Institute for Economic Studies, Keio University
Keio-IES Discussion Paper Series

A Tale of Two Countries:
Global Value Chains, the China Trade Shock, and Labor Markets
Jaerim Choi, Masahiro Endoh, Akira Sasahara
1 May, 2024
DP2024-012
https://ies.keio.ac.jp/en/publications/23768/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
1 May, 2024

A Tale of Two Countries: Global Value Chains, the China Trade Shock, and Labor
Markets
Jaerim Choi, Masahiro Endoh, Akira Sasahara
Keio-IES DP2024-012
1 May, 2024
JEL Classification: F14, F16, F66
Keywords: The China trade shock, imported inputs, exports, global value chains,
manufacturing employment
Abstract
This study investigates the effects of imports from China and exports to therest of the world on labor markets using the data from two major trading
partners of China: Japan and the US. An analysis shows that imports of final
goods from China and exports to the rest of the world have the same effects on
manufacturing employment in the two countries: the former effect is negative,
and the latter is positive. In contrast, imported inputs are shown to havedifferent effects on manufacturing employment across the two countries: positivein Japan but negative in the US. We show that these contrasting effects relate
to the extent to which these countries integrate into global value chains. In
particular, we focus on areas specializing in more downstream sectors in the twocountries and uncover that cheaper access to Chinese intermediate inputs allow
Japanese input buyers to boost manufacturing employment through input-output
linkages. However, the US experienced negative employment effects in thoseareas, suggesting that the US input buyers do not take advantage of thecomplementary effects of global value chains, especially with China.
Jaerim Choi
School of Economics, Yonsei University
03722 Yonsei University Graduate School, 50 Yonsei-Ro, Seodaemun-Gu, Seoul,
South Korea
jaerimchoi@yonsei.ac.kr
Masahiro Endoh
Faculty of Business and Commerce, Keio University
2-15-45 Mita, Minato City, Tokyo 108-0073
endoh@keio.jp
Akira Sasahara
Faculty of Economics, Keio University
2-15-45 Mita, Minato City, Tokyo 108-0073
sasahara@keio.jp
Acknowledgement: Masahiro Endoh and Akira Sasahara are grateful for financial
support from JSPS Kakenhi under grant numbers 21H00713, 21K13293, and 22H00063.

A Tale of Two Countries: Global Value Chains, the China Trade Shock,and Labor Markets*
Jaerim Choi†
Yonsei University

Masahiro Endoh‡
Keio University

Akira Sasahara§
Keio University

May 2024

Abstract
This study investigates the effects of imports from China and exports to the rest of the world on labor markets using
the data from two major trading partners of China: Japan and the US. An analysis shows that imports of final goods
from China and exports to the rest of the world have the same effects on manufacturing employment in the two countries: the former effect is negative, and the latter is positive. In contrast, imported inputs are shown to have different
effects on manufacturing employment across the two countries: positive in Japan but negative in the US. We show thatthese contrasting effects relate to the extent to which these countries integrate into global value chains. In particular,
we focus on areas specializing in more downstream sectors in the two countries and uncover that cheaper access toChinese intermediate inputs allow Japanese input buyers to boost manufacturing employment through input-output
linkages. However, the US experienced negative employment effects in those areas, suggesting that the US input
buyers do not take advantage of the complementary effects of global value chains, especially with China.
Key Words: The China trade shock, imported inputs, exports, global value chains, manufacturing employment
JEL codes: F14, F16, F66* The authors would like to thank Reshad Ahsan, Jevan Cherniwchan, Mitsuo Inada, Jota Ishikawa, Tadashi Ito, Kozo Kiyota, Naoto Mikawa, Ryo
Nakajima, Lourenço Paz, Raymond Riezman, Eiichi Tomiura, Vladimir Tyazhelnikov, Ariel Weinberger, seminar participants at Keio University,
Hokkaido University, Kobe University, the University of Hawaii at Mānoa, Gakushuin University, Yonsei University, SWET at the Otaru Universityof Commerce, the JSIE Kanto Workshop, the 2023 JSIE Annual Meeting at Nihon University, Midwest Trade Conference at Georgia Tech, andthe RSAI Annual Meeting at Osaka University for their helpful comments. Masahiro Endoh and Akira Sasahara are grateful for financial support
from JSPS Kakenhi under grant numbers 21H00713, 21K13293, 22H00063, and 24K16371. This study was previously circulated under the title“The China shock and supply chains: when rising imports raise exports.” The views expressed in this paper are those of the authors and do notnecessarily reflect the positions or policies of the institutions mentioned above. Any remaining errors are our own. Declarations of interest: none.
† School of Economics, Yonsei University. E-mail: jaerimchoi@yonsei.ac.kr‡ Faculty of Business and Commerce, Keio University. E-mail: endoh@keio.jp§ (Corresponding author) Faculty of Economics, Keio University. E-mail: sasahara@keio.jp

1

1 Introduction
An increase in imports with China has been shown to influence several aspects of local labor markets adversely,
including declining manufacturing employment and higher unemployment rates (e.g., Autor et al., 2013; Pierce andSchott, 2016; and Acemoglu et al., 2016). Additionally, recent studies attempt to understand the mechanisms throughwhich the China trade shock has had such sizable adverse effects on the US economy (e.g., Xu et al., 2023; Eriksson
et al., 2021). In contrast, existing studies document that the negative effects of imports from China were limited in
the Japanese context (e.g., Taniguchi, 2019; Kainuma and Saito, 2022). Nevertheless, manufacturing employment
decreased at almost the same magnitude in the two countries during the 1990s and 2000s. However, employment
adjustments reacting to imports from China are shown to be substantially greater in the US than in Japan.
This paper proposes one answer for contrasting responses to imports from China in the two major trading partnersof China. We analyze how the cross-country differences in integration patterns to global value chains (hereafter GVCs)
shapes labor market effects of international trade. Our analysis suggests that the difference comes from the role ofimported inputs affecting manufacturing employment, which is driven by the extent to which these two countries
participate in GVCs.
With the rise of GVCs over the past decades, firms in developed countries have taken advantage of global production sharing, especially relying on production tasks conducted in Chinese factories.1 Despite its potential positive (orjob-creating or export-facilitating) effects of GVC aspects of the China trade shock, much less has been explored on
this channel. We identify these aspects by separating total imports from China into (i) final goods and (ii) intermediate
goods: the former could capture the competing effect; the latter could capture the complementary effect.
Japan, as a comparison to the US, offers an ideal setting to investigate this research question, based on the following
two characteristics. First, Japan has not only faced rising import competition from China, but it has also received
increasing complementary shocks through intermediate input imports from China. The share of intermediate inputs
within total imports from China grew from 30% to 40% during the 2000-2007 period according to the World InputOutput Tables of Timmer et al. (2015) and Woltjer et al. (2021). It shows how Japanese producers have relied on
Chinese inputs in their production, thereby increasing the output. Second, a greater share of Japanese firms conductboth international import and export transactions than US firms.2 This feature indicates that imports of intermediate
goods and exports are tightly linked within each Japanese firm and therefore complementary shocks are expected tobe observed more clearly in Japan than in the U.S.
We begin by investigating the effects of imports from China and exports to the rest of the world (hereafter ROW)
on Japanese and US manufacturing employment. Our baseline analysis is based on commuting zone (hereafter CZ)
level data and employs shift-share instruments to identify causal effects. We find that imports reduced employment
1 Several examples illustrate how firms in developed countries can benefit by sending production tasks to China. For example, an article has
documented that China specializes in labor-intensive products, while Japan concentrates on high-tech products (Brooke, 2002). The same article
says that Nintendo produces 70% of its GameBoy Advance units in China while keeping other tasks in Japan.
2 Based on Bernard et al. (2007), in 1997, 11% of all U.S. manufacturing firms engaged in both exporting and importing, the ratio is smaller than
the ratios of firms only importing (14%) and only exporting (27%). On the contrary, according to the Basic Survey of Japanese Business Structure
and Activities by METI, 15% of all medium-sized and large Japanese manufacturing firms conduct both exporting and importing, the ratio being
larger than the ratios of import-only firms (6%) and export-only firms (10%). In 2016, these figures increased to 25%, 8%, and 12%, respectively,
revealing that Japanese firms conducting both imports and exports still dominated the other two categories.

2

while exports had an employment-sustaining effect, confirming the existing studies in the US context (Autor et al.,2013, Feenstra et al., 2019) and providing new insight into the literature in the Japanese context where the effects ofexports have been less explored.3
The second step of our analysis is to disaggregate import shocks into two components, (i) imported final goodsand (ii) imported intermediate inputs. The results show that, although an increase in imported final goods from China
had adverse effects on manufacturing employment in both countries, an increase in imported inputs from China had
contrasting effects across the two countries. While imported inputs had positive employment effects in Japan, the same
type of imports had negative employment effects in the US. The overall quantified effects of imported final goods andintermediate goods in Japan are calculated to be 70 thousand job gains for the 1990-2000s. On the other hand, thesame calculation led to 1.82 million job losses in the US.
As the third step, based on our empirical observation suggesting that Japan is more deeply integrated into GVCs
than the US, we examine how countries’ integration patterns to GVCs affect the employment effects of imports. To
do so, we use the ‘upstreamness’ and ‘downstreamness’ measures of Miller and Temurshoev (2017), which quantify
the complexity and thickness of value chains sellers and buyers of intermediate goods face, respectively. The countrysector level measures of up/downstreamness are converted to the CZ level variables using sector-CZ employment
as weights. Then, we estimate the effects of the exposure to trade on manufacturing employment by the level ofup/downstreamness to examine how the integration level to GVCs affects the effects of import competition.
We find that, in the US, the adverse effects of imported inputs come from CZs with lower levels of ‘upstreamness’ or higher levels of ‘downstreamness.’ It suggests that, when the CZ is specializing in less upstream or more
downstream sectors, imported inputs from China reduce manufacturing employment in the CZ greatly. Our conjecture is that, when US manufacturers are in an upstream position, a greater share of imported inputs are used to
produce either next-round inputs or final goods, which in turn requires a greater amount of labor. On the other hand,when US manufacturers are in a downstream position with a fewer stages production going forward, imported inputs
do not generate employment-creation effects and pro-competitive effects dominate. Results from Japan suggest thatup/downstreamness matter less presumably because sectors in Japan are more strongly linked with GVCs, generating
employment creation/sustaining effects of imported inputs in almost all manufacturing sectors. Our results are foundto be robust to the approach proposed by Borusyak et al. (2022), re-constructing sector level variables to mitigate
spacial correlation of exogenous shocks.
This study is related to three different strands of the literature. First, we contribute the literature on the China trade
shock. In the US context, Autor et al. (2013), Acemoglu et al. (2016), and many other studies have found a sizable
manufacturing job reduction caused by imports from China. Other studies attempt to understand why the adverse
effects on US manufacturing employment were so large. For example, Xu et al. (2023) find that the US housing
market collapse in the 2000s worked to magnify the effects of the China trade shock. Eriksson et al. (2021) find that
the China trade shock hit the US labor markets when the US manufacturing industry was at later stages of the product3 Kainuma and Saito (2022) examine the effects of exports to China on manufacturing employment in Japan. In contrast, the current study
examines the effect of exports to the ROW. Also, Kainuma and Saito (2022)’s “export growth” variable turns out to be statistically insignificant
after controlling for their upstream and downstream variables.

3

cycle, which was responsible for generating large adverse effects. Our paper adds an additional explanation by arguingthat limited employment-creation (or employment-sustaining) effects of imported inputs come from the difference inthe countries’ integration patterns to GVCs.4 This paper echoes with Shen and Silva (2018) where they examine theeffects of imports from China on US manufacturing employment by considering value-added contents of trade and thepositions in GVCs. They find that the adverse effects on US manufacturing employment is greater when imports from
China include a greater share of foreign-value added contents.5
In the Japanese context, imported inputs from China or the propagation effects of imports from China through theimporting country’s upstream sectors to downstream sectors have been shown to increase manufacturing employment
by Taniguchi (2019), Kainuma and Saito (2022), and Endoh (2023) using region level data, Kiyota et al. (2021) using
industry level data, and by Hayakawa et al. (2021) using firm level data.6 They argue that imported inputs from China
help Japanese firms in downstream industries to compete and survive in the domestic market, generating employment.
This paper goes one step further from these prior studies by comparing the trade effects in the two countries andexamining the sources of different effects.
Second, this paper contributes to the literature on the effects of offshoring on employment because we examine
the effects of imported inputs from China, which could be seen as offshoring of tasks to China. In the context of
the US economy, previous studies offer varied outcomes. For example, by utilizing variations in offshoring costs
caused by bilateral tax treaties, Kovak et al. (2021) find that offshoring in general increased US employment, however,offshoring of sales activities reduced it during the 1987-2007 period. Wright (2014) finds that, using US data during the
2001-2007 period, offshoring reduced low-skilled employment and offshoring to China increased employment of all
workers.7 Other influential studies on the US economy include Harrison and McMillan (2011), Ottaviano et al. (2013),
Monarch et al. (2017), and their results depend on certain factors and their strengthness.8 In the Japanese context,
existing studies find that expansion of offshoring had a positive effect on domestic employment (e.g., Yamashita andFukao, 2010; Ito and Tanaka, 2014; Ando and Kimura, 2015; Kiyota et al., 2022). Overall, previous studies analyzing
the effects on the US economy propose mixed results. In contrast, those analyzing the effects on the Japanese economy
show positive effects, which are consistent with our findings from the two countries.
4 Wang et al. (2018) incorporate the upstream and downstream effects of the China trade shock and show that the overall effects on employment
are positive. Nevertheless, our paper is not inconsistent with their findings. First, our imported input variables are constructed differently. Theirvariables measure supply chains across China and the US and inter-sectoral linkages within each CZ. In contrast, our measures are simply imported
intermediate inputs from China. Second, Wang et al. (2018) show that downstream effects positively affect non-manufacturing employment, andtheir effects on manufacturing employment are mostly null. The latter part is consistent with our findings and the former part–the effects on
non-manufacturing employment– is not examined in the current study.
5 Shen and Silva (2018)’s results–the adverse effects of imports from China are greater in CZs specializing in sectors with a greater
downstreamness–is consistent our results. However, they use value-added contents in US final goods imports from China as a measure of “downstreamness” (a higher value-added content, a higher downstreamness). On the other hand, we use the up/downstreamness measure of Miller and
Temurshoev (2017), which quantify the number and thickness of value chains comprehensively. In addition, although Shen and Silva (2018) examine the effects of overall value-added contents, we examine the effects of final goods imports and intermediate inputs imports separately to examine
different roles of these two types of imports in shaping labor market effects of imports from China.
6 The other existing studies examining the effects of import competition or offshoring in the Japanese context include Endoh (2018), Endoh(2021a), Endoh (2021b), Matsuura (2022), and Bellone et al. (2022).
7 Wright (2014) finds that offshoring from the US to abroad reduced the employment of production workers (via the job replacement effects) andincreased the employment of non-production workers (via the productivity effects).
8 Harrison and McMillan (2011) finds that offshoring to low-income countries reduces domestic employment while offshoring of tasks thatare not performed at domestic affiliates expands domestic employment. Ottaviano et al. (2013) illustrate that, using the data from the 2000-2007
period, the positive productivity effects of offshoring largely offset the negative displacement effects of offshoring. Monarch et al. (2017) showthat increased offshoring reduced US employment and the adverse effects were stronger for less productive offshoring firms using the data from the
1999-2006 period.

4

In the context of the China trade shock, Mion and Zhu (2013) examine the effects of import competition from China
and offshoring to China on Belgium manufacturing firms’ employment during the 1996-2007 period. They show that,while import competition had an adverse effect on employment, offshoring had a positive effect. Aghion et al. (2023)
find negative pro-competitive effects of imported final goods and positive complementary effects of imported inputs
on sales, employment, and innovation using French firm level data. The results from these two studies, Mion and Zhu(2013) and Aghion et al. (2023), are similar to our findings. On the other hand, Flaaen and Pierce (2021) find thatrising US tariffs on Chinese inputs (negative shocks reducing offshoring) during the 2018-19 US-China Trade War
reduced manufacturing employment in the US. We conjecture that the difference in the sample period (1990-2011 inthis study versus 2018-2019 in their study) and horizon (long-run effects in this study versus short-run effects in theirstudy) are the primary sources of the difference in the results.
Third, this paper is related to the literature on estimating the employment effects of exports. Feenstra et al.
(2019) find that, while imports from China reduced US manufacturing employment, US exports to the ROW increased(sustained) it. Kainuma and Saito (2022) examine employment effects of Japan’s exports to China, and find limited
effects. The current study examines the effect of exports to the ROW. The logic is as follows. While less expensive
imports from China may have different pro-competitive effects on labor markets, a positive demand shock from any
destination country should operate in the same manner to increase exports hence create employment in exporting
countries theoretically. We find that exports to the ROW had positive and significant effects on employment in both
Japanese and US contexts. Choi and Xu (2019) show that Korean exports to China had a positive effect on Korean
manufacturing employment during the 1993–2013 period.9
The rest of the paper proceeds as follows. The next section provides an empirical background motivating ouranalysis. Section 3 outlines our empirical approach. Section 4 summarizes data sources and provides an overview ofvariables. Section 5 presents baseline regression results. Section 6 investigates the mechanisms leading to our main
results. Section 7 concludes. Additional details are documented in Appendix.

2 Background
This section provides an overview of empirical regularities motivating our analysis. Panel A of Figure 1 showsthat, in the US, manufacturing employment decreased from 18 million to 12 million during the 1987-2012 period. In
Japan, manufacturing employment was about 15.5 million at the peak and decreased to 11 million during the same
period. Figure 1 Panel B shows manufacturing imports from China as a share of GDP in Japan and the US, by two
types of imports, final goods and intermediate inputs. It indicates that imports from China are substantially increasing
in both countries and both types of goods. One important difference between the two countries is that, while final
9 The other existing studies investigating the labor market effects of exports to China (or the other countries or the ROW) include Sasahara(2019), Feenstra and Sasahara (2018), Kiyota et al. (2021), Nishioka and Olson (2022), and Choi et al. (2023). Sasahara (2019) and Feenstra andSasahara (2018) use an input-output analysis to estimate the effects of exports on employment. Kiyota et al. (2021) estimate sector level regressions
with shift-share instruments using data from many countries during the 2000–2014 period. Nishioka and Olson (2022) examine the effects of tradewith Japan on political preferences in the US through labor markets using the data from the 1976–1992 period. Choi et al. (2023) investigate theeffects of an increase in Chinese exports on Korean firms’ product churning and product creation through the third market competition by affecting
Korean firms’ exports. All these studies find positive effects of exports–exports increase employment and product creation–although the effects
may be statistically insignificant in some studies.

5goods imports are rising more rapidly in the US than in Japan, intermediate input imports are rising more rapidly inJapan than in the US. These observations indicate that China and Japan have a tighter link within GVCs than China
and the US.
F IGURE 1: Manufacturing employment and import penetration from China

1987

.015

.02

Final goods imports in USA

Final goods imports in Japan.01

Input imports in Japan

Input imports in USA
0

10

Japan

Panel B: Import peneration from China by types of goods

.005

14

USA
12

Million workers

16

Manufacturing imports from China/GDP

18

.025

Panel A: Manufacturing employment

1992

1997

2002

2007

2012

2017

1985

1990

1995

2000

2005

2010

Note: The US manufacturing employment data in Panel A come from the Bureau of Labor Statistics and the Bureau of Economic Analysis. The
Japanese employment data come from the Labor Force Survey of the Statistics Bureau, Ministry of Internal Affairs and Communications. Panel
B shows the value of manufacturing imports from China divided by the import country’s GDP. Authors’ calculations based on the data from the
WIOT 2022 version for the 1985-2000 period and the WIOT 2016 version for the 2001-2012 period.

The differences in the sectoral composition of imports from China are described in Figure 2. Each bar is a change
in China’s sector s’s exports to the country k = JPN, USA, as a share of overall change in China’s exports to the sameP
CHN →k
CHN →k. It shows that the largest increase in imports from China came from thecountry ∆IMs,t/ s ∆IMs,ttextile industry in the 1990s in Japan, and the communication equipment industry took over the place in the 2000s.

The communication equipment industry has continuously been the greatest import-growing sector in the US in thetwo decades. The consumer electrical machinery industry and the industrial electrical machinery industry are ranked
in the 3rd and 4th places, respectively, in the two countries. Overall, the sectoral compositions of imports from China
are not very different between the two countries.
k→ROW
We also discuss the sectoral composition of growth of exports to the ROW, ∆EXs,t
/

P

k→ROW
, in
s ∆EXs,tJapan and the US. As Autor et al. (2013) emphasized, imports from China differ from imports from other countries atleast in two respects. First, imports from China increased substantially due to its productivity growth and accession
to the WTO in 2001. This increase can be seen as an exogenous shock. Second, China’s products are less expensive,
have a stronger pro-competitive effect, and are more likely to replace existing products. Therefore, on the import side,
we focus on imports from China. However, when it comes to the export side, one would assume that exports to China
and exports elsewhere would have the same labor market effects. Based on this consideration, we examine the effects
of exports to the ROW.
Figure 3 demonstrates that the two countries’ sectoral composition of export growth differs. While the largest
6

F IGURE 2: The sectoral composition of imports from China
Panel A: Sorted based on Japan, 2000s

Panel B: Sorted based on the US, 2000s

Communication equipment

Communication equipment

TextilesTextilesConsumer electrical machinery

Consumer electrical machinery

Industrial electrical machinery

Industrial electrical machinery

Air conditioning apparatus

Air conditioning apparatus

Plastic products

Toys and sporting goods

Toys and sporting goods

Plastic products

Industrial machine

Wooden/construction material
Japan, 1991−2001

Agricultural products

Japan, 1991−2001

Industrial machineJapan, 2001−2012

Japan, 2001−2012

USA, 1991−2001

Steel and related products

USA, 1991−2001

Autos and their related products

USA, 2001−2012
0

.05

.1

.15

.2

USA, 2001−2012

.25

0

.05

.1

.15

.2

.25

Note: The figure shows the share of the changes in exports to the rest of the world (ROW). The top 10 sectors out of 62 sectors in Japan’s exports
in the 2000s are shown. The data comes from the UN Comtrade.

F IGURE 3: Sectoral composition of exports to the ROW
Panel A: Sorted based on Japan, 2000s

Panel B: Sorted based on the US, 2000s

Autos and their related products

Refined oils and cokeIndustrial electrical machinery

Autos and their related products

Industrial machine

Agricultural products

Steel and related products

Ornaments

Plastic products

Plastic products

Air conditioning apparatus

Medical apparatus and instruments

Engines and turbines motors

Air conditioning apparatus

Japan, 1991−2001
Japan, 2001−2012
USA, 1991−2001
USA, 2001−2012

Japan, 1991−2001
Vessels and their related products

Medical products

Japan, 2001−2012
USA, 1991−2001

Construction and mining machinery

Steel and related products

USA, 2001−2012

Refined oils and coke

Inorganic chemicals
0

.05

.1

.15

.2

0

.05

.1

.15

.2

Note: The figure shows the share of the changes in exports to the rest of the world (ROW). The top 10 sectors out of 62 sectors in Japan’s exports
in the 2000s are shown. The data comes from the UN Comtrade.

increase comes from the auto industry in Japan, it comes from the oil and coke industry in the US. The other growing
industries in exports include the industrial electrical machinery industry and the industrial machinery industry in Japan.
As there are higher shares of trade in parts and components in the auto and machinery industries, it is consistent with
our understanding that Japan is more deeply connected with GVCs than the US.
We link this empirical observation with our regression analysis to investigate the mechanisms leading to different
employment effects of imports across countries. To do so, we employ two measures of the degree of participation in
GVCs, ‘upstreamness’ and ‘downstreamness’ of Miller and Temurshoev (2017). The ‘upstreamness’ quantifies thenumber and the thickness of value chains that input suppliers face, measuring the country-sector’s forward integration
to GVCs. Using an input-output table, it is defined as follows:f(k,s)+2uks =x(k,s)P Pk′s′ a(k,s),(k′ ,s′ ) f(k′ ,s′ )x(k,s)

+3

P P P
k′

7

s′

k′′

P

s′′ a(k,s),(k′ ,s′ ) a(k,s),(k′′ ,s′′ ) f(k′′ ,s′′ )x(k,s)+ ...

The variable f(k,s),k′ indicates the value of final goods produced by sector s of country k and purchased by country k ′ .
PAs a result, f(k,s) = k′ f(k,s),c′ indicates the total value of final goods produced in sector s of country k. We omit
year subscript t for simplicity. The variable a(k,s),(k′ ,s′ ) = m(k,s),(k′ ,s′ ) /x(k′ ,s′ ) denotes the input-output coefficients,
where m(k,s),(k′ ,s′ ) indicates the value of intermediate goods produced by sector s of country k and used by sector s′of country k ′ , and x(k′ ,s′ ) denotes the gross output. Using the world input-output table, the upstreamness measures
are computed as follows: u = (I − x̂T)−1 i, where x̂ is a (K × S) × (K × S) matrix including gross output x(k,s)
as diagonal entries and zeros in off-diagonal entries. K denotes the number of countries and S denotes the number ofsectors. T denotes the input matrix. The “downstreamness” quantifies the number and the thickness of value chainsthat input buyers face, measuring backward integration to GVCs. It is defined as follows:v(k,s)+2dks =x(k,s)P Pk′s′ v(k,s) v(k,s),(k′ ,s′ )x(k,s)

+3

P P P
k′

s′

k′′

P

s′′ v(k,s) b(k,s),(k′ ,s′ ) b(k,s),(k′′ ,s′′ )x(k,s)+ ...

where v(k,s) denotes value-added, and b(k,s),(k′ ,s′ ) = m(k,s),(k′ ,s′ ) /x(k,s) denotes ‘output’ coefficients. Using theworld input-output table, it is computed as follows: d′ = i′ x̂(I − x̂T)−1 x̂−1 .
F IGURE 4: Upstreamness and downstreamness

3

3.5

Panel A: Upstreamness

Panel B: Downstreamness

China

USA

Downstreamness
2.5
3

Upstreamness
2.4
2.6

Japan

2.2

2.8

China

Japan

1985

2

2

USA

1990

1995

2000

2005

2010

1985

1990

1995

2000

2005

2010

Note: Authors’ calculations based on the data from the WIOT 2022 version for the 1985-2000 period and the WIOT 2016 version for the
2001-2012 period.

Figure 4 displays the value-added weighted average up/downstreamness in the manufacturing sectors in China,
Japan, and the US, computed using the data from the WIOD.10 It shows that China’s up/downstreamness takes thehighest values and the US takes the lowest values among the three countries, and Japan is in the middle throughout
the sample period. It suggests that China’s forward and backward integration to GVCs is at the highest level while
the US’s integration level is the lowest among the three. In particular, Panel A shows that US upstreamness declined
around 2000 and stayed lower during the 2000s. In contrast, Panel B shows that US downstreamness increased since2000 until the 2008-09 Global Financial Crisis. These trends suggest that the US was moving down the GVCs and
10 We use the WIOD 2022 Release for the 1985-1999 period and the WIOD 2016 Release for the 2000-2012 period.

8increased its specialization in products closer to final consumers. We argue that alterations in integration patterns
within GVCs elucidate some of the employment effects of the exposure to trade.
To enhance our understanding of the value chains between China and Japan, and those between China and the US,
we estimate the value-added content contributed by China and embedded in Japan and the US’s domestic production,
as well as to their exports. To do so, we take the approach outlined as the “value-added content in gross exports” inJohnson (2018). For simplicity, consider a world with two countries, country 1 and country 2, each of which consistsof two sectors, manufacturing (denoted by M ) and non-manufacturing (denoted by N ) sectors. By including country
1’s manufacturing sector’s sales in country k, f(1,M ),k , as the only non-zero element in the final demand vector,
value-added contents embedded in such final goods flows are estimated as follows:vax(1,M ),k











 vax(1,N ),k 



−1 
v(I − A) 

 =b



vax(2,M ),k vax(2,N ),kf(1,M ),k
0
0
0







,




b denotes a vector including
where A denotes a 4 × 4 matrix of input-output coefficients, I denotes an identity matrix, v

the value-added to gross output ratios as diagonal elements and zeros as off-diagonal elements. The share of valueadded by country 2’s manufacturing sector included in f(1,M ),k is calculated as vax(2,M ),k /f(1,M ),k .
Figure 5 Panel A displays the share of value-added content by China’s manufacturing sectors included in Japan andthe US’s manufacturing sectors’ domestic sales, respectively. It shows that Japan and the US had similar shares andshared their trends until around 2000. On the contrary, in the 2000s, China’s value-added contribution increased more
rapidly in Japan than in the US. Figure 5 Panel B presents the share of value-added content by China’s manufacturingsectors included in Japan and the US’s manufacturing exports to the ROW, respectively. It shows similar patterns
as Panel A: synchronized trends between Japan and the US by the end of the 1990s, and diversion in the 2000s. In
addition, the share of China’s value-added was more rapidly growing in Japan’s exports than Japan’s domestic sales in
the 2000s. Interestingly, although the up/downstreamness measures in Figure 4 suggest the US is moving down along
GVCs, the value-added content measures in Figure 5 indicate that the level of the US’s backward integration to value
chains with China is less rapidly increasing relative to Japan.
Overall, the data suggest three facts regarding the US’s integration to GVCs: (i) the US’s forward integration to
GVCs (upstreamness) was declining since around 2000, (ii) the US’s backward integration to GVCs (downstreamness)
was almost constant during the 1990s and 2000s, and (iii) China’s value-added embedded in US domestic sales andexports was less rapidly increasing than Japan since 2000s. These facts imply that, after China’s accession to the WTO
and the acceleration of China’s integration to the world economy, the deepness of the US’s integration to value chainswith China was declining. Combined with the exceptional increase in US imports of final goods from China shownin Figure 1, we argue that import penetration from China in the US was predominantly a penetration of final goods,
generation a stronger pro-competitive effect rather than a complementary or productivity-enhancing effect.

9Panel A: Within domestic sales

Panel B: Within exports

.015

.015

F IGURE 5: China’s value-added content included in Japanese and US production

China’s value added content share
.005
.01

.005

.01

Japan

1985

USA

0

USA

0

China’s value added content share

Japan

1990

1995

2000

2005

2010

1985

1990

1995

2000

2005

2010

Note: The figure shows China’s manufacturing sectors’ value-added included in Japan and the US’s manufacturing sales in the domestic market(Panel A) and their manufacturing exports (Panel B). Authors’ calculations based on the data from the WIOT 2022 version for the 1985-2000
period and the WIOT 2016 version for the 2001-2012 period.

3 Methodology
3.1 Baseline regression model
Our empirical approach follows the ones used in Autor et al. (2013) and Feenstra et al. (2019), investigating theeffects of trade shocks on manufacturing employment and other labor market outcomes. Specifically, our benchmark
regression model for country k = JPN, USA, is as follows:k∆lit= β1 ∆IP WitCHN →k + β2 ∆EOWitk→ROW + Xitβ 3 + αt + αr + uit ,k=where i indicates CZ and t indicates period.11 The dependent variable ∆lit



Lk
Lk
it+1
it
− W APkk
W APit+1
it(1)


is the decennialchange in manufacturing employment share of the working-age population.12 αt indicates period fixed effects and αrdenotes the regional fixed effects (i.e., the region dummies for Japan and the statistical division dummies for the US).
One of our key explanatory variables, ∆IP WitCHN →k , captures import penetration from China in CZ i during theperiod t, and it is defined as∆IP WitCHN →k =

X

s∈SM

∆imCHN →k
Lkist
P× P st kks′ ∈SA Lis′ t
i′ Li′ st

!

.

(2)

The variable Lkist denotes the number of employees in sector s of CZ i at the beginning of period t in country k. The
CHN →kvariable ∆imst
denotes the change in imports of sector s’s goods from China.13 SA is the set of all sectors and
11 Throughput the paper, subscript t indicates “period” unless otherwise indicated. Depending on the context, t also indicates the start year of aperiod.
12 The data on working age population comes from the Census of Japan, which provides us with the data in every five years, 1990, 1995, 2000,
2005, 2010. The manufacturing employment data come from the Establishment and Enterprise Census and the Economic Census for Business
Activity of Japan, which provide us with the data in 1991, 1996, 2001, 2006, and 2012. As a result, there is a one-year gap (and a two-year gap forthe last year) when calculating Lit /W APit .
13 The data on imports from China are obtained from UN Comtrade and expressed in nominal USD. Therefore, we convert the data to Japanese
yen using the exchange rate from Penn World Table and adjust them to real values using Japanese sectoral CPI. As indicated, this import penetration
variable is the weighted average of decennial changes in sectoral imports from China where the weights are sector level employment. The sectoral10

SM is the set of manufacturing sectors. This variable is the employment-weighted average of the per worker exposure
to the import competition. As the denominator of the weight includes all sectors including non-manufacturing sectors,
the exposure to import competition takes a lower value if the CZ has a lower share of manufacturing employment in
the first place.
In addition to import penetration, we include country k’s exports to the ROW, by replacing imports from China
CHN →kleads to the following.
in equation (2) with exports to the ROW ∆exk→ROW
∆imst
st

∆EOWitk→ROW =

X

s∈SM

∆exk→ROW
Lkist
P
P st k×kL′′s ∈SA is t
i′ Li′ st

!

.

(3)

The variable Xit denotes a vector of control variables including. When running regressions with the Japanese data,
Xit includes (1) the initial manufacturing employment share, (2) the unemployment rate, (3) the labor market participation rate, (4) the foreign population share, (5) the urban population share, (6) the female employment share, (7)log of “manufacturing employment divided by the number of manufacturing affiliates,” (8) the share of non-full-time
manufacturing employment as a share of total manufacturing employment, (9) log of population density, (10) the shareof population of aged 15 or less to total population, and (11) the region dummies.
When running regressions with the US data, Xit includes (1) the initial manufacturing employment share, (2) the
share of educated population, (3) the share of foreign-born population, (4) the share of female employment, (5) the
share of employment with routine occupations, (6) the average offshorability index, and (7) the statistical division
dummies. To allow different slope parameters for control variables across periods, the period dummy dt interacts withthe controls Xit (besides the region dummies and statistical division dummies). The error term is denoted by uit .

3.2 Identification
The variables, ∆IP WitCHN →k and ∆EOWitk→ROW , are endogenous because labor market conditions can potentially affect imports and exports through various channels (e.g., purchasing powers of consumers and firms’ economic
activities in country k). Therefore, we employ the instrumental variable approach with shift-share instruments following Autor et al. (2013) for the import side and Feenstra et al. (2019) for the export side.
In particular, ∆IP WitCHN →k is instrumented by the following variable:
CHN →OT H\k∆IP Wit=Xs∈SM

CHN →OT H\kwhere ∆imst

CHN →OT H\k
Lkist−1
∆imst
P
P
×
k
k
s′ ∈SA Lis′ t−1
i′ Li′ st−1

!

,

(4)

indicates decennial changes in China’s exports of sector s’s goods to eight other developed

countries from the view point of country k.14 China’s exports to these countries are used to capture China’s supply
shocks leading to export growth, including, for example, its accession to the WTO and productivity growth. The
weighted average is computed using employment data in all sectors, not only manufacturing sectors but also non-manufacturing sectors following
Autor et al. (2013).
14 The eight countries are selected following Autor et al. (2013). For the Japanese perspective, it includes Australia, Denmark, Finland, Germany,
New Zealand, Spain, Switzerland, and the US. For the US perspective, it includes Japan instead of the United States.

11

employment data used to compute the weighted average come from the previous period.15 The reason for using
lagged employment data is to alleviate the potential endogeneity issue where workers may choose their locations towork with their expectations on the upcoming arrival of the China trade shocks.
We take the symmetric approach for the export side. The instrument used for the endogenous variable ∆EOWitk→ROW
is
OT H\k→ROW∆EOWit=Xs∈SM
OT H\k→ROW

where ∆exst

OT H\k→ROW
Lkist−1
∆exst
P
P
×
k
k
s′ ∈SA Lis′ t−1
i′ Li′ st−1

!

,

(5)

indicates decennial changes in exports from sector s in the eight other developed countries

to the ROW. This variable is meant to capture the world’s demand shocks. As in equation (4), we use the lagged
employment data to compute the weighted average of the world’s demand shocks.

3.3 Disaggregating the import variables
As found in previous studies (e.g., Taniguchi, 2019; Endoh, 2023; and Aghion et al., 2023), we assume different
labor market effects across types of goods because final goods are expected to have a greater pro-competitive effectin the goods market than inputs. Echoing these prior studies, we classify imported goods into imported final goods
and intermediate goods. The variable f(CHN,s),J indicates the dollar value of final goods produced in China’s sectors and consumed by Japan, and m(CHN,s),(J,s′ ) indicates the dollar value of intermediate goods produced in China’s
sector s and used by Japan’s sector s′ . Hereafter, time subscripts t are dropped for simplicity except the employment
variables.
The degree of import penetration of final goods from China is measured by

CHN →k∆IP WFinal,i
=

X

s∈SM

∆f(CHN,s),k
Lkist
P
× P k
k
L
s′ ∈SA is′ t
i′ Li′ st

!

,

(6)

where the decennial change in final goods imports from China per employment ∆f(CHN,s),k /P

ki′ Li′ st is utilized

to find the weighted average of the exposures to final goods imports from China. Similarly, the degree of importpenetration of intermediate goods from China is measured by

CHN →k∆IP WInputs,i
=

X

s∈SM

Lkist
P
×
k
s′ ∈SA Lis′ tP

s′ ∆m(CHN,s),(k,s′ )P ki′ Li′ st

!

,

(7)

where the decennial change in imported inputs from China is used to find the exposure to imported inputs from China.
We also introduce a slightly different measure of imported inputs to quantify the amount of inputs used by sectors in
the importing country. By replacing the sector subscripts for summation in the variable m(CHN,s),(k,s′ ) , we construct
the following variable:

CHN →k∆IP WInputs
used,i =

X

s∈SM

Lkist
P
×
k
s′ ∈SA Lis′ tP

s′ ∆m(CHN,s′ ),(k,s)
P ki′ Li′ st

!

,

(8)

15 Due to the availability of the Japanese employment data, we use the 1986 employment data for the 1991-2001 change, the 1996 employment
data for the 2001-2012 change to construct instruments for the Japanese analysis.

12where the decennial change in imported inputs from China that are used by Japan’s sector s per employment ∆m(CHN,s′ ),(k,s)
is used to find the exposure to imported inputs from China. We expect that imported inputs used in each sector in Japan(rather than imported inputs produced in each sector in China) are more appropriate to measure the effect of the utilization of imported inputs from China.
These variables are constructed using the input-output tables from the WIOD 2016 Release for the 2000s and using
the WIOD 2022 Release for the 1990s. Limitations of using the datasets include (i) the sectoral aggregation is lessdetailed, covering 17 manufacturing sectors for the 2016 version and 12 manufacturing sectors for the 2022 version,16and (ii) the list of the eight developed countries used to construct the instruments differ from the eight developed
countries used to construct equations (4) and (5) because New Zealand is not included as a separate country in either
version of the WIOD and Switzerland is not included in the WIOD 2022 Release. See Appendix C for details.
We construct the corresponding instruments for each of the endogenous variables (6)-(8).17 Hereafter, we omit
CHN →kCHN
the superscript “→ k” to simplify the notations. For example, we express ∆IP WFinal,ias ∆IP WFinal,iand weCHN →OT H\kexpress ∆IP Wit

as ∆IP WitCHN →OT H .

3.4 Upstreamness and downstreamness
We investigate the role of the upstreamness and downstreamness in affecting the effects of imports from China
on manufacturing employment. The measure of upstreamness ukst and downstreamness dkst obtained from the inputoutput table are at the country k-sector s level in the start year of period t. Japan and the US’s sector level upstreamnessand downstreamnes measures are converted to CZ level by taking the weighted average as follows:

ukit =

X

s∈SM

P

Lkist
k
s′ ∈SM Lis′ twhere the denominator of the weight,P× ukst!

anddkit =

X

s∈SM

P

Lkist
k
s′ ∈SM Lis′ t× dkst

!

,

(9)

k
s′ ∈SM Lis′ t , includes the manufacturing employment only. Without excluding

the non-manufacturing employment from the denominator, the upstreamness and downstreamness variables would
have high correlations with the exposure variables to trade by construction because these variables would take lower
values in the CZs with lower manufacturing employment shares. These two CZ level variables are utilized to examine
the effects of the exposure to trade differ depending on the level of the upstreamness and downstreamness.18

3.5 The BHJ approachStudies have clarified conditions under which shift-share instruments are valid. Adão et al. (2019) show that if
observations are exposed to a shock at similar degrees, it leads to correlated residuals and incorrect standard errors.
To overcome the issue, Goldsmith-Pinkham et al. (2020) propose an approach with exogenous exposure shares and
16 We construct a crosswalk between the WIOD’s more aggregated manufacturing sectors and 392 manufacturing sectors used in Autor et al.
(2013) and Feenstra et al. (2019). Then, we aggregate the sector-CZ level employment data from Feenstra et al. (2019) to the WIOD aggregationlevel, and utilize them to construct variables (6)-(8) and their instruments. We take the same approach to construct the CZ level up/downstreamnessof equation (9) and the CZ level capital-labor ratios used in Appendix F.
17 See Appendix C for the equations of the instruments used to identify the explanatory variables (6)-(8). See Appendix D for first-stage regression
results predicting these endogenous explanatory variables.
18 See Figure A1 in Appendix E for the spatial distribution of the CZ level upstreamness and downstreamness in Japan and the US, respectively.

13

a (possibly) endogenous shock. In addition, Borusyak et al. (2022) propose an approach with (possibly) endogenous
exposure shares and an exogenous shock.
We re-run regressions by taking the approach by Borusyak et al. (2022) (hereafter BHJ) and confirm the robustnessof our results. Specifically, as the first step, we regress the dependent variable on the set of control variables and obtainresiduals, ỹi , which is at the CZ level with CZ subscript i. A residualized endogenous explanatory variable x̃i andinstrument z̃i are also obtained by taking the same step. As the second step, convert the CZ level residualized variables,ỹi , x̃i , and z̃i are converted to the sector level by taking sectoral employment-weighted averages, ŷs , x̂s , and ẑs . As
the last step, regress ŷs on x̂s with the instrument ẑs , where regressions are weighted by sectoral employment. We
report results from the BHJ approach in every regression table for the second-stage.
One limitation of our dataset when conducting this test is that the underlying sectoral aggregation levels differ
between variables (4)-(5) and variables (6)-(8). Variables (4)-(5) are constructed using the employment and trade
data from 63 Japanese manufacturing sectors and 292 US manufacturing sectors. On the other hand, variables (6)(8) are based on 12 or 17 sectors manufacturing sectors both in Japanese data and US data because we use moreaggregated data from the WIOD. When taking the BHJ approach for regressions with variables (6)-(8), CZ level
import competition variables based on 12 or 17 sectors are converted to 63 Japanese sectors or 292 US sectors, which
could potentially lead to bias. Nevertheless, as we obtain broadly consistent results from the CZ level regressions andsector level regressions.

4 Data4.1 Data sources
Our Japanese dataset is constructed using data from multiple sources. First, the employment data come fromtwo sources including (1) the Establishment and Enterprise Census of Japan (Jigyōsho Kigyō Tōkei Chōsa, hereafter
EECJ), where the survey years include 1986, 1991, 1996, and 2001, and (2) the Economic Census for Business Activityof Japan (Keizai Sensasu Katsudō Chōsa, hereafter ECBAJ), where the survey year is 2012. The control variables areconstructed based on the data from the databases (1)-(2) and the Regional Statistics Database (System of Social and
Demographic Statistics) of Japan. The population-related variables come from the Census of Japan, which provides
us with the data in every five years, 1990, 1995, 2000, and 2005, which are used for the 1991-1996, 1996-2001,
2001-2006, 2006-2012, respectively. The Japanese manufacturing value-added data are obtained from the Census of
Manufacturer (Kōgyo Tōkei Chōsa). The value-added data are deflated using prefecture level GDP deflators obtained
from the Prefectural Accounts of the Cabinet Office (Naikakufu).
We construct trade shock variables for Japan using trade flows retrieved from UN Comtrade. To do so, we createa sectoral concordance connecting the EECJ (the ECBAJ for 2012) and UN Comtrade. Maintaining a consistent listof sectors throughout the sample period for EECJ and ECBAJ yields 63 sectors.19 The data on trade flows obtained
from UC Comtrade are converted to real JPY values, in the 2020 prices, using the exchange rate from the Penn World
19 See Table A1 in Appendix A for the list of 63 sectors. We use the same secoral aggregation and concordance table as Sasahara et al. (2023).

14

Table and CPIs from the Statistics Bureau of Japan (Sōmushō Tōkei Kyoku). The cross-sectional unit of our datasetis ‘commuting zones’ constructed by Adachi et al. (2021), including 308 CZs, which is strongly balanced across thetwo stacked periods.20 Given the availability of the data in the Japanese dataset, the first period spans from 1991 to
2001, and the second period spans from 2001 to 2012. The variables for the second period is re-scaled to the decennial
length by multiplying 10/11.
The US CZ level dataset is based on the data from Autor et al. (2013) and Feenstra et al. (2019), with 722 CZs in
the contiguous US. The manufacturing employment data and trade shock variables and their instruments come from
Feenstra et al. (2019).21 The control variables, the percentage of educated workers, the percentage of foreign-bornworkers, the percentage of female workers, and the percentage of routine occupations, come from Autor et al. (2013).
Additionally, we use the World Input-Output Tables (hereafter WIOT) of Timmer et al. (2015) and Woltjer et al.
(2021) when constructing variables such as imported final goods and imported inputs. Therefore, the definition ofimported inputs in this study is a ‘broad’ definition including not only parts and components but also finished goods
used as inputs. Trade flows obtained from the WIOT are converted to real values using the price indices obtained fromthe Socio Economic Accounts of the same database. Japanese trade variables are converted to JPY using the exchange
rates, while the original USD data are used to construct instruments of Japanese trade variables and US trade variables.

4.2 Summary statistics
Table 1 Panel A shows summary statistics of variables in the Japanese data and Panel B shows those in the US data.
Each of these tables displays summary statistics of the dependent and key control variables, endogenous explanatory
variables, and instruments in the stacked cross-section data for the two periods, the 1990s and 2000s.22 Table 1 showsthat, for example, the average change in the share of manufacturing employment is -1.48 percentage points (hereafterpp). The average of the import penetration from China, ∆IP W CHN , is 101 thousand JPY. The average of the export
opportunities to the ROW, ∆EOW ROW , is 198 thousand JPY.
Table 1 Panel B presents the summary statistics of US variables. It shows that, for example, the average change
in the manufacturing employment as a share of the working age population is 1.34 pp, with minimum of -17 pp andthe maximum of 15 pp. The key explanatory variables ∆IP W CHN and ∆EOW ROW and their instruments comefrom Feenstra et al. (2019) and these variables are normalized variables. The average of ∆IP W CHN is 0.80 and theaverage of ∆EOW ROW is 1.34. The other trade exposure variables are authors’ calculations and the unit of thesevariables is 1,000 USD in the current prices.
20 We employ Adachi et al. (2021)’s commuting zones based on the 1995 commuting patterns, originally including 315 CZs.

However, after
excluding CZs with no manufacturing employment and those with exceptional population changes (i.e., the one in Fukushima where there was
evacuation due to the explosion in the Fukushima Daiichi Nuclear Powerplants), the number of CZs in our sample is reduced to 308. Whileprevious studies employ other cross-sectional units such as prefectures (e.g., Taniguchi, 2019) and sectors (Kiyota et al., 2021), CZs are consideredas the right cross-sectional units when investigating labor market outcomes (see Adachi et al., 2021, for further details). Existing studies using CZlevel datasets include Kainuma and Saito (2022) and Endoh (2023).
21 Feenstra et al. (2019) obtained manufacturing employment data from the County Business Patterns (CBP). Their trade data come from UN
Comtrade with the 6-digit HS product classification, and they merge the trade data with 392 manufacturing industries following the steps by
Acemoglu et al. (2016).
22 See Tables A2 and A6 in Appendix B for summary statistics of the instruments and control variables, and correlation matrices of the variables.

15

TABLE 1: Summary statistics of main variables
Panel A: Japan, k = JP N
Mean

Std. dev.

Min

Max

Dependent and control variables
100 ×∆(mnfg emp / working age pop)
Initial 100×(mnfg emp / working age pop)
Upstreamness (U p)
Downstreamness (Down)

-1.48
12.59
2.22
2.40

2.36
6.40
0.22
0.10

-14.88
0.32
1.58
2.20

14.75
35.57
2.97
2.72

Endogenous explanatory variables∆IP W CHN →k∆EOW k→ROW
CHN →k∆IP WFinal
goods
CHN →k∆IP WInputs
CHN →k∆IP WInputs
used
CHN →k∆IP WFinal goods × U p
CHN →k∆IP WInputs
used × U p
CHN →k∆IP WFinal goods × Down
CHN →k∆IP WInputs
used × Down

1.01
1.98
1.12
0.37
0.24
0.53
0.13
0.57
0.14

0.76
2.29
0.88
0.38
0.27
0.60
0.19
0.65
0.21

-0.09
-0.25
0.00
0.00
-0.05
0.00
-0.02
0.00
-0.03

5.19
13.73
5.67
1.82
1.82
3.87
1.55
4.02
1.97

Panel B: The US, k = U SA
Mean

Std. dev.

Min

Max

Dependent and control variables
100 ×∆(mnfg emp / working age pop)
Initial 100 ×(mnfg emp / working age pop)
Upstreamness (U p)
Downstreamness (Down)

-1.34
9.57
2.44
2.41

3.05
6.33
0.23
0.17

-17.98
0.00
1.66
2.00

14.95
37.86
3.34
2.80

Endogenous explanatory variables∆IP W CHN →k∆EOW k→ROW
CHN →k∆IP WFinal
goods
CHN →k∆IP WInputs
CHN →k∆IP WInputs
used
CHN →k∆IP WFinal goods × U p
CHN →k∆IP WInputs
used × U p
CHN →k∆IP WFinal goods × Down
CHN →k∆IP WInputs
used × Down

0.80
1.34
1.04
0.38
0.24
0.66
0.15
0.68
0.16

0.92
1.51
1.20
0.38
0.22
1.02
0.19
1.06
0.20

-0.02
-4.35
0.00
0.00
0.00
0.00
0.00
0.00
0.00

9.51
23.11
11.02
3.28
2.07
10.83
1.66
11.37
2.19

Note: In Panel A, the sample size is 616 for all variables, consisting of 308 CZs for the two periods. The unit of the endogenous explanatory
variables is 100,000 JPY (in the 2000 prices). The unit of the instruments is 1,000 USD (nominal prices).
In Panel B, the sample size is 1,444 for all variables, consisting of 722 CZs for the two periods. Panel B’s manufacturing employment variables,∆IP W CHN and ∆EOW ROW , and these two endogenous variables’ instruments come from Feenstra et al. (2019). These endogenous variables
and instruments are normalized variables. The other trade exposure variables and their instruments are authors’ calculations, and the unit of thesevariables is 1,000 USD (nominal prices).

4.3 Trade exposures in JapanTo gain a better grasp of the spatial distribution of the trade exposure variables, we present top three, median, andbottom three CZs (among 40 largest CZs) in terms of the key trade exposure variables computed as the sum of the two
decades.
Table 2 Panel A displays the ranking based on overall import penetration from China, ∆IP W CHN . It indicatesthat CZs including Ohta-shi, Gunma, Otsu-shi, Shiga, and Yao-shi, Osaka–where these locations specialize in importoriented manufacturing–had the greatest exposure to import penetration. On the other hand, CZs including Fukuokashi, Fukuoka, Sapporo-shi, Hokkaido, and Naha-shi, Okinawa–where these locations specialize in non-manufacturingindustries–had the smallest exposure. The median CZs were those include Kobe-shi, Hyogo, and Takasaki-shi, Gunma.

16

TABLE 2: Commuting zones with top, median, and bottom exposures, Japan
Panel A: Import penetration from China
No.
1
2
3

Commuting zone
Ohta-shi and Ashikaga-shi, Gunma and Tochigi
Otsu-shi and Higashiomi-shi, Shiga
Yao-shi and Matsubara-shi, Osaka

∆var
6.79
5.20
4.33

∆l
-9.07
-5.46
-3.59

20
21

Kobe-shi and Akashi-shi, Hyogo
Takasaki-shi and Maebashi-shi, Gunma

2.43
2.42

-3.56
-4.28

38
39
40

Fukuoka-shi and Kasuga-shi, Fukuoka
Sapporo-shi and Ebetsu-shi, HokkaidoNaha-shi and Okinawa-shi, Okinawa

0.66
0.42
0.20

-1.93
-1.68
-0.65

Panel B: Export opportunities to the ROW
No.
1
2
3

Commuting zone
Toyota-shi and Okazaki-shi, Aichi
Mito-shi and Hitachi-shi, Ibaraki
Yokkaichi-shi and Tsu-shi, Mie

∆var
17.04
14.31
12.01

∆l
-5.05
-4.84
-4.16

20
21

Shizuoka-shi and Yaizu-shi, Shizuoka
Gifu-shi and Kakamigahara-shi, Gifu

6.35
6.17

-4.69
-5.43

38
39
40

Kagoshima-shi and Satsumasendai-shi, Kagoshima
Sapporo-shi and Ebetsu-shi, HokkaidoNaha-shi and Okinawa-shi, Okinawa

1.42
1.09
0.95

-1.24
-1.68
-0.65

Panel C: Imported inputs used − final goods from China
No.
1
2
3

Commuting zone
Toyota-shi and Okazaki-shi, Aichi
Hiroshima-shi and Kure-shi, Hiroshima
Naha-shi and Okinawa-shi, Okinawa

∆var
-0.07
-0.11
-0.21

∆l
-5.05
-3.13
-0.65

20
21

Kagoshima-shi and Satsumasendai-shi, Kagoshima
Takamatsu-shi and Marugame-shi, Kagawa

-0.93
-0.94

-1.24
-3.09

38
39
40

Gifu-shi and Kakamigahara-shi, Gifu
Ohta-shi and Ashikaga-shi, Gunma and Tochigi
Otsu-shi and Higashi-Ohmi-shi, Shiga

-1.76
-2.02
-2.08

-5.43
-9.07
-5.46

CHN
CHN
Note: In Panels A, B, and C, ∆var indicates ∆IP W CHN , ∆EOW ROW , and ∆IP WInputs
used − ∆IP WFinal goods , respectively. The tableshows the exposure to each type of trade shocks in the top 40 commuting zones in terms of the total population size in 1991. The municipalities
listed are representative municipalities of the CZs and not the full list of municipalities in the CZs. The unit of the trade shocks is 100,000 JPY (inthe 2000 prices). The unit of ∆l is the percentage point change as a share of working age population. See the main text for data sources.

The table also shows that CZs with greater import exposures experienced greater reductions in the share of manufacturing employment (i.e., 9.1 pp, 5.5 pp, and 3.6 pp reductions in the top three CZs, respectively).
Table 2 Panel B summarizes the exposure to export opportunities, ∆EOW JP N , showing that CZs including
export-oriented manufacturing industries such as Toyota-shi, Aichi, Mito-shi, Ibaraki, and Yokkaichi-shi, Mie were
exposed to greater export opportunities. On the other hand, CZs in Hokkaido and Okinawa were less exposed to thoseshocks because these locations are specializing in non-manufacturing sectors such as agriculture and tourism, exposed
to export opportunities at a smaller extent.
Table 2 Panel C displays the exposure to “imported inputs used − imported final goods from China.” We use
the “imported inputs used” (equation (8)) rather than the conventional “imported inputs” (equation (7)) because theformer captures the imported inputs used in the manufacturing sectors in Japan, more suited in our analysis shedding17

F IGURE 6: Exposures to imports from China and exports to the ROW, Japan
Panel A: Imports from China, total

Panel C: Imports from China, final goods

Panel B: Exports to the ROW

Panel D: Imports from China, imported inputs used

Note: The figure shows the exposures to the imports from China and exports to the rest of the world (ROW). All panels are based on the averagesof the two periods, the 1990s and 2000s. Authors’ calculations using the data obtained from the data sources written in the text.

light on GVCs. A greater value indicates that the CZ is exposed to imported inputs more greatly than imported final
goods. It shows that CZs including Toyota-shi, Aichi, Hiroshima-shi, Hiroshima, and Naha-shi, Okinawa were facing
greater amounts of imported inputs rather than imported final goods from China. On the other hand, CZs including
Gifu-shi, Gifu, Ohata-shi, Gunma, and Otsu-shi, Shiga were facing greater amounts of imported final goods rather
than imported inputs from China.
Figure 6 displays nation-wide geographical distributions of the trade variables in Japan. Panels A, B, C, D display
imports from China, exports to the ROW, imported final goods from China, and inputs used by Japanese sectors andimported from China, respectively. The darker colored CZs are those with greater exposures. These panels show thatthe exposures to trade shocks are not uniformly distributed across space.

18

TABLE 3: Commuting zones with top, median, and bottom exposures, the US
Panel A: Import penetration from China
No.
1
2
3

Commuting zone
San Jose CA
Providence RI
Los Angeles CA

∆var
5.25
3.28
1.97

∆l
-10.27
-7.53
-5.89

20
21

Columbus OH
Miami FL

1.17
1.13

-3.58
-3.65

38
39
40

Orlando FL
Washington DC
New Orleans LA

0.59
0.55
0.32

-3.13
-0.63
-1.89

Panel B: Export opportunities to the ROW
No.
1
2
3

Commuting zone
Providence RI
San Jose CA
Milwaukee WI

∆var
10.76
8.75
4.46

∆l
-7.53
-10.27
-4.18

20
21

Houston TX
Columbus OH

2.28
2.21

-1.96
-3.58

38
39
40

West Palm Beach FL
Washington DC
Seattle WA

1.09
0.62
0.34

-2.69
-0.63
-4.96

Panel C: Imported inputs used − final goods from China
No.
1
2
3

Commuting zone
New Orleans LA
West Palm Beach FL
Washington DC

∆var
-0.51
-0.55
-0.56

∆l
-1.89
-2.69
-0.63

20
21

Sacramento CA
Columbus OH

-1.29
-1.30

-1.75
-3.58

38
39
40

Providence RI
Buffalo NY
San Jose CA

-2.64
-3.14
-5.36

-7.53
-5.88
-10.27CHN
CHN
Note: In Panels A, B, and C, ∆var indicates ∆IP W CHN , ∆EOW ROW , and ∆IP WInputs
used − ∆IP WFinal goods , respectively. The tableshows the exposure to each type of trade shocks in the top 40 commuting zones in terms of the total population size in 1991. The trade variables

come from Feenstra et al. (2019) and they note that “the change in US import (or export) exposure is computed by dividing 100 × the annualized
increase in the value of US imports (exports) over the indicated periods by 1991 US market value (1991 US industry output) in that industry.” Theunit of ∆l is the percentage point change as a share of the working age population.

4.4 Trade exposures in the US
The top three, medium and bottom three CZs (among the largest 40 CZs) in terms of the three trade exposures in
the US are shown in Table 3. Panel A shows that CZs, including San Jose, CA, and Providence, RI, and Los Angeles,
CA had the greatest exposures to import penetration from China. On the other hand, those including Orlando, FL,
Washington, DC, and New Orleans, LA, had the smallest exposures in terms of import penetration.
Table 3 Panel B shows that the top two CZ in import penetration, San Jose, CA, and Providence, RI, are also listed
at the top two CZs in terms of export opportunities. The bottom CZs in terms of the exposure to export opportunities
are West Palm Beach, FL, Washington DC, and Seattle, WA. Washington D.C. is also listed as one of the CZs withthe smallest exposure to import penetration. It has a smaller exposure to trade shocks in general because its share ofmanufacturing employment is small. Panel C displays the top, medium, and bottom CZs in terms of “imported inputs
used − imported final goods from China.” It lists New Orleans LA, West Palm Beach FL, and Washington, DC as the
top three CZs and Providence RI, Buffalo, NY, and San Jose, CA as the bottom CZs.

19

The geographical distribution of these trade variables in the US are presented in Figure 7, imports from China,
exports to the ROW, imported final goods from China, and inputs used by US sectors and imported from China in
Panels A, B, C, and D, respectively. Again, as in the case of Japan, the figure shows that the exposure to the trade
shocks vary substantially across locations within the US.
F IGURE 7: Exposures to imports from China and exports to the ROW, the US
Panel A: Imports from China, total

Panel C: Imports from China, final goods

Panel B: Exports to the ROW

Panel D: Imports from China, imported inputs used

Note: The figure shows the exposures to the imports from China and exports to the rest of the world (ROW). All panels are based on the averagesof the two periods, the 1990s and 2000s. Authors’ calculations using the data obtained from the data sources written in the text.

5 Baseline results
5.1 Baseline first-stage results
We discuss our regression results, starting with first-stage results. Table 4 summarizes the results from regressing
the endogenous explanatory variables on the instruments and the control variables, using the initial population as
weights. Columns (1)-(4) report the results from the Japanese data and columns (5)-(8) reports the results from the US
data, and both are structured symmetrically. Column (1) and (5) regress ∆IP W CHN on ∆IP W CHN →OT H and thecontrol variables. Both coefficients are positive and statistically significant at the 1%, and corresponding F -statistics
for excluded instruments are 477 and 213, respectively, indicating that the instrument ∆IP W CHN →OT H works well
to explain the endogenous variable ∆IP W CHN .
Columns (2) and (6) regress the same endogenous variable ∆IP W CHN on ∆IP W CHN →OT H and ∆EOW OT H→ROW
because we include the two endogenous variables in the same regression equation in our main specifications. In column

20

(2), although ∆EOW OT H→ROW has a significant negative coefficient, the coefficient is tiny, -0.004.23 In column (6),∆EOW OT H→ROW has an insignificant coefficient. In either case (in columns (2) and (6)), ∆IP W CHN →OT H continue to have significant positive coefficients and these two regressions produce high enough first-stage F -statistics,
256 and 109, respectively.
TABLE 4: First-stage regression results
Dep. var.
∆IP W CHN →OT H\k∆EOW OT H\k→ROW
Obs.
R-sq.
F -stat.
p-value of F -stat.

k =JPN∆IP W CHN →k∆EOW k→ROW
(1)
(2)
(3)
(4)
0.19***
0.19***-0.07(0.01)(0.01)(0.06)-0.004*** 0.08***
0.08***(0.00)(0.01)(0.01)
616
616
616
616
0.94
0.94
0.87
0.87
476.82255.75
32.72
17.62
0.000
0.000
0.000
0.000

k =USA∆IP W CHN →k∆EOW k→ROW
(5)
(6)
(7)
(8)
1.02*** 1.03***-0.01(0.07)(0.07)(0.14)-0.01
0.39***
0.39***(0.01)(0.05)(0.06)
1,444
1,444
1,444
1,444
0.82
0.82
0.61
0.61
213.48109.28
54.94
31.08
0.000
0.000
0.000
0.000

Note: The table shows the results from first-stage regressions. All regressions include constant term and control variables and region fixed effects,
prefecture dummies for Japan, and statistical division dummies for the US. The total initial population weights all regressions. Standard errors,
clustered at the prefecture level for Japan, and the state level for the US, are in parentheses. The shown F -statistics are those for excluded instruments
only. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.

Columns (3) and (7) regress ∆EOW ROW on ∆EOW OT H→ROW and the controls. The two columns show
highly significant positive coefficients with F -statistics of 33 and 55, respectively. Lastly, columns (4) and (8) regress∆EOW ROW on ∆EOW OT H→ROW and ∆IP W CHN →OT H . Although ∆IP W CHN →OT H does not have significant coefficients in the two columns, F -statistics remain high, 18 and 31, respectively.
Figure 8 visually describes the goodness-of-fit of the first-stage regressions, taking the endogenous variable on
the vertical axis and the predicted endogenous variable using the corresponding instruments on the horizontal axis.
The top two panels are based on Japanese data and the bottom two are based on the US data. Panels A and C
display the correlations between the import penetration variable and its predicted variable. Panels B and D display thecorrelations between the export opportunity variable and its predicted variable. All panels show that there are striking
positive correlations between the two variables.

5.2 Baseline second-stage results
Using the instruments shown to be valid in the previous section, this section discusses second-stage results. Table 5 shows results from Japan in Panel A and results from the US in Panel B, constructed symmetrically. In column (1), using OLS, we regress changes in the manufacturing employment as a share of working age population,∆lit = 100 × (Lit+1 /W APit+1 − Lit /W APit ), on import penetration from China, ∆IP W CHN . The variable∆IP W CHN has statistically significant and negative coefficients in both panels, meaning that imports from China
reduced manufacturing employment in the two countries.
As the OLS results include endogeneity bias potentially, column (2) uses ∆IP W CHN →OT H as an instrument.
23 This suggests that the Japanese sectors that were expecting increasing global demand (greater ∆EOW OT H→ROW ) were facing smaller
import penetration from China, reflecting the fact that Japanese CZs specializing in import-competing sectors differ from Japanese CZs specializing
in export-oriented sectors.

21

F IGURE 8: First-stage fits
Panel B: EOW to ROW, Japandelta.EOW to ROW per worker (in 100K JPY)
0
5
10
15

delta.IPW from China per worker (in 100K JPY)
0
1
2
3
4
5

Panel A: IPW from China, Japan

0

2

4

6

−10

−5

0

5

10

Panel C: IPW from China, the US

Panel D: EOW to ROW, the US

2

4

6

20
10
0

delta.EOW to ROW per worker (normalized)
0

Predicted delta.IPW from China per worker (normalized)

−10

8
6
4
2
−2

30

Predicted delta.EOW to ROW per worker (in 100K JPY)

10

Predicted delta.IPW from China per worker (in 100K JPY)

0

delta.IPW from China per worker (normalized)

−2

−5

0

5

10

15

Predicted delta.EOW to ROW per worker (normalized)

Note: The figure shows the scatter plots showing the correlations between the endogenous explanatory variables and their predictions based on thecorresponding instruments.

The absolute values of the IV coefficients are greater than the OLS coefficients in both panels, suggesting that there
is attenuation bias caused by measurement errors and other types of endogeneity leading to upward bias. The bottomof each panel shows the quantified causal effects of the trade shock on manufacturing employment.24 It shows that, inJapan, the import penetration from China reduced 350 thousand and 730 million jobs in the 1990s and 2000s, leading
to a 1.08 million job reduction in the two periods. The corresponding number of job losses is slightly lower in the US,
230 thousand in the 1990s and 730 thousand in the 2000s, a total of 960 thousand.
Column (3) replaces the explanatory variable with ∆EOW ROW . It uses the OLS approach, showing that exports
to the ROW has a positive effect on manufacturing employment in both countries. Column (4) shows that using the IV
approach does not change the result qualitatively. The bottom of column (4) lists the employment effects of exports,
displaying that Japan had 59 thousand job gains and the US had 18 thousand job gains during the two periods.
The two variables ∆IP W CHN and ∆EOW ROW are jointly included in the same regressions in the last two
24 To quantify the causal effect of the trade shock on manufacturing employment during the period starting at year t with h-year horizon, we use
CHN
the following equation: ∆Lcausal
× β̂IV /100 × R2 where W APt denotes the number of working= 12 (W APt + W APt+h ) × ∆IP W tt
CHNdenotes the working age population weighted average of the trade shock, βIV denotes the IV coefficient of the trade
age population, ∆IP W tvariable, and R2 is a Partial R-squared. Autor et al. (2013) and Dauth et al. (2014) find the “causal” component of the effect using the combinationof the OLS coefficient, IV coefficient, and the coefficient from a residual regression. That component is replaced with a Partial R-squared as donein Acemoglu et al. (2016), Asquith et al. (2019), and Kainuma and Saito (2022).

22

TABLE 5: Effects of imports and exports on manufacturing employment
Panel A: Japan∆IP W CHN →JP N(1)
OLS-0.58**(0.24)(2)IV-0.73***(0.26)∆EOW JP N →ROW
Obs.
K.-P. rk Wald F -stat.

616

616
476.82

(3)
OLS

(4)
IV

0.31***(0.07)
616

0.37***(0.05)
616
32.72

(5)
OLS
-0.53***(0.19)
0.30***(0.06)
616(6)IV-0.62***(0.21)
0.34***(0.06)
616
16.73

(7)
BHJ
-0.65*(0.34)
0.38***(0.13)
126
7.28

Quantified employment effects (million workers)

1991-2001
2001-2012
1991-2012∆IP W CHN →U SA

-0.35
-0.73
-1.08
(1)
OLS
-0.32***(0.07)∆EOW U SA→ROW
Obs.
K.-P. rk Wald F -stat.

1,444

0.15
0.44
0.59

Panel B: The US
(2)
(3)
(4)
IV
OLS
IV
-0.51***(0.12)
0.11* 0.15*(0.06) (0.09)
1,444
1,444 1,444
213.48
54.94

-0.13
-0.15
-0.29
(5)
OLS
-0.36***(0.07)
0.13**(0.06)
1,444

(6)
IV
-0.58***(0.13)
0.24**(0.11)
1,444
25.75

(7)
BHJ
-0.60***(0.21)
0.35**(0.15)
784
8.15

Quantified employment effects (million workers)

1991-1999
1999-2011
1991-2011

-0.23
-0.73
-0.96

0.10
0.08
0.18

-0.11
-0.70
-0.80

k
k ) for k = JP N , U SA. All regressions include constant term andNote: The dependent variable is 100 × (Lki,t+1 /W APi,t+1− Lki,t /W APi,tcontrol variables and region fixed effects, prefecture dummies for Japan, and statistical division dummies for the US. The total initial population
weights all regressions. Standard errors, which are clustered at the prefecture level and the state level in the CZ level regressions in Japan and theUS, respectively, and clustered at the sector level in the BHJ approach, are in parentheses. ***, **, and * indicate statistical significance at the 1%,5%, and 10% level, respectively.

columns, with OLS in column (5) and IV in column (6). As shown in Panel A, in Japan, the quantified employment
effects of the two trade shocks are 29 thousand job reductions in the two periods. The negative effects of imports from
China slightly outweigh the positive effects of exports to the ROW. As shown in Panel B, in the US, the trade shocks
led to a reduction of 80 thousand jobs, comparable to the estimate of 65 thousand job reduction by Feenstra et al.
(2019).25
Column (7) displays the results from the BHJ approach, indicating that the coefficients remain qualitatively similarand statistical significance is also in line with the CZ level results in column (6). Overall, the results in Table 5 suggestthat these trade shocks had greater adverse effects on US manufacturing employment than in Japan.

5.3 Considering types of imports from China
We seek to answer the question presented in the previous section: Why does the adverse effect of trade muchgreater in the US than in Japan. A potential candidate explaining the difference between the two countries is thedifference in the “composition” of trade. As discussed in Section 2, Japan imports a greater share of inputs than final
25 The slight difference comes from the fact that we use a different set of instruments. Feenstra et al. (2019) use several instruments including
the “predicted” instruments constructed by regressing trade flows on tariffs, an uncertainty variable, and other variables. We use only a simpler
instrument conducted by foreign demand and supply. This is because our analysis includes not only the US but also Japan, and China did not facea change in the Permanent Normal Trade Relation Status when it comes to trade with Japan.

23

goods from China compared with the US. These two types of imports, final goods and imported inputs, may havedifferent effects on manufacturing employment, as shown in existing studies in the Japanese context (Taniguchi, 2019;Endoh, 2023).
Table 6 shows results from introducing separate import penetration variables: final goods and inputs. Although
our baseline regressions in the previous sub-section include the export opportunity variable, we omit the variable inthis section to keep the number of endogenous variables minimal, ensuring the effectiveness of the instruments.
Columns (1)-(4) display results with the Japanese data, and columns (5)-(8) display results with the US data.
CHN
CHN
Columns (1) and (5) regress ∆l on ∆IP WFinal,iand ∆IP WInputs,i(equation (7)). It shows that, in Japan, while im-ported final goods from China has a job-reducing effect, imported inputs work to sustain manufacturing employment.
The quantified effects on employment is shown to be 250 thousand job losses in the 1990s and 240 thousand job losses
in the 2000s, leading to a total of 480 thousand job losses, suggesting that the negative effects of final goods imports
outweigh the positive effects of imported inputs.
Contrary to the positive effect of imported inputs in Japan, column (5) show that imported inputs have a negative
effect on manufacturing employment in the US. The effect of final goods imports is shown to be insignificant, although
the sign of the coefficient is negative. The quantified job losses in the 1990s and 2000s add up to 1.15 million in the
US. These CZ level regression results are generally supported by the results from the BHJ approach shown in columns(2) and (6), respectively.
TABLE 6: Effects of imports from China by types of imports, IV

CHN →k∆IP WFinal
goods
CHN →k∆IP WInputs

CZ level(1)-0.90***(0.21)
1.51***(0.52)

CHN →k∆IP WInputs
used

Obs.
K.-P. Wald F -stat.

616
1678.02k =JPNBHJ
CZ level(2)(3)-0.89** -0.84***(0.38)(0.15)-1.90(2.05)
3.39***(0.65)
126616
144.68255.62

BHJ
(4)
-0.47
(0.36)

4.60***(1.55)
126
389.23

CZ level(5)-0.14(0.12)-0.85*(0.45)

1,444
62.34

k =USA
BHJ
CZ level
(6)
(7)
0.04
-0.24**(0.08)(0.11)-1.24**(0.55)-2.18***(0.72)
784
1,444
53.03
103.05

BHJ
(8)
-0.12
(0.10)-3.07***(0.88)
784
32.78

Quantified effects (million workers)

1990s
2000s
1990s and 2000s

-0.25
-0.24
-0.48

-0.09
0.15
0.07

-0.26
-0.88
-1.15

-0.45
-1.37
-1.82

k = 100 × (LkkkkNote: The dependent variable is ∆lit
it+1 /W APit+1 − Lit /W APit ). All regressions include the control variables explained in thetext, including region dummies. The initial total population weights all regressions. Standard errors, which are clustered at the prefecture level andthe state level in the CZ level regressions in Japan and the US, respectively, and clustered at the sector level in the BHJ approach, are in parentheses.
***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.
CHN
CHN
Columns (3) and (7) regresses ∆l on ∆IP WFinal,iand ∆IP WInputs
used,i (equation (8)). Column (3) shows that,in Japan, while imported final goods have a significant negative effect on manufacturing employment, imported inputs
used by Japanese sectors have a positive effect. Both coefficients are statistically significant at the 1% level. These
results are supported by the BHJ approach result presented in column (4). The suggested effect is that these two shocks
overall let to 70 thousand job gains in Japan during the two periods as shown in the bottom of column (3).
In contrast, column (7) shows that, in the US, both final goods imports and imported inputs used by US sectors
24have negative effects on manufacturing employment. The coefficients of the two variables are negative and significant
at the 5% and 1% level, respectively. The BHJ approach shown in column (8) also supports the CZ level result. The
bottom part of column (7) shows that, in the US, these two trade shocks let to a 1.82 million job losses.
Overall, the results suggest that, in Japan, imported inputs have a positive effect on manufacturing employment asshown in existing studies (e.g., Taniguchi, 2019). However, the results do not support such positive effects of imported
inputs in the US context. We further examine the differential effects in the next section.

6 Mechanisms leading to different effects
6.1 Input-output linkages
We have uncovered the different effects of imported inputs on manufacturing employment across the two countries.
Our results suggest that Japan and the US differ not only in the composition of imports from China but also theemployment effects. This section introduces an additional element, i.e. downstreamness, as a measure to quantify
the degree to which a country integrates GVCs, into the regression model to understand the mechanisms behind thedifferent employment effects. The downstreamness measures the number of input-output linkages and the size oftransactions through those linkages that input buyers are facing. Therefore, a greater value of downstreamness meansthat the sector is at a downstream position of GVCs. In particular, we run regressions by focusing on a sample such thatthe value of downstreamness (equation (9)) is above the median, where we use the median value of all observations inthe stacked cross-section data from each country. If cheaper access to Chinese intermediate inputs has complementary
effects, then input buyers can benefit from global value chains and thus increase manufacturing employment more.
This input-output linkage can be tested once we focus on areas specializing in more downstream sectors.
Column (4) in Panel A shows that final goods imports have adverse effects on manufacturing employment andimported inputs have positive effects. The result suggests that in Japan downstream firms can purchase cheaper
Chinese intermediate inputs, thereby expanding production and consequently employment. In other words, Japanis more integrated into GVCs, especially with China, and takes advantage of complementary effects. In contrast,
results from the US shown in Panel B indicate that the effects of intermediate inputs differ from the ones in Japan.
Column (4) in Panel B indicates that the effects of imported inputs are negative in CZs specializing in sectors withhigher downstramness. The result suggests that the degree to which a country integrates into GVCs is related withthe employment effects of input imports. If a country is deeply integrated into GVCs, a greater amount and wider
range of imported inputs are combined with domestic inputs, which in turn generating job-creating effects of imports.
On the contrary, a country that could not benefit from imported inputs through GVCs lose competitiveness in global
markets relative to other countries, such as Japan, those that reap the benefits of cheaper inputs, and thereby yielding
job-reducing effects of imports.
Our results from the US data may seem to contradict the findings by Flaaen and Pierce (2021), where they findthat rising US input tariffs on Chinese products during the 2018-19 US-China Trade War reduced US manufacturing
employment. We argue that there are several reasons why results could be different between the two studies. First,
25most importantly, Flaaen and Pierce (2021) focused on tariffs specific to the US and China, unlike the China trade
shock which had huge impacts on other countries. The intermediate input channel allowed Japanese firms to purchasecheaper inputs via global value chains, thereby increasing competitiveness in global markets. On the contrary, thesame channel worked in the opposite direction, which reduced competitiveness of US firms in global markets. Second,
Flaaen and Pierce (2021) analyze the data in the late 2010s, a period marked by a substantial slowdown in the declineof manufacturing employment. Third, they use monthly data. Hence, their results quantify the short-run effects ofan acute and sudden rise of input tariffs, whereas almost all ‘China shock’ studies, including ours, examine decennial
data, quantifying the long-run effects. Fourth, Flaaen and Pierce (2021) use the sector level data whereas our analysis
is based on the CZ level observations, capturing labor market and goods market effects within each CZ.
Table 7 summarizes results. Columns (1) and (2) show results based on the sample with lower and higher upstreamness, respectively. Columns (3) and (4) are based on downstreamness. Finally, columns (5) and (6) are based
on upstreamness minus downstreamness. Panel A displays the results from Japan. It shows that there is no systematicand sizable difference based on the upstreamness or downstreamness. In either case, final goods imports have adverse
effects on manufacturing employment and imported inputs have positive effects. The employment effects of importcompetition do not differ depending on the upstreamness or downstreamness presumably because all sectors in Japanare more integrated into GVCs than the US as demonstrated in Figure 4.26
TABLE 7: Up/downstreamness and imports from China, IV
Selection based on
SampleCHN →k∆IP WFinal
goods
CHN →k∆IP WInputs
used

Obs.
K.-P. Wald F -stat.
Selection based on
SampleCHN →k∆IP WFinal
goods
CHN →k∆IP WInputs
used

Obs.
K.-P. Wald F -stat.

Up
Low
(1)
-1.05***(0.18)
4.01***(0.91)
308
99.4

Panel A: Japan
Down
High
Low
High
(2)
(3)
(4)
-0.72*** -1.03** -0.64***(0.19)(0.44)(0.15)
2.93***
3.35*
3.51***(0.92)(1.90)(0.76)
308
308
308
228.1
31.5
146.9

Up−Down
Low
High
(5)
(6)
-0.88***-0.61***(0.15)(0.23)
3.52***
4.17***(0.67)(1.07)
308
308
111.8
103.6

Panel B: The US
Down
High
Low
High
(2)
(3)
(4)
-0.44***
0.22-0.63***(0.13)(0.17)(0.11)-0.37
1.39-1.22**(0.61)(2.30)(0.62)
722
722
722
88.6
74.4
73.9

Up−Down
Low
High
(5)
(6)
-0.48***
0.09(0.10)(0.16)-1.72***-1.11(0.66)(0.93)
722
722
52.6
84.4

Up
Low
(1)
-0.09
(0.13)-4.31***(1.01)
72232.8kk ) for k = JP N , U SA. The interaction terms are instrumented by
Note: The dependent variable is 100 × (Lki,t+1 /W APi,t+1− Lki,t /W APi,tthe interaction terms between the instruments for the import competition variables and the upstreamness (downstreamness) variable. All regressions
include constant term and control variables and region fixed effects, prefecture dummies for Japan, and statistical division dummies for the US.

The total initial population weights all regressions. Standard errors, clustered at the prefecture level for Japan, and the state level for the US, are inparentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.

In contrast, results from the US shown in Panel B indicate that the effects of import penetration differ depending
26 The summary statistics shown in Table 1 indicate that the CZ level upstreamness and downstreamness take greater values in the US than inJapan, which is due to fact that these are weighted averages based on sector-CZ level employment. On average, Japanese CZs might be more
specializing in sectors with lower levels of upstreamness or downstreamness than the US.

26

on the level of the upstreamness and downstreamness. Columns (1) and (2) show that the effects of imported inputs arenegative in CZs specializing in sectors with lower levels of upstreamness. In comparison, the effects are not statisticallysignificant in CZs with higher levels of upstreamness. Our interpretation is as follows: as the upstreamness measures
the number of input-output linkages and the size of transactions through those linkages that input sellers are facing,a greater value of upstreamness means that the sector is at an upstream position of GVCs. When a producer is at
an upstream position, imported inputs are used to produce next-round inputs to supply, which requires more labor,
mitigating the pro-competitive effects of imported inputs.
Columns (3) and (4) are based on downstreamness. The results indicate that the effects of imported inputs arenegative in CZs specializing in sectors with higher downstramness while it is statistically insignificant in the other
CZs. The downstreamness measures the number of input-output linkages and the size of transactions through thoselinkages that input buyers are facing. Therefore, a greater value of downstreamness means that the sector is at adownstream position of GVCs. When a producer is at a downstream position, as imported inputs would go througha fewer stages of production going forward, positive employment effects of imported inputs were so weak that they
were overshadowed by the pro-competitive effects of inputs, leading to adverse employment effects.
The last two columns, columns (5) and (6), are based on the upstreamness minus downstreamness, meant to be apositive measure of the ‘upstream’ position in GVCs. It shows that adverse employment effects of imported inputs
come from CZs with lower values of U p − Down and the effects are insignificant in CZs with higher values of thevariable. In these columns, a similar pattern is observed for the effects of final goods also although these are not our
main focus in this section. Overall, the results in these columns are consistent with those in columns (1)-(4).
To summarize, the results show that there is no difference in the employment effects depending on the level of
upstreamness or downstreamness in Japan presumably because Japan’s integration to GVCs is at a higher level. On
the other hand, in the US, the job-reducing effects of imported inputs come from CZs specializing in sectors withlower upstreamness or higher downstreamness. These results suggest that the degree to which a country integrates
into GVCs is related with the employment effects of imports. If a country is deeply integrated into GVCs, a greater
amount and wider range of imported inputs are combined with domestic inputs, which in turn requires more labor,
mitigating the job-reducing effects of imports.27

6.2 Other possible mechanisms
There might be other explanations behind the different effects across the two countries. This section summarizes
other mechanisms that could potentially lead to our empirical results.
1. The balance between direct job replacement effects and productivity effects: Ottaviano et al. (2013) and
Wright (2014) find that offshoring has two employment effects working in different directions: the direct replacement
effect reducing employment at home, and the productivity effect improving the productivity of firms outsourced theirtasks, increasing employment. If such adjustments occur within the manufacturing sector, it could be the case that
27 This result is echoing with Kurokawa (2011) who finds that an increase in the number of variety offshored has labor market effects throughskill-variety complementarity.

27

the job creation effect dominates the job replacement effect of offshoring in Japan, whereas vice versa might occur in
the US. Due to data limitations, our paper cannot identify the contribution of each of these channels. Nevertheless,we show in Appendix F that, while imported inputs have positive effects on labor productivity in the US, we findinconclusive results regarding the effects of the same type of imports on labor productivity from the Japanese data.
Although the previous studies suggest that improved productivity increases labor demand, our results suggest that laborproductivity increased more in CZs that experienced greater reductions in manufacturing employment. A positive link
between labor productivity growth and increased manufacturing employment is not observed from the Japanese data
either, which makes us think that job-creating productivity-enhancing effects of offshoring are not strongly working
in the context of the effects of the China shock at the CZ level in the US and Japan.
2. Different labor market institutions: The Japanese labor markets are known to be more rigid than those of theother advanced countries such as the US. Ono (2010) argue that “[j]ob mobility remains considerably lower in Japan
than in other advanced economies (particularly the US)” because of Japan’s “informal lifetime employment contracts.”
Japan’s rigid labor markets appear in other aspects also. For example, Genda et al. (2010) document that a negative
macroeconomic shock has as a persistent long-term effect on the cohort graduating in the recession years while theeffect is temporary in the US. They also find that starting wages are almost constant from the 1980s until the late
2000s. Sasahara et al. (2023) find that imports from China had limited effects on wages using Japan’s prefecture level
data. Part of the contrasting empirical results between Japan and the US in this study could come from different labormarket institutions between the two countries. Due to data limitations, our paper does not investigate the role of labormarket institutions.
3. Different degree of China shock-induced servification of manufacturing: Fort et al. (2018) find that US
manufacturing value-added had been rising while US manufacturing employment was declining rapidly during the‘China shock’ period. Fort (2023) also find that US manufacturing firms rapidly expanded their knowledge workers
and conducted domestic innovation actively. These findings suggest that US manufacturing employment was reallocated into service sectors, including the R&D sector. In Appendix F, we examine the effects of imports and exports on
manufacturing labor productivity and find suggestive evidence of such productivity-enhancing effects of import competition from the US data but not from the Japanese data. Caliendo et al. (2019) and Feenstra and Sasahara (2018) also
document similar inter-sectoral labor reallocation towards services. However, such inter-sectoral adjustments may belimited in Japan.
4. Different production functions: Another potential explanation is that the production function could be different across countries. For instance, final goods’ production function in the US could be a CES or Cobb-Douglas form
combining labor and intermediate inputs. In this case, labor and inputs are substitutes, meaning that an increase in the
input usage reduces labor used for production. In contrast, in the Japanese case, using imported inputs may entirely
change the cost structure, leading to an increase in labor input. However, it is beyond the scope of the current study.
5. The China shock affecting the production functions differently: Lastly, the exposure to trade may havealtered each country’s production function differently. A greater exposure to import competition in Japan may haveincreased its labor share because, for example, tasks associated with handling inputs from China are labor-intensive.

28

In contrast, import competition in the US may have increased the capital share because, for example, it transformed its
production process in a cost-saving manner. In Appendix F, we pursue this possibility by examining the effects on thecapital-labor ratios. Results suggest that, in the US, the capital-labor ratios increased due to an increase in final goods
imports and intermediate goods imports. On the other hand, in Japan, results were somewhat inconsistent between CZ
level regressions and the BHJ regressions.

6.3 Limitations and a remaining puzzle
We have analyzed the effects of trade on labor markets by focusing on GVC aspects. In so doing, this study
runs reduced-form regressions. This section acknowledges a need for caution when interpreting the results. Caliendoand Parro (2022) notes, in the context of reduced-form shift-share analysis, “the estimation strategy does not identify
the overall impact of trade liberalization on the outcome, but it instead measures whether some districts (authors’
note: CZs in our context) are affected more than others.”28 Autor et al. (2021) also acknowledge the same point:“This strategy identifies the relative impact of the trade shock–e.g., whether manufacturing employment fell by morein more-trade-exposed locations–but not the absolute impact of the shock, e.g., whether the trade shock reduced
manufacturing employment nationally.”29 In our context, for example, although our results suggest that imports from
China contributed to sustain 70 thousand manufacturing jobs, this counterfactual analysis assumes that CZs with zero
exposure to the trade shock experienced no change in manufacturing employment (Helpman, 2018), which may not
be the case. To overcome this issue of a reduced-form analysis, Caliendo et al. (2019) and Caliendo and Parro (2023)
use quantitative trade models and demonstrate that the China trade shock is not the main cause of the decrease in the
US manufacturing employment.
Our analysis implies that the adverse effect of the trade shock on Japanese manufacturing employment is lessthan that of the US (and the effect is even positive). Nevertheless, as shown in Figure 1, Japanese manufacturing
employment decreased by about 4 million since the early 1990s, which is almost the same magnitude as the US. The
open question now is: Why did Japanese manufacturing employment decrease by almost the same magnitude as theUS?

7 Concluding remarks
This paper has examined the effect of imports from China and exports to the rest of the world on manufacturing
employment in Japan and the US. We have documented the differences between the two countries in terms of theirtrade with China: (i) Japan imported a greater share of intermediate inputs from the US throughout the sample period,and (ii) while imported inputs from China had positive effects on manufacturing employment in Japan, such positiveeffects of imported inputs were not observed in the US data.
We have examined a mechanism leading to different employment effects of imported inputs between the twocountries by focusing on their integration patterns into GVCs, quantified by up/downstreamness. The results show that,
28 See pages 227-228 in Caliendo and Parro (2022). They mention that Topalova (2010) makes the same point.
29 See page 3 in Autor et al. (2021). They note this point by referencing Heckman et al. (1998) and Helpman (2018).

29

in the US, the adverse effects of imported inputs from China are particularly strong in CZs specializing in less upstream
and more downstream sectors. On the other hand, the Japanese data do not suggest such different employment effects
of imported inputs based on up/downstreamness, presumably because Japan’s integration to GVCs is at a higher level
than the US. This study has highlighted cross-country differences in the effects of exposure to the China trade shock
and proposed an explanation based on GVCs. We hope that it fosters a deeper comprehension of the variations ininteractions between trade shocks and labor markets across countries.

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33

Appendix for “A Tale of Two Countries: Global Value Chains, the China Trade
Shock, and Labor Markets”
Jaerim Choi, Masahiro Endoh, and Akira Sasahara
May 2024

A

List of manufacturing sectors in the Japanese data
Table A1 shows the list of sectors used to construct trade exposure variables in the analysis with the Japanese data.
TABLE A1: Sectors used when constructing trade exposures in the analysis with the Japanese data
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32

Description
Live stock food products
Seafood products
Vegetable, fruits, agricultural products
Beverages (non alcoholic)Alcoholic drinks
Teas and coffees
Fertilizers
Tobaccos
TextilesWooden products and construction materials
Paper and pulp
Chemical fertilizers
Inorganic chemicals
Organic chemicals
Fats and oils processed products
Medical products
Other chemical products
Refined oils and coke
Paving material
Plastic products
Tires and robber products
Leather and fur
Bags
Glass
CementClay for construction
Ceramics
Refractories
Carbon and graphite
Abrasive
Masonry materials
Steel

A1

No.
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63

Description
Nonferrous metals
Tin, aluminium, and other metals
HardwareAir conditioning apparatus
Architectural metal products
Bolts, nuts, and rivets
Engines and turbines motors
Industrial machine
Agricultural machinery
Construction and mining machinery
Textile machinery
Office machinery
Electrical measuring instrument
Medical apparatus and instruments
Optical instruments
Communication equipment
Industrial electrical machinery
Consumer electrical machinery
Electric lighting fixtures
Autos and their related products
Rail cars and their related products
Vessels and their related products
Aircrafts and their related products
Other transportation equipment
Ornaments
Watches and clocks
Musical instruments and records
Toys and sporting goods
Office supplies and equipment
Weapons
MiscellaneousB Summary statistics and correlations between variables
This section provides summary statistics of control variables and correlation matrices between variables. Table
A2 shows the summary statistics of the control variables used in the regressions with the Japanese data. Table A3
shows correlation coefficients between the trade exposure variables used in the analysis with the Japanese data. The
CHN
correlation coefficient between ∆IP W CHN and ∆EOW ROW is 0.67. The correlation between ∆IP WFinalgoods andCHN
∆IP WInputs
used is 0.73. These pairs of variables included in the same regression equation. Although the correlation

coefficients are not so low, these are not substantially high to be concerned about the multicollinearity. Table A4
shows correlation coefficients between the instrumental variables in the Japanese data. Table A5 shows correlation
coefficients between the control variables in the Japanese data.
TABLE A2: Summary statistics of the instruments control variables, Japan, k = JP N
Mean

Std. dev.

Min

Max

Controls
Unemployment rate
Labor market participation rate
Foreign population share
Urban population share
Female employment shareln(Affiliate size)
Non-full-time employee shareln(Population density)
Young population share
Decade dummy, the 2000s = 1
Region dummy, Hokkaido and Tohoku
Region dummy, Kanto
Region dummy, Chubu
Region dummy, Kansai
Region dummy, Chugoku
Region dummy, Shikoku
Region dummy, Kyushu and Okinawa

3.24
51.89
0.61
15.24
42.92
1.98
11.54
1.53
0.25
0.50
0.16
0.07
0.08
0.13
0.20
0.05
0.30

1.46
4.04
0.55
24.45
3.32
0.24
7.88
1.09
0.02
0.50
0.37
0.26
0.27
0.34
0.40
0.22
0.46

0.00
39.97
0.00
0.00
21.02
0.70
0.00
-1.89
0.16
0
0
0
0
0
0
0
0

10.78
81.74
3.71
97.44
54.13
2.46
69.86
4.54
0.34
1
1
1
1
1
1
1
1

Instruments∆IP W CHN →OT H\k∆EOW OT H\k→ROW
CHN →OT H\k∆IP WFinal goods

4.02
15.04
2.64

4.07
15.70
2.39

0.00
-11.730.01

22.92
103.13
15.53

CHN →OT H\k∆IP WInputs

CHN →OT H\k∆IP WInputs used
CHN →OT H\k∆IP WFinal goods
× Up
CHN →OT H\k∆IP WInputs used
× Up
CHN →OT H\k∆IP WFinal goods
× DownCHN →OT H\k∆IP WInputs used
× Down

0.91

0.93

0.00

4.54

0.51

0.48

0.00

3.31

1.38

1.61

0.00

10.38

0.27

0.34

0.00

2.81

1.47

1.74

0.00

11.62

0.29

0.37

0.00

3.57

Note: The sample size 616. The female employment share is the share of female manufacturing employment as a share of total manufacturing
employment. ln(Affiliate size) is the natural log of the number of manufacturing employees divided by the number of manufacturing affiliates. The
young population share is the share of the population younger than 15 years old as a share of the population between the ages 15 and 64.

TABLE A3: Correlations between the endogenous explanatory variables, Japan, k = JP N
(1)(1) ∆ mnfg share1(2) ∆IP W CHN →k-0.16(3) ∆EOW k→ROW
0.08
CHN →k(4) ∆IP WFinal
-0.08
goods
CHN →k(5) ∆IP WInputs
0.10
CHN →k
0.06(6) ∆IP WInputs
used
Note: The sample size 616.

(2)

(3)

(4)

(5)

(6)

1
0.67
0.81
0.72
0.71

1
0.58
0.87
0.88

1
0.76
0.73

1
0.97

1

A2

TABLE A4: Correlations between the instruments, Japan, k = JP N
(3)

∆ mnfg share∆IP W CHN →OT H\k∆EOW OT H\k→ROW
CHN →OT H\k∆IP WFinal goods

(1)
1
0.04
0.08
-0.14

(2)

(1)
(2)
(3)
(4)

1
0.77
0.90

1
0.65

1

(5)

∆IP WInputs

0.04

0.90

0.87

0.85

1

(6) ∆IP WInputs used
Note: The sample size 616.

0.02

0.82

0.90

0.76

0.95

CHN →OT H\k

CHN →OT H\k

(4)

(5)

(6)

1

TABLE A5: Correlations between the control variables, Japan, k = JP N
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)

∆ mnfg share
Initial mnfg emp. share
Unemployment rate
Labor market part. rate
Foreign population share
Urban population share
Female employment shareln(Affiliate size)
Non-full-time emp. shareln(Population density)
Young population share
Upstreamness
Downstream

(1)
1
-0.51
0.18
0.03
-0.22
-0.05
-0.08
-0.11
0.14
-0.24
0.11
-0.07
0.34

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

1
-0.17
0.13
0.47
-0.02
0.04
0.35
-0.28
0.33
-0.08
0.26
-0.06

1
-0.51
-0.08
0.28
0.34
0.24
-0.29
0.43
0.19
0.25
0.38

1
0.13
-0.11
-0.27
0.01
0.21
-0.28
-0.26
-0.04
0.09

1
0.26
-0.26
0.36
-0.05
0.44
0.06
0.18
-0.001

1
-0.09
0.43
-0.05
0.44
-0.05
0.15
0.09

1
-0.11
-0.33
0.26
-0.01
0.13
0.12

1
-0.31
0.44
-0.07
0.36
0.22

1
-0.30
-0.07
-0.44
-0.27

1
0.06
0.34
0.12

1
-0.14
-0.02

1
0.47

1

Note: The sample size is 616. The female employment share is the share of female manufacturing employment as a share of total manufacturing
employment. ln(Affiliate size) is the natural log of the number of manufacturing employees divided by the number of manufacturing affiliates. The
young population share is the share of the population younger than 15 years old as a share of the population between the ages 15 and 64.

Table A6 shows the summary statistics of the control variables used in the regressions with the US data. Table
A7 shows correlation coefficients between the trade exposure variables used in the analysis with the US data. The
CHN
correlation coefficient between ∆IP W CHN and ∆EOW ROW is 0.31. The correlation between ∆IP WFinal
goods
CHN
and ∆IP WInputs
used is 0.46. These correlation coefficients are not so high to be concerned about the multicollinearity.

Table A8 shows correlation coefficients between the instrumental variables in the US data. Table A9 shows correlation
coefficients between the control variables in the US data.

A3

TABLE A6: Summary statistics of the instruments and control variables, the US, k = U SA
Instruments∆IP W CHN →OT H\k∆EOW OT H\k→ROW
CHN →OT H\k∆IP WFinal goods
CHN →OT H\k∆IP WInputs

CHN →OT H\k∆IP WInputs used
CHN →OT H\k∆IP WFinal goods
× Up
CHN →OT H\k∆IP WInputs used
× Up
CHN →OT H\k∆IP WFinal goods
× DownCHN →OT H\k∆IP WInputs used
× DownControls
Share of educated population
Share of foreign-born population
Share of female employment
Share of employment with routine occupations
Average offshorability index
Decade dummy, the 2000s = 1
Census division dummy 1
Census division dummy 2
Census division dummy 3
Census division dummy 4
Census division dummy 5
Census division dummy 6
Census division dummy 7
Census division dummy 8

Mean

Std. dev.

Min

Max

0.75
2.47
1.15

0.78
3.00
1.59

-0.97
-9.48
0.00

5.25
28.93
15.19

0.65

0.76

0.00

6.94

0.41

0.48

-0.02

3.45

0.71

1.26

0.00

10.79

0.25

0.37

-0.02

3.11

0.74

1.32

0.00

11.48

0.26

0.39

-0.03

3.65

45.26
4.96
62.74
28.57
-0.51
0.50
0.02
0.04
0.12
0.23
0.15
0.10
0.15
0.13

9.09
5.86
7.06
3.14
0.43
0.50
0.15
0.19
0.32
0.42
0.35
0.30
0.36
0.34

19.94
0.38
33.24
19.99
-1.64
0
0
0
0
0
0
0
0
0

70.56
48.91
79.61
37.75
1.24
1
1
1
1
1
1
1
1
1

TABLE A7: Correlations between the endogenous explanatory variables, the US, k = U SA
(1)(1) ∆ mnfg share1(2) ∆IP W CHN →k-0.47(3) ∆EOW k→ROW-0.08
CHN →k(4) ∆IP WFinal
-0.45
goods
CHN →k(5) ∆IP WInputs-0.55
CHN →k-0.52(6) ∆IP WInputs
used
Note: The sample size 1,444.

(2)

(3)

(4)

(5)

(6)

1
0.31
0.55
0.55
0.55

1
0.14
0.38
0.43

1
0.59
0.46

1
0.91

1

TABLE A8: Correlations between the instruments, the US, k = U SA
(1)(1) ∆ mnfg share1(2) ∆IP W CHN →k-0.56(3) ∆EOW k→ROW-0.47
CHN →k(4) ∆IP WFinal
-0.53
goods
CHN →k(5) ∆IP WInputs
-0.58
CHN
→k
-0.58
(6) ∆IP WInputs used
Note: The sample size 1,444.

(2)

(3)

(4)

(5)

(6)

1
0.60
0.64
0.63
0.64

1
0.52
0.71
0.79

1
0.78
0.70

1
0.94

1

TABLE A9: Correlations between the control variables, the US, k = U SA
(1) ∆ mnfg share(2) Initial mnfg employment share(3) Share of educated population(4) Share of foreign-born population(5) Share of female employment(6) Share of employment with routine occupations(7) Average offshorability index(8) Upstreamness(9) Downstream
Note: The sample size 1,444.

(1)
1
-0.41
0.01
-0.04
-0.02
-0.31
-0.11
-0.07
-0.42

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

1
-0.28
-0.23
0.19
0.52
0.34
-0.21
0.10

1
0.20
0.62
0.16
0.24
0.10
0.28

1
-0.17
0.05
0.21
-0.05
0.15

1
0.29
0.34
-0.10
0.23

1
0.71
-0.06
0.18

1
-0.26
-0.14

1
0.17

1

A4

C

Instrumental variables for the other import competition variables
The instrument used to identify the exposure to imported final goods (equation (6)) is
CHN →OT H\k∆IP WFinal,i
=

X

s∈SM

Lkist
P
×
′
s′ ∈SA Lis tP

k′ ∈OT H\k ∆f(CHN,s),k′P k′i′ Li′ st

!

,

(A1)

where the superscript in the left-hand side CHN → OT H\k indicates that it is exports from China to “other”Pcountries from country k’s perspective. In the right-hand side, k′ ∈OT H\k ∆f(CHN,s),k′ denotes the sum of exports

from China’s sector s to country k ′ over destination countries k ′ included in the set of the “other” countries. The
instrument used to identify the exposure to imported imported inputs (equation (7)) is
CHN →OT H\k∆IP WInputs,i
=

X

s∈SM

Lkist
P
×
′
s′ ∈SA Lis tP

k′ ∈OT H\kPs′ ∆m(CHN,s),(k′ ,s′ )P ki′ Li′ st

!

.

(A2)

The instrument used to identify the exposure to imported imported inputs used by sectors in the destination country(equation (8)) is
CHN →OT H\k∆IP WInputs used,i
=

X

s∈SM

Lkist
P
×
s′ ∈SA Lis′ t

CHN →OT H\JP N

When constructing ∆IP WiP

k′ ∈OT H\kPs′ ∆m(CHN,s′ ),(k′ ,s)P ki′ Li′ st

!

.

(A3)

, the eight developed countries used are: Australia, Denmark, Fin-land, Germany, New Zealand, Spain, Switzerland, and the US. However, some of these countries are not included in
the WIOD.
We use the Long-run WIOD Version 1.1, March 2022 Release, covering the 1965-2000, to construct variables (6)(8) and (A1)-(A3) for the 1990s (1991-2000 for Japan; 1991-1999 for the US). For the WIOD 2022 version, the eightdeveloped countries used to construct the instruments for Japan are Australia, Canada, Denmark, Finland, Germany,
Spain, the UK, and the US. The eight developed countries used to construct the instruments for the US are Australia,
Canada, Denmark, Finland, Germany, Japan, Spain, the UK.
We use the WIOD 2016 Release, covering the 2000–2014 period, to construct variables (6)-(8) and (A1)-(A3) for
the 2000s (2001-2012 for Japan; 2000-2011 for the US). For the WIOD 2016 version, the eight developed countries
used to construct the instruments for Japan are Australia, Canada, Denmark, Finland, Germany, Spain, Switzerland,
and the US. The eight developed countries used to construct the instruments for the US are Australia, Canada, Denmark, Finland, Germany, Japan, Spain, and Switzerland. Table A10 summarizes the lists of the “eight developed”
countries.

A5

TABLE A10: The list of eight developed countries used to construct instruments

1
2
3
4
5
6
7
8
9
10

ISO
AUS
CAN
CHE
DEU
DNK
ESP
FIN
GBR
NZL
USA

Country
Australia
CanadaSwitzerland
Germany
Denmark
Spain
Finland
The UK
New Zealand
The USAUN Comtrade
X
X
X
X
X
X

WIOD 2022
version (1990s)
X
X
X
X
X
X
X

X
X

WIOD 2016
version (2000s)
X
X
X
X
X
X
X

X

X

The lists of manufacturing sectors used to construct variables (6)-(8) and (A1)-(A3) are shown in Tables A11 andA12.
TABLE A11: The list of manufacturing sectors in WIOD 2022 Release, used for the 1990s’ trade data
1
2
3
4
5
6
7
8
9
10
11
12

WIOD code
D15t16
D17t19
D21t22
D23
D24
D25
D26
D27t28D29
D30t33
D34t35
Dnec

Sector description
Food, beverages, and tobacco
Textiles, leather and footwear
Pulp, paper, printing and publishing
Coke, refined petroleum and nuclear fuelChemicals and chemical products
Rubber and plastics
Other non-metallic mineral
Basic metals and fabricated metal
Machinery, nec
Electrical and optical equipment
Transport equipment
Manufacturing, nec; recycling

TABLE A12: The list of manufacturing sectors in WIOD 2016 Release, used for the 2000s’ trade data
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

WIOD code
C10-C12C13-C15
C16
C17, 18
C19
C20
C21
C22
C23
C24
C25
C26
C27
C28
C29
C30
C31, 32

Sector description
Manufacture of food products, beverages, and tobacco products
Manufacture of textiles, wearing apparel and leather products
Manufacture of wood and of products of wood and cork
Manufacture of paper products, printing of recorded media
Manufacture of coke and refined petroleum products
Manufacture of chemicals and chemical products
Manufacture of basic pharmaceutical products
Manufacture of rubber and plastic products
Manufacture of other non-metallic mineral products
Manufacture of basic metals
Manufacture of fabricated metal products
Manufacture of computer, electronic and optical products
Manufacture of electrical equipment
Manufacture of machinery and equipment n.e.c.
Manufacture of motor vehicles, trailers and semi-trailers
Manufacture of other transport equipment
Manufacture of furniture; other manufacturing

Note: In the original input-output tables, C17 and C18 are available as separate sectors. However, we combine these two sectors to match with theemployment data.

A6

DFirst-stage for the other import competition variables
Table A13 shows first-stage regression results with different import competition variables. The import competition

CHN →JP N
CHN →JP N
variables, (1) final goods imports from China ∆IP WFinal, (2) imported inputs from China ∆IP WInputs,
goods
CHN →JP N
and (3) imported inputs from China used by Japanese sectors ∆IP WInputs, are regressed on the corresponding
used

instruments. The results show that the instruments have strong statistical power in explaining the corresponding endogenous variables. Table A14 shows first-stage results from the US. The results suggest that the instruments work
well in explaining the corresponding endogenous variables in the US data as well.
TABLE A13: First-stage regression results, different types of imports, Japan
Dep. Var. =CHN →OT H\k∆IP WFinal goods

Final
goods
(1)
0.35***(0.01)

CHN →OT H\k∆IP WInputs

Inputs(2)0.36***(0.01)

Final
goods
(3)
0.37***(0.01)-0.21***(0.05)

Inputs(4)-0.02***(0.00)
0.39***(0.01)

CHN →OT H\k∆IP WInputs used

R-sq.
F -stat.
p-value of F -stat.

0.97
1062.20.000

0.99
2112.3
0.000

0.97
800.0
0.000

0.99
1661.70.000

Inputs
used
(5)

Final
goods
(6)
0.34***(0.01)

Inputsused(7)
0.02***(0.00)

0.43***(0.02)
0.98
487.3
0.000

-0.34***(0.06)
0.97
509.4
0.000

0.44***(0.02)
0.98
374.1
0.000

Note: The table shows the results from first-stage regressions with the import variables with different types of goods, k = JP N . The sample sizeof the regressions is 616. All regressions include constant term and control variables and region fixed effects, and the prefecture dummies. The total
initial population weights all regressions. Standard errors, clustered at the prefecture level, are in parentheses. The shown F -statistics are those for
excluded instruments only. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.

TABLE A14: First-stage regression results, different types of imports, the US
Dep. Var. =CHN →OT H\k∆IP WFinal goods

Final
goods
(1)
0.78***(0.04)

CHN →OT H\k∆IP WInputs

Inputs(2)

0.39***(0.04)

Final
goods
(3)
0.76***(0.04)
0.14**(0.07)

Inputs(4)-0.06***(0.02)
0.45***(0.04)

CHN →OT H\k∆IP WInputs used

R-sq.
F -stat.
p-value of F -stat.

0.89
505.7
0.000

0.85
89.5
0.000

0.89
295.5
0.000

0.87
66.1
0.000

Inputs
used
(5)

Final
goods
(6)
0.78***(0.03)

Inputsused(7)-0.04***(0.01)

0.36***(0.04)
0.87
105.0
0.000

0.09
(0.09)
0.89
261.8
0.000

0.39***(0.03)
0.89
109.0
0.000

Note: The table shows the results from first-stage regressions with the import variables with different types of goods, k = U SA. The sample sizeof the regressions is 1,444. All regressions include constant term and control variables and region fixed effects, and the statistical division dummies.
The total initial population weights all regressions. Standard errors, clustered at the state level, are in parentheses. The shown F -statistics are thosefor excluded instruments only. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.

A7

E Upstreamness and downstreamness
Figure A1 Panels A and B show the spatial distributions of upstreamness and downstreamness based on equation(9) in Japan. Panels C and D show those from the US.
F IGURE A1: CZ level upstreamness and downstreamness
Panel A: Upstreamness, Japan

Panel B: Downstreamness, Japan

Panel C: Upstreamness, the US

Panel D: Downstreamness, the US

Note: The figure shows the upstreamness and downstremaness converted to the CZ level. All panels are based on the averages of the two periods,
the 1990s and 2000s. Authors’ calculations using the data obtained from the data sources written in the text.

A8

F Additional analyses
This section examines the effects of imports and exports on additional variables. First, we examine the effects on
the annualized manufacturing labor productivity growth rate by usingklpgi,t=
100 × ln(V Aki,t+h /Lki,t+h ) − ln(V Aki,t /Lki,t )has the dependent variable, where k indicates country, i indicates CZ, and t indicates the start year of the period. The
variable V Aki,t denotes the real manufacturing value-added and in CZ i in year t in country k and Lki,t denotes the
manufacturing employment for k = JP N , U SA, and h indicates the number of years in that period. Although t
indicates ‘period’ in the main text, it indicates ‘year’ in this section. The data on Japanese manufacturing valueadded come from the Census of Manufacture of Japan (Kōgyō Tōkei Hyō), 1991, 2001, and 2012. The nominal
manufacturing value-added are deflated by the prefecture level GDP deflator, which are dividing by manufacturing
employment to convert to the real value-added per worker. The data on US manufacturing value-added come fromthe Regional Economic Accounts, CAGDP9 Manufacturing (31-33), thousands of chained 2012 dollars. As the data
series starts at 2001, we report regression results from the 2000s only for the US.
Second, we examine the effect of the exposure to trade shocks on the manufacturing capital-labor ratio. As the CZ
level capital-labor ratio is not available, we obtain the sector level capital-labor ratio and convert it to the CZ level bytaking the employment-based weighted average:

k
kli,t
=

X

s∈SM

(

"
100
P
×
ln
′
h
s′ ∈SM Lis t
Lkistk
Ks,t+hLks,t+h

!

− ln

k
Ks,t
Lks,t

!#)

,

k
where where Ks,tdenotes the capital stock in sector s in year t and Lks,t denotes the employment. It measuresthe employment-weighted average of the annualized capital-labor ratio growth rate. The denominator of the weight
includes manufacturing sectors only because it is the summation over sectors in the set SM . The data come fromthe WIOD 2016 Release. The data on capital stock are the “nominal capital stock (in millions of national currency,
Series K)” deflated by dividing by the “price levels of intermediate inputs, 2010=100 (Series II PI).” The labor is the“number of persons engaged (thousands, Series EMP).”
Third, we examine the effect of imports on exports by utilizing equation (5) as the dependent variable. Table A15
shows summary statistics of the variables used for an additional analysis. See Table 1 for summary statistics of theexport variable (5). Figure A2 displays the spatial distribution of the labor productivity growth rates and capital-laborratio growth rates in the two countries. See Figures 6 and 7 for the spatial distribution of the export variable. All
regressions in this section are estimated using the same instrumental variable approach as the main text.

F.1 Effects on manufacturing labor productivity
Table A16 Panel A reports regression results for Japan, and Panel B reports the result for the US. In Japan,
columns (1) and (2) show that while overall import penetration has no effect on manufacturing labor productivity,
A9

TABLE A15: Summary statistics of the variables used for an additional analysis
Sample period

Obs

Mean

Std. dev.

Min

Max

Panel A: Japan
Annualized labor productivity growthln(Initial labor productivity)/10
Annualized capital-labor ratio growth rateln(Initial capital-labor ratio)/10

1990s and 2000s
1990s and 2000s
2000s
2000s

553
558
308
308

3.15
61.78
2.01
95.84

5.42
8.20
0.66
1.71

-26.57
13.92
-1.29
91.74

46.65
79.67
4.24
105.01Panel B: The US
Annualized labor productivity growthln(Initial labor productivity)/10
Annualized capital-labor ratio growth rateln(Initial capital-labor ratio)/10

2000s
2000s
2000s
2000s

688
689
722
722

5.13
41.54
2.80
44.38

4.39
6.89
0.98
2.46

-25.64
-7.99
-1.98
37.05

32.37
61.49
5.86
59.26

Note: Authors’ calculations using the data obtained from the data sources written in the text.

F IGURE A2: CZ level labor productivity and capital-labor ratios
Panel A: Labor productivity growth rates, Japan

Panel B: Capital-labor ratio growth rates, Japan

Panel C: Labor productivity growth rates, the US

Panel D: Capital-labor ratio growth rates, the US

Note: Panel A is based on the average of the two periods, the 1990s and 2000s. Panels B-D are based on one period, the 2000s. Authors’calculations using the data obtained from the data sources written in the text.

export opportunities have a positive effect on it. On the other hand, in the US, while overall import penetration has apositive effect on labor productivity, export opportunities have no effect on it. These two contrasting results across thetwo countries are somewhat surprising given the results reported in Table 5 which demonstrates that overall importpenetration reduced manufacturing employment and export opportunities increased it in both countries.
The contrasting effects on manufacturing labor productivity suggest that, in Japan, export opportunities not only

A10

sustain manufacturing employment but also increase the labor productivity of the sustained employment. On theother hand, in the US, reduced manufacturing employment due to import competition contributes to increasing laborproductivity.
TABLE A16: Effects of imports and exports on manufacturing labor productivity, IV
Panel A: Japan, k = JP N
∆IP W CHN →OT H\k∆EOW OT H\k→ROW

CZ level(1)
0.15(0.33)
0.42***(0.16)

BHJ
(2)
-0.13
(0.37)
0.30*(0.18)CZ level
(3)

BHJ
(4)

CZ level
(5)

BHJ
(6)

-0.72**(0.34)
2.76***(0.87)-0.70**(0.30)
0.23(1.06)-0.49(0.34)-0.96***(0.23)

2.48*(1.31)
553
329.3-2.00***(0.54)
126
393.7

CZ level
(5)

BHJ
(6)

0.57**(0.22)

0.32(0.23)

3.33**(1.37)
688
103.6

7.51***(2.77)
392
26.4

CHN →k∆IP WFinal
goods
CHN →k∆IP WInputs
CHN →k∆IP WInputs
used

Obs.
K.-P. Wald F -stat.

∆IP W CHN →OT H\k∆EOW OT H\k→ROW
CHN →k∆IP WFinal
goods
CHN →k∆IP WInputs

553
14.6

126
7.3

553
324.6

126
167.6

Panel B: The US, k = U SACZ level
BHJ
CZ level
BHJ
(1)
(2)
(3)
(4)
1.25*** 1.48***(0.41)(0.39)-0.07
0.25(0.31)(0.46)
0.25-0.22(0.24)-0.265
3.21*** 6.65***(1.11)(1.78)

CHN →k∆IP WInputs
used

Obs.
K.-P. Wald F -stat.

688
95.0

392
10.2

688
62.9

392
29.0

h
i
Note: The dependent variable is 100 × ln(V Aki,t+1 /Lki,t+1 ) − ln(V Aki,t /Lki,t ) for k = JP N , U SA. All regressions include constant term,
the initial productivity level, the control variables and region fixed effects, prefecture dummies for Japan, and statistical division dummies for the
US. The total initial population weights all regressions. Standard errors, clustered at the prefecture level for Japan, and the state level for the US,
are in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.

Columns (3)-(4) display the effects of final goods imports and intermediate goods imports from China. In Japan,
according to the BHJ result in column (4), while final goods imports have a negative effect on manufacturing laborproductivity, intermediate goods imports have no effect on it. In the US, both columns show that while final goods imports do not affect manufacturing labor productivity, intermediate goods imports have a positive effect on it. Combinedwith the results shown in Table 6, in the US, imported inputs had an adverse effect on manufacturing employment,
which in turn improved labor productivity.
Columns (5)-(6) display the effects of final goods imports and intermediate goods imported from China and usedby sectors in the importing country. The BHJ result shown in column (6) indicates that, in Japan, both final goods andintermediate goods imports reduced the manufacturing labor productivity. In contrast, in the US, both columns showthat imported inputs increased manufacturing labor productivity.
These results are consistent with Fort (2023)’s findings where the US manufacturing sectors are increasing the
A11allocation of their resources to productivity-enhancing activities however, as we do not observe such productivityenhancing effects of import penetration from the Japanese data, such resource allocations may be limited in theJapanese context.

F.2 Effects on manufacturing capital-labor ratio
Table A17 presents results from regression the annualized capital-labor ratio growth rate on the ‘exposure to
trade’ variables. Columns (1) and (2) show that import competition increases the capital-labor ratio while export
opportunities have no effects in Japan and the US. We acknowledge that the first-stage F -statistics are not high enoughin Japan and in the BHJ result in the US as we need to interpret the results cautiously.
Columns (3)-(4) examine the effects of final goods imports and imported inputs. As for Japan, while column(3) shows that the exposure to imported inputs has a negative coefficient in the CZ level regression, the BHJ result incolumn (4) indicates that the effect turns out to be positive. The same pattern is observed in columns (5)-(6), where thesecond import competition variable is the variable measuring imported inputs used by importing sectors. The effectsof final goods imports on the capital-labor ratios are estimated to be positive except in column (4). Overall, while
the inconsistent results between the CZ level regressions and the BHJ regressions in columns (3)-(6) require cautious
interpretation, they suggest that increased imports from China increases the capital-labor ratios.
In the case of the US, columns (1)-(2) indicate that while an increase in imports from China raises capital-laborratios, a rise in exports to the ROW reduces them, echoing the trends observed in Japan. Columns (3)-(4) overallsuggest that an increase in final goods and intermediate goods imports increases the capital-labor ratios in the US.
The effects of imported inputs from China differ between the two countries: positive in Japan and negative in the
US. However, based on the results from the BHJ approach, the effects on the capital-labor ratios are similar across thetwo countries. It suggests that, in Japan, imported inputs from China increase capital stock more than labor.

F.3 Effects on manufacturing exports to the rest of the world
Results from regressing the ‘exports to the world’ variable on the import competition variables are presented inTable A18. The dependent variable is equation (3), which is the employment-weighted averages of changes in per
worker sectoral exports to the world. It does not quantify actual exports from each CZ. Nevertheless, we use thisvariable as a proxy of exports by assuming that the same amount of labor in a sector in all CZs generates the sameamount of exports in the same sector.
Regarding the effects in Japan, the result from a CZ level regression in column (1) suggests that overall imports
from China increase exports. However, as shown in the next column, this result is not robust to the BHJ approach.
Columns (3)-(6) also show that the CZ level results and BHJ results differ. Although the effect of final goods imports
on exports is negative in CZ level regressions presented in columns (3) and (5), the results are null in the corresponding
BHJ results, respectively. Regarding the effects of imported inputs, although CZ level regressions suggest a positiveeffect on exports, the results are null in the corresponding BHJ result.

A12

TABLE A17: Effects of imports and exports on manufacturing capital-labor ratio, IV
Panel A: Japan, k = JP N
CZ level(1)
0.25**(0.10)-0.07(0.05)∆IP W CHN →OT H\k∆EOW OT H\k→ROW

BHJ(2)
0.45***(0.16)-0.05(0.10)

CHN →k∆IP WFinal
goods
CHN →k∆IP WInputs

CZ level
(3)

BHJ
(4)

CZ level
(5)

BHJ
(6)

0.55***(0.13)-0.65**(0.31)

0.32(0.21)
1.37**(0.68)

0.53***(0.09)

0.59***(0.14)-0.88***(0.31)
280
141.0

0.72**(0.35)
63
183.6

CZ level
(5)

BHJ
(6)

0.18***(0.04)

0.15***(0.02)

0.76***(0.22)
722
79.2

1.60***(0.20)
392
25.7

CHN →k∆IP WInputs
used

Obs.
K.-P. Wald F -stat.

280
4.9

63
3.2

280
111.9

63
27.3

Panel B: The US, k = U SACZ level
BHJ
CZ level
BHJ
(1)
(2)
(3)
(4)
0.25*** 0.32***(0.05)(0.07)-0.07-0.23(0.06)(0.24)
0.10***-0.01(0.04)(0.05)
0.75*** 1.54***(0.12)(0.29)∆IP W CHN →OT H\k∆EOW OT H\k→ROW
CHN →k∆IP WFinal
goods
CHN →k∆IP WInputs
CHN →k∆IP WInputs
used

Obs.
K.-P. Wald F -stat.
Note: The dependent variable is

P

s∈SM

(

722
101.4

P

Lk
ist

s′ ∈SM Lis′ t

392
9.3

722
54.9

392
30.4

  k

 k )Ks,t+h
Ks,t
× 100
ln
for k = JP N , U SA. All regressions
−
ln
k
k
h
Ls,t+hLs,tinclude constant term, the initial capital-labor ratio, the control variables and region fixed effects, prefecture dummies for Japan, and statisticaldivision dummies for the US. The total initial population weights all regressions. Standard errors, clustered at the prefecture level for Japan, andthe state level for the US, are in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.

Regarding the effects in the US, column (1) shows that overall imports from China increase exports; however, thisresult is not robust to the BHJ approach, as shown in column (2), indicating a null result. On the other hand, CZ level
results are broadly consistent with BHJ results in columns (3)-(6), examining the effects of final goods imports andintermediate goods imports separately. These columns suggest show that imported final goods reduce exports while
imported inputs increase exports.
To summarize, combined with the other results presented in this study, an increase in imported inputs from China
reduces manufacturing employment, however, it increases manufacturing labor productivity, capital-labor ratio, andexports to the ROW. In contrast, in Japan, an increase in imported inputs sustains manufacturing employment. However, it does not lead to clear positive effects on manufacturing labor productivity, capital-labor ratio, and exports to
the ROW.

A13

TABLE A18: Effects of imports from China on manufacturing exports to the world, IV
Panel A: Japan, k = JP N
CZ level(1)
0.51**(0.21)∆IP W CHN →OT H\k

BHJ
(2)
0.53
(0.86)

CHN →k∆IP WFinal
goods
CHN →k∆IP WInputs

CZ level
(3)

BHJ
(4)

CZ level
(5)

BHJ
(6)

-0.94***(0.23)
4.04***(0.88)-0.19(0.54)
2.96(2.73)-0.66***(0.16)

0.001(0.59)

4.78***(1.28)
558
140.6

-0.46
(6.85)
126
25.2

CZ level
(5)

BHJ
(6)

-0.14
(0.09)-0.19***(0.07)

3.24***(0.79)
1,444
164.3

1.99*
(1.17)
784
67.7

CHN →k∆IP WInputs
used

Obs.
K.-P. Wald F -stat.

∆IP W CHN →OT H\k
CHN →k∆IP WFinal
goods
CHN →k∆IP WInputs

558
826.1

126
173.3

558
393.6

126
265.2

Panel B: The US, k = U SACZ level
BHJ
CZ level
BHJ
(1)
(2)
(3)
(4)
0.42***
0.13(0.10)(0.13)-0.30*** -0.34***(0.10)(0.09)
2.29***
1.94***(0.32)(0.56)

CHN →k∆IP WInputs
used

Obs.
K.-P. Wald F -stat.

1,444
357.4

k→ROW =
Note: The dependent variable is ∆EOWit

P

784
211.4

1,444
124.9



Lk
ist
k
′
s ∈S L ′

s∈SM

P

A

is t

784
56.5
×

k→ROW
∆exst
P
k
i′ L ′i st

for k = JP N , U SA. All regressions

include constant term, the control variables and region fixed effects, prefecture dummies for Japan, and statistical division dummies for the US.
The regressions for Japan includes the employment-weighted average of the initial export level as a control. The total initial population weights all
regressions. Standard errors, clustered at the prefecture level for Japan, and the state level for the US, are in parentheses. ***, **, and * indicate
statistical significance at the 1%, 5%, and 10% level, respectively.

G Robustness checks
This section provides results from the BHJ approach associated with regressions in Table 7, examining (potentially) different effects of the exposure to trade on manufacturing employment by up/downstreamness. To take the
BHJ approach, we generate the CZ level residualized dependent variable, residualized endogenous explanatory variables, and residualized instruments. These CZ level residualized variables and the CZ level upstramness variables areconverted to the sector level using sector-CZ level employment as weights. As the last step, we regress the residualized dependent variable on the residualized explanatory variables using the residualized instruments as instruments,
separately for the (i) observations with lower upstreamness variable and (ii) observations with higher upstreamness
variable. We take the same step for the downstream variable. The cutoff for division is the median value among all
observations in the stacked cross-sections.
Table A19 shows results from the BHJ approach. The results are broadly consistent with results from CZ level
regressions in Table 7. Results from Japan are presented in Panel A. It shows that the effects of imported inputs are
positive and statistically significant in less upstream or downstream sector-year observations as shown in columns(1) and (2). However, the effects of imported inputs turn out to be insignificant sector-year observations with more
upstream or downstream sectors as shown in columns (3) and (4). These results are inconsistent with the results in

A14

Table 7. Nevertheless, the results presented in columns (5) and (6) indicate that the effects of imported inputs are
similar: positive and statistically significant in sector-year observations with low and high levels of “uptreamness −downstreamness.”
Results from the US are presented in Panel B. Columns (1) and (2) show that the adverse effects of imported
inputs are greater in sector-year observations with low upstreamness variable, which is consistent with the resultsfrom Table 7. Columns (3) and (4) show that less downstream sector-year observations are less adversely affected byimported inputs than those with more downstream sector-year observations, which is consistent with Table 7. The lasttwo columns show that adverse effects of imported inputs come from sector-year observations with lower levels of“uptreamness − downstreamness,” which is also consistent with Table 7.
TABLE A19: Up/downstreamness and imports from China, BHJ
Selection based on
SampleCHN →k∆IP WFinal
goods
CHN →k∆IP WInputs
used

Obs.
K.-P. Wald F -stat.
Selection based on
SampleCHN →k∆IP WFinal
goods
CHN →k∆IP WInputs
used

Obs.
K.-P. Wald F -stat.

Panel A: Japan
Down
High
Low
High
(2)
(3)
(4)
1.34
-1.09*** -0.67**(1.47)(0.34)(0.27)
4.14
9.55**
0.09(2.58)(3.90)(0.41)
64
63
63
43.7
12.6
38.5

Up−Down
Low
High
(5)
(6)
-0.95***
1.51(0.10)(0.94)
3.08***
3.68*(0.53)(1.88)
63
63
179.4
55.6

Panel B: The US
Down
High
Low
High
(2)
(3)
(4)
-0.38*** 1.91*** -0.55***(0.12)(0.37)(0.11)-3.03**
5.15*-3.89(1.31)(2.66)(2.70)
392
392
392
45.0
59.3
7.9

Up−Down
Low
High
(5)
(6)
-0.43***
1.54***(0.14)(0.32)-4.49***-1.23(1.65)(1.31)
392
392
15.7
19.5

Up
Low
(1)
-0.86***(0.10)
3.41***(0.50)
62
199.4
Up
Low
(1)
0.05
(0.25)-4.11***(1.48)
392
83.2

Note: The table shows results from the BHJ approach associated with the CZ level regressions in Table 7. Standard errors, clustered at the sector
level, are in parentheses. In Panel A, the observations are not split into the same size of sub-samples because there is an observation that takes theexact same value as the median. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.

A15