The Empirics of the China Trade Shock:

A Summary of Estimation Methods and A Literature Review

Akira Sasahara
May 25, 2022
KEO Discussion Paper No. 169

KEO Discussion Paper No. 169

The Empirics of the China Trade Shock:
A Summary of Estimation Methods and A Literature Review∗
Akira Sasahara†
Keio University
May 25, 2022

Abstract
Given a number of trade liberalization episodes and the advent of newer empirical methods, there
has been a growth in the number of empirical studies on trade. This paper surveys existing studies
focused on the impact of import competition, especially from China, on various aspects of the economy. First, it summarizes estimation methods commonly employed to investigate the effect of importcompetition and clarifies the methodological development. Second, it discusses existing studies onimport competition by country and topic. Lastly, it outlines future research directions.
Key Words: China trade shock, import competition, local labor markets, shift-share instruments
JEL codes: F14, F16, F66∗ This paper is an English and updated version of the Japanese article of the same title and author, published in the
Mita Journal of Economics (Mita Gakkai Zasshi), Vol. 114, No. 4, January 2021. Permission to translate the original
paper and make it publicly available is granted based on Article 7 of the Keio Economic Society’s Publication Policy. The
author would like to thank Masahiro Endoh, Takako Fujiwara-Greve, Kazunobu Hayakawa, Hayato Kato, Kozo Kiyota,
Ryo Makioka, Toshiyuki Matsuura, Shigenori Shiratsuka, Yoichi Sugita, Yuta Suzuki, Mina Taniguchi, Kensuke Teshima,
Eiichi Tomiura, Naoki Yamashita, an anonymous referee, and participants at the 18th Symposium of the Keio Economic
Society for their comments and suggestions. Special thanks are given to Kensuke Teshima, who provided the author withextremely helpful comments and improved the quality of the paper substantially. Financial support from the Japan Societyof Promotion of Science KAKENHI under grant numbers 21H00713 and 21K1329 is greatly acknowledged. All errors are
the author’s responsibility.
† Faculty of Economics, Keio University. E-mail: sasahara@keio.jp1

1Introduction
Since the 2010s, a growing number of empirical studies have been conducted to understand the impact

of international trade on the economy. I believe that there are two reasons behind the increasing focus
on this topic. The first is the rise of the Chinese economy. Its rapid economic growth and accession to
the WTO has led to a drastic increase in Chinese exports. This phenomenon is called the “China shock”
in extant literature.1 To what extent the economy has been affected by this shock, is one of the mostimportant research questions for trade economists today.
The second reason for the increasing focus on this topic, in my view, is innovations in estimation
methods. Empirical investigations on the effect of trade on the economy, especially labor markets, were
not popular in academia prior to the 1990s. This may be due to the difficulty in identifying the causal
relationship from imports to employment, which has drawn interest from economists. Typically, observed
data reflect on the reverse causality from employment to imports. As a result, it is difficult to extractthe effect of imports per se on employment. For example, when the economy is booming, employment
increases, raising the purchasing power of consumers. As a result, imports also increase. Other potential
issues include measurement errors of variables and omitted variable bias. However, since the mid-2000s,
there were two important innovations that changed these perceptions.
The first innovation was the introduction of “local labor markets.” Most studies in the early 2000s
employed industry-level or plant-level data (Tomiura, 2003; Bernard et al., 2006). However, economists
introduced a notion of local labor markets, converting a national-level trade policy variable (such as
tariffs) into a variable that differs across domestic regions by exploiting regional employment (Topalova,
2007). This local labor market level variable provided economists with the ability to consider geographicalaspects of a trade shock.
The second innovation was the usage of shift-share instruments (or Bartik instruments) in the context
of international trade. Autor, Dorn, and Hanson (2013) (hereafter ADH) utilized this approach to examine
the effect of imports from China, caused by China’s supply shock, on U.S. labor markets. Although there
are critics, most economists today believe that this approach is one of the best ways to identify the causal
effect of imports on the economy. These two innovations have prompted a number of economists to carryout empirical studies on the effect of trade.
This paper summarizes empirical studies that are related to at least one of the following keywords: (1)the China shock, (2) local labor markets, and (3) shift-share instruments.2 The first key term, the China
1 Autor (2018) defines the “China shock” as “denoting China’s rapid market integration in the 1990s and its accession
to the World Trade Organization in 2001.” To clarify that it is a shock in international trade, it is also referred to as the“China shock in trade” (Feenstra, Ma, Sasahara, et al., 2018) and the “China trade shock” (Caliendo, Dvorkin, et al., 2019).
Sometimes, it is referred to as the “China syndrome” (Autor, Dorn, and Hanson, 2013; Choi and Xu, 2020). Throughout
this paper, it is referred to as the “China shock.”
2 Autor, Dorn, and Hanson (2016) also summarize existing studies on the China shock and Pavcnik (2017) provides
a survey on the effect of import competition in developing countries. This paper covers more articles and provides acomprehensive survey. Survey articles (or columns) written in Japanese include Tomiura (2012), Tanaka (2016), Matsuura(2018), and Sato (2019).

2

shock, is commonly used to refer to import competition from China. Therefore, this paper discusses
studies on import competition, not necessarily with China but with other countries as well. We also lookat studies investigating the effect of exports to China and offshoring (i.e., trade in intermediate goods).3
Studies on the impact of trade on China’s economy and the effect of the 2018–2019 U.S.–China trade war
are not discussed in this paper.4 The area inside of the dashed line in Figure 1 summarizes the specifictopics covered in this study. Primarily, this paper discusses existing studies on the effect of imports
from China from the 1990s to 2000s through the intensified competition in the goods market and on
import competition in general. It especially focuses on studies using local labor markets and shift-share
instruments.
Figure 1: Topic coverage of this study
Does the study investigate the effect of China’s
integration to the world economy?
Yes

No

Analyzed country
Type of the shock

Not China

China

Type of the shock
Offshoring /intermediate good
trade
Exportopportunities

Offshoring /intermediate good
trade
Importcompetition

Importcompetition

Exportopportunities

The 2018–19
US–China tradewar

Note: The topics inside of the dashed line are covered in this study.

The remainder of this paper is organized as follows: Section 2 discusses the methodological development in estimation techniques. Section 3 summarizes existing studies by country. Section 4 summarizes
studies extending the import competition variable of ADH. Section 5 summarizes existing studies by
topic. Section 6 provides future directions for research.

2

Development of Estimation Methods
This section summarizes empirical approaches estimating the effects of import competition. First, I

explain notations used throughout this section. The Greek letter β is used to denote parameters in all
regression equations to avoid running out of Greek letters. Bold notations indicate that the parameter
3 Feenstra and Hanson (2003) and Kurokawa (2014) provide surveys on the effects of offshoring on wage inequality
4 As an example regarding the effect of trade on China’s economy, see Feng et al. (2017). Regarding the effect of the
2018–2019 U.S.–China trade war, see, for example, Caliendo and Parro (2021).

3

(or the variable) is a vector rather than a scalar. ∆ indicates a change. Whether it is lag or forward
and the number of lagged (or forwarded) years are defined in each equation. The term ln A indicatesa natural log of variable A. As a result, ∆ ln A is a continuous-time compounded rate of change of A.
Subscript i indicates industry, p indicates plant, r indicates region (e.g., commuting zones, metropolitan
areas, counties, prefectures), c indicates country, and t indicates year. X is a vector of control variables
including a constant term. δ and ε with appropriate subscript(s) indicate fixed effects and the error term,
respectively.

2.1

Analysis with Import Prices

In traditional trade models such as the Ricardian model, trade shocks are modelled as a change in
relative prices. Following this idea gives us a regression equation with import prices on the right-handside. For example, using Japanese industry-level data, Tomiura (2003) estimates the following regression
equation:∆ ln Lit = β1 ∆ ln Pit + β2 [M Sit × ln Pit ] + X′itβ 3 + εit ,where Lit is industry i’s employment in year t. Equation ∆Lit = ln Lit − ln Lit−1 is the employment
growth rate. Variable ∆Pit is the rate of change in the import price. In addition, an interaction term
between ∆Pit and M Sit is introduced. As a decline of import prices is expected to reduce employment,
the expected sign of β1 is positive. Furthermore, the negative effect of imports on employment is expected
to be greater in industries with higher import shares. Therefore, the expected sign of β2 is also positive.
Variable Xit includes control variables such as a lagged dependent variable ∆Lit−1 and input costs.
If the Japanese economy is considered a small open economy, ∆Pit can be seen as an exogenous
variable. However, M Sit is an endogenous variable and is expected to correlate with the error term.
Therefore, Tomiura (2003) employs a lagged import share M Sit−1 and input costs as instruments. He
finds that β1 > 0 and β2 > 0 during the 1993–1995 period where the Japanese economy was experiencing
a recession and the yen was appreciating. Other studies employing the same approach include Tomiura(2004) and Sasaki (2007).

2.2

Analysis with Import Penetration at the Industry-level

Import competition can also be measured by import values as a share of domestic absorption, which
we call “import penetration.” Bernard et al. (2006) define import penetration as follows:

IPit = MitL /(Mit + Qit − Xit ),where MitL denotes industry i’s imports from low-income countries in year t; Mit denotes such imports
from all countries; Qit is domestic production, and Xit is exports. As a result, the denominator measures4

the domestic absorption. They estimate the following equation:∆ ln Lpt = β1 IPit + X′iptβ 2 + δt + δp + εpt ,(1)

where ∆ ln Lpt denotes the employment growth rate of plant p from year t to year t+5. Similar approaches
are employed by Ito (2005), Inui et al. (2011), Auer and Fischer (2010), Khandelwal (2010), Kneller et al.
(2012a), Kneller et al. (2012b), Federico (2014), and Acemoglu et al. (2016).
It is difficult to estimate equation (1) owing to the endogeneity of the import penetration variable
IPit . To overcome this issue, Bernard et al. (2006) use variables, such as tariffs, as instruments. To
be a valid instrument, two conditions need to be satisfied: the relevance condition and the exclusion
restriction.5 The relevance condition appears to be satisfied as tariffs influence imports. However, theexclusion restriction, which requires that tariffs do not affect employment directly, does not appear tobe satisfied. Because tariffs are an endogenous policy instrument of a country, policymakers may change
tariffs by reflecting changes in domestic employment levels, leading to an endogeneity issue. However, as
we will discuss in the next section, a drastic tariff cut caused by political reasons may be an exogenous
shock (e.g., India, Indonesia, and Brazil in the 1990s and China’s accession to the WTO).

2.3

Analysis with Local Labor Markets

The previous sections have discussed studies employing industry-level data. However, newer studies
since the mid-2000s employ data from local labor markets, making it possible to examine geographicalaspects of a trade shock. For example, Petia Topalova estimates the following equation

yrt = β0 + β1 τrt + P ostt + δr + εrt ,where yrt denotes the dependent variable (the poverty level and the consumption level in Topalova, 2007,
and Topalova, 2010, respectively) of region r in year t. P ostt indicates the dummy variable taking unity
after trade liberalization (after the year 1991). The variable τrt denotes the weighted-average tariff ofregion r, defined as follows:
τrt =

P

i∈N Lr,i,1991 × T arif fit

Pi∈N Lr,i,1991,(2)

where Lr,i,1991 denotes industry i’s employment in region r in the year 1991. The variable T arif fit
denotes industry i’s import tariffs in year t, and N indicates the set of all industries. Although thetrade shock variable T arif fi,t varies only across industries, it is converted to a variable that varies acrossregions within a country based on regional employment levels.
Topalova sets T arif fit = 0 for non-tradable industry i. As a result, regions that specialize in tradable
5 For example, Felbermayr and Gröschl (2013) show that these two conditions are satisfied in the context of estimating
the effects of trade on income levels.

5

industries may have systematically greater reductions in τrt because T arif fit does not decrease by construction. Therefore, the effect of tariffs on employment may be overestimated. To deal with this issue,
Topalova employs an instrument variable of the weighted-average tariff based on tradable industries only:

T
τrt
=

P

i∈N T Lr,i,1991 × T arif fit

Pi∈N T Lr,i,1991,

where N T indicates the set of tradable industries. Similar approaches are employed by Edmonds et al.
(2010), Gaddis and Pieters (2012), Law (2019), and Anukriti and Kumler (2019).
However, it is controversial to include non-tradable industries in τrt in the first place.6 Kovak (2013)and Dix-Carneiro, Soares, et al. (2018) propose a variable based on tradable industries. They call it theregional tariff change (RTC) and define it as follows:

RT Cr =Pi∈T (λrt /ϕi ) × ∆ ln(1 + T arif fi )Pi∈T (λrt /ϕi ),(3)where T arif fi denotes industry i’s tariff rate, λri denotes the employment share of industry i in region r,
ϕi denotes one minus the wage payment share of industry i, and T denotes the set of tradable industries.
Differing from equation (2), equation (3) reflects wages to measure the size of each industry. In addition,
there is a theoretical foundation as this equation is derived from the specific-factors model. Studies
on the Brazilian economy use equation (3) to show that, in import-competing regions, wages declined(Kovak, 2013), employment levels and incomes declined (Dix-Carneiro and Kovak, 2017), and the crime
rate increased (Dix-Carneiro, Soares, et al., 2018).
Hakobyan and McLaren (2016) also employ data from local labor markets to examine the effect of
U.S. imports from Mexico on the U.S. economy. Their import-competition variable is as follows:

∆τr =

P

× RCAi × ∆T arif fi
i Lr,i,1991
P
i Lr,i,1991 × RCAi,

P M EX
M EX
Xi,1990/ i Xi,1990
with RCAi = ROW P ROW
Xi,1990 / i Xi,1990

where Lr,i,1990 denotes industry i’s employment in region r in the year 1990. The variable T arif fi denotes
the change in industry i’s tariff rate imposed by the U.S. on Mexican exports during the 1990–2000 period.
The main difference from equation (2) is that the term “revealed comparative advantage,” RCAi , is used.
M EX
The variable Xi,1990
denotes Mexican exports from industry i to countries other than the U.S. The variable
ROW
Xi,1990
denotes exports from countries other than Mexico and the U.S. (Rest Of World) from industry

i to countries other than the U.S.7 If RCAi > 1, Mexican exports from industry i are greater thanother countries’ exports from industry i. Therefore, Mexico has a comparative advantage in industry i.
6 Gaddis and Pieters (2017), which is the published version of Gaddis and Pieters (2012), treat τ T as an exogenous shockrt
and employ the fixed effects model, instead of estimating an IV model. Kis-Katos and Sparrow (2011) raise a concern
T as an explanatory variable instead of using τ .
regarding τrt , by citing a critique by Hasan et al. (2007). They employ τrtrt
7 Exports to the U.S. are excluded from the calculation presumably because they attempt to exclude demand shocks in
the U.S.

6

Hakobyan and McLaren (2016) utilize this variable to show that import competition reduced wages of
low-skilled workers in the U.S.
Thus, seminal work by Topalova, Kovak, Dix-Carneiro, Hakobyan, McLaren and others have introduced the concept of “local labor market” in the context of international trade.8 This innovation madeit possible to capture the geographical differences in the degree of trade liberalization. This idea is also
employed by ADH.

2.4

Analysis with Shift-share Instruments

The change in the degree of import competition faced by each firm or industry can be decomposed
into changes caused by macroeconomic factors and changes caused by other factors. As macro factors
are exogenous for individual firms or individual industries, macro components can be utilized as an
instrument. Specifically, we construct a variable equal to the “initial exposure to import competition”
multiplied by the “economy-wide change in imports.” This variable can be seen as a change in the exposure
to import competition caused by an economy-wide shock, plausibly exogenous. It is called a shift-share
instrument or a Bartik instrument, after Timothy J. Bartik (1991).9
For example, to examine the effect of imports from China on firms’ innovations, Bloom et al. (2016)
estimate the following equation:∆ ln T ECHpict = β1 ∆IM PitCH + δct + εpict ,where ∆ ln T ECHpict denotes the rate of change of an innovation variable (such as the number of patents)of plant p in industry i in country c. The variable ∆IM PitCH is a change in IM PitCH = MitCH /MitW orld ,where MitCH denotes country c’s imports from China’s industry i and MitW orld denotes country c’s imports
from all countries’ industry i. To deal with the endogeneity of ∆IM PitCH , Bloom et al. (2016) utilize theCHCH
W orldinitial exposure to imports from China IM Pit−1= Mit−1/Mit−1
multiplied by the increase in the EU’s

overall imports from China ∆MtCH as an instrument in Section 5.2 of their study.
CH
As both the initial exposure IM Pit−1
and the overall increase in imports ∆MtCH are exogenous,CHthe product of the two, IM Pit−1
× ∆MtCH , is also exogenous for individual industries. Bloom et al.

(2016) utilize variations in the shift-share variable as an exogenous shock to examine the effect of importcompetition from China. They show that import competition led to firm innovations and reallocation ofworkers from low-tech to high-tech firms. Other studies employing similar ideas include Majlesi (2016)
and Dell et al. (2019). See Section 3.3.1 for their findings.
8 Ebenstein et al. (2014) construct a region-level import penetration variable using a sectoral import penetration variable
and employment data. To measure region-level export opportunities in Vietnam, McCaig (2011) converts the sectoral
tariff rates imposed by the U.S. to Vietnam to a region-level variable using employment data. To examine the effects oftrade liberalization in Mexico, Hanson (1998) classifies his sample into a pre-liberalization sample (1980-1984) and a postliberalization sample (1985-1993). To examine the effects of trade on wages in China, Han et al. (2012) define liberalizing
and non-liberalizing areas based on distance from the coast.
9 Jaeger et al. (2018) summarize existing studies using shift-share instruments in immigration research.

7

2.5

Analysis with Local Labor Markets and Shift-share Instruments

2.5.1

Import Penetration at the Local Labor Market Level

This section discusses the method employed by ADH, a workhorse approach in the field of international
trade today. I would like to emphasize that even their seminal approach is a combination of previous
approaches, suggesting the importance of considering previous studies in drafting a creative and influential
paper. First, their approach utilizes the concept of “import penetration,” similar to Bernard et al. (2006).
Second, it uses data from “local labor markets” as Topalova and Kovak. Third, they use the data on(lagged) initial employment levels to construct their instrument as in Bartik (1991).
Specifically, using the commuting zone level data, ADH estimates the following equation:C∆Lrt = β1 ∆IP Wrt+ X′rtβ 2 + δt + εrt ,where ∆Lrt denotes the change in the share of the manufacturing employment in the working age population in region r in year t. The degree of import penetration from China is measured as follows:C∆IP Wrt=CU
rit × ∆miti LP

Pi Lritwith ∆mCU
it =,∆MitCU,
Lit(4)

where Lrit denotes the employment level of industry i in region r. The variable ∆MitCU denotes thechange in industry i’s exports from China to the U.S. The import penetration variable can be re-writtenPC
PLrit and× ∆mCUas ∆IP Wrt= i PLritit , which can be decomposed into two components:
L
L
k

rkt

k

rkt

∆mCU
it . The former measures the degree of industrial specialization in region r and the latter measures
the import value per worker in industry i.
2.5.2

The Shift-share Instrument of ADH

To deal with the endogeneity issue, ADH usesO
∆IP Wrt=CO
rit−1 × ∆miti LP

Pi Lrit−1,with ∆mCO
it =∆MitCO
,
Lit−1

(5)

as an instrument. Comparing it with equation (4), notice that the subscript for the employment variable
is now replaced with t − 1 rather than t. The intention of this modification is to deal with potential
endogeneity issues stemming from U.S. workers’ expectation of China’s rise in year t. It is possible that
U.S. workers had expected the arrival of the China shock and that expectation could have reflected on
the initial geographical and industrial distribution of employment levels. Using the employment datafrom year t − 1 alleviates that concern.
The second modification is that ∆MitCH is now replaced with ∆MitCO , a change in U.S. imports from8

other developed countries.10 Following the conventional shift-share approach, one would use a change intotal U.S. imports from China, ∆MtCH , instead of ∆MitCO . However, ADH uses ∆MitCO because they
assume that China’s exports to the other developed countries can be seen as China’s supply shock. U.S.
imports from China, ∆MitCU (even aggregated imports; ∆MtCU ) includes U.S. demand shocks, which willcorrelate with the error term.11 Therefore, it does not satisfy the exclusion restriction.
As we will discuss, there are some critiques. Nevertheless, this approach is widely accepted in academia
and a significant number of studies have used this approach (See Table 1 of Section 2.7). Some studies
also employ a similar idea to construct their instruments. For example, to examine the effect of imports
from China on demand for tasks, Lu and Ng (2013) use China’s exports to the U.K. as an instrument.
Choi and Xu (2020) use China’s exports to Japan as an instrument for China’s exports to Korea. Endoh(2018) uses Japan’s imports from the rest of the world as an instrument for Japan’s imports from China.
2.5.3

Validity of Shift-share Instruments

Studies have conducted rigorous research on the validity of shift-share instruments from an econometrics point of view (Adão, Kolesàr, et al., 2019; Goldsmith-Pinkham et al., 2020; Borusyak et al.,2021).12 In order for the coefficient to be unbiased, two conditions need to be met: (1) controlling for
explanatory variables and fixed effects, the error term does not correlate with the macro shock, ∆mCOit ;and (2) controlling for explanatory variables and fixed effects, the error term does not correlate with
the initial employment share, PLrit
L
k

rkt

). If either of the two conditions are met, the coefficient will beconsistent. Future studies will have to discuss if these two conditions are satisfied or not.
Kim and Vogel (2021) use a trade model with frictional labor markets to examine whether estimatesobtained from a reduced-form regression with a shift-share instrument can be utilized to gauge welfare
effects of a trade shock for each group of workers. They argue that the estimates can be used to find welfare
effects for groups of workers classified by region, education, and gender. Adão, Arkolakis, et al. (2019)
examine the link between estimating a reduced-form regression equation, such as ADH, and a generalequilibrium analysis, such as Caliendo, Dvorkin, et al. (2019). They derive the optimal instrumental
variable and use it to estimate the effects of the China shock. They find that (1) manufacturing andnon-manufacturing employment decreased, (2) a positive welfare effect of price reductions offsets half ofthe employment losses, and (3) considering the regional interactions through the goods market offsets
the negative effect of the China shock almost entirely.
Fischer and Saure (2018) argue that the estimation approach by ADH does not identify the causal
effect of imports correctly. They argue that China’s exports to other developed countries correlate with
10 The eight developing countries in ADH are Australia, Denmark, Finland, Germany, Japan, New Zealand, Spain, andSwitzerland.
11 ADH argues that an increase in U.S. imports from China was mainly caused by supply-factors such as China’s accession
to the WTO and China’s productivity growth.
12 This explanation is based on the description in Section 2.4.2 of Lu, Sugita, et al. (2020). Section 2.3.2 of Caliendo andParro (2021) also discusses a theoretical background of shift-share instruments.

9U.S. demand factors, which makes ADH’s approach less reliable. They propose a plausibly exogenous
measure of import penetration using a monopolistic competition model of trade.

2.6

Analysis with PNTR as an Exogenous ShockThe U.S. Congress granted Permanent Normal Trade Relations (PNTR) status to China in 2001
(Handley and Limão, 2017). Prior to that, Chinese firms exporting to the U.S. faced uncertainty regarding
U.S. tariffs subject to annual revision. Most studies consider this policy change an exogenous shock. If so,
OLS estimates will be unbiased and consistent. As a result, there is no need to employ an IV approach.
2.6.1

Industry- or Firm-level Data and PNTR

Pierce and Schott (2016) use the gap between non-NTR tariffs and NTR tariffs,

N T R Gapi = N on N T R Ratei − N T R Ratei ,(6)

to measure the size of the removal of the tariff uncertainty. A greater N T R Gapi indicates that industry
i experienced a greater reduction in policy uncertainty. They estimate the following regression equation:ln Lit = β1 (P ostP N T Rt × N T R Gapi ) + β2 P ostP N T Rt + X′iβ 3 + X′itβ 4 + δt + δi + εit ,where ln Lit indicates the natural log of industry i’s employment in year t; P ostP N T Rt indicates thedummy variable taking unity after China was given PNTR status in 2001. The parameter for theinteraction term, β1 , measures the effect of the policy shock, which shows that β1 < 0.
2.6.2

Local Labor Markets and PNTR

Although N T R Gapi is an industry-level variable, Pierce and Schott (2018) convert it to a variable
that changes across regions using data on regional employment in each industry. Specifically, the size ofa policy shock faced by region r is measured as follows:

N T R Gapr =P

× N T R Gapi
i Lr,i,1990
P
i Lr,i,1990,(7)

where Lr,i,1990 is industry i’s employment in region r in 1990 and N T R Gapi is as defined in equation (6).
Replacing ∆mCU
it with N T R Gapi in equation (4) leads to equation (7). Using this variable, Pierce andSchott (2020) show that import competition with China raised the mortality rate by furthering drug abuse.
Similar approaches are employed by Che, Lu, et al. (2016), Greenland, Lopresti, and McHenry (2019),
and Besedeš et al. (2021). Kondo (2018) uses equation (7), instead of equation (5), as an instrument forequation (4), a strategy which combines the ADH approach and the Pierce-Schott approach. McManus

10

and Schaur (2016) and Lu, Shao, et al. (2018) use both the ADH and Pierce-Schott approach.

2.7

Other Identification Strategies

2.7.1

China’s Accession to the WTO and the Removal of the MFA Quotas

Bloom et al. (2016), which we discussed in Section 2.4, use the removal of quotas following China’s
accession to the WTO as an instrument. Utar (2014) and Utar (2018) also consider the removal of theMulti-Fiber Arrangement (MFA) quotas and China’s accession to the WTO as an exogenous shock andemploys these events in a difference-in-difference estimation. It is shown that import competition has anegative effect on Danish firms’ employment, value-added, and intangible assets (Utar, 2014), and that anegative effect is concentrated on workers with manufacturing industry-specific education (Utar, 2018).
Sugita et al. (2021) also utilize the removal of the MFA quotas to examine the effect of trade liberalization
on matching patterns between exporters and importers.
2.7.2

Exchange Rate Fluctuations and Other Strategies

Some studies use exchange rate fluctuations as an exogenous shock. Mion and Zhu (2013) use theexchange rate as an instrument for their import competition variable, showing that the China shock
reduced manufacturing employment in Belgium and led to an upgrade in skilled workers. Campbell (2020)
uses exchange rate fluctuations as an exogenous shock to show that an appreciation of the U.S. dollar
reduces U.S. exports and employment. Yokoyama et al. (2021) also utilize exchange rate fluctuations
to examine employment adjustment behavior of Japanese firms. They show that an appreciation of theJapanese yen reduced non-regular employment in industries that rely more on exports. Ebenstein et al.
(2014) use the prevalence of access to the internet and education levels as instruments for offshoring. Theyfind globalization induced labor reallocation from high-wage industries to low-wage industries, resulting
in a decline of the average wage.
Table 1 summarizes the studies we have discussed so far. All of these studies estimate reduced-form
regression models. Topalova (2010) and Caliendo and Parro (2021) acknowledge the need for prudence ininterpreting results from such an analysis. They note that estimation results obtained from a differencein-difference approach or a local labor markets analysis show us whether import competing regions havea significantly greater decline of employment (or other outcome variables) relative to other regions. Thisimplies that, even if the estimated coefficient is insignificant, we cannot conclude that the overall effecton the economy is zero. To understand the overall effect on the economy, an input–output analysis or ananalysis based on a quantitative trade model is more appropriate.

11

Table 1: Identification strategies to estimate the effect of import competition
Identification strategy(1) Lagged dependent variable
and input costs as IVs(2) Tariff cuts as an exogenous
shock

Industry / firm / worker levelTomiura (2003, 2004), Bernard
et al. (2006)(3) Shift-share instruments withaggregate imports(4) Shift-share instruments with
China’s exports to countries
other than analyzed country

Bloom et al. (2016)(5) PNTR gap as an exogenous
shock(6) Both (4) and (5)(7) China’s accession to the
WTO and the removal of the
MFA quotas as an exogenous
shock(8) Exchange fluctuations as an
exogenous shock

Acemoglu et al. (2016), Keller
and Utar (2018), Endoh (2018),
Choi and Xu (2020), Kiyota etal. (2021), Hayakawa et al.
(2021a, 2021b),
Pierce and Schott (2016, 2018)

McManus and Schaur (2016)
Utar (2014, 2018), Bloom et al.
(2016), Sugita et al. (2021)

Local labor market level

Topalova (2007, 2010), Kovak(2013), Dix-Carneiro and
Kovak (2017), Dix-Carneiro etal. (2018), Hakobyan and
McLaren (2016)
Majlesi (2016), Dell et al.
(2019)
Autor et al. (2013), Dauth et al.
(2014), Mendez (2015),
Malgouyres (2017), Taniguchi(2019)
Che et al. (2016), Kondo(2018), Greenland et al. (2019),
Pierce and Schott (2020),
Besedeš et al. (2021)
Lu et al. (2018)

Mion and Zhu (2013),
Ebenstein et al. (2014),
Campbell (2020), Yokoyama etal. (2021)
Note: This table lists major studies only and is not comprehensive.

2.8

Analysis with Input–Output Tables

An input–output analysis provides us with an estimate of a direct effect as well as an indirect effectcaused by the propagation of the direct effect. In addition, industry-by-industry effects can be easily estimated. However, observed data reflects supply-side and demand-side factors of the labor market, making
it difficult to extract the effects of changes in labor demand per se. Furthermore, various assumptions
are made to construct an input–output table, making an estimate less reliable.
2.8.1

Analysis with Domestic Input–Output Tables

Kiyota (2012) estimates the effect of foreign demand on domestic employment using a Japanese
input–output table. It covers n = 108 industries during the 1975–2006 period. The size of the input
coefficient matrix A is n × n. The size of the final demand matrix ∆F is n × 1. Using these, theemployment effect ∆L is estimated as follows:

L = p [I − (I − m)A]

−1

∆F,

(8)

where p is an n × n matrix with the employment divided by value-added in diagonal entries and zeros inother entries. I denotes an identity matrix. m is another n×n matrix with imports divided by domestic

12

P
absorption, mi = Mi /( j qij +di ), in diagonal entries and zeros in other entries. The variable Mi denotes

the import value of industry i. The variable qij indicates the value of intermediate goods produced inindustry i and used in industry j. Variable di denotes the value of final demand faced by industry i.
Feenstra and Hong (2010) estimate the employment effects of China’s exports using the same approach.
Sakurai (2004) and Sakurai (2011) also estimates the employment effect of Japan’s net exports.
Numerous studies have been conducted using the same approach. However, in the early 2000s, the research shifted toward simulation analyses based on more detailed economic models because input–output
analyses suffered from some issues as mentioned. Nevertheless, owing to the introduction of international
input–output tables which connects countries’ domestic input–output tables using data on trade flows,
researchers re-started using an input–output analysis in academic studies on trade.
2.8.2

Analysis with International Input-output Tables

In an international input–output table with k countries, the size of the input–output matrix, A, andthe employment-to-“value-added” share matrix, p, is (k × n) × (k × n). The size of the final demandmatrix, F, and the resulting change in employment, ∆L, is (k × n) × 1. The employment effects of foreign
demand are estimated as follows:
L = p [I − A]

−1

∆F.

(9)

The difference from equation (8) is that equation (9) does not include (I − m). In an international
input–output table, international trade in intermediate goods is already reflected in A. As a result, there
is no need to adjust trade in intermediate goods using (I − m).
For example, Feenstra and Sasahara (2018) investigate the employment effects of trade using aninternational input–output table. They find that imports and exports created approximately 0.7 million
jobs in the U.S. economy during the 1995–2011 period. Other studies conducting an analysis using aninternational input–output table include Los et al. (2015), Kiyota (2016), Feenstra and Sasahara (2019),
and Sasahara (2019). Some studies utilize an international input–output table to estimate value-addedexports, which are included in their regressions. Such studies include Wang et al. (2018), Shen and Silva(2018), and Jakubik and Stolzenburg (2021).

2.9

Quantitative Trade Models

We have discussed that estimating a reduced-form regression does accurately convey the overall effectof a trade shock and that an input–output analysis does not give us the effect of an exogenous labordemand shock. A simulation analysis based on quantitative trade models can address these issues. For
example, Caliendo, Dvorkin, et al. (2019) build a quantitative trade model with internal migration costs,
input–output linkages, and frictional labor markets. They find that imports from China (1) account13

for 16% of the employment decline during the 2000–2007 period, (2) induced labor reallocation frommanufacturing to non-manufacturing, and (3) led to a 0.2% increase in U.S. welfare.
Lyon and Waugh (2019) develop a dynamic Ricardian model with incomplete markets and frictional
labor markets. They find that the China shock increased the labor supply by strengthening workers’
precautionary saving motive and raised GDP by 2% in a five-year span. Brussevich (2018) estimates
sectoral job switching costs in a frictional labor market for women and men separately, utilizing them toestimate the effect of the China shock on welfare levels by gender.
Thus, “frictional labor market” is a keyword in this series of studies. It is essential to include africtional labor market in a model to reflect realistic labor market conditions where changing sectors
and jobs require monetary and non-monetary costs. McLaren (2017) emphasizes this point and discusses
quantitative trade models from that perspective. Caliendo and Parro (2021) also review quantitative trademodels with input–output linkages and local labor markets. These adverse effects of the China shock arepresumably due to the fact that imports from China are less expensive and have greater pro-competitiveeffects.13

3

Impacts of the China Trade ShockThis section summarizes existing studies on the China trade shocks and other import competition bycountry.

3.1

Impacts on the U.S. Economy

3.1.1

Adverse Effects on Employment

ADH shows that imports from China led to a loss of the manufacturing jobs of 1.53 million people during the 1990-2007 period.14 Acemoglu et al. (2016) find that the China shock led to a loss ofmanufacturing jobs of one million people during the 1999–2011 period. They also show that considering
input–output linkages between manufacturing and non-manufacturing industries increases the number oflost jobs to 2 million. Pierce and Schott (2016) utilize PNTR gaps as an exogenous shock to examine theeffect of the China shock. They show that adverse effects on employment are greater in the U.S. than in
EU nations. Furthermore, Autor, Dorn, and Hanson (2021) show that the adverse employment effects of
the China shock are persistent even in 2019. They show that 55% of the decrease in employment since2000 can be explained by the China trade shock.
13 For example, using establishment-level data from the U.S. manufacturing sector between 1997 and 2012, Kamal andLovely (2017) show that imports from middle- and high-income countries are not correlated with employment changes while
imports from low-income countries are associated with employment losses.
14 See footnote 31 on page 2140 of ADH for this figure.

14

3.1.2

Types of Affected Workers

There are studies investigating the effect of the China shock by type of worker. For example, delAngel et al. (2019) consider several categories of workers based on wage, labor type, and education level.
They show that the China shock adversely affected less educated, low-wage manual laborers the most.
Ferriere et al. (2021) show that the negative shock strongly affected non-college graduates and that thecollege enrollment rates increased in import-competing regions.
Autor, Dorn, and Hanson (2015) examine the effect of the China shock at the task level. They showthat the China shock reduced routing task jobs while increasing abstract and manual task jobs. Lu andNg (2013) investigate the effects of import competition during the 1971–2001 period. They find thatimport competition affected cognitive and interpersonal skill intensive industries. Lu and Ng (2013) alsofind similar effects using imports from countries other than China. Based on this, they argue that theirresults are not driven by imports from China only.
3.1.3

Channels and Mechanisms

It is important to understand both the mechanisms through which the China shock reduced employment and the transitions of workers who lost their jobs. There are three studies which attempt to answer
these questions: they find that (1) workers in the import competing industry have a higher probabilityof losing their jobs (Autor, Dorn, Hanson, and Song, 2014; worker-level data); (2) employment losses
are caused by the closure of establishments (Asquith et al., 2019; establishment-level data); and (3) the
number of new businesses declined and business closures increased in import competing industries (Aslan
and Kumar, 2021; household-level data).
Magyari (2017) points out that the China shock triggered “servification,” a transition of manufacturing
plants to non-manufacturing plants. She shows that employment declined in the manufacturing plants
producing goods, but it increased in manufacturing plants doing RD. Furthermore, the job creation effects
offset the job destruction effects, leading to an annual average increase in employment of 2% during the
1997–2012 period.
3.1.4

Considering U.S. Exports and Non-manufacturing Industries

The U.S. manufacturing industry is considered a comparative disadvantage industry (Eriksson et al.,2021). Therefore, it is theoretically natural that import competition has a negative effect on manufacturing employment. Some studies consider the effects of U.S. exports and include non-manufacturingindustries in their analyses. For example, Feenstra, Ma, and Xu (2019a) apply the ADH approach to
examine the employment effects of U.S. exports. They find that overall trade (imports and exports) led
to 0.4 million lost manufacturing jobs based on industry-level analysis, and that the effect of overall tradeis almost zero at the commuting zone level. Liang (2021) conducts a similar analysis and finds that U.S.

15

exports created 1.6 million jobs during the 1991-–2007 period.
Uysal et al. (2015) examine U.S. firm-level data and show that the adverse effect of import competition
on employment increases with firm productivity for non-exporters while the adverse effect decreases withfirm productivity for exporters. The result suggests that exporting creates additional jobs and mitigates
the job destruction effect of import competition.15 A global input–output analysis by Feenstra andSasahara (2018) and an analysis with a quantitative trade model by Caliendo, Dvorkin, et al. (2019) also
consider non-manufacturing sectors.
3.1.5

Reasons for Sizable Impacts

The adverse effects of the China trade shock in the U.S. economy are discussed in a sensational way,
not just in academia but also in policy debates. Economists have investigated why the effects of imports
from China have been so enormous in the U.S.
Product cycles: Eriksson et al. (2021) investigate the long-run effects of trade on the U.S. economy
from the view of product cycles. They argue that the adverse effects of the China shock have been sizable
because, during the 1990–2007 period, the U.S. manufacturing industry had entered a later stage of the
product cycle, had been employing less educated workers and lacked innovation. By contrast, they argue
that the U.S. manufacturing industry was at an early stage of the product cycle when the Japan shock(1975–1985) and the Tiger shock16 (1975–1988) hit the U.S. economy, which resulted in a smaller effecton manufacturing employment.17
Housing markets: Feenstra, Ma, and Xu (2019b) argue that the impacts of the China shock had
become sizable because the U.S. economy was doubly hit by the China shock and the collapse of the
housing market.18 They show that the inclusion of housing market variables in the ADH regression
equation halves the absolute value of the coefficient. In other words, if the housing market did not reactat all, the decline of manufacturing employment would have been one half of the actual decline.
Limited regional labor mobility: A limited labor mobility across regions may be the cause of theconcentration of adverse effects of imports on import-competing regions.19 Greenland, Lopresti, and
15 The effects of exports are estimated to be positive. However, it is not necessarily correct to assume that imports
always have a negative effect and exports always have a positive effect on employment. For example, a boom leads toemployment growth, raising the purchasing power of consumers and increasing imports, leading to a positive correlation
between employment and imports (Krugman, 1994; Irwin, 2020, Chapter 4). Furthermore, Atkin (2016) finds that regionswith export-oriented industries experienced a decline in the high school graduation rate by raising opportunity costs ofgraduating from high school in Mexico. Hummels et al. (2016) show that an increase in export opportunities raised workers’
business hours, associated mental stress, and work-related injuries in Denmark. Thus, there are negative aspects of exports.
16 The Tiger shock refers to an increase in imports from Taiwan, Korea, Singapore, Thailand, and Hong Kong.
17 There are other studies on the effects of trade during the 1970s and 1980s. Batistich and Bond (2019) argue that theJapan shock deteriorated labor market conditions of African American workers, leading to civil rights movements. Nishioka
and Olson (2020) examine the effects of the Japan shock on U.S. politics. As a result of the Japan shock, the Democratic
party implemented a protectionist policy. However, the Reagan administration attempted to increase exports to Japan byutilizing Section 301. Nishioka and Olson (2020) argue that, owing to the different policies, Republican party supporters
decreased in the Midwest where the Japan shock hit severely.
18 Housing prices spiked in California and Florida during the 2000–2007 period. By contrast, housing prices did notincrease much in areas that were exposed to the China shock, such as the Midwest. According to Feenstra, Ma, and Xu(2019b), housing market reactions worked to magnify the effect of the China shock. The effects on the housing market arealso considered in Barrot et al. (2018) and Feler and Senses (2017).
19 Ganong and Shoag (2017) show that a decline in regional labor mobility, caused by a sharp increase in urban housing

16

McHenry (2019) demonstrate that the China shock resulted in a relocation of workers from importcompeting regions to elsewhere. However, such adjustments may not have been enough to absorb theentire effect of the China shock. Caliendo, Dvorkin, et al. (2019) show that, while the welfare effect of
the China shock is positive when there is full regional labor mobility, it is negative when labor mobility islimited. Kondo (2018) shows that an increase in workers with Trade Adjustment Assistance leads to two
additional unemployed workers, suggesting that the negative effects are concentrated within each region.
He also shows that, when regional labor mobility is muted, import competition increases regional income
inequality.
Measurement issues: The expanding global value chains have increased trade in intermediate goods.
Some studies consider that aspect by introducing the value-added contents in trade rather than grosstrade. For example, Shen and Silva (2018) show that value-added imports from China’s upstream industries have smaller effects on U.S. employment. Jakubik and Stolzenburg (2021) show that an analysis
with value-added imports leads to a smaller employment effect compared with ADH.

3.2

Impacts on the Japanese Economy

3.2.1

Earlier Studies

Greater attention has been paid to the effects of trade on employment in Japan due to its proximity
to low-income Asian countries since the early 2000s. Using industry-level data from 1988–1995, Tomiura(2003) finds that the combination of the recession and the appreciation of the yen reduced employment.
Sasaki (2007) updated the data to the 1994–2003 period. He finds that imports had a stronger negative
effect on employment in the labor-intensive industries. Tomiura (2004) shows that a decline in theprices of imports reduced employment by triggering exits of manufacturing plants. Using industry-level
data during the 1975–1994 period, Dekle (1998) finds that the appreciation of the yen reduced thecompetitiveness of Japanese exports, resulting in a decrease in employment.
Using firm-level data from the late 1990s, Ito (2005) shows that imports from low-income countries
reduced the sales and employment growth rate. Using data from the 1981–2000 period, Inui et al.
(2011) find that imports from low-income countries reduced the survival rate of incumbent firms andemployment. However, they also show that imports have a smaller effect on more productive firms andthat imports from non-low-income countries have a positive effect on the survival rate and employment.
Using the 1994–2005 period data, Kneller et al. (2012a) show that there is no systematic relationshipbetween imports from low-income countries and plants’ exits and that imports have a negative effect onplant-level productivity. Kneller et al. (2012b) analyze the effects of imports on firms’ organizational
structures such as the number of owned plants and the exit rate. They find no significant relationshipbetween imports and plants’ exits.
prices, was one of the causes of declining income convergence across regions.

173.2.2

Recent Studies with Aggregated Data

Prefectures and commuting zones: A growing number of studies have analyzed the effects of the
China shock on Japanese labor markets. Taniguchi (2019) applies the ADH approach to the Japanese
prefecture-level data from the 1995–2007 period. She finds that imports from China boosted employment
in Japan by 0.32 million, which is in contrast with the adverse employment effects found in the U.S. The
positive employment effects are explained by the fact that China’s exports to Japan include a sizable
share of intermediate goods.
The cross-sectional unit in Taniguchi (2019) is “prefectures.” However, Kainuma and Saito (2022)
analyze “commuting zone” level data, whereas, commuting zones in Japan are defined by Adachi et al.
(2021).20 They show that imported inputs from China had a positive effect on Japan’s downstream
industries’ employment and that there is no significant effect on upstream industries’ employment.
Industries: Using the employment data from a Japanese input–output table, Hayakawa et al. (2021a)
show that imports from China reduced employment and that an increase in imports caused by regional
free trade agreements increased employment.
3.2.3

Recent Studies with Micro Data

Employment: A growing number of studies use micro data such as firm-level data and worker-level
data. For example, Matsuura (2020) and Matsuura (2021) construct a firm-level import penetration
variable to investigate the effects of imports from China on manufacturing employment. These studies
find that import competition resulted in “servification,” a reallocation of labor from manufacturing plants
to non-manufacturing plants within the manufacturing industry. Bellone et al. (2021) examine data on
multi-product firms. They find that imports from China reduced the number of products produced byeach firm. Hayakawa et al. (2021b) analyze firm-level data and find that imports from China reduced
downstream firms’ employment and increased upstream firms’ employment.
Endoh (2021a) decomposes job flows into three factors — the regional, industrial, and common components –— and analyzes their effects on imports. He finds that imports from China reduced job creation
by 0.3% and increased job destruction by 5.3%. Tomiura and Suzuki (2021) show that imports from
China affected job turnover of older workers and job switching of younger workers. They also show thatimports from China had little effect on workers’ relocation across prefectures.21
Wages: Endoh (2018) analyzes wages paid by large firms. He finds that import competition has
almost no effect on wages paid to workers with high school diplomas and has a positive effect on workers
with college degrees. Endoh (2021c) includes small- and medium-scale firms in his sample and finds the
20 For example, the southern region of Saitama, Tokyo, the northern region of Kanagawa, and the western region of Chiba
can be considered as one commuting zone. A large prefecture like Hokkaido consists of multiple commuting zones.
21 Regarding the limited effects of the China shock on regional labor mobility, the authors argue that labor mobility acrossprefectures was already small. In addition, during the 1992–1997 period, global value chains between Japan and China had
not yet expanded, resulting in a smaller China shock effect.

18

following: (1) imports from Asia had a positive effect on high-wage workers and a negative effect on
low-wage workers; and (2) that these effects are small compared with overall variations in wages. Endoh(2021b) shows that offshoring has essentially no effect on skill premium and gender gaps measured bybase salaries. However, it increases skill premium measured by hourly wages and expands the gender gap
in annual income.
Others: Yamashita and Yamauchi (2020) report that imports from China increased Japanese firms’
innovation activities and patents. However, they also find that imports from China reduced the qualityof innovation measured by the number of citations. Endoh (2022) shows that upstream firms that are
given to access to international markets through firm-to-firm transactions with downstream firms havehigher survival rates. Ito (2021) examines the data on Japan’s national elections during the 2009–2014
period. He finds that candidates in import-competing regions tend to have protectionist policies and thispattern is more strongly observed from challengers rather than incumbents.
3.2.4

Impacts of Exports

Studies have investigated the effects of exports as well. Reduced-form analyses have shown thefollowing: (1) exports increased manufacturing workers’ business hours, but it did not affect the share of
non-regular employment to total employment (Tanaka, 2013); and (2) the product churning effects of the
China shock are mitigated by export opportunities (Bellone et al., 2021). Input–output analyses haveshown the following: (1) imports during the 1980–1990 period reduced manufacturing jobs by 0.53 million,
which account for 4.7% of the initial employment level (Sakurai, 2004); (2) a decrease in net exports during
the 1990–2000 period reduced jobs by 0.57 million (Sakurai, 2011, Chapter 6); (3) the export dependency
is higher in the transport and wholesale industry, and the indirect effects through input–output linkages
have greater impacts on employment than the direct effects of foreign demand (Kiyota, 2012); and (4)industries that export greater value-added content have larger employment effects per export (Sasahara,2019).

3.3

Impacts on Countries Other Than the U.S. and Japan

3.3.1

MexicoStudies have examined the effects of China’s exports to Mexico on the Mexican economy. For example,
Iacovone et al. (2013) examine firm-level and product-level data. They find that imports from China
hastened exits of firms with lower sales but did not affect firms with higher sales as much. In addition,using municipality level data, Majlesi and Narciso (2018) show that import-competing municipalities had
higher out-migration rates.
Given that Mexico is one of the major trading partners of the U.S., some studies have investigated theeffects of China’s exports to the U.S. on the Mexican economy through trade diversion. Dell et al. (2019)19

show that, in Mexican regions with industries that faced import competition with China in the U.S.
market, the unemployment rate rose and drug transactions increased, resulting in an increase in violent
crimes. Utar and Ruiz (2018) show that Mexican industries that faced import competition with China
in the U.S. economy had lower employment and sales growth. Majlesi (2016) finds that fiercer importcompetition in the U.S. increased job opportunities for women compared to men, giving women moredecision-making power, and improving health conditions of children. Mendez (2015) also investigates
the direct effects of China’s exports to Mexico and the indirect effects of China’s exports to the U.S. on
Mexican manufacturing employment. He shows that a decline in Mexican exports to the U.S. caused by
the rise of the Chinese economy reduced Mexican manufacturing employment.
3.3.2

CanadaStudies on the Canadian economy define each metropolitan area as a local labor market. Murray(2017) argues that imports from China account for 20% of the observed decline in manufacturing employment during the 2001–2011 period. Using the data from the 1991–2011 period, Kim (2018) showsthat imports from China had a greater adverse effect on female workers than male workers. Albouy et al.
(2019) show that the adverse effects of the China shock on the Canadian economy were smaller than thoseon the U.S. economy. They argue that the difference comes from Canada employing a larger-scale income
redistribution policy than the U.S. and that Canada accepts a greater share of high-skilled immigrants.
Yang et al. (2021) show that import competition increased firms’ product innovation (the developmentof new products), but reduced process innovation (the improved efficiency of the production process).
3.3.3

Denmark

Using Danish matched firm-employee data, Keller and Utar (2018) show that imports from China
increased the gender wage gap, resulting in increased family time for women. They also show thatthe marriage rate increased, divorce rate decreased, and birth rate increased. Utar (2018) utilizes theremoval of the MFA quotas as an exogenous shock to increase imports from China. She shows that thisshock reduced manufacturing employment and wages. Traiberman (2019) shows that the China shockincreased workers likelihood of pursuing education, presumably because these workers are adjusting toa negative shock by acquiring human capital. He shows that import competition during the 1995–2005
period reduced workers’ income by 5% and that import shocks at the occupation-level account for 60%
of overall import shocks’ variations.
3.3.4

Germany

Dauth et al. (2014) apply the ADH approach on the import- and export-side of the German economy.
They find that exports have a positive employment effect while imports have a negative employment

20

effect. They also show that the negative effect of imports is mainly caused by imports from China andthe positive effect of exports is mainly caused by exports to Eastern Europe. Dauth et al. (2021) use
worker-level data and show that income earned by workers increased in export-oriented industries and did
not significantly change in import-competing industries. Surprisingly, they also show that the negative
effects of import competition are concentrated in workers employed by high-wage plants.
3.3.5

FranceUsing French commuting zone level data during the 1995–2007 period, Malgouyres (2017) showsthat import-competing regions experienced a decline of employment and wages in manufacturing andnon-manufacturing industries. Furthermore, studies have shown the following: (1) imports from China
reduced firms’ markups but the negative effect is smaller for firms with export opportunities (Caselli
and Schiavo, 2020); (2) imports from China improved firms’ productivity and products’ quality (Caselli,
Nesta, et al., 2021); and (3) firms that compete with final goods from China reduced their employment,
while firms that purchase inputs from China did not reduce employment substantially (Aghion et al.,2021).
3.3.6

BrazilBrazil experienced a large tariff cut in the 1990s and studies have examined its effects. For example,
it has been shown that import competition reduced employment and wages, and increased the crime rate
(Kovak, 2013; Dix-Carneiro, 2014; Dix-Carneiro and Kovak, 2017). These studies argue that a limited
labor reallocation across industries is one of the causes of these adverse effects. Costa et al. (2016)
show that import competing regions have smaller wage growth rates and that export-oriented regions
experienced an increase in wage and formal sector jobs. Benguira and Ederington (2021) show thatimport competition reduced the gender wage gap in Brazil.
3.3.7

Other Countries

Studies have examined the effects on other countries as well. They show the following: (1) in Spain,
an increase in non-manufacturing employment almost entirely offset a decrease in manufacturing employment (Donoso et al., 2015); (2) in Belgium, the employment growth rate of low-tech manufacturing firms
decreased and workers upgraded their skill levels (Mion and Zhu, 2013); (3) in Norway, the unemployment
rate among low-skilled workers increased but the China shock accounted for only 10% of observed decline
in manufacturing employment (Balsvik et al., 2015); (4) in Portugal, a decline in employment caused byimport competition stemmed from adjustments in non-regular workers’ employment (Branstetter et al.,2019); (5) in Italy, support for right-wing parties increased (Caselli, Francasso, et al., 2020), and employment in low-skilled labor intensive and less RD focused manufacturing industries decreased (Federico,

21

2014); (6) in the U.K., workers’ mental stress increased in import-competing industries (Colantone, Crino,
et al., 2018b); and (7) in South Korea, imports from China reduced employment but exports to China
offset the negative effect, resulting in an increase in employment of 0.52 million (Choi and Xu, 2020).
3.3.8

Cross-Country Analyses

Kiyota et al. (2021) analyze country-industry level data from the World Input–Output Tables to
examine the effects of imports from China on employment in the U.S., Japan, Germany, the U.K., France,
and South Korea. They show that (1) imports of final goods from China had a negative employment
effect, (2) imports of intermediate goods from China and exports (of final and intermediate goods) to
China had a positive employment effect, and (3) the overall employment effect in a country depends
on the balance between these effects. Bloom et al. (2016) and Feenstra and Sasahara (2019), whichwere discussed in Section 2.4 and Section 3.1, respectively, also investigate the effects on employment in
multiple countries.

4

Different Types of Import Shocks
This section summarizes existing studies using import competition variables that reflect industries’

characteristics.

4.1

Upstream and Downstream

4.1.1

Industry-level Data and Domestic Input–Output Linkages

Acemoglu et al. (2016) define the “downstream→upstream” import shock on industry i as follows:∆IPiDown→U p =P

T otalj µ(i,U ),(j,U ) × ∆mjPj µ(i,U ),(j,U ),where ∆mTj otal denotes the change in imports of industry i’s goods from China. The variable µ(i,U ),(j,U )denotes the value of intermediate goods produced in industry i in the U.S. and used in industry j in
the U.S. The variable ∆mTj otal captures the propagation effect of an import competition shock on theimporting country’s downstream industry j into the same country’s upstream industry i. Replacing theorder of the subscripts of µ(i,U ),(j,U ) leads to the “upstream→downstream” import shock on industry i:∆IPiU p→Down =PT otalj µ(j,U ),(i,U ) × ∆mjPj µ(j,U ),(i,U ),The variable ∆mTj otal denotes the change in imports of upstream industry j’s goods. Therefore, it
captures the propagation effect from upstream industries into downstream industries in the importing
country.
22

Acemoglu et al. (2016) show that the coefficient for ∆IPiDown→U p is negative and statistically significant while the coefficient for ∆IPiU p→Down is insignificant. In other words, a negative shock on
downstream industries adversely influenced upstream industries. However, a negative shock on upstream
industries did not affect downstream industries. This is because import competition in domestic upstream
industries may cause domestic final goods producers to purchase imported inputs, escaping the negative
shock on domestic upstream industries.
Federico (2014) and Hayakawa et al. (2021b) also construct similar variables. However, the labels
are completely different from the ones in Acemoglu et al. (2016). Federico (2014) and Hayakawa et al.
(2021b) add labels based on industries that were initially hit by import competition shock. However,Acemoglu et al. add labels based on industries to which the initial shock propagated. Although it isconfusing to compare these studies owing to the different labelling rules, Federico and Hayakawa et al.
also obtain similar results as Acemoglu et al. using Italian and Japanese data, respectively. A slightdifference is that, although ∆IPiU p→Down is negative in Acemoglu et al., it is positive in Federico and
Hayakawa et al.
4.1.2

Commuting Zone Level Data and International Input–Output Linkages

U.S. producers purchasing imported inputs from China may benefit from the China shock because
they can use less expensive inputs from China. To examine if there is such an effect, Wang et al. (2018)
define their downstream shock as follows:

Down∆IP Wrt=P× ∆IPiDown
i LritPi Lrit,with∆IPiDown =Intj µ(j,C),(i,U ) × ∆mjPPj µ(j,C),(i,U ),where µ(j,C),(i,U ) denotes the value of intermediate goods produced in industry j in China and used inindustry i in the U.S. The variable ∆mInt
denotes the change in the value of imported inputs fromj
China’s industry j. Therefore, ∆IPiDown captures the effect of imported inputs from China on U.S.
downstream industry i. However, the upstream shock is defined as follows:Up∆IP Wrt=× ∆IPiU P
i LritP

P

i Lrit,with∆IPiU p =Intj µ(i,U ),(j,U ) × ∆mjPPj µ(i,U ),(j,U ),where µ(i,U ),(j,U ) denotes the value of intermediate goods produced in industry i in the U.S. and used
in industry j in the U.S. Therefore, ∆IPiU p measures the effect of imported inputs on U.S. upstream
industry i.
There are three differences from the variables in Acemoglu et al. (2016): first, Acemoglu et al. use
total imports from China, ∆mT otal , while Wang et al. use imported inputs from China, ∆mInt ; second,
Acemoglu et al. use µ(j,U ),(i,U ) to capture input–output linkages within U.S. industries, while Wang etal. use µ(j,C),(i,U ) to capture international input–output linkages between China and the U.S.; third,
23

Acemoglu et al.’s variables are at the industry-level, while Wang et al. transform industry-level data into
local labor market variables.
Up
As U.S. downstream industries can benefit from imported inputs from China, the effect of ∆IP Wrt
Down
is expected to have a negativeon U.S. employment is expected to be positive. However, ∆IP Wrt

effect on U.S. employment because U.S. upstream industries compete with China’s intermediate goods
producers. Wang et al. (2018) obtain results that are consistent with these considerations.
4.1.3

Commuting Zone Level Data and Domestic Input–Output Linkages

Using Japanese commuting zone level data, Kainuma and Saito (2022) examine the effects of importcompetition on employment. They also introduce separate upstream and downstream variables. Theirdefinitions are a combination of Acemoglu et al. (2016) and Wang et al. (2018). The approaches of
Kainuma and Saito (2022) and Wang et al. (2018) are similar in that both convert industry-level shocks
to the commuting zone level. Kainuma and Saito (2022)’s approach is similar to Acemoglu et al. (2016)’s
approach in that the import penetration variables are constructed based on the importing country’s
domestic input–output table. Kainuma and Saito (2022) show that an import shock on Japan’s upstream
industries had a positive employment effect on Japan’s downstream industries (i.e., ∆IPiU →D is positive).
Table 2 summarizes the upstream and downstream variables in the three studies we have discussed.
Table 2: Upstream and downstream variables in the three existing studies

Acemoglu et al. (2016)
Wang et al. (2018)
Kainuma and Saito (2022)

4.1.4

Variables’ cross-sectional unit
Industry
Commuting zone✓✓✓

Inputoutput table
Domestic International✓✓✓

Considering Value-added Content in Trade

Shen and Silva (2018) examine the effects of value-added imports, rather than gross imports, from
China. They define the downstream and upstream shock,

Down∆IP Wrt=∆V AXiC × Di
i Lrit ×P

Pi LritandUp∆IP Wrt=P

AXiC × (1 − Di )i Lrit × ∆V
Pi Lrit,respectively. The variable ∆V AXiC denotes value-added imports of industry i’s goods from China. The
variable i denotes the dummy variable taking unity if China’s export industry i’s downstream level isabove the median. They compute the downstream level based on an international input–output table. The
Down
downstream shock ∆IP Wrt
is the effect of value-added imports from China’s downstream industries.

Therefore, it is expected to have a pro-competitive effect on the importing country’s goods market,
Upleading to a greater negative employment effect. Moreover, the upstream shock ∆IP Wrt
is the effect of24

value-added imports from China’s upstream industries. Therefore, it is expected to have a limited effecton the importing country’s goods market competition. Using U.S. data, they obtain expected results.
Table 3 summarizes the upstream and downstream effects we have discussed. It shows that thedefinition of “downstream” and “upstream” effects vary across extant literature and the expected impacts
on labor markets also differ. Readers need to check the definitions carefully to understand each study’s
empirical results.
Table 3: Upstream and downstream variables in existing studies

Acemoglu et al.
(2016)
Kainuma andSaito (2022)

Federico (2014),
Hayakawa et al.
(2021b)Wang et al.
(2018)

Shen and Silva(2018)

Upstream effects
EffectInterpretation
Negative
The propagation effect ofimports in the importing
Ambiguous country’s downstream
industries on upstream
industries in the same
country
Positive
The propagation effect ofimports in the importing
country’s upstream
industries on downstream
industries in the same
country
Negative
The effect of imported
inputs from China on theimporting country’s
upstream industries (procompetitive effects ofimported inputs)
Ambiguous The effect of value-added
imports from China’s
upstream industries on theimporting country’s goods
market competition

Downstream effects
EffectInterpretation
Ambiguous The propagation effect ofimports in the importing
country’s upstream
Positiveindustries on downstream
industries in the same
country
Negative/
The propagation effect of
Ambiguous imports in the importing
country’s downstream
industries on upstream
industries in the same
country
Positive
The effect of imported
inputs from China on theimporting country’s
downstream industries(production complement
effects of imported inputs)
Negative
The effect of value-added
imports from China’s
downstream industries on
the importing country’s
goods market competition

4.2

Vertical and Horizontal shocks

4.2.1

Local Labor Markets and Trade in Intermediate Goods

Taniguchi (2019) examines the effects of final goods imports and intermediate goods imports from
China separately. In doing so, she uses industry i’s final goods share, θiF , and intermediate goods share,θiI , where θiF + θiI = 1, to decompose the ADH import penetration variable into the final goods import
shock and the intermediate goods import shock:∆IP WrF =

P

F
× ∆mC
i θi
i Lri
P
i Lri

and

∆IP WrI =

I
× ∆mC
i θi
i Lri
P

P

i Lri

,

respectively. She finds that ∆IP WrF has a negative employment effect while ∆IP WrI has a positive
employment effect. The negative effect of ∆IP WrF is consistent with the negative downstream effect ofShen and Silva (2018). The positive effect of ∆IP WrI is consistent with the positive downstream effect25

of Wang et al. (2018).
4.2.2

Firm-level Trade in Final and Intermediate Goods

Using French firm-level data, Aghion et al. (2021) consider whether firms compete with imports from
China in the goods market (the horizontal relationship with imports from China) or purchase imported
inputs from China (the vertical relationship with imports from China). Their variable capturing thehorizontal shock is∆IPpH =PC
pi × ∆Mi
i xP

,

j xpi

where xpi denotes firm p’s exports of industry i’s goods and ∆MiC denotes the change in imports ofindustry i’s goods from China. Because firm p exports industry i’s goods abroad, it can be argued thatit competes with the same industry’s goods imported from China. As a result, it is interpreted as ahorizontal shock. However, the horizontal shock is∆IPpV =

P× ∆MiC
im
Ppi
j mpi

,

where mpi denotes firm p’s imports of industry i’s goods. As the firm directly imports the goods from
China, it can be said that the goods are used as intermediate goods, which is a vertical relationship. Using
these variables, Aghion et al. (2021) examine the effects on employment, patents, and other outcomes.
They show that the horizontal shock has significant negative effects while the vertical shock has either
positive effects or statistically insignificant effects. The horizontal and vertical shocks in Aghion et al.
(2021) are similar to the final goods and the intermediate goods shock in Taniguchi, respectively. Table
4 summarizes the shocks we have discussed.
Table 4: Differences in upstream and downstream variables in existing studies
Effects of imports from China’s
downstream industries on theimporting country’s goods market(Employment effects are negative)
Wang et al. (2018)
Shen and Silva (2018)
Taniguchi (2019)
Aghion et al. (2021)

5

Downstream shock
Final goods import shock
Horizontal shock

Effects of imports of inputs from
China on the importing country’s
downstream industries(Employment effects are positive or
ambiguous)
Downstream shock
Intermediate goods import shock
Vertical shock

Impacts by TopicThis section summarizes existing studies by topic. It includes topics that are both intensively studied

and under-investigated.

26

5.1

Labor Market Aspects

5.1.1

Patents, Research & Development, Investment, and Productivity

Existing studies have examined the effect of import competition on firm behavior. Studies on the
U.S. economy have found that, due to import competition, (1) firms’ incentive for innovation and thenumber of patents declined (Autor, Dorn, Hanson, Pisano, et al., 2020), (2) investment declined (Pierce
and Schott, 2016), and (3) firms with higher RD capital stock suffer less from the China shock (Hombertand Matray, 2018). In addition, studies have pointed out that, while manufacturing employment declinedby 25% during the 2000-2012 period, manufacturing output increased, hence labor productivity increased(Pierce and Schott, 2016; Fort et al., 2018).
Studies on countries other than the U.S. have found that import competition: (1) increased innovationand triggered labor reallocation from low-tech firms to high-tech firms in EU nations (Bloom et al., 2016),(2) increased firms’ productivity and improved product quality in France (Caselli, Nesta, et al., 2021), (3)increased the number of patents but reduced the number of citations in Japan (Yamashita and Yamauchi,2020), and (4) increased product innovation but reduced process innovation in Canada (Yang et al., 2021).
5.1.2

GenderImport competition affects certain industries more strongly than others. In addition, gender compositions vary across industries. As a result, it is easy to understand that import competition affects eachgender differently on average. For example, Autor, Dorn, and Hanson (2019) show that import competition reduced the marriage rate of U.S. male workers. They argue this is because imports adversely
affected male-intensive manufacturing sectors, reducing the marriage opportunities for men. Besedeš etal. (2021) show that imports from China reduced the gender wage gap and the gender employment gap
in the U.S. Brussevich (2018) finds that import competition raised the relative welfare level of women
because women have lower job switching costs. Sasahara and Mori (2021) construct a theoretical model
to quantify the role of international trade in explaining narrowing gender gaps in the U.S.
There are also studies focusing on countries other than the U.S. These studies have shown that importcompetition (1) increased the gender wage gap, marriage rate, and birth rate, and reduced the divorce
rate in Denmark (Keller and Utar, 2018), (2) reduced the gender wage gap by increasing the number ofwomen with high-wage occupations in Brazil (Benguira and Ederington, 2021), and (3) improved women’s
rights within their families by increasing job availability for women in Mexico (Majlesi, 2016).
5.1.3

Human Capital

Research has found that a negative effect caused by the China shock increased human capital by raising
benefits from higher education. For example, (1) import competition reduced employment opportunities
for workers who did not graduate high school, resulting in an increase in high school graduation rates in27

the U.S. (Greenland and Lopresti, 2016); (2) the college enrollment rates increased in import-competing
regions in the U.S. (Ferriere et al., 2021); and (3) import competition raised enrollment in education ofworkers who were employed in import-competing industries in Denmark (Utar, 2018). On the contrary,
import competition during the 1990s in India dampened enrollment in education (Edmonds et al., 2010).
A study shows that workers with greater human capital have higher job switching costs, and ignoringthese switching costs results in biased estimates (Traiberman, 2019).
5.1.4

Migration

Studies have found that import-competing regions experienced a reduction in in-migration and anincrease in out-migration in the U.S. (Greenland, Lopresti, and McHenry, 2019) and Mexico (Mendez,
2015; Majlesi and Narciso, 2018). As discussed in Section 3.2.3, Tomiura and Suzuki (2021) examine
the effects of the China shock on migration across prefectures in Japan. Faber et al. (2020) examines
migration responses to two shocks–the China shock and an increase in the prevalence of robots between
1990 and 2015. They find that robots cause a greater migration response than the China shock.

5.2

Outside of the Labor Market

5.2.1

PricesTraditional gains from trade include a reduction in consumer prices and an associated rise in consumer
welfare. Indeed, Amiti et al. (2020) find that imports from China reduced manufacturing prices, raising
the welfare of U.S. consumers. A study with bar-code level data shows that, during the 2004–2015 period,
an increase in imports from China resulted in an annual average decline in the price index of 0.19% (Baiand Stumpner, 2019). In addition, a study with U.S. industry-level data shows that a 1% increase inimports from China reduces the producer price index by 2.4% (Auer and Fischer, 2010).
5.2.2

Fiscal Revenue

A negative effect on local labor markets caused by imports may make it difficult for municipal governments to collect taxes. This could result in a reduction in the provision of public goods. For example,
Feler and Senses (2017) show that firms’ sales and land prices declined in import competition regions in
the U.S., which reduced government expenditure for police and education. Dix-Carneiro, Soares, et al.
(2018) show that import competing regions in Brazil experienced a rise in unemployment rates and adecrease in tax revenue. They show that these changes resulted in a decrease in public expenditure andan increase in high school dropout rates.

28

5.2.3

Politics

A number of existing U.S studies that have investigated the political aspects of the China shock haveshown that import competition: (1) increased votes for the Democratic party, a party which attempts
to employ policies enhancing relief from competition and expanding benefits for unemployed workers(Che, Lu, et al., 2016); (2) caused political polarization (Autor, Dorn, Hanson, and Majlesi, 2020)22 ;(3) reduced the positive attitude toward giving the U.S. president special authority regarding free trade
agreements (Che and Xiao, 2020); and (4) led to local newspapers from import-competing regions toreport negative news about China (Lu, Shao, et al., 2018). Bombardini et al. (2020) examine if US
politicians had expected the adverse effects of imports from China when they voted for pro-China Normal
Trade Relations status between 1990 and 2001.
Studies on countries other than the U.S. show that import competition: (1) increased citizens’ support
for right-wing parties (Colantone and Stanig, 2018a, 15 Western European nations; Caselli, Francasso,
et al., 2020, Italy; Dippel et al., 2022, Germany), (2) decreased votes for left-wing presidential candidates(Ogeda et al., 2021, Brazil), and (3) resulted in a greater share of people voting to leave the EU in
import-competing regions in the U.K. (Colantone and Stanig, 2018b).
The effects on politicians’ views are also analyzed. Feigenbaum and Hall (2015) examine the dataon members of the House of Representatives in the U.S. during the 1990–2010 period. They find thatpoliticians in import-competing regions tend to support protectionist policies. Ito (2021), discussed in
Section 3.2.3, is also included in this category.
5.2.4

HealthA decline in income levels caused by import competition may make it difficult to pay medical bills
and maintain healthy lifestyles. In the U.S., for example, import competition had the following effects:(1) deteriorated physical and mental health, and raised the mortality rate (Adda and Fawaz, 2020); (2)affected mental health more strongly than physical health (Lang et al., 2019); (3) increased opioid abuse(Charles et al., 2018); (4) raised the mortality rate by increasing drug abuse (Pierce and Schott, 2018);
and (5) increased work-related injuries in import-competing industries’ plants (McManus and Schaur,2016).23 A study also shows that workers in import competing industries in the U.K. experienced anincrease in mental stress (Colantone, Crino, et al., 2018b).
22 Autor, Dorn, Hanson, and Majlesi (2020) show that import competition increased the support for conservative Republican candidates in regions with greater shares of Caucasian Americans. By contrast, the same shock increased the support
for Democratic candidates in regions with greater shares of racial minorities.
23 As noted in footnote 15, a study with Danish data shows that export opportunities increased work hours, resulting in
an increase in work-related injuries and mental stress (Hummels et al., 2016).

29

5.2.5Crime

An increase in unemployment caused by import competition may lead to a surge in crime rate. Studies
have shown the following: (1) the crime rate increased in import-competing regions in the U.S. (Che,Xu, et al., 2018); (2) violent crimes increased in Mexican regions with industries that faced tougher
competition with China in the U.S. market (Dell et al., 2019); and (3) the crime rate increased in
Brazilian regions with industries experiencing greater tariff cuts (Dix-Carneiro, Soares, et al., 2018).
Thus, existing studies have analyzed the effects of imports from China on various aspects of theeconomy. Research on the exact mechanisms through which the China shock resulted in these changes
and the effects on new aspects of the economy will be an important contribution to the literature.

6

SummaryThis paper has summarized empirical approaches, existing studies on the China trade shock, andother studies on import competition by country and topic. Numerous studies on the effects of the China
shock on the U.S. economy have been conducted. However, there is still room for further studies on theimpacts of the China shock on non-U.S. economies and explanations of different impacts of the China
shock across countries. For example, existing studies have shown that adverse effects on EU nations andCanada were smaller than those on the U.S. economy (Pierce and Schott, 2016; Albouy et al., 2019).
Additionally, positive employment effects were found for the Japanese and Korean economies (Taniguchi,
2019; Choi and Xu, 2020).
Possible explanations for the cross-country differences may come from the following: (1) labor market
institutions, laws, and norms; (2) the social welfare systems such as unemployment benefits; (3) development stages of the manufacturing industry, whether it is a comparative advantage or disadvantage
industry; (4) the degree of integration in global value chains (whether traded goods are final goods orintermediate goods); and (5) trade balances.
Regarding (3), Eriksson et al. (2021) have shown that the U.S. manufacturing industry since the2000s is a comparatively disadvantaged industry. However, such an analysis has not yet been conducted
to understand the effects of the China shock in Japan. Regarding (4), Taniguchi (2019) investigates theeffect of imported inputs. Kiyota et al. (2021) also attempt to explain cross-country differences in themagnitude of the China shock effect by type of goods (intermediate goods or final goods) and the directionof trade (imports or exports). Regarding (5), Choi and Xu (2020) attribute the cause of the positive
employment effects of trade with China to a positive trade surplus in the Korean economy. However, as
noted in footnote 15, exports may not necessarily have a positive effect on the economy. Therefore, weneed to carefully examine the effects of exports. There is a lack of existing studies on (1) and (2) andthese require further research.

30

In addition, dependent variables other than employment will be an important focus of future studies.
For example, U.S. manufacturing labor productivity has been drastically improved (Pierce and Schott,2016; Fort et al., 2018). By contrast, Japan’s manufacturing productivity has not grown as much as
the U.S. since the 1990s (Sasahara, 2019). The association between the China trade shock and the
manufacturing productivity is an interesting topic to pursue. Furthermore, in studies focusing on Japan,
unique economic situations of the Japanese economy such as aging, proximity to Asian countries, anddeeper integration in global value chains will be important aspects to be considered.
Lastly, I would like to point out that existing studies mainly focus on import competition with China
through the good market. The effects of China’s integration in the global trade system on the Chinese
economy, countries’ offshoring to China, and the 2018–2019 U.S.–China trade frictions are important
topics to pursue. In addition, the difference between the effects of trade and those of technologicalprogress are not yet well studied in the context of the China shock.24 Such focus should be included in
the future research agenda.

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