Adjustments of Multinational’s Production Activities in
Response to the US-Sino Trade War: Evidence fromJapanese affiliate-level data∗
LIANG Licheng†

MATSUURA Toshiyuki‡

May 12, 2022

Abstract
Using factual affiliate-level data of Japan’s multinational firms from 2017 through 2019,
this study investigates the impact of a trade shock (the 2018 US-Sino trade war in thiscase) on multinational firms’ overseas production activities. Focusing on Japaneseaffiliates in the Association of South East Asian Nations (ASEAN) countries, we findevidence of a potential production shift from China to the ASEAN member countries.
According to our empirical results, in response to the trade war, those affiliates in the
ASEAN with vertically integrated Chinese siblings belonging to the same multinational
parent’s value chains may increase their export to North America and see a growth intotal sales. Fast substitution of export and production occurs through the production
network within Japanese multinationals when a part of which is negatively affectedby the trade shock. The study highlights the positive role of setting up a diversified
production network for multinationals.
Keywords: trade shock; multinational enterprise (MNE); affiliates
JEL Codes: F13; F14; F23∗

This study is conducted as a part of the project "Restructuring of multinational production networks
in response to economic and institutional changes" undertaken at the Joint Usage and Research Center
Programs, Institute of Economic Research, Hitotsubashi university. The microdata access in this study is
granted by the National Statistics Center and used in the onsite facilities (statistical research data center)set up by universities and research institutions collaborating with the National Statistics Center. The
authors wish to thank the Joint Usage and Research Center Programs under Hitotsubashi University for theirgreat support. The authors are also grateful for helpful comments and suggestions by Nobuaki Yamashita(Swinburne University of Technology) and Chih-Hai Yang (National Central University).
†
PhD student, Keio University‡
Associate Professor, Keio Economic Observatory, Keio Univeristy

1Introduction

How do multinational enterprises (MNEs) respond to an unexpected negative trade shock?
Would they adjust the production network when a part of it is affected by such a shock? To
explore the impact of trade shock on MNEs’ overseas organization of production activities, a
trade war between the US and China starting in 2018 can serve as a quasi-natural experiment,
given that the representative event of the beginning is relatively exogenous. In 2018 March,
the Trump administration of the US announced tariffs on up to $60 billion of goods imported
from China, and subsequent retaliatory custom duties were imposed by China later, leading
to a war-type trade conflict. By 2021, with persisting escalated tariffs, the trade war has
racked up nearly $100 billion in duties. Against this background of an escalating situation,
the economic impact of the trade war attracts worldwide attention, and related issues areextensively explored both from macro and micro perspectives. A branch of previous studies
has examined the economic impact of the trade war on US income and investment (Amiti
et al., 2019; Amiti et al., 2020), the financial performance of American and Chinese listedcompanies (Huang et al., 2019), and overseas operations by multinational enterprises fromthe view of a third country (Sun et al., 2019). Relative to previous studies, instead ofevaluating the direct impact on firm performance, we mainly utilize a detailed Japanese
overseas affiliates’ data set to investigate potential adjustments of production activities, tobe more specific, a shift of production within multinationals as a response to the US-Sinotrade war.
Although amounting tariffs lead to a decline in trade flows between US and China,
the impact can be heterogeneous across regions and countries. Contrary to our perception,
predicted global trade in the products targeted did not come to a halt but even increased
after the war since many bystander countries substituted Chinese exports to the US market
along with newly created export opportunities (Fajgelbaum et al., 2021). For example, theautomotive industry is targeted by both the US and China, making it heavily affected by
the trade war. Comparing trade values (measured by Free Alongside Ship price in US$) of1

automotive parts imports to the US by various countries between 2015 and 2020 (see Figure
1 below), we can see that imports from China dropped dramatically since 2018. However,meanwhile, the ASEAN imports kept growing and saw a sudden rise, especially from two
member countries– Thailand and Vietnam – witnessed steadily and significantly the growthof export to the US in automotive parts after the trade war started. Seemingly, ASEAN
countries function as the new export platform, and their export to the American market
benefited from the war.
Insert Figure 1 about here.
From the micro perspective, multinational firms dominate international trade. A partof multinational’s value chains located in the "war zone" is unlikely to avoid the hit oftrade shock, and consequently, those multinationals get motive to relocate part of chainsto relatively safe destinations– some other export platform countries(Flaaen et al., 2020).
Regarding the case of Japan, the trade war somehow accelerates such a relocation process.
Prior to the trade war, due to soaring labor costs in China, low-wage neighboring countries
were found to benefit from the competition to inviting inward foreign direct investment (FDI)(Donaubauer and Dreger, 2018). Compared to China, the ASEAN countries like Indonesia
and Vietnam became more attractive for Japanese FDI due to their lower labor costs andlower exposure to tariffs. According to a report by the Japan External Trade Organization(JETRO) in 2020 1 , a bunch of Japanese multinational manufacturers investing in China
tend to move a part of production lines from China to other countries, including Thailand,
Vietnam, other ASEAN states, Mexico (due to geographical proximity to the US), and evenhome country to avoid rising Chinese labor cost or higher trade war tariffs. As summarized by the intention survey, among 293 interviewed MNEs, at least 9.2% MNEs show awillingness to move or have already been working on it. Regarding the scale of production
relocation, 42.3% of those MNEs planned to or have already moved 10% to 30% production
1

Related information and data can be found at https://www.jetro.go.jp/biz/areareports/special/2019/120
1/b9bc9720fbf660d4.html

2

outward China. Moreover, 30.8% of them prefer to move out more, ranging from 30% to
100%. A few of the big names appear on the wish list, such as Mitsubishi Electric, Ricoh,
Sharp, etc., across multiple industries– including automotive, chemicals, electronic equipment, and manufacturing machinery.
Taking the experience of Japanese manufacturing MNEs into account, we use the 2018
US-China trade war as an exogenous shock and hypothesize that this shock would cause
further adjustments to the production network toward the ASEAN by japan’s MNEs, increasing export from affiliates located in the ASEAN to the North American market. At
this point, our study documents direct evidence of production relocation/substitution within
multinationals due to the negative shock induced by the trade conflict.
Our study relies on detailed information about Japanese multinationals’ parents and theirforeign affiliates from the Basic Survey on Overseas Business Activities (BSOBA) compiledby the Ministry of Economy, Trade and Industry (METI). By adopting a simple differencein-differences (DID) setting for a data set on the information of all affiliates in the ASEAN
region operated by Japanese multinationals, we find that those with affiliates established in
China belonging to the same Japanese parent multinational’s production network (from now
on referred to as "Chinese siblings") may see an increase on total sales after the burst of the
trade war. Furthermore, by distinguishing the type of Chinese sibling affiliates, we find that
affiliates in the ASEAN with vertically integrated Chinese siblings in the same multinational
value chains tend to increase their export to North America. More importantly, sampling
multinationals and their affiliates in the ASEAN differ in many ex-ante features, such as
degree of exposure to trade with America, firm size, productivity, etc. These productionrelevant features may bias the estimation of the impact of the trade war on production andexport performance. In order to address such a potential problem of selection bias, we combine our DID setting with the propensity score matching approach for the baseline empiricalestimation. Compared with results obtained from unmatched DID analysis, DID on matchedsamples shows a more significant and economically sizable positive effect of the trade war

3

on exports to North America and total sales for focused affiliates in the ASEAN. To sum it
up, we find that ASEAN-located Japanese affiliates with Chinese siblings highly integrated
into the multinational’s production network would function as a new export platform for the
North American market, implying that the trade war leads to further relocation of overseas
production within the Japanese multinationals.
This paper is organized as the followings. Section 2 presents the literature review, while
the empirical strategy is explained in section 3. Then, after briefly explaining our data in
section4, we show estimation results in section 5. Section 6 concludes this paper.

2

Literature Review

Our paper is mainly related to three strands of literature. First, this paper contributes tothe nascent literature on the trade war in general. It is grounded on the recent work by Sun
et al. (2019) suggesting that Japanese multinationals’ operations in China are negatively
affected by the trade war, and the higher degree of reliance on trade with North America,
the more severe the impact could be. Moreover, as shown by their finding, the affiliates
located in China that are highly dependent on trade with the US are more likely to see a
decline in total sales, driven by a drop in exports to third countries. Accordingly, Huang etal. (2019) examines the market response of US firms to the initial round of tariff hike andreveals that dependence on trade with China can explain a firm’s worsening financial performance; moreover, their finding addresses that production linkages intervene in the effect ofthe 2018 trade war leads to heterogeneous responses by US firms. Benguira (2019) examines
the impact of the trade war on revenue earned by many companies worldwide and points outthat firms benefit from export exposure to the US but are hurt by export exposure to China.
The mechanism behind this suggests that firms with higher exposure to the US can takethe chance of substituting Chinese goods due to the trade war. Ito (2022) finds that trade
war tariffs improved Japan’s trade terms and led to an increase in exports from Japanese

4

industries positioned upstream within the value chains to Chinese downstream industries.
Second, there is another growing body of literature on multinationals’ reorganization of
production under supply-chain shocks. Becker and Muendler(2010) documents that multinational firms would reallocate employment across existing affiliates in response to wage
differentials among locations. Flaaen et al. (2020) provides indirect and partial evidence on
multinational firms’ production relocation behavior. They find that relocation of production
by multinationals intervenes in the pass-through of tariffs to consumer prices. Because of
potential production relocation, the price effect of tariffs becomes non-monotone. Regarding
the related experience of Japan, Hayakawa et al. (2015) documents how Japanese affiliates
change their procurement patterns after a natural disaster. They focus on the impact of theThai Flood in 2011 on Japanese affiliates in ASEAN countries and find that supply-chain
disruptions caused by natural disasters change Japanese affiliates’ import and local procurement.
Moreover, this study is also related to the issue of shock propagation mechanics throughthe production chains. Todo et al. (2015) examines Japan’s firm-level data, and their finding
documents the advantages of a diversified supply chain network. In terms of the finding, eventhough a widely expanded supply network may delay the recovery from a natural disastershock, firms can easily replace damaged firms with surviving ones in the same network toreceive support. Thus, diversified or non-concentrated supply chain networks may improve
resilience of firms to exogenous shocks. Similarly, Mendes (2021) also shows that firms that
adjusted and diversified their supplier network in the years following a natural disaster may
gain immunity to other exogenous shocks in the future (i.e., the COVID pandemic as thecase).
By testing factual affiliate-level data, this study will contribute to the literature on trade
friction between the US and China from a third-country perspective and provide new andmore direct empirical evidence on production adjustment behavior by firms when adverse
shocks hit them. The next section will discuss the empirical strategy for our analysis.

5

3

Empirical strategy

3.1

DID estimation with propensity score matching

Adopting a DID setting with matching in this context, we first defined the treatment andthe control groups. For simplicity, we classify ASEAN-located affiliates with Chinese siblings
as the treatment group and those without any as the control group. As mentioned before,
post-war outcomes (e.g., exporting performance) of the ASEAN-located affiliates may be
influenced by their pre-war characteristics, so there is a potential concern that affiliates in
the control group may be inherently under-performed on exporting even there was no tradewar. In other words, the affected affiliates are unlikely from a random sample. In order
to avoid such selection bias and find suitable pairs of affiliates in comparison, we conduct
propensity score matching before the DID analysis. In this way, we attempt to find affiliates
in the control group having similar pre-war characteristics as those in the treatment groups.
Accordingly, to compute the propensity score of affiliates falling into the treatment group,
we estimate a logistic model to investigate determinants for the types of MNEs based on
pre-war characteristics of parent firms at the first stage. Considering the impact of the trade
war on overseas activities, a problem of potential simultaneity selection of FDI location may
arise, so we only use the pre-war characteristics of parent firms to estimate the choice. We
consider the model:

P (Da,s,P re−war = 1) = Λ(Xh,P re−war + δs + vc )where indicator D implies whether the parent firm h set up affiliates within industry s in both
China and the ASEAN countries before the trade war, taking one if the parent firm invests inboth locations, 0 for the opposite. X denotes characteristics of parent firms (e.g., firm size,
overseas experience, profit earning from affiliates, royalties, dividend share to total sales). We
also consider the industry-specific effect, δs , and host country effect, vc . Using the obtained6

propensity score for every affiliate, we conduct the matching between observations in control
and treatment groups. Caliper matching is the baseline method for matching procedure 2 ,and we also employ alternative methods, including local linear regression, Nearest 1 to 1
without replacement, and Nearest 2 to 1 matching.
Based on the matched pairs of observations, we run the DID specification above at
the second stage to estimate the impact of the trade war on outcomes regarding affiliates’
production activities. Overall, the estimator of DID with propensity score matching approachis given by:
P SM − DID =X
1 X[∆y1,i,t0 −w(psi , psj )∆y0,j,t0 ]ni i∈I1j∈I0

In the equation, I1 is the set for the treatment group (I1 = {i : D = 1}), and I0 is the set forthe control group (I0 = {i : D = 0}). Given t the pre-war period and t0 the post-war period,
we have ∆y1,i,t0 = yi,t0 − yi,t and ∆y0,j,t0 = yj,t0 − yj,t , indicating that the difference in outcome
y before and after the occurrence of the trade war for the treated affiliates and controlled
ones, respectively. w(.) denotes the weight for the matching between samples i and j; psiand psj are respective propensity scores for the treatment group and control group.

3.2

Further decomposition of treatment groups

To make it a more precise identification of the heterogeneous impact, for the DID estimation,
we further split the treatment group into two subgroups: treatment group (1) consists of allaffiliates located in the ASEAN with purely vertical FDI-typed Chinese siblings; treatmentgroup (2) is the group of those with horizontal and other FDI-typed Chinese siblings. We
have two significant reasons to use this classification: first, as mentioned already, affiliates in
ASEAN with Chinese siblings may react actively to the shock, especially those affiliated to
an MNE prone to move production outward China, as they can relocate their productions
across borders within the production network; second, classifying the FDI types of sibling
affiliates in China may enable us to more precisely identify the heterogeneous impact of2

The caliper is set as 0.05 for our matching process7

trade shocks on MNEs’ production networks. We expect affiliates in ASEAN may operate
independently with HFDI type Chinese siblings, while those with other vertically integrated
affiliates in different locations are more sensitive or vulnerable to the shock. To split sibling
affiliates in China into two categories in terms of their FDI types, here we refer to themeasure proposed by Alfaro and Charlton (2009) and firstly distinguish the FDI forms of
those affiliates by their industry affiliation. Due to the data limitation, we adopt a simplified
version of this measure. We classify a Chinese affiliate as Vertical-FDI (VFDI) affiliate if
her owner parent operates in a different industry and as a Horizontal-FDI (HFDI) affiliate
if the owner parent operates in the same industry. Since VFDI affiliates are inter-industry
investments mainly intended to produce for foreign sales and mean to generate export, ourmain concern in the analysis is the response of treatment group (1)– ASEAN affiliate with
VFDI Chinese siblings– to the trade war. Based on the classification by industry affiliation,
there are more than 1000 Chinese affiliates categorized as vertical FDI, taking into accountfor 25% of the total3 .
The DID setting for the control and treatment groups is shown as the following:
ya,c,t =β1 · ChinaSiba · P ostt · V F DIchnsib + β2 · ChinaSiba · P ostt · OtherF DIchnsib+ ua + γs,t + vc + a,s,c,twhere ya,c,t denotes outcomes (e.g., sales to North America, import from North America,
total sales, investment, and the number of employees) for the affiliate a in an ASEAN
country c at year t. ChinaSiba equals one if the affiliate in ASEAN has a sibling affiliate
in China at t and 0 otherwise; P ostt is a dummy variable indicating the period after theoccurrence of a trade war (t=2018 and aftermath in this case); V F DIchnsib represents anindicator variable corresponding to 1 if Chinese siblings of target affiliate are classified asvertical FDI, 0 otherwise; OtherF DIchnsib takes 1 when Chinese siblings are in the categoryof horizontal FDI or others (note that there could be the case that both VFDI and HFDI
3

The distribution of VFDI affiliates in China is shown by Table A1 in Appendix A

8Chinese siblings are in operation). ua is included to control affiliate-level fixed effects; γs,trepresents the industry-year fixed effect controlled to filter out industry-specific trend that
may intervenes the outcomes; vc is set for country fixed effects and a,s,c,t denotes the error
term. After the matching procedures, we test this DID specification based on the matchedsamples. In the next section, we discuss data issues and a summary of statistics.

4

Data

4.1

Data source

Affiliate-level data of Japanese multinational firms are from Japan’s governmental surveys.
Our data source is the Basic Survey on Overseas Business Activities (BSOBA), compiledby the ministry of economy, trade and industry (METI). The raw data set on this survey
contains more than 25,000 observations each year for Japanese affiliates worldwide in allindustries except finance and insurance. We construct our primary data set by using theinformation provided by this survey. Company profiles in this data source enable us to identify company name, location, industrial classification, and affiliation for all existing overseas
subsidiaries operated under Japanese MNEs during the sample period. Besides, one meritof using this data source is that it contains detailed information on affiliate-level sales andprocurement, which are decomposed into shipment destinations and procurement origins(e.g., local market, North America, Asia, Europe, and rest of the world). The decomposed
export is critical for our analysis since the change of exports to North America, especially
the US, is our primary concern. Note that exports to the US would be a more appropriate
candidate variable for the analysis, but the data on affiliate-level sales does not include any
country-level categories, so we use the exports to North America instead to proxy it 4 . It
is a reasonable choice because exports to the US have accounted for most of the ASEAN’s
4

In BSOBA, the category of North America includes only the US and Canada.

9

exports to North America during the sample period 5 . Additionally, we can obtain various
affiliate-level characteristics from the source, such as the number of regular employees, paidin-capital, etc. Our data are panel data covering the three years from 2017 to 2019 6 .
Information on parent firms’ characteristics comes from the same data source, BSOBA,
but a different subsection of the surveys titled the survey form for parent companies. From
the data on the survey designed for parent companies, we can refer to the profile of parent
firms (i.e., company name, identification code, industrial classification, paid-in capital) andtake variables including total sales, regular employees, total export, export to affiliates, royalties and profit from affiliates. Corresponding to the affiliate-level data set, we also have athree-year data set for all the parent companies, spanning from 2017 to 2019.
Our final data set covers all Japanese manufacturing affiliates located in the ASEAN
countries (i.e., Singapore, Malaysia, Thailand, Indonesia, the Philippines, Cambodia, Brunei,
Lao PDR, Vietnam, Myanmar) between 2017 and 2019, their sales, imports, exports, investment, and employment. Because both surveys for affiliates and their parent firms covercompany profiles, the corresponding data contains parent firm ID, subsidiary ID, and secondary subsidiary ID. Referring to these three IDs, we can combine an affiliate with herparent company.

4.2

Description of statistics

Statistics of control and two treatment groups for the DID analysis are summarized in Table
1 between 2017 and 2019. Due to the fact that there are many HFDI affiliates in China,
treatment group 2 (titled "Other Chinese siblings" on the table) has the greatest number ofaffiliates and observations. There are 2,146 Chinese siblings classified as HFDI or other types
in our dataset. In contrast, the number of VFDI Chinese siblings is the lowest, and only 563
5

According to UN Comtrade Database, in 2017, before the trade war, ASEAN exports to the US weremore than $143 billion, while the exports to Canada were about $ 7.8 billion. The US export share in totalexport to the US and Canada is around 95%.
6
In this study, data analysis proceeded at research data center set up by universities and research
institutions in collaboration with the National Statistics Center, where the annual affiliate-level data before
2017 is not available. So we limit our sample period to be from 2017 to 2019.

10

affiliates fall into this category. Regarding the share of export to North America (hereinafterreferred to as "NA") in total sales, computed by the ratio between export to NA and total
sales, the average values are almost identical for all the three groups, which are around 1%.
However, as for the log of export value to NA, ASEAN-located affiliates with HFDI Chinesesiblings contribute the highest on average. Contrasting to the control group, both treatmentgroups have a higher average value on export to NA during the observation period, 0.509 and
0.651, respectively. Total sales and number of employees are also more remarkable for theaffiliates with Chinese siblings than those without any. We can also find that affiliates with
HFDI Chinese siblings take the highest value of the investment, and the lowest is for thosewith no Chinese siblings. Notably, affiliates with VFDI Chinese siblings see the highest
imports from NA, and they also have the highest share of NA imports measured by theratio between imports from NA and total purchases. This group of affiliates accounts forthe largest share of home-country imports measured by the cost of goods sold. Seemingly,
affiliates with VFDI Chinese siblings maintained relative closely production linkage withJapanese suppliers, so they are more likely to function as new exporting platforms when
Chinese siblings are troubled by the trade war.
Insert Table 1 about here.
Significant heterogeneity between the treated and controlled affiliates can also be observed from distributions of some affiliates’ characteristics. Visualization of distributions for
total sales, employment, and investment is illustrated in Figure 2. Taking the distributionof total sales as an example, we find that both treatment groups are located at the right ofthe control group and show a more considerable mean value, which is in line with the finding from summary statistics. Similarly, for the case of investment, both treatment groups
are characterized by higher average investment than the control, but there seems to be noapparent difference between the two treatment groups. Given the existence of innegligible
differences, matching is a proper tool to help us at least reduce the bias of sample selection
for analysis later.
11

Insert Figure 2 about here.
Bearing in mind the difference in characteristics between groups, we investigate thesecond difference – the difference in these characteristics before the trade war and after. For
this purpose, we plot the time trend for respective characteristics within each group during
the sample period. Since export to NA is the variable of interest, we show the time series ofexport to NA separately for each treatment and control group in Figure 3. The green dashedline represents the control group, the solid yellow line represents the treatment group (1),and the solid blue line refers to the treatment group (2). A vertical red dotted line indicating
the timing of the trade war separates the whole time into the pre-war and post-war periods.
In post-war period, export to NA increased for the group of affiliates with VFDI Chinesesiblings, enlarging the gap with the affiliates having no Chinese siblings. In contrast, thegroup of affiliates with other types of FDI roughly remained at the same export level to NAeven after the trade war. Figure 4 shows the logged total sales time series for three groups,
respectively. Two treatment groups share a similar upward tendency of logged total sales
after the trade war, while the control group sees a slight decline in total sales. Figure 5 shows
identical patterns of the trend for investment across all the affiliates in the ASEAN. Afterthe trade war, investment was likely to increase for affiliates in the ASEAN to facilitate therelocation of production. Figure 6 shows employment changes for each group over the years.
Compared to the control group, both treatment groups increased their labor input slightly
after the trade war. Finally, there could be many explanations for the decreasing labor foraffiliates in the control group. One explanation is that those affiliates face more competition
from other affiliates or local firms, thus adjusting labor input.
Insert Figure 3 - Figure 6 about here.
From the graphical representation of the difference in affiliates-level characteristics before
and after the trade war, we find that the trade war may lead to MNEs’ adjustment of
production activities for those affiliates with Chinese siblings. We then conduct the analysis,
and the next section shows related results.
12

5Results

5.1

Baseline

In our analysis, propensity score matching is conducted before DID. To check the effectivenessof matching, we use the balancing test. The test reveals that matched pairs of affiliates fromcontrol and treatment groups have very similar pre-war characteristics. Also, pseudo-R2
is applied as another test. The vastly reduced explanatory power of pseudo-R2 confirms avalid matching, so we also compared the pseudo-R2 from the logistic estimation on samples
before and after matching. In the case of caliper matching (the benchmark matching for ourstudy), the pseudo-R2 decreases from 0.313 for the logit estimation using the sample before
matching to 0.02 that obtained from the exact estimation on matched samples7 .
Table 2 shows estimation results of DID with caliper matching and simple DID without
matching. The upper panel provides the results obtained by DID with caliper matching as
the baseline results for this study. According to the baseline estimation, we can first see that
affiliates with VFDI Chinese siblings (the treatment group (1)) have a significant positiveand sizable increase after the trade war. Hiking tariffs may lead to around a 28% increasein exports to NA for this group of affiliates in the ASEAN compared to ASEAN-locatedaffiliates with no Chinese siblings (the control group). This result supports our hypothesis.
As one consequence of increasing export to NA, the share of NA-oriented export in total
sales witnessed a significant growth of 0.5%. Second, we also see a 10.7% increase in total
sales at the 90% confidence level, possibly driven by export growth to NA. Third, an 8.3%
increase in labor input is also found for the treatment group (2). More use of labor may
also explain the expansion of sales. As suggested by the earlier finding, the trade war leads
to a decline in labor within Japanese affiliates in China (Sun et al., 2019), and this result
complementarily indicates silent substitution of labor through the production network of
7

For the baseline model, after matching, our matched sample contains 440 affiliates without Chinesesiblings as a match for the 319 affiliates with HFDI and other typed FDI Chinese siblings and 100 affiliateswith VFDI Chinese siblings.

13

MNEs (Muendler and Becker, 2010). Compared to the treatment group (1), we do notfind any significant trade war impact on the treatment group (2). However, it confirms
the heterogeneous effect of trade shock on affiliates differently connected to their siblings in
the same network. More specifically, affiliates with vertically embedded siblings are more
affected than those operating independently.
Insert Table 2 about here.
However, we can not find any significant results for imports from NA. Regardless ofimport measured by value or share, there seems to be no statistical difference in either oneof the treatment groups before the trade war and after compared to the control group. In
fact, due to deepening industrial agglomeration within the ASEAN, agglomerated suppliersof parts and components facilitated intra-region trade, and affiliates do not highly rely on
imports from NA. So, the trade shock is not very likely to increase imports from NA for
affiliates located in the ASEAN.
In addition to PSM-DID results, the lower panel of Table 2 describes results for thesimple DID with unmatched samples. Including the observations from the whole sample,
we can still find a barely significant increase in export to NA for the treatment group (1).
Relative to affiliates without Chinese siblings, a 9.7 % increase in export to NA is still foundfor affiliates with VFDI Chinese siblings due to the trade war. Accordingly, total sales go
up by 8% for them. Apart from the treatment group (1), it is worth noting that affiliates
in the treatment group (2) also tend to have a 4% increase in total sales. This increase
may be driven by less competition from Chinese firms in the ASEAN market, resulting
from a reduction in Chinese exports due to the trade war (Sun et al., 2019). Given theunchanged export to NA for this group of affiliates, an increase in total sales naturally leads
to a lower NA export share. Thus, we can find a decrease in NA-oriented export share forthe treatment group (2). The baseline estimation and simple DID show us consistent resultsfor NA-oriented export and total sales for a specific group of ASEAN affiliates, indicatingthat potential relocation of production is undertaken through the network within Japanese14

MNEs.

5.2

Robustness

To check the robustness of the findings, we implement other matching methods before the
DID analysis. Results from estimations with alternative matching methods are summarizedin Table 3. The entire table is separated into three panels by two lines, and the upper panel
shows the results obtained from DID with local linear matching, the panel in the middle
provides the results for Nearest 1 to 1 matching (with no replacement), and the lower panel
provides the results for Nearest 2 to 1 matching. All three methods show us consistent resultsthat the trade shock has significantly increased NA-oriented exports by 25.4% to 26.8% foraffiliates with VFDI Chinese siblings. The share of NA-oriented exports in total sales seesa significant increase for the case of Nearest 2 to 1 matching. However, for the other two
matching methods, the coefficients on the share of exports to NA are still positive but notsignificant. No matter what matching method is applied, the trade war positively affects
total sales for the same group of affiliates. After the trade war, the increase in total sales
is ranged from 11.1% to 23.9% across three cases. Similar effects are found for employment
as well. The results show that the trade war may lead to 7.7% to 8.85% more labor input
for affiliates with Chinese VFDI siblings. There is no finding of any significant results forthe other treatment group, those with HFDI Chinese siblings. Again, the results imply that
relatively independent affiliates operating in the ASEAN would not be affected too much by
the shock.
Insert Table 3 about here.
For three alternative matching techniques, regardless of whether local linear regression,
Nearest 1 to 1, or Nearest 2 to 1 matching is applied, the trade war shows positive effects onexports to NA, total sales, and labor inputs for affiliates with VFDI Chinese siblings thatare statistically significant at 10% or higher level. Such results further strongly convince us15that Japanese MNEs adjusted their production network by relocation.
The trade war effect on outcome variables may depend on the way how we split thetreatment group. In order to check the robustness of our model, we redefined the measure of
FDI types for Chinese siblings. Instead of classifying FDI as intra-industry or inter-industry
investment, we use export intensity as the criterion and define VFDI as an export-intensive
investment. Export intensity is computed as the ratio of export to total sales. Any affiliateswith a higher export ratio relative to the industrial median will be classified as VFDI affiliates;
the reverse cases will fall into the category of HFDI. We rerun the benchmark PSM-DID
estimator based on differently classified Chinese siblings, and the applied matching method
is caliper matching. Corresponding results are stored in Table 4. We can see the significant
positive impact of the trade war on export to NA, total sales, investment, and employment
for the first treatment group. The consistent results indicate that our results are robust
even if we change the classification for treatment groups. To be Specific, we observe a 23.6%
increase in export to NA for the first treatment group and a 14.5% increase in total sales,
together with a 9.62% increase in employment. Contrary to our expectations, investment
may also increase after the trade war.
Insert Table 4 about here.

5.3

Country-level heterogeneous effects

The trade war effects could be heterogeneous across affiliates located in different countries.
Corresponding to this concern, we examine whether affiliates located in less industrialized
ASEAN member countries – Cambodia, Laos, Myanmar, and Vietnam – known as the CLMV
countries, performed differently from other affiliates in the relatively more developed ASEAN
countries around and after the trade war.
We add interactions between DID terms and a dummy variable that indicates affiliates
were set up in the CLMV countries. Table 5 represents the corresponding results for thecountry-intervened effect of the trade war on treatment groups. Affiliates with VFDI Chinese
16

siblings are still the most affected ones. The interaction between DID term for this specificgroup of affiliates and the CLMV dummy carries a significant negative sign, indicating that
CLMV-located affiliates who operate with VFDI Chinese siblings tend to increase export to
NA in the aftermath of the trade war. While on average, the magnitude of growth in export to
NA is smaller than that from the same type of affiliates in other ASEAN countries (34.2%26.6%=7.6%). In contrast, labor increases substantially for the same group of affiliates(4.97%+13.2%=18.17%). That is to say, the impact of the trade war is even more positiveon employment for CLMV-located affiliates with VFDI Chinese siblings.
Insert Table 5 about here.
Since CLMV countries are less industrialized countries in the ASEAN, the formation ofindustrial agglomeration is still in progress, so they still concentrate on producing laborintensive products. Accordingly, when relocation of production happens in the aftermath of
the trade war, those affiliates located in CLMV are more likely to increase labor input verysoon in response to an expansion of production and sales.

5.4

Industry-level heterogeneous effects

Provided that some affiliates are producing in industries included by the tariff lists of 2018,
but some others are not. In this section, we investigate the heterogeneous effects of industries.
Firstly, we split the whole data set of matched affiliates into sub-samples in terms of industry
affiliations, and then we run the DID on the respective sub-sample.
Table 6 shows the results for affiliates within all machinery manufacturing industries.
Again, export to NA markedly increases for the affiliates with VFDI Chinese siblings (40.4%).
The increase in export value drives an increase in export share to NA. After the tradewar, the export share to NA increased by around 1%. The coefficient on total sales ispositive but not significant for the same group of affiliates. Also, The trade war leads toa 12.3% increase in employment at a very significant level. While we still can not find17

any significant results on imports from NA for affiliates with Chinese VFDI. No significant
effects of the trade war are found for affiliates with HFDI siblings either. The results for all
machinery manufacturers are consistent with the results we obtained from earlier estimations.
In this case, machinery manufacturers are likely to recruit more employees and expand the
production of exporting commodities in the ASEAN. So, ASEAN-located affiliates producing
within machinery industries may benefit from such rearrangement of production.
Insert Table 6 about here.
Next, we conduct the DID analysis based on the further breakdown of industries. We
select industries including electrical machinery and ICT equipment, transportation equipment, business machinery, chemicals, iron, steel, and non-ferrous metals. For comparison,
we also examine the impact of the trade war on industries less likely to be affected by 2018
tariffs. We run the analysis on the sample for textile and textile mill products industries.
The upper part of Table 6 shows results for affiliates producing within electrical machineryand ICT equipment industries. As estimated, the exports to NA increased by 26.2% for thosewith VFDI Chinese siblings. For the same group, employment and investment experienced asizable increase. Being prioritized industries in the ASEAN, manufacturing of electrical andelectronics contributed the biggest proportion of total exports 8 , With the current industrial
foundation, production relocation brings new opportunities for this group of affiliates. The
middle part of the table shows results for manufacturing transportation equipment and business machinery. Similarly, for the group of affiliates with VFDI Chinese siblings, both value
and share of export to NA witnessed a substantial increase. While in this case, only positive
but insignificant results can be seen for total sales and employment. A few ASEAN countries have actively integrated into the value chain of transportation equipment, especiallythe automobile industry. Well-established automobile industries already exist in countries
like Thailand and Indonesia. Substitution of production is relatively easy to undertake by
8

According to the report Global Value Chains in ASEAN published by the ASEAN-Japan Centre,
the percentage is 27% in 2021. Related infomation can be found through https://www.asean.or.jp/ja/wpcontent/uploads/sites/2/GVCs_Electronics_Paper-13_full_web.pdf

18

affiliates set up in these countries. Regarding the results for industrial chemicals, iron, steel,and non-ferrous metals, we can not find any significant results for treated affiliates. The
explanation could be straightforward: For all these capital-intensive industries, production
relocation is quite impossible to be implemented within a short period. So, even though thetrade war occurred, prompt relocation is unlikely to be carried on.
Insert Table 7 about here.
The 2018 tariff list did not cover the industries of textile mill products. We examine
the impact of the trade war on the industries of textile mill products but find no significant
results for either treatment group. Again, we do not see any significant increase in export
to NA. Since textile is not a highly fragmented industry in the ASEAN, the trade shock isless likely to propagate through MNEs’ value chains.
To sum it up, our analysis documents evidence of the heterogeneous effect of the trade
war on affiliates across multiple industries. Affiliates operating in targeted industries may
benefit from the trade war and increased exports to North America. In contrast, thoseaffiliates operating in industries with heavy capital spending or industries excluded by the
first round of hiking tariffs seem less affected.

6Conclusion

In this study, utilizing a PSM-DID approach, we examined the impact of the 2018 US-Sinotrade war on production acclivities of ASEAN-located affiliates for Japanese MNEs. We findthat those affiliates with Chinese siblings may see an increase in total sales around and after
the trade war, particularly compared to independently operated affiliates, those affiliateswith vertically integrated Chinese siblings within the same value chains of a Japanese MNE
tend to increase their export to the North American market. Also, their expanding sales
to North America may lead to more labor input. This finding document the evidence ofpotential prompt relocation of production undertaken by Japanese MNEs in response to a19

trade shock. Furthermore, the impact of the trade war is heterogeneous across affiliates indifferent locations and industries. Affiliates in CLMV countries tend to see a slight increasein exports to North America but a substantial increase in labor relative to more developed
ASEAN countries (ASEAN-6). As for industry heterogeneity, we find that affiliates with
Chinese VFDI siblings operating in some affected industries would see export expansion to
North America around and after the trade war.
Our study documents empirical evidence for MNEs’ adjustments to overseas production
activities. It may derive implications for MNEs to set up a diversified overseas production
network.

20

References
Alfaro, L. and Charlton, A. (2009). Intra-industry foreign direct investment. American
Economic Review, 99(5):2096–2119.
Amiti, M., Kong, S. H., and Weinstein, D. (2020). The effect of the us-china trade war on
us investment. Technical report, National Bureau of Economic Research.
Amiti, M., Redding, S. J., and Weinstein, D. E. (2019). The impact of the 2018 tariffs on
prices and welfare. Journal of Economic Perspectives, 33(4):187–210.
Benguria, F. (2019). The global impact of the us-china trade war: Firm-level evidence.
Available at SSRN 3413229.
Donaubauer, J. and Dreger, C. (2018). The end of cheap labor: Are foreign investors leaving
china? Asian Economic Papers, 17(2):94–107.
Fajgelbaum, P., Goldberg, P. K., Kennedy, P. J., Khandelwal, A., and Taglioni, D. (2021).
The us-china trade war and global reallocations. Technical report, National Bureau of
Economic Research.
Flaaen, A., Hortaçsu, A., and Tintelnot, F. (2020). The production relocation and priceeffects of us trade policy: the case of washing machines. American Economic Review,110(7):2103–27.
Hayakawa, K., Matsuura, T., and Okubo, F. (2015). Firm-level impacts of natural disasters
on production networks: Evidence from a flood in thailand. Journal of the Japaneseand International Economies, 38:244–259.
Huang, Y., Lin, C., Liu, S., and Tang, H. (2019). Trade networks and firm value: Evidence
from the us-china trade war. Available at SSRN 3504602.
Ito, T. (2022). The effects of trump’s trade war with china on japan’s trade. Technicalreport, Research Institute of Economy, Trade and Industry (RIETI).
Mendes, B. M. D. M. (2021). Do firms create immunity to production network shocks?
Evidence from the Covid-19 global pandemic. PhD thesis.
Muendler, M.-A. and Becker, S. O. (2010). Margins of multinational labor substitution.
American Economic Review, 100(5):1999–2030.
Sun, C., Tao, Z., Yuan, H., and Zhang, H. (2019). The Impact of the US-China Trade War
on Japanese Multinational Corporations. RIETI Working Paper.
Todo, Y., Nakajima, K., and Matous, P. (2015). How do supply chain networks affect theresilience of firms to natural disasters? evidence from the great east japan earthquake.
Journal of Regional Science, 55(2):209–229.

21

Tables
Table 1: Summary statistics
ASEAN affiliates of Japan MNEs, 2017-2019
No Chinese siblings
VFDI Chinese siblings
Other Chinese siblings(# of affiliates: 1573)(# of affiliates: 563)(# of affiliates: 2146)
VARIABLES
export to NA/total saleslog (export to NA)
import from NA/total purchaselog (import from NA)
log (total sales)log (investment)log (employees)

N

Mean

SD

N

Mean

SD

N

Mean

SD

3,696
3,696
3,696
3,696
2,961
2,225
3,021

0.012
0.389
0.004
0.108
6.602
3.249
4.682

0.07
1.40
0.04
0.69
1.65
2.12
1.43

1,413
1,413
1,413
1,413
1,144
923
1,167

0.008
0.509
0.007
0.263
7.609
4.045
5.169

0.04
1.63
0.05
1.14
1.80
2.16
1.56

5,685
5,685
5,685
5,685
4,838
3,859
4,876

0.011
0.651
0.003
0.238
7.949
4.222
5.381

0.06
1.88
0.03
1.08
1.89
2.31
1.68

Note: This table shows the summary of statistics of Japanese affiliates in the ASEAN countries by threegroups: affilate without any Chinese siblings, affiliates with HFDI Chinese siblings and affiliates with VFDI
Chinese siblings.

22

23
10,0530.858

0.000134(0.00204)-0.00290*(0.00175)

2,419
0.849

0.00573*(0.00302)
0.000963(0.00293)

10,0530.895

0.0970*(0.0519)-0.0141(0.0382)

2,419
0.892

0.279***(0.0926)
0.0739(0.0618)

10,0530.808-0.000137(0.00111)-9.42e-05(0.00111)

2,419
0.901

10,0530.870

Simple DID
0.0200(0.0280)-0.00313(0.0234)

2,419
0.842

Caliper matching (baseline)-0.000241
0.0543(0.00145)(0.0485)-0.00112-0.00968(0.00190)(0.0412)

8,226
0.978

0.0803**(0.0352)
0.0396*(0.0213)

2,226
0.973

0.107*(0.0578)0.0493(0.0343)

6,258
0.867

-0.0381(0.104)0.0493(0.0740)

1,776
0.840

-0.0866(0.226)0.167(0.127)

8,316
0.983

-0.00465(0.0188)0.00695(0.0167)

2,239
0.981

0.0828***(0.0267)
0.0316(0.0288)

(1)
(2)
(3)
(4)
(5)
(6)
(7)
Export share (NA) log of export (NA) Import share (NA) log of import (NA) log (Total sales) log (Investment) log (Employees)

Note: Firm, country and industry-year level fixed effects are included in estimation for each case. Identification of industry is based on 4-digit
industrial classification. Standard errors are clustered at affiliate level. Robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

NR-squared

1.Post#1.ChinaSib#1.OtherFDI

1.Post#1.ChinaSib#1.VFDI

N
R-squared

1.Post#1.ChinaSib#1.OtherFDI

1.Post#1.ChinaSib#1.VFDI

Dependent variables

Table 2: Impact of trade war on production activities of ASEAN affiliates

24
2,410
0.846

0.00548*(0.00307)0.000960(0.00294)

1,495
0.843

0.00233(0.00533)
0.000774(0.00427)

1,782
0.822

0.00557(0.00426)
0.00178(0.00330)

2,410
0.889

0.268***(0.0941)0.0723(0.0616)

1,495
0.886

0.254*(0.144)0.113(0.0914)

1,782
0.880

0.257**(0.116)0.0826(0.0710)
1,782
0.833

local linear matching
0.0668(0.0681)0.0133(0.0470)
1,642
0.971

0.143*(0.0780)
0.0324(0.0441)

1,495
0.767

2,410
0.901

2,410
0.840

Nearest 2 to 1 matching-0.000242
0.0462(0.00147)(0.0486)-0.00112-0.00725(0.00190)(0.0412)

1,495
0.757

2,217
0.973

0.111*(0.0592)
0.0514(0.0343)

1,382
0.961

Nearest 1 to 1 without replacement-0.00134
0.03000.239***(0.00173)(0.0746)(0.0910)-0.00262-0.01630.0358(0.00254)(0.0508)(0.0566)

1,782
0.860

0.000910(0.00186)
0.000761(0.00191)

1,768
0.840

-0.105(0.232)0.167(0.128)

1,061
0.848

-0.103(0.336)-0.0525(0.164)

1,304
0.848

0.0160(0.321)0.204(0.150)

2,230
0.981

0.0881***(0.0268)
0.0334(0.0288)

1,385
0.987

0.0885**(0.0386)
0.0396(0.0309)

1,652
0.982

0.0770**(0.0354)0.0322(0.0368)

(1)
(2)
(3)
(4)
(5)
(6)
(7)
Export share (NA) log of Export (NA) Import share (NA) log of Import (NA) log (Total sales) log (Investment) log (Employees)

Note: Firm, country and industry-year level fixed effects are included in estimation for each case. Identification of industry is based on 4-digit
industrial classification. Standard errors are clustered at affiliate level. Robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

NR-squared

1.Post#1.ChinaSib#1.OtherFDI

1.Post#1.ChinaSib#1.VFDI

N
R-squared

1.Post#1.ChinaSib#1.OtherFDI

1.Post#1.ChinaSib#1.VFDI

N
R-squared

1.d#1.ChinaSib#1.OtherFDI

1.d#1.ChinaSib#1.VFDI

Dependent variables

Table 3: Impact of trade war on production activities of ASEAN affiliates: alternative matching methods

25
2,419
0.849

0.00539(0.00358)
0.00138(0.00278)
2,419
0.892

0.236**(0.111)
0.0972(0.0594)
2,419
0.901

0.00138(0.00179)-0.00136(0.00183)
2,419
0.842

0.0802(0.0698)-0.00988(0.0381)

2,226
0.973

0.145**(0.0689)0.0470(0.0337)

1,776
0.841

0.397**(0.193)0.0549(0.130)

2,239
0.981

0.0962*(0.0512)0.0332(0.0270)

(1)
(2)
(3)
(4)
(5)
(6)
(7)
Export share (NA) log of Export (NA) Import share (NA) log of Import (NA) log (Total sales) log (Investment) log (Employees)

Note: Firm, country and industry-year level fixed effects are included in estimation for each case. Identification of industry is based on 4-digit
industrial classification. Standard errors are clustered at affiliate level. Robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

NR-squared

1.Post#1.ChinaSib#1.OtherFDI

1.Post#1.ChinaSib#1.VFDI

Dependent variables

Table 4: Impact of trade war on production activities of ASEAN affiliates: redefined FDI types

26
2,419
0.849

0.00669**(0.00337)
0.00155(0.00323)-0.00398(0.00339)-0.00344(0.00334)
2,419
0.892

0.342***(0.113)0.0862(0.0696)-0.266**(0.118)-0.0721(0.0670)
2,419
0.901

-0.000200(0.00143)-0.00138(0.00205)-0.000194(0.00131)0.00152(0.00163)
2,419
0.842

0.0358(0.0474)-0.0123(0.0448)0.0779(0.175)0.0156(0.0429)

2,226
0.973

0.0333(0.0493)0.0299(0.0344)0.287*(0.156)0.109(0.0766)

1,776
0.841

0.0273(0.259)0.218(0.135)-0.450(0.462)-0.297(0.279)

2,239
0.981

0.0497*(0.0270)0.0275(0.0335)
0.132**(0.0575)
0.0229(0.0521)

(1)
(2)
(3)
(4)
(5)
(6)
(7)
Export share (NA) log of Export (NA) Import share (NA) log of Import (NA) log (Total sales) log (Investment) log (Employees)

Note: CLMV countries are Cambodia, Lao PDR, Myanmar and Vietnam. Firm, country and industry-year level fixed effects are included inestimation for each case. Identification of industry is based on 4-digit industrial classification. Standard errors are clustered at affiliate level. Robust
standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

NR-squared

1.Post#1.ChinaSib#1.OtherFDI#1.CLMV

1.Post#1.ChinaSib#1.VFDI#1.CLMV

1.Post#1.ChinaSib#1.OtherFDI

1.Post#1.ChinaSib#1.VFDI

Dependent variables

Table 5: Impact of trade war on affiliates in CLMV

27
1,089
0.921

0.00981*(0.00577)-0.000881(0.00444)
1,089
0.904

0.404**(0.168)0.0863(0.0995)
1,089
0.973

0.000216(0.000859)
0.000286(0.00102)
1,089
0.817

0.0624(0.0901)-0.0657(0.0592)

1,000
0.977

0.0406(0.0558)
0.0483(0.0431)

806
0.844

0.238
(0.352)0.0163(0.182)

1,016
0.975

0.123***(0.0410)0.0137(0.0495)

(1)
(2)
(3)
(4)
(5)
(6)
(7)
Export share (NA) log of Export (NA) Import share (NA) log of Import (NA) log (Total sales) log (Investment) log (Employees)

Note: Firm, country and industry-year level fixed effects are included in estimation for each case. Identification of industry is based on 4-digit
industrial classification. Standard errors are clustered at affiliate level. Robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

NR-squared

1.Post#1.ChinaSib#1.OtherFDI

1.Post#1.ChinaSib#1.VFDI

Dependent variables

Table 6: Impact of trade war on all machinery industries

28
94
0.959

0.000149(0.00351)-0.00394(0.00351)

485
0.494

-0.00265(0.00353)-0.00484(0.00511)

621
0.949

0.0167*(0.00935)
0.000398(0.00460)

333
0.687

0.000944(0.00509)-0.0115(0.00886)

94
0.966

0.310
(0.339)-0.230(0.190)485
0.852

0.0283(0.0669)
0.0379(0.0879)

621
0.916

0.531*(0.274)0.0344(0.126)333
0.926

0.262*(0.136)-0.0135(0.131)
333
0.798

296
0.972

621
0.843

582
0.979

94
0.958

0.00524(0.00889)
0.00153(0.00216)

485
0.787

94
0.997

textile mill products-0.0620(0.0575)0.00135(0.0117)

485
0.767

87
0.977

0.481
(0.470)0.0721(0.0898)

446
0.986

Industrial-oriented chemicals, iron, steel and non-ferrous metals
0.00301
0.146-0.126(0.00197)(0.116)(0.0782)0.004220.0751
0.0222(0.00283)(0.106)(0.0714)

621
0.977

Transportation equipment and business machinery-0.000584-0.06800.0390(0.00126)(0.0522)(0.0820)-0.000342-0.0792
0.0693(0.00132)(0.0815)(0.0470)

333
0.750

Electrical machinery and ICT equipment
0.00147
0.361
0.0226(0.00141)(0.283)(0.0831)0.00223
0.0228-0.0465(0.00222)(0.0989)(0.107)68
0.834-0.678(0.678)0.618(0.663)

367
0.845

0.113
(0.499)0.249(0.298)495
0.789

0.0839(0.498)0.0786(0.236)229
0.909

0.803**(0.331)-0.193(0.329)86
0.993

0.0800(0.127)0.0278(0.0814)

448
0.989

-0.0392(0.0530)-0.0271(0.0436)

596
0.981

0.0826(0.0524)0.0848(0.0517)

297
0.957

0.226***(0.0839)-0.139(0.137)

Note: Firm, country and industry-year level fixed effects are included in estimation for each case. Identification of industry is based on 4-digit
industrial classification. Standard errors are clustered at affiliate level. Robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

NR-squared

1.Post#1.ChinaSib#1.OtherFDI

1.Post#1.ChinaSib#1.VFDI

N
R-squared

1.Post#1.ChinaSib#1.OtherFDI

1.Post#1.ChinaSib#1.VFDI

N
R-squared

1.Post#1.ChinaSib#1.OtherFDI

1.Post#1.ChinaSib#1.VFDI

N
R-squared

1.Post#1.ChinaSib#1.OtherFDI

1.Post#1.ChinaSib#1.VFDI

Dependent variables

Table 7: Impact of trade war on selected industries
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Export share (NA) log of Export (NA) Import share (NA) log of Import (NA) log (Total sales) log (Investment) log (Employees)

Figures
Figure 1: U.S. Imports of Automotive Parts

Data source: US’ department of commerce, 2021
Note: The solid red line represents China, and solid blue line represents the case of ASEAN. The left
vertical axis indicates the import value from ASEAN, Thailand, and Vietnam, and the right vertical axis
indicates the import value from China.

29

Figure 2: Distribution of variables (log of total sales / log of employment / log of investment)

30

Figure 3: Before and after the trade war ( export to NA)

Figure 4: Before and after the trade war ( log of total sales)31Figure 5: Before and after the trade war (log of investment)

Figure 6: Before and after the trade war (log of employment)

32

Appendix A.
Table A1: Distribution of VFDI affiliates in China#affiliates in China
Type of industry
VFDI Total Proportion
food, beverage and animal feed
13
133
10%
textile40
254
16%
lumber and wood products, paper
19
68
28%
chemicals
105
341
31%
ceramic, stone and clay products
24
111
22%
iron and steel
16
70
23%
non-ferrous metals
43
143
30%
fabricated metal products
63
250
25%
general-purpose machinery
53
185
29%
production machinery
95
379
25%
business oriented machinery
38
115
33%
electrical machinery
120
350
34%
information and communication equipment
113
414
27%
transportation equipment
161
668
24%
Miscellaneous manufacturing
121
581
21%

33