPL

NA
0.821

0.059
NA

Notes: UEA, MFG, and EMPL stand for urban employment area, manufacturing, andemployment, respectively. NA means not applicable. *** and ** indicate statisticalsignificance at the 1 percent and 5 percent levels, respectively. Figures in brackets
indicate the heteroskedasticity robust standard errors. Regression models are weighted
by the start of period UEA share of national manufacturing employment.
Source: For the data sources, see Section 2.2.
(1) includes multiple endogenous variables.
We highlight the following four findings. First, in column (2), Shea’s adjusted partial R2 indicates high values, which suggests that our instruments are strong, meaning
the problem of the weak instruments is not an issue. Second, column (1) indicatesthat neither offshoring nor imports present significant coefficients. By contrast, column(2) indicates that offshoring exposure has significantly positive effects on employment,
while import exposure has significantly negative effects. This implies that OLS underestimates the impact of offshoring and imports from China.
Third, column (3) shows a larger coefficient for offshoring exposure than column(2). By contrast, column (4) presents an insignificant coefficient for offshoring exposure. These results suggest that, without addressing the endogeneity of offshoring andimports simultaneously, the coefficients on offshoring are either overestimated or underestimated. Furthermore, the standard error of the coefficient on import exposure issmaller in column (2) than in columns (3) and (4), implying that the estimated coeffi20

cients are more precise when we control for the endogeneity of offshoring and import
exposure simultaneously, than when we consider the endogeneity of only one of thesevariables.
Finally, our main specification (column (2)) indicates that Chinese import competition has significantly negative effects on local employment. This result is consistent
with the findings of Autor, Dorn, and Hanson (2013), who found a negative Chinese
shock in the United States. By contrast, the effect of offshoring is significantly positive,
suggesting that offshoring exposure contributes to local employment growth.
Why does offshoring exposure have positive effects on employment? There areseveral possible mechanisms underlying this phenomenon. First, offshoring in the laststage of production leads to an increase in the production of intermediate stages inJapan, raising the labor demand in the country. Second, because of offshoring, plants
may reallocate workers from offshored activities to other activities. Third, as a resultof offshoring unskilled-intensive activities, the demand for skill-intensive activities increases. If the unskilled-intensive activities effect exceeds the skill-intensive, overall
employment increases, although the composition of workers changes in this case.19
One may argue that the results are trivial because they reflect the industry composition of the local labor market. For instance, if an industry is productive, it will actively
engage in offshoring while increasing domestic employment and sales. Therefore, ourresults reflect that such a productive industry is located in the local labor market. Because the variables used in our analysis are similar to other local labor market studies,measured at the local labor market level, it is difficult to consider industry productivity
changes explicitly. Nonetheless, this argument suggests a positive correlation between
the changes in domestic sectoral employment and offshoring. As a shortcut, we verify
whether such a correlation is observed.
Figure 6 presents this correlation for 1995–2016. The horizontal axis indicates thechanges in domestic employment, and the vertical axis, the changes in offshoring. The
size of the circle represents the employment size in 1995. Industries with offshoring
19

Head and Ries (2003) also found that additional affiliate employment in low-income countries increases the skill intensity of Japanese multinationals.

21

changes greater than 300 percent are regarded as outliers and dropped from the figure. The share of domestic employment of these dropped industries combined accounts
for only 1.1 percent of total domestic manufacturing employment.20 The dotted line
indicates the predicted value from the linear regression.
There are two notable findings. First, we confirm a positive correlation betweenchanges in domestic sectoral employment and offshoring. This finding suggests thatthe industry characteristics are important. Second, offshoring increased in almost allindustries. Because the changes in offshoring are clustered between 20 and 100 percent, it is difficult to determine which industries actively engage in offshoring. A similar argument was made by Bernard, Eaton, Jensen, and Kortum (2003). Focusing onexports by plants, they pointed out that “while previous work has sought to link tradeorientation with industry, exporting producers are in fact quite spread out across industries” (p. 1271). This result implies that, although industry characteristics matter in
explaining the relationship between offshoring and domestic employment, they cannot
explain all aspects. Given the firm heterogeneity within an industry, our results are thus
not necessarily trivial.
We should also note that although offshoring exposure contributes to local employment, its magnitude is limited. The estimated coefficient on offshoring exposure indicates that a 10 percent increase in foreign manufacturing employment drives a 1.3
percent increase in local employment. As per Figure 1, overall manufacturing employment declined at the national level, suggesting that offshoring mitigated the negative
effects of imports from China.
Thus far, we have focused on the manufacturing activity of foreign affiliates, excluding non-manufacturing activities. Moreover, we define the local labor market by UEAs
rather than jurisdictional areas. One may ask whether these points matter because, if
not, the analysis will be much simpler and easier to implement. To answer this question, we examine the validity of the definitions of foreign affiliates and the local labormarket as a supplementary analysis. The results, presented in Section A.2, indicate that
20

These industries are: chemical fertilizers, electronic equipment and electric measuring instruments,
and prepared animal foods and organic fertilizers.

22Figure 6: Changes in Offshoring and Domestic Employment between 1995 and 2016,
by Industry

Notes: Industries with greater than 300 percent change in offshoring are regarded as outliers
and are dropped from this figure. The dotted line indicates the predicted value from the linear
regression.
Source: For the data sources, see Section 2.2.

23

if we include the activity of non-manufacturing foreign affiliates, the positive effects ofoffshoring exposure will be overestimated. The results also show that the instruments
become weak when jurisdictional areas are utilized rather than UEAs. In sum, the focus
on the manufacturing activities of foreign affiliates and the use of UEAs matter.

4

Robustness Checks

4.1

Alternative measure of offshoring

One concern is that our results are sensitive to the measurement of offshoring. In themain analysis, we measured offshoring by the employment of foreign affiliates. However, the results may change if an alternative measure of offshoring is employed. Hence,
we measure offshoring by investment in manufacturing foreign affiliates as follows:∆Offshoringj,t =X Kij,0 ∆Offshoringi,t
i

Ki,0

Lj,0

,

(8)

where i indicates the industry of the foreign affiliate, Kij,0 /Ki,0 is the share of industry
i’s capital stock in UEA j in industry i’s total capital stock in Japan in the initial year
(i.e., t = 0), and ∆Offshoringi,t is the change in industry i’s offshoring from the initial
year (i.e., t = 0) to year t. We use the capital stock rather than employment for weights
because we measure offshoring by the value of investments rather than employment.
The value of investments by foreign affiliates was obtained from BSOBA.21 For capitalstock, we aggregate the tangible assets of the plants from the CM.
Column (2) in Table 3 presents the estimation results of the alternative measure ofoffshoring, whereas column (1) indicates our main results (i.e., column (2) in Table 2).
Column (2) indicates that Shea’s adjusted partial R2 is 0.67 and 0.74 for offshoring andimports, respectively. Although these are smaller than those in column (1), they arestill relatively high. The sign is the same as in column (1) for offshoring and imports.
Although the significance level slightly declines for offshoring, it remains significant at
21

For the investment value in 1995, the investment value in 1995 is used directly. For the investment
value in 2016, we use the cumulative value of the investment from 1995 to 2016.

24

Table 3: Robustness Check∆Offshoring∆Imports(1)
IV
0.09851***[0.02950]-0.06828***[0.00782]
213
UEA

(2)
IV
0.00814**[0.00337]-0.07743***[0.01386]
213
UEA

N
Local market
Industry
Parent
MFG
MFG
Foreign affiliates
MFG
MFG
Offshoring
EMPL
Investment
2First stage: Shea’s adjusted partial R
Offshoring
0.920
0.668
Imports
0.894
0.738
Area
All
All
Period1995–2016
1995–2016(3)IV
0.11541***[0.02504]-0.08482***[0.01126]
212
UEA

(4)
IV
0.10832***[0.02294]-0.05983***[0.00621]
213
UEA

(5)
IV
0.06270***[0.02322]-0.05363***[0.01178]
222
UEA

MFG
MFG
EMPL

MFG
MFG
EMPL

MFG
MFG
EMPL

0.929
0.949
Non-Tokyo
1995–2016

0.684
0.747
All
1995–2005

0.952
0.971
All
2006–2016

Notes : UEA, MFG, and EMPL stand for urban employment area, manufacturing, andemployment, respectively. *** and ** indicate statistical significance at the 1 percent
and 5 percent levels, respectively. Figures in brackets indicate the heteroskedasticity
robust standard errors. Regression models are weighted by the start of period UEA
share of national manufacturing employment.
Source: For the data sources, see Section 2.2.
the 5 percent level. Note that the magnitudes of the coefficients are not directly comparable because employment and investment use different units. These results imply that
our main result holds even when we employ an alternative measure of offshoring.

4.2

The Tokyo Metropolitan Area

Another concern may be that our results are mainly driven by the Tokyo Metropolitan Area because, in Japan, the economic activity is strongest in this area. In 1995,
23.5 percent of manufacturing employment was concentrated in the Tokyo Metropolitan Area. If this area is an outlier, our results may not hold for other areas. To address
this concern, we verify whether our results are sensitive to the inclusion of the Tokyo
Metropolitan Area.
Column (3) presents the estimation results that exclude the Tokyo Metropolitan Area25

from the analysis.22 Since the Tokyo Metropolitan Area was excluded, the number ofobservations declined from 213 to 212. The results indicate that Shea’s adjusted partial
R2 is 0.93 and 0.95 for offshoring and imports, respectively, meaning the instruments
are strong. The sign and significance levels are the same as those in column (1) for
offshoring and import exposure. The magnitude of the coefficients is slightly larger
in absolute terms for both offshoring and import exposure. These results suggest thatthe positive effect of offshoring is more evident, whereas the negative effect of imports
from China is more severe in areas other than the Tokyo Metropolitan Area. Overall,
we can conclude that our results are robust to excluding the Tokyo Metropolitan Area.

4.3

Sub-sample analysis

One may be further concerned that our results are sensitive to the choice of sample
period because our main analysis covers more than two decades. If the positive effects
of offshoring are limited to a particular period, our results do not have external validity.
Hence, we split the sample period in half (i.e., 1995–2005 and 2006–2016) and estimate
equation (1) for each period.
Columns (4) and (5) present the estimation results for 1995–2005 and 2006–2016,
respectively.23 The instruments are weaker for 1995–2005 than for 2006–2016 as Shea’s
adjusted partial R2 is 0.68 and 0.75 for offshoring and import exposures, respectively.
Nonetheless, the values are reasonably high. The sign and significance levels are thesame as those in column (1) for offshoring and import exposure.
An important difference may be the magnitude of the coefficients. The coefficienton offshoring exposure declines from 0.11 to 0.06 from 1995–2006 to 2006–2016. This
result implies that, although the effect of offshoring exposure is significantly positive,
its effect has declined in recent years. Whereas this is beyond the scope of our study,
clarifying it is essential for future research.
22

The weight of instruments Lij,0 /Li,0 is also recalculated when dropping the Tokyo Metropolitan
Area.
23
For the weight of instruments Lij,0 /Li,0 , the initial year is set to 2006 if the analysis focuses on the
period between 2006 and 2016.

26

5

Extensions

5.1

Difference between offshoring firms and non-offshoring firms

As discussed in Section 3, within the same industry, some firms engage in offshoring,whereas others do not. While we did not consider this issue, firm heterogeneity becomes
essential when we discuss the relationship between offshoring and non-offshoring firms.
Offshoring may have negative or insignificant effects on employment at the firmlevel. However, it is unclear whether this issue affects other non-offshoring firms in thesame local market. For example, Greenstone, Hornbeck, and Moretti (2010) found thatthe establishment of a large plant had positive effects on the productivity of other existing plants in the same country. Because offshoring firms are generally large, their plants
are also possibly large. If a large domestic plant shuts down because of offshoring, existing plants in the same region could have negative productivity effects. By contrast,
Ito and Tanaka (2014) examined the effects of firms’ offshoring on the employmentof their suppliers in the home country using firm-level transaction data. The analysis
revealed statistically positive effects. Noting that transactions are more likely to occur
between firms in neighboring areas, their results imply that the expansion of offshoring
could positively affect the local labor market.
To address these issues further, we examine the differential effects on offshoringand non-offshoring firms as follows:∆LOffshoring= λt + β∆Offshoringj,t + γ∆Importsj,t + εj,t
j,t

(9)

∆LNon-offshoring= λt + β∆Offshoringj,t + γ∆Importsj,t + εj,t ,
j,t

(10)

where Loffshoringand LNon-offshoring
denote the employment of offshoring and non-offshoringj,tj,tfirms, respectively. Their sum is equal to the total employment in UEA j: Loffshoring+j,t
LNon-offshoring= Lj,t . The definitions of the other variables are the same as before.
j,tTable 4 presents the estimation results. The number of observations for offshoringfirms in column (1) is smaller than that for non-offshoring firms in column (2) because27Table 4: Difference between Offshoring and Non-offshoring Firms∆Offshoring∆Imports(1)
IV
0.03960[0.03200]-0.04635***[0.00903]
177
UEA

(2)
IV
0.08560***[0.02197]-0.06527***[0.00630]
213
UEA

N
Local market
Industry
Parent
MFG
MFG
Foreign affiliates
MFG
MFG
Offshoring
EMPL
EMPL
2
First stage: Shea’s adjusted partial R
Offshoring
0.858
0.936
Imports
0.879
0.900
Firm
Offshoring Non-offshoring
Area
All
All
Period1995–2016
1995–2016
Notes: UEA, MFG, and EMPL stand for urban employment area, manufacturing, andemployment, respectively. *** and ** indicate statistical significance at the 1 percent
and 5 percent levels, respectively. Figures in brackets indicate the heteroskedasticity
robust standard errors. Regression models are weighted by the start of period UEA
share of national manufacturing employment.
Source: For the data sources, see Section 2.2.
the offshoring firms do not have any plants in some UEAs. Table 4 indicates that all
Shea’s adjusted partial R2 exceed 0.86, which indicates strong instruments.
The sign and significance levels of import exposure are the same as those of column (1). However, the coefficient on offshoring exposure becomes insignificant foroffshoring firms, whereas that on non-offshoring firms continues to be significantly positive. These results suggest a positive spillover effect from offshoring to non-offshoringfirms. In other words, offshoring has a significantly positive effect on the employment of non-offshoring firms in the same UEA. As Greenstone, Hornbeck, and Moretti(2010) have shown, this may be due to the productivity spillovers from offshoring firms.
Alternatively, as pointed out by Ito and Tanaka (2014), offshoring exposure may contribute to the growth of local employment through supply chain linkages. It should be
noted that offshoring exposure does not have an insignificant effect on offshoring firms’
28employment, implying a limited threat of employment, although manufacturing firms
increase the offshoring of their manufacturing activities.

5.2

Possible mechanisms

In Section 3, we find that offshoring exposure has a positive effect on employment. Asa possible mechanism, it is possible that the domestic production and/or productivityof plants increases as a consequence of offshoring. To further investigate these possibilities, we replace the dependent variable in equation (1) from the changes in local
employment with the changes in gross output, value added, average wages, and laborproductivity.
Table 5 presents the estimation results. Columns (1)–(4) show the results when we
utilize the changes in gross output, value added, average wages, and labor productivity,
respectively. Labor productivity is measured by the per-capita value added. Table 5shows that Shea’s adjusted partial R2 is 0.920 for offshoring exposure and 0.894 forimport exposure, which implies the strongness of the instrumental variables.24 The
coefficient on import exposure is significantly negative in all the columns. These results
suggest that high import exposure results in a decline in gross output, value added,
average wages, and labor productivity.
By contrast, the coefficient on offshoring exposure is significantly positive for gross
output, value added, and average wages. This implies that, as expected, offshoring contributes to increases in output production, which leads to increases in value added andaverage wages. However, the coefficient on offshoring exposure is insignificant for labor productivity. While only indicative, these results suggest that productivity spillovers
do not explain the positive effects of offshoring exposure on local employment.
Shea’s adjusted partial R2 are the same between columns (1) and (4) because the first stage regressionis the same.
24

29

Table 5: Possible Mechanisms

Dependent variable∆Offshoring∆Imports

(1)
(2)
IV
IV
Gross output Value added
0.16629***0.14239*[0.04174][0.07715]-0.10930*** -0.09949***[0.00993][0.01873]
213
213
UEA
UEA

N
Local market
Industry
Parent
MFG
MFG
Foreign affiliates
MFG
MFG
2
First stage: Shea’s adjusted partial R
Offshoring
0.920
0.920
Imports
0.894
0.894
Firm
All
All
Area
All
All
Period1995–2016
1995–2016(3)
IV
Average wages
0.06275***[0.00773]-0.02667***[0.00228]
213
UEA

(4)
IV
Labor productivity
0.02548[0.03503]-0.02084**[0.00881]
213
UEA

MFG
MFG

MFG
MFG

0.920
0.894
All
All
1995–2016

0.920
0.894
All
All
1995–2016

Notes: UEA, MFG, and EMPL stand for urban employment area, manufacturing, andemployment, respectively. ***, **, and * indicate statistical significance at the 1 percent, 5 percent, and 10 percent levels, respectively. Figures in brackets indicate theheteroskedasticity robust standard errors. Regression models are weighted by the startof period UEA share of national manufacturing employment.
Source: For the data sources, see Section 2.2.

30

6Concluding Remarks

In this study, we examined the local labor market effects of offshoring exposure on
manufacturing activities. One of the main contributions of this study is that we developed matched-foreign affiliate-domestic parent-domestic plant data in Japan to measure
manufacturing employment and offshoring of manufacturing activities at the local level.
The data cover the period between 1995 and 2016, when offshoring rose and manufacturing employment declined.
Our results indicate that, while imports from China negatively affect local manufacturing employment, offshoring exposure contributes to mitigating such negative effects.
We find that a 10 percent increase in foreign manufacturing employment drives a 1 percent increase in local employment. This result is majorly robust even if we employ analternative measure of offshoring, if we exclude the Tokyo Metropolitan Area, or if we
perform a sub-sample analysis. We also found that offshoring exposure has a significantly positive effect on the employment of non-offshoring firms in the same local labormarket.
These results have important policy implications. Policies supporting firms’ offshoring are sometimes controversial because they may negatively affect domestic employment. Our results suggest that this is not the case, even at the local labor market
level. Indeed, offshoring manufacturing activities can mitigate the decrease in domestic manufacturing employment. Overall, our results indicate that offshoring positively
affects local employment.
A limitation of this study is that plant-level data in Japan report employment only at
the aggregate level. The impact of Chinese import competition and offshoring exposure
could differ between the skills of workers and/or between tasks. Another limitationis that data on technology-related variables, such as information and communication
technology, are unavailable at the plant level. Thus, we could not consider the effects oftechnology shocks at the UEA level. Owing to data limitations, these issues are beyond
the scope of our study.

31

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A

Appendix

A.1

Data Appendix

This appendix presents the employment of foreign affiliates in manufacturing and nonmanufacturing industries between 1995 and 2016. The parent firms are limited to manufacturing firms.
Table A1: Share of Non-manufacturing Employment
Total
1995 1,632
1996 2,098
1997 2,281
1998 2,169
1999 2,594
2000 2,840
2001 2,645
2002 2,863
2003 3,128
2004 3,420
2005 3,592
2006 3,727
2007 3,821
2008 3,630
2009 3,7702010 4,031
2011 4,154
2012 4,277
2013 4,299
2014 4,574
2015 4,483
2016 4,344
Average 3,381

Manufacturing
1,480
1,888
2,049
1,928
2,308
2,522
2,403
2,585
2,854
3,136
3,318
3,445
3,536
3,244
3,356
3,593
3,705
3,793
3,839
4,068
3,955
3,834
3,038

Non-manufacturing
152
210
232
241
286
318
242
278
274
284
274
282
285
387
414
438
448
484
460
505
529
510
342

Share
9.3%
10.0%
10.2%
11.1%
11.0%
11.2%
9.1%
9.7%
8.8%
8.3%
7.6%
7.6%
7.5%
10.6%
11.0%
10.9%
10.8%
11.3%
10.7%
11.0%
11.8%
11.7%
10.1%

Notes: The unit is thousand workers. Manufacturing and Non-manufacturing indicate
the employment of foreign affiliates in manufacturing and non-manufacturing, respectively. Share indicates the share of non-manufacturing workers to total workers.
Source: For the data sources, see Section 2.2.

A.2

Validation of the definitions of foreign affiliates and local labormarkets

In the main analysis, we focused on the manufacturing activities of foreign affiliates andexcluded non-manufacturing activities. Moreover, we defined the local labor market
using UEAs rather than jurisdictional areas. To determine whether these points matteror complicate the analysis unnecessarily, we examine the validity of the definitions offoreign affiliates and the local labor market as a supplementary analysis.
37

Table A2: Validation of the Definitions of Foreign Affiliates and Local Labor Markets∆Offshoring∆Imports(1)
IV
0.09851***[0.02950]-0.06828***[0.00782]
213
UEA

(2)
IV
0.14142***[0.04859]-0.07870***[0.01206]
213
UEA

(3)
IV
0.18278***[0.06240]-0.13096**[0.05251]
47
Prefecture

MFG
All
EMPL

MFG
MFG
EMPL

0.866
0.884

0.554
0.383

N
Local market
Industry
Parent
MFG
Foreign affiliates
MFG
Offshoring
EMPLFirst stage: Shea’s adjusted partial R2
Offshoring
0.920
Imports
0.894

Notes : UEA, MFG, and EMPL stand for urban employment area, manufacturing, andemployment, respectively. *** and ** indicate statistical significance levels at 1 percent
and 5 percent, respectively. Figures in brackets indicate the heteroskedasticity robust
standard errors. Regression models are weighted by the start of period UEA share ofnational manufacturing employment.
Source: For the data sources, see Section 2.2.
We first measure the offshoring variable, incorporating the employment of manufacturing and non-manufacturing foreign affiliates. The results are presented in column(2) of Table A2, whereas column (1) presents our main results (i.e., column (2) in Table2). This table indicates that the sign and significance levels are the same for offshoringand import exposure.
However, the magnitude is different, particularly for the offshoring exposure. Its
coefficient is 0.14, which is more than 40 percentage points greater than that of ourmain result (0.10). That implies that if we include the activity of non-manufacturing
foreign affiliates, the positive effects of offshoring exposure will be overestimated. This
is not surprising if non-manufacturing activities include export-platform activities suchas sales branches. If the activity of sales branches in foreign countries expands, exports
from domestic plants to sales branches also increase, which is expected to have positiveeffects on the domestic activity in general. In other words, even if we focus only on
manufacturing activities, we find significantly positive effects on the local labor market,
similar to our main results.
Next, we redefine the local labor market for the 47 prefectures in Japan rather thanthe 213 UEA level. Column (3) presents the estimation results. First, Shea’s adjusted
partial R2 is small: 0.554 for offshoring exposure and 0.383 for import exposure, suggesting these instruments are not strong. That may be because jurisdictional areas are soaggregated that they cannot capture differential effects within each jurisdictional area.
Our results imply that the definition of the local labor market matters when analyzing
the offshoring effects.

38