KEO Discussion Paper No. 176

The COVID-19 Pandemic and the World Machinery Trade Network
Kozo Kiyota
Keio University, RIETI, and TCER

August 18, 2023

The COVID-19 Pandemic and the World Machinery Trade Network †Kozo Kiyota ‡Keio University, RIETI, and TCER
Abstract
In light of the importance of machinery trade in the world trade, this paper examines whether
or not the patterns of machinery exports changed significantly after the COVID-19 pandemic outbreak.
We apply a framework of the network analysis and that of the structural break analysis to the monthlylevel bilateral export data from January 2016 to March 2022. Our main findings are threefold. First,
we find positive structural change in exports in the major machinery exporting countries. Second, we
find negative structural change in the centrality in Japan and some ASEAN countries, which implies
the decline in the relative importance of these countries in the world machinery network. Third, thedecline in the Japanese centrality is not caused by the decline in export values or number of destination
countries. Rather, it is attributable to the decline in the centrality of Japan’s export destination countries
such as ASEAN countries. Noting that Japan has a relatively strong trade relationship with ASEAN
countries, these results together suggest that the negative shock of the pandemic spread through the
supply chain, which lead to the decline in the relative importance of some countries such as Japan in
the world machinery trade network.
JEL classification: F14, F40
Keywords: Machinery trade; COVID-19 pandemic; Network; Centrality† I have benefited from conversations on this topic with Fukunari Kimura. I also benefitted from helpful comments

on earlier drafts from Doan Ha, Paresh Kumar Narayan, Shujiro Urata, and Hongyon Zhang. This research was
supported by the Economic Research Institute for ASEAN and East Asia (ERIA) project ‘Global Trade andEconomic Recovery in the Post-Pandemic World’. I also acknowledge the financial support of the Japan Society for
the Promotion of Science (JSPS) Grants-in-Aid (JP22H00063). The usual disclaimer applies.
‡ Kozo Kiyota, address: Keio Economic Observatory, Keio University, 2-15-45, Mita, Minato-ku, Tokyo 10883345, Japan. E-mail: kiyota@keio.jp

1. Introduction
Whether and how the global supply chain is resilient to the various shocks such as the coronavirus
disease (COVID-19) pandemic is one of the concerns for policy makers. For example, building
resilient supply chains is featured in the US White House (2022). Freeman and Baldwin (2022) in their
VoxEU article also argued that “Supply disruptions caused by systemic shocks such as Brexit, Covid,
and Russia-Ukraine tensions have catapulted the issue of risk in global supply chains to the top ofpolicy agendas’’.
Despite the fact that the year 2020 was marked by some of the largest reductions in trade sinceWorld War II, the world economy is rapidly recovering from the coronavirus disease (COVID-19)
pandemic. According to the International Monetary Fund (IMF) (2022), the global output growth was-3.1 percent in 2020 and it recovered to 5.9 percent in 2021. Similarly, world trade volume declined 8.2 percent in 2020 but increased 9.3 percent in 2021. These estimates suggest that the negative impacts
are temporal rather than perpetual. In other words, trade was resilient against the shock of thepandemic. 1
When discussing supply chains, however, people will assume manufacturing products rather
than trade as a whole, especially machinery products such as iPad, car, and airplane because they have
many parts and components whose production process spreads across different countries. While thenegative effect of the pandemic on overall goods trade was disappeared because of a V-shaped recovery,
the effect on machinery trade is inconclusive. 2
For example, Ando, Kimura, and Obashi (2021) examined the impacts of the COVID-19
pandemic on Japanese machinery trade. Using the data between January 2017 and October 2020, theirresults indicate that trade relationships for parts and components were robust even amid the pandemic.
Moreover, international production networks in machinery sectors were almost intact. Ando, Kimura,and Yamanouchi (2022) found that the negative impacts of COVID-19 pandemic on exports were muchsmaller for East Asia than for North America and Europe.
In contrast, Arriola, Kowalski, and van Tongeren (2021) argued that the variation in trade
impacts across the different product categories in 2020 was not only larger than during the global
financial crisis, but also larger than in any other year in the past two decades. Based on the detailed
descriptive analysis of the trade data between January 2000 and January 2021, they pointed out thatIn this paper, we limit our focus to goods trade due to the limited availability of the monthly data. Ando and
Hayakawa (2022) utilized the quarterly data and examined the impact of the pandemic on trade in services. They
found that the pandemic had a more significantly negative impact on the services trade than the goods trade.
2 Kiyota (2022) examined how the pandemic affected global trade, using the monthly-level bilateral trade datafrom January 2000 and March 2021. The study found no evidence for major trading countries and ASEAN
countries that changed trade significantly after the pandemic began.
11the changes in the trade structure caused by the pandemic in a single year was a similar magnitude tochanges otherwise typically seen over five years. Hayakawa and Mukunoki (2021) examined theeffects of the COVID-19 pandemic on the trade of finished machinery products from January—June
2019 and January—June 2020. Their study found that, on the one hand, the pandemic did not have asignificant effect on demand for finished machinery products in importing countries. On the other hand,the finished machinery trade is significantly hurt by higher rates of COVID-19 infection in countries
exporting finished machinery products as well as countries exporting machinery parts to thosecountries.
In a nutshell, whilst the previous studies made significant contributions to the literature, it isstill not necessarily clear whether or not the patterns of machinery trade changed significantly after the
COVID-19 pandemic outbreak. Did the pandemic cause the structural change in the pattern ofmachinery trade? 3 How the relative importance of countries in machinery trade changed after the
pandemic outbreak?
Figure 1 presents the share of machinery exports in the world manufacturing exports in theworld between 2016 and 2021. Figure 1 indicates that the share of machinery exports in total
manufacturing exports was 41.0 percent in 2016, and slightly increased to 42.7 percent in 2021.
Moreover, the machinery exports were dominated by two categories: electrical machinery andtransport equipment. Indeed, the share of these two categories exceeded 40 percent in manufacturing
trade in 2021. Because machinery exports indicate the large share of manufacturing exports, it isessential to answer these questions for a deeper understanding of the impact of the pandemic oninternational trade.
Figure 1
Share of Machinery in Manufacturing Exports in the WorldAs we will explain below, the structural breaks are defined as the break points in the time series of variables,
where break points are defined as a significant shift in mean or trend in the variables concerned.
23Notes: Manufacturing is defined as the Harmonized System (HS) categories from 16 to 96. Machinery exports arefrom 84 to 92, where 84 is electrical machinery and 85 is transport equipment.
Source: UN Comtrade Database.

Based on this background, this study investigates whether or not the patterns of machinery
trade changed significantly after the COVID-19 pandemic outbreak. Our study builds upon Kiyota(2022) that examined how total trade and the centrality of each country in the world trade changed
after the pandemic outbreak. 4 Following Kiyota (2022), this study focuses on trade and centrality.
Unlike Kiyota (2022), however, this study focuses particularly on the machinery trade. 5 This paper
thus contributes to the literature, by providing a more detailed analysis on the machinery trade and by
employing formal statistical analysis to evaluate the significance of the changes in the world machinery

After the COVID-19 pandemic outbreak, several studies employed the framework of the network analysis toanalyze the trade patterns between countries. See, for example, Vidya and Prabheesh (2020), Antonietti, Falbo,
Fontini, Grassi, and Rizzini (2022), and Kiyota (2022).
5 In this connection, Hayakawa and Mukunoki (2020) estimated a gravity model using data for 186 countries. Oneof their important findings was that the negative impacts of the pandemic are particularly evident in exports fromdeveloping countries. Although they presented interesting findings, the network structure of trade is beyond thescope of their study.
3
4

trade network after the pandemic outbreak. We believe that such disaggregated-level analysis would
clarify whether there are shifts in the global value chains landscape as a result of the COVID-19
pandemic.
The analysis of this paper could also have important policy implications. Understanding thevulnerability of supply chains helps policy makers to identify the sources of uncertainty in policy
making. Our analysis attempts to clarify whether and which machinery products were vulnerable orresilient against the pandemic outbreak. Based on the importance of machinery trade in Asian countries,
our study will provide policy makers with useful information.
2. Methodology and Data
2.1.

Network analysis

This study employs the framework of the network analysis in order to identify the relative importanceof each country in the world machinery trade and the framework of the structural break to statisticallydetect the structural change in machinery trade and the centrality. In the framework of network analysis,
each country is represented as a node while trade relationship between countries is represented as alink. The world machinery trade network thus is represented by the nodes and links, which is calledgraph.
There are three advantages to employing the framework of network analysis. First, the datarequirement for the analysis is relatively low. Basically, only the information on the bilateral trade isnecessary for the analysis. It thus is easy to implement the analysis. Second, trade data is helpful to
examine the current economic situation because they are available on a monthly basis. Finally, thenetwork analysis is outstanding in terms of visualization. The network analysis visualizes the networkof countries, based on graph theory. It thus is helpful to capture the relative importance of each country
in a relatively easy manner.
The relative importance of each node is represented by centrality measures. There are several
centrality measures such as the closeness centrality that are based on the distance between nodes andthe degree centrality that is based on the number of links. However, because countries generally tradewith many countries simultaneously, these centrality measures are not necessarily useful for the
analysis of the world machinery trade network.
Several recent economic studies such as Acemoglu, Carvalho, Ozdaglar, and Tahbaz-Salehi(2012) and Carvalho (2014) utilized eigenvector centrality, which is also called Bonacich centrality.
However, it is not applicable to directed graphs. Therefore, it is not applicable to analyzing the world
machinery trade network because trade has a direction (from exporting countries to importing
countries).

4

To overcome this problem, this study utilizes PageRank centrality, which was originallydeveloped to evaluate the ranking of webpages (Page, Brin, Motwani, and Winograd, 1999). PageRankcentrality is a variant of eigenvector centrality but has the following two advantages. First, likeeigenvector centrality, PageRank centrality considers not only the number of edges (i.e., traderelationship) that a node (i.e., country) has, but also the number of edges that other directly connected
nodes have. Indeed, as Kiyota (2023) showed, PageRank centrality is consistent with the index of
upstreamness. Second, unlike eigenvector centrality, this centrality is applicable to a directed graph.
Because trade has directions (from origin to destination), this is another desirable property for the
analysis of trade.

where:

Let the number of nodes be 𝑛𝑛. We denote the adjacency matrix as 𝐴𝐴:
𝑎𝑎11 ⋯ 𝑎𝑎1𝑗𝑗 ⋯ 𝑎𝑎1𝑛𝑛
⋮
⋱
⋮
⋱
⋮
⎛
⎞
𝐴𝐴 = ⎜ 𝑎𝑎𝑖𝑖1 ⋯ 𝑎𝑎𝑖𝑖𝑖𝑖 ⋯ 𝑎𝑎𝑖𝑖𝑖𝑖 ⎟,
⋮
⋱
⋮
⋱
⋮
𝑎𝑎
⋯
𝑎𝑎
⋯
𝑎𝑎
𝑛𝑛𝑛𝑛
𝑛𝑛𝑛𝑛 ⎠⎝ 𝑛𝑛1
𝑎𝑎𝑖𝑖𝑖𝑖 = �1,0,

if there is a link from node 𝑖𝑖 to node 𝑗𝑗;
othewise.

(1)

(2)

Now, let us introduce time dimension 𝑡𝑡. Let PageRank centrality be 𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖 for country 𝑖𝑖 attime 𝑡𝑡. Then, it is defined as:𝑛𝑛

𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖 = 𝜓𝜓 � 𝑎𝑎𝑖𝑖𝑖𝑖𝑖𝑖
𝑗𝑗=1

𝑃𝑃𝑃𝑃𝑗𝑗𝑗𝑗+ 𝜒𝜒,
𝑘𝑘𝑗𝑗𝑗𝑗(3)

where 𝜓𝜓 and 𝜒𝜒 are positive constants and 𝑘𝑘𝑗𝑗𝑗𝑗 is the outdegree. In computing PageRank centrality,
we use exports as a link weight. Equation (1) thus means that PageRank centrality for country 𝑖𝑖

becomes high if 1) the number of country 𝑖𝑖's partners increases; 2) country 𝑖𝑖's trade increases; and 3)

PageRank for country 𝑖𝑖's partner increases. Conventionally, we set 𝜓𝜓 = 0.85 and 𝜒𝜒 = 1. To makecomparisons between years, we also adjust PageRank centrality such that its total equals to one.
2.2. Structural break analysis
2.2.1. Regression equations

Once we obtain the centrality measures, we conduct structural break analysis to identify
whether there are break points in machinery trade and centrality measures in their time series, where
break points are defined as a significant shift in mean or trend in the variables concerned. To detect thestructural break, we employ the test developed by Clemente, Montañés, and Reyes (1998) that allows
5for estimation of two events within the observed history of a time series. 6 This allows us to investigate
whether countries experienced significant changes in trade and their centrality during the sample
period.
We denote an outcome variable of country 𝑖𝑖 as 𝑦𝑦𝑡𝑡 (suppressing the country subscript 𝑖𝑖 ),where outcome variable is either the log value of machinery exports or PageRank centrality. Consider
that the time series of outcome 𝑦𝑦𝑡𝑡 experiences one structural break during the sample. There are two

types of models that can capture the structural break: an additive outlier (AO) model that captures asudden change, and an innovational outliers (IO) model that captures a gradual shift in the mean of theseries. 7
The AO model consists of the following two steps. In the first step, we estimate the following
regression equation:(4)

𝑦𝑦𝑡𝑡 = 𝜇𝜇 + 𝛿𝛿1 𝐷𝐷𝐷𝐷1𝑡𝑡 + 𝛿𝛿2 𝐷𝐷𝐷𝐷2𝑡𝑡 + 𝑦𝑦�𝑡𝑡 ,where:

𝐷𝐷𝐷𝐷𝑚𝑚𝑚𝑚 = �

1,
0,

(5)

if 𝑡𝑡 > 𝑇𝑇𝑏𝑏𝑏𝑏 ;otherwise,

where 𝑚𝑚 = 1, 2; 𝑇𝑇𝑏𝑏1 and 𝑇𝑇𝑏𝑏2 are the break points to be located by grid search. and 𝑦𝑦�𝑡𝑡 denotes theresiduals. In the second step, the residuals from this regression are used as the dependent variable forthe following equation:

where:

𝑑𝑑

𝑑𝑑

𝑑𝑑

𝜏𝜏=1

𝜏𝜏=1

𝜏𝜏=1

𝑦𝑦�𝑡𝑡 = � 𝜔𝜔1𝜏𝜏 𝐷𝐷𝐷𝐷𝑏𝑏1,𝑡𝑡−𝜏𝜏 + � 𝜔𝜔2𝜏𝜏 𝐷𝐷𝐷𝐷𝑏𝑏2,𝑡𝑡−𝜏𝜏 + 𝛼𝛼𝑦𝑦�𝑡𝑡 + � 𝜃𝜃𝜏𝜏 Δ𝑦𝑦�𝑡𝑡−𝜏𝜏 + 𝜀𝜀𝑡𝑡 ,1,
𝐷𝐷𝐷𝐷𝑏𝑏𝑏𝑏,𝑡𝑡 = �0,

if 𝑡𝑡 = 𝑇𝑇𝑏𝑏𝑏𝑏 + 1;
otherwise,

(6)

(7)

for 𝑚𝑚 = 1, 2. The lag order 𝑑𝑑 is also unknown. The second regression is estimated over feasible

values of 𝑇𝑇𝑏𝑏𝑏𝑏 to search for the minimal 𝑡𝑡-statistic to test whether the autoregressive parameter 𝛼𝛼 =1 (i.e., the strongest rejection of the unit root null hypothesis) for all the break time combinations,while 𝑑𝑑 is determined by a set of sequential 𝐹𝐹 -tests. 8 The significance level of this minimal 𝑡𝑡 statistic is investigated based on the critical values provided by Perron and Vogelsang (1992).

There are several structural break tests. To compare the results with those of Kiyota (2022), we employ the testdeveloped by Clemente, Montañés, and Reyes (1998). In addition, for the test developed by Clemente, Montañés,
and Reyes (1998), stata code is available (and thus easy to implement and replicate).
7 One may concern that trade involves future contracts and thus could not change suddenly. Because the IO model
captures a graduate shift in the mean series, it would capture the gradual change in futures contracts if that were thecase.
8 We set the maximum lag number as 12 to reduce the computational burden and to account for seasonality (i.e.,
𝑑𝑑 = 12). Note that there is no intercept because the mean of 𝑦𝑦�𝑡𝑡 is zero.
66

In contrast, the IO model is based on the one-step procedure. The following regression equation
is estimated:
𝑦𝑦𝑡𝑡 = 𝜇𝜇 + 𝛿𝛿1 𝐷𝐷𝐷𝐷1𝑡𝑡 + 𝛿𝛿2 𝐷𝐷𝐷𝐷2𝑡𝑡 + 𝜙𝜙1 𝐷𝐷𝐷𝐷𝑏𝑏1,𝑡𝑡 + 𝜙𝜙2 𝐷𝐷𝐷𝐷𝑏𝑏2,𝑡𝑡𝑑𝑑(8)+𝛼𝛼𝑦𝑦𝑡𝑡−1 + � 𝜃𝜃𝜏𝜏 Δ𝑦𝑦𝑡𝑡−𝜏𝜏 + 𝜀𝜀𝑡𝑡 .
𝜏𝜏=1As in the AO model, the regression equation is estimated over feasible values of 𝑇𝑇𝑏𝑏𝑏𝑏 to search forthe minimal t-statistic to test whether the autoregressive parameter 𝛼𝛼 = 1 (i.e., the strongest rejectionof the unit root null hypothesis) for all the break time combinations, while 𝑑𝑑 is determined by a setof sequential F-tests.

Note that it is necessary to choose some trimming values because the test is not defined at thelimits of the sample (Clemente, Montañés, and Reyes, 1998). Banerjee, Lumsdaine, and Stock (1992)
suggested using a window (0.15, 0.85). However, Perron and Vogelsang (1992) pointed out that “thischoice is arbitrary” (p.305). To adapt the largest window possible in both the theoretical derivations
and the empirical applications, following Zivot and Andrews (1992), we set the values((𝑑𝑑 + 2)/𝑇𝑇, (𝑇𝑇 − 1)/𝑇𝑇) interval. 9

2.2.2. Hypothesis

The COVID-19 pandemic is an ongoing global pandemic of coronavirus disease 2019. The virus was
first identified from an outbreak in Wufan, China, in December 2019 and then spread to other areas of
the world. The World Health Organization (WHO) declared the outbreak a public health emergency ofinternational concern on January 30th, 2020, and a pandemic on March 11th, 2020. Although the
pandemic is still ongoing, most of the strict measures including lockdown were introduced in early2020 and therefore we expect a significant change in the patterns of machinery trade, if any, appearedin 2020. We thus focus on year 2020 and examine whether we can find a break point in the patterns ofmachinery trade in 2020. Our hypothesis is as follows.
Hypothesis: If the COVID-19 pandemic has a significantly negative impact on the patterns ofmachinery exports, we will find significantly negative coefficients in 2020.
Note that the focus of our analysis is to identify the timing of the structural change. AlthoughThis interval was also adopted by Perron and Vogelsang (1992) and Lumsdaine and Papell (1997). It is
technically difficult to set the interval and lag order simultaneously. As a short cut, we use maximum lag order (i.e.,
𝑑𝑑 = 12) for the interval while determining the lag order by a set of sequential 𝐹𝐹-test.
7

9

investigating the sources of the structural change is beyond the scope of our study, we will discuss thepossible factors for further research. It is also important to note that there were several shocks during
the sample period (January 2016—March 2022). If those shocks are more significant than the
pandemic outbreak, those shocks will be the break points. In other words, our question is whether the
pandemic outbreak had more significant effects on exports than other shocks.
2.2.3. Data
We mainly use the monthly export data from January 2016 to March 2022 (75 months), obtained fromthe UN Comtrade Database. In the UN Comtrade Database, monthly imports are valued at the c.i.f.
price (i.e., cost, insurance, and freight price) while monthly exports are valued at the f.o.b. (i.e., freeon-board price). To exclude the effects of the freight charges and the shipping insurance, we utilize
exports rather than imports. 10
Note that the availability of the monthly-level trade data varies between countries. Table 1
summarizes the data availability for major machinery exporting countries and ASEAN countries.
Because monthly export data are not available for China between October 2012 and December 2015,
we focus on the period after 2015.
Table 1
Sample Period in the UN Comtrade Database
Major
countries

ASEAN

10

CHN
DEU
HKG
JPN
USA
BRN
IDN
KHM
LAO
MMR
MYS
SGP
VNM
PHL
THA

Start
2016m1
2010m1
2010m1
2010m1
2010m1
2015m1
2015m1
2015m1
2015m1
2011m1
2014m8
2013m5
2015m1End
2022m32022m32022m32022m32022m32020m122022m32020m12
2020m122021m122021m122021m62021m12

T
75
147
147
147
147
72
87
72
72
132
89
98
84

Not available
2012m10-2015m12

2014m1-2014m12
2013m9 & 2014m11-2014m12
2010m1-2014m12 & 2021m12010m1-2014m12 & 2021m12010m1-2010m12 & 2022m12014m7 & 2022m1
2013m4 & 2021m72010m1-2014m12 & 2022m12019m6
2019m10

As a robustness check, we also utilized import data, as we will discuss later.
8

Following Ando, Kimura, and Obashi (2022), we focus on 2-digit classifications of theHarmonized System (HS) codes between 84 and 92: general machinery (HS84), electrical machinery(HS85), transport equipment (HS86-89), and precision machinery (HS90-92). We first aggregate theseclassifications and focus on machinery trade as a whole as a benchmark. A more disaggregated-level
analysis will be provided in the discussion section (Section 4).
Throughout the paper, we mainly focus on five major machinery exporting countries: China,
Germany, Hong Kong, Japan, and the United States because these countries have been ranked in the
top five machinery exporters for most of the years between 2016 and 2021. In 2021, these fivecountries account for over 50 percent of the world machinery exports. South Korea also ranked in the
top five for some years during this period. However, South Korean export data are not available from2020 at the monthly level in the UN Comtrade Database. We thus mainly focus on the above fivecountries. In Section 5.1, however, we also look at ASEAN countries where machinery is actively
traded through the international production networks (e.g., Ando, Kimura, and Yamanouchi, 2022),
accounting for more than 11 percent of the world machinery exports in 2021 (UN Comtrade Database).
Note that the HS classification codes were revised several times during the sample period.
While we acknowledge the importance of tracing the same product throughout the period, it is difficult
to do so at the 2-digit level because classification changes occurred at the detailed product level. In
addition, for machinery products, most of the products are classified within the same 2-digit levels,
although some of the electrical machinery products are classified into different categories. We thus
decided not to pursue this issue here and continue to use the HS84-92 codes.
Note also that we have to trim the data for the structural break analysis. As mentioned above,
we use ((𝑘𝑘 + 2)/𝑇𝑇, (𝑇𝑇 − 1)/𝑇𝑇) interval, where 𝑘𝑘 = 12 and 𝑇𝑇 = 75. This means that the intervalis (14, 74), which dropped 10 percent of the sample (i.e., the first 13 months at the beginning of thesample period and the last month of the sample). Because the pandemic started from March 2020 (𝑡𝑡 =51), we have basically 23(= 74 − 51) months after the pandemic outbreak. Our analysis thus has
the enough observations after the pandemic outbreak.
2.2.4. Continuing export countries
Figures 2 and 3 present the changes in the total value of machinery exports and the total number ofdestination countries in the world. The total value indicates the sum of all the countries’ exports while
the total number of destination countries indicates the number of origin countries times the number ofdestination countries. We highlight two findings. First, the value of the machinery exports declined
when the COVID-19 pandemic started around March 2020, it showed a quick recovery from mid-2020.
9

Second, in contrast, the number of destination countries declined around March 2020, and thencontinued to decline with some fluctuation afterword.
Figure 2
Value of the Machinery Exports in the World

Notes: Total value of machinery exports indicates the aggregation of export values for each country. The grayshaded area indicates the year 2020.
Source: UN Comtrade Database.

Figure 3
Number of Destination Countries of the Machinery Exports in the World

10

Notes: Total number of destination countries indicates the aggregation of export destination countries for eachcountry. The gray shaded area indicates the year 2020.
Source: UN Comtrade Database.

Note that, in the UN Comtrade Database, monthly export data are not available for somecountries for a certain period, especially after the pandemic outbreak. As mentioned above, South
Korean monthly export data are not available from 2020. Similarly, monthly export data are notavailable from 2021 for some ASEAN countries such as Cambodia, Lao PDR, and Viet Nam (Table
1). Chinese monthly export data are not available from October 2012 to December 2015 (Table 1).
Because PageRank centrality is affected by the presence or absence of trade between countries, thedata availability could affect the results.
To address this issue, we restrict our sample to countries that continue to be available fromJanuary 2016 to March 2022. These are countries that continued to export machinery products to atleast one country throughout the period. In 2019 (before the pandemic), there are about 117 exporting
countries (monthly average). The number of continuing export countries is 48. In 2019, the share ofthese continuing export countries is 58.9 percent of total machinery exports in the world.
Figures 4 and 5 present the changes in the total value of machinery exports and the total number11

of destination countries in the world for continuing export countries. There are two notable findings.
First, like the results of all exporting countries (Figure 2), although the value of the machinery exports
declined when the COVID-19 pandemic started around March 2020, it showed a quick recovery frommid-2020. Second, unlike the results of all exporting countries (Figure 3), the number of export
destination countries did not decline significantly except for the period between March 2020 and April
2020. These results imply that the decline in the number of destination countries in Figure 3 is
attributable to the data availability.
Figure 4
Value of the Machinery Exports in the World: Continuing Export Countries

Notes: Total value of machinery exports indicates the aggregation of export values for each country. The grayshaded area indicates the year 2020.
Source: UN Comtrade Database.

Figure 5
Number of Destination Countries of the Machinery Exports in the World: Continuing Export Countries
12Notes: Total number of destination countries indicates the aggregation of export destination countries for eachcountry. The gray shaded area indicates the year 2020.
Source: UN Comtrade Database.

Figure 6 presents the machinery exports for the five countries from January 2016 to March
2022. There are three notable findings. First, machinery exports declined significantly in the early 2020
for these countries. Second, the exports of Hong Kong dropped significantly in the mid-2018. This isbecause the exports of many machinery products including electrical machinery and transport
equipment are missing in July 2018 in the UN Comtrade Database. Although this may be regarded as
an outlier, a caution is needed in interpreting the results of Hong Kong. Third, it is difficult tell whichmonth-year indicates the break points. In the next section, we employ an econometric analysis to
identify the break points in a precise manner.
Figure 6
Machinery Exports: Major Machinery Exporting Countries

13Notes: Machinery exports are from HS84 to 92, where 84 is electrical machinery and 85 is transport equipment.
The gray shaded area indicates the year 2020.
Source: UN Comtrade Database.

3. Estimation Results
3.1. Structural breaks in machinery exports
We first investigate whether or not we observe a structural break in exports in 2020. We estimate
equations (4) and (6) for the AO model and equation (8) for the IO model, using machinery export dataas an outcome variable. Because we take the log for the dependent variable, the coefficients areinterpreted as the percentage change of the dependent variable.
Table 2 presents the estimation results for major machinery export countries: China (CHN),
Germany (DEU), Hong Kong (HKG), Japan (JPN), and the United States (USA). The upper part
indicates the results of the AO model while the lower panel indicates the lower part indicates the resultsof the IO model. Figures A1-A5 indicate the break points to visually check them.
Table 214

Break Points of Machinery Exports: Major Machinery Export Countries
AO modelBreak point 1

Break point 2

IO modelBreak point 1

Break point 2

CHN
2018m120.016(0.527)2019m120.076**(2.558)DEU
2020m1-0.123***(-5.874)
2020m60.121***(5.549)HKG
2018m5-0.033(-0.321)2021m40.178(1.524)JPN
2020m2-0.111***(-6.479)
2020m80.113***(6.188)USA
2020m1-0.110***(-6.603)2020m70.079***(4.484)CHN
2019m1-0.007(-0.548)2020m1
0.043***(3.653)DEU
2020m2-0.239***(-7.291)
2020m70.225***(6.963)HKG
2018m6-0.002(-0.113)2020m70.109***(5.519)JPN
2020m2-0.134***(-5.910)2020m80.126***(5.415)USA
2020m2-0.171***(-9.187)
2020m70.147***(7.323)

Notes: Countries are represented by their ISO codes. The symbols ***, **, * indicate statistical significance at the
1%, 5%, and 10% levels, respectively. The figures in parentheses are t-statistics. The break points indicate theestimated year and month when the structural breaks are identified. Bold letters indicate significantly negative
coefficient in 2020 while gray-highlights indicate significantly positive coefficients.
Source: UN Comtrade Database.

Let us highlight three findings in the AO model results. First, for China and Hong Kong, theresults indicate no significantly negative coefficient in 2020. This implies that our hypothesis isrejected. Machinery exports in these countries were resilient to the shocks from the COVID-19
pandemic. This result is consistent with the findings of Ando, Kimura, and Obashi (2021) and Ando,
Kimura, and Yamanouchi (2022) that found the resilience of machinery trade in East Asia during the
pandemic period.
Second, in contrast, the results indicate significantly negative coefficient in January 2020 for
Germany and the United States and February 2020 for Japan. This result suggests that, for Germany,
Japan, and the United States, the hypothesis is not rejected. The COVID-19 pandemic had a significant
impact on the machinery exports of these three countries. However, we also confirmed significantly
positive coefficients in mid-2020 for all these three countries. Note that the absolute value of thecoefficients indicates similar magnitude: -0.123 and 0.121 for Germany; -0.111 and 0.113 for Japan;
and -0.110 and 0.079 for the United States. Noting that the coefficients indicate the percentage change
of dependent variable, this implies the significant drop in exports was mostly offset by their increases
15

in mid-2020. These findings are also basically in line with the finding of Ando, Kimura, and Obashi(2021) and Ando, Kimura, and Yamanouchi (2022).
The results of the IO model are very similar to those of the AO model. One notable differenceis that we now confirm significantly positive coefficients in 2020 for China. This implies that that the
COVID-19 pandemic positively affected the machinery exports of China. This may be due to thepositive demand shocks.
One may be concerned about a lag between the business contract and the actual transaction of
the products (i.e., trade). This means that the effects of the pandemic outbreak do not appear
instantaneously. Rather, the effects appear with some lags, which in turn implies that the effects appear
in 2021 rather than in 2020. However, Table 2 does not indicate any significant break points in 2021.
The effects of the future contract, if any, do not have any significant effects on our results.
In sum, the impacts of the COVID-19 pandemic on machinery exports were basically not
negative but positive in China and Hong Kong. The impacts on Germany, Japan, and the United States
were significantly negative, but they were temporal as we observed both negative and positive breakpoints in 2020. These results together suggest that the machinery exports were basically resilient forthese five countries.
3.2. Structural breaks in centrality
Section 3.1 confirmed that the machinery exports were basically resilient for major exporting countries.
Now, let us estimate equations (4) and (6) for the AO model and equation (8) for the IO model, using
PageRank centrality as an outcome variable.
Figure 7 presents the changes in PageRank centrality between January 2016 and March 2022.
Table 3 presents the estimation results for PageRank centrality. The upper part indicates the results of
the AO model while the lower part indicates the results of the IO model. Figures A6-A10 indicate thebreak points to visually check them.
Figure 7
Centrality of Machinery in Manufacturing Exports in the World

16

Notes: Machinery exports are from HS84 to 92, where 84 is electrical machinery and 85 is transport equipment.
The gray shaded area indicates the year 2020.
Source: UN Comtrade Database.

Table 3
Break Points of the Centrality of Machinery Exports: Major Machinery Export Countries

17

AO model
Break point 1

Break point 2

IO modelBreak point 1

Break point 2

CHN
2018m5-0.031(-1.268)
2019m120.082***(3.717)

DEU
2017m100.036**(2.437)2019m100.012(1.149)HKG
2017m100.014(0.655)2019m11-0.037**(-2.475)

JPN
2020m2-0.022(-1.644)2021m3-0.069***(-3.863)USA
2018m20.029*(2.104)2019m11-0.044***(-3.795)

CHN
2018m6-0.022(-1.100)
2020m10.082***(3.826)DEU
2019m110.005(0.561)Not
detected

HKG
2017m110.009(0.362)
2018m11-0.045**(-2.517)JPN
2020m3-0.040***(-3.255)2021m4-0.057***(-2.993)USA
2017m110.029*(1.836)2019m9-0.043***(-3.436)

For notes and source, see Table 2.

While the AO model indicates no significant coefficients in 2020 for these countries, the IO
model indicates a significantly negative coefficient in 2020 for Japan. This result supports the
hypothesis, implying that the COVID-19 pandemic had significantly negative impacts on the relative
importance of Japan in the world machinery trade network. This result is different from the result of
overall exports. Indeed, Kiyota (2022) employed the same framework and found no significant
coefficient after the COVID-19 pandemic outbreak, using overall trade data. This in turn suggests thatthe patterns of machinery trade are slightly different from those of overall trade.
A concern may be that there is a lag between the business contract and the actual transactionof the products (i.e., trade), which suggests that the effects appear with some lags. However, Table 3
indicates significantly negative coefficient only for Japan in both AO and IO models. This result
strengthens our main message. Even if we take into account the lagged effect, our main message does
not change.
We found that the relative importance of top five countries except for Japan did not change
when the COVID-19 pandemic started. This result implies that Japanese relative importance in theworld machinery export network declined after the pandemic started. In the next section, we will
examine whether this pattern is confirmed at the more disaggregated levels and for ASEAN
countries. 11 We also discuss possible reasons why Japanese relative importance in the world
11

We also estimated the break points of machinery imports and centrality for these five countries. We similarly
18machinery export network declined after the pandemic outbreak.
4. Discussion
4.1. Electrical machinery and transport equipment
Section 3 presents the results of all machinery exports, covering the HS categories between 84 and 92.
However, the results may be different between sectors. For example, Ando, Kimura, and Obashi (2021)
found that the negative impacts of the COVID-19 pandemic on exports were larger in the transport
equipment industry than in other machinery industries. This section thus presents the disaggregatedlevel analysis, focusing on two major machinery categories: electrical machinery and transport
equipment.
Tables 4 and 5 present the estimation results for the exports of electrical machinery andtransport equipment, respectively. We cannot estimate the break points for Hong Kong because theexports of electrical machinery and transport equipment are missing in July 2018, as mentioned above.
We highlight two findings. First, for the exports of electrical machinery, the results in Table 3 are very
similar to the results in Table 1. One notable difference is that the significantly negative coefficient isconfirmed before 2020 for Japan in the IO model. Although the month is sometimes different, the same
patterns are confirmed in Tables 2 and 4. This in turn means that the export patterns of machinery
goods as a whole are basically the same as those of electrical machinery.
Table 4
Break Points of Electrical Machinery Exports: Major Machinery Export Countries

found positive coefficients for imports and negative coefficients for centrality in 2020. The results are presented inTables A1 and A2.
19

AO model
Break point 1

Break point 2

IO modelBreak point 1

Break point 2

CHN
2018m120.024(0.737)2019m120.076**(2.441)

DEU
2020m1-0.062***(-4.234)
2020m70.105***(6.750)HKG
Notavailable

JPN
2020m2-0.046***(-2.976)
2020m80.093***(5.697)

USA
2020m1-0.050***(-4.853)
2020m90.074***(6.638)CHN
2019m1-0.006(-0.444)
2020m10.039***(2.902)

DEU
2020m2-0.123***(-6.115)2020m70.172***(6.669)HKG
Notavailable

JPN
2019m11-0.030**(-2.116)2020m70.057***(3.237)

USA
2020m2-0.091***(-5.495)
2020m80.124***(5.593)For notes and source, see Table 1.

Second, for the transport equipment, the results in Table 5 are also very similar to the results in Table
2. A notable difference is that the significantly positive coefficient is confirmed after 2019 for China
in the AO model. The month is sometimes different, but basically the same patterns are confirmed inTables 2 and 5. This in turn means that the export patterns of machinery goods as a whole are also
basically the same as those of transport equipment.
Table 5
Break Points of Transport Equipment Exports: Major Machinery Export Countries

20

AO model
Break point 1

Break point 2

IO modelBreak point 1

Break point 2

CHN
2019m11-0.052(-1.612)
2020m80.231***(6.677)DEU
2020m1-0.222***(-5.820)2020m60.186***(4.683)HKG
Notavailable

JPN
2020m3-0.257***(-9.260)
2020m70.207***(7.173)USA
2020m1-0.225***(-7.835)
2020m70.118***(3.902)CHN
2019m12-0.160***(-3.549)
2020m70.325***(4.551)

DEU
2020m2-0.453***(-7.692)
2020m6
0.398***(7.222)HKG
Notavailable

JPN
2020m2-0.309***(-9.420)2020m70.159***(5.551)

USA
2020m2-0.414***(-12.102)
2020m70.282***(6.529)For notes and source, see Table 1.

Tables 6 and 7 present the estimation results for PageRank centrality of exports of electricalmachinery and transport equipment, respectively. Table 6 indicates the significantly negative
coefficients in 2020 for Japan: February 2020 in the AO model and March 2020 in the IO model. This
result implies that the relative importance of Japan in the network of electrical machinery exports
declined during the pandemic period. Table 7 presents that, for the transport equipment, we confirm
significantly negative coefficients in 2020 for Japan and the United States in both AO and IO models.
The decline in the centrality of machinery exports for Japan may be due to the decline in the centralityof transport equipment export. This finding is in line with the finding of Ando, Kimura, and Obashi(2021) that found that the negative impacts of the COVID-19 pandemic on exports were larger in thetransport equipment industry than in other machinery industries.
4.2. Results for ASEAN countries
Thus far, we found that the decline in the relative importance of major machinery exporting countries
in the world machinery trade network. This may be due to the increases in the relative importance ofother countries, particularly in ASEAN countries where the machinery trade is active, as mentioned
above. This subsection runs the same regressions as those in Section 3, focusing on ASEAN countries.
Due to the limited availability of the data (Table 1), we focus on the period between January 2015 andJune 2021 for Indonesia (IDN), Myanmar (MMR), Malaysia (MYS), Singapore (SGP), and Viet Nam
21

(VNM). 12 To save space, we present the results of PageRank centrality. The results for exports arepresented in the Appendix.
Table 6
Break Points of the Centrality of Electrical Machinery Exports: Major Machinery Export Countries
AO modelBreak point 1

Break point 2

IO modelBreak point 1

Break point 2

CHN
2019m120.136***(7.346)2021m7-0.098***(-3.562)

DEU
2017m40.050**(2.227)
2020m100.044***(4.056)HKG
Notavailable

JPN
2018m90.007(0.496)2020m2-0.040***(-3.075)

USA
2018m20.044(1.671)
2018m10-0.004(-0.193)

CHN
2020m1
0.113***(4.895)2021m7-0.093***(-3.447)

DEU
2019m110.015(1.554)Not
detected

HKG
Notavailable

JPN
2018m100.009(0.890)2020m3-0.041***(-3.686)USA
2017m11
0.069(1.120)2018m110.039(1.592)For notes and source, see Table 2.

Table 7
Break Points of the Centrality of Transport Equipment Exports: Major Machinery Export Countries

In the monthly-level Comtrade Database, the data for June 2019 is not available for the Philippines and October
2019 is not available for Thailand (See Table 1).
22

12

AO model
Break point 1

Break point 2

IO modelBreak point 1

Break point 2

CHN
2018m12-0.128***(-3.333)
2019m120.081**(2.165)DEU
2018m100.018(0.992)
2019m100.016(0.924)HKG
Notavailable

JPN
2019m90.059*(1.861)
2020m6-0.143***(-4.231)USA
2020m1-0.066*(-1.778)
2020m60.106**(2.739)CHN
2018m11-0.121***(-3.232)
2020m10.101***(2.934)

DEU
2018m110.044**(2.333)2019m110.029*(1.922)HKG
Notavailable

JPN
2019m100.089**(2.491)
2020m5-0.167***(-3.733)

USA
2020m60.144**(2.516)2020m10-0.122*(-1.986)

For notes and source, see Table 2.

Tables 8 and 9 present the estimation results for machinery exports and PageRank centrality,
respectively, for ASEAN countries. In Table 8, Indonesia presents both negative and positivecoefficients in 2020. This result is similar to the results of Germany, Japan, and the United States inTable 2. Malaysia, Singapore, and Viet Nam indicate positive coefficients in 2020, which is similar to
the results of China and Hong Kong in Table 2. Myanmar does not indicate any significant break points.
Overall, these results indicate that the results of ASEAN countries are similar to those of major
exporting countries.
Table 8
Break Points of the Machinery Exports: ASEAN Countries

23

AO model
Break point 1

Break point 2

IO modelBreak point 1

Break point 2

IDN
2020m1-0.084***(-3.371)
2020m70.148***(5.089)MMR
2016m6-0.449**(-2.143)2017m20.401***(2.878)MYS
2017m50.104***(7.125)2020m30.041***(2.751)

SGP
2016m120.051***(4.606)2020m70.067***(6.021)

VNM
2017m50.162***(7.722)
2020m30.106***(4.937)IDN
2020m2-0.204***(-5.862)
2020m70.309***(6.362)

MMR
2017m30.350***(3.285)2020m3-0.126(-1.249)

MYS
2017m60.060**(2.341)
2020m40.063***(4.493)

SGP
2020m40.038***(3.747)VNM
2020m40.079***(3.376)

For notes and source, see Table 2.

Table 9
Break Points of the Centrality of Machinery Exports: ASEAN Countries
AO modelBreak point 1

Break point 2

IO modelBreak point 1

Break point 2

IDN
2019m1-0.006(-0.391)
2020m4-0.050**(-2.787)

MMR
2017m12-0.018**(-2.628)
2019m60.010(1.434)

MYS
2017m11-0.096***(-6.098)
2020m7-0.048**(-2.390)

SGP
2016m7-0.033(-0.703)2020m2-0.080**(-2.531)

VNM
2017m4-0.064**(-2.465)
2018m30.060***(2.691)

IDN
2019m2-0.018(-1.374)
2020m5-0.042**(-2.501)

MMR
2018m1-0.033***(-3.714)2019m70.021***(3.285)MYS
2017m12-0.140***(-3.004)
2020m6-0.060**(-2.424)

SGP
2016m8-0.027(-0.838)
2020m3-0.093***(-3.898)

VNM
2017m5-0.092***(-3.575)
2018m4
0.097***(4.231)For notes and source, see Table 2.

In Table 9, we find significantly negative coefficients for Indonesia (April 2020 in the AO
model and May 2020 in the IO model), Malaysia (July 2020 in the AO and June 2020 in the IO model)
and Singapore (February 2020 in the AO model and March 2020 in the IO model). This result implies24

that the decline in the relative importance of these three ASEAN countries. These results are similar to
the results of Japan in Table 3. These results have two important implications. First, the decline in therelative importance of Japan in the world machinery trade network is not attributed to the increases in
the relative importance of these ASEAN countries. Second, the decline in the relative importance ofJapan might be due to the close relationship with these ASEAN countries, because PageRank centrality
reflects not only the importance of Japan but also that of her export partner countries. To investigate
this issue, we conducted the same structural break analysis, replacing the log of the value of exports tothe log of the number of destination countries.
Tables 10 and 11 present the estimated break points for the number of destination countries for
major machinery export countries and ASEAN countries, respectively. We highlight two findings. First,
Table 10 indicates significantly negative coefficients in 2020 for Hong Kong (November 2020 in the
IO model) and the United States (January 2020 in the AO model). This implies that the number ofdestination countries declined for Hong Kong and the United States after the pandemic outbreak.
Second, Japan did not indicate significantly negative coefficients in both AO and IO models. Finally,
Table 11 presents significantly negative coefficients for four out of five countries (Indonesia, Myanmar,
Malaysia, and Singapore). This suggests that the number of destination countries also decreased forthese ASEAN countries after the pandemic outbreak.
Table 10
Break Points of the Number of Machinery Export Destination Countries: Major Machinery Export
Countries

25

AO model
Break point 1

Break point 2

IO modelBreak point 1

Break point 2

CHN
2019m11-0.008**(-2.386)
2020m40.003(0.965)DEU
2017m9-0.013**(-2.517)2018m60.024***(6.225)HKG
2017m7-0.011(-0.239)
2018m5-0.009(-0.281)JPN
2018m20.000(0.068)2019m11-0.008**(-2.477)USA
2017m110.004(0.919)2020m1-0.011***(-3.594)CHN
2019m12-0.015***(-3.581)
2020m50.004(1.036)DEU
2017m10-0.016***(-2.821)2018m60.027***(4.911)

HKG
2018m60.005(1.263)2020m11-0.008**(-2.184)

JPN
2019m12-0.011***(-3.184)2021m5-0.003(-0.747)

USA
2018m70.015***(3.165)2019m2-0.020***(-4.071)

For notes and source, see Table 2.

Table 11
Break Points of the Number of Machinery Export Destination Countries: ASEAN Countries
AO modelBreak point 1

Break point 2

IO modelBreak point 1

Break point 2

IDN
2018m40.035***(4.217)
2020m3-0.049***(-5.091)

MMR
2017m20.406***(6.238)2019m10-0.172***(-3.048)

MYS
2020m2-0.083***(-5.653)
2020m70.078***(4.608)SGP
2017m120.029***(5.392)
2020m2-0.013**(-2.186)

VNM
2016m100.013***(3.066)2021m10.009*(1.693)IDN
2018m20.058***(4.741)
2020m2-0.078***(-5.701)

MMR
2016m80.225**(2.617)
2020m12-0.167*(-1.963)

MYS
2019m12-0.099***(-5.434)
2020m6
0.092***(4.727)

SGP
2016m60.039***(3.630)2017m110.018***(3.124)VNM
2016m110.015***(2.916)2021m20.014**(2.288)For notes and source, see Table 2.

As we discussed earlier, PageRank centrality (equation (3)) increases when 1) the number ofcountry 𝑖𝑖's partners increases; 2) country 𝑖𝑖's trade increases; and 3) PageRank for country 𝑖𝑖's partner.
26

Table 1 indicates that the negative shock of the pandemic on the machinery export of Japan is temporal.
Table 10 presents no significant negative effects of the pandemic on the number of machinery export
destination countries in 2020. These results together suggest that the decline in the Japan’s centrality
is not caused by the decline export values or the number of her destination countries. Rather, it isattributable to the decline in the centrality of Japan’s export destination countries such as ASEAN
countries. Indeed, Table 11 confirms the decline in the PageRank of the ASEAN countries in 2020.
Noting that Japan has a relatively strong trade relationship with ASEAN countries, these results
together suggest that the negative shock of the pandemic spread through the supply chain, which leadto the decline in the relative importance of some countries such as Japan in the world machinery trade
network.
In sum, the world machinery trade network was basically resilient against the pandemic
outbreak. However, this is not applicable to all countries. Some countries were negatively affected by
the pandemic and their relative importance declined. While only indicative, our results suggest thetransmission of negative shocks through the supply chain during the pandemic period. This finding isimportant because, when we focus on the overall trade, Kiyota (2022) did not find negative structuralchange in 2020. Our results thus suggest the importance of detailed disaggregated level analysis.
5. Concluding Remarks
In light of the importance of machinery trade in the world trade, this paper examines whether or not
the patterns of machinery trade changed significantly after the COVID-19 pandemic outbreak. To doso, we employ the framework of the network analysis in order to identify the relative importance ofeach country in the world machinery trade and the framework of the structural break to statisticallydetect the structural change in machinery trade and the centrality.
We utilized the monthly-level bilateral export data from January 2016 to March 2022. Our main
findings are threefold. First, we find positive structural change in exports in the major machinery
exporting countries. Second, we find negative structural change in the centrality in Japan and someASEAN countries, which implies the decline in the relative importance of these countries in the world
machinery network. Third, the decline in the Japanese centrality is not caused by the decline in export
values or number of destination countries. Rather, it is attributable to the decline in the centrality ofJapan’s export destination countries such as ASEAN countries. Noting that Japan has a relatively
strong trade relationship with ASEAN countries, these results together suggest that the negative shockof the pandemic spread through the supply chain, which lead to the decline in the relative importanceof some countries such as Japan in the world machinery trade network.
In sum the world machinery trade network was basically resilient against the pandemic
outbreak. However, this is not applicable to all countries. Some countries were negatively affected by
the pandemic and their relative importance declined. While only indicative, our results suggest the27transmission of negative shocks through the supply chain during the pandemic period. This finding isimportant because, when we focus on the overall trade, Kiyota (2022) did not find negative structuralchange in 2020. Our results thus suggest the importance of detailed disaggregated level analysis. In
this context, it is also interesting to examine the difference between the exports of intermediate goods
and those of consumption goods. This issue will be explored in the next stage of our research.
References
Acemoglu, Daron, Vasco M. Carvalho, Asuman Ozdaglar, and Alireza Tahbaz-Salehi (2012), ‘The
Network Origins of Aggregate Fluctuations’, Econometrica, 80: 1977-2016.
Ando, Mitsuyo, Fukunari Kimura, and Ayako Obashi (2021), ‘International Production Networks Are
Overcoming COVID-19 Shocks: Evidence from Japan’s Machinery Trade’, Asian Economic
Papers, 20: 40-72.
Ando, Mitsuyo, Fukunari Kimura, and Kenta Yamanouchi (2022), ‘East Asian Production Networks
Go Beyond the Gravity Prediction,’ Asian Economic Papers, 21: 78-101.
Ando, Mitsuyo and Kazunobu Hayakawa (2022), ‘Impact of COVID-19 on Trade in Services’, Japanand the World Economy, 62: 101131.
Antonietti, Roberto, Paolo Falbo, Fulvio Fontini, Rosanna Grassi, Giorgio Rizzini (2022), ‘The World
Trade Network: Country Centrality and the COVID-19 Pandemic,’ Applied Network Science,
7: 18.
Arriola, Christine, Przemyslaw Kowalski, and Frank van Tongeren (2021), ‘The Impact of COVID-19
on Directions and Structure of International Trade’, OECD Trade Policy Paper, No. 252.
Banerjee, Anindya, Robin L. Lumsdaine, and James H. Stock (1992), ‘Recursive and Sequential Testsof the Unit-Root and Trend-Break Hypotheses: Theory and International Evidence,’ Journal ofBusiness and Economic Statistics, 10: 271-287.
Carvalho, Vasco M. (2014), ‘From Micro to Macro via Production Networks’, Journal of Economic
Perspectives, 28: 23-48.
Clemente, Jesús, Antonio Montañés, and Marcelo Reyes (1998), ‘Testing for a Unit Root in Variables
with a Double Change in the Mean’, Economics Letters, 59: 175-182.
Freeman, Rebecca and Richard Baldwin (2022) “Global Supply Chain Risk and Resilience,” VoxEU
Column, April 6th, 2022.
Hayakawa, Kazunobu and Hiroshi Mukunoki (2021), ‘Impact of COVID-19 on Global Value Chains’,
Developing Economies, 59: 154-177.
International Monetary Fund (IMF) (2022), World Economic Outlook Update: Rising Caseloads,
Disrupted Recovery, and Higher Inflation, Washington, D.C.: IMF.
28

Kiyota, Kozo (2022), ‘The COVID-19 Pandemic and the World Trade Network’, Journal of Asian
Economics, 78: 101419.
Kiyota, Kozo (2023), ‘The Anatomy of a Global Network of Multinationals: The Case of Japan’,manuscript, Keio University.
Lumsdaine, Robin L. and David H. Papell (1997), ‘Multiple Trend Breaks and the Unit-Root
Hypothesis,’ Review of Economics and Statistics, 79: 212-218.
Page, Lawrence, Sergey Brin, Rajeev Motwani, and Terry Winograd (1999), ‘The PageRank Citation
Ranking: Bringing Order to the Web’, Technical Report, Stanford InfoLab.
Perron, Pierre and Timothy J. Vogelsang (1992), ‘Nonstationarity and Level Shifts with an Application
to Purchasing Power Parity’, Journal of Business & Economic Statistics, 10: 301-320.
Vidya, C. T. and K. P. Prabheesh (2020), ‘Implications of COVID-19 Pandemic on the Global Trade
Networks,’ Emerging Markets Finance and Trade, 56: 2408-2421.
White House (2022) Economic Report of the President, April 2022.
Zivot, Eric and Donald W.K. Andrews (1992), ‘Further Evidence on Great Crash, the Oil-Price Shock,and the Unit-Root Hypothesis,’ Journal of Business and Economic Statistics, 10: 251-270.
Appendix
Figure A1
Machinery Exports and Estimated Break Points: China

29

Note: Chinese export data are not available from October 2012 to December 2015 in the UN Comtrade Database.
The gray shaded area indicates the year 2020. The red lines indicate the estimated breakpoints by AO or IO model.
We did not distinguish them because sometimes both AO and IO models estimated the same break points.
Source: UN Comtrade Database.

Figure A2
Machinery Exports and Estimated Break Points: Germany

30

Note: The gray shaded area indicates the year 2020. The red lines indicate the estimated breakpoints by AO or IO
model. We did not distinguish them because sometimes both AO and IO models estimated the same break points.
Source: UN Comtrade Database.

Figure A3
Machinery Exports and Estimated Break Points: Hong Kong

31

Note: The exports of many machinery products including electrical machinery and transport equipment are missingin July 2018 in the UN Comtrade Database. The gray shaded area indicates the year 2020. The red lines indicate theestimated breakpoints by AO or IO model. We did not distinguish them because sometimes both AO and IO models
estimated the same break points.
Source: UN Comtrade Database.

Figure A4
Machinery Exports and Estimated Break Points: Japan

32

Notes: The gray shaded area indicates the year 2020. The red lines indicate the estimated breakpoints by AO or IO
model. We did not distinguish them because sometimes both AO and IO models estimated the same break points.
Source: UN Comtrade Database.

Figure A5
Machinery Exports and Estimated Break Points: United States

33Notes: The gray shaded area indicates the year 2020. The red lines indicate the estimated breakpoints by AO or IO
model. We did not distinguish them because sometimes both AO and IO models estimated the same break points.
Source: UN Comtrade Database.

Figure A6
PageRank Centrality and Estimated Break Points: China

34

Notes: The gray shaded area indicates the year 2020. The red lines indicate the estimated breakpoints by AO or IO
model. We did not distinguish them because sometimes both AO and IO models estimated the same break points.
Source: UN Comtrade Database.

Figure A7
PageRank Centrality and Estimated Break Points: Germany

35

Notes: The gray shaded area indicates the year 2020. The red lines indicate the estimated breakpoints by AO or IO
model. We did not distinguish them because sometimes both AO and IO models estimated the same break points.
Source: UN Comtrade Database.

Figure A8
PageRank Centrality and Estimated Break Points: Hong Kong

36

Notes: The gray shaded area indicates the year 2020. The red lines indicate the estimated breakpoints by AO or IO
model. We did not distinguish them because sometimes both AO and IO models estimated the same break points.
Source: UN Comtrade Database.

Figure A9
PageRank Centrality and Estimated Break Points: Japan37

Notes: The gray shaded area indicates the year 2020. The red lines indicate the estimated breakpoints by AO or IO
model. We did not distinguish them because sometimes both AO and IO models estimated the same break points.
Source: UN Comtrade Database.

Figure A10
PageRank Centrality and Estimated Break Points: United States

38Notes: The gray shaded area indicates the year 2020. The red lines indicate the estimated breakpoints by AO or IO
model. We did not distinguish them because sometimes both AO and IO models estimated the same break points.
Source: UN Comtrade Database.

Table A1
Break Points of Machinery Imports: Major Machinery Export Countries

39

AO model
Break point 1

Break point 2

IO modelBreak point 1

Break point 2

CHN
2018m12-0.014(-0.860)
2020m80.104***(6.163)DEU
2020m1-0.077***(-4.882)
2020m70.113***(6.766)HKG
2020m80.065***(3.209)2021m40.050**(2.031)

JPN
2019m11-0.039***(-3.082)
2020m90.070***(5.005)USA
2020m1-0.085***(-5.462)
2020m70.122***(7.427)CHN
2019m1-0.054***(-3.010)2020m70.117***(4.730)DEU
2020m2-0.157***(-6.938)
2020m70.199***(7.417)HKG
2020m70.084***(3.456)2021m40.073(1.416)JPN
2019m12-0.074***(-4.982)
2020m80.103***(5.919)USA
2019m12-0.065***(-4.212)
2020m40.085***(5.235)For notes and source, see Table 2.

Table A2
Break Points of the Centrality of Machinery Imports: Major Machinery Export Countries
AO modelBreak point 1

Break point 2

IO modelBreak point 1

Break point 2

CHN
2019m120.084***(4.900)2021m80.077***(2.863)DEU
2019m7-0.051***(-6.368)2021m8-0.060***(-4.768)

HKG
2018m3
0.098***(3.826)2018m110.004(0.197)JPN
2019m2-0.049***(-3.389)
2020m2-0.017(-1.175)

USA
2020m2-0.107***(-9.364)2021m10-0.051**(-2.468)

CHN
2020m10.080***(4.830)2021m50.072***(3.222)

DEU
2019m4-0.041***(-4.239)2021m6-0.053***(-3.951)

HKG
2018m20.110**(2.660)2018m12-0.005(-0.148)

JPN
2018m7-0.044***(-2.915)2020m3-0.050***(-3.327)

USA
2020m3-0.094***(-5.968)2021m7-0.026*(-1.762)

For notes and source, see Table 2.

40