KEO Discussion Paper No. 163

The COVID-19 Pandemicand the World Trade Network
Kozo KiyotaabcaKeio Economic Observatory, Keio University, 2-15-45, Mita, Minato-ku, Tokyo 108-8345, Japan; E-mail:kiyota@sanken.keio.ac.jpbResearch Institute of Economy, Trade and Industry (RIETI), Japanc
Tokyo Center for Economic Research (TCER), Japan

The COVID-19 Pandemic
and the World Trade Network*
Kozo Kiyota†
Keio University, RIETI, and TCER
November 2021

Abstract
Global trade suffered a significant contraction in 2020 as a result of the
COVID-19 pandemic, and its growth is expected to remain below the prepandemic trend. Did the relative importance of countries in the world
trade network change as a result of the pandemic? The answer to thisquestion is particularly important for the Association of Southeast Asian
Nations (ASEAN) countries because of their relative importance in world
trade as well as their strong trade linkages with China, where the COVID19 virus originated. This paper examines how the world trade network haschanged since the COVID-19 pandemic, with a particular focus on ASEAN
countries. Tracking the changes in centrality from January 2000 to March
2021, we find no evidence for most ASEAN and major trading countries
that centrality changed significantly after the pandemic began. Our results
suggest the resilience of the trade pattern for these countries.
Key words: International trade, COVID-19 pandemic, network, centrality,
resilience
JEL classification codes: F14, F40* I have benefited from discussions on this topic with Fukunari Kimura and Kazunobu
Hayakawa. I thank Rashesh Shrestha for their helpful comments. I acknowledge the financial support of the Japan Society for the Promotion of Science Grants-in-Aid (JP18H03637,
JP19H00598). This research was also supported by the Economic Research Institute for ASEAN
and East Asia (ERIA) project “ERIA Research on COVID-19 and Regional Economic Integration.” The usual disclaimers apply.
†
Keio Economic Observatory, Keio University, 2-15-45, Mita, Minato-ku, Tokyo 108-8345,
Japan; Research Institute of Economy, Trade and Industry, 1-3-1, Kasumigaseki, Chiyoda-ku,
Tokyo 100-8901, Japan; Tokyo Center for Economic Research, 1-7-10-703, Iidabashi, Chiyodaku, Tokyo 102-0072, Japan; E-mail: kiyota@sanken.keio.ac.jp1

1

Introduction

The COVID-19 pandemic has had significant negative impacts on the global
economy. According to the International Monetary Fund (IMF) (2021b), global
output growth was −3.2% in 2020, whereas global trade in goods and services
suffered a much larger contraction of −8.3% due to weaker demand. Althoughmerchandise trade volumes have returned to pre-pandemic levels, the World
Trade Organization (WTO) (2021) projected that the growth of trade would
likely slow, remaining below the pre-pandemic trend.
Such negative impacts on trade could vary between countries. Therefore,
the question arises: did the relative importance of countries in the world trade
network change as a consequence of the pandemic? The answer to this questionis particularly important for Association of Southeast Asian Nations (ASEAN)
countries for two reasons. First, they have strong trade linkages with China,
where the COVID-19 virus originated. Second, ASEAN countries are well embedded in global value chains and possess one of the largest shares in global
trade. In 2018, ASEAN had a 7.2% share in global trade in goods, ranked fourth
after the European Union, China, and the United States (The ASEAN Secretariat, 2019). However, answering this question is not straightforward becauseof the complexity of the world trade network, as well as the heterogeneous impacts of the pandemic across countries.
Given this context, this study asks how the world trade network has changed
since the COVID-19 pandemic, with a particular focus on the ASEAN countries. This paper contributes to two strands of literature. The first strand is
the literature on the impacts of COVID-19 on international trade. Hayakawa
and Mukunoki (2021b) investigated the effects of COVID-19 on international
trade by comparing January–August 2019 with the same period for 2020 andconfirmed the significant negative effects. Hayakawa and Mukunoki (2021a)
extended Hayakawa and Mukunoki (2021b), focusing on finished machinery
trade in the first six months of 2019 and 2020. They found that export of finished machinery products significantly decreased when the COVID-19 burden
was more severe in countries exporting finished machinery products or supplying machinery inputs. Hayakawa and Mukunoki (2021c) examined the effects of lockdown policies on international trade in the first six months of 2019and 2020 and found that workplace closures had significant negative effects
on trade. Zhang (2021) focused on the exports of Japanese foreign affiliates
and found that their exports to Japan and other countries significantly declined
during the first three quarters of 2020.1
1

In this connection, based on a series of survival analyses, Obashi (2021) found that trade2

The second strand of literature to which this paper contributes involves thestudies on the network structure of international trade. A number of studies
have analyzed the international trade network using network analysis.2 For
example, De Benedictis and Tajoli (2011) examined the changes in the networkof international trade between 113 countries from 1960 to 2000. They found thatthe centrality of the network changed from European countries to the UnitedStates over the period.
Both strands of literature have made significant contributions to economic
understanding. However, the network structure of trade is beyond the scopeof the first strand of research, and, within the second strand, to the best of
our knowledge, only Vidya and Prabheesh (2020) have examined changes inthe trade network after the COVID-19 pandemic. Furthermore, although theyfound changes in centrality measures after the pandemic, their study involved asimple descriptive analysis, and no statistical tests were provided. In addition,
as they focused on the top 15 global trading countries,3 most of the ASEAN
countries were excluded from their study. Thus, changes in the relative importance of the ASEAN countries in the world trade network remain unclear.
This paper attempts to fill this gap in the two strands of literature. That is,
we extend the network analysis to ASEAN countries and employ formal statistical analysis to evaluate the significance of the changes in the international
trade network after the COVID-19 pandemic. There are three advantages to
employing a network analysis. First, the data requirement for the analysis isrelatively low. Information on bilateral trade only is required for the analysis, which means that it is easy to implement and replicate. Second, trade
data are suitable for examining the current economic situation because they areavailable on a monthly basis. Our sample period is between January 2000 andMarch 2021. Noting that the abovementioned related studies did not cover theyear 2021, our study presents the latest update of the analysis on international
trade in the literature. Finally, network analysis enables us to provide a superior visual representation of the results. It involves visualizing the networkof countries based on graph theory, which is helpful in capturing the relative
importance of each country in a reasonably simple manner.
To measure the relative importance of the ASEAN countries in the world
trade network, we compute each country’s centrality using the bilateral traderelationships in the East Asian region were resilient even in the midst of the COVID-19 shocks.
2
Several studies, such as Smith and White (1992) and Garlaschelli and Loffredo (2004), haveapplied network analysis to international trade data, but they did not provide an economic
basis for their analyses.
3
The top 15 global trading countries are Canada, China, France, Hong Kong, India, Indonesia, Italy, Japan, Germany, the Netherlands, Russia, Singapore, South Korea, the United Kingdom, and the United States.

3

data between January 2000 and March 2021. Then, we investigate whether thecentrality changed significantly after the pandemic, employing the econometric framework of structural breaks. We find statistically significant breakpoints
in the changes in centrality before 2020 for most ASEAN countries. The result
suggests that the trade shock after the pandemic is temporary rather than perpetual.
The paper is organized as follows. The next section explains the methodology and data used in this study. Section 3 presents the estimation results.
Section 4 checks the robustness of the results, and Section 5 presents extensionsof our analysis. Section 6 provides our conclusions.

2

Methodology and Data

2.1

Methodology

2.1.1

Centrality

As noted above, this study employs network analysis. In this framework, eachcountry is represented as a node, while the trade relationship between countries
is represented as a link. Thus, the world trade network is represented by nodes
and links, which make up a graph. The relative importance of each node isrepresented by centrality measures. Measures of centrality include closenesscentrality, which is based on the distance between nodes, and degree centrality,
which is based on the number of links. However, because countries generally
trade with many countries simultaneously, these centrality measures are notnecessarily useful for analysis of the world trade network.
Several studies, including Acemoglu, Carvalho, Ozdaglar, and Tahbaz-Salehi(2012) and Carvalho (2014), proposed theoretical models in which the influence of individual firms or sectors on aggregate outcomes is determined bytheir eigenvector centrality, which is also called Bonacich centrality.4 However,
eigenvector centrality is not applicable to directed graphs; therefore, it is notapplicable to analyzing the world trade network because trade has a direction(from exporting countries to importing countries).
To overcome this problem, this study utilizes PageRank centrality, which
was originally developed to evaluate the ranking of webpages (Page, Brin, Motwani, and Winograd, 1999). PageRank centrality is a variant of eigenvector centrality but has the following two advantages. First, like eigenvector centrality,
4

Behrens, Lamorgese, Ottaviano, and Tabuchi (2007) theoretically investigated the relationship between eigenvector centrality and international trade.

4PageRank centrality considers not only the number of edges that a node has
but also the number of edges that other directly connected nodes have. Indeed,as Kiyota (2021a) showed, PageRank centrality includes an index of forward
linkages in input–output analysis as a special case. Second, unlike eigenvector centrality, this centrality is applicable to a directed graph. This is anotherdesirable property for the analysis of trade.
Let the number of nodes be n. We denote the adjacency matrix as A:

a11 · · ·
 .
...
 ..


A = (aij ) =  ai1 · · ·
 .
..
 .
.
 .
an1 · · ·
where

(
aij =

a1j · · ·
..
..
.
.
aij
..
.

···
..
.

anj · · ·


a1n
.. 
. 


ain  ,
.. 

. 
ann

1 if there is a link from node i to node j;
0 otherwise,

(1)

(2)

where a link is measured by trade (e.g., country j imports from country i).
Now, let us introduce time dimension t. Let the PageRank centrality be P Rit
for country i at time t. Then, it is defined as:
P Rit = ψ

n
X

aijt

j=1

P Rjt
+ χ,
kjt

(3)

where ψ and χ are positive constants and kjt is the outdegree, which is measured by the number of export destination countries of country j. In computing
PageRank centrality, we use the share of imports of country i from country j tototal imports of country i as a weight.5 Thus, equation (3) means that PageRankcentrality for country i becomes high if 1) the number of country i’s partners
increases; 2) country i’s trade increases; and 3) the PageRank for country i’s
partners increases. Conventionally, we set ψ = 0.85 and χ = 1. To make comparisons between years, we also adjust PageRank centrality such that its total
equals one. We examine how this centrality measure changes after the COVID19 pandemic to check the relative importance of ASEAN countries within theworld trade network.
5
Note that it is difficult to use the share of exports as a weight because PageRank is computed by summing over all the import partner countries (j). If, for example, the import ofcountry i from country j is zero, aij = 0. Therefore, export share from country j to country
i will not be reflected for the PageRank calculation. In turn, this means that the sum of theweights will not be equal to one.

5

2.1.2

Structural break

Our main research question is whether the relative importance of the countries
in the world trade network changed as a result of the pandemic. Note that suchchanges occur over the analysis period due to, for example, each country’s economic growth. Thus, we investigate whether countries experienced statisticallysignificant changes in their centrality paths by employing a structural change
framework.6 Examples of the structural breaks in trade are trade liberalization(Ben-David and Papell, 1997) and the 1970s oil shocks (Abu-Bader and AbuQarn, 2010). We employ the approach developed by Zivot and Andrews (1992)
and extended by Perron and Vogelsang (1992), which is summarized in ourcontext as follows.
We denote an outcome variable of country i as yt (suppressing the country
subscript i). Although our main outcome variable is PageRank centrality, we
also utilize trade as an outcome variable in the preliminary analysis to check the
impacts on trade. Consider that the time series of outcome yt experiences one
structural break during the sample period. There are two types of models thatcan capture the structural break: an additive outlier (AO) model that captures
a sudden change, and an innovative outlier (IO) model that captures a gradualshift in the mean of the series.
The AO model consists of two steps. In the first step, we estimate the following regression equation:(4)

yt = µ + δDUt + ỹt ,
where
DUt =


1

if t > Tb ;0otherwise,(5)

where Tb is the time of the unknown breakpoint to be located by grid search;and ỹt denotes the residuals. In the second step, the residuals from this regression are used as the dependent variable for the following equation:
ỹt =

d
X

ωτ DTb,t−τ + αỹt−1 +

τ =1

d
X

θτ ∆ỹt−τ + εt ,

(6)

τ =1

where
DTbt =

1

if t = Tb + 1;

0otherwise.

6(7)

A recent study by Constantinescu, Mattoo, and Ruta (2020) employed a similar structuralchange framework to examine global trade patterns over the past four decades.

6

The lag order d is unknown. The second regression is estimated over feasible
values of Tb to search for the minimal t-statistic to test whether the autoregressive parameter α = 1 (i.e., the strongest rejection of the unit root null hypothesis) for all the break time combinations, whereas d is determined by a set ofsequential F -tests.7 . Note that there is no intercept because the mean of ỹt iszero. The significance level of this minimal t-statistic is investigated based on
the critical values provided by Perron and Vogelsang (1992).
By contrast, the IO model is based on a one-step procedure. The following
regression equation is estimated:
yt = µ + δDUt + φDTbt + αyt +dXθτ ∆yt−τ + εt .

(8)

τ =1

As in the AO model, the regression equation is estimated over feasible valuesof Tb to search for the minimal t-statistic to test whether the autoregressive parameter α = 1 (i.e., the strongest rejection of the unit root null hypothesis) for
all the break time combinations, whereas d is determined by a set of sequential
F -tests. Note that it is necessary to choose some trimming value because thetest is not defined at the limits of the sample period (Clemente, Montañés, andReyes, 1998). To adopt the largest window possible, we drop the first and thelast observations, which is equivalent to setting the trimming value at 0.5% for
the beginning and the end of the sample period.
2.1.3

Hypothesis

Equation (3) indicates that the centrality is affected by 1) changes in the number of countries, 2) changes in trade, and 3) changes in linkages (through thechanges in other centrality). The pandemic could potentially affect all of thesefactors. For example, the demand and/or supply shocks from the pandemic
could change the number of partner countries and/or volume of trade.8 Even
though the ASEAN countries successfully weathered the shocks, their centrality would change if the relative importance of other countries changed.
As mentioned above, it is not easy to predict how the trade network haschanged since the pandemic began because of the complexity of the world
trade network, as well as the heterogeneous impacts of the pandemic acrosscountries. Nevertheless, several studies such as Obashi (2010) and Ando and7

We set the maximum lag number as 12 to reduce the computational burden and to accountfor seasonality
8
Bacchetta, Bekkers, Piermartini, Rubinova, Stolzenburg, and Xu (2021) argued that the
COVID-19 pandemic could contribute to the diversification of sources of supply, which in turnwould affect both the number of partner countries and the volume of trade.

7

Kimura (2012) have argued that the production networks in East Asia are stable and resilient against demand/supply shocks, such as the global financial
crisis and the Great East Japan Earthquake. Thus, we hypothesize that the relative importance (measured by PageRank centrality) of the ASEAN countries
in the world trade network did not change following the COVID-19 pandemic.
In turn, this would mean that δ is significant before the pandemic started (i.e.,
before 2020).

2.2

Data and descriptive analysis

In measuring the trade network, we use the monthly bilateral trade data fromthe IMF’s Direction of Trade Statistics for the period from January 2000 to March
2021 for 204 countries.9 That is, aij in the adjacency matrix is measured by theimports of country i from country j. Imports are measured by their cost, insurance, and freight prices. The imports do not cover services trade. For each country, the total number of observations is 255 (= 12 months × 21 years + 3 months).
While our main focus is the 10 ASEAN countries (i.e., Brunei Darussalam, Cambodia, Indonesia, the Lao People’s Democratic Republic (PDR), Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Viet Nam), we also compute
PageRank centrality for four major trading countries (i.e., China, Germany,
Japan, and the United States) for reference.
Figure 1 presents the changes in the overall trade for four major trading
countries (i.e., China, Germany, Japan, and the United States) and one of the
major trading ASEAN countries, Singapore, from January 2015 to March 2021.10
The overall trade is defined as the sum of exports to and imports from theworld. We highlight two findings in this figure. First, overall trade dropped
when the COVID-19 pandemic commenced (around March 2020), but it increased again around June 2020. Second, compared with the four major trading
countries, the overall trade of Singapore, as our representative ASEAN country, is rather small even though ASEAN as a whole possesses one of the largest
shares in the global economy. This implies that it would be difficult to determine the pattern of trade by comparing the ASEAN countries in this mannerwith the four major trading countries given the differences in the scale of trade.
In what follows, we present the figures for ASEAN countries separately.
Figure 2 presents the changes in the overall trade for ASEAN countries for
9

Taiwan is not included in the Direction of Trade Statistics.
These four major trading countries have been ranked in the top four countries in PageRankcentrality in the world trade network since 2001 (Kiyota, 2021b). We focus on the period fromJanuary 2015 to highlight the pandemic period. Figure A1 presents the results for full sample
period (i.e., from January 2000 to March 2021).
108

Figure 1: Trade Patterns for Major Trading Countries, January 2015 to March
2021
Exports + Imports (Billions of USD)

500

100
50

2021m1

2020m7

2020m1
2020m32019m72019m1

2018m72018m1

2017m72017m1

2016m7

2016m1

2015m7

2015m1

10Year and month
China
Germany
Singapore

United StatesJapan

Note: The vertical axis indicates the value of overall trade (exports + imports) in log
terms. The solid line indicates March 2020, when the COVID-19 pandemic commenced
in many countries.
Source: IMF (2021a).

9

January 2015 to March 2021.11 For ease of exposition, we divide the 10 ASEAN
countries into two groups based on the scale of trade.12 Panel A indicatesthe overall trade for Indonesia, Malaysia, Singapore, Thailand, and Viet Nam,
while Panel B presents the overall trade for Brunei Darussalam, Cambodia, theLao PDR, Myanmar, and the Philippines. Similar to Figure 1, we find increases
in trade value after the sharp drop when the pandemic started for all ASEAN
countries. It is interesting to note that the overall trade for Myanmar has large
values in September–October 2014. Although this is due to increases in exports
to China, we could not identify the specific reason for this, and, thus, we must
be cautious in interpreting the changes in trade patterns for Myanmar during
this period.
Figure 2: Trade Patterns for ASEAN Countries, January 2015 to March 2021
Panel A
Exports + Imports (Billions of USD)

100

50

2021m1

2020m7

2020m1
2020m32019m72019m1

2018m72018m1

2017m72017m1

2016m7

2016m1

2015m7

2015m1

10Year and month
Malaysia
ThailandIndonesia

11

Viet Nam
Singapore

Figure A2 presents the results for the full sample period (i.e., from January 2000 to March
2021).
12
As the vertical axis indicates, the scale of trade differs between the four major trading countries and the ASEAN countries, and between the ASEAN countries in Panels A and B.

10

Panel B
Exports + Imports (Billions of USD)

50

10
5

1
0.5

2021m1

2020m7

2020m1
2020m32019m72019m1

2018m72018m1

2017m72017m1

2016m7

2016m1

2015m7

2015m1

0.1

Year and monthPhilippines
Cambodia
Lao PDR

MyanmarBrunei DarussalamNote: The vertical axis indicates the value of overall trade (exports + imports) in log
terms. The solid line indicates March 2020, when the COVID-19 pandemic commenced
in many countries.
Source: IMF (2021a).

Figure 3 presents PageRank centrality for the four major countries and Singapore between January 2015 and March 2021.13 This figure indicates that thechanges in PageRank centrality differ across countries after the start of the pandemic. For China and Germany, PageRank centrality dropped around the timethat the pandemic began but increased around June 2020. By contrast, for Japanand the United States, PageRank centrality dropped and did not increase after
the start of the pandemic. For Singapore, it is difficult to determine the change
because the value is small compared with the four major countries. Thus, wereport the results for the ASEAN countries separately from the four major trading partners as follows.
13

Figure A3 presents the results for the full sample period (i.e., from January 2000 to March
2021).

11

2021m1

2020m7

2020m1
2020m32019m72019m1

2018m72018m1

2017m72017m1

2016m7

2016m1

2015m7

2015m10.05.1PageRank Centrality.15

Figure 3: PageRank Centrality for Major Trading Countries, January 2015 toMarch 2021

Year and month
China
Germany
Singapore

United StatesJapan

Note: The vertical axis indicates PageRank centrality. The solid line indicates March
2020, when the COVID-19 pandemic commenced in many countries.
Source: Author’s estimation based on IMF (2021a).

Figure 4 presents PageRank centrality for the ASEAN countries for January
2015 to March 2021.14 Similar to overall trade, we divide the 10 ASEAN countries into two groups based on the scale of centrality for ease of exposition.15
Panel A indicates the PageRank centrality for Indonesia, Malaysia, Singapore,
Thailand, and Viet Nam, while Panel B presents the PageRank centrality for
Brunei Darussalam, Cambodia, the Lao PDR, Myanmar, and the Philippines.
Figure 4 also indicates that the changes in PageRank centrality differ and do
not present any common patterns between ASEAN countries. This result implies that it is not necessarily clear whether the relative importance of ASEAN
countries in the world trade network changed after the pandemic. To address
this issue further, the next section employs regression analyses. For Myanmar,
it is important to note that PageRank centrality exhibits extremely high values
for September–October 2014, which should be treated with caution.
14

Figure A4 presents the results for full sample period (i.e., from January 2000 to March 2021).
As was the case for the size of trade, PageRank centrality differs between the four major
trading countries and the ASEAN countries, and between ASEAN countries in Panels A and B.
15

12

2020m1
2020m32020m72021m1

2020m1
2020m32020m72021m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m7

2016m1

2015m7

2015m1

.01

.012

.014

PageRank Centrality

.016

.018

Figure 4: PageRank Centrality for ASEAN Countries, January 2015 to March
2021
Panel A

Year and month
Malaysia
ThailandIndonesia

Viet Nam
Singapore

2019m72019m1

2018m72018m1

2017m72017m1

2016m7

2016m1

2015m7

2015m1

.001

.002

.003

PageRank Centrality

.004

.005

Panel B

Year and monthPhilippines
Cambodia
Lao PDR

MyanmarBrunei DarussalamNote: The vertical axis indicates PageRank centrality. The solid line indicates March
2020, when the COVID-19 pandemic commenced in many countries.
Source: Author’s estimation based on IMF13(2021a).

3

Estimation Results

3.1

Structural break in trade

Before analyzing centrality, we determine whether a structural break in trade
is observed after the pandemic. We estimate equations (4) and (6) for the AO
model and equation (8) for the IO model using aggregate bilateral trade (exports + imports) as an outcome variable.16 This enables us to investigate whether
the structural break in the centrality coincides with the changes in the aggregate
trade volume.
Table 1 presents the results for the AO model. We highlight two findings.
First, a structural break is confirmed in all countries, as indicated by the significant coefficients. Moreover, all the coefficients are positive. These results imply
that the ASEAN countries experienced a positive structural change in trade in
the sample period. Second, the breakpoint varies across countries, ranging from
December 2004 (Singapore) to October 2015 (the Philippines). All the breakpoints are located before 2020. This result implies that the trade shock from thepandemic is not necessarily regarded as a point of structural change.
Table 1: Estimation Results: Additive Outlier Model for Trade
BRN
KHM
IDN
LAO
MYS
Break point 2005m4
2012m4 2006m12 2008m10 2005m12
yt−1
0.312*** 0.680*** 0.432*** 0.771*** 0.310***[19.339] [27.614] [32.031] [30.840] [27.760]
MMR
PHL
SGP
THA
VNM
Break point 2010m12 2015m10 2004m12 2006m12 2010m12
yt−1
0.583*** 0.242*** 0.347*** 0.389*** 0.729***[32.004] [16.694] [29.254] [30.025] [26.351]
Note: Countries are represented by their International Organization for Standardization (ISO) codes (see Table A1 in the Appendix). The symbols ***, **, and * indicate
statistical significance at the 1%, 5%, and 10% levels, respectively. The figures in parentheses are t-statistics. The breakpoints indicate the estimated month and year when the
structural breaks are identified.
Source: Author’s estimation based on IMF (2021a).

Table 2 presents the results for the IO model. There are two notable findings.
First, the significantly positive coefficient is estimated for nine of the 10 countries. For Viet Nam, the estimated coefficient is insignificant. This means that
the IO model did not identify a structural break for Viet Nam during the sample period. Second, similar to the IO model, the breakpoint is different between
16

The estimation is based on the Stata commands clemao1 and clemio1 developed by
Baum (2005).

14

countries, ranging from January 2004 (Singapore) to January 2010 (Myanmar).
However, the estimated breakpoints in the IO model are different from thosein the AO model. For example, for the Philippines, the estimated breakpoint is
October 2015 in the AO model, whereas it is March 2009 in the IO model. The
results suggest that the estimated breakpoints could vary between the AO andIO models. Nevertheless, all the breakpoints are located before 2020, suggesting that the trade shock during the pandemic is not regarded as a breakpoint
during the period.
Table 2: Estimation Results: Innovative Outlier Model for Trade
BRN
KHM
IDN
LAO
MYS
Break point 2005m1 2010m1 2003m11 2005m1 2009m1
yt−1
0.033* 0.028*** 0.017** 0.035*** 0.015**[1.822] [3.230][2.135][2.795] [2.448]
MMR
PHL
SGP
THA
VNM
Break point 2010m1 2009m3 2004m1 2009m1 2009m1
yt−1
0.048** 0.015**
0.021**
0.013*
0.008[2.425] [2.205][2.438][1.900] [0.992]
Note: Countries are represented by their ISO codes (see Table A1 in the Appendix).
The symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels,
respectively. The figures in parentheses are t-statistics. The breakpoints indicate theestimated year and month when the structural breaks are identified.
Source: Author’s estimation based on IMF (2021a).

These results together suggest that although there was a large decline inoverall trade after the pandemic, it is not necessarily regarded as a breakpoint
between January 2000 and March 2021. Thus, the shock from the pandemic
can be regarded as a temporary one. However, the relative importance of theASEAN countries in overall trade may still change after the pandemic. The next
section addresses this issue.

3.2

Structural break in centrality

3.2.1

Single structural breakNow, we estimate equations (4) and (6) for the AO model and equation (8)for the IO model, using PageRank centrality as an outcome variable. Table 3
presents the results for the AO model. There are four notable findings. First,
the structural break is confirmed in all countries, as indicated by the significant coefficients. However, the signs of the coefficients vary across countries.
Whereas seven countries have positive signs, Brunei Darussalam, the Philippines, and Singapore have negative signs. The results suggest that the direction15

of structural change is different between countries.
Table 3: Estimation Results: Additive Outlier Model for Centrality
BRN
KHM
IDN
LAO
MYS
Break point 2013m12
2014m92008m92013m82020m2yt−1-0.0003*** 0.0006*** 0.0005*** 0.0002*** 0.0014***[-21.147][30.815][8.496][31.727][7.926]
MMR
PHL
SGP
THA
VNM
Break point 2014m7
2007m7
2015m82008m2
2015m2yt−1
0.0005*** -0.0013*** -0.0016*** 0.0019*** 0.0088***[14.665][-24.278][-16.171][16.989][35.232]
Note: Countries are represented by their ISO codes (see Table A1 in the Appendix).
The symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels,
respectively. The figures in parentheses are t-statistics. The breakpoints indicate theestimated year and month when the structural breaks are identified.
Source: Author’s estimation based on IMF (2021a).

Second, in line with the results for trade in Table 1, the breakpoint varies
between countries, ranging from July 2007 (the Philippines) to February 2020
(Malaysia). Among the 10 countries, only Malaysia has a breakpoint in 2020.
This result seems to suggest that Malaysia experienced a structural change
when the pandemic started. However, note that the shock is positive rather
than negative. We will discuss this point again with the results of the IO model.
Third, the estimated breakpoints differ for the trade and centrality results.
For example, for Brunei Darussalam (see Table 1), the estimated breakpoint intrade is April 2005, whereas for centrality it is December 2013. Indeed, none of
the countries has the same breakpoint for trade and centrality. The results imply
that significant changes in the aggregate bilateral trade do not necessarily meanchanges in the relative importance of the country.
Finally, it is interesting to note that the estimated breakpoint for Myanmar is
July 2014, which is close to the period when the outliers are confirmed (Figure4). Thus, the estimated structural break for Myanmar may be affected by theexistence of outliers.
Table 4 indicates the results for the IO model. For one of the 10 countries, thecoefficient is insignificant. Nevertheless, all the signs of the IO model in Table
4 are the same as those of the AO model in Table 3. Moreover, although theestimated breakpoints in the IO model for each country are different from thosein the AO model, they are very similar. For example, for Brunei Darussalam,
the breakpoints are December 2013 and January 2014 for the AO and IO models,
respectively. Similarly, those for Cambodia are September 2014 and October
2014 for the AO and IO models, respectively. These results suggest that, in
16

contrast to aggregate bilateral trade, for centrality, both the AO and IO models
estimate similar breakpoints.
Table 4: Estimation Results: Innovative Outlier Model for Centrality
BRN
KHM
IDN
LAO
MYS
Break point 2014m1
2014m10
2008m52013m42019m1yt−1-0.0001*** 0.00004**
0.0001*0.00001** 0.0004***[-3.069][2.173][1.747][2.091][3.640]
MMR
PHL
SGP
THA
VNM
Break point 2014m8
2007m82015m7
2008m3
2014m2yt−1
0.0001*** -0.0002*** -0.0005***
0.00010.0003**[3.845][-4.565][-3.240][1.335][2.380]Note: Countries are represented by their ISO codes (see Table A1 in the Appendix).
The symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels,
respectively. The figures in parentheses are t-statistics. The breakpoints indicate theestimated year and month when the structural breaks are identified.
Source: Author’s estimation based on IMF (2021a).

For Malaysia, in contrast to the AO model results, the IO model estimatedthat the breakpoint occurred in January 2019. This indicates that the structural
break occurred before the pandemic started if it involves a gradual rather thana sudden change. Although we confirmed that a structural break occurred in
February 2020 in the AO model, it is sensitive to the choice of model. Thus,
it is difficult to assert definitively that a structural change occurred after the
pandemic started.
It is interesting to note that the breakpoints are concentrated in 2007–2008
for three countries (Indonesia, the Philippines, and Thailand) and in 2013–2014
for five countries (Brunei Darussalam, Cambodia, the Lao PDR, Myanmar, andViet Nam). The world economy experienced the global financial crisis around2008, while Cambodia, the Lao PDR, Myanmar, and Viet Nam enacted zero
tariff rates on virtually all imports for ASEAN members between 2008 and 2015
(Okabe and Urata, 2014). The results suggest that significant changes for our
10 countries are more likely to be related to the global financial crisis and tradeliberalization than to the pandemic.
As mentioned above, one of the key advantages of network analysis is theability to present the results visually. However, it is difficult to present meaningful results for the trade network for all ASEAN countries and their partnercountries, given that each country trades with many countries. Therefore, we
focus on the top 20 partner countries for each ASEAN country and compute
their PageRank centrality. Trade between non-ASEAN countries is excluded tofocus on trade by ASEAN countries.
17

Figures 5 and 6 present the trade networks of the ASEAN countries in March
2000, 2010, 2019, and 2020.17 The visualization is based on the circle layout algorithm. Each country is located on the circle in alphabetical order by International Organization for Standardization (ISO) codes. The location of the countries changes over the period because of changes in the top 20 partner countries.
The size of the ISO code and node represent the size of the country’s gross domestic product (GDP) and per capita GDP, respectively.18 Note that the locationof nodes and the length of edges do not have any meaning because they depend
upon the algorithm employed to depict the figure. The important issue here iswhich nodes are connected with each other.
Figure 5 indicates that the connection with China increased from March 2000
to March 2010 in many ASEAN countries, including Indonesia, Malaysia, Singapore, and Thailand. Figure 6 indicates that such connections with China became much larger in March 2019. The network graphs in March 2019 and March
2020 are almost identical. This supports our result that there is no significant
change in the trade network of the ASEAN countries before or after the pandemic.
3.2.2

Multiple structural breaks

A concern with the analysis in the previous subsection is that both the AO andIO models assume a single structural change during the sample period. However, because the sample covers more than 20 years, there may be more than
one structural change in each country. To address this issue, we employ the testdeveloped by Clemente, Montañés, and Reyes (1998) that allows for the estimation of two events within the observed history of a time series. The estimation
procedure for the double-break model is almost the same as that for the singlebreak model. For the AO model, the first-step regression equation is writtenas:yt = µ + δ1 DU1t + δ2 DU2t + ỹt ,
(9)
where
DUmt =

1

if t > Tbm ;0otherwise,

17

(10)

We choose March because the latest available month is March (2021). We also present theresults for March 2021 in Figure A5 in the Appendix for reference.
18
For the ISO code, see Table A1 in the Appendix. When data for a given year were notavailable, the data for the latest available year were used.

18

Figure 5: Trade Network of ASEAN Countries, March 2000 and March 2010
March 2000

March 2010

Note: The size of the ISO code and node represent the size of each country’s GDP and
per capita GDP, respectively. The network is based on three major trading partners.
Source: Author’s estimation based on IMF (2021a).

19

Figure 6: Trade Network of ASEAN Countries, March 2019 and March 2020
March 2019

March 2020

Note: The size of the ISO code and node represent the size of each country’s GDP and
per capita GDP, respectively. The network is based on three major trading partners.
Source: Author’s estimation based on IMF (2021a).

20

for m = 1, 2. As for the single breakpoint model, Tb1 and Tb2 are the breakpoints
to be located by grid search. The corresponding second step equation is:
ỹt =

d
X
τ =1

ω1τ DTb1,t−τ +dX

ω2τ DTb2,t−τ + αỹt−1 +

τ =1

where
DTbm,t =dXθτ ∆ỹt−τ + εt ,

(11)

τ =1


1

if t = Tbm + 1;

0otherwise(12)

for m = 1, 2. The second regression is estimated over feasible values of Tbm,tand d. For the IO model, the regression equation is written as:
yt = µ + δ1 DU1t + δ2 DU2t + φ1 DTb1,t + φ2 DTb2,t + αyt−1 +dXθτ ∆yt−τ + εt . (13)

τ =1

The locations of Tbm and d are determined by the grid search. As in the baseline
single-break analysis, we drop the first and last observations, which is equivalent to setting the trimming value at 0.5% for the beginning and the end of thesample period.
Table 5 presents the estimation results of the AO model with two breaks.19
There are two notable findings. First, the significance and the signs of the estimated coefficients vary across countries. Second, the breakpoints are different
between countries. Moreover, the two breakpoints do not necessarily coincidewith the breakpoint estimated by the single-break AO model. Nonetheless, all
but one breakpoint is confirmed before 2020. This means that even if we allow
for two structural breaks, the trade shock after the pandemic is not identified
as a structural breakpoint.
Table 6 presents the results for the IO model. As for the results of the AO
model, the signs and significance levels of the estimated coefficients differ between countries, and the breakpoints vary across countries. The estimatedbreakpoints are not necessarily the same between the single- and double-breakmodels. Here, all the estimated breakpoints are located prior to 2020. In short,the estimated breakpoints are sensitive to the choice of the models and the number of possible breaks. However, we cannot find evidence to support changes
in the relative importance of the ASEAN countries in world trade after the pandemic. Thus, our main messages continue to hold even when the analysis takes
into account the existence of two structural breaks.
19

The estimation is based on the Stata commands clemao2 and clemio2 developed by
Baum (2005).

21

Table 5: Estimation Results for Two Structural Breaks: Additive Outlier Model
for Centrality
BRN
KHM
IDN
LAO
MYS
Break point 1 2004m9
2014m92009m92013m8
2012m2yt−1
0.0001*** 0.0005*** 0.0009*** 0.0002*** -0.0002**[3.620][27.173][10.970][32.191][-2.493]
Break point 2 2013m12
2019m3
2012m10 2020m1 2019m10
yt−1-0.0003*** 0.0003*** -0.0006*** 0.0001*** 0.0014***[-21.367][10.582][-6.878][10.025][8.453]
MMR
PHL
SGP
THA
VNM
Break point 1 2013m11
2008m5
2005m10 2006m4 2014m10
yt−1
0.0004*** -0.0016***0.00010.0015*** 0.0078***[4.392][-31.292][0.538][15.310][33.802]
Break point 2 2014m7
2014m102015m82015m4 2019m10
yt−1
0.00010.0004*** -0.0016*** 0.0014*** 0.0040***[1.436][8.044][-15.217][13.423][9.413]
Note: Countries are represented by their ISO codes (see Table A1 in the Appendix).
The symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels,
respectively. The figures in parentheses are t-statistics. The breakpoints indicate theestimated year and month when the structural breaks are identified.
Source: Author’s estimation based on IMF (2021a).

Table 6: Estimation Results for Two Structural Breaks: Innovative Outlier
Model for Centrality
BRN
KHM
IDN
LAO
MYS
Break point 1 2013m4
2014m5
2009m10
2013m4
2010m7yt−1-0.0001*** 0.0001*** 0.0003*** 0.00002*** -0.0001[-3.024][3.654][3.130][2.703][-1.188]
Break point 2 2014m7
2019m4
2012m1
2018m72019m1yt−10.00000.0001*** -0.0003*** 0.00001** 0.0005***[-1.038][2.791][-2.879][1.996][4.015]
MMR
PHL
SGP
THA
VNM
Break point 1 2014m1
2007m8
2012m12
2006m5
2011m4yt−1
0.0003*** -0.0003*** -0.0003** 0.0003*** 0.0003***[5.553][-5.777][-2.206][2.705][2.840]
Break point 2 2014m8
2014m3
2015m92014m10 2014m11
yt−1-0.0001
0.0001*** -0.0004** 0.0003*** 0.0003**[-1.590][3.485][-2.156][3.046][2.375]
Note: Countries are represented by their ISO codes (see Table A1 in the Appendix).
The symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels,
respectively. The figures in parentheses are t-statistics. The breakpoints indicate theestimated year and month when the structural breaks are identified.
Source: Author’s estimation based on IMF (2021a).

22

3.3

Implications of the results

In sum, although we found statistically significant breakpoints in the changes
in centrality between February 2000 and March 2021 for most ASEAN countries, they generally occur prior to 2020, which supports our hypothesis. These
results suggest that the trade shock following the pandemic is temporary rather
than perpetual. This indicates the resilience of the trade pattern for almost all
the ASEAN countries.
One may ask why we did not find a breakpoint during the pandemic. There
are several possible reasons. First, as Hayakawa and Mukunoki (2021c) pointed
out, many Asian countries permitted factory operations for specific industries
if firms took adequate infection control measures. Second, inventory adjustment might enable firms in ASEAN countries to export without the need for
production operations.
Third, strong monetary and fiscal policies by many governments helped
prevent a larger drop in global demand (WTO, 2021). Fourth, lockdowns andtravel restrictions caused consumers to shift their spending from non-traded
services to goods, while innovation and adaptation by businesses and households kept economic activity from falling even more. These are some possible
reasons why we did not find a breakpoint during the pandemic, although more
rigorous analyses are required for further discussions.

4

Robustness Check

4.1

Results for major trading countries

In Section 3, we found that the structural changes in centrality occurred prior to2020. There may be concerns regarding the external validity of this result; thatis, the results may change if we focus on major trading countries. To address
this concern, we estimate the same AO and IO models for the four major trading
countries mentioned in Subsection 2.2: China, Germany, Japan, and the UnitedStates.
Table 7 presents the estimation results. The upper part of the table shows theresults of the AO model, and the lower part contains those for the IO model.
We highlight four findings. First, the estimated coefficients of the AO model
are significantly positive for China, and significantly negative for the UnitedStates, Germany, and Japan. This result implies that except for China, the major
trading countries experienced negative structural changes in centrality during
the sample period. Second, for the IO model, the estimated coefficients become23insignificant for China, but the same signs are maintained for the other countries. Moreover, similar to the results for ASEAN countries in Tables 3 and 4, theestimated breakpoints are quite similar between the AO and IO models. Thissuggests that the results of the IO model are generally consistent with those of
the AO model.
Table 7: Estimation Results: Major Trading Countries
Additive Outliers Model
CHN
USA
DEU
JPN
Break point 2009m2
2003m3
2008m2
2010m6yt−1
0.0394*** -0.0267*** -0.0083*** -0.0149***[24.022][-31.450][-21.190][-27.452]
Innovative Outliers Model
CHN
USA
DEU
JPN
Break point 2008m3
2002m3
2009m10
2010m9yt−1-0.0001-0.0018** -0.0013*** -0.0009***[-0.092][-2.542][-3.426][-3.013]
Note: Countries are represented by their ISO codes (see Table A1 in the Appendix).
The symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels,
respectively. The figures in parentheses are t-statistics. The breakpoints indicate theestimated year and month when the structural breaks are identified.
Source: Author’s estimation based on IMF (2021a).

Third, all the estimated breakpoints occur prior to 2020 for these four countries. This result means that the trade shock following the pandemic is notregarded as a breakpoint in terms of changes in centrality. In the previous subsection, we found that the majority of breakpoints occurred prior to 2020 for the
ASEAN countries. Thus, our results suggest that the same is true for the major
trading countries.
Finally, it is interesting to note that the estimated breakpoints were between
February 2008 and September 2010 for China, Germany, and Japan. The global
financial crisis may have significant effects on the importance of these countries
in the world trade network. This result is in line with the results of the IO modelfor the ASEAN countries (Table 4). By contrast, the estimated breakpoints for
the United States were March 2003 and March 2002 in the IO and AO models,
respectively. Although we cannot argue for causality in a precise manner, thisresult seems to suggest that China’s entry into the WTO, which occurred in December 2001, immediately before these breakpoint dates, may have influenced
the decline in the relative importance of the United States in the world trade
network.

24

4.2

Shorter period

We use a relatively longer time series (i.e., January 2000 to March 2021) to examine a structural break in centrality measures. One possible concern is that thelonger time series may make it harder to determine structural breaks during
the pandemic. To address this concern, we focus on a shorter period of analysis, namely a five-year period, starting from January 2015, and then estimate
the AO and IO models.
Tables 8 and 9 present the estimation results for the AO and IO models,
respectively. Table 8 indicates that among the 10 countries, Indonesia, Lao PDR,
Malaysia, the Philippines, and Singapore have a breakpoint in 2020. Althoughthese countries appear to experience a structural change when the pandemic
commenced, the shock is positive rather than negative except for Singapore. It
is interesting to note that the result for Malaysia is consistent with the findingof the results of the single structural break model for the longer time period in(Table 3).
Table 8: Estimation Results for Shorter Analysis Period: Additive Outlier Model
for Centrality
BRN
KHM
IDN
LAO
MYS
Break point
2019m92019m32020m2
2020m1
2020m2yt−1
0.00007*** 0.00027*** 0.00043*** 0.00010*** 0.00124***[4.361][7.566][3.239][9.029][7.033]
MMR
PHL
SGP
THA
VNM
Break point
2019m32020m2
2020m62017m122019m10yt−10.00023***0.00009-0.00085*** -0.00042** 0.00388***[8.960][1.509][-3.477][-2.614][8.867]
Note: Countries are represented by their ISO codes (see Table A1 in the Appendix).
The symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels,
respectively. The figures in parentheses are t-statistics. The breakpoints indicate theestimated year and month when the structural breaks are identified.
Source: Author’s estimation based on IMF (2021a).

Table 9 indicates that for Indonesia and Singapore, in contrast to the AO
model results, the IO model estimated that breakpoints occurred in 2019 forIndonesia and in 2015 for Singapore. Similar to the baseline results, the structural break occurred before the pandemic commenced, if the structural change
is gradual rather than sudden. Although we confirmed that a structural breakoccurred for Indonesia and Singapore when the pandemic started in the AO
model, this result is sensitive to the choice of model.
Table 9 also indicates that for Lao PDR, Malaysia and the Philippines, theestimated breakpoint in the IO model was March 2020, which is close to the25Table 9: Estimation Results for Shorter Period: Innovative Outlier Model forCentrality
BRN
KHM
IDN
LAO
MYS
Break point 2019m10
2019m42019m52020m32020m3yt−1
0.00007*** 0.00036*** 0.00034*** 0.00005*** 0.00117***[3.788][6.414][2.823][2.740][3.202]
MMR
PHL
SGP
THA
VNM
Break point
2019m42020m3
2015m92017m82019m5yt−1
0.00017*** 0.00010** -0.00061** -0.00029** 0.00097***[3.753][2.154][-2.421][-2.161][2.830]Note: Countries are represented by their ISO codes (see Table A1 in the Appendix).
The symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels,
respectively. The figures in parentheses are t-statistics. The breakpoints indicate theestimated year and month when the structural breaks are identified.
Source: Author’s estimation based on IMF (2021a).

points in the AO model. For these countries, the pandemic has had a significant impact if we limit the analysis to the sample period between January 2015
and March 2021. These results suggest that, even if we focus on the shorter
period, seven of 10 countries indicate the breakpoint before 2020. Moreover,the shock is positive rather than negative for the other three countries (i.e., Lao
PDR, Malaysia and the Philippines). Thus, we can argue that the relative importance of ASEAN countries in the world trade network did not decline as aconsequence of the pandemic.

5

Extensions

5.1

Alternative approach

A further concern with our analysis is that the changes in centrality follow nonlinear trends. Because our baseline analysis accommodates only a linear trend,
the main results may not hold if the analysis accounts for nonlinear trends. To
address this issue, following Ben-David and Papell (1997) and Abu-Bader and
Abu-Qarn (2010), we estimate the following version of the augmented Dickey–
Fuller regression, which accommodates both linear and nonlinear trends:
yt = µ + β1 t + β2 t2+ δDUt + φ1 DTt + φ2 DTt2 +dXθτ yt−τ + εt ,

(14)

τ =1

where yt is PageRank centrality, DUt is the break dummy variable, which is thesame as in the baseline analysis, and DTt captures the changes in the trend after
26

the breakpoint. Then, we have:

DTt =


t − T

if t > Tb ;0otherwise,

b

(15)

where Tb is the time of the breakpoint, that is, the period in which the change
in the trend function parameters occurs. For the lag d, we use a 12-month lag(i.e., d = 12) to account for seasonality.
Conducting the test involves the following steps. First, before estimating
equation (14), we examine whether PageRank centrality contains a unit root
based on the Phillips–Perron test (Phillips and Perron, 1988), the null hypothesis of which is that PageRank centrality does contain a unit root.20 We find thatthe null hypothesis is rejected for all ASEAN countries except for Viet Nam.
Second, we conducted Vogelsang’s (1997) sup-Wald (or sup Wt ) test, estimating
equation (14). Sup Wt is the maximum of the standard F -test statistics times
three for each year for testing the null hypothesis δ = φ1 = φ2 = 0 over all
possible trend breaks. The month and year when the maximum is identified
are regarded as the breakpoint. Finally, we investigate the significance level of
sup Wt based on the critical values calculated in Vogelsang (1997).21
Table 10 presents the regression results of equation (14). Breakpoints arefound in 2020 only for the Lao PDR. For other countries, breakpoints occur
prior to 2020. This implies that relative importance did not change just before
or after the pandemic for most ASEAN countries. In short, our main messages
are largely unchanged even if we employ an alternative approach.

5.2

Exports and imports

In Section 3, we estimated equations (4) and (6) for the AO model and equation(8) for the IO model using aggregate bilateral trade (exports + imports) as an
outcome variable. One question that may arise is how the results change if
the analysis utilizes exports or imports only. To answer this question, we use
aggregate bilateral exports and imports separately as outcome variables andreestimate equations (4) and (6) for the AO model and equation (8) for the IO
model.
Tables 11 and 12 present the results of exports and imports, respectively, for
20

The alternative is that PageRank is generated by a stationary process. We include a trend
variable in the regression. The test is based on the Stata command pperron.
21
For Viet Nam, the critical values are obtained from Vogelsang (1997, Table 2, p = 2 &∗λ = 0.01). For other countries, the critical values are obtained from Vogelsang (1997, Table 1,
p = 2 & λ∗ = 0.01).

27

Table 10: Regression Results: Alternative Approach
BRN
KHM
IDN
LAO
MYS
Unit-root test Reject*** Reject** Reject*** Reject*** Reject***
Break point
2011m8 2019m6 2009m12 2020m4 2014m12
Sup Wt22.0***
61.0***
19.3**
20.4**
21.7***
MMR
PHL
SGP
THA
VNM
Unit-root test Reject*** Reject* Reject*** Reject*** Not reject
Break point
2014m9 2007m10 2006m2 2014m12 2018m2
Sup Wt92.8***
27.0***
15.0
24.0***
39.7***
Note: Countries are represented by their ISO codes (see Table A1 in the Appendix).
The symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels,
respectively.
Source: Author’s estimation based on IMF (2021a).

the AO model, whereas Tables 13 and 14 indicate the results of exports andimports, respectively, for the IO model. We highlight two findings. First, estimated breakpoints are different between exports and imports for all countries
but Singapore. Nevertheless, estimated breakpoints in Table 1 are consistent
with both Tables 11 and 12. For example, the estimated breakpoints in Tables 1
and 11 are the same for Malaysia, Singapore, Thailand, and Viet Nam, whereas
the estimated breakpoints in Tables 1 and 12 are the same for Indonesia, Myanmar, the Philippines, and Singapore. Similar patterns are confirmed for the IO
model in Tables 13 and 14.
Table 11: Estimation Results: Additive Outlier Model for Exports
BRN
KHM
IDN
LAO
MYS
Break point 2004m9 2012m3 2005m10 2009m9 2005m12
yt−1
0.295*** 0.668*** 0.370*** 0.844*** 0.315***[14.813] [26.395] [30.458] [30.475] [28.171]
MMR
PHL
SGP
THA
VNM
Break point 2008m3 2004m12 2004m12 2006m12 2010m12
yt−1
0.556*** 0.191*** 0.336*** 0.386*** 0.786***[23.236] [18.203] [29.370] [31.685] [27.969]
Note: Countries are represented by their ISO codes (see Table A1 in the Appendix).
The symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels,
respectively. The figures in parentheses are t-statistics. The breakpoints indicate theestimated year and month when the structural breaks are identified.
Source: Author’s estimation based on IMF (2021a).

Second, the results confirm that none of the estimated breakpoints occurred
after the pandemic started. This in turn implies that the changes in exports
and imports of the ASEAN countries before and after the pandemic cannot be28

Table 12: Estimation Results: Additive Outlier Model for Imports
BRN
KHM
IDN
LAO
MYS
Break point 2007m8 2011m12 2006m12 2007m7 2008m12
yt−1
0.356*** 0.692*** 0.563*** 0.757*** 0.252***[16.935] [27.568] [32.891] [32.696] [20.140]
MMR
PHL
SGP
THA
VNM
Break point 2010m12 2015m10 2004m12 2008m12 2008m11
yt−1
0.621*** 0.337*** 0.354*** 0.348*** 0.683***[36.569] [19.119] [27.489] [21.709] [24.582]
Note: Countries are represented by their ISO codes (see Table A1 in the Appendix).
The symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels,
respectively. The figures in parentheses are t-statistics. The breakpoints indicate theestimated year and month when the structural breaks are identified.
Source: Author’s estimation based on IMF (2021a).

Table 13: Estimation Results: Innovative Outlier Model for Exports
BRN
KHM
IDN
LAO
MYS
Break point 2004m5
2010m4 2002m1 2005m4 2009m1
yt−1
0.030*
0.035*** 0.027*** 0.055*** 0.014**[1.898][3.429][3.060][3.167] [2.110]
MMR
PHL
SGP
THA
VNM
Break point 2007m12 2008m12 2005m1 2005m1 2010m1
yt−1
0.054**
0.012**
0.019**
0.012
0.013*[2.001][2.288][2.169][1.572] [1.790]
Note: Countries are represented by their ISO codes (see Table A1 in the Appendix).
The symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels,
respectively. The figures in parentheses are t-statistics. The breakpoints indicate theestimated year and month when the structural breaks are identified.
Source: Author’s estimation based on IMF (2021a).

Table 14: Estimation Results: Innovative Outlier Model for Imports
BRN
KHM
IDN
LAO
MYS
Break point 2007m2 2010m1 2007m1 2005m7 2009m1
yt−1
0.036
0.029** 0.037*** 0.039*** 0.019**[1.009] [2.346] [2.758][2.662] [2.563]
MMR
PHL
SGP
THA
VNM
Break point 2010m7 2015m4 2004m1 2009m1 2009m1
yt−1
0.074*** 0.027** 0.025**
0.015*
0.008[3.543] [2.343] [2.473][1.731] [0.686]
Note: Countries are represented by their ISO codes (see Table A1 in the Appendix).
The symbols ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels,
respectively. The figures in parentheses are t-statistics. The breakpoints indicate theestimated year and month when the structural breaks are identified.
Source: Author’s estimation based on IMF (2021a).

29

regarded as structural breaks. This result reinforces the resilience of the tradepattern for all ASEAN countries. Thus, our main messages remain unchanged
even when we analyze exports and imports separately.

6

Concluding Remarks

Global trade was expected to suffer a significant contraction as a result of the
COVID-19 pandemic. However, as such negative impacts on trade could vary
between countries, we wished to investigate whether the relative importance ofcountries in the world trade network changed as a result of the pandemic. We
consider that the answer to this question is particularly important for ASEAN
countries because of their strong trade linkages with China, where the COVID19 virus originated. More generally, the answer to this question is importantbecause of the complexity of the world trade network and the heterogeneous
impacts of the pandemic across countries.
This paper examined how the world trade network changed after the COVID19 pandemic, particularly focusing on ASEAN countries. Tracking the changes
in centrality from January 2000 to March 2021, we found no evidence that centrality changed significantly after the pandemic started for most ASEAN countries. Our results suggest that the relative importance of the ASEAN countries
in the world trade network remains unchanged even after the pandemic.
If we view the results optimistically, the COVID-19 pandemic has not had
a destructive impact on the world trade network. Rather, the effects are temporary and limited. World trade is strong enough to resist the threat from thepandemic. In particular, the pattern of trade is resilient to the pandemic for almost all the ASEAN countries. This seems to be a positive message, althoughcaution is required because our analysis focuses on a short period after the pandemic owing to the limited availability of data.
Before closing this study, we point out several directions for future research.
First, extending the analysis to a longer period is an important avenue for future
research. As mentioned above, this study covers only a short period after the
pandemic started (i.e., March 2020 to March 2021) owing to the limited availability of data. However, the effect of the pandemic may be more evident in
the medium to long run. For example, Jordà, Singh, and Taylor (2020) argued
that significant macroeconomic aftereffects of pandemics persist for decades asa result of reductions in the relative labor supply and/or a shift to greater precautionary savings. It may be premature to reach definitive conclusions at thispoint, and it remains essential to carefully monitor the effects of the pandemic

30

on global trade.
Second, it is important to examine the impact of the pandemic at a more detailed product level. Our analysis focused on the aggregate bilateral trade, buteven if aggregate trade did not change significantly, the pandemic might haveinfluenced the composition of trade. Finally, it would be interesting to investigate the effects on services trade. As a result of the pandemic, the mobility ofpeople has been restricted to a greater degree than that of goods, with more limits between countries than within each country. Such restrictions would havesignificant effects on some services trade, such as tourism services. We plan toinclude these issues in our future research agenda.

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Appendix
Figure A1: Trade Patterns for Major Trading Countries, January 2000–March
2021
Exports + Imports (Billions of USD)

500

100
50

2021m1

2020m1

2019m1

2018m1

2017m1

2016m1

2015m1

2014m1

2013m1

2012m1

2011m1

2010m1

2009m1

2008m1

2007m1

2006m1

2005m1

2004m1

2003m1

2002m1

2001m1

2000m1

10Year and month
China
Germany
Singapore

United StatesJapan

Note: The vertical axis indicates the value of overall trade (exports + imports) in log
terms. The solid line indicates March 2020, when the COVID-19 pandemic started inmany countries.
Source: IMF (2021a).

34

Figure A2: Trade Patterns for ASEAN Countries, January 2000–March 2021
Panel A
Exports + Imports (Billions of USD)

100
50

10
5

2018m1

2019m1

2020m12021m1

2018m1

2019m1

2020m12021m1

2017m1

2016m1

2015m1

2014m1

2013m1

2012m1

2011m1

2010m1

2009m1

2008m1

2007m1

2006m1

2005m1

2004m1

2003m1

2002m1

2001m1

2000m1

1Year and month
Malaysia
ThailandIndonesia

Viet Nam
Singapore

Panel B

10
5

1
0.5

2017m1

2016m1

2015m1

2014m1

2013m1

2012m1

2011m1

2010m1

2009m1

2008m1

2007m1

2006m1

2005m1

2004m1

2003m1

2002m1

2001m1

0.1
2000m1

Exports + Imports (Billions of USD)

50

Year and monthPhilippines
Cambodia
Lao PDR

MyanmarBrunei DarussalamNote: The vertical axis indicates the value of overall trade (exports + imports) in log
terms. The solid line indicates March 2020, when the COVID-19 pandemic started inmany countries.
Source: IMF (2021a).

352021m1

2020m1

2019m1

2018m1

2017m1

2016m1

2015m1

2014m1

2013m1

2012m1

2011m1

2010m1

2009m1

2008m1

2007m1

2006m1

2005m1

2004m1

2003m1

2002m1

2001m1

2000m10.05.1PageRank Centrality.15

Figure A3: PageRank Centrality for Major Trading Countries, January 2000–
March 2021

Year and month
China
Germany
Singapore

United StatesJapan

Note: The vertical axis indicates PageRank centrality. The solid line indicates March
2020, when the COVID-19 pandemic started in many countries.
Source: Author’s estimation based on IMF (2021a).

Table A1: Country CodeISO
ASEAN countries
BRN Brunei Darussalam
MMR
KHM Cambodia
PHL
IDN
Indonesia
SGP
LAO Lao People’s Democratic Republic THA
MYS Malaysia
VNM
Major trading countries
CHN China
JPN
DEU Germany
USA
ISO

CountryCountry
MyanmarPhilippines
Singapore
Thailand
Viet NamJapan
United States

Note: ISO indicates the 3-digit code developed by International Organization for Standardization.
Source: International Organization for Standardization’s website ().

36

2017m1

2018m1

2019m1

2020m12021m1

2018m1

2019m1

2020m12021m1

2016m1

2017m1

2015m1

2014m1

2013m1

2012m1

2011m1

2010m1

2009m1

2008m1

2007m1

2006m1

2005m1

2004m1

2003m1

2002m1

2001m1

2000m1

0

.005

.01

PageRank Centrality

.015

.02

Figure A4: PageRank Centrality for ASEAN Countries, January 2000–March
2021
Panel A

Year and month
Malaysia
ThailandIndonesia

Viet Nam
Singapore2016m1

2015m1

2014m1

2013m1

2012m1

2011m1

2010m1

2009m1

2008m1

2007m1

2006m1

2005m1

2004m1

2003m1

2002m1

2001m1

2000m1

PageRank Centrality.001 .002 .003 .004 .005 .006

Panel B

Year and monthPhilippines
Cambodia
Lao PDR

MyanmarBrunei DarussalamNote: The vertical axis indicates PageRank centrality. The solid line indicates March
2020, when the COVID-19 pandemic started in many countries.
Source: Author’s estimation based on IMF (2021a).

37

Figure A5: Trade Network of ASEAN Countries, March 2021

Note: The size of the ISO code and node represent the size of country’s GDP and percapita GDP, respectively. The network is based on three major trading partners. We
use the data for the latest available year because the data for 2021 are not available (asof September 2021).
Source: Author’s estimation based on IMF (2021a).

38