Diversification in trade and foreign investment and resilience to the
COVID-19 shock: Firm-level evidence using Japanese customs data
Running title: Resilience to the COVID-19 shock
Toshiyuki Matsuura †Keiko Ito §Naoto Jinji *Abstract
Using data from the COVID-19 period, this study examines whether firms’ participationin global value chains (GVCs) makes them more resilient to external negative shocks,
distinguishing between multinational and non-multinational firms and between trading
and non-trading firms. Using Japanese customs data matched with firm-level data and asurvey on foreign direct investment (FDI) we construct a dataset that contains export
destinations, import origins, and investment destinations. We then examine which firms
were more affected by the COVID-19 pandemic. We find that export growth rates arehigher for firms with more diversified export destinations and import origins, and theresult is more pronounced for the intermediate goods trade. However, no such effect isseen for diversification of FDI destination countries.
Keywords: COVID-19 pandemic; Multinational firms; Global Value Chains
JEL code: F23; F14†

Keio Economic Observatory, Keio University; Visiting Research Fellow, Policy
Research Institute Ministry of Finance, corresponding author.
§
Graduate School of Social Sciences, Chiba University; Visiting Research Fellow, Policy
Research Institute Ministry of Finance*
Graduate School of Economics, Kyoto University; Visiting Research Fellow, Policy
Research Institute Ministry of Finance11. Introduction
International trade declined significantly in 2020 due to the spread of COVID-19.
The pandemic led to lockdowns in many countries that halted manufacturing activities,
and shocks spread to other countries as the supply of intermediate goods was disrupted
across global value chains (GVCs). The COVID-19 pandemic was not the first time
GVCs were disrupted; similar shocks occurred during the global financial crisis (GFC)of 2008–2009 and the great East Japan earthquake (GEJE) of 2011. The effects of external
shocks are amplified through GVCs because many firms and countries involved in GVCs
are interdependent, so an external shock in one part of a GVC will have a cascading effecton other parts. 1 Therefore, understanding the mechanisms of such spillovers has attracted
attention not only among academic researchers but also policymakers. However, if
intermediate goods are sourced from multiple countries it may be easier to procure themfrom other countries if production stops in one location due to a shock. Similarly, if
exports are made to multiple countries it may be relatively easy for exporters to findalternative export destinations if faced with a drastic reduction in demand in one country
due to a shock. In other words, firms that engage with a more diverse range of countries
and industries in their GVCs may recover relatively quickly from temporary shocks, even
if the initial impact is significant.
In addition, given that multinational enterprises (MNEs) play an essential role in
GVCs the question of whether those MNEs are vulnerable or resilient to external shocks
to GVCs is also a policy issue. On the one hand, MNEs are often considered resilient toshocks as they operate in multiple countries and can diversify such risks. Still, the impact
of shocks is likely to be more severe if a firm engages in intrafirm trade of intermediate
goods that are difficult to procure from other suppliers.
In fact, numerous empirical analyses using country-level trade data suggest
reductions in the supply of intermediate goods due to the COVID-19 shock were
amplified through supply chains, leading to a decrease in domestic exports (e.g., Friedt
and Zhang, 2020, Kejzar and Velic, 2020, Hayakawa and Mukunoki, 2021a). However,most of these studies are based on country-level monthly trade data, while there is little
empirical evidence based on firm-level data. A few exceptions include Jordaan (2023)
and Lafrogne-Joussier et al. (2023). While Jordaan (2023) analyzes firms in many
countries using firm-level data compiled by the World Bank Enterprise Survey (WBES)
project, he mainly examines the impact of firm-level export and import intensity on sales
1Studies such as Acemoglu et al. (2012), Calvalho et al. (2021), and di Giovanni et al.
(2024) show that as economic shocks propagate through inter-firm and/or inter-industry
input-output relationships, they are amplified to become larger shocks.
2but due to data constraints does not take diversification of export destinations and importsources into account. Using detailed firm-level trade data for French firms, LafrogneJoussier et al. (2023) focus on the short-run impact of the January 2020 lockdown in
China on French firms’ imports, exports, and domestic sales. Although they are interested
in diversification with respect to sourcing and compare firms that import from China withthose that import from other countries, their study does not consider diversity in export
destinations. Thus, there is not yet sufficient empirical evidence at the firm level todetermine whether firms that are part of GVCs that diversified their risks recovered fasterfrom the COVID-19 trade shock. Against the backdrop, this study uses firm-level datafrom Japan combined with detailed export and import customs data to examine changes
in firm performance due to the COVID-19 pandemic and compare resilience to external
shocks among firms participating in GVCs, both MNEs and non-MNEs. 2
The following two features distinguish this study from previous research. First, we
examine the resilience of firms that participate in GVCs to shocks by focusing on
diversity among both importing and exporting countries. While previous studies on shockpropagation, such as Todo et al. (2023) and Barrot and Sauvagnat (2016), use original
firm surveys or trade partner data for listed firms, this study uses comprehensive firmlevel customs data. The second distinguishing feature of this study is that we analyze theresilience of MNEs to shocks by taking into account the number of foreign direct
investment (FDI) destination countries. Many studies that compare MNEs with nonMNEs, such as Alfaro and Chen (2012), do not consider differences in MNEs’ tradepatterns and production locations in different countries. We use the number of FDI
destination countries to consider the diversity of MNEs’ production locations, thereby
examining the effects of diversification in terms of countries where MNEs operate.
Our key findings can be summarized as follows: diversity in export and importdestinations contributes to export recovery during a pandemic, and this effect is more
pronounced for the trade of intermediate goods. Given that the number of export-importdestinations is particularly large for MNEs, the improved performance of MNEs during
pandemic periods analyzed in previous studies is explained by the diversity of MNEs’
export-import destinations. By contrast, the number of MNEs’ investment destinations
has no significant impact on export and import growth rates.
2

Zhang (2021) analyzes the impact of COVID-19 on sales, exports, and imports offoreign affiliates of Japanese multinational firms. However, his study relies on quarterly
aggregate-level data for Japanese affiliates in foreign countries, not on firm-level data.
Moreover, while he focuses on affiliates of Japanese MNEs, our study focuses on parent
firms of Japanese MNEs.
3

This study is related to three strands of the literature: the first focuses on shocktransmission mechanisms in supply chains. Barrot and Sauvagnat (2016) use data on U.S.
listed firms to analyze the extent to which natural disasters in the U.S. spill over to trading
partners through supply chains and find that spillover effects are significant in industries
that produce differentiated goods. Kashiwagi et al. (2021) analyze how damage from ahurricane in the U.S. spreads through global supply chains to domestic and international
trading partner firms, finding that the impact is smaller for firms doing business withoverseas firms. 3 Using French firm-level trade transaction data, Bricongne et al. (2022)
find the decline in macro-level trade during the GFC and the COVID-19 pandemic period
is mainly attributable to the decrease in exports by top exporters. In particular, they point
to the high sensitivity of top exporters to changes in foreign demand.
Second, several studies focus on the COVID-19 pandemic. 4 Among them, Ando and
Hayakawa (2022) examine the relationship between the geographic diversity of importing
countries and exports of machinery in 35 countries and find that the impact of thepandemic was smaller for transportation machinery and precision machinery in countries
that import intermediate goods from more geographically diverse countries. Todo et al.
(2023) analyze the geographic distribution of suppliers and customers and the impact of
COVID-19 shocks based on original firm-level surveys in ASEAN and India. Using firmtransaction-level data they analyze the geographic concentration of suppliers andcustomers and the robustness of trading relationships. They show supply chains are morerobust when suppliers and customers are geographically dispersed. Our study is similar
to Ando and Hayakawa (2022) and Todo et al. (2023) regarding the diversity of sales andprocurement sources. We extend previous studies by distinguishing trade of final goods
versus intermediate goods and examining the characteristics of MNEs using Japanesecustoms data combined with comprehensive firm-level data. This is the first study toutilize firm-level data matched with the Japanese customs data to analyze the impact of
the COVID-19 pandemic on firm activities. An advantage of using customs data is that
one can observe the level of engagement in trade (both import and export), including thenumber of trading partner countries, at the firm-product level. Thus, we can consider
details of GVC participation.
The third stream of the literature focuses on how MNEs respond to shocks. Using
3

Others include Ando and Kimura (2012) and Abreha et al. (2020) for the global financial
crisis and GEJE, Todo et al. (2015) and Boehm et al. (2019) for the GEJE in 2011,
Hayakawa et al. (2015) for the Thai flood in 2011.
4
For example, see Hayakawa and Mukunoki (2021b), Meier and Pinto (2020), Ando etal. (2021), Borino et al. (2021).
4

data on Irish firms, Görg and Stroble (2003) show MNEs are more footloose, having ahigher probability of exit than non-MNEs. In a recent study, Kiyota et al. (2020) compare
employment volatility across firms and find it is higher in MNEs engaged in intrafirm
trade. They interpret this to mean that because MNEs engaging in intrafirm trade transact
intermediate goods via firm-specific investment within firms, an external shock would
disrupt intrafirm trade in these intermediate goods, hurting firms’ performance.
Conversely, a body of literature suggests MNEs exhibit greater resilience toeconomic shocks. For example, Alfaro and Chen (2012) use establishment-level data on
MNEs to analyze whether MNEs and non-MNEs responded differently to the GFC andfind that MNEs are more resilient to shocks. Jordaan (2023), using a World Bank surveyof firms, analyzes changes in firm performance during the COVID-19 pandemic and finds
the negative effects are smaller for MNEs engaged in imports and exports. Benguria(2021) uses firm-level trade transaction data for Colombia matched with firm balance
sheet information to examine patterns of declining trade values, finding that subsidiaries
of MNEs experience a smaller decline in trade. Our study differs from prior research byexamining the resilience of MNEs to shocks, incorporating factors such as the number ofcountries in which they invest, as well as the number of exporting and importing countries.
The remainder of this study is organized as follows. Section 2 presents the analyticalframework. Section 3 explains our data sources and data construction procedure, andpresents a data overview. We present the estimation results in Section 4, and Section 5
concludes.

2. Analytical framework
To examine the impact of the COVID-19 pandemic on global trade through GVCs, weestimate the following equation using data for the period from 2017 through 2020:∆𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑌𝑌𝑖𝑖𝑖𝑖𝑡𝑡0 + 𝛽𝛽𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖−1 + 𝛽𝛽 2020 ∙ 𝐷𝐷2020 ∙ 𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖−1 + 𝜇𝜇𝑗𝑗 + 𝜆𝜆𝑡𝑡 + 𝜖𝜖𝑖𝑖𝑖𝑖𝑖𝑖 (1)where, ∆𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖 are annual changes in the performance indicators for firm i in industry j inyear t, and 𝑌𝑌𝑖𝑖𝑗𝑗𝑗𝑗0 is the level at the beginning of the sample period, i.e., 2017. 𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖−1 is avector of firm characteristics, including indicators of a firm’s internationalization. To
control for the link between pre-pandemic firm performance indicators and firm attributes,
we include a pre-pandemic sample in the analysis, and examine the pandemic’s impact
by including a cross-term between the firm attribute variables and a 2020 dummy variable,i.e., D2020, that takes a value of one for the year 2020, and zero otherwise. 𝜇𝜇𝑗𝑗 , 𝜆𝜆𝑡𝑡 , and 𝜖𝜖𝑖𝑖𝑖𝑖
are industry fixed effects, year-fixed effects, and an error term, respectively.
5

We use total sales, domestic sales, and export and import values in logarithmic form
as firm performance indicators. Export and import values are decomposed into final andintermediate goods. Final goods are defined using the United Nations’ Broad Economic
Category product codes, and goods other than final goods are classified as intermediate
goods. Variables for firm characteristics are firm size, measured by the number of
employees, firm age, and exporter, importer, foreign-owned company, and MNE
dummies. We also control for the number of FDI destination countries, export-destination
countries, and import-source countries as a measure of the involvement of GVCs at thefirm-level. All firm characteristics variables are lagged by one year.

3. Data and data overview
3.1 Data
We combine three datasets for our analysis. The first is a firm-level survey, the Basic
Survey of Japanese Business Structure and Activities (BSJBSA, Kigyo Katsudo Kihon
Chosa, in Japanese) conducted by Japan’s Ministry of Economy, Trade and Industry(METI). This survey covers all firms operating in the target industries with over 50
employees and capital exceeding 30 million yen. The target industries include Mining,
Manufacturing, Wholesale and Retail, and some service sectors. The BSJBSA is acomprehensive survey of Japanese corporations and offers valuable insights into theirdiversification, globalization, and R&D strategies. It contains data on firms’ sales,
expenses, debts, assets, profits, employment, exports and imports, outsourcing, and R&D
activities. Although the BSJBSA offers data on the amounts of exports and imports, itdoes not include information on export destinations, import-source countries, exported orimported products.
To obtain this level of detail we use a second dataset, from Japan Customs, that
contains firm-product-destination-level trade transaction data. This dataset has recently
been made available to academic researchers through collaborative research projects with
the Policy Research Institute (PRI) of Japan’s Ministry of Finance (MOF). It contains
detailed trade transaction information, such as the value and quantity of exports andimports at the HS 9-digit level, export-destination, import origin, invoice currency, andthe importer’s or exporter’s name and location.
Third, we use the METI’s survey on FDI, namely the Basic Survey on Overseas
Business Activities (hereafter BSOBA, Kaigai Jigyou Katsudou Kihon Chosa inJapanese), conducted annually, to capture Japanese MNEs’ production networks throughtheir foreign affiliates. The BSOBA is based on a questionnaire distributed to all Japanesecompanies with overseas affiliates, except those in the financial and insurance industries.
6

The BSOBA contains basic information on overseas affiliates including sales, expenses,
levels of exports and imports, and employment.
The procedure for data construction is as follows: BSJBSA and BSOBA use the same
firm identification number; thus, we can easily match firm-level data from these twodatasets. We match BSJBSA and trade transaction data from Japan Customs by referring
to corporate numbers provided by the National Tax Agency (NTA), as these two data sets
have had NTA’s corporate numbers since 2018. However, firms that ceased their tradetransactions before 2018 do not have an NTA corporate number; therefore, we match thetwo data sets by referring to company name, location, and phone number. 5 Our study
focuses on manufacturing firms over the period 2017–2020.
To identify involvement in GVCs we use two groups of measures. First, we use tradetransactions data from Japan Customs to identify exporter and importer status, and we
calculate the number of exporting and importing countries. Second, following Kiyota etal. (2020) we distinguish two types of MNEs: foreign-owned companies (FOCs) andJapanese MNEs. Using BSJBSA, we define FOCs as firms with a foreign capital share
greater than 50% and a headquarters outside of Japan. Japanese MNEs are defined as
firms that own at least one foreign manufacturing affiliate based on BSOBA’s data. 6 The
remaining firms are classified as domestic firms. The number of FDI countries, also
obtained from BSOBA, is defined as the number of countries where MNEs’
manufacturing subsidiaries are located. The definitions of the variables, summary
statistics, and correlation matrix are presented in Appendix Table A1.
3.2 Data overview
Table 1 shows the number of exporters, importers, and MNEs in the BSJBSA data in 2019.
Since two-way trading firms are often referred to as GVC firms (e.g., Baek and Urata
2021; Reddy et al. 2022), we distinguish two-way trading, export-only, and import only
firms in Table 1 (a). Two-way trading firms account for just 43% (4,714/10,842) of thetotal number of firms but they dominate in terms of import and export value, accounting
for 99.7% of all manufacturing firms in terms of export value (43,226/43,351) and 97%
in terms of import value (20,417/21,001). Similarly, Table 1(b) shows the number of
MNEs, FOCs, and domestic firms and the value of their exports and imports. MNEs
account for 11% of the total number of firms (1207/10842), 77% of total exports(33,597/43,351), and 49% of total imports (10,350/21,001). FOCs account for 1%
5

For more details of matching procedures, see Ito et al. (2024).
A foreign-owned companies with foreign manufacturing affiliates are classified as FOCs
not as MNEs.

6

7

(122/10842) of the total number of firms but 6% of total exports (2,617/43,351) and 12%of total imports (2,531/21,001). Focusing on two-way trading MNEs and FOCs, we find
92% of MNEs (1,114/1,207) and 93% of FOCs (114/122) are engaged in two-way trading
and are responsible for more than 99% of MNEs’ and FOCs’ export and import values(33,589/33,597 and 10,333/10,350 for MNEs’ export and import values and 2,615/2,617
and 2,530/2,531 for FOCs’ export and import values). Roughly 74% of the two-way
trading firms are domestic firms (3,486/4,714), but they account for only 16% of totalexport value (7,022/43,226) and 37% of total import value (7,554/20,417) for two-way
trading firms. We confirm that that Japanese manufacturing exports and imports are
dominated by MNEs and two-way trading firms.
== Table 1 ==Table 2 compares the number of exporting and importing countries across MNEs, FOCs,
and domestic firms. FOCs and MNEs export to an average of 14.15 and 16.93 countries,
respectively, and import from 11.52 and 9.03 countries, respectively. The number of both
exporting and importing countries is even higher for two-way trading FOCs and MNEs,
at 15.12 and 18.23 countries, and 12.23 and 9.74 countries, respectively. However,domestic firms export to just 2.81 countries and import from only 1.79 countries. When
limited to domestic firms engaged in two-way trading, the number of exporting andimporting countries increases significantly to 7.12 and 4.31, respectively. This reveals
significant differences in the diversity of export and import destinations among domestic
firms.
== Table 2 ==Figure 1 Panel (a) compares the growth rates of sales, imports, and exports in 2019-2020
for MNEs, FOCs, domestic firms, and exporting and importing firms. 7 The −11.2%
decline in sales for FOCs over this period is larger than the −9.0% decline for domestic
firms. However, the sales decline for importing-only firms was only −8.0%. Other firms
show a similar decline of roughly −9%. Interestingly, a comparison of trading andnontrading firms shows the decline in sales for nontrading firms was similar to that of
trading firms, suggesting that even firms not directly engaged in exporting or importing
were indirectly affected by the pandemic through their transaction relationships withtrading firms. Panel (b) shows changes in the value of exports and imports. The decreases
for MNEs for the period are −13.7% and −15.5%, respectively, showing smaller declines
7

The status of MNEs, FOCs, and exporting-importing firms is based on information asof 2019.
8

than those for domestic firms. In contrast, exports and imports for FOCs declined by−16.1% and −22.3%, respectively. For other firms, there is no significant difference inthe rate of decline for exports versus imports. In sum, Figure 1 suggests that MNEs andimport-export firms except FOCs, did not suffer worse declines during the COVID-19
pandemic. MNEs performed better than other types of firms in terms of export and import
growth rates.
== Figure 1 ==4. Results
Table 3 shows the estimation result of the baseline regression presented in Section 2.
Controlling for before-pandemic performance (2017 performance indicators), we see thatsales growth rates are higher for younger firms and firms with more employees (column
1). The growth rates for domestic sales (column 2) and exports (column 3) are also
positively associated with the number of employees. For the growth rate of imports(column 4), none of the coefficients without interaction terms are significant.
When the coefficient of import status in the export growth rate estimation equationof column (3) (and the coefficient of export status in the import growth rate estimation
equation of column (4)) were positive, it would suggest that engaging in both imports andexports would result in higher growth rates. However, for both export and import growth
rates, the coefficient on the lag term for import and export status was insignificant, withor without the 2020 dummy.
Looking at the coefficient of the cross-terms with 2020 dummies, which captures thedifference in effects before and during the pandemic, no statistically significant variables
were obtained for the growth rate of total sales, domestic sales, and export and import.
One exception is the coefficients of the cross-term between the employee size and the2020 dummy are positive and significant for the growth rates of imports (columns 4). It
suggests that larger firms tend to increase (reduce) imports more (less) than smaller firms
during the pandemic. 8== Table 3 ==Table 4 introduces the number of import-source/export-destination countries and thenumber of FDI-destination countries where manufacturing subsidiaries are located. Since
the number of import-source/export-destination countries is highly correlated with eachother, they are added as independent variables one by one. Columns (1) and (2) of Table
8

One may be interested in the performance of two-way trading, export-only and import
only firms. Appendix Table A2 presents the estimation results with the dummy variable
for these firms but we do not find any systematic differences with baseline results.
94 examine the correlation between export growth rates and a firm’s degree of diversity ofimport-source/export-destination and FDI-destination countries. Focusing on thecoefficients for the pandemic period, i.e., the cross term with the 2020 dummy, thecoefficients for the number of exporting and importing countries are positive andsignificant. These results indicate that firms that diversify their export destinations andprocurement origins are more likely to recover their exports more quickly during anegative shock. Ando and Hayakawa (2022) point out that the diversity of import sources
contributed to the recovery of machinery exports during the pandemic. Our result is
consistent with their findings. Furthermore, we find this diversity is important not only
on the import side of a business but also on the export side.
With respect to diversification of firms’ investment destinations, the coefficient on
the cross-term between the diversification of FDI destinations and the 2020 dummy
variable is not significant, suggesting that diversification of FDI destination countries has
no systematic effect on export recovery.
Columns (3) through (4) in Table 4 present the results for import growth. The crossterms for the number of exporting/importing countries, and the number of FDI countries
with the 2020 dummy variable are all insignificant, indicating that diversification ofdestinations for exports, imports, and FDI did not affect the recovery of import values
during the pandemic period.
== Table 4 ==Table 5 compares exports of final goods and intermediate goods. Columns (1) and(2) present the determinants of the growth rate of final goods exports. Looking at thecoefficient of the cross-terms, the results are the same as in Table 4 except for the numberof importing countries, suggesting that the recovery in export growth for final goods is
related to diversification of a firm’s export destinations. For intermediate goods exports
as shown in columns (3) and (4), the coefficients on the number of export-destination andimport-source countries are both positive and significant. However, the coefficients on
the number of FDI destination countries are insignificant both for final goods andintermediate goods exports.
== Table 5 ==Table 6 compares the results for final versus intermediate goods imports. For thegrowth in final goods imports in the pre-pandemic period, the effect of diversity of importsources and export destinations is positive and significant. However, the cross-term of
these variables with the 2020 dummy is insignificant, as shown in Table 3. In contrast,
for the growth rate of intermediate goods imports, as shown in columns (3) and (4) of
Table 6, the cross-term between the number of export-destination and import-source10countries and the 2020 dummy is positive and significant. Comparing the coefficients ofthe cross-terms for the number of trading countries in columns (3) and (4), the value forimport-sourcing countries is larger, suggesting that diversity in procurement sources ismore important for a recovery of imports of intermediate goods.
== Table 6 ==Figure 1(b) shows MNEs’ perform better in terms of export-import growth, but neither
the MNE dummy nor the diversity of FDI destinations is statistically significant in the
results shown in Table 3 through Table 6. To check the robustness of these results, we
exclude diversity of FDI destinations or diversity of export/import countries. The results,
presented in Appendix Tables A6 and A7, confirm our main results do not change
regardless of the combination of the diversification measures. Table 2 shows MNEs tend
to export to and import from a large number of countries. Thus, their better performance
as shown in Figure 1(b) can be attributed to diversity of both their export-destination andimport-source countries.

5. Conclusion
This study uses comprehensive firm-level customs data to investigate the resilienceof firms participating in GVCs to shocks caused by the COVID-19 pandemic, focusing
on diversity in the firms’ import-source and export-destination countries. We also
examine the resilience of MNEs to shocks by considering the number of FDI destination
countries. Our findings suggest that diversity in export destinations and import sources
helps firms to recover their export levels during a pandemic, explaining the better
performance of MNEs. This effect is particularly pronounced for the trade of intermediate
goods.
While this study presents interesting evidence, it also raises several questions to beaddressed in future research. The first is the long-term impact of COVID-19 on firm-level
trade. While this study focuses on firm performance in 2019-2020, analyzing performancethrough 2021 and beyond is necessary to examine post-pandemic firm strategies. Second,
analyzing firms’ responses to other external negative shocks could provide useful insights
for policymakers. For example, our dataset would allow us to explore how firms reacted
to U.S.-China trade frictions. Such studies could help shed light on the mechanisms ofinternational shock spillovers through supply chains.
Acknowledgement
This study is an outcome of research conducted jointly with the Policy Research Institute11

(PRI) after submitting a request for use of customs’ export and import declaration data to
the Ministry of Finance (MOF) based on the “Guideline on the utilization of customs’import and export declaration data in joint research with Policy Research Institute,” andreceiving approval in February 2022. The views expressed in this paper are the authors’personal responsibility and do not represent the official views of the MOF or PRI. The
authors would like to thank Toshihiro Okubo for his helpful comments. This study was
supported by the JSPS KAKENHI Grants 22K01482 (Matsuura), 23H00817 (Ito andJinji), 23K01396 (Ito), and 23K25514 (Ito and Jinji). All errors are our own.
Data availability statement
Due to confidentiality requirements, research data are not shared.
ORCID
Toshiyuki MATSUURA https://orcid.org/0000-0001-8240-6861
Keiko ITO https://orcid.org/0000-0002-2162-2517
Naoto JINJI https://orcid.org/0000-0001-7438-1879

12

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15

Table 1 Number of firms and trade values by type of firms in 2019
Panel (a)# of firms
Two-way traders
Export only
Import only
No traders
Total

4,714
772
1,127
4,229
10,842

Exportvalues43,2261250043,351

Importvalues
20,4170583
021,001Panel (b)All firms# of firms
Domestic firms
FOC
MNEs
Total

9,513
122
1,207
10,842

Exportvalues
7,137
2,617
33,59743,351

Twoway traders
ImportExport# of firms
valuesvalues
8,120
3,486
7,022
2,531
114
2,615
10,3501,114
33,58921,001
4,714
43,226

Importvalues7,554
2,530
10,333
20,417

Source: Authors’ calculation based on BSJBSA and trade transaction data by Japan
Customs. Units: Billion yen
Table 2 Number of export-destination/import-source countries in 2019
All firms
Number of exporting countries
Number of importing countries
Number of firms
Two-way traders
Number of exporting countries
Number of importing countries
Number of firms

Domestic
firms
2.81
1.79
9513
Domestic
Total
firms
9.94
7.12
5.78
4.31
4714
3486

Total
4.51
2.71
10842

FOCs
14.15
11.52
122

MNEs
16.93
9.03
1207

FOCs
15.12
12.23
114

MNEs
18.23
9.74
1114

Source: Authors’ calculation based on BSJBSA and trade transaction data by Japan
Customs

16

Table 3 Baseline results(1)
Δln(Sales)ln(Sales)t0ln(DSales)t0

(2)
(3)
(4)
Δln(DSales) Δln(Export) Δln(Import)-0.0107**(0.00485)-0.0164***(0.00459)

ln(Export)t0
ln(Import)t0
MNE dummy
FOC dummy
Exporter dummy
Importer dummyln(Age)ln(Emp)MNE dummy*D2020
FOC dummy*D2020
Exporter dummy*D2020
Importer dummy*D2020
ln(Age)*D2020ln(Emp)*D2020

Observations
R-squared
N of firms-7.93e-05(0.00292)-0.00811(0.0137)-0.00163(0.00329)
0.00247(0.00324)-0.00530**(0.00246)
0.0114**(0.00558)-0.00138(0.00781)-0.0185(0.0306)-0.00566(0.00687)
0.00602(0.00803)-0.00614(0.00669)
0.00445(0.00449)

0.00392(0.00366)-0.0230(0.0324)-0.00147(0.00361)
0.00470(0.00363)-0.00267(0.00287)
0.0175***(0.00547)-0.0131(0.00979)
0.0479(0.0901)-0.00629(0.00726)
0.000110(0.00717)-0.00145(0.00901)
0.00188(0.00598)

32,5260.096
10842

32,3730.060
10809-0.0165***(0.00288)

0.0146(0.0143)-0.00406(0.0449)-0.0110(0.0309)0.00542(0.0138)
0.0198**(0.00894)-0.00972(0.0302)0.103(0.103)0.0569(0.0616)-0.0250(0.0329)
0.0263(0.0167)
11,7080.025
3975-0.0130***(0.00393)
0.00572(0.0146)0.0600(0.0424)-0.0285(0.0280)-0.00221(0.00999)
0.0108(0.00710)
0.0328(0.0293)-0.118(0.0757)
0.0313(0.0577)-0.0218(0.0266)
0.0270*(0.0138)
10,402
0.035
3524

Note: ***,**, and * indicate statistical significance at the 1%, 5%, and 10% levels,
respectively. Figures in parentheses are robust standard errors clustered at the 2-digit
industry level. Year and 3-digit level industry fixed effects are included.

17

Table 4 Diversification of export, FDI destinations, and import origins(1)(2)
Δln(Export)
Number of FDI countries
Number of Exporting countries
Number of Importing countries
Number of FDI countries*D2020
Number of Exporting countries*D2020
Number of Importing countries*D2020

Observations
R-squared
N of firms

0.00117(0.00184)-9.06e-06(0.000392)-0.00209(0.00307)0.00171*(0.000888)

11,7080.025
3975

0.00200(0.00183)-0.00130(0.00132)-0.00350(0.00324)

0.00548*(0.00274)
11,708
0.025
3975

(3)
(4)
Δln(Import)-0.00278(0.00202)
0.000795(0.000554)

0.00446(0.00362)-0.00138(0.00105)

10,4020.034
3524

0.000820(0.00166)-0.00321*(0.00185)
0.000544(0.00325)

0.00185(0.00262)
10,402
0.035
3524

Note: ***, **, and * indicate statistical significance at the 1%, 5%, and 10%, levels, respectively. Figures in parentheses are robust
standard errors clustered at the 2-digit industry level. Year and 3-digit level industry fixed effects and variables for firm characteristics
are included. Full results are presented in Table A3 in the Appendix.

18

Table 5 Diversification of export, FDI destinations, and import origins: export of final goods vs. export of intermediate goods.
VARIABLES
Number of FDI countries
Number of Exporting countries
Number of Importing countries
Number of FDI countries*D2020
Number of Exporting countries*D2020
Number of Importing countries*D2020

Observations
R-squared
N of firms(1)(2)
Δln(Export-Final Goods)
0.00487*(0.00268)
0.00366***(0.000851)-0.000771(0.00591)
0.00400***(0.00139)

10,735
0.036
3855

0.00467*(0.00257)

0.00701***(0.00208)
0.00354(0.00595)

0.00296(0.00325)(3)(4)Δln(Export-Intermediate Goods)
0.00338(0.00515)
0.00770***(0.00177)-0.00146(0.00628)
0.00565**(0.00276)

10,735
0.035
3855

14,1250.026
4923

0.00584(0.00536)

0.0125***(0.00378)-0.000177(0.00592)

0.0102*(0.00574)
14,1250.025
4923

Note: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Figures in parentheses are robust
standard errors clustered at the 2-digit industry level. Year and 3-digit level industry fixed effects and variables for firm characteristics
are included. Full results are presented in Table A4 in the Appendix.

19

Table 6 Diversification of export, FDI destinations, and import origins: import of final goods vs. import of intermediate goods.
VARIABLES
Number of FDI countries
Number of Exporting countries
Number of Importing countries
Number of FDI countries*D2020
Number of Exporting countries*D2020
Number of Importing countries*D2020

Observations
R-squared
N of firms(1)(2)
Δln(Import-Final Goods)
0.00434(0.00331)
0.00168*(0.000952)

0.00185(0.00550)-0.00207(0.00191)

10,195
0.028
3676

(3)
(4)
Δln(Import-Intermediate Goods)

0.00411(0.00319)

0.000678(0.00373)
0.00234(0.00146)

0.00433*(0.00229)
0.00213(0.00485)-0.00166(0.00718)
0.00478*(0.00252)-0.00510(0.00447)
10,1950.028
3676

14,8350.031
5186-0.00116(0.00256)0.00772**(0.00349)-0.00457(0.00594)

0.0141***(0.00522)
14,8350.031
5186

Note: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Figures in parentheses are robust
standard errors clustered at the 2-digit industry level. Year and 3-digit level industry fixed effects and variables for firm characteristics
are included. Full results are presented in Table A5 in the Appendix.

20

Figure 1 Comparison of the performance indicators among MNEs, FOCs, exporters,
importers and non-trading firms
Panel (a) Growth rate of sales from 2019 to 2020

Panel (b) Growth rate of Export and Import Value from 2019 to 202021

Appendix Table A1 Descriptive Statistics: Panel (a) Variable Definitions and Summary Statistics
VariableΔln(Sales)Δln(DSales)Δln(Export)
Δln(Import)
MNE dummy
FOC dummy

Changes in log sales
Changes in log domestic sales
Changes in log export
Changes in log import
Dummy for firms that own manufacturing foreign affiliates
Dummy for firms whose foreign capital share is greater than 50%
and their headquarters outside of Japan.

Exporter dummy

Dummy variable for exporting firms

Importer dummy

Dummy variable for importing firmsln(Age)logged firm ageln(Emp)logged the number of employeesln(Sales)t0

logged sales in year in 2017ln(DSales)t0

logged domestic sales in year in 2017ln(Export)t0

logged exports in year in 2017

ln(Import)t0

logged imports in year in 2017
Number of countries where manufacturing foreign subsidiaries arelocatedNumber of FDI countries
Number of Exporting countries

Number of exporting countries, calculated with Customs data

Number of Importing countries

Number of importing countries, calculated with Customs data

# of obs Mean
SD
p10
p90
32526
-0.0300.167-0.1950.116
32373-0.0300.218-0.2000.120
11708-0.0460.716-0.6330.534
10402-0.0460.655-0.585
0.470
32526
0.109
0.312
3.237
4.350
32526

0.011

0.104

4.226

6.619

32526
32526
32526
32526
32526
32373
11708
10402

0.506
0.535
3.890
5.254
8.556
8.556
6.240
5.692

0.500
0.499
0.531
1.001
1.358
1.357
2.470
2.297

0.000
0.455
0.000
0.000
0.000
7.017
3.155
2.800

0.318
1.455
1.000
0.000
1.000
10.222
9.357
8.597

32526

0.367

1.809

0.000

1.000

11708
11708

11.146
5.711

13.338
6.764

1.455
1.455

25.45513.455

Source: Authors’ calculation based on BSJBSA and trade transaction data from Japan Customs. The values of p10 and p90 are averagesof 10 samples around the p10 and p90 firms to avoid confidentiality violations.

22Panel (b) Correlation Matrix
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]

Δln(Sales)Δln(DSales)Δln(Export)
Δln(Import)
MNE dummy
FOC dummyln(Age)ln(Emp)ln(Sales)t0ln(DSales)t0ln(Export)t0
ln(Import)t0
Number of FDI countries
Number of Exporting countries
Number of Importing countries

[1]
1.000

[2]

[3]

0.518

1.000

0.265

0.086

1.000

0.284
-0.0120.001-0.034-0.015-0.022-0.007-0.046-0.028-0.024-0.010-0.020

0.142
0.000
0.006
-0.006-0.015-0.021-0.041-0.012-0.011-0.0050.006-0.0070.215-0.003-0.002-0.007
0.005
0.009
0.016
-0.030-0.008-0.007-0.012-0.011

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

[15]

1.000
0.007
0.003
-0.015
0.011
0.009
0.011
0.004
-0.018-0.0040.004-0.0151.000-0.113
0.134
0.332
0.329
0.275
0.366
0.256
0.519
0.327
0.319

1.000
-0.157
0.004
0.043
0.025
0.071
0.098
0.005
0.066
0.143

1.000
0.120
0.091
0.113
0.057
0.061
0.137
0.118
0.069

1.000
0.921
0.888
0.633
0.549
0.468
0.541
0.654

1.000
0.952
0.687
0.646
0.449
0.519
0.649

1.000
0.527
0.579
0.385
0.426
0.573

1.000
0.542
0.440
0.638
0.616

1.000
0.349
0.393
0.561

1.000
0.526
0.548

1.000
0.746

1.000

Source: Authors’ calculation based on BSJBSA and trade transaction data from Japan Customs.

23

Table A2 Includes two-way, export-only, import only dummies
VARIABLESln(Sales)t0ln(DSales)t0(1)
Δln(Sales)-0.0107**(0.00485)

ln(Export)t0(2)
Δln(DSales)-0.0164***(0.00457)

ln(Import)t0
MNE dummy
FOC dummy
Two-way dummy
Ex-Only dummy
Im-Only dummylog(Age)log(Emp)
MNE dummy*D2020
FOC dummy*D2020
Two-way dummy*D2020
Ex-Only dummy*D2020
Im-Only dummy*D2020
log(Age)*D2020
log(Emp)*D2020-0.000218(0.00288)-0.00828(0.0135)0.000870(0.00379)-0.00286(0.00531)
0.00151(0.00356)-0.00529**(0.00246)
0.0114**(0.00558)-0.000944(0.00791)-0.0181(0.0305)
0.000256(0.0105)-0.00191(0.00907)
0.00882(0.00971)-0.00619(0.00667)
0.00452(0.00449)

0.00375(0.00369)-0.0232(0.0323)
0.00326(0.00423)-0.00294(0.00575)
0.00355(0.00423)-0.00265(0.00287)
0.0175***(0.00543)-0.0125(0.00981)
0.0486(0.0901)-0.00632(0.0105)-0.000833(0.00928)
0.00418(0.00858)-0.00152(0.00900)
0.00198(0.00597)

32,5260.096
10842

32,3730.060
10809

Observations
R-squared
N of firms

See Table 3 for table notes.

24(3)Δln(Export)-0.0165***(0.00288)(4)Δln(Import)

0.0146(0.0143)-0.00406(0.0449)-0.0110(0.0309)-0.0130***(0.00393)
0.00572(0.0146)0.0600(0.0424)-0.0285(0.0280)

0.00542(0.0138)
0.0198**(0.00894)-0.00972(0.0302)0.103(0.103)0.0569(0.0616)-0.00221(0.00999)
0.0108(0.00710)
0.0328(0.0293)-0.118(0.0757)
0.0313(0.0577)-0.0250(0.0329)
0.0263(0.0167)-0.0218(0.0266)
0.0270*(0.0138)

11,7080.025
3975

10,402
0.035
3524

Table A3 Full results of Table 4(1)(2)Δln(Export)ln(Export)t0
ln(Import)t0
FOC dummy
Exporter dummy
Importer dummylog(Age)log(Emp)Number of FDI countries
Number of Exporting countries
Number of Importing countries
FOC dummy*D2020
Exporter dummy*D2020
Importer dummy*D2020
log(Age)*D2020
log(Emp)*D2020
Number of FDI countries*D2020
Number of Exporting countries*D2020
Number of Importing countries*D2020

Observations
R-squared
N of firms-0.0174***(0.00305)-0.0166***(0.00311)-0.00673(0.0440)-0.00778(0.0308)

9.54e-05(0.0443)-0.00460(0.0307)0.00590(0.0136)
0.0215**(0.0102)0.00117(0.00184)-9.06e-06(0.000392)

0.00600(0.0137)0.0244**(0.0112)0.00200(0.00183)

0.0955(0.102)0.0502(0.0612)-0.00130(0.00132)
0.0720(0.103)0.0375(0.0616)-0.0276(0.0325)0.0170(0.0203)-0.00209(0.00307)0.00171*(0.000888)-0.0259(0.0326)
0.00849(0.0213)-0.00350(0.00324)

11,7080.025
3975

11,7080.025
3975

See notes in Table 4.

25

0.00548*(0.00274)(3)(4)Δln(Import)-0.0126***(0.00390)
0.0537(0.0427)-0.0108**(0.00408)
0.0743(0.0465)-0.0329(0.0287)-0.00207(0.00962)
0.00956(0.00766)-0.00278(0.00202)
0.000795(0.000554)-0.0253(0.0278)-0.00148(0.00993)
0.0202**(0.00865)
0.000820(0.00166)-0.119(0.0757)-0.00321*(0.00185)-0.138*(0.0802)

0.0463(0.0586)-0.0188(0.0259)0.0342**(0.0168)
0.00446(0.00362)-0.00138(0.00105)

0.0343(0.0584)-0.0202(0.0265)
0.0236(0.0185)0.000544(0.00325)

10,402
0.034
3524

10,4020.035
3524

0.00185(0.00262)

Table A4 Full results of Table 5
VARIABLESln(Export-Final Goods)t0ln(Export-Intermediate Goods)t0
FOC dummy
Importer dummylog(Age)log(Emp)Number of FDI countries
Number of Exporting countries
Number of Importing countries
FOC dummy*D2020
Importer dummy*D2020
log(Age)*D2020
log(Emp)*D2020
Number of FDI countries*D2020
Number of Exporting countries*D2020
Number of Importing countries*D2020

Observations
R-squared
N of firms(1)(2)
Δln(Export-Final Goods)-0.0765***(0.00564)-0.0727***(0.00599)

0.0278(0.0684)
0.0984*(0.0512)0.0146(0.0165)0.0594***(0.0125)0.00487*(0.00268)
0.00366***(0.000851)-2.02e-05(0.0671)0.0807(0.0510)0.0205(0.0160)
0.0514***(0.0130)
0.00467*(0.00257)-0.0213(0.179)-0.0116(0.0965)-0.0568(0.0644)0.0114(0.0191)-0.000771(0.00591)
0.00400***(0.00139)

10,7350.036
3855

0.00701***(0.00208)-0.0283(0.185)-0.00945(0.0970)-0.0497(0.0649)
0.0183(0.0221)0.00354(0.00595)

0.00296(0.00325)
10,7350.035
3855

See notes in Table 5.

26

(3)
(4)
Δln(Export-Intermediate Goods)-0.105***(0.0155)
0.189***(0.0583)
0.0818(0.0784)
0.0374(0.0270)0.0914***(0.0311)0.00338(0.00515)
0.00770***(0.00177)-0.0697(0.159)-0.103*(0.0579)
0.00590(0.0383)-0.104(0.154)-0.00146(0.00628)
0.00565**(0.00276)

14,1250.026
4923-0.101***(0.0149)
0.149**(0.0627)0.0540(0.0789)0.0451*(0.0269)0.0845**(0.0326)
0.00584(0.00536)

0.0125***(0.00378)-0.108(0.172)-0.0944(0.0580)-0.000742(0.0417)-0.120(0.146)-0.000177(0.00592)

0.0102*(0.00574)
14,1250.025
4923

Table A5 Full results of Table 6
VARIABLESln(Import-Final Goods)t0
ln(Import-Intermediate Goods)t0
FOC dummy
Exporter dummylog(Age)log(Emp)Number of FDI countries
Number of Exporting countries
Number of Importing countries
FOC dummy*D2020
Exporter dummy*D2020
log(Age)*D2020
log(Emp)*D2020
Number of FDI countries*D2020
Number of Exporting countries*D2020
Number of Importing countries*D2020

Observations
R-squared
N of firms(1)(2)
Δln(Import-Final Goods)-0.0679***(0.00594)-0.0691***(0.00617)

0.146*(0.0829)0.105***(0.0377)-0.00567(0.0205)
0.0406***(0.0131)0.00434(0.00331)
0.00168*(0.000952)

0.130(0.0854)
0.109***(0.0383)-0.00293(0.0194)
0.0362**(0.0138)0.00411(0.00319)-0.221(0.183)-0.0951(0.0752)-0.0402(0.0375)
0.0660***(0.0239)0.00185(0.00550)-0.00207(0.00191)

10,1950.028
3676

See notes in Table 6.

27

0.00433*(0.00229)-0.201(0.183)-0.100(0.0729)-0.0438(0.0367)
0.0722**(0.0287)
0.00213(0.00485)-0.00510(0.00447)
10,195
0.028
3676

(3)
(4)
Δln(Import-Intermediate Goods)-0.102***(0.0169)
0.225***(0.0667)
0.139***(0.0521)-0.0501(0.0344)
0.109***(0.0233)
0.000678(0.00373)
0.00234(0.00146)

0.0835(0.0901)-0.0137(0.135)0.0505(0.0849)
0.0278(0.0328)-0.00166(0.00718)
0.00478*(0.00252)

14,8350.031
5186-0.105***(0.0176)
0.198***(0.0630)
0.146***(0.0542)-0.0470(0.0349)0.101***(0.0220)-0.00116(0.00256)0.00772**(0.00349)
0.0220(0.0964)-0.00483(0.130)0.0591(0.0855)
0.00723(0.0361)-0.00457(0.00594)

0.0141***(0.00522)
14,835
0.031
5186

Table A6 Panel (a) results excluding the number of FDI destination countries(1)(2)
Δln(Export)ln(Export)t0
ln(Import)t0
MNE dummy
FOC dummy
Exporter dummy
Importer dummyln(Age)ln(Emp)Number of Exporting countries
Number of Importing countries
MNE dummy*D2020
FOC dummy*D2020
Exporter dummy*D2020
Importer dummy*D2020
ln(Age)*D2020ln(Emp)*D2020
Number of Exporting countries*D2020
Number of Importing countries*D2020

Observations
R-squared
N of firms-0.0178***(0.00309)-0.0169***(0.00314)

0.0165(0.0139)-0.00219(0.0449)-0.00909(0.0307)0.0181(0.0137)0.00430(0.0449)-0.00658(0.0305)

0.00523(0.0136)
0.0211**(0.00969)-6.00e-06(0.000406)

0.00551(0.0136)0.0241**(0.0111)-0.0192(0.0300)
0.0906(0.105)0.0523(0.0613)-0.0271(0.0325)0.0173(0.0198)
0.00168*(0.000894)

11,7080.025
3975

See notes in Table 6.

28-0.00115(0.00130)-0.0217(0.0287)
0.0679(0.105)0.0409(0.0615)-0.0257(0.0326)
0.00856(0.0213)0.00516**(0.00252)
11,708
0.025
3975

(3)
(4)
Δln(Import)-0.0130***(0.00391)
0.00236(0.0151)
0.0557(0.0439)-0.0111***(0.00412)
0.0113(0.0142)0.0774(0.0466)-0.0316(0.0287)-0.00299(0.00974)
0.00791(0.00747)
0.000557(0.000528)-0.0267(0.0281)-0.00184(0.00994)
0.0197**(0.00866)

0.0408(0.0301)-0.108(0.0773)

0.0393(0.0581)-0.0198(0.0261)
0.0339**(0.0164)-0.00133(0.00104)

10,402
0.035
3524-0.00317*(0.00178)0.0293(0.0300)-0.128(0.0824)0.0296(0.0582)-0.0220(0.0266)
0.0220(0.0183)0.00151(0.00259)
10,402
0.035
3524

Table A6 Panel (b) results without the number of FDI destination countries(1)(2)
Δln(Export-Final Goods)ln(Export-Final Goods)t0ln(Export-Intermediate Goods)t0
MNE dummy
FOC dummy
Importer dummyln(Age)ln(Emp)Number of Exporting countries
Number of Importing countries
MNE dummy*D2020
FOC dummy*D2020
Importer dummy*D2020
ln(Age)*D2020ln(Emp)*D2020
Number of Exporting countries*D2020
Number of Importing countries*D2020

Observations
R-squared
N_firm-0.155***(0.0112)-0.147***(0.0114)0.0761***(0.0248)0.0407(0.0745)
0.109**(0.0542)-0.00395(0.0212)0.108***(0.0170)0.0102***(0.00134)0.0843***(0.0245)-0.0244(0.0721)0.0696(0.0527)0.0116(0.0204)
0.0920***(0.0172)-0.00725(0.0450)-0.0140(0.169)-0.0374(0.0891)-0.0549(0.0595)
0.00882(0.0180)
0.00337***(0.00123)

11,201
0.087
4180

See notes in Table 6.

29

0.0181***(0.00296)
0.00424(0.0465)-0.0305(0.178)-0.0440(0.0906)-0.0467(0.0607)0.0134(0.0228)

0.00339(0.00347)
11,201
0.083
4180

(4)
(3)
Δln(Export-Intermediate Goods)-0.107***(0.0155)
0.124***(0.0409)
0.222***(0.0601)
0.0747(0.0776)0.0307(0.0273)
0.0845***(0.0302)
0.00714***(0.00182)

0.0300(0.0817)-0.0596(0.161)-0.111(0.153)-0.105*(0.0590)0.00370(0.0378)0.00532**(0.00261)

14,1250.026
4923-0.103***(0.0150)
0.132***(0.0408)
0.186***(0.0634)
0.0474(0.0781)0.0381(0.0275)
0.0787**(0.0324)0.0119***(0.00359)
0.0401(0.0799)-0.0936(0.172)-0.128(0.145)-0.0981(0.0591)-0.00300(0.0422)0.00973*(0.00531)
14,1250.026
4923

Table A6 Panel (c) results excluding the number of FDI destination countries
VARIABLESln(Import-Final Goods)t0
ln(Import-Intermediate Goods)t0
MNE dummy
FOC dummy
Importer dummyln(Age)ln(Emp)Number of Exporting countries
Number of Importing countries
MNE dummy*D2020
FOC dummy*D2020
Importer dummy*D2020
ln(Age)*D2020ln(Emp)*D2020
Number of Exporting countries*D2020
Number of Importing countries*D2020

Observations
R-squared
N_firm(1)(2)
Δln(Import-Final Goods)-0.0674***(0.00589)-0.0690***(0.00615)-0.0316(0.0337)0.135(0.0857)
0.106***(0.0375)-0.00225(0.0196)0.0455***(0.0131)0.00232**(0.000959)-0.0290(0.0329)0.116(0.0880)0.112***(0.0379)0.00127(0.0184)0.0401***(0.0139)0.130**(0.0520)-0.185(0.191)-0.112(0.0729)-0.0490(0.0370)0.0581**(0.0234)-0.00297(0.00184)

10,1950.028
3676

See notes in Table 6.

30

0.00556**(0.00228)0.126**(0.0515)-0.161(0.192)-0.120*(0.0707)-0.0538(0.0361)
0.0651**(0.0284)-0.00655(0.00441)
10,195
0.028
3676

(3)
(4)
Δln(Import-Intermediate Goods)-0.133***(0.0202)0.0486(0.0483)0.244***(0.0808)0.257**(0.121)-0.0627(0.0468)
0.124***(0.0281)0.00180(0.00172)

0.0576(0.0982)
0.194*(0.108)0.153(0.309)0.0432(0.112)0.0350(0.0426)
0.00453*(0.00257)

10,6580.046
4125-0.105***(0.0176)
0.0128(0.0391)
0.203***(0.0646)0.145**(0.0555)-0.0483(0.0349)
0.0992***(0.0219)0.00734**(0.00331)
0.0345(0.0919)0.0375(0.105)-0.00951(0.135)0.0546(0.0853)
0.00358(0.0355)

0.0128**(0.00512)
14,835
0.031
5186

Table A7 results without the number of export-destination and import-source countries(1)

VARIABLESln(Export)t0
ln(Export-Final Goods)t0

(2)
(3)
(4)
Δln(Export- Δln(ExportΔln(Export)
FinalIntermediate Δln(Import)
Goods)
Goods)-0.0160***(0.00292)ln(Export-Intermediate Goods)t0-0.0673***(0.00508)ln(Import)t0-0.0965***(0.0146)ln(Import-Final Goods)t0-0.0128***(0.00389)ln(Age)ln(Emp)Number of FDI countries
FOC dummy*D2020
ln(Age)*D2020ln(Emp)*D2020
Number of FDI countries*D2020

Observations
R-squared
N of firms-0.0663***(0.00546)-0.00898(0.0443)0.00629(0.0137)0.0198**(0.00927)
0.000787(0.00200)
0.109(0.100)-0.0273(0.0324)0.0270(0.0170)0.000821(0.00342)

0.0488(0.0677)0.0195(0.0159)
0.0721***(0.0125)
0.00864***(0.00236)-0.00787(0.184)-0.0523(0.0652)
0.0275(0.0184)
0.00553(0.00562)

0.225***(0.0548)0.0444(0.0269)
0.118***(0.0307)0.0152**(0.00599)-0.0507(0.149)-0.0938(0.0579)
0.0250(0.0316)0.00723(0.00810)

0.0557(0.0421)-0.00179(0.00982)
0.0117(0.00718)-0.00141(0.00187)-0.123(0.0748)-0.0196(0.0260)
0.0302**(0.0150)
0.00218(0.00347)

0.160*(0.0833)-0.00185(0.0212)0.0495***(0.0102)0.00675*(0.00345)-0.239(0.185)-0.0474(0.0392)
0.0589**(0.0221)-0.00201(0.00488)-0.104***(0.0180)0.245***(0.0662)-0.0391(0.0374)
0.127***(0.0212)0.00295(0.00396)
0.0826(0.0935)-0.000576(0.0835)
0.0433(0.0365)0.0107(0.00791)

11,7080.025
3975

10,7350.033
3855

14,125
0.024
4923

10,402
0.034
3524

9,109
0.028
3369

12,6930.032
4595

ln(Import-Intermediate Goods)t0
FOC dummy(5)(6)Δln(Import- Δln(ImportFinal
Intermediate
Goods)
Goods)

See notes in Table 6.

31