KEO Discussion Paper No. 157

The China Syndrome: A Cross-Country Evidence
Kozo Kiyotaab , Sawako Maruyamac ,and Mina Taniguchid†a

Keio Economic Observatory, Keio University, 2-15-45, Mita, Minato-ku, Tokyo 108-8345, Japan; E-mail:kiyota@sanken.keio.ac.jpbResearch Institute of Economy, Trade and Industry (RIETI), Japanc

Faculty of Economics, Kindai University, 3-4-1 Kowakae, Higashiosaka City, Osaka 577-8502, Japan;
E-mail: maruyama@eco.kindai.ac.jpdDepartment of Economics and MGSE, LMU Munich, Kaulbachstrasse 45, 80539, Germany; E-mail:mina.taniguchi@econ.lmu.de†
Corresponding author

January 7, 2021

The China Syndrome: A Cross-Country Evidence*
Kozo Kiyota abaKeio Economic Observatory, Keio University, 2-15-45, Mita, Minato-ku, Tokyo 108-8345, Japan;
E-mail: kiyota@sanken.keio.ac.jpbResearch Institute of Economy, Trade and Industry (RIETI), Japan

Sawako Maruyama ccFaculty of Economics, Kindai University, 3-4-1 Kowakae, Higashiosaka City, Osaka 577-8502,
Japan; E-mail: maruyama@eco.kindai.ac.jp

Mina Taniguchi d†dDepartment of Economics and MGSE, LMU Munich, Kaulbachstrasse 45, 80539, Germany; Email: mina.taniguchi@econ.lmu.de†
Corresponding author

Abstract
While in many advanced countries the increasing import competition
from China on employment is a major concern for policymakers and thegeneral public, its impact of Chinese import competition could be different
across countries, depending upon the volume and the composition of the
products. This paper examines the impact of the China shock on employment in six advanced countries. We find that the import penetration of final goods from China has negative effects on manufacturing employment
in these countries, whereas the import penetration of intermediate inputs
from and the exports to China could have positive effects. Moreover, such
positive effects could offset or even outweigh the negative effects in somecountries. These results together suggest that a careful interpretation isneeded when evaluating the external validity of the China shock that is
obtained in one country.
Keywords: The China shock, Cross-country comparisons, Final goods and intermediate inputs, Imports and exports
JEL classification codes: F16, J21* Kozo Kiyota gratefully acknowledges financial support received from the JSPS Grants-in-Aid(JP18H03637, JP19H00598). Mina Taniguchi gratefully acknowledges support from the German
Research Foundation (DFG). The usual disclaimers apply.

1

Introduction

For many advanced countries, import competition from low-wage countries is always one of the major concerns for policymakers and the general public because
it is considered to be one of the most important adjustment processes in globalization.1 In particular, the impact on employment of increasing import competition
from China, which is also called “the China Syndrome” or “the China shock,” hasbeen a major topic of debate in the United States for the last two decades due to therapid growth of the Chinese economy. Accordingly, several studies have examinedthe effects of imports from China on the U.S. employment (e.g. Autor, Dorn, andHanson, 2013, 2015; Acemoglu, Akcigit, and Kerr, 2015; Acemoglu, Autor, Dorn,
Hanson, and Price, 2016; Pierce and Schott, 2016; and Wang, Wei, Yu, and Zhu,
2018).
Among these studies on the China shock, one of the most influential studies
is Acemoglu et al. (2016). They examined the effects of imports from China on
U.S. employment between 1999 and 2011. Using detailed input–output data, theyfound that job losses from rising Chinese import competition for the above periodamount to 2.0–2.4 million. Due to the huge negative impact on U.S. employment,
this number was featured in stories by news publications such as the Washington
Post (12/15/2014) and the New York Times Magazine (9/5/2016).
Concern about the China shock is not only limited to the United States but isalso shared with other advanced countries. Figure 1 compares the Chinese importpenetration and manufacturing employment for six advanced countries: France,
Germany, Japan, South Korea, the United Kingdom, and the United States for theperiod between 2000 and 2014.2 These are top six destination countries to which
China exports intermediate inputs. On the one hand, import penetration from
China increased throughout the period in all six countries. On the other hand,manufacturing employment declined over the period for all countries except South
Korea. Indeed, the studies on the China shock thus have expanded from the UnitedStates to various other countries. For example, Dauth, Findeisen, and Suedekum(2014) have investigated the effects of imports from China and Eastern Europe on
1

For the earlier studies on this issue, see Revenga (1992) for the case of the United States andTachibanaki, Morikawa, and Nishimura (1998) and Tomiura (2003) for the case of Japan.
2
The data come from the World Input–Output Database. Next section explains about the data
used in this paper in more detail.

1German employment. Taniguchi (2019) and Choi and Xu (2020) have studied theeffects of imports from China on Japanese and Korean employment, respectively.
[Figure 1 about here.]
However, to our knowledge, the previous studies have paid little attention to
the cross-country differences about the China shock. It is possible that the China
shock could be different across countries, depending upon the volume and thecomposition of the products. Although the studies on the China shock have expanded from the United States to other countries such as Germany (Dauth et al.,2014) and Japan (Taniguchi, 2019), these studies are conducted independently.
Thus, their results are difficult to compare with each other due to differences in
the period and industry coverage as well as differences in industry classification.
More comprehensive analysis is needed for cross-country comparisons.
In this paper, we examine the effects of imports from China on employment insix advanced countries: France, Germany, Japan, South Korea, the United Kingdom, and the United States. Our empirical approach is similar to Acemoglu etal. (2016), but we extend their analysis in the following three aspects. First, we
extend their analysis to cross-country comparisons during the same period under
the same industry classification that enables us to compare the results across countries.3 To do so, this paper utilizes the data from the World Input–Output Database(WIOD) between 2000 and 2014. This extension enables us to identify similarities
and differences in the China shock across countries, based on the same analyticalframework during the same period under the same industry classification.
Second, unlike Acemoglu et al. (2016), this paper distinguishes between imports of final goods and those of intermediate inputs. The imports of final goods
could yield negative effects on domestic producers of final goods from import competition. In contrast, the imports of intermediate inputs can have two opposite
effects. On the one hand, it could compete with domestic production of intermediate inputs. On the other hand, it could contribute to domestic production of
final goods, and thereby could have positive effects on employment of final goods
producers. Without considering such positive effects explicitly, the negative effects could be overemphasized. Indeed, Figure 1 also indicates that the imports
3

Our main focus is on the cross-country comparisons of the China shock on overall employment.
Due to the limited availability of the local labor market data across countries, local labor market
issue is not pursued here.

2

of intermediate inputs increased in the six advanced countries, where imports ofintermediate inputs are defined as imports that are not used for final demands.
Nonetheless, there are still only a few studies that distinguish between the imports of final goods and those of intermediate inputs. Taniguchi (2019) examinedthe effects of increased imports from China on Japanese local labor markets. She
found that increases in the imports of intermediate inputs from China had positiveeffects on employment. Wang et al. (2018) and Caliendo, Dvorkin, and Parro (2019)
also found similar positive effects of imported intermediate inputs from China on
U.S. employment, where both studies utilize the WIOD to capture the imports ofintermediate inputs. Building upon these studies and using the WIOD, this paper
distinguishes the difference in the effects of the imports of final goods and those ofintermediate inputs.
Finally, we take into account the effects of exports as well as imports. As Dauthet al. (2014) pointed out, while the growth of China increased import competition,
it simultaneously leads to a substantial rise in market opportunities for companies
in advanced countries. Without considering the effects of exports explicitly, one
could overestimate the negative effects of foreign exposure on employment. Indeed, Dauth et al. (2014) found significantly positive effects of trade exposure on
employment in Germany. In spite of the importance of exports, however, only afew studies such as Dauth et al. (2014), Feenstra and Sasahara (2018), Feenstra,
Ma, and Xu (2019), and Choi and Xu (2020) explicitly took into account the effects
of exports as well as imports in recent studies on the China shock.4 Based on thisbackground, this paper explicitly focuses on the effects of exports as well as thoseof imports.
To clarify the similarity in and the difference between the previous studies andour study, we summarize the related studies in Table 1. This table indicates thatthe use of the WIOD allows one to distinguish the imports of intermediate inputs
and final goods while restricting the number of industries.
[Table 1 about here.]
The major findings of our paper are twofold. First, the import penetration of final goods from China has a negative effect on manufacturing employment in most4

In this connection, several studies have found the positive relationship between exports andemployment. See, for example, Kiyota (2012) for the case of Japan. Kiyota (2016) extended theanalysis of Kiyota (2012) to China, Indonesia, and South Korea as well as Japan.

3

of the six countries, whereas the import penetration of intermediate inputs fromand the exports to China show positive coefficients while they are statistically insignificant in most countries. Second, in the counterfactual analysis, we show thatsuch positive effects could offset or even outweigh the negative effects in somecountries. For the United Kingdom and the United States, the negative effects of
the imports of final goods outweigh the positive effects of the imports of intermediate inputs and exports. In contrast, for France and Japan, the negative effects of
the imports of final goods offset the positive effects of the imports of intermediate
inputs and exports. For South Korea and Germany, the positive effects outweighthe negative effects. These results together suggest that a careful interpretation isneeded when evaluating the external validity of the China shock that is obtainedin one country.
These results have an important caveat. Our analysis is based on small sample. This could cause the small sample problem, which results in the less precise
estimates. Noting that the small sample is caused by the aggregation of industries,
this could also magnify the problem of within-industry heterogeneity. Therefore,
our estimation results should be interpreted with caution.
The rest of this paper is organized as follows. The next section explains themethodology and data used in this paper. Section 3 presents the estimation results. Section 4 addresses issues to be discussed further on our approach and theestimation results. A summary of our findings and their implications are presented
in the final section.

2

Methodology and Data

2.1

Methodology

2.1.1

Preliminary analysis

We first examine the effect of total imports on employment as a preliminary analysis. Following Acemoglu et al. (2016), the specification in our preliminary analysis

4has the following form:5∆Lj,τ = ατ + β∆IPj,τ + εj,τ ,(1)where ∆Lj,τ is 100 times the log change in employment in industry j in country cover the period τ ; ατ is a country- and period-specific constant; ∆IPj,τ is 100 times
the change in import penetration from China in industry j in country c over thetime period τ ; and εj,τ is an error term. For ease of presentation, we omit country
notation c, unless otherwise noted.
The change in import penetration from China is defined as follows:∆IPj,τ =

CHN
∆Mj,τ
,
Yj,0 − Ej,0 + Mj,0

(2)

CHN
where ∆Mj,τ
is the change in imports during the period τ ; Yj,0 − Ej,0 + Mj,0 is
the initial absorption (measured as industry outputs, Yj,0 , plus industry imports,
Mj,0 , minus industry exports, Ej,0 ). Equation (1) is estimated using two-stage leastsquares (2SLS) as well as ordinary least squares (OLS) specifications.
An instrumental variable (IV) for 2SLS is:
CHN,O
∆Mj,τ
,
∆IP Oj,τ = O
OO
Yj,0 − Ej,0+ Mj,0(3)

CHN,O
where ∆Mj,τ
is the change in imports from China during the period τ in otherOOO
is the initial absorption of other high+ Mj,0− Ej,0high-income countries; Yj,0income countries. For the initial absorption, we choose the absorption value in2000. We would note that using the absorption value in 2000 might lead to bias if
the included economic variables are affected by an anticipated increase in imports
and/or exports with China. If we use the earlier version of the WIOD, we may beable to choose the previous (or earlier) year for the absorption. However, because
the industry classification in the earlier version of the WIOD is more aggregated,
the sample size becomes further small. As a compromise, we choose the initial
5

One may argue that we employ alternative estimation strategy such as difference-in-differences(DID) design. However, the DID is based on a common trends assumption, which should be tested
before the China shock (the early 2000s). As we will explain below, the data we use covers from2000. The period of our data thus is not long enough to test this assumption, which makes it
difficult to employ the DID design.

5year of our sample for the absorption. Noting that the major export and importdestination countries vary between final goods and intermediate inputs and acrosscountries, we choose other high-income countries in which the correlation between
CHN
is relatively high and the first-stage F -value is also high.6
IV and ∆Mj,τ
2.1.2

Benchmark specification

We extend the specification in the preliminary analysis in two ways. First, similar to Taniguchi (2019) and Wang et al. (2018), we distinguish between imports
of intermediate inputs and those of final goods. As mentioned above, without
considering the positive effects of the imported intermediate inputs explicitly, thenegative effects of imports could be overemphasized.
Second, we control for the effects of exports as well as imports. As was pointed
out by Dauth et al. (2014) and Choi and Xu (2020), employment could be affected
not only by imports but also by exports. We thus include exports to the regression
equation in an analogous measure.7
Our main regression is specified as follows:
IM
FN∆Lj,τ = ατ + β1 ∆IPj,τ
+ β2 ∆IPj,τ
+ γ∆EPj,τ + εj,τ ,

where
IM
∆IPj,τ
=

∆xCHN
j,τ
,
Yj,0 − Ej,0 + Mj,0

(4)

(5)

denotes the changes
where superscript IM denotes intermediate inputs and ∆xCHNj,τ
in the imports of intermediate inputs from China to industry j in the importing
country over the period τ ;FN∆IPj,τ
=

CHN
∆fj,τ
,
Yj,0 − Ej,0 + Mj,0

(6)

CHN
where superscript F N denotes final goods and ∆fj,τ
denotes the changes in theimports of final goods from China to industry j in the importing country over the6

The choice of other high-income countries thus varies between imports and exports and between final goods and intermediate inputs.
7
Choi and Xu (2020) employed similar indexes to export–output ratio that indicates the changes
in Korean exports as well as those in Japanese exports as an instrument.

6period τ . Their instruments are:IM=
∆IP Oj,τ

∆xCHN,Oj,τ
O
OO
Yj,0 − Ej,0+ Mj,0andFN=∆IP Oj,τ

CHN,O
∆fj,τ
,
O
O
O
Yj,0
− Ej,0+ Mj,0(7)

FNIM
are the change in the imports of intermediate inand ∆IP Oj,τwhere ∆IP Oj,τputs and final goods, respectively, from China to industry j in other high-income
countries during the period τ .
Similarly, ∆EPj,τ is 100 times the change in exports to China relative to output
in industry j in country c over the time period τ :∆EPj,τ =

CHN
∆Ej,τ
,
Yj,0

(8)

CHN
where ∆Ej,τ
is the change in exports from country c to China. Its instrument is:∆EP Oj,τ =

CHN,O
∆Ej,τ
,
O
Yj,0

(9)

CHN,O
where ∆Ej,τ
is the change in exports from other high-income countries toChina during the period τ . Equation (4) is estimated using 2SLS as well as OLS
FNIM, and ∆EP Oj,τ .
, ∆IP Oj,τspecifications with IVs of ∆IP Oj,τNote that Acemoglu et al. (2016) featured the general equilibrium effect of anincrease in imports from China including indirect effects through sectoral linkages. However, since the WIOD has limited number of industries, the inclusionof indirect effects cause severe multicollinearity and loss of a degree of freedom.
Thus, while we follow the empirical specification with focusing on direct effect in
Acemoglu et al. (2016), we extend the analysis on the direct effect, which explicitly
distinguishes the imports of final goods, those of intermediate inputs, and exports.

2.1.3

Instrumental variables

Our instrumental strategy is similar to that of the previous studies such as Autor et al. (2013) and Acemoglu et al. (2016). That is, to instrument the imports
from China by a target country c, we use the imports from China by other OECD
countries which experienced a similar surge in the imports from China during thesample period. As in the previous literature, we choose a set of countries as the7IV candidate that have characteristics similar to a target country regarding tradewith China. We then take the mean of these countries’ Chinese import penetration
ratios and export-output ratios to form the instruments.
We choose countries to construct IV for each explanatory variable as follows.
First, following the literature, we select nine high-income OECD countries that areavailable in the WIOD and experienced a large increase in trade with China. We
consider these nine countries as a baseline set of countries, which consists of Australia, Canada, France, Germany, Japan, South Korea, Taiwan, the United Kingdom, and the United States. Then, in the case where the baseline set of countries
does not satisfy the conditions that are required to be valid instruments, we modify
a set of countries by adding or excluding some of these countries to satisfy these
conditions.8 We select the countries that have high correlations with a target country in terms of the imports of intermediate inputs or of final goods from China. We
also adjust the set of countries in order to include at least three countries and not
to choose the target countries and other IV countries from only one region. This
would avoid the IV correlating to unobserved labor demand shocks that would
also affect the employment change in the target country. The countries we use toconstruct the IV for each explanatory variable in six target countries are listed inTable A2.
We form the IVs for the export–output ratios in a similar way with those forthe import penetration ratios of final good and intermediate inputs. To reduce thecorrelations between the IVs for each explanatory variable, we further adjust thesets of IV countries such that we do not have too low Shea’s adjusted partial R2 ,which is an indicator for a valid IV in a multivariate model (see Shea, 1997).
8
We consider a set of IV valid if the IV satisfies the following conditions at the first stage: 1)
the IV is well correlated with the explanatory variable, 2) F -value in the first-stage regression ishigh enough, and 3) the IV is not strongly correlated with the other IVs (for example, the IV for
import penetration ratio of final goods does not have a high correlation with the IV for importpenetration ratio of intermediate inputs and the IV for export–output ratio), which means Shea’s
adjusted partial R2 is high (see Shea, 1997). These first-stage statistics are provided in the Tables
A4–A6.

8

2.2

Data

2.2.1

SourceThis paper uses data from the WIOD for the period from 2000 to 2014.9 The
WIOD is built on national accounts data and was developed within the 7th Framework Programme of the European Commission. The WIOD provides time-seriesof global IO tables for 28 EU countries, 15 other major countries and the rest-of-theworld (ROW). The 15 countries include non-EU OECD member countries such as
Japan and the United States as well as emerging economies such as China and
Mexico. These tables are constructed on the basis of officially published IO tables
in conjunction with national accounts and international trade statistics.
One advantage of using the WIOD is that it provides Socio Economic Accounts
which include annual data such as employment at the industry level. This enables us to examine the effects of trade on employment more precisely. Moreover, throughout the data collection effort, harmonization procedures are applied
to ensure the international comparability of the data. This enables us to conductcomparative analysis across countries for the same period under the same industry
classification. If the period or the industry classification is different, one cannot figure out whether the difference of the effects of the China shock can be attributable
to the differences in country, period, or industry classification.
Another advantage of using the WIOD is that it includes information on theuse of imported goods, whether for intermediate inputs or for final demand. It
provides data for domestic and imported intermediate inputs as well as domestic
and imported final demands separately and by country. In our analysis, imports
in the intermedate demand sector are regarded as imports of intermediate inputs,
whereas imports in the final demand sector are regarded as imports of final goods.
In addition, information on both source and destination industries is also available. Note that the use of and the destination industry of imported goods are notreported in standard trade data. Similarly, the national input–output table reports
the imports as a total and does not distinguish between intermediate inputs and
9
The WIOD and all satellite accounts are available at http://www.wiod.org. The satellite accounts include National IO Tables, Socio Economic Accounts (i.e. data on employment, capitalstocks, etc.) and Environmental Accounts. In this paper, we utilize World IO Tables released in
November 2016 and Social Economic Accounts data released in February 2018. For a detailed description of the database construction, see Timmer, Dietzenbacher, Los, Stehrer, and de Vries (2015).

9

final goods. These features in turn mean that the WIOD enables us to capture theimports of manufacturing goods for intermediate inputs as well as for final demand. Thus, the WIOD is useful for cross-country comparisons of international
trade flows between a particular pair fo countries with a separation of intermediate and final goods.
In contrast, a disadvantage of the WIOD is that the industry classification is lessdisaggregated than the classification in the previous studies. This makes it difficult to analyze the inter-industry linkages precisely, even though the recent studies
such as Acemoglu et al. (2016) emphasized the importance of the general equilibrium effects. Therefore, this study does not pursue the issue of inter-industry
linkages. In addition, many Eastern European and South East Asian countries are
not included in the WIOD. This paper focuses on the imports from China rather
than those from low-wage countries.
Note that the China shock became evident from the early 2000s. For example, Autor et al. (2013) confirmed that the the share of imports from China in the
United States increased from 2001 when China joined the World Trade Organization (WTO).10 Similarly, Taniguchi (2019) pointed out that, in Japan, imports from
China in 2002 exceeded imports from the United States that was the largest importing partner for a long time. Because the WIOD covers the period from 2000, it
is desireable to examine the effects of the China shock.11
2.2.2

Definition of key variables

There are two key variables in our analysis: employment and trade (imports andexports). In Socio Economic Accounts in the WIOD, employment is defined as
the number of persons engaged (EMP in the WIOD).12 Note that there is neither
distinction between temporary and permanent workers nor distinction between
part-time and full-time workers in the WIOD. Therefore, employment in our analysis includes temporary as well as permanent workers.
10

In contrast, Pierce and Schott (2016) argued that the increased imports from China are attributable to the changes in US trade policy rather than the China’s entry to the WTO.
11
The Release 2013 version of the WIOD covers the period between 1995 and 2011. However,the number of sectors is much smaller (34 sectors) than the current version (the Release 2016). This
makes a small sample problem much severe in our analysis. This paper thus uses the Release 2016
rather than the Release 2013.
12
Although the WIOD provides us with the number of persons engaged (EMP) and that of employees (EMPE), we use the former because the latter excludes self-employed workers.

10

Trade is measured as the transactions between countries. Imports of final goods
are defined as the imports that are used for final demand. The rest of the imports
are defined as the imports used for intermediate inputs. To ensure the comparability of our findings with previous studies, we focus on the effects of manufacturing trade; therefore, industries are limited to industries with the WIOD industrial
codes from 5 to 23.13 In this study, we define manufacturing by the supply side
sector.14 This, in turn, means that the imports of intermediate inputs in manufacturing do not include the imports from non-manufacturing industries such as
natural resources because they do not directly cause competition in manufacturingindustries.15 When we measure procurement from China to industry j in a target
country, the imports of intermediate inputs are based on the user side sector. xCHNj
in equation (5) indicates the imports of intermediate inputs from industries 5 to 23
in China to industry j in a target country. Note that an exporting industry can bedifferent from an importing industry in the WIOD. EjCHN in equation (8) indicatesthe exports from industry j in the target country to industries 5 to 23 in China.
To compute the growth rate with enough observations, we split the sample into
two sub-periods: 2000–2007 and 2007–2014. The growth rate is computed for 2000–
2007 and for 2007–2014. The initial year for the first sub-period (2000–2007) is theyear 2000. The year 2007 is the initial year for the second sub-period (2007–2014).
One may propose the use of overlapping data (e.g., 2000–2007, 2001–2008, etc.)
rather than non-overlapping data (i.e., 2000–2007 and 2007–2014). As Clark and
Coggin (2011) point out, the use of overlapping data sometimes allows us to obtaingreater statistical efficiency. However, overlapping data creates a moving average
error term and thus OLS parameter estimates would be inefficient.16 Besides, theprevious studies on the China shock (e.g., Autor et al. 2013) used non-overlapping
data. In conformity with the existing literature, we use non-overlapping data.
13

For the list of industries, see Table A1 in Appendix A2.
Appendix A1 explains the structure of the WIOD in more detail.
15
For example, according to Japan Foreign Trade Council, the 1st and the 2nd major products of
the Japanese imports in 2018 are oil (10.8 percent) and liquefied natural gas (5.7 percent), respectively. It is difficult to imagine that these products bring competition in manufacturing industries.
16
For more detail about the overlapping data problems, see Harri and Brorsen (2009).
1411

2.2.3

Descriptive statistics

Table 2 presents the descriptive statistics for the main variables in the regression
analysis (i.e., equations (1) and (4)). We highlight three main findings. First, manufacturing employment declined for all countries except for South Korea. Second,
the growth of the imports of final goods from China is greater than that of intermediate inputs except for South Korea. Finally, total imports from China grew fasterthan total exports to China from the United States, Japan, the United Kingdom,and France while total exports grew faster than total imports for Germany andSouth Korea. These results suggest that the effects of imports from and exports to
China could be different across these six countries.
[Table 2 about here.]3

Estimation Results

3.1

Preliminary analysis

Table 3 presents the OLS and 2SLS regression results of equation (1). We use
small option in Stata software to make degrees-of-freedom adjustments and report
small-sample statistics to take into account the small sample problem. To avoid
the potential endogeneity problem, the focus is on 2SLS results while the OLS results are presented as references. We highlight two results. First, the first-stage
partial R2 is relatively high in all countries.17 This result supports the validity of
our instruments.
[Table 3 about here.]
Second, the imports from China have significantly negative effects on employment in most countries. Table 3 indicates that the significantly negative coefficientsof Chinese import penetration (∆IP ) are confirmed in the United States, Japan, the17Table A3 indicates the first-stage results. For each country, the coefficients in the first-stage
estimations, F -value, and partial R2 are listed for each explanatory variable. The results indicatethat the correlations between explanatory variable and its instrument, F -values, and partial R2 arehigh enough in each target country, which suggests that our instrumental variables are not weak
and thus valid.

12United Kingdom, and France. This result implies that import competition from
China negatively affected for employment in these countries.
As discussed in Section 2, however, the effects of import penetration may bedifferent if the difference between intermediate inputs and final goods or the effects of exports are taken into account. Section 3.2 addresses these issues in moredetail.

3.2

Benchmark results

Table 4 indicates the OLS and 2SLS regression results of equation (4). As in the
preliminary analysis, we focus on 2SLS results to avoid the potential endogeneity
problem.18 We highlight three results. First, the effects of imports of final goods
from China on employment are generally negative and significant. Significantly
negative coefficients of the imports of final goods are confirmed in all target countries except South Korea. The results imply that the increasing imports of final
goods from China could pose a threat to employment in many advanced countries.
[Table 4 about here.]
Second, however, the imports of intermediate inputs have different effects fromthose of final goods. The positive coefficients are confirmed in all target countries
except the United States. Moreover, the coefficient is statistically significant at the
5 percent level for Germany. The results indicate that the increasing imports ofintermediate inputs are not threats in all countries but it could affect employment
positively in many of these countries.
Finally, the effects of exports are generally positive although insignificant. Insignificantly positive coefficients of export–output ratio are confirmed in all countries but Germany. The results weakly suggest that the increasing exports to China
also affect employment positively in these countries.
These results together imply that the import penetration of final goods from
China could have significantly negative effects on manufacturing employment in
18

Table A6 indicates the first-stage results. Like the preliminary analysis, results indicate that thecorrelations between explanatory variable and its instrument, F -values, and partial R2 are highenough in each target country, which suggests that our instrumental variables are not weak andthus valid.

13

six target countries. In contrast, the import penetration of intermediate inputs fromand the exports to China could have weak but positive effects in most of thesecountries. These results seem to suggest that these six advanced countries facesimilar reactions to the China shock. However, the magnitude may be different
across countries. In Section 3.3, the issue of magnitude is discussed further.

3.3

Counterfactual manufacturing employment

In Section 3.2, we found that the import penetration of final goods from China
has significantly negative effects on employment while the import penetration ofintermediate inputs from and exports to China commonly have weak positive effects across most of these countries. However, even if the results are similar acrosscountries in terms of statistical significance, their economic significance may be different. To address this issue, we estimate changes in counterfactual employment
when there is no increase in trade with China.19
The difference between actual and counterfactual manufacturing employmentof country c, ∆Lcf
τ , is:
∆Lcf
τ = −

X




g j,τ ,
f F N − γ̂∆EP
f IM − βˆ2 ∆IP
Lj,τ 1 − exp −βˆ1 ∆IP
j,τ
j,τ

(10)

j

f IM
f FN
where βˆ1 , βˆ2 , and γ̂ are the 2SLS coefficient estimates.20 ∆IPj,τ and ∆IP j,τ indicate the increases in import penetration ratio from China for intermediate inputs
g j,τ indicates the increases in export–output
and for final goods, respectively; ∆EP
f IM is obtained by multiplyratio to China. Following Acemoglu et al. (2016), ∆IPj,τ
IMing the observed increase in import penetration ∆IPj,τ with the partial R-squaredf F N and ∆EP
g j,τ are estifrom the first-stage regression on the instrument. ∆IPj,τmated in a similar manner. As for time period τ , the estimation covers two periods. Changes in employment and ratios from 2000 to 2007 as well as changes from2007 to 2014 are examined.
Table 5 presents the results. Each figure indicates the difference between actualand counterfactual employment. For example, the figure in the top-left corner in
19This means that the counterfactual employment is estimated under the assumption that there
is no change in imports of intermediate inputs, final goods, and exports.
20
Unlike Acemoglu et al. (2016), we multiply the difference by −1 such that the sign of thedifference becomes consistent with the sign of the effects of trade.

14this table indicates −1237.6, which means that the U.S. employment would havedecreased by 1.2 million workers in comparison to the case where there was noincrease in the imports of intermediate inputs and final goods from, as well as theexports to, China between 2000 and 2007.
[Table 5 about here.]
The effect of the imports of final goods is generally negative on manufacturing
employment while the effects of the imports of intermediate inputs and exports are
generally positive. However, the magnitude is different across six countries. For
the United Kingdom and the United States, the negative effects of the imports offinal goods outweigh the positive effects of the imports of intermediate inputs andexports. These results suggest the significant negative effects of the China shockon manufacturing employment in these two countries, which may be consistent
with the recent surge of anti-globalization activities in these two countries.
For France and Japan, in contrast, the negative effects of the imports of final
goods offset the positive effects of the imports of intermediate inputs and exports.
For example, negative effects are reduced to one-tenth for Japan if the effects ofimports of intermediate inputs and exports are taken into account. Therefore, theeffect of the China shock in France and Japan may be much smaller than in the
United Kingdom and the United States.
For South Korea and Germany, positive effects outweigh negative effects. For
example, for Germany, the employment would have decreased by 318 thousand
workers if there were no imports from and exports to China. A similar finding is
confirmed in South Korea. The China shock thus might have positive effects on
manufacturing employment in these two countries. These results together imply
that the effects of import competition from China vary across countries. Therefore,a careful interpretation is needed for the external validity of the results that are
obtained in one country.
It is important to note that the negative effects of the China shock could be overestimated if the analysis does not take into account exports as well as the imports
of intermediate inputs. Table 6 presents the results of counterfactual employment,
based on equation (1).21 The results indicate negative employment effects in these21
cf
Counterfactual employment
 is computed from the 2SLS results and ∆LτP
f j,τ .
− j Lj,τ 1 − exp −β̂∆IP

15=six countries, which is consistent with the results of final good imports in Table 5.
The results suggest that the negative effects of the China shock could be overemphasized without accounting for the effects of imports of intermediate inputs andthose of exports.
[Table 6 about here.]
It is also important to note that the negative effects of the imports of final goods
from China declined from 2000–2007 period to 2007–2014 period in these six countries. These results suggest that the significantly negative effects of the China shock
were mainly observed in the 2000s right after China’s entry into the WTO. The
negative shock seems to have declined in the 2010s. The recent decline in manufacturing employment may be attributable to other factors such as the substitution
between capital and labor caused by the growing use of robots, although moredetailed analysis is needed to determine the exact factors behind these changes.

4

Discussion

4.1

Alternative specifications

One may concern the consistency between the results of our study and those of theprevious studies. Because none of the previous studies take into account the effects
of exports and the difference between intermediate inputs and final goods simultaneously, we re-estimate our benchmark equation, dropping exports or using total(intermediate inputs + final goods) imports. Table 7 indicates the results without
exports while Table 8 indicates the regression results of equation (4) without distinction between intermediate inputs and final goods, both of which are similar tothe specifications employed by the previous studies.22[Table 7 about here.][Table 8 about here.]
For the United States, if we drop exports from our benchmark equation, we
can find a positive but insignificant coefficient for the imports of intermediate inputs (Table 7). Wang et al. (2018) also employed a similar specification and found22

For the first-stage results, see Tables A5 and A6.

16

the positive effects of imported intermediate inputs from China. Strictly speaking,however, our results are not directly comparable to their results because their positive effects are confirmed through downstream linkages, which we are unable toaddress due to the small sample size.
For Japan, even if we drop exports, we continue to find a positive but insignificant coefficient for the imports of intermediate inputs (Table 7). Taniguchi (2019)
also found that the increases in the imports of intermediate inputs from China had
positive effects on employment. Note, however, that her study is based on theregional variation (i.e., cross-region analysis) while our study is based on the industry variation (i.e., cross-industry analysis). It is therefore not surprising that
our results are slightly different from her results.
For Germany, if we use total imports, we can confirm a significantly positivecoefficient for exports (Table 8), which is consistent with the findings of Dauth etal. (2014) where they found significantly positive effects of trade exposure on employment in Germany. However, when they focus on trade with China, they findsignificantly negative effects of imports while insignificant effects on exports. Note
that, like Taniguchi (2019), however, their study is based on the regional variation.
Their sample period is also different from ours (Table 1). This may be one of thereasons why our results are slightly different from their results.
For South Korea, if we use total imports, we continue to find a positive but
insignificant coefficient for exports (Table 8), which is consistent with the findingof Choi and Xu (2020) where they also found the positive effects of exports. Their
analysis is based on more detailed industry-level data, which may allow them tocapture the variations across industries more precisely.

4.2

Why is the impact so large in the United States?

Our estimation and counterfactual analysis suggest that the United States had thelargest negative impact from the China shock in our six target countries. The coefficient of interest in our benchmark specification is the largest; as well, the numberof counterfactual employment loss outweighs the numbers in the other five countries.
What causes this stronger “China shock” in the United States? A close lookat industries shows that industry 6 (C13–C15 in ISIC) – Manufacture of textiles,

17

wearing apparel and leather products, had a distinct behavior in changes in employment. In 2000, employment in the textile industry in the United States wasover 1.2 million, but the number fell to almost one-half in 2007. During 2000–2007,
this industry experienced the harshest employment decline as well as the largest
increase in imports of final goods from China in the U.S. manufacturing sector for
the whole sample period. In the U.S. textile industry, losses for the entire period
from 2000 to 2014 were 744.5 thousand jobs.
Our benckmark estimation results show how much this single industry affected
employment in the United States. To briefly look at this effect, we estimate ourbenchmark model excluding the textile industry. Without this industry, in the 2SLS
estimation, the effect of a one-percent increase in the import penetration ratio of
final goods from China on manufacturing employment is −1.38. This number isnearly one-third of the coefficient in the estimation including the textile industry.
In addition, the coefficient of the import penetration ratio of intermediate inputs
turns to positive, although it remains insignificant. This exercise suggests that thelarge employment decline in the U.S. manufacturing in our benckmark results islargely attributable to the textile industry’s experience.
The counterfactual employment change using the estimation result without
the textile industry is also quite different from our main specification result for
the United States. According to a new counterfactual exercise using the estimates
without the textile industry, the decrease of employment caused by Chinese tradeis 257.1 thousand workers during 2000–2014, which is almost one-sixth of 1,530.2
thousand, the number in the exercise that includes the textile industry. In particular, in 2007–2014, the counterfactual employment change is 1.44 thousand, whichis small but positive in contrast to the number in our benchmark exercise. If weassume that all of the employment decline in the textile industry, 744.5 thousand,was attributable to the China shock, the sum of this decline and the employment
loss estimated without the textile industry is approximately 1 million (= 257.1 +744.5) during the sample period, which is almost two-thirds of the number in ourbenckmark exercise. Given these large differences, our results suggest that the import exposure in the textile industry would play an important role in the effect of
the import penetration from China and the distinctive number of counterfactual
employment change in the United States.

18

4.3

The small sample problem

We utilized the WIOD in our analysis. On the one hand, because the WIOD covers
the same period based on the same industry classification, the use of it enables us
to investigate the effects of the China shock in an internationally comparable manner. Besides, because the WIOD is based on the world input-output table, it allows
us to distinguish the imports of intermediate inputs and those of final goods in aconsistent way. Indeed, a number of studies utilized the WIOD in analyzing the effects of trade on employment.23 For example, Feenstra and Sasahara (2018) utilized
the WIOD to examine the effects of exports and imports on the U.S. employment.
Caliendo et al. (2019) utilized the WIOD to examine the effects of trade on labormarket dynamics, calibrating the model to 22 sectors. The wide use of the WIOD
in the literature implies the relatively high reliability of the WIOD.
On the other hand, the use of the WIOD prevents us from using the detailed
industry classification, which in turn leads to the small sample size, as was indicated in Table 1. This could cause the following two problems. One is the problem
arises from the statistical aspect. The smaller the sample size, the less the precision of the statistical accuracy would be. Indeed, several studies such as Cravino
and Sotelo (2019) also faced the problem of small sample, although their study did
not discuss this problem explicitly. To address this issue, we use small option in
Stata software to make degrees-of-freedom adjustments and report small-sample
statistics, which would mitigate the problem. Nonetheless, a careful interpretationis needed for the results of our analysis. 24
The other is the problem arises from the aggregation of industries. The WIOD
is available only at the aggregated level. Because of the aggregation, there may be
a large within-industry heterogeneity. For example, within Manufacture of chemicals and chemical products in the WIOD industry classification, there may be ahuge variation of Chinese imports and exports. If one can utilize the data withmore detailed industry classification, such problem could be alleviated. However,23

For more detail, see the WIOD website (http://www.wiod.org/published)
As a robustness check, we perform a regression with the benchmark specification that also
includes non-manufacturing industries, following the previous studies such as Wang et al. (2018)
and Caliendo et al. (2019) (see Table 1). It has sample size of over one hundred. Our main messages
from the benchmark results are unchanged: the coefficients of the imports of final goods from China
are significantly negative in most countries while the imports of intermediate inputs do not show
negative impacts. See Appendix B for the result.
24

19

even when one can utilize firm-level data, international comparative analysis prevents us from the use of detailed industry classification because of, for example,
differences in industry classification across countries. For example, Bellone, Kiyota, Matsuura, Musso, and Nesta (2014) examined the cross-country productivity
gap of exporters using firm-level data in France and Japan. For the comparison between countries, they aggregate the data into 18 manufacturing industries. Dobbelaere, Kiyota, and Mairesse (2015) estimated the productivity and markup of firms
using the firm-level data in France, Japan, and the Netherlands. They aggregate
the data into 30 manufacturing industries. For the international comparative studies, it is generally difficult to rely on the detailed industry-level classification at thecurrent moment.
Note also that the problem of within-industry heterogeneity may not be solved
even if the internationally comparable detailed product-level data (along with employment data) are available. For example, Schott (2004) found that the unit values of U.S. manufacturing imports varied widely even within 10-digit Harmonized System (HS) product code. Similarly, Kiyota (2010) found such heterogeneity within 9-digit HS product code for the Japanese imports. These studies suggest
that, even if we use the internationally comparable detailed product-level data, we
may still face the same problem.

5

Concluding Remarks

While in many advanced countries the increasing import competition from China
on employment is a major concern for policymakers and the general public, its
impact could be different across countries, depending upon the volume and composition of the products. This paper examines the impact of the China shock on
employment in six advanced countries: France, Germany, Japan, South Korea, theUnited Kingdom, and the United States. One of the contributions of this paper
is that we extend the previous studies to cross-country comparisons, based on thesame analytical framework and the same dataset. We used the data from the WIOD
between 2000 and 2014.
Our major findings are twofold. First, the import penetration of final goods
from China has a negative effect on manufacturing employment in most of the six
countries, whereas the import penetration of intermediate inputs from and the ex20

ports to China show positive coefficients while they are statistically insignificant
in most countries. Second, in the counterfactual analysis, we show that such positive effects could offset or even outweigh the negative effects in some countries.
For the United Kingdom and the United States, the negative effects of the imports
of final goods outweigh the positive effects of the imports of intermediate inputs
and exports. In contrast, for France and Japan, the negative effects of the imports
of final goods offset the positive effects of the imports of intermediate inputs andexports. For South Korea and Germany, the positive effects outweigh the negative
effects. These results together suggest that a careful interpretation is needed whenevaluating the external validity of the China shock that is obtained in one country.
It is also important for policymakers to focus on positive as well as negative aspects of trade with China. Furthermore, we should note that consumers generally
receive benefits from the imports of low-priced goods, as standard trade theories
suggest. Of course, the negative aspects of globalization should not be ignored,
but they should not be overemphasized.
It is important to note that these results have an important caveat. Our analysis is based on small sample. This could cause the small sample problem, which
results in the less precise estimates. Noting that the small sample is caused by theaggregation of industries, this could also magnify the problem of within-industry
heterogeneity. Therefore, our estimation results should be interpreted with caution.
In conclusion, several future research issues are worth mentioning. First, further investigation of the China shock is an important extension. Recent studies
have focused on the effects of Chinese import competition on various outcomesother than employment. For example, Autor, Dorn, and Hanson (2019) focusedon the effects on mortality. Che, Xu, and Zhang (2018) focused on the effects oncrime. However, to our knowledge, none of these studies distinguish between
the imports of final goods and those of intermediate inputs. It is important toextend these studies to take into account such differences. Second, although our
instrumental strategy followed Autor et al. (2013), some recent studies such asGoldsmith-Pinkham, Sorkin, and Swift (2019) and Jaeger, Ruist, and Stuhler (2019)
point out potential problems of the use of such shift-share instrument. Exploringalternative instrumental strategy may be an interesting avenue for future research.
Finally, it is also essential to extend the analysis to more detailed industry-level21

data. The use of more detailed industry-level analysis could mitigate the small
sample problem. To conduct such analyses, it is imperative that the quality andcoverage of the industry-level data must be improved and expanded.

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24

Figure 1: Import Penetration Ratio from China (left scale)and Share of Manufacturing Employment (right scale)

Year

2010

Chinese Import Penetration Ratio (%)
2
4
6

2015

2000

USA: Chinese Import Penetration Ratio
USA: Chinese Import Penetration Ratio, Intermediate Inputs
USA: Manufacturing Employment (right scale)

Chinese Import Penetration Ratio (%)
2
4
6
8

2015

2000

2010

2005

Year

2010

2015

GBR: Chinese Import Penetration Ratio
GBR: Chinese Import Penetration Ratio, Intermediate Inputs
GBR: Manufacturing Employment (right scale)2015

Chinese Import Penetration Ratio (%)
5
10
15
20

Panel F: South Korea

0

1000
2000
3000
4000
Manufacturing Employment (thousands)

Chinese Import Penetration Ratio (%)
1
2
3
4
0

Year

1000
2000
3000
4000
Manufacturing Employment (thousands)

2010

Panel E: France

2005

2015

0

5000
7000
9000
Manufacturing Employment (thousands)

Chinese Import Penetration Ratio (%)
2
4
6

Year

DEU: Chinese Import Penetration Ratio
DEU: Chinese Import Penetration Ratio, Intermediate Inputs
DEU: Manufacturing Employment (right scale)

2000

2010

Panel D: United Kingdom

0

2005

Year

JPN: Chinese Import Penetration Ratio
JPN: Chinese Import Penetration Ratio, Intermediate Inputs
JPN: Manufacturing Employment (right scale)

Panel C: Germany

2000

2005

2000

FRA: Chinese Import Penetration Ratio
FRA: Chinese Import Penetration Ratio, Intermediate Inputs
FRA: Manufacturing Employment (right scale)

2005

Year

2010

2000
3000
4000
Manufacturing Employment (thousands)

2005

0

5000
10000
15000
20000
Manufacturing Employment (thousands)

Chinese Import Penetration Ratio (%)
2
4
6
0
2000

7000
10000
13000
Manufacturing Employment (thousands)

Panel B: Japan

Panel A: United States

2015

KOR: Chinese Import Penetration Ratio
KOR: Chinese Import Penetration Ratio, Intermediate Inputs
KOR: Manufacturing Employment (right scale)Notes: The order of the country is based on the size of the GDP. Imports are limited to
manufacturing sector.
Sources: World IO Tables released in November 2016 and Social Economic Accounts data
released in February 2018.

25

26
US
Japan
Japan

JIP Database
JIP Database;
Japan-China InputOutput Table
World InputOutput Database
World InputOutput Database
World InputOutput Database

1995-2007

1996-2009

1991-2011

2000-2014

2000-2014

US

Inter-Country
Input-Output
tables (OECD)UN Comtrade

1991-2009

US, Japan, UK,
Germany, France,
South Korea

USUN Comtrade

1993-2013

2000-2007

South Korea

UN Comtrade

1988-2008

US

Germany

UN Comtrade

1991-2007

1995-2011US

UN Comtrade

1990-2007

PeriodUSUSUN Comtrade

Country

All (35)
Manufacturing (13)All (22)
Manufacturing (12)Manufacturing (19)Manufacturing (52)Manufacturing (50)

Manufacturing (392)All (34)
Manufacturing (16)Manufacturing (392)Manufacturing (180)

Manufacturing (97)

Manufacturing (392)

Industry(# of industries)
Manufacturing (397)

Yes

Yes (expressed ininverse matrix)
Yes

Yes

No

No

Yes (only in downstream channel)

No

No

No

No

Imports: Separationof Final demands andIntermediate inputs
No

Notes: “Yes (to China)” in the last column indicates that only exports to China are included, whereas “Yes (to the world)”
indicates that exports to the world are included.

Our study

Feenstra andSasahara (2019)
Caliendo et al.
(2019)

Feenstra et al.
(2019)
Fabinger et al.
(2017)
Taniguchi(2018)

Autor et al.
(2013)
Acemoglu et al.
(2016)
Dauth et al.
(2014)
Choi and Xu(2020)
Acemoglu et al.
(2015)
Wang et al.
(2018)

SourceTable 1: Summary of the Trade Data of the Related Studies

Yes(to China)

Yes(to the world)
Yes

No

Yes
(to the world)No

Yes(to China)

Yes(to China)
Yes(to China)
No

No

No

Exports

27

Japan
Mean
S.D.
-10.282 17.367
1.962
2.541
1.337
2.356
0.625
0.563
1.36
1.687
FranceMean
S.D.
-16.672 14.968
2.143
3.558
1.37
3.313
0.774
0.880
1.373
2.346

Germany
Mean
S.D.
-4.342 11.482
2.730 4.492
1.874 3.972
0.856 0.796
2.953 3.173
South Korea
Mean
S.D.
5.171 22.672
6.415 5.130
2.012 3.318
4.403 3.668
7.861 8.664

Note: For the definition of variables, see main text.
Sources: World IO Tables released in November 2016 and Social Economic Accounts data released in February 2018.

United States
Mean
S.D.
∆ Employment (∆L)
-16.492 15.149∆ Imports (∆IP )
2.333
2.907
FN
∆ Imports of final goods (∆IP )
1.693
2.908
∆ Imports of intermediate inputs (∆IP IM ) 0.640
0.542∆ Exports (∆EP )
0.646
0.828
United Kingdom
Mean
S.D.
∆ Employment (∆L)
-20.812 16.759∆ Imports (∆IP )
2.938
3.831
FN
∆ Imports of final goods (∆IP )
2.227
3.753
IM
∆ Imports of intermediate inputs (∆IP ) 0.711
0.545∆ Exports (∆EP )
1.27
2.818

Table 2: Descriptive Statistics

Table 3: Estimation Results: Preliminary Analysis

Import penetration(∆IP )N
Period fixed effectFirst-stage partial R2

Import penetration(∆IP )N
Period fixed effectFirst-stage partial R2

United States
OLS
2SLS-2.636∗∗∗ -3.139∗∗∗(0.863)(0.826)
38
38
Yes
Yes
0.9070
United Kingdom
OLS
2SLS
∗∗∗
-2.650-2.553∗∗∗(0.551)(0.573)
38
38
Yes
Yes
0.8826

Japan
OLS
2SLS
-1.788 -2.353∗(1.285) (1.357)
36
36
Yes
Yes
0.7928
FranceOLS
2SLS∗-1.811 -2.483∗∗(0.918) (1.015)
38
38
Yes
Yes
0.8885Germany
OLS
2SLS-0.378 -0.519(0.503) (0.608)
38
38
Yes
Yes
0.8601
South Korea
OLS
2SLS-0.695 -0.063(0.596) (1.049)
36
36
Yes
Yes
0.7387

Notes: This table presents the estimation results of regression equation (1) with
instruments (i.e., equations (2) and (3)) for 2SLS. ***, **, and * indicate the significancelevels at 1%, 5%, and 10%, respectively. Figures in parentheses indicate heteroskedasticity
robust standard errors. Observations are weighted by the 2000 employment level in thedata set. The sample period consists of two sub-periods: 2000–2007 and 2007–2014. The
number of industries thus is N /2.
Sources: World IO Tables released in November 2016 and Social Economic Accounts data
released in February 2018.

28

Table 4: Estimation Results: Benchmark Specification
United States
OLS
2SLS
Import penetration
Final goods(∆IP F N )Intermediate inputs(∆IP IM )Export-output ratio(∆EP )N
Period fixed effectFirst-stage partial R2∆IP F N∆IP IM
∆EP-2.722∗∗∗(0.833)
2.128(6.150)1.530(3.486)38
Yes-3.263∗∗∗(0.765)-3.084(8.905)5.285(6.525)38
Yes

0.73630.21530.1396
United Kingdom
OLS
2SLS

Import penetration
Final goods-2.719∗∗∗(∆IP F N )(0.549)Intermediate inputs
0.420
IM(∆IP )(2.747)
Export-output ratio
0.009(∆EP )(0.364)N
38
Period fixed effectYesFirst-stage partial R2∆IP F N∆IP IM
∆EP-2.680∗∗∗(0.561)
0.294(3.377)0.451(0.678)38
Yes
0.91300.71260.7343

Japan
OLS
2SLS
-2.283∗(1.249)4.977(6.948)0.350(1.906)36
Yes-2.925∗∗(1.095)6.408(7.406)
1.149(2.012)36
Yes

0.75280.58640.6143
FranceOLS
2SLS-1.888∗∗(0.912)-3.513(5.081)1.599(1.373)38
Yes-2.950∗∗(1.249)1.045(4.372)0.783(1.419)38
Yes

Germany
OLS
2SLS-1.579∗∗∗(0.507)7.134∗∗(2.888)-0.107(0.440)38
Yes-2.000∗∗∗(0.613)8.972∗∗(4.187)-0.393(0.575)38
Yes

0.5401
0.54350.5593
South Korea
OLS
2SLS-1.705∗∗(0.747)0.716(1.640)0.002(0.836)36
Yes

0.79930.69700.7754

Notes: This table presents the estimation results of regression equation (4) with
instruments (i.e., equations (7) and (9)) for 2SLS. ***, **, and * indicate the significancelevels at 1%, 5%, and 10%, respectively. Figures in parentheses indicate heteroskedasticity
robust standard errors. Observations are weighted by the 2000 employment level in thedata set. The sample period consists of two sub-periods: 2000–2007 and 2007–2014. The
number of industries thus is N /2.
Sources: World IO Tables released in November 2016 and Social Economic Accounts data
released in February 2018.

29-1.056(0.987)1.457(2.272)0.217(0.989)36
Yes
0.86620.59650.6114

30
2000-07-180.018.6
28.7-132.6

2000-07-529.6
376.7
207.7
54.8

Japan
2007-14-398.3
276.5
67.3
-54.5
France2007-14
-8.6
13.3
16.8
21.6
2000-14-188.531.9
45.6-111.02000-14-927.9
653.2
275.0
0.3

Germany
2000-07 2007-14 2000-14-215.0-75.5-290.5
458.8
276.5
735.3
-61.4
-65.1
-126.4
182.4
135.9
318.3
South Korea
2000-07 2007-14 2000-14-133.6-84.9-218.5
127.6
204.6
332.2
34.6
57.3
91.9
28.6
177.0
205.6

Notes: The counterfactual employment is estimated from equation (10). The unit is thousand workers. ‘Imports’ means importpenetration ratio while ‘exports’ means export–output ratio.
Sources: World IO Tables released in November 2016 and Social Economic Accounts data released in February 2018.

United States
2000-07 2007-14 2000-14
Imports: final goods-1237.6-316.7-1554.4
Imports: intermediate inputs
-67.5
-72.5
-139.9
Exports
88.8
75.2
164.1
Total
-1216.3-314.0-1530.2
United Kingdom
2000-07 2007-14 2000-14
Imports: final goods-284.6-99.9-384.5
Imports: intermediate inputs
4.6
4.8
9.4
Exports
9.9
12.8
22.7
Total
-270.1-82.3-352.4

Table 5: Counterfactual Manufacturing Employment

31
2000-07-221.1

2000-07-627.3Japan
2007-14-468.0France2007-14-47.2
2000-14-268.3

2000-14-1095.3

Germany
2000-07 2007-14 2000-14-130.9-55.9-186.8South Korea
2000-07 2007-14 2000-14
-13.7
-14.9
-28.7

Notes: The counterfactual employment is estimated from equation (10). The unit is thousand workers. ‘Imports’ means importpenetration ratio while ‘exports’ means export–output ratio.
Sources: World IO Tables released in November 2016 and Social Economic Accounts data released in February 2018.

United States
2000-07 2007-14 2000-14
Total imports -1708.0 -671.7 -2379.7
United Kingdom
2000-07 2007-14 2000-14
Total imports -310.6-140.8-451.4

Table 6: Counterfactual Manufacturing Employment: Alternative Specification

Table 7: Estimation Results: Alternative Specification 1
United States
OLS
2SLS
Import penetration
Final goods(∆IP F N )Intermediate inputs(∆IP IM )N
Period fixed effectFirst-stage partial R2∆IP F N∆IP IM

Import penetration
Final goods(∆IP F N )Intermediate inputs(∆IP IM )N
Period fixed effectFirst-stage partial R2∆IP F N∆IP IM-2.683∗∗∗(0.814)3.991(2.734)38
Yes-3.099∗∗∗(0.776)
3.063(3.339)
38
Yes

Japan
OLS
2SLS
-2.283∗(1.244)5.744(4.772)36
Yes-2.807∗∗(1.147)
8.665(5.586)36
Yes

0.90080.7523
United Kingdom
OLS
2SLS

0.77080.6814
FranceOLS
2SLS-2.719∗∗∗(0.541)0.428(2.826)38
Yes-1.911∗(0.992)-0.772(3.072)38
Yes-2.699∗∗∗(0.552)0.295(3.366)38
Yes
0.91920.6889-2.975∗∗(1.307)2.437(2.944)38
Yes
0.80450.7363Germany
OLS
2SLS-1.541∗∗∗(0.462)6.705∗∗∗(1.879)38
Yes-1.971∗∗∗(0.535)
7.958∗∗∗(2.878)38
Yes

0.50530.4896South Korea
OLS
2SLS-1.705∗∗(0.717)0.719(0.845)36
Yes-1.008(1.075)2.007(1.615)36
Yes
0.81840.7353

Notes: This table presents the estimation results of regression equation (4), dropping
exports, with instruments (i.e., equations (7)) for 2SLS. ***, **, and * indicate thesignificance levels at 1%, 5%, and 10%, respectively. Figures in parentheses indicate
heteroskedasticity robust standard errors. Observations are weighted by the 2000
employment level in the data set. The sample period consists of two sub-periods:
2000–2007 and 2007–2014. The number of industries thus is N /2.
Sources: World IO Tables released in November 2016 and Social Economic Accounts data
released in February 2018.

32

Table 8: Estimation Results: Alternative Specification 2

Import penetration(∆IP )
Export-output ratio(∆EP )N
Period fixed effectFirst-stage partial R2∆IP∆EP

Import penetration(∆IP )
Export-output ratio(∆EP )N
Period fixed effectFirst-stage partial R2∆IP∆EP

United States
OLS
2SLS-2.767∗∗∗ -3.267∗∗∗(0.811)(0.703)3.899∗∗
5.379∗(1.472)(2.991)
38
38
Yes
Yes

Japan
OLS
2SLS
-2.039 -2.892∗∗(1.204) (1.058)
1.882
3.063∗(1.453) (1.556)
36
36
Yes
Yes

Germany
OLS
2SLS-0.600 -0.847(0.557) (0.648)
0.846∗∗ 1.042∗∗(0.397) (0.451)
38
38
Yes
Yes

0.87460.4428
United Kingdom
OLS
2SLS
∗∗∗
-2.648-2.530∗∗∗(0.557)(0.580)0.150
0.587(0.427)(0.731)
38
38
Yes
Yes

0.71560.7076
FranceOLS
2SLS∗∗-1.983-2.678∗∗(0.967) (1.081)
1.254
1.471
(0.849) (1.069)
38
38
Yes
Yes

0.79130.5577
South Korea
OLS
2SLS-1.005 -0.702(0.687) (0.937)
0.496
0.677
(0.533) (0.695)
36
36
Yes
Yes

0.87420.75390.84910.79030.75880.7540Notes: This table presents the estimation results of regression equation (4), aggregating
imports of intermediate inputs and final goods into total imports, with instruments (i.e.,equations (3) and (9)) for 2SLS. ***, **, and * indicate the significance levels at 1%, 5%, and10%, respectively. Figures in parentheses indicate heteroskedasticity robust standard
errors. Observations are weighted by the 2000 employment level in the data set. The
sample period consists of two sub-periods: 2000–2007 and 2007–2014. The number ofindustries thus is N /2.
Sources: World IO Tables released in November 2016 and Social Economic Accounts data
released in February 2018.

33

Appendix A
A.1 Calculation using the WIOD
This paper uses data from the WIOD. The WIOD is useful to our analysis for thefollowing reasons. First, the WIOD provides information on the use of imported
goods. In the WIOD, data of imported intermediate input is separated from imported final demands. Second, the WIOD provides information on both sourceand destination industries. The latter is not obtained in standard trade data. The
information of destination industry is used when we focus on manufacturing sector. Third, exports and imports are reported by country. In the national inputoutput tables, it is impossible to distinguish between imports from China and total
imports. These features of the WIOD enable us to calculate the import penetration
ratio from China, separating intermediate inputs and final goods. Meanwhile, ‘imports’ or ‘exports’ used in the calculation is not indicated explicitly in the WIOD,
because there is no notation in the tables; therefore, this appendix aims to indicatecomponents of calculations in the WIOD.
Suppose that there are S industries in N countries.25 For ease of presentation,
we omit time subscript t, unless otherwise noted. Note also that this subsection
utilizes i and j for industry subscripts, following the standard notation in the IO
analysis. Therefore, the subscripts below are not necessarily the same as those usedin the main text.
As usual IO tables, transactions are divided into two broad sectors of ‘intermediate demand sector’ and ‘final demand sector’. In the intermediate demand
sector, an element of xm,nindicates the value of transactions from industry j in
jicountry m to industry i in country n. The superscript m denotes the country ofa source or a supplier, whereas n denotes a country of a destination or a user. A
supplier industry is denoted as j, and a user industry is denoted as i. We regard
imports in the intermediate demand sector as imports of intermediate inputs, andthis is used in equation (5). Similarly, in the final demand sector, an element fjm,nindicates the value of transactions in industry j provided from country m to country n. We regard imports in the final demand sector as imports of final goods which
is used in equation (6). Total output of industry j in country m, Yjm , is produced tosatisfy domestic and foreign final demands, or to be used as intermediate inputs
in domestic and foreign production. Therefore, the sum of each row in a horizontal direction, adding elements in the intermediate demand sector and those in the
25

In the WIOD, S equals to 56 including 23 manufacturing industries, and N equals to 44 including the rest of the world. In this paper, strictly speaking, goods include services. For ease of
explanation, however, this paper uses the word ‘goods’ rather than the word ‘goods and services.’

34

final demand sector, equals to total output:
Yjm =

N X
S
X

xm,n
ji +

n=1 i=1

NX

fjm,n .

(A1)

n=1

For sake of simplicity, we construct three-country IO table, which consists ofChina (CHN), Japan (JPN), and the rest of the world (ROW) , see Figure A1. Total
output in each industry is produced to satisfy domestic and foreign final demands
or to be used as intermediate inputs in domestic and foreign production. Let YjJP N
denote the value of output of industry j in Japan. YjJP N consists of intermediate inputs used in China, Japan, and the ROW as well as final goods provided in China,
Japan, and the ROW. Using the expressions of xm,nfor intermediate inputs and
ji
m,n
JP N
is expressed as the sum of x-s and f -s in a horizontal
fj for final demands, Yj
direction in the following equation:
YjJP N =

S
X

S
X

N,CHN
+
xJP
ji

S
X

N,JP N
xJP
+
ji

i=1

i=1

N,ROW
xJP
+fjJP N,CHN +fjJP N,JP N +fjJP N,ROW .
ji

i=1

(A2)
Excluding domestic transactions from YjJP N , we obtain exports from industry j in
Japan to the world, EjJP N :EjJP N =

S
X

N,CHN
+
xJP
ji

S
X

N,ROW
xJP
+ fjJP N,CHN + fjJP N,ROW .
ji

(A3)

i=1

i=1

Exports in equation (A3) are used in the denominator of ∆IP and ∆EP in theequation (2). Similarly, exports from industry j in Japan to China, EjJP N,CHN , is:
EjJP N,CHN =

S
X

N,CHN
xJP
+ fjJP N,CHN .
ji

(A4)

i=1

Exports in equation (A4) are used in the numerator of ∆EP , expressed in equation(8). Imports from industry i (a supplier industry) in China to industry j (a user
industry) in Japan is expressed as follows:
MjCHN,JP N =S
XNxCHN,JP+ fjCHN,JP N .
ij

(A5)

i=1

Note that industry j includes industries 5 to 23 of the WIOD industry code whenexports or imports of intermediate inputs are limited to manufacturing. In orderto calculate total imports from the world to Japan, add the value of imports from35

the ROW:
MjJP N =

S
X
i=1

N
xCHN,JP
+
ij

S
X

N
xROW,JP+ fjCHN,JP N + fjROW,JP N .
ij

(A6)

i=1

The import penetration ratio and export–output ratio of industry j in Japan
from/to China are respectively calculated as follows:
IPjJP N =MjCHN,JP N
YjJP N − EjJP N + MjJP N

andEPjJP N =EjJP N,CHN.
YjJP N(A7)

Next, we extend it to many-country IO. In the regression analysis, we use thechange of the import penetration ratio and export-output ratio from the initial period, as shown in Section 2.1. The change of the import penetration ratio at theperiod τ of a target country c such as Japan, ∆IPj,τ , is derived as follows. The
numerator of the ratio is a change in imports from the initial period 0 to the period
CHN. We omit the subscript c, unless otherwise noted. Theτ , expressed as ∆Mj,τ
denominator is the initial value of domestic absorption. Therefore, the change ofthe import penetration ratio from China to industry j in the target country, ∆IPj,τ ,is expressed as follows:∆IPj,τ =

CHN
∆Mj,τ
,
Yj,0 − Ej,0 + Mj,0(A8)which corresponds to equation (2). As for an instrument variable, ∆IP Oj,τ expressed in equation (3), we use data of other high-income countries as a target.
Similarly, the change of the export-output ratio is calculated as follows:∆EPj,τ =

CHN
∆Ej,τ
,
Yj,0

(A9)

CHN
where ∆Ej,τ
is the change in exports from 0 to τ . This corresponds to equation(8).As for an instrument variable, ∆EP Oj,τ expressed in equation (9), we calculate
it using data of other high-income countries as a target.
We further derive separate expressions of the import penetration ratio of intermediate inputs in equation (5) and final demands in equation (6). Let xCHN
denoteij
the value of imported intermediate inputs from China to the target country. The
sum of imports of intermediate inputs from China to industry j in the target country is:S
X
xCHN= xCHN
.
(A10)
ij
j
i=1

In IO tables, final demand sector does not provide the information of user indus36

tries. Therefore, we assume that imports from industry j in China satisfy demandsin the same industry in the target country. Total imports from China to industry jin the target country are expressed as follows:+ fjCHN ,MjCHN = xCHN
j

(A11)

where MjCHN is utilized as a numerator of the import penetration ratio as noted
below.
Domestic absorption of industry j, which is a denominator of the import penetration ratio, is Yj −Ej +Mj , where Yj indicates total output of industry j in countryc; Ej is total exports to the world; and Mj is total imports from the world in thesame industry. Total exports from the target country c to the world, Ej , is:
Ej =

N X
S
X

xc,n
ji +

n=1 i=1

N
X

fjc,n

(n 6= c),

(A12)

n=1

where xc,nji denotes intermediate inputs from industry j in country c to industry iin country n. In a similar manner, Mj is expressed as the sum of imported intermediate inputs and imported final goods from all the N trade partners:
Mj =

N X
S
X

xn,c
ij +

n=1 i=1

N
X

fjn,c

(n 6= c).

(A13)

n=1

Using these equations, the import penetration ratio of industry j is calculated
as follows:
MjCHN
IPj =
.
(A14)
Yj − Ej + Mj
When we separate intermediate inputs from final goods, the first term of theright-hand side of equation (A13) is used as a numerator of the import penetration
ratio. The second term, on the other hand, is used in the calculation of the importpenetration ratio of final goods. The change of the import penetration ratio ofIMintermediate inputs, ∆IPj,τ
, is calculated as follows:IM∆IPj,τ
=

∆xCHN
j,τ
,
Yj,0 − Ej,0 + Mj,0(A15)

where superscript IM denotes intermediate inputs. This corresponds to equation(5). Similarly, the change of the import penetration ratio of final goods is calculated
as follows:
CHN
∆fj,τ
FN
∆IPj,τ
=,(A16)
Yj,0 − Ej,0 + Mj,0where superscript F N denotes final goods. This corresponds to equation (6). We
37

IM
FN
derive instrument variables ∆IP Oj,τand ∆IP Oj,τin equation (7) in a similarmanner, using data of other high-income countries as a target.

Figure A1: An Example of A Three-Country Input-Output Table
Intermediate demand sector
CHN
JPN
ROW
1 . . i . . 56
1 . . i . . 56
1 . . i . . 56

CHN

JPN

ROW

1
:
j
:
56
1
:
j
:
56
1
:
j
:
56

Value
added
Total
outputxCHN,CHN
ji

N,CHN
xJP
ji
[export]xROW,CHN
ji

N
xCHN,JPji[import ofintermediate
goods]

N,JP NxJP
ji

N
xROW,JPji[import ofintermediate
goods]

CHN

Final demand sector
JPN
ROW

xCHN,ROW
ji

fjCHN,CHN

N,ROW
xJP
ji
[export]fjJP N,CHN[export]xROW,ROW
ji

fjROW,CHN

viCHN

viJP N

viROW

YiCHN

YiJP N

YiROW

fjCHN,JP N[importof final
goods]
fjJP N,JP N

fjROW,JP N[importof final
goods]

fjCHN,ROW

YjCHN

fjJP N,ROW[export]YjJP N

fjROW,ROW

YjROW

Notes: Blocks with a notation [export] are included in exports from Japan, whereas blocks with
[import] are included in imports to Japan. The final demand sector is divided into five items,
although they are omitted in this table for simplicity.

38

Total
outputA.2 List of countries and industries in the WIOD
Table A1: Countries and Industries in the WIODClassification
Target of this paper
Other OECD countries

Non-OECD countries

WIOD
Code
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

Name

Countries
Countries
France, Germany, Japan, South Korea, the United Kingdom,
the United States
Australia, Austria, Belgium, Canada, Czech Republic, Denmark,Estonia, Finland, Greece, Hungary, Ireland, Italy, Latvia,
Luxembourg, Mexico, Netherlands, Norway, Poland, Portugal,
Slovakia, Slovenia, Spain, Sweden, Switzerland, Taiwan, Turkey
Bulgaria, Brazil, Cyprus, Croatia, India, Indonesia, Lithuania,
Romania, Russia
Industries

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, except furniture;
manufacture of articles of straw and plaiting materials
Manufacture of paper and paper products
Printing and reproduction of recorded media
Manufacture of coke and refined petroleum products
Manufacture of chemicals and chemical products
Manufacture of basic pharmaceutical products and pharmaceutical preparations
Manufacture of rubber and plastic products
Manufacture of other non-metallic mineral products
Manufacture of basic metals
Manufacture of fabricated metal products, except machinery and equipment
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
Repair and installation of machinery and equipment

39A.3 First-stage results
Table A2: List of IV Countries
Country
US

Japan

Germany

UK

Variable
Imports: intermediate inputs
Imports: final goods
Exports
Imports: intermediate inputs
Imports: final goods
Exports
Imports: intermediate inputs
Imports: final goods
Exports
Imports: intermediate inputs
Imports: final goods

France

South
Korea

Exports
Imports: intermediate inputs
Imports: final goods
Exports
Imports: intermediate inputs
Imports: final goods
Exports

IV countries
France; Australia, Portugal
France, Germany, Japan, South Korea, UK;
Australia, Canada, Taiwan
Germany, Japan; Belgium
UK; Australia, Italy, Portugal, Sweden
South Korea, UK, US; Australia, Canada, Spain
US; Belgium, Canada, Taiwan
Japan; Finland, Ireland, Portugal, Sweden
France, Japan, South Korea, UK, US;
Australia, Canada, Taiwan
US; Australia, Finland, Italy, Sweden
South Korea; Australia, Canada, Netherlands,
Sweden, Taiwan
France, Germany, Japan, South Korea, US;
Australia, Canada, Taiwan
US; Italy, Portugal
US; Portugal, Sweden
Germany, Japan, US; Austria, Italy, Portugal
US; Spain, Italy
Germany, UK; Australia, Canada, Taiwan
US; Italy, Portugal, Taiwan
France, Germany, Japan, UK, US; Australia,
Canada, Taiwan

Notes: Countries before a semicolon are chosen from other target countries, while
countries after the semicolon are chosen from other OECD countries.

40

Table A3: First Stage Results: Preliminary Analysis
United States
First-stage coefficient∆IP∆IP
0.596∗∗∗(0.030)F -value
201.64
Partial R2
0.907
United Kingdom
First-stage coefficient∆IP∆IP
0.758∗∗∗(0.088)F -value
38.32
2Partial R
0.883

Japan
∆IP
0.431∗∗∗(0.046)50.94
0.793
France∆IP
1.348∗∗∗(0.120)
72.44
0.889

Germany∆IP
0.814∗∗∗(0.114)25.61
0.860
South Korea∆IP
1.812∗∗∗(0.283)
21.42
0.739

Notes: ***, **, and * indicate the significance levels at 1%, 5%, and 10%, respectively.
Figures in parentheses indicate heteroskedasticity robust standard errors. Observations
are weighted by the 2000 employment level in the data set.
Sources: World IO Tables released in November 2016 and Social Economic Accounts data
released in February 2018.

41

42

United States∆IP F N∆IP IM
∆EP
∗∗∗
∗∗∗
0.612
−0.026−0.017(0.025)(0.009)(0.013)−0.2270.650∗∗∗
0.522(0.210)(0.140)(0.383)0.164∗
0.071∗
0.277∗∗∗(0.091)(0.035)(0.047)156.08
15.72
28.57
0.736
0.215
0.140
United Kingdom∆IP F N∆IP IM
∆EP
∗∗∗
0.835
0.001
−0.051(0.057)(0.010)(0.047)−0.1750.311∗∗∗−0.002(0.114)(0.018)(0.162)0.071
0.099∗∗∗
1.931∗∗∗(0.118)(0.017)(0.278)
56.37
98.18
16.50
0.913
0.713
0.734
∆IP F N
1.315∗∗∗(0.167)−0.118(0.482)0.167(0.197)
22.38
0.799

∆IP F N0.425∗∗∗(0.026)0.455(0.524)−0.067(0.131)
213.51
0.753

Japan∆IP IM−0.013∗(0.007)0.838∗∗∗(0.097)0.034∗(0.019)34.39
0.586
France∆IP IM
0.010(0.047)1.483∗∗∗(0.303)0.034(0.085)
12.70
0.697
∆EP
−0.032(0.032)0.619∗∗(0.265)1.673∗∗∗(0.337)
10.25
0.775

∆EP
−0.019(0.013)0.450(0.434)
0.468∗∗∗(0.052)
33.92
0.614

Germany∆IP IM∆EP∗∗
0.039
0.159∗(0.015)(0.093)0.473∗∗∗−0.368(0.047)(0.312)0.067∗∗∗
1.768∗∗∗(0.024)(0.232)
127.30
44.16
0.544
0.559
South Korea∆IP F N∆IP IM
∆EP
∗∗∗
1.814
−0.1160.084(0.194)(0.093)(0.226)0.080
3.494∗∗∗
3.830∗∗∗(0.301)(0.331)(1.009)−0.350∗∗ −0.470∗∗∗ 1.700∗∗∗(0.149)(0.077)(0.253)
44.70
29.31
51.17
0.866
0.597
0.611
∆IP F N0.674∗∗∗(0.060)1.039∗∗(0.406)−0.383∗(0.205)
34.29
0.540

Notes: ***, **, and * indicate the significance levels at 1%, 5%, and 10%, respectively. Figures in parentheses indicate
heteroskedasticity robust standard errors. Observations are weighted by the 2000 employment level in the data set.
Sources: World IO Tables released in November 2016 and Social Economic Accounts data released in February 2018.

F -value
Shea’s adjusted partial R2∆EP∆IP IMFirst-stage coefficient∆IP F NF -value
Shea’s adjusted partial R2∆EP∆IP IMFirst-stage coefficient∆IP F N

Table A4: First Stage Results: Benchmark Specification

Table A5: First Stage results: Alternative Specification 1
First-stage coefficient∆IP∆EP
F -value
Shea’s adjusted partial R2First-stage coefficient∆IP∆EP
F -value
Shea’s adjusted partial R2

United States∆IP F N ∆IP IM
0.583∗∗∗ −0.008(0.019) (0.010)
0.206∗∗ 0.374∗∗∗(0.079) (0.064)
305.01
15.86
0.875
0.443
United Kingdom∆IP F N ∆IP IM
0.757∗∗∗ −0.045(0.092) (0.042)
0.173
1.931∗∗∗(0.216) (0.274)
33.62
17.32
0.874
0.754

Japan
∆IP
∆IP IM
0.424∗∗∗ −0.012(0.029) (0.016)
0.040
0.507∗∗∗(0.133) (0.048)
206.59
40.23
0.716
0.708
France∆IP F N ∆IP IM
1.328∗∗∗
0.004(0.124) (0.026)
0.209
1.801∗∗∗(0.163) (0.342)
62.16
14.85
0.849
0.790
FN

Germany∆IP F N ∆IP IM
0.821∗∗∗
0.088(0.122) (0.058)−0.066 1.602∗∗∗(0.173) (0.202)
18.41
52.51
0.791
0.558
South Korea∆IP F N ∆IP IM
1.959∗∗∗
0.605(0.223) (0.398)−0.351∗ 2.638∗∗∗(0.193) (0.355)
37.52
18.49
0.759
0.754

Notes: ***, **, and * indicate the significance levels at 1%, 5%, and 10%, respectively.
Figures in parentheses indicate heteroskedasticity robust standard errors. Observations
are weighted by the 2000 employment level in the data set.
Sources: World IO Tables released in November 2016 and Social Economic Accounts data
released in February 2018.

43

Table A6: First Stage Results: Alternative Specification 2First-stage coefficient∆IP F N∆IP IMF -value
Shea’s adjusted partial R2First-stage coefficient∆IP F N∆IP IMF -value
Shea’s adjusted partial R2

United States∆IP F N∆IP IM
0.617∗∗∗ −0.024∗∗∗(0.031)(0.007)−0.0290.735∗∗∗(0.187)(0.108)131.17
18.22
0.901
0.752
United Kingdom∆IP F N∆IP IM
∗∗∗
0.835
0.002
(0.055)(0.010)−0.1740.312∗∗∗(0.119)(0.020)
81.41
99.98
0.919
0.689

Japan
∆IP
∆IP IM
0.419∗∗∗ −0.010(0.043) (0.007)
0.251
0.944∗∗∗(0.540) (0.110)
35.08
26.85
0.771
0.681
France∆IP F N ∆IP IM
1.316∗∗∗
0.011(0.167) (0.045)
0.091
1.526∗∗∗(0.417) (0.263)
27.36
17.11
0.805
0.736
FN

Germany∆IP F N ∆IP IM
0.700∗∗∗ 0.035∗∗(0.067) (0.016)
0.670∗ 0.537∗∗∗(0.389) (0.049)
41.72
101.61
0.505
0.490South Korea∆IP F N ∆IP IM
1.812∗∗∗ −0.119(0.205) (0.152)−0.514 2.696∗∗∗(0.322) (0.351)
33.16
19.84
0.818
0.735

Notes: ***, **, and * indicate the significance levels at 1%, 5%, and 10%, respectively.
Figures in parentheses indicate heteroskedasticity robust standard errors. Observations
are weighted by the 2000 employment level in the data set.
Sources: World IO Tables released in November 2016 and Social Economic Accounts data
released in February 2018.

44

Appendix B
Table B1: Estimation Results: Benchmark Specification with All Industries
United States
OLS
2SLS
Import penetration
Final goods-2.603*** -3.066***
FN(∆IP )(0.803)(0.926)
Intermediate inputs
8.910**
5.859
IM(∆IP )(4.256)(18.654)
Export–output ratio-1.415-1.116(∆EP )(1.202)(4.552)N
110
110
Sector*Period Fixed Effect
Yes
Yes
2
First-stage partial R∆IP F N0.8521∆IP IM
0.2826∆EP
0.1098
United Kingdom
OLS
2SLS
Import penetration
Final goods-2.724*** -2.664***
FN(∆IP )(0.522)(0.589)Intermediate inputs
1.176
6.362
IM
(∆IP )(5.243)(5.840)
Export–output ratio
0.108-2.646(∆EP )(0.621)(2.785)N
110
110
Sector*Period Fixed EffectYes
YesFirst-stage partial R2∆IP F N0.8779∆IP IM
0.4131∆EP
0.0764

Japan
OLS
2SLS

Germany
OLS
2SLS-2.182*(1.276)-3.823(7.716)1.985(1.995)102
Yes-1.188*(0.666)
3.149(3.809)0.574(0.554)110
Yes-2.999**(1.348)5.545(13.089)
1.477(4.178)102
Yes

0.5486
0.51690.3940FranceOLS
2SLS

0.58700.51120.6034South Korea
OLS
2SLS

-2.023*(1.056)
3.922(3.645)0.469(0.978)110
Yes-1.507*(0.884)1.714(1.247)-0.129(0.518)106
Yes-4.181***(1.356)7.390(7.629)1.547(2.322)
110
Yes
0.54150.37940.2508Notes: ***, **, and * indicate the significance levels at 1%, 5%, and 10%, respectively.
Figures in parentheses indicate heteroskedasticity robust standard errors. Observations
are weighted by the 2000 employment level in the data set.
Sources: World IO Tables released in November 2016 and Social Economic Accounts data
released in February 2018.

45-2.170***(0.819)9.774(6.339)0.073(0.885)
110
Yes-1.315(1.308)2.028(1.760)-0.228(0.742)
106
Yes
0.65340.62330.5976

Table B2: List of IV Countries (All Industries)
Country
US

Variable
Imports: intermediate inputs
Imports: final goods

Japan

Exports
Imports: intermediate inputs
Imports: final goods

Germany

Exports
Imports: intermediate inputs
Imports: final goods

UK

Exports
Imports: intermediate inputs
Imports: final goods

FranceExports
Imports: intermediate inputs
Imports: final goods
Exports

South
Korea

Imports: intermediate inputs
Imports: final goods
Exports

IV countries
France, Germany, Japan, South Korea, UK;
Australia, Canada, Taiwan
France, Germany, Japan, South Korea, UK;
Australia, Canada, Taiwan
France, South Korea, UK; Australia, Italy
UK; Italy, Portugal
France, Germany, South Korea, UK, US;
Australia, Canada, Taiwan
Germany, South Korea; Italy, Taiwan
Finland, Italy, Mexico, Portugal
France, Japan, South Korea, UK, US;
Australia, Canada, Taiwan
Japan, South Korea, UK
South Korea; Italy, Taiwan
France, Germany, Japan, South Korea, US;
Australia, Canada, Taiwan
South Korea, US; Italy, Australia
Germany, Japan, South Korea, UK, US;
Australia, Canada, Taiwan
Germany, Japan, South Korea, UK, US;
Australia, Canada, Taiwan
Germany, Japan, South Korea, UK, US;
Australia, Canada, Taiwan
US; Italy, Netherlands, Portugal, Taiwan
France, Germany, Japan, UK, US; Australia,
Canada, Taiwan
France, Germany, Japan, UK, US; Australia,
Canada, Taiwan

Notes: Countries before a semicolon are chosen from other target countries, while
countries after the semicolon are chosen from other OECD countries.

46