Institute for Economic Studies, Keio University
Keio-IES Discussion Paper Series

Margins, concentration, and the performance of firms ininternational trade: Evidence from Japanese customs data
Keiko Ito, Masahiro Endoh, Naoto Jinji,
Toshiyuki Matsuura, Toshihiro Okubo, Akira Sasahara
18 June, 2024
DP2024-017
https://ies.keio.ac.jp/en/publications/23972/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
18 June, 2024

Margins, concentration, and the performance of firms in international trade: Evidence fromJapanese customs data
Keiko Ito, Masahiro Endoh, Naoto Jinji, Toshiyuki Matsuura, Toshihiro Okubo, Akira Sasahara
Keio-IES DP2024-017
18 June, 2024
JEL Classification: F10, F14, L25
Keywords: Japan’s international trade, customs data, intensive and extensive margin of trade,
exporter premia
Abstract
This study is the first to comprehensively investigate international trade at the firm-level using
Japan’s customs data for the 2014-2020 period. We first decompose international trade into theintensive and extensive margin and show that the intensive margin accounts for around 30% and
40% of the variation in partner country-specific exports and imports, respectively. We next find a
substantial concentration of trading firms: in 2017, the top 10% of exporters accounted for 96.6%
of all exports, while the top 10% of importers were responsible for 94.6% of all imports. Finally, wematch the customs data with other firm-level datasets and estimate the performance premia ofexporting firms. Our findings indicate that exporting firms outperform non-exporting firms in all
aspects we consider: sales, value added, the number of employees, the capital-labor ratio,
productivity, and wages. Interestingly, the exporter premia for value added, labor productivity, andtotal factor productivity decreased between 2014 and 2016 and then increased until 2019, whereas
the exporter premium for the average wage steadily increased.
Keiko Ito
Graduate School of Social Sciences,
Chiba University
1-33 Yayoichō, Inage Ward, Chiba, 263-0022
keiko-i@chiba-u.jp

Masahiro Endoh
Faculty of Business and Commerce,
Keio University
2-15-45 Mita, Minato City, Tokyo 108-8345
endoh@keio.jp

Naoto Jinji
Graduate School of Economics,
Kyoto University
Yoshidahonmachi, Sakyo-ku, Kyoto 606-8501
jinji@econ.kyoto-u.ac.jp

Toshiyuki Matsuura
Keio Economic Observatory,
Keio University
2-15-45 Mita, Minato City, Tokyo 108-8345
matsuura@sanken.keio.ac.jp

Toshihiro Okubo
Faculty of Economics, Keio University
2-15-45 Mita, Minato City, Tokyo 108-8345
okubo@econ.keio.ac.jp

Akira Sasahara
Faculty of Economics, Keio University
2-15-45 Mita, Minato City, Tokyo 108-8345
sasahara@keio.jp

Acknowledgement: This study is an outcome of the research conducted jointly with the Policy
Research Institute (PRI) after submitting a request for use of customs’ export and importdeclaration data to the Ministry of Finance (MOF) based on the Guideline on the utilization ofcustoms’ import and export declaration data in a joint research with Policy Research Institute,” andreceiving approval in February 2022. The views expressed in this paper are those of the authors’personal responsibility and do not represent the official views of MOF or PRI. The authors would
like to thank Kenta Ando, Shintaro Negishi, and Uraku Yoshimoto, Fumiharu Ito of the PRI of the
MOF Japan for their guidance on the dataset used in this research and helpful comments. Financial
support from the Japan Society of the Promotion of Science, KAKENHI, under grant numbers
9K01658, 20H01501, 23H00817, 23K01396, 23K25514, and 24K16371 is gratefully acknowledged.
All subsequent errors are our own.

Margins, concentration, and the performance of firms in international
trade: Evidence from Japanese customs data*
Keiko Ito†
Toshiyuki Matsuura‡

Masahiro Endoh§Toshihiro Okubo¶

Naoto Jinji||Akira Sasahara**

June 2024

Abstract
This study is the first to comprehensively investigate international trade at the firm-level using Japan’s
customs data for the 2014-2020 period. We first decompose international trade into the intensive andextensive margin and show that the intensive margin accounts for around 30% and 40% of the variationin partner country-specific exports and imports, respectively. We next find a substantial concentrationof trading firms: in 2017, the top 10% of exporters accounted for 96.6% of all exports, while the top
10% of importers were responsible for 94.6% of all imports. Finally, we match the customs data withother firm-level datasets and estimate the performance premia of exporting firms. Our findings indicatethat exporting firms outperform non-exporting firms in all aspects we consider: sales, value added, thenumber of employees, the capital-labor ratio, productivity, and wages. Interestingly, the exporter premia
for value added, labor productivity, and total factor productivity decreased between 2014 and 2016 andthen increased until 2019, whereas the exporter premium for the average wage steadily increased.
Key Words: Japan’s international trade; customs data; intensive and extensive margin of trade; exporter
premiaJEL codes: F10; F14; L25* This study is an outcome of the research conducted jointly with the Policy Research Institute (PRI) after submitting a request foruse of customs’ export and import declaration data to the Ministry of Finance (MOF) based on the “Guideline on the utilization ofcustoms’ import and export declaration data in a joint research with Policy Research Institute,” and receiving approval in February
2022. The views expressed in this paper are those of the authors’ personal responsibility and do not represent the official viewsof MOF or PRI. The authors would like to thank Kenta Ando, Shintaro Negishi, and Uraku Yoshimoto, Fumiharu Ito of the PRIof the MOF Japan for their guidance on the dataset used in this research and helpful comments. Financial support from the Japan
Society of the Promotion of Science, KAKENHI, under grant numbers 19K01658, 20H01501, 23H00817, 23K01396, 23K25514,
and 24K16371 is gratefully acknowledged. All subsequent errors are our own.
†
Graduate School of Social Sciences, Chiba University; Visiting Research Fellow, PRI‡
Keio Economic Observatory, Keio University; Visiting Research Fellow, PRI§
Faculty of Business and Commerce, Keio University; Visiting Research Fellow, PRI¶
Faculty of Economics, Keio University; Visiting Research Fellow, PRI||
Graduate School of Economics, Kyoto University; Visiting Research Fellow, PRI** Faculty of Economics, Keio University; Visiting Research Fellow, PRI

1

1 Introduction
In most countries, international trade is dominated by a small number of exceptionally performing firms(Bernard et al., 2007; Bernard et al., 2018; Greenaway and Kneller, 2007). Moreover, while most exporters
export only one product to one country, a tiny fraction of firms export many products to many countries(Bernard et al., 2009a; Bernard et al., 2009b). The patterns are similar for importers. Mounting theoreticaland empirical research has found that these empirical observations are consistent with the seminal model by
Melitz (2003).1
The goal of this study is to confirm these empirical regularities in the Japanese context and provide
additional insights in terms of a wide range of dimensions, including the destination and origin of exports
and imports, the products involved, and the characteristics of firms engaged in international trade. To thisend, we utilize newly available Japanese transaction-level customs data for the 2014-2020 period.2 Ourdataset has two major advantages over datasets previously used in research in this context. First, unlike
censored datasets, it includes all trading firms.3 Second, the accuracy of trade values in customs data aremuch higher than in self-reported survey data.4
In the trade literature, the first studies using countries’ customs data emerged in the 2000s. It has
now been almost two decades since academic research began to utilize customs data to investigate thedetailed behavior of individual firms in exporting and importing. Issues such studies have focused on include
the extensive and intensive margin of international trade (Bernard et al., 2009a, Bernard et al., 2009b,
for the United States), product quality (Crozet et al., 2012, for French wine; Bastos and Silva, 2010, forPortuguese trade), and destinations (Eaton et al., 2004, Eaton et al., 2011, for France; Lawless, 2009, for
Ireland). Countries that have made their customs data available to researchers since a relatively early dateinclude Belgium, Brazil, China, France, Hungary, Ireland, Norway, Portugal, Thailand, and the UnitedStates, facilitating academic research on international trade.5 More recently, detailed empirical studies havebeen conducted by connecting customs data with firm-level data (e.g., Aghion et al. (2024), Albornoz et al.
(2023), and Koenig and Poncet (2022) for France, Adão et al. (2022) for Ecuador, Fan et al. (2023) for
China, and Gimenez-Perales (2024) for Colombia).
This study contributes to this literature using countries’ customs data by making use of newly available
transaction-level customs data for a major trading nation, Japan. While this is not the first study to examine
trade at the firm-level in the Japanese context, most existing studies on this matter use a dataset provided
by the Ministry of Economy, Trade and Industry (METI) called the Basic Survey of Japanese Business
Structure and Activity (e.g., Kimura and Kiyota, 2006; Wakasugi et al., 2014).6 However, as mentioned,
1

Moreover, several recent studies (e.g., Costinot et al., 2020; Bergstrand et al., 2023) have demonstrated the importance of firm
heterogeneity in designing trade policy.
2
Japanese customs data are collected by Japan Customs under the jurisdiction of the Ministry of Finance (MOF). The data
became available to researchers starting in 2022 through a new scheme under which researcher can propose a collaborative projectwith the Policy Research Institute (PRI) of the MOF and, if accepted, gain access to the data. The authors of this study form one of
the first groups working on a collaborative project with the PRI.
3
For example, the Ministry of Economy, Trade and Industry (METI)’s data only provide aggregate export and import information, missing details on destinations and products. As a result, numerous aspects of Japanese firms’ trading activities remain
unexplored.
4
For example, METI’s firm-level data only include firms with more than 50 employees and capital assets exceeding 30 million
yen, which means that smaller firms are excluded. Furthermore, METI’s aggregate export and import data are based on firms’
self-reporting, potentially including large measurement errors. Therefore, previous estimates based on the METI data may bebiased.
5See, for example, Muûls and Pisu (2009) for Belgium, Békés and Muraközy (2012) for Hungary, and Manova and Zhang(2012) for China.
6
A few studies use other data. For example, Tomiura (2005) uses the Survey of Commercial and Manufacturing Structure andActivities, which provides information on exports and foreign outsourcing, while Okubo and Tomiura (2019) use plant-level data2

this dataset has limitations in terms of coverage and accuracy of the information, which has been an obstacle
to further advancement of Japanese firm-level studies on international trade.
This study overcomes such issues by employing Japanese customs data. The customs data compriseexhaustive export and import declaration information, i.e., transaction-level information, covering all exports and imports by Japanese firms in all industries. The advantage of this comprehensive dataset is that
all transactions are included, without any thresholds, and port, destination or origin, and product details areincluded for each transaction. In addition, as the customs data are administrative data, measurement errors
can be assumed to be smaller than in previously used datasets.
Our analysis consists of three parts. First, we decompose trade into the intensive and the extensive margin. Second, we examine the degree of concentration of trading firms. And third, matching the customs
data with METI’s firm-level data, we estimate the performance premia of exporting firms. The first analysis
reveals that, during 2014-2020, the intensive margin accounts for around 30% of the variation in Japan’s
partner country-specific export amounts, while the remainder is explained by the extensive margin. Similarly, around 40% of the variation in Japan’s partner country-specific import amounts is explained by theintensive margin, and about 60% by the extensive margin.
Second, regarding the degree of concentration of trading firms, in 2017, the top 10% of exporters accounted for 96.6% of all exports and the top 10% of importers for 94.6% of all imports. Moreover, thetop 1% of firms that engage in both exports and imports accounted for 76.7% of the total trade, whereas
the top 1% of exporters were responsible for 75.9% of exports and the top 1% of importers for 73.9% ofimports. Focusing on manufacturing industries, 21.1% of exporting firms exported only one HS9-digit levelproduct to only one destination country. On the other hand, 11.3% of exporting firms exported more than 10
products to more than 10 destination countries. On the import side, the share of the firms that imported one
HS9-digit level product from a single country is slightly higher (24.0%) and that of those importing morethan 10 products from more than 10 countries is much lower (4.7%) than on the export side.
Third, we find exporters outperform non-exporters in all aspects we consider: sales, value added, thenumber of employees, the capital-labor ratio, labor productivity, total factor productivity (TFP), and wages,
echoing previous studies (e.g., Wakasugi et al., 2014). Our new findings include that the exporter premia
with respect to value added, labor productivity, and TFP decreased from 2014, the start of our observation
period, until 2016 and then increased until 2019, whereas the exporter premium with respect to the average
wage steadily increased. The export participation ratio steadily increased from 47.1% in 2014 to 49.9% in2018 but slightly decreased thereafter.
Our observation period, 2014-2020, overlaps with the period of the ‘Abenomics’ policies initiated by
Former Prime Minister Shinzo Abe, which included three “arrows”: aggressive monetary policy, a flexible
fiscal policy, and a growth strategy.7 To facilitate international trade, several important regional trade agreements, such as the Japan-Australia Economic Partnership Agreement, the Comprehensive and Progressive
Agreement for Trans-Pacific Partnership (CPTPP), and the Japan-EU Economic Partnership Agreement,
were signed and entered into force during the Abenomics period.8 Our study helps to examine the impactof these policies on firms engaged in international trade.
This study contributes to the literature examining the characteristics of exporters using firm- or plantlevel data. Early studies by Bernard and Jensen (1995), Aw et al. (2000), and Bernard and Wagner (2001)
show that only a small number of firms engage in exporting, and that they tend to be more productivefrom the Census of Manufacture, which provides export shares of total output.
7
See Ito (2021) for monetary and fiscal policies and Solís and Urata (2018) for trade policies as part of the growth strategy.
8
Felbermayr et al. (2019) provide a quantitative analysis on the Japan-EU EPA.

3

and larger firms.9 Melitz (2003) constructs a model to describe these findings and demonstrates that thebenefits of trade liberalization have primarily accrued to these exporting firms.10 Subsequent studies haveinvestigated firm behavior with regard to exports and imports (e.g., Bernard et al., 2007; Greenaway andKneller, 2007), learning by exporting (or importing) (De Loecker, 2007; De Loecker, 2013; Vogel and Wagner, 2010), the choice between export and foreign direct investment (FDI) (e.g., Helpman et al., 2004), andoutsourcing (e.g., Girma and Görg, 2004; Görg et al., 2008; Tomiura, 2005; Tomiura, 2007). Meanwhile,
ISGEP (2008) has estimated export premia for 14 countries. More recently, the availability of micro-level
customs data for individual trade transactions has spurred research into various facets of trade behavior, as
explained above.
Existing studies in the Japanese context have focused on the characteristics of exporters and firms conducting FDI.11 For instance, using METI’s firm-level data, Wakasugi et al. (2014) and Kimura and Kiyota(2006) find that the most productive firms tend to engage in both exporting and FDI, while moderately productive firms tend to engage in either exporting or FDI.12 Kimura and Kiyota (2006) and Hayakawa andMatsuura (2015) investigate FDI decisions and firm productivity. Likewise, Tanaka (2012) discusses therelationship between firm productivity and the number of FDI destinations rather than export destinations.
Bellone et al. (2014) conduct a cross-country comparison of exporters’ productivity premia using Japaneseand French data. As Japanese transaction-level customs data were unavailable in the past, these existingstudies exclusively focus on the performance of international firms rather than export destinations or products. Our study differs from these prior studies in that we analyze firm behavior more comprehensively,
including in terms of these two dimensions.
The remainder of this study is organized as follows. Section 2 explains the data used in this study andprovides stylized facts gleaned from the customs data. Section 3 decomposes Japan’s trade into the intensive
and extensive margin of trade. Section 4 investigates the degree of concentration of Japan’s trade in a small
number of large firms. Section 5 matches the customs data with Japanese firm-level data and estimates theperformance premia of exporting firms. Finally, Section 6 concludes.

2 Data and Stylized Facts
2.1 Transaction-level data from Japan Customs
Our study employs transaction-level customs data from Japan Customs, Ministry of Finance, covering
the period 2014-2020. This dataset contains a comprehensive record of both exports and imports at thetransaction level. It includes transaction details such as the names and addresses of exporters and importers,
the registration codes of these entities, HS9-digit product codes, shipping ports, destination/source countries,
declaration dates, declared export (FOB) and import (CIF) values and quantities, invoice currencies, andso on. The dataset encompasses all seaports and airports in Japan and the declaration-level data include
9

See Wagner (2012) for a survey of studies until 2012.
As is well known, Melitz (2003) is the first study to construct a heterogeneous-firm trade model. Since then, a number ofsimilar types of models have been constructed by, for example, Helpman et al. (2004), Melitz and Ottaviano (2008), and Chaney(2008).
11
Some studies focus on the dynamics of export decisions (Inui et al., 2015), survival in export markets (Inui et al., 2017),
and foreign outsourcing and intra-firm trade (Tomiura, 2005; Tomiura, 2007). Tomiura (2007) found that the most productive
firms engage FDI, whereas less productive firms tend toward exporting and/or outsourcing. Apart from studies focusing on export
participation and export behavior, there are studies investigating the impact of firms’ trade on firm organization, employment(Tanaka, 2013), and research and development (R&D) investment (Ito and Tanaka, 2016). Okubo and Tomiura (2019) decompose
export premia by region (prefecture).
12
Another study in this vein is Todo (2011), which obtains similar findings.
10

4

various types of transactions: (1) regular transactions exceeding 200,000 yen in value, and (2) miscellaneoustransactions including special trade activities like gold transactions and small-scale transactions valued at
200,000 yen or less. We exclusively use the data for regular transactions exceeding 200,000 yen in value,
which fall under the general trade classification), since our study focuses on the exports and imports of firms.

2.2 Overview of the data
This sub-section provides an overview of our transaction-level customs data. Table 1 presents an
overview of the number of transactions (Panel A) and the total value (Panel B) for exports and imports
spanning the period from 2014 to 2020. Regular transactions, which we focus on in this paper, are listed in
the column labelled “General Trade.” These transaction counts (number of declaration columns) and tradevalues are stable over this period, although there was a discernible decline in the value of trade in 2020 due
to the COVID-19 pandemic. Both exports and imports amount to approximately 70 to 80 trillion yen, with
imports exceeding exports in all years except 2016.
TABLE 1: Overview of Export-Import Declaration Data
Panel A: Number of declaration columns
General trade

Other

Total
Part A1: Exports
2014
18,678,628
2015
18,813,924
2016
18,779,405
2017
20,182,821
2018
20,859,383
2019
19,258,079
2020
18,241,865

12,283,614
12,348,526
12,167,046
12,926,968
13,111,466
12,235,596
10,871,711(65.8%)(65.6%)(64.8%)(64.0%)(62.9%)(63.5%)(59.6%)

6,395,014
6,465,397
6,612,359
7,255,853
7,747,917
7,022,483
7,370,154(34.2%)(34.4%)(35.2%)(36.0%)(37.1%)(36.5%)(40.4%)Part A2: Imports
2014
20,948,209
2015
20,931,258
2016
21,794,316
2017
23,517,002
2018
25,179,862
2019
26,569,348
2020
27,724,720

10,407,848
10,306,199
10,217,918
10,764,750
10,951,621
10,751,986
10,013,860(49.7%) 10,540,361(49.2%) 10,625,059(46.9%) 11,576,398(45.8%) 12,752,252(43.5%) 14,228,241(40.5%) 15,817,362(36.1%) 17,710,860(50.3%)(50.8%)(53.1%)(54.2%)(56.5%)(59.5%)(63.9%)

Panel B: Export/Import amount (Unit: billion yen)
Total

General trade

Other

Part B1: Exports
2014
78,801
2015
79,411
2016
73,862
2017
82,012
201885,584
2019
80,963
2020
71,16173,741
75,26670,19578,233
81,21976,68868,342(93.6%)(94.8%)(95.0%)(95.4%)(94.9%)(94.7%)(96.0%)

5,060
4,145
3,667
3,779
4,364
4,275
2,819

(6.4%)(5.2%)(5.0%)(4.6%)(5.1%)(5.3%)(4.0%)Part B2: Imports
2014
89,057
2015
81,428
2016
69,911
2017
79,721
201887,685
2019
83,538
2020
71,922

84,81077,35065,48375,05182,72978,74968,455(95.2%)(95.0%)(93.7%)(94.1%)(94.3%)(94.3%)(95.2%)

4,248
4,078
4,428
4,670
4,956
4,789
3,467

(4.8%)(5.0%)(6.3%)(5.9%)(5.7%)(5.7%)(4.8%)

Next, Table 2 presents basic statistics for the number of destination countries or countries of origin, thenumber of products traded, and the amount of trade per firm, by firms’ industry at the NACE 1-digit level.13
Note that to determine exporters’ and importers’ main industry, we use information from the Orbis database
13“NACE” stands for “Nomenclature of Economic Activities” and is the classification of economic activities used in the European
Union. Figures for the manufacturing sector at the 2-digit level are presented in Appendix Table A3.

5

provide by Bureau van Dijk.14 Starting with exports, the average number of products exported per firm in2017 stood at 10.3, while the average number of destinations per firm was 3.7, and the average export value
was 1,192 million yen. In the case of imports, the average number of products imported per firm was 8.2,
the average number of countries of origin per firm stood at 2.7, and the average import value at 804 million
yen. In sum, exporters tended to deal with a larger number of products and a larger number of partnercountries than importers. However, there is substantial variation across industries. Specifically, the numberof products and partner countries is particularly large for firms in the manufacturing and wholesale and retailsectors. The manufacturing sector, in particular, stands out with the highest number of products exported
per firm (14.1), the largest number of export destinations (5.3), and the largest export value per firm (3,093
million yen).
TABLE 2: Exports and Imports by Industry, 2017
Exports

NACE 1-digit classification
A Agriculture, forestry and fishing
B Mining and quarrying
C Manufacturing
D Electricity, gas, and other utilities
E Water supply; sewerage; etc.
F Construction
G Wholesale and retail trade, etc.
H Transportation and storage
I Accomodation and food service activities
J Information and communication
K Financial and insurance activities
L Real estate activities
M Professional services
N Administrative and support services
O Public administration and defence, etc.
P Education
Q-T Other services
n.a. Unclassified
All industries

No. ofproducts (HS6-digit) per
firm

4.2
7.4
14.1
5.6
4.6
10.6
11.6
12.2
5.1
3.3
9.8
4.4
5.1
6.1
1.9
4.1
3.5
6.0
10.3

Imports

No. ofcountries
per firm

Exportvalue perfirm (millionyen)

No. ofproducts (HS6-digit) per
firm

No. ofcountries
per firm

Importvalue perfirm (million
yen)

2.7
3.8
5.3
2.0
2.4
2.4
3.7
3.5
1.9
2.3
3.1
1.8
2.2
2.3
1.3
2.2
2.4
2.3
3.7

123
1,449
3,093
70
88
228
820
707
37
264
558
41
92
276
5
17
58
156
1,192

3.5
6.6
8.9
5.2
5.7
4.4
11.0
9.7
4.4
4.1
6.1
4.5
4.2
4.9
1.4
3.7
4.6
5.1
8.2

2.3
3.3
3.1
2.6
1.9
1.8
3.2
2.9
2.0
2.2
2.3
1.9
1.9
2.1
1.5
2.0
2.0
1.9
2.7

X
X
1,535
13,480
333
87
874
1,361
158
283
X
82
120
241
9
20
259
278
804

Note: For sectors A (Agriculture, forestry and fishing), B (Mining and quarrying), and K (Financial and insurance activities), theimport values per firm are withheld.
14

In this section and Section 3, we conduct analyses by connecting the customs data, i.e., export and import declaration data, withcorporate information from the Orbis database provided by Bureau van Dijk, which comprehensively covers companies worldwide,
including those in Japan. The customs data include the corporate number designated by the National Tax Agency for each exporterand importer, and we link this data with the Orbis database at the firm level using the corporate number. See Appendix A for theoverview of our matched dataset. As shown in Appendix Table A1, we were able to successfully link approximately 97% of theexport/import declarations observed in the regular transaction data in the customs data with Orbis corporate information. It should
be noted, however, that even for firms recorded in the Orbis database, there is a significant amount of missing information suchas with regard to revenue, the number of employees, and the industry classification. Moreover, the industry classification of firms
used in Sections 2 and 3 is based on the most recent information available from Orbis as of June 2022, and data for the years 2014
to 2020 for the industry classification were not available to us. This means that our analysis is based on the assumption that theindustry classification of firms in our sample did not change between 2014 and June 2022.

6

3 Trade Decomposition: Extensive and Intensive Margins
3.1 Decomposition of factors contributing to cross-section variation in Japan’s trade
Trade amounts with each partner country are likely to be linked to the number of firms trading with thatcountry and the number of products traded. The impact of the number of firms and products on the amountof trade is referred to as the extensive margin, while the impact of the average trade amount per firm-productpair on the total amount of trade is referred to as the intensive margin. In this subsection, following Bernard
et al. (2009a) and Bernard et al. (2009b), we analyze to what extent the extensive or the intensive margin
explains the variation in Japan’s trade across partner country.
The trade amount xc with partner country c can be expressed using the following equation:
xc = fc pc dc x̄c ,(1)where fc represents the number of firms trading with country c, pc represents the number of products tradedwith country c, dc represents the trade density, and x̄c represents the average trade amount per firm-productpair. Trade density is defined as oc /(fc pc ), where oc denotes the number of firm-product pairs in trade withcountry c, while fc pc denotes the number of all potential firm-product combinations that could potentiallybe involved in trade with country c. Trade density takes into account the fact that only a small fraction of
all possible firm-product combinations is actually involved in trade with each country. The average trade
amount x̄c is calculated as xc /oc , representing the intensive margin.
Taking the logarithm of equation (1) above, we get:ln xc = ln fc + ln pc + ln dc + ln x̄c ,(2)

In equation (2), the logarithm of the export or import amounts in each year and for each partner country isused as the dependent variable. To decompose Japan’s exports to or imports from each partner country into
the extensive and the intensive margin, we regress the dependent variable on each term on the right-handside of equation (2). Table 3 shows the results for 2014, 2017, and 2020.
TABLE 3: Decomposition of Japan’s Exports and Imports Across Partner Countries for the Years 2014,2017, and 2020
(1)
2014
0.588
(0.013)

Exports
(2)
2017
0.619
(0.013)

(3)
2020
0.622
(0.013)

(4)
2014
0.495
(0.019)

Imports
(5)
2017
0.522
(0.018)

(6)
2020
0.526
(0.018)

Number of products

0.531(0.011)0.541(0.011)0.554(0.011)

0.462(0.019)0.480(0.017)0.478(0.017)

Density-0.431(0.011)-0.450(0.011)-0.457(0.012)-0.385(0.015)-0.403(0.015)-0.405(0.014)

Intensive margin

0.312(0.013)0.289(0.014)0.281(0.014)0.428(0.023)0.401(0.021)0.401(0.021)

234

231

233

228

229

228

Number of firms

No. of partner countries

Note: For each year, the estimated coefficients of each explanatory variable and their standard errors (in parentheses) arepresented. The number of observations in each year corresponds to the number of countries with which Japanese firms engaged inexports or imports.

The coefficients shown in Table 3 indicate how much each factor on the right-hand side of equation
7

(2) contributes to the variation in the dependent variable on the left-hand side. Columns (1) to (3) indicatethat the intensive margin (ln x̄c ) explains around 30% of the variation in Japan’s partner country-specificexport amounts. On the other hand, the variation in the number of export firms (ln fc ) and number ofexported products (ln pc ) account for approximately 60% and 55%, respectively, of the partner countryspecific export amounts. Meanwhile, trade density is negatively related to export amounts, so that the sumof these three coefficients is around 70%, indicating that the majority (about 70%) of the variation in partnercountry-specific export amounts is explained by the extensive margin. Thus, although the intensive margin,
at around 30%, is quite large, the extensive margin, at around 70%, is larger, indicating that larger overall
exports are primarily due a larger number of exporting firms and exported products. In other words, thedecomposition implies that in the case of countries that account for a large amount of Japan’s exports, alarge number of firms export a large number of products, while in the case of countries that account for asmall amount of exports, a small number of firms export a smaller number of products.
A similar pattern is observed for the variation in Japan’s imports across countries. Specifically, columns(4) to (6) show that around 60% of the variation can be explained by the extensive margin, while around
40% is explained by the intensive margin. These figures indicate that imports are more concentrated thanexports in that fewer firms account for a larger amount of imports from each partner country, so that theextensive margin makes a smaller contribution than in the case of exports.
While the decomposition results in Table 3 generally align with the findings of Bernard et al. (2009a) andBernard et al. (2009b) for the United States, in the case of Japan, the share explained by the intensive margin
in both exports and imports is about 10 percentage points larger than in the United States. This suggests thatin Japan, trade is more concentrated among a smaller number of firms than in the United States.

3.2 Decomposition of factors contributing to time-series variation in Japan’s trade
Next, we examine the determinants of changes in Japan’s trade over time. The change in Japan’s totalexports and imports compared to the previous year can be decomposed into three components: (1) the
increase due to firms newly engaging in exports or imports, (2) the decrease due to firms stopping to export
or import, and (3) the increase or decrease resulting from changes in the amount traded by continuing
exporters or importers. Therefore, the change in aggregate exports or imports from t − 1 to year t, ∆xt , canbe decomposed using the following equation:
∆xt =

X

xf t −

X

xf t−1 +∆xf t ,

(3)

f ∈Ct

f ∈Et

f ∈Nt

X

where f refers to a firm, Nt is the set of firms that newly started exporting or importing in year t, Et is theset of firms that stopped exporting or importing in year t, and Ct is the set of firms that continued exportingor importing from year t − 1 to year t.
The change in exports or imports of firms continuing to engage in export or import activitiesPf ∈Nt xf t ,can be further decomposed into (i) the changes due to the adding or dropping of new partner country-productpairs, and (ii) the changes due to an increase or decrease in the trade amount for continuing partner countryproduct pairs. Therefore, for continuous exporter or importer f , the change in exports or imports from yeart − 1 to year t, ∆xf t , can be decomposed using the following equation:∆xf t =

X

j∈Af txf jt −

X

xf jt−1 +j∈Df tX

j∈Gf t8∆xf jt +X

j∈Sf t∆xf t ,

(4)

where j refers to a partner country-product pair, Af t is the set of partner country-product pairs where firm f
started trading in year t, Df t is the set of partner country-product pairs where firm f stopped trading in yeart, Gf t is the set of partner country-product pairs where firm f ’s trade amount increased from year t − 1 toyear t, and Sf t is the set of partner country-product pairs where firm f ’s trade amount decreased from yeart − 1 to year t.
The first two terms on the right-hand side of equation (3) represent the entry into exporting or importing
and thus represent the extensive margin. The first two terms on the right-hand side of equation (4) represent
another extensive margin, namely, the switching of country-products by exporters or importers. The thirdand fourth terms on the right-hand side of equation (4) represent the intensive margin, reflecting the change
in trade for continuing exporters or importers.
The results of decomposing changes in Japan’s exports are presented in Table 4, while the corresponding
results for imports are shown in Table 5. Specifically, Table 4 presents the decomposition of year-on-yearchanges in Japan’s nominal exports, followed by the decomposition of changes over two years (2014-2016,2017-2019) and three years (2017-2020). Rows 1 to 9 show the results of the decomposition into theextensive and intensive margins, while row 10 presents the overall change in exports. Rows 11 through 13
report the net contribution of each margin as a percentage of the overall change in exports.
TABLE 4: Decomposition of Changes in Japan’s Exports over Time (Unit: Billion Yen)

20142015
Panel A: Export entry and exit
(1) Exporter entry(2) Exporter exit
(3) Net entry

287
-68
219

Year-on-year changes
2015201720182016
2018
2019

20192020

2-year201420172016
2019

3-year20172020

414
-106
308

1,111
-228
882

279
-706
-428

266
-334
-68

878
-184
694

2,101
-1,160941

2,232-1,538694

Panel B: Product-country switching by continuing exporters(4) New product-country
3,959
3,745
(5) Discontinued product-country-3,800 -3,879(6) Net product-country
159
-133

4,024
-4,076-52

3,821-4,599-779

3,365-4,160-794

5,916-6,159-243

5,994-7,093-1,0996,786-8,951-2,166

16,013 16,837-19,120 -17,715-3,108-878-2,657 -1,036

15,658-23,587-7,928-9,400Panel C: Intensive margin for continuing exporters(7) Product-country growth
14,598 11,385 15,575 13,194 11,057(8) Product-country decline-13,255 -15,936 -12,940 -16,488 -18,559(9) Net intensive margin
1,343 -4,552
2,634 -3,294 -7,502(10) Total change in exports
1,721 -4,377
3,464 -4,500 -8,364
Panel D: Percent of annual growth due to:(11) % Net entry and exit
(12) % Net product-country addition(13) % Net intensive margin

13
9
78

-7
3
104

25
-2
76

10
17
73

1
9
90

-26
9
117

-91
106
85

-7
23
84

Note: Row (11) is computed as ((1)+(2))/(10). Row (12) is computed as ((4)+(5))/(10). Row (13) is computed as ((7)+(8))/(10).

Row 10 indicates that Japan’s total exports exhibited a decreasing trend from 2014 to 2020. Furthermore,row 13 shows that a significant share of the year-on-year – i.e., short-run – changes in total exports is
explained by the intensive margin, which is consistent with the findings by Bernard et al. (2009a) andBernard et al. (2009b) for the United States. In the case of Japan, the intensive margin accounted for an
average 84% of the year-on-year changes in exports, ranging from a low of 73% for 2018 to 2019 to a highof 104% for 2015 to 2016.15 As Japan’s total exports followed a downward trend tend during the period from
15
This finding regarding the share of the intensive margin is comparable to Bernard et al. (2009a) results for the United States:
they find that over the period from 1993 to 2003, but excluding 2000 to 2002, when the U.S. economy was in recession, on average
76% of annual export growth was due to the intensive margin.

9

2014 to 2020, this means that continuing exporters’ exports of incumbent products to incumbent destinations(row 8) made the largest contribution to the overall decline in Japan’s exports during this period. In addition,
the intensive margin accounts for over 80% of the changes over the two- and three-year intervals.
Turning to the extensive margin and looking at product-country switching by continuing exporters showsthat except in 2014-2015, the contribution of the discontinuation of exports of incumbent products to incumbent destinations (row 5) always exceeded the contribution of the addition of new product-country combinations (row 4). Next, looking at export entry and exit shows that the contribution of export market entry
followed an increasing trend until 2018 but then declined (row 1). As a result, from 2018 onwards, thenegative contribution of export market exit exceeded the positive contribution of export market entry, sothat the net contribution of entry and exit turned negative (rows 2 and 3). While possible reasons for thesedevelopments include external factors such as the U.S.-China trade tensions and the COVID-19 pandemic,
more detailed analysis is required to understand the factors contributing to the recent decrease in new entries
into export markets.
TABLE 5: Decomposition of Changes in Japan’s Imports over Time (Unit: Billion Yen)

20142015
Panel A: Export entry and exit
(1) Importer entry(2) Importer exit
(3) Net entry

291
-114
177

Year-on-year changes
2015- 2017- 20182016
2018
2019

20192020

2-year2014- 20172016
2019

3-year20172020

269
-108
161

490
-557
-67

1,532
-380
1,152

388
-902
-515

667
-214
452

2,273
-1,142
1,130

2,816-2,76947

Panel B: Product-country switching by continuing importers(4) New product-country
3,635
2,885
(5) Discontinued product-country-3,028 -2,919(6) Net product-country
607
-35

4,008
-2,656
1,351

3,080-3,236-156

2,792-3,144-352

5,283-5,447-164

5,992-4,791
1,200

7,130-7,06170

10,876 16,831-28,569 -14,950-17,693
1,881-17,405
4,211

14,435-20,511-6,075-5,959Panel C: Intensive margin for continuing importers(7) Product-country growth11,612
7,963 16,458 11,383
9,362(8) Product-country decline-18,850 -19,039 -9,610 -16,299 -18,666(9) Net intensive margin-7,239 -11,077
6,848 -4,917 -9,303(10) Total change in exports-6,455 -10,950
8,132 -3,920 -10,170
Panel D: Percent of annual growth due to:(11) % Net entry and exit
(12) % Net product-country addition(13) % Net intensive margin

-3
-9
112

-1
0
101

-1
17
84

-29
4
125

5
3
91

-3
1
102

27
29
45

-1
-1
102

Note: Row (11) is computed as ((1)+(2))/(10). Row (12) is computed as ((4)+(5))/(10). Row (13) is computed as ((7)+(8))/(10).

Next, Table 5 presents the decomposition of changes in Japan’s nominal imports. Similar to exports,
Japan’s imports followed a decreasing trend from 2014 to 2020 (row 10). Furthermore, as with exports,
a substantial share of year-on-year changes in total imports is explained by the intensive margin. In otherwords, the overall decline in Japan’s imports is primarily due to the decrease in continuing importers’ imports of incumbent products from incumbent countries of origin (row 8). However, the contribution of theintensive margin (row 13) varies over time. Moreover, comparing the contribution of the intensive margin
for imports and exports shows that, generally, it makes a larger contribution in the case of imports thanexports.
Tables 4 and 5 show that a large part of the overall change in both exports and imports is explained by
the intensive margin. The similarity in the patterns of the decomposition results for exports and imports
can be explained by the fact that large firms engaged in both exports and imports account for a substantial
share of a country’s total trade. We show that Japan’s trade is concentrated among a small number of large
10firms engaged in both exporting and importing in Table 9 of Section 4. For firms that are engaged in both
exports and imports, it is likely that if there is a decrease in the export of final goods for some reason, theirintermediate goods imports also decrease. As a result, the contribution of the intensive margin to changes inexports and imports is likely to follow similar patterns.
As discussed in previous studies such as Mayer and Ottaviano (2008), Wakasugi et al. (2014), andBernard et al. (2018), the majority of a country’s total exports or imports tends to be carried by a very small
number of large firms, and these large firms are continuing exporters and/or importers. It is for this reason
that the contribution of the intensive margin to changes in aggregate trade is much larger than that of theextensive margin. Against this background, the next section examines the extent to which Japan’s trade isconcentrated among a small number of firms.

4 Concentration of Japan’s International Trade
We next examine the concentration of Japan’s international trade in a small number of large firms. Using
data for 2017, we start by aggregating the exports and imports of each firm by product (HS9-digit level) andpartner country. The number of products and countries each firm exports to or imports from is then counted.
For exporters, Panel A of Table 6 shows the share of the total number of firms in each category based on
the number of exported products and destination countries. Panel B of Table 6 presents the share of export
values for each category of firm.
Panel A of Table 6 shows that firms exporting one product to one destination country make up thelargest share, accounting for 21.1% of all exporting firms classified under manufacturing in the industry
classification of the Orbis database. In terms of the number of destination countries, firms exporting to
one or two countries account for 55.6% of all manufacturing exporting firms (39.5% + 16.1%). Moreover,firms exporting only one or two products account for 37.6% of all manufacturing exporting firms (24.4% +13.2%). On the other hand, firms exporting to more than 10 countries and exporting more than 10 products
make up 11.3% of all manufacturing firms. Thus, firms exporting only one product to one country andfirms exporting more than 10 products to more than 10 countries make up the largest shares in the panel,indicating that a large number of exporting firms broadly fall into one of two categories.
Next, looking at the distribution of the share of export value (Panel B of Table 6), it becomes clear thatfirms exporting more than 10 products and to more than 10 countries account for 90.3% of the total value ofexports. In other words, approximately 11% of all manufacturing exporters make up over 90% of the total
value of exports. Such concentration of exports in large firms exporting many products to many countries
is observed not only in Japan but also in other countries (e.g., Mayer and Ottaviano, 2008; Bernard et al.,2018).
Next, Table 7 shows the corresponding results for imports. Similar to exports, only importing firms
classified under manufacturing in the Orbis database are considered.
As in the case of exports, firms importing only one product from a single country constitute the largest
share, accounting for 24.0% of all manufacturing importing firms. In terms of the number of source countries, firms importing from one or two countries make up a substantial share, totaling 68.0% (47.9% +20.1%). Meanwhile, firms importing one or two products account for 40.7% (25.7% + 15.0%) of all manufacturing importers. On the other hand, firms importing more than 10 products and from more than 10
countries constitute 4.7% of all manufacturing importers.
Comparing this figure with the corresponding figure for exports (Panel A of Table 6) shows that the11TABLE 6: Distribution (%) of Manufacturing Exporting Firms by Number of Products and Destination
Countries, 2017
Number of countries12
3
4
5 6-10Panel A: Percentage of exporting firms
1
21.1
2.1
0.6
0.3
0.1
0.2
2
7.1
3.9
1.0
0.5
0.3
0.3
3
3.2
2.5
1.2
0.6
0.2
0.5
4
1.8
1.5
1.2
0.6
0.3
0.5
5
1.2
1.2
0.8
0.5
0.4
0.5
6-10
2.7
2.4
2.3
1.6
1.0
2.6
11+
2.5
2.4
2.2
2.0
1.8
6.9
All
39.5 16.1
9.3
6.0
4.1 11.6
Panel B: Percentage of export value
1
0.1
0.0
0.1
0.0
0.0
0.0
2
0.1
0.0
0.0
0.2
0.0
0.0
3
0.0
0.0
0.0
0.0
0.0
0.1
4
0.0
0.0
0.1
0.0
0.0
0.2
5
0.0
0.0
0.0
0.0
0.0
0.0
6-10
0.1
0.1
0.1
0.1
0.1
0.6
11+
0.7
0.3
0.4
0.5
0.6
3.8
All
1.0
0.5
0.7
0.9
0.8
4.7
No. ofproducts

11+

All

0.1
0.1
0.1
0.2
0.2
1.2
11.3
13.3

24.4
13.2
8.4
6.2
4.8
13.9
29.1
100

0.0
0.0
0.0
0.1
0.1
0.8
90.3
91.3

0.3
0.4
0.3
0.4
0.2
1.7
96.7
100

TABLE 7: Distribution (%) of Manufacturing Importing Firms by Number of Products and Source Countries, 2017
Number of countries12
3
4
5 6-10Panel A: Percentage of importing firms
1
24.0
1.4
0.2
0.1
0.0
0.0
2
9.3
5.0
0.6
0.1
0.0
0.0
3
4.4
3.4
1.3
0.3
0.1
0.0
4
2.7
2.3
1.3
0.5
0.2
0.1
5
2.0
1.7
1.0
0.5
0.2
0.1
6-10
3.6
4.0
3.1
2.1
1.2
1.5
11+
2.0
2.3
2.3
2.1
2.1
6.2
All
47.9 20.1
9.7
5.7
3.8
8.0
Panel B: Percentage of import value
1
0.5
0.1
0.1
0.0
0.1
1.0
2
0.3
0.2
0.1
0.1
0.0
0.8
3
0.2
0.2
0.2
0.0
0.1
0.0
4
0.2
0.2
0.1
0.1
0.1
0.0
5
0.1
0.2
0.1
0.2
0.0
0.1
6-10
0.4
0.6
0.4
0.5
0.3
0.7
11+
0.5
0.8
1.1
1.3
1.7
7.1
All
2.2
2.4
2.0
2.2
2.3
9.8
No. ofproducts

11+

All

0.0
0.0
0.0
0.0
0.0
0.1
4.7
4.8

25.7
15.0
9.5
7.0
5.5
15.5
21.7
100

0.0
0.3
0.0
0.0
0.0
4.1
74.8
79.1

1.8
1.9
0.7
0.7
0.8
7.0
87.2
100

percentage of firms importing many products from many countries is lower. However, looking at the distribution of the value of imports (Panel B of Table 7), we find that firms importing more than 10 products
and from more than 10 countries are responsible for 74.8% of total imports. In other words, in terms ofthe number of firms, approximately 5% of all manufacturing importers account for three-quarters of total
imports. This pattern is similar to that observed in the United States, suggesting that, also in the case ofimports, large firms importing many products from many countries dominate.
Table 8 provides a more detailed breakdown of trade concentration. It shows the import shares of firms
when these are sorted in ascending order in terms of their amount of trade and split into deciles or percentiles.
The column labeled “Exports + Imports” indicates that the top 10% of firms (the 10th decile) account for
96.2% of the total trade amount. Looking at exports only, the top 10% of firms are responsible for 96.6% of
total exports, while for imports only, the top 10% of firms account for 94.6% of total imports.

12

We further divide the distribution into percentiles within the top 10% and show the 91st to 100th percentiles in the table. It can be seen that the top 1% of firms (the 100th percentile) account for 76.7% of total
trade. The figures are similar when looking at exports or imports only, indicating that the concentration of
trade among the top 1% of firms is very high.
TABLE 8: Distribution of Trade Shares, 2017
Exports + Imports Exports Imports
Panel A: Deciles
1
0.000
0.000 0.000
2
0.000
0.000 0.000
3
0.000
0.000 0.000
40.000
0.000 0.001
50.001
0.001 0.001
6
0.002
0.001 0.003
7
0.003
0.003 0.005
8
0.008
0.007 0.012
9
0.024
0.022 0.032
10
0.962
0.966 0.946
Panel B: Percentiles
91
0.005
92
0.006
93
0.007
94
0.009
95
0.012
96
0.016
97
0.023
98
0.038
99
0.080
100
0.767

0.005
0.006
0.007
0.009
0.012
0.017
0.024
0.040
0.088
0.759

0.006
0.007
0.009
0.011
0.014
0.019
0.025
0.040
0.075
0.739

Note: The table shows the share of trade accounted for by each decile or percentile of firms. The figures are based on data for2017 and for firms classified under manufacturing based on the industry classification in the Orbis company database.

For comparison, in the United States, the top 10% of firms account for 96.3% of total trade, with the top
1% of firms responsible for 81.8% of the total trade (Bernard et al., 2018). Both Japan and the United States
exhibit a high concentration of trade, with the top 1% of firms making up around 70-80% of trade in termsof the total as well as exports or imports only. Mayer and Ottaviano (2008) report lower concentration levels
for European countries, such as Belgium (48%), France (44%), Germany (59%), Hungary (77%), Italy(32%), Norway (53%), and the United Kingdom (42%). Although European countries also show a highconcentration among the top 1% of firms, the concentration in Japan and the United States is substantiallyhigher. This difference is likely due to the close proximity between European countries, with shared borders,the establishment of a single market within the European Union (EU), and the overall ease of trade within
the EU. In addition, the smaller market size of individual European countries may encourage businesses toexport to other countries within the region.
Next, in Table 9 we examine the distribution of exports and imports across firms by again grouping firms
into deciles and percentiles based on the amount of trade they are engaged in.16 Starting with column (1),this shows the share of firms in each decile or percentile that are engaged in both exporting and importing(each decile comprises 2,557 or 2,558 firms, while each percentile comprises 256 or 257 firms). The shareof firms engaged in both exporting and importing rises for each decile (and broadly increases over thepercentiles). For instance, among firms belonging to the bottom 10% in terms of their total value of trade(1st decile), only 2.9% are involved in both exporting and importing, whereas among firms in the top 10%
16

We further examine the diversity of exports and imports at the firm-level. The statistics are reported in Appendix B.2.

13

(10th decile) the corresponding share is 96.4%.
Column (2) shows the average value of the total amount of trade (exports and/or imports) per firm, while
column (3) shows the average value of the intra-industry trade (IIT) index for each firm within each decile
or percentile, where IIT is defined as the export and import of products in the same HS6-digit level category.
The IIT index is calculated for each firm and then averaged within each decile or percentile. The IIT index,
known as the Grubel-Lloyd index, is calculated as follows:
IITf pt = 1 −|Xf pt − Mf pt |,Xf pt + Mf pt

where Xf pt stands for firm f ’s value of exports of product p in year t, and Mf pt and IITf pt stand for thecorresponding value of imports and IIT (where exports and imports are aggregated at the product level andno distinction of trading partners is made). This index approaches 1 when the difference between the export
and import amounts is small, indicating a significant level of IIT. Furthermore, the firm-level IIT index isdefined as the weighted average of the IIT indices for each product p, where the weights are given by theshare of product p in the total trade amount of firm f :IITf t =X  Xf pt + Mf pt 
pX f t + Mf t

IITf pt ,Column (2) of Table 9 shows that the average trade amounts of the top 10%, and especially the top 1%,
of firms are much larger than those of other firms. Moreover, column (3) shows that the top 10% of firms tosome extent both export and import products in the same category, even at the relatively detailed HS6-digit
product category. Columns (4) to (6) and columns (8) to (10) present the average number of product-country
pairs for exports and import, the average number of products, and the average number of partner countries,
respectively. Here again, we find that the top 10%, and particularly the top 1%, of firms trade significantly
more products with more partner countries than other firms. Comparing the figures in columns (4) to (6)and columns (8) to (10) shows that large exporters tend to trade a much larger number of products with amuch larger number of countries than importers.
Next, column (7) shows the average export market share of firms’ top export product. Specifically, we
first identify the top-ranking product (at the HS9-digit level) in a firm’s exports to a specific country in termsof the export value and then calculate the share of this in Japan’s total exports of that product to that country,
which we call the market share. Next, we calculate for each firm the average of the market shares across all
product-country pairs. Finally, for column (7), the firm-level average top product market shares are further
averaged for each decile or percentile. Column (11) show the corresponding values for imports.
For example, column (7) indicates that the top-ranking product of the top 1% of firms in terms of tradevolume, on average, accounts for a 48% share of exports to each export market. In contrast, the top-ranking
product of medium-sized exporters falling into the 5th decile has an average market share of only 13%. Thissuggests that among firms exporting the same product to the same country, large firms dominate the market,
holding substantial power in the export market from Japan to that specific destination country. Similar
patterns can be observed for imports, as shown in column (11).

14TABLE 9: Distribution of Trade by Decile/Percentile of the Value of Total Trade, 2017
Exports + Imports
(1)
(2)
(3)
Share offirms
engagedin bothexportandimport(4)

Exports
(5)
(6)

(7)

Average
Averageno. ofmarketAverage
Average share of
productno. of
country products no. of
the toppartnerpairs
export(HS9countries product(HS9digit
digit
for eachlevel)
level)
firm(8)

Imports
(9)
(10)

(11)

Average
Averageno. ofmarketAverage
Average share of
productno. of
country products no. of
the toppartnerpairs
import(HS9countries product(HS9digit
digit
for eachlevel)
level)
firm

Averagevalue oftrade
(millionyen)

Average
IIT index(HS6digit
level)

Panel A: Deciles
1 0.0291
2 0.1242
3 0.206
5
4 0.29412
5 0.400
27
6 0.51356
7 0.644117
8 0.774265
9 0.886793
10 0.964 32,226

0.003
0.013
0.021
0.026
0.035
0.040
0.054
0.073
0.099
0.145

1.2
1.9
2.8
3.9
5.0
6.8
10.4
16.7
32.6
176.5

1.2
1.7
2.4
3.1
3.9
4.9
7.3
10.9
19.0
65.8

1.1
1.3
1.6
2.0
2.3
2.8
3.6
4.7
7.2
17.2

0.10
0.10
0.11
0.13
0.13
0.14
0.16
0.17
0.21
0.31

1.2
1.8
2.4
3.1
4.3
5.7
7.2
9.8
16.2
65.9

1.2
1.7
2.3
2.9
4.0
5.1
6.3
8.3
13.0
38.8

1.1
1.2
1.4
1.5
1.7
2.0
2.3
2.7
3.9
9.8

0.09
0.10
0.12
0.14
0.15
0.16
0.19
0.20
0.24
0.32

Panel B: Percentiles
91 0.949
1,651
92 0.934
1,977
93 0.945
2,413
94 0.969
3,013
95 0.957
3,863
96 0.961
5,357
97 0.973
7,734
98 0.977 12,567
99 0.988 26,739
100 0.984 257,616

0.122
0.118
0.143
0.133
0.140
0.153
0.149
0.156
0.180
0.156

55.6
57.9
61.6
76.3
84.3
107.3
132.4
162.5
274.7
740.0

30.3
29.9
31.6
37.9
42.9
53.8
59.2
70.8
102.4
195.4

9.4
10.9
10.9
12.7
12.5
14.6
17.3
18.1
25.9
38.9

0.24
0.25
0.26
0.28
0.26
0.29
0.33
0.33
0.39
0.48

23.3
22.7
29.4
31.1
38.2
41.5
47.6
62.6
95.5
265.5

17.5
17.2
21.8
22.5
26.7
29.1
32.1
40.1
56.6
123.1

4.8
5.5
6.3
6.5
7.7
8.0
8.8
10.9
14.4
24.4

0.26
0.27
0.30
0.29
0.31
0.30
0.32
0.35
0.38
0.45

Note: The figures are for firms classified under manufacturing based on the Orbis industry classification.

5 Performance Premia of Firms Engaged in International Trade
In this section, we analyze the performance premia of exporting firms by matching the customs data with
firm-level questionnaire information from the Basic Survey of Japanese Business Structure and Activities(BSJBSA). Details of the BSJBSA and the characteristics of the matched data are provided in Appendix C.

5.1 Performance premia of exporting firms
We compare the firm characteristics of exporting firms and non-exporting firms. In this section, we
restrict our analysis to manufacturing firms in the matched data and use regression analysis to examine how
firm performance differs between exporting and non-exporting firms. The specific estimation equation is asfollows:
yf t = α + βDf t + γXf t + ǫf t ,(5)where yf t represents the performance indicator of firm f in year t, Df t is a dummy for exporters, and Xf t
represents other control variables. The performance indicators include sales, value added, labor productivity(value added per employee), the number of employees, the capital-labor ratio, TFP, and the average wage.17
17

TFP is calculated from the residuals of the estimated production function for each industry. The production function is estimated using the method of Wooldridge (2009).

15

All these variables are in logs.
Taking the exponent of the estimated coefficient β̂ of the exporter dummy, exp(β), allows us to calculate
the factor by which the performance indicators of exporting firms exceed those of non-exporting firms. As
a control variable, we use the log of the number of employees as a proxy for firm size. We also include
two-digit-level industry fixed effects and year fixed effects. The basic statistics of the variables used in theanalysis are presented in Table C4.
The estimation results of equation (5) are presented in Table 10, where columns (1), (2), and (3) show theestimation results with different combinations of fixed effects and control variables. The values in the table
represent the coefficients on the exporter dummy, with standard errors in parentheses. For example, column(1) shows that the exporter premium with respect to sales is 0.761, implying that the sales of exporters areapproximately twice those of non-exporters (exp(0.761) = 2.14).
The following three observations can be made from Table 10. First, the coefficients on the exporter
dummy are positive and statistically significant in all cases, indicating that firms engaged in exporting perform better than non-exporting firms. Second, the difference between exporting and non-exporting firms
tends to decrease when industry fixed effects and firm size are controlled for (most of the coefficients in column (3) are smaller than those in columns (1) and (2)). Still, the coefficient on the export dummy remains
statistically significant even after these factors are controlled for.18
TABLE 10: Estimation Results for Exporter Premia

Sales
Value added
Labor productivity
No. of employees
Capital-labor ratio
TFP
Average wage
Year FE
Industry (2-digit) FE
Size control(1)0.761*(0.019)0.745*(0.017)0.230*(0.007)0.519*(0.014)0.262*(2)
0.819*(0.020)0.727*(0.017)0.173*(0.007)0.557*(0.015)0.281*(3)
0.193*(0.011)0.120*(0.007)0.119*(0.007)(0.014)0.313*(0.010)0.165*(0.005)Yes
No
No(0.014)0.188*(0.006)0.116*(0.004)
Yes
Yes
No(0.015)0.072*(0.006)0.083*(0.005)
Yes
Yes
Yes

N
78,76878,57578,57578,7680.185*

78,37078,18078,768

Note: The table shows the coefficient of the exporter dummy when each variable in the left column is used as the dependent
variable. Numbers in parentheses are robust standard errors clustered by firm. Firms in the top and bottom 1% in terms of their log
value added, capital per worker, and TFP are dropped as outliers in the estimations where these variables are used as the dependent
variables. “n.a.” stands for “not applicable.” We do not estimate the exporter premia in the case where the number of employees isused as the dependent variable in column (3) because the variable to control for firms’ size is based on the number of employees. *denotes statistical significance at the 1% level.

Table 11 presents estimates of export premia by year using the exporter dummy.19 The exporter premia
with respect to sales and the capital-labor ratio are stable over time, but those with respect to the other indicators show an increasing trend. For example, for the average wage, the coefficient on the exporter dummy
18

In Appendix D, instead of the exporter dummy, we use an importer dummy and a dummy for two-way traders, i.e., firms thatare engaged in both exporting and importing. The importer and two-way trader dummies based on the custom data tend to haveslightly larger coefficients than those based on the BSJBSA. However, overall, the results are qualitatively similar to those obtained
from the BSJBSA.
19
See Appendix D for import premia and two-way trader premia.

16increases from 0.07 to 0.09. Theoretically, an increase in export premia can be observed in the following
cases: assuming the performance of domestic firms remains constant, either (1) exporters’ performance improves, or (2) poorly performing exporters withdraw from foreign markets and export participation declines.
The export participation rate shown in the bottom row of Table 11 remains almost unchanged, suggestingthat the rise in export premia is driven by the improved performance of exporting firms. A possible interpretation of this result is that domestic firms’ performance has been sluggish due to slow growth in the domestic
market while exporting firms have improved their performance by capturing growth in foreign markets.
TABLE 11: Exporter Premia over Time
2014

2015

2016

2017

2018

2019

2020

0.192*
0.114*
0.110*
0.181*
0.065*
0.072*
0.544*

0.185*
0.105*
0.104*
0.190*
0.057*
0.074*
0.535*

0.185*
0.106*
0.102*
0.182*
0.058*
0.079*
0.548*

0.208*
0.127*
0.127*
0.199*
0.077*
0.091*
0.559*

0.198*
0.134*
0.135*
0.179*
0.089*
0.083*
0.561*

0.195*
0.134*
0.134*
0.188*
0.085*
0.090*
0.569*

0.189*
0.126*
0.124*
0.174*
0.079*
0.093*
0.582*

Exporter premia
Sales
Value added
Labor productivity
Capital-labor ratio
TFP
Average wage
Number of employees
Number of firms
Export participation ratio

11,262 11,328 11,255 11,324 11,136 11,168 11,295
47.1% 48.0% 48.1% 49.4% 49.9% 49.1% 47.1%

Note: Two-digit industry fixed effects are included. Firm size, measured by the number of employees, is controlled for in all cases
except where the number of employees is used as the dependent variable for firm performance. * denotes statistical significance atthe 1% level.

5.2 Link between firm size and the extensive margins of trade
To explore the link between firm size and the number of countries and products traded, we estimate thefollowing simple regression model:
yf st = exp(α + β ln(Empf st ) + λs + ηt + ǫf st ),(6)

where yf st is the number of destination or source countries or the number of exported or imported products
for firm f in industry s and year t. ln(Empf st ) is the number of employees, a proxy for firm size. We also
include industry and year fixed effects. The estimation results of equation (6) are presented in Table 12.
Since our sample covers non-exporting and non-importing firms, there are many zero values in thedependent variables, so that we use a negative binomial model. The coefficients on firm size are all positiveand significant for the number of countries and products traded for both exports and imports, indicating thatthe larger a firm, the more countries it trades with and the larger the number of products it trades. Looking at
the magnitude of the coefficients on firm size, those for exports are larger than those for imports, suggestingthat larger exporters tend to trade more goods with a larger number of countries than importers.
Finally, Table 13 presents estimates of the same model for three industry categories: manufacturing,
wholesale and retail trade, and other. The coefficients on firm size are positive and significant for all industry
categories. Comparing the magnitude of the coefficients between the manufacturing and wholesale/retailindustries, the coefficient for the wholesale/retail industry is about half that of the manufacturing industry.
A possible explanation is that wholesale/retails firms trade many products with many different countries
even though they tend to be relatively small compared to manufacturers.

17

TABLE 12: Link between Firm Size and Extensive Margins of Trade, All Industries(1)
No. ofdestination
countries(2)
No. ofsourcecountries(3)
No. ofexported
products(4)
No. ofimported
productsln(Emp )0.604*(0.098)0.574*(0.087)0.768*(0.098)0.667*(0.077)N
log-likelihood

208,741-314961

208,741-312919

208,741-390534

208,741-402255

Note: Two-digit industry and country fixed effects are included. Figures in parentheses are robust standard errors clustered at thetwo-digit industry level. * denotes statistical significance at the 1% level.

TABLE 13: Link between Firm Size and Extensive Margins of Trade by Industry
(1)

(2)

(3)

(4)

No. ofdestination
countries

No. ofsourcecountries

No. ofexported
products

No. ofimported
products

Panel A: Manufacturingln(Emp ) 0.773*(0.046)
N
89,706log-likelihood -179,088

0.724*(0.025)89,706-158,286

0.949*(0.048)89,706-221,959

0.822*(0.029)89,706-210,074

Panel B: Wholesale and retailln(Emp ) 0.304*(0.011)
N
64,137log-likelihood -99,153

0.316*(0.008)64,137-108,299

0.463*(0.013)64,137-123,742

0.432*(0.010)64,137-138,778

Panel C: Other industriesln(Emp ) 0.666*(0.094)
N
54,898log-likelihood -29,092

0.638*(0.086)54,898-35,5830.809*(0.103)54,898-34,368

0.678*(0.081)
54,898-42,441

Note: Two-digit industry and year fixed effects are included. Figures in parentheses are robust standard errors clustered at thetwo-digit industry level. * denotes statistical significance at the 1% level.

6 Conclusion
This study provides first insights into Japanese firms’ export and import behavior using transaction-level
customs data. While previous studies working with METI’s firm-level survey data (e.g., Wakasugi et al.,2014) examine the performance of exporters, our study investigates firm behavior in terms of a greater number of dimensions, including various margins and the level of concentration in terms of products and partnercountries. We found that during 2014-2020 approximately 70% of the variation in Japan’s export values
across partner countries was due to the number of exporting firms and the number of exported products (i.e.,
the extensive margin). Similarly, the extensive margin can explain about 60% of the variation on the importside. We also found that a limited number of firms accounts for a large share of both exports and imports,
and they trade many products with many partner countries, whereas a large share of firms that engage ininternational trade export only one product to one destination country or import only one product from one
source country. Notably, the distributions of the value of trade per firm, the number of products per firm,
and the number of destinations per firm exhibit a high degree of skewness.
Furthermore, we found interesting trends in the performance premia of Japanese exporting firms in the
period 2014-2020. Specifically, exporter premia with respect to value added and productivity (both laborproductivity and TFP) decreased until 2016 and then increased until 2019, whereas the exporter premium
18with respect to the average wage steadily increased in this period. On the other hand, the export participation
ratio steadily increased until 2018 and slightly decreased thereafter. A possible explanation for these changes
in exporter premia is the introduction of Abenomics in 2013, consisting of policies by the Abe administration
to boost growth and overcome deflation. While it is beyond the scope of this study to investigate how
economic policy in this period affected Japanese firms’ engagement in international trade, doing so might
be feasible by combining Japanese customs data with firm-level data and hence is an important topic for
future research.

ReferencesAdão, Rodrigo, Paul Carrillo, Arnaud Costinot, Dave Donaldson, and Dina Pomeranz (2022). “Imports, exports, and earnings inequality: Measures of exposure and estimates of incidence”. In: Quarterly Journalof Economics 137.3, pp. 1553–1614.
Aghion, Philippe, Antonin Bergeaud, Matthieu Lequien, Marc J. Melitz, and Thomas Zuber (May 2024).
“Opposing firm-level responses to the China shock: Output competition versus input supply”. In: American Economic Journal: Economic Policy 16.2, pp. 249–269.
Albornoz, Facundo, Héctor F. Calvo Pardo, Gregory Corcos, and Emanuel Ornelas (2023). “Sequentially
exporting products across countries”. In: Journal of International Economics 142, p. 103735.
Aw, Bee Yan, Sung Chung, and Mark J Roberts (2000). “Productivity and turnover in the export market:
Micro-level evidence from the Republic of Korea and Taiwan (China)”. In: The World Bank Economic
Review 14.1, pp. 65–90.
Bastos, Paulo and Joana Silva (2010). “The quality of a firm’s exports: Where you export to matters”. In:
Journal of International Economics 82.2, pp. 99–111.
Békés, Gábor and Balázs Muraközy (2012). “Temporary trade and heterogeneous firms”. In: Journal ofInternational Economics 87.2, pp. 232–246.
Bellone, Flora, Kozo Kiyota, Toshiyuki Matsuura, Patrick Musso, and Lionel Nesta (2014). “International
productivity gaps and the export status of firms: Evidence from France and Japan”. In: European Economic Review 70, pp. 56–74.
Bergstrand, Jeffrey H., Stephen R. Cray, and Antoine Gervais (2023). “Increasing marginal costs, firm heterogeneity, and the gains from “deep” international trade agreements”. In: Journal of International Economics 144, p. 103774.
Bernard, Andrew B and J Bradford Jensen (1995). “Exporters, jobs, and wages in U.S. manufacturing:
1976–1987”. In: Brookings Papers on Economic Activity: Microeconomics 26, pp. 67–119.
Bernard, Andrew B, J Bradford Jensen, Stephen J Redding, and Peter K Schott (2007). “Firms in international trade”. In: Journal of Economic Perspectives 21.3, pp. 105–130.
— (2018). “Global firms”. In: Journal of Economic Literature 56.2, pp. 565–619.
— (2009a). “The margins of U.S. trade (long version)”. NBER Working Paper No. 14662.
— (2009b). “The margins of US trade”. In: American Economic Review: Papers & Proceedings 99.2,
pp. 487–493.
Bernard, Andrew B and Joachim Wagner (2001). “Export entry and exit by German firms”. In: Weltwirtschaftliches
Archiv 137.1, pp. 105–123.
Costinot, Arnaud, Andrés Rodríguez-Clare, and Iván Werning (2020). “Micro to macro: Optimal trade policy with firm heterogeneity”. In: Econometrica 88.6, pp. 2739–2776.

19

Crozet, Matthieu, Keith Head, and Thierry Mayer (2012). “Quality sorting and trade: Firm-level evidence
for French wine”. In: Review of Economic Studies 79.2, pp. 609–644.
De Loecker, Jan (2007). “Do exports generate higher productivity? Evidence from Slovenia”. In: Journal ofInternational Economics 73.1, pp. 69–98.
— (2013). “Detecting learning by exporting”. In: American Economic Journal: Microeconomics 5.3, pp. 1–
21.
Eaton, Jonathan, Samuel Kortum, and Francis Kramarz (2004). “Dissecting trade: Firms, industries, andexport destinations”. In: American Economic Review 94.2, pp. 150–154.
— (2011). “An anatomy of international trade: Evidence from French firms”. In: Econometrica 79.5,
pp. 1453–1498.
Fan, Jingting, Yi Lu, and Wenlan Luo (2023). “Valuing domestic transport infrastructure: A view from theroute choice of exporters”. In: Review of Economics and Statistics 105.6, pp. 1562–1579.
Felbermayr, Gabriel, Fukunari Kimura, Toshihiro Okubo, and Marina Steininger (2019). “Quantifying the
EU-Japan economic partnership agreement”. In: Journal of the Japanese and International Economies
51, pp. 110–128.
Gimenez-Perales, Victor (2024). “The dynamics of importer-exporter connections”. In: European Economic
Review 161, p. 104638.
Girma, Sourafel and Holger Görg (2004). “Outsourcing, foreign ownership, and productivity: Evidence fromUK establishment-level data”. In: Review of International Economics 12.5, pp. 817–832.
Görg, Holger, Aoife Hanley, and Eric Strobl (2008). “Productivity effects of international outsourcing:
Evidence from plant-level data”. In: Canadian Journal of Economics 41.2, pp. 670–688.
Greenaway, David and Richard Kneller (2007). “Firm heterogeneity, exporting and foreign direct investment”. In: Economic Journal 117.517, F134–F161.
Hayakawa, Kazunobu and Toshiyuki Matsuura (2015). “Trade liberalization in Asia and FDI strategies
in heterogeneous firms: Evidence from Japanese firm-level data”. In: Oxford Economic Papers 67.2,
pp. 494–513.
Helpman, Elhanan, Marc J Melitz, and Stephen R. Yeaple (2004). “Export versus FDI with heterogeneousfirms”. In: American Economic Review 94.1, pp. 300–316.
Inui, Tomohiko, Keiko Ito, and Daisuke Miyakawa (2015). “Overseas market information and firms’ export
decisions”. In: Economic Inquiry 53, pp. 1671–1688.
— (2017). “Export experience, product differentiation and firm survival in export markets”. In: Japanese
Economic Review 68, pp. 217–231.
ISGEP (2008). “Understanding cross-country differences in export premia: Comparable evidence for 14
countries”. In: Review of World Economics 144.4, pp. 596–635.
Ito, Banri and Ayumu Tanaka (2016). “External R&D, productivity, and export: Evidence from Japanesefirms”. In: Review of World Economics 152, pp. 577–596.
Ito, Takatoshi (2021). “An assessment of Abenomics: Evolution and achievements”. In: Asian Economic
Policy Review 16.2, pp. 190–219.
Kimura, Fukunari and Kozo Kiyota (2006). “Exports, FDI, and productivity: Dynamic evidence from Japanesefirms”. In: Review of World Economics 142, pp. 695–719.
Koenig, Pamina and Sandra Poncet (2022). “The effects of the Rana Plaza collapse on the sourcing choices
of French importers”. In: Journal of International Economics 137, p. 103576.

20

Lawless, Martina (2009). “Firm export dynamics and the geography of trade”. In: Journal of International
Economics 77.2, pp. 245–254.
Manova, Kalina and Zhiwei Zhang (2012). “Export prices across firms and destinations”. In: Quarterly
Journal of Economics 127.1, pp. 379–436.
Mayer, Thierry and Gianmarco I. P. Ottaviano (2008). “The happy few: The internationalisation of European
firms new facts based on firm-level evidence”. In: Intereconomics 43, pp. 135–148.
Melitz, Marc J. (2003). “The impact of trade on intra-industry reallocations and aggregate industry productivity”. In: Econometrica 71.6, pp. 1695–1725.
Melitz, Marc J. and Gianmarco I. P. Ottaviano (2008). “Market size, trade, and productivity”. In: Review of
Economic Studies 75.1, pp. 295–316.
Muûls, Mirabelle and Mauro Pisu (2009). “Imports and exports at the level of the firm: Evidence from
Belgium”. In: The World Economy 32.5, pp. 692–734.
Okubo, Toshihiro and Eiichi Tomiura (2019). “Regional variations in exporters’ productivity premium: Theory and evidence”. In: Review of International Economics 27.3, pp. 803–821.
Solís, Mireya and Shujiro Urata (2018). “Abenomics and Japan’s trade policy in a new era”. In: Asian
Economic Policy Review 13.1, pp. 106–123.
Tanaka, Ayumu (2012). “Firm productivity and the number of FDI destinations: Evidence from a nonparametric test”. In: Economics Letters 117.1, pp. 1–3.
— (2013). “The causal effects of exporting on domestic workers: A firm-level analysis using Japanesedata”. In: Japan and the World Economy 28.C, pp. 13–23.
Todo, Yasuyuki (2011). “Quantitative evaluation of the determinants of export and FDI: Firm-level evidence
from Japan”. In: The World Economy 34.3, pp. 355–381.
Tomiura, Eiichi (2005). “Foreign outsourcing and firm-level characteristics: Evidence from Japanese manufacturers”. In: Journal of the Japanese and International Economies 19.2, pp. 255–271.
— (2007). “Foreign outsourcing, exporting, and FDI: A productivity comparison at the firm level”. In:
Journal of International Economics 72.1, pp. 113–127.
Vogel, Alexander and Joachim Wagner (2010). “Higher productivity in importing German manufacturingfirms: Self-selection, learning from importing, or both?” In: Review of World Economics 145, pp. 641–
665.
Wagner, Joachim (2012). “International trade and firm performance: A survey of empirical studies since2006”. In: Review of World Economics 148.2, pp. 235–267.
Wakasugi, Ryuhei, Banri Ito, Toshiyuki Matsuura, Hitoshi Sato, Ayumu Tanaka, and Yasuyuki Todo (2014).
“Features of Japanese internationalized firms: Findings based on firm-level data”. In: Internationalization of Japanese Firms: Evidence from Firm-level Data. Ed. by Ryuhei Wakasugi. Springer, pp. 15–
45.
Wooldridge, Jeffrey M. (2009). “On estimating firm-level production functions using proxy variables tocontrol for unobservables”. In: Economics Letters 104.3, pp. 112–114.

21

Appendix for “Margins, concentration, and the performance of firms ininternational trade: Evidence from Japanese customs data”
A

Overview of the Matched Customs/Orbis Data

In Sections 3 and 4, we conducted our analysis by connecting the customs data with corporate information from the Orbis database. In this appendix, we provide an overview of our matched dataset, in which we
linked the customs data with the Orbis database. The original customs data are based on export and importdeclarations that contain detailed information such as the exporting/importing firm, the HS9-digit productcode, the trading partner country, etc. The customs data include a huge number of observations for exports
and imports (approximately 20 million declarations for exports and over 20 million declarations for imports
each year), as shown in Table 1. For the analysis in Sections 3 and 4, we used the export and import declarations that fall under the general trade classification and are aggregated by year, exporting/importing firm,
HS6-digit product, and trading partner country. The total annual number of observations in the aggregateddata for exports and imports still each exceeds on million, as shown in Appendix Table A1. We then linked
the aggregated customs data with the Orbis database using firms’ corporate number. As shown in Table A1,
we were able to link about 97% of all year-firm-product-country-level observations with the Orbis corporate
information during the period 2014–2020.
TABLE A1: Number of Observations Aggregated by Year, Firm, HS6-digit Product, and Partner Country

Total obs.
2014
2015
2016
2017
2018
2019
2020
All years

1,219,386
1,258,900
1,238,000
1,274,327
1,289,765
1,253,764
1,162,458
8,696,600

Exports

Imports

No. of obs. linked with
the Orbis data (%)

Total obs.

No. of obs. linked with
the Orbis data (%)

1,038,975
1,040,014
1,007,615
1,050,017
1,067,551
1,071,846
1,038,981
7,314,999

955,111
973,103
956,759
1,008,905
1,055,288
1,061,266
1,030,113
7,040,545

1,147,786
1,195,177
1,186,252
1,233,216
1,276,616
1,243,034
1,153,620
8,435,701(94.1%)(94.9%)(95.8%)(96.8%)(99.0%)(99.1%)(99.2%)(97.0%)(91.9%)(93.6%)(95.0%)(96.1%)(98.9%)(99.0%)(99.1%)(96.2%)

Appendix Table A2 provides a more detailed overview for the year 2017, showing a breakdown of thenumber of firms, export and import values, etc., based on the industry to which exporting or importing firms
belong. The Orbis database primarily uses the European Union’s Nomenclature of Economic Activities(NACE Rev. 2) industry classification, and we follow this example. NACE is closely related to the International Standard Industrial Classification (ISIC). It should be noted that (as mentioned in footnote 13 of
the main text), information regarding the industry classification of firms for the years 2014 to 2020 was notavailable, so that we classified firms based on their industry classification in June 2022. This means that in
our regression analyses we assume that firms’ industry classification in 2014 to 2020 was the same as in June
2022. Moreover, there are some firms that we managed to link with the Orbis database using the corporate
number but for which no industry classification is provided. Exports and imports by firms for which theindustry classification is unavailable account for approximately 4% of the total export value and 10% of theimport value (Appendix Table A2). Possible reasons why the Orbis database does not provide informationon the industry classification is that such information was not available because the firms in question were
small or did not disclose such information because they are the subsidiaries of foreign companies.
Looking at firms for which the industry classification is available, Appendix Table A2 shows that the
majority of international trade is conducted by firms in the manufacturing or wholesale and retail industries.
Manufacturing firms account for 70% of the total export value, while wholesale and retail firms accountfor 25%. Meanwhile, both manufacturing and wholesale and retail firms each account for 42% of thetotal import value. The higher share of wholesale and retail firms in imports than exports may be dueto the fact that Japan imports a substantial amount of undifferentiated goods, such as natural resourcesand grains/agricultural products. These goods are often mass imported by large trading companies (sogoA1shosha).
TABLE A2: Number of Exporting and Importing Firms and Export and Import Values by Industry, 2017

NACE 1-digit Classification
A
Agriculture, forestry and fishingB
Mining and quarrying
C Manufacturing
D Electricity, gas, and other utilities
E
Water supply; sewerage; etc.
F
Construction
G Wholesale and retail trade, etc.
H Transportation and storage
I
Accomodation and food service activities
J
Information and communication
K
Financial and insurance activities
L
Real estate activities
M Professional services
N Administrative and support services
O Public administration and defence, etc.
P
Education
Q-T Other services
n.a. Unclassified
All industries

Exports

No. of firms
in the Orbis
database

No. offirms

Share ofexportingfirms (%)

(A)
16,0581,329
198,194
2,909
11,214
270,145
307,979
44,87330,80636,404
12,40593,032
99,43462,2652,285
12,531
156,530
2,066,740
3,425,133

(B)
112
40
17,621
23100
728
23,488
631
164
627
119
161
650
895
17
92
378
19,666
65,512

(B/A)
0.7
14
3.0
58
8.9 54,500
0.8
2
0.9
9
0.3
166
7.6 19,250
1.4
446
0.5
6
1.7
166
1.0
66
0.2
7
0.7
60
1.4
247
0.7
0
0.7
2
0.2
22
1.0 3,064
1.9 78,083

Total
amount(billionyen)

Imports
Share ofexports
(%)

0.02
0.07
69.80
0.00
0.01
0.21
24.65
0.57
0.01
0.21
0.09
0.01
0.08
0.32
0.00
0.00
0.03
3.92
100

Total
amount(billionyen)

No. offirms

Share ofimportingfirms (%)

Share ofimports
(%)

(C)
300
40
20,31295
90
1,885
35,709
708
354
1,055
209
349
1,092
1,694
15
247
881
28139
93,174

(C/A)
X
1.9
X
3.0
X
X
10.2 31,170 41.63
3.3 1,281
1.71
0.8
30
0.04
0.7
164
0.22
11.6 31,200 41.67
1.6
964
1.29
1.1
56
0.07
2.9
299
0.40
1.7
X
X
0.4
29
0.04
1.1
132
0.18
2.7
408
0.55
0.7
0
0.00
2.0
5
0.01
0.6
228
0.30
1.4 7,808 10.43
2.7 74,880
100

Note: For sectors A (Agriculture, forestry and fishing), B (Mining and quarrying), and K (Financial and insurance activities), thetotal import amount and the share of importers are withheld.

TABLE A3: Exports and Imports by 2-digit Manufacturing Industry, 2017
Exports
No. offirms
NACE 2-digit classification
10 Food products
11, 12 Beverages and tobacco
13 Textiles
14 Wearing apparel
15 Leather and related products
16 Wooden products
17 Paper and paper products
18 Printing, recorded media
19 Coke and refined petroleum products
20 Chemicals and chemical products
21 Pharmaceutical products
22 Rubber and plastic products
23 Other non-metallic mineral products
24 Basic metals
25 Fabricated metal products
26 Computer, electronic and optical products
27 Electrical equipment
28 Machinery and equipment n.e.c.
29 Motor vehicles, trailers and semi-trailers
30 Other transport equipment
31 Furniture
32 Other manufacturing
33 Repair of machinery and equipment
All manufacturing industries

913
363
410
378
161
161
337
210
71
1,114
277
1,274
421
463
1,976
1,994
1,068
3,533
763
104
150
1,008
472
17,621

Imports

No. ofExportproducts
value
No. of
per firm countries per firm
(HS 6-digit per firm (million
level)
yen)
4.7
2.4
8.9
15.0
6.4
5.1
8.1
8.2
11.5
15.9
9.7
10.9
11.8
14.9
9.1
19.6
18.2
18.2
28.2
32.6
6.0
10.3
9.2
14.1

2.8
2.8
4.2
2.6
2.4
2.4
4.2
3.5
7.4
7.4
7.2
3.8
5.1
4.8
3.5
7.2
5.6
6.4
6.6
9.0
3.0
6.9
3.3
5.3

177
163
331
191
69
128
698
1,127
20,120
3,710
2,443
727
1,561
2,920
461
5,027
2,799
2,628
22,31021,730
106
959
1,952
3,093

No. of
firms
1,183
210
604
537
322
481
389
278
65
1,099
319
1,481
491
481
2,257
2,261
1,216
3,364
816
126
364
1,359
609
20,312

No. ofImportproducts
No. of value perper firm countries
firm(HS 6-digit per firm (million
level)
yen)
4.3
4.7
7.5
13.3
4.9
4.1
6.8
6.9
6.8
10.8
11.1
6.3
7.8
9.3
5.9
11.9
10.5
10.8
14.7
22.7
7.0
7.8
7.6
8.9

Note: The figures are for firms classified under manufacturing based on the Orbis industry classification.

A2

2.5
945
3.0
1,071
2.1
227
2.1
453
2.1
174
2.2
393
2.6
715
2.1
261
4.8 121,900
4.0
1,716
5.6
7,959
2.3
517
2.9
620
3.4
7,256
2.1
302
4.0
1,877
3.1
1,286
3.5
656
4.3
2,808
4.5
2,706
2.3
160
3.0
330
2.6
176
3.1
1,535

In Table 2, we presented basic statistics for the year-firm-product-country-level data by firms’ industry
at the 1-digit industry level. Table A3 presents the same basic statistics for firms in the manufacturingsector, broken down by 2-digit manufacturing industry using the NACE classification. We see substantial
heterogeneity across industries within the manufacturing sector. Some industries, such as the chemical,
metal products, and machinery industries, have a large number of exporters and importers, whereas lightindustries such as the leather, wood, and furniture industries only have a small number of exporters andimporters. Another notable fact is that the number of exporters in the petroleum products, motor vehicles,
and other transport machinery industries is relatively small (71, 763, and 104, respectively) but the export
value per firm is much higher than in the other industries, exceeding 20 billion yen. This pattern suggests thatthese industries are characterized by economies of scale and oligopolistic markets. In addition, the motor
vehicles and other transport machinery industries are characterized by a substantial number of products per
firm (28.2 and 32.6, respectively), reflecting the fact that these industries are parts and components intensive.

B Concentration/Diversity of Traded Products and Partner Countries at the
Firm Level
Next, this appendix examines the concentration or diversity of exports and imports at the firm level.
Specifically, similar to Table 9 in Section 4, firms are grouped into deciles or percentiles based on their
total amount of trade (i.e., exports and imports). Next, various indicators of the concentration/diversity of
products traded and partner countries are calculated, and the averages of these indicators are then calculated
for each decile or percentile. We calculate four types of indicators to examine the degree of concentration/diversification of traded products and partner countries. The first is the Herfindahl-Hirschman Index(HHI), which measures the concentration of partner countries and products based on the share of each partner country-product pair in the total export or import value of each firm. A smaller HHI value indicatesa larger diversity. The second indicator we use is the standard deviation of the share of partner countryproduct pairs, with a smaller value indicating a lower bias toward specific products or partner countries andhence greater diversity.
The third and fourth indicators are entropy indexes. Specifically, we calculate the diversity of partnercountries for each of firm f’s export or import products in a given year:
EfP =X  Number of partner countries for product p of firm f pTotal number of product − country pairs for firm f×ln1
Number of partner countries for product p for firm f
Total number of product−country pairs for firm f!

Similarly, we calculate the diversity of products for each of firm f ’s partner countries in a given year:
X  Number of products for partner country c of firm f 
C
×ln
Ef =
Total number of product − country pairs for firm fc1
Number of products for partner country c of firm f
Total number of product−country pairs for firm f

In these entropy indexes, a larger value suggests greater diversity.
The results are shown in Appendix B1. They show that firms that trade more (i.e., firms with a larger
amount of exports and/or imports) tend to have more diverse trading patterns; that is, there is a positive
correlation between the amount of trade and the diversity of trade. For instance, among the top 10% of
exporting firms (10th decile), the average HHI is 0.23, while for the bottom 10% of exporting firms (1stdecile), the average is 0.95, indicating that the trade of firms that trade less tends to be concentrated in fewer
products and fewer trading partners. Turning to the average of the standard deviation of the share of partnercountry-product pairs, we find that although it does not monotonically fall with the amount of trade, thestandard deviation for the top 1% of exporters and importers (100th percentile) is smaller than that of allother firms. This implies that the trade of firms that trade a lot is more evenly distributed across products
and countries than the trade of firms that trade less.
Turning to the two entropy indexes, these also show that the trade of firms that trade more is morediverse. On implication of the results is the following: if greater diversification in trade in terms of the
product variety and partner countries mitigates the impact of economic shocks overseas, firms that trade
A3

!

.

.

more are likely to be more resilient to shocks. That said, how resilient firms are in practice likely will
also depend on the relative shares accounted for by domestic and overseas procurement and the number ofpartner firms in a particular country with which a particular firm in Japan trades the same product.
TABLE B1: Diversity of Trade by Decile/Percentile of the Value of Total Trade, 2017

Note: The figures are for firms classified under manufacturing based on the Orbis industry classification.

C

Overview of the Matched Customs/BSJBSA Data

For the analysis in Section 5, we matched the customs data with firm-level information from the Basic
Survey of Japanese Business Structure and Activities (BSJBSA). The BSJBSA is collected and compiled by
the Ministry of Economy, Trade and Industry (METI), Japan, and covers all firms with over 50 employees
and capital exceeding 30 million yen in mining, manufacturing, wholesale, retail, and some service sectors.
The BSJBSA contains variables such as sales, costs, debt, assets, profits, employment, exports, and imports.
Many previous studies, such as Wakasugi et al. (2014), use the BSJBSA to examine the characteristics ofexporting or importing firms in Japan. The matching of both datasets is primarily based on the corporate
number provided by the National Tax Agency. Firms we could not match based on the corporate number
were identified and matched, where possible, by referring to the company name, location, or phone number.
Firms surveyed by the BSJBSA report financial data on a fiscal year basis, and the majority of them use
April to March as their fiscal year. On the other hand, the customs data we used are on a calendar year basis,starting from the beginning of January 2014 to the end of December 2020. We matched the customs datawith firm-level information from the BSJBSA on a fiscal year basis. Appendix Table C1 shows the coverage
ratio of the matched data. In Appendix Table C1, the number of exporting or importing firms for 2013 and2020 is smaller than that for the other years. This is because the data for 2013 here covers the period from
A4

January to March 2013 and the data for 2020 covers the period from April to December 2020. For otheryears, the annual data covers the period from April to March.
Regarding the number of importing and exporting firms, the coverage rate of the matched data is about
16% on the export side and 12% on the import side. For reference, we checked the number of firms thatreport export or import values in the BSJBSA, which is smaller than the corresponding numbers in thematched data.
TABLE C1: Number of Firms in the Customs Data and the Matched Data
(1)
(2)
Customs data
Matched data
Panel A: Number of exporters
2013
40,8358,021
2014
59,6209,582
2015
61,841
9,823
2016
62,6609,913
2017
66,618
10,151
2018
67,250
10,2202019
66,3509,985
2020
58,596
9,592
Total
483,770
77,287

19.6%
16.1%
15.9%
15.8%
15.2%
15.2%
15.0%
16.4%
16.0%

Panel B: Number of importers
2013
65,4402014
86,667
2015
87,372
2016
88,1572017
94,722
2018
96,416
2019
96,9782020
92,355
Total
708,107

13.7%
12.2%
12.3%
12.2%
11.7%
11.6%
11.4%
11.6%
12.0%

8,986
10,55010,70910,77011,071
11,218
11,102
10,73485,140(2)/(1)

BSJBSA

7,220
7,019
6,988
6,955
6,955
6,890

7,244
7,086
7,097
7,022
7,022
6,952

Next, Appendix Table C2 compares the total import and export values of the customs data and thematched data. Similar to Appendix Table C1, the values for 2013 and 2020 are smaller than those for otheryears because of the difference in the period covered. The coverage rate of the matched data ("(2)/(1)") isgenerally over 80% for the value of exports and close to 70% for the value of imports and is relatively stable
over our observation period. As mentioned, since the BSJBSA only covers firms with 50 or more employees
and in a limited number of industries, it is not surprising that the coverage rate in terms of the number offirms is low but in terms of the value of exports or imports is relatively high at around 70-80%. This likely
reflects that a small number of large firms account for most exports and imports.
Turning to the industry-specific characteristics of the matched data, Appendix Table C3 compares thenumber of importing and exporting firms, the average number of export and import partner countries, and theaverage number of exported or imported goods in the manufacturing, wholesale, retail, and other industries
in 2018.
In manufacturing, the average number of countries to which firms exported was nine, while the average
number of countries from which firms imported was five. The average number of products exported was
26, while the corresponding number for products imported was 16. Manufacturing firms export to more
destinations than they import from, and they export more items than they import. On the other hand, for thenumber of trading partner countries and products traded for firms in the wholesale and retail industry, thefigures for exports and imports are very similar. Table C4 shows summary statistics of the matched data.

A5

TABLE C2: Export and Import Values in the Customs Data and the Matched Data
(1)
Customs data
Panel A: Export value
2013
17,351
2014
75,025
2015
73,7792016
71,591
2017
78,854
2018
80,2602019
75,6982020
50,019
Total
522,579
Panel B: Import value
2013
21,541
2014
82,4042015
74,356
2016
67,1482017
76,157
2018
82,237
2019
77,2632020
50,268
Total
531,374(2)
Matched data (2)/(1)14,66963,25762,01559,81966,20866,77862,03541,530436,311

84.5%
84.3%
84.1%
83.6%
84.0%
83.2%
82.0%
83.0%
83.5%

14,88555,75649,93843,32351,26957,404
52,61533,810359,000

69.1%
67.7%
67.2%
64.5%
67.3%
69.8%
68.1%
67.3%
67.6%

BSJBSA

77,72677,55373,06280,621
82,95475,46540,86941,61435,65940,37947,723
43,832

Note: Figures are in billion yen.

TABLE C3: Industry-specific Characteristics of the Matched Data, 2018
Average Average
Averagenumber of export
Number number
exported value perofof export
goods
firm
exporters partner(HS 6(million
countries
digit level)yen)Panel A: Exports
Manufacturing
Wholesale and retail
Other
Total

6,324
2,958
846
10,128

8.8
8.3
4.3
8.3

26.2
34.7
14.0
27.7

7,446
6,289
639
6,540

Panel B: Imports
Manufacturing
Wholesale and retail
Other
Total

6,665
3,328
1,078
11,071

4.9
7.7
3.8
5.7

15.5
33.1
10.3
20.3

3,445
7,601
2,794
4,631

TABLE C4: Summary Statistics of the Matched Data
N

Sales (log)Value added (log)
Labor productivity (log)Number of employees (log)
Capital-labor ratio (log)
TFP (log)
Average wage (log)Exporter dummy (BSJBSA)
Exporter dummy (Customs data)
Importer dummy (BSJBSA)
Importer dummy (Customs data)
Two-way trader dummy (BSJBSA)
Two-way trader dummy (Customs data)

78,76878,57578,57578,76878,37078,18078,76878,76878,76878,76878,76878,76878,768A6

No. of firms
14,186
14,162
14,162
14,186
14,104
14,08014,18614,18614,18614,18614,18614,18614,186

Mean Std. Dev.
8.417
1.229
7.026
1.103
1.868
0.463
5.161
0.904
2.402
0.891
2.125
0.637
1.466
0.316
0.368
0.482
0.484
0.500
0.329
0.470
0.511
0.500
0.250
0.433
0.412
0.492

D

Estimation Results for the Performance Premium of Firms Engaged in
TradeThis appendix provides additional estimates of the performance premium of firms engaged in international trade. Table 10 in Section 5 provides the performance premium of exporters based on exporting
information obtained from the customs data. Table D1 provides the same premia estimated using the export
dummy based on the BSJBSA’s export information. They show that the performance premia of exporters are
qualitatively similar. However, the performance premia of exporters are slightly greater when the exporter
dummy is based on the customs data as shown in columns (1) and (2). Nevertheless, as shown in column(3), when controlling for both industry fixed effects and firm size measured by the number of employees,
the difference between the two estimates becomes smaller. The performance premium of exporters based on
the BSJBSA even becomes greater for some variables such as value-added and the capital labor-ratio whencontrolling for industry fixed effects and firm size.
While Tables 10 and D1 show the performance premia of exporters, Table D2 reports the performance
premia of importers. In Panel A, the importer dummy is based on import information based on the customs data, while in Panel B it is based on the BSJBSA. These panels show that importers outperform
non-importers: the sales, value added, labor productivity, capital-labor ratio, TFP, and average wage of importers is higher than that of non-importers, and they employ a greater number of employees. While thedifferences in the importer premia estimated using the importer dummy based on the customs data (PanelA) and that based on the BSJBSA (Panel B) are not large, they are slightly larger when using the importer
dummy constructed based on the customs data.
TABLE D1: Estimation Results for Exporter Premia based on BSJBSA

Sales
Value added
Labor productivity
No. of employees
Capital-labor ratio
TFP
Average wage
Year FE
Industry (2-digit) FE
Size control(1)0.709*(0.021)0.718*(0.019)0.233*(0.007)0.488*(0.016)
0.294*(0.014)0.322*(0.011)0.169*(0.005)
Yes
No
No

(2)
0.744*(0.022)0.687*(0.020)0.172*(0.007)0.517*(0.017)0.302*(0.014)0.181*(0.007)0.118*(0.005)
Yes
Yes
No

(3)
0.158*(0.011)0.123*(0.007)0.121*(0.007)

N
78,76878,57578,57578,7680.213*(0.015)0.073*(0.006)0.088*(0.005)
Yes
Yes
Yes

78,37078,18078,768

Note: The table shows the coefficient of the exporter dummy when each variable in the left column is used as the dependent
variable. Numbers in parentheses are robust standard errors clustered by firm. Firms in the top and bottom 1% in terms of their log
value added, capital per worker, and TFP are dropped as outliers in the estimations where these variables are used as the dependent
variables. “n.a.” stands for “not applicable.” We do not estimate the exporter premia in the case where the number of employees isused as the dependent variable in column (3) because the variable to control for firms’ size is based on the number of employees. *denotes statistical significance at the 1% level.

Lastly, we estimate the performance premia of firms engaged in both exports and imports relative to allother firms. Table D3 presents the two-way trader premia, the premia of firms engaged in both exports andimports. In Panel A, the two-way trader dummy is constructed based on trade information obtained fromthe customs data, while in Panel B it is based on the BSJBSA. These panels show that two-way traders
outperform firms engaged in export only, in import only, or not engaged in international trade at all. Table
D4 presents developments in the importer premia and two-way trader premia over time during the 2014-

A7

2020 period. The performance premium of importers in terms of sales followed an increasing trend between
2014 and 2017, decreased between 2017 and 2019, and then increased again from 2019 to 2020. The othervariables follow similar patterns. Nevertheless, overall, the fluctuations in these variables are not substantial.
TABLE D2: Estimation Results for Importer PremiaPael A: 
(1)
Sales 0.796(0.019)
Value added 0.737(0.017)
Labor productivity 0.214(0.007)
No. of employees 0.528(0.014)
Capital-labor ratio 0.240(0.014)
TFP 0.288(0.010)
Average wage 0.149(0.005)
Yes
Year FE
No
Iustry (2-digit) FE
No
Size 

Sales
Value added
Labor productivity
No. of employees
Capital-labor ratio
TFP
Average wage
Year FE
Iustry (2-digit) FE

Size 

Pael B:(1)
0.654
(0.022)0.592(0.020)0.173(0.007)0.421(0.017)0.171(0.015)0.258(0.011)0.126(0.005)YesNoNos data
(2)
(3)
0.853
0.229
(0.019)(0.011)0.718
0.109(0.017)(0.007)0.164
0.109(0.007)(0.007)0.557(0.014)0.266
0.169(0.014)(0.015)0.182
0.066(0.006)(0.006)0.105
0.071(0.004)(0.005)
Yes
Yes
Yes
Yes
No
Yes



SJBSA
(2)
0.682
(0.022)0.557(0.020)0.126(0.007)0.433(0.017)0.189(0.014)0.149(0.007)0.0868(0.005)
Yes
Yes
No

(3)
0.191
(0.011)
0.082(0.007)0.080(0.007)

N
78,76878,57578,57578,76878,37078,18078,768N
78,76878,57578,57578,7680.106(0.014)0.057(0.006)0.059(0.005)
Yes
Yes
Yes

78,37078,18078,768

Note: The table shows the coefficient of the importer dummy when each variable in the left column is used as the dependent
variable. Numbers in parentheses are robust standard errors clustered by firm. Firms in the top and bottom 1% in terms of their log
value added, capital per worker, and TFP are dropped as outliers in the estimations where these variables are used as the dependent
variables. “n.a.” stands for “not applicable.” We do not estimate the importer premia in the case where the number of employees isused as the dependent variable in column (3) because the variable to control for firms’ size is based on the number of employees. *denotes statistical significance at the 1% level.

A8

TABLE D3: Estimation Results for Two-way Trader Premia
P!el A" #$%&s data(1)(2)
0.93
Sales 0.84 (0.020)(0.020)0.81
Value added 0.81
(0.018)(0.018)0.17 
Labor productivity 0.23
(0.007)(0.007)0.63
No. of employees 0.58
(0.015)(0.015)0.27
Capital-labor ratio 0.24
(0.014)(0.014)0.21
TFP 0.33 (0.010)(0.006)
0.11 
Average wage 0.16 (0.005)(0.004)es
es
ear FE
es
Noustry (2-digit) FE
NoNoSi 

Sales
Value added
Labor productivity
No. of employees
Capital-labor ratio
TFP
Average wage
ear FE
ustry (2-digit) FE

Si 

P!el B" 'SJBSA(1)(2)
0.79
0.76
(0.024)(0.024)0.72 
0.69
(0.022)(0.022)0.21
0.15
(0.008)(0.008)0.54
0.52
(0.019)(0.019)0.22
0.22 
(0.015)(0.015)0.18
0.32
(0.012)(0.008)0.15
0.10
(0.005)(0.005)
es
es
No
es
No
No

(3)
0.22
(0.011)0.12
(0.007)
0.11 
(0.007)

N
78,76878,57578,57578,7680.16
(0.015)0.08
(0.006)0.08
(0.005)
es
es
es

78,370(3)
0.18
(0.011)0.09
(0.008)0.09
(0.008)N
78,76878,18078,76878,57578,57578,7680.12 
(0.015)0.06 
(0.007)0.07
(0.005)
es
es
es

78,37078,18078,768

Note: The table shows the coefficient of the importer-and-exporter dummy when each variable in the left column is used as thedependent variable. Numbers in parentheses are robust standard errors clustered by firm. Firms in the top and bottom 1% in termsof their log value added, capital per worker, and TFP are dropped as outliers in the estimations where these variables are used as
the dependent variables. “n.a.” stands for “not applicable.” We do not estimate the importer-and-exporter premia in the case where
the number of employees is used as the dependent variable in column (3) because the variable to control for firms’ size is based on
the number of employees. * denotes statistical significance at the 1% level.

A9

TABLE D4: Importer and Two-way Trader Premia over Time
Panel A: Importer premia
2014

2015

2016

2017

2018

2019

2020

Sales
Value added
Labor productivity
Capital-labor ratio
TFP
Average wage
Number of employees

0.215*
0.106*
0.102*
0.174*
0.059*
0.061*
0.537*

0.224*
0.103*
0.103*
0.176*
0.057*
0.069*
0.547*

0.234*
0.100*
0.098*
0.164*
0.057*
0.071*
0.552*

0.245*
0.120*
0.120*
0.182*
0.075*
0.076*
0.563*

0.239*
0.117*
0.119*
0.173*
0.076*
0.074*
0.556*

0.219*
0.114*
0.117*
0.164*
0.075*
0.078*
0.571*

0.229*
0.106*
0.103*
0.150*
0.065*
0.070*
0.576*

Number of firms
Export participation rate

11,262
49.5%

11,32850.3%

11,25550.2%

11,32452.0%

11,13652.9%

11,16852.5%

11,29550.1%

PremiaPanel B: Two-way trader premia
2014

2015

2016

2017

2018

2019

2020

Sales
Value added
Labor productivity
Capital-labor ratio
TFP
Average wage
Number of employees

0.205*
0.108*
0.103*
0.160*
0.065*
0.069*
0.613*

0.220*
0.108*
0.108*
0.170*
0.066*
0.078*
0.620*

0.223*
0.109*
0.104*
0.161*
0.066*
0.081*
0.627*

0.236*
0.130*
0.131*
0.172*
0.089*
0.089*
0.635*

0.234*
0.134*
0.136*
0.159*
0.097*
0.087*
0.637*

0.221*
0.134*
0.135*
0.157*
0.096*
0.091*
0.649*

0.216*
0.119*
0.117*
0.148*
0.080*
0.084*
0.669*

Number of firms
Export participation rate

11,262
40.6%

11,32841.0%

11,25540.9%

11,32441.9%

11,13642.5%

11,16841.9%

11,295
39.4%

PremiaNote: Two-digit industry fixed effects are included. Firm size, measured by the number of employees, is controlled for in all cases
except where the number of employees is used as the dependent variable for firm performance. * denotes statistical significance atthe 1% level.

A10