Kyoto University,
Graduate School of Economics
Research Project Center Discussion Paper Series

Firm Heterogeneity and the Structure of Export and FDI:
Evidence from Japanese Manufacturing Industries

Ayumu Tanaka

Discussion Paper No. E-09-003

Research Project Center
Graduate School of Economics
Kyoto University
Yoshida-Hommachi, Sakyo-ku
Kyoto City, 606-8501, Japan

March 2010

Firm Heterogeneity and the Structure of Export and FDI
Evidence from Japanese Manufacturing Industries
Ayumu Tanaka†
March 31, 2010

Abstract
The percentage of exporters and multinational enterprises (MNEs)varies substantially across industries. We extend the ﬁrm heterogeneity model presented in Helpman et al. (2004) to derive testable predictions about the prevalence of these internationalized modes. The
model indicates that intra-industry ﬁrm heterogeneity and R&D intensity play large roles in the inter-industry variation of the number ofinternationalized ﬁrms. We investigate whether these factors as wellas import tariﬀ aﬀect the structure of export and foreign direct investment (FDI) using Japanese industry-level data. We obtain results thatare consistent with the model. First, industries with larger productivity dispersion have a larger percentage of non-MNE exporters, a larger
percentage of MNEs, and a larger percentage of the sum of exporters
and MNEs. Second, MNEs are heavily concentrated in R&D intensive
industries. In addition, we reveal that lower import tariﬀs raise thepercentage of exporters and MNEs in line with Melitz (2003).
Keywords: Firm heterogeneity; Multinationals; Exports; Foreign direct investment
JEL Classiﬁcation: F1, F23†

Kyoto University and Research Institute of Economy, Trade and Industry (RIETI).
E-mail: ayumu21<at>gmail.com

1

1

Introduction

The percentage of exporters and multinational enterprises (MNEs) vary substantially across industries, and almost all industries have at least one exporter or MNE. Recent empirical research in international trade and foreign
direct investment (FDI) provides ﬁrm-level evidence that ﬁrms that export
or conduct FDI are relatively rare. However, the percentage of ﬁrms thatexport or conduct FDI within each industry category ranges rather widely.
For example, according to Bernard et al. (2007), the number of ﬁrms exporting is nearly 40% in some U.S. manufacturing industries but less than
10% in others.
In this paper we use the ﬁrm heterogeneity model presented in Helpman
et al. (2004) to derive the theoretical relationship between ﬁrm heterogeneity
and the percentage of internationalized ﬁrms. The ﬁrm heterogeneity models
of Helpman et al. (2004) assume that ﬁrms diﬀer in productivity and must
incur the ﬁxed costs of exporting and FDI. They predict that only ﬁrms thatare productive enough to cover the ﬁxed cost of exporting can export. Since
the ﬁxed cost of FDI is larger than that of exporting, ﬁrms that conduct
FDI must be more productive than ﬁrms that only export.
Based on the model of Helpman et al. (2004), we show that industries
with a larger degree of productivity dispersion have a larger percentage
of MNEs, a larger percentage of the sum of exporters and MNEs, and alarger relative percentage of MNEs over exporters, although the eﬀect of
an increase in the dispersion of productivity on the percentage of exporters
can be either positive or negative. In addition, we show that R&D intensive
industries have an advantage in conducting FDI. Our approach resembles
Antràs and Helpman (2004, 2008) who focused on the prevalence of suchorganizational forms as foreign outsourcing and FDI; Helpman et al. (2004)
focused on the relative magnitude of exports and FDI sales.
We also use Japanese industry-level data to examine the model’s implications. Many previous empirical studies have conﬁrmed that exporters are
more productive than non-exporters (Bernard and Jensen, 1999) and that
MNEs are more productive than ﬁrms that only export (Tomiura, 2007).
Such ﬁrm-level evidence supports the standard ﬁrm heterogeneity models
of Melitz (2003) and Helpman et al. (2004). Helpman et al. (2004) also
provide empirical evidence at the industry level that industries with larger
productivity dispersion have smaller relative export sales over FDI sales aspredicted by their theoretical model. However, no evidence exists that conﬁrms the large role of ﬁrm heterogeneity and R&D intensity in the variationof the percentage of internationalized ﬁrms across industries. The results2

support the predictions of our heterogeneous ﬁrm model that ﬁrm heterogeneity and R&D play key roles in the structure of international trade and
FDI and additionally reveal that import tariﬀs matter. First, industries
with a larger degree of productivity dispersion have more exporters, a larger
percentage of MNEs, a larger relative percentage of MNEs over exporters,
and a larger percentage of the sum of exporters and MNEs. Second, MNEs
are concentrated heavily in R&D intensive industries. Third, we additionally test and conﬁrm the reallocation eﬀect that lower import tariﬀs raise
the percentage of internationalized ﬁrms.
The remainder of this paper is divided into four sections. In Section
2, we brieﬂy describe the Japanese manufacturing data used in this paperand show that the variation of the percentage of exporters and MNEs issystematic. In Section 3, we use a version of Helpman et al. (2004) to derivepredictions about the prevalence of internationalized modes. In Section 4,
we introduce our estimation approach. In Section 5, we present the resultsof our empirical analysis. The summary and conclusion are presented in theﬁnal section.

2

A ﬁrst glance at the data

There is tremendous variation in the percentage of exporters and MNEs
across industries, as Bernard et al. (2007) and Tomiura (2007) have shown.
In addition, this section reveals that this variation is systematic. First, thepercentage of exporters is higher in industries with a larger dispersion ofsales. Second, the percentage of MNEs is also higher in industries witha larger dispersion of sales. Third, relative to all active ﬁrms, MNEs isheavily concentrated in R&D intensive industries. Finally, the percentagesof exporters and MNEs are higher in less protected industries. This section
unveils these patterns in the Japanese manufacturing industry-level data.
The facts in this section motivate the theoretical model and more rigorous
empirical analysis in the following sections.
This study uses the industry-level data for the period 1997-2005 constructed from the conﬁdential ﬁrm-level data collected by the Ministry of
Economy, Trade, and Industry (METI). METI conducts annual surveys
called the Basic Survey of Japanese Business Structure and Activities, which
covers all ﬁrms with 50 employees or more and capital of 30 million yen or
more. We focus on ﬁrms whose main line of business is manufacturing andexclude those whose main line of business is weapons and the munitionsindustry because Japanese law prohibits the export of such products. 57

3manufacturing industries remain. Table 4 provides three-digit METI industry codes and descriptions. In this section we use the data averaged overnine years: 1997-2005.
Figure 1 illustrates that the percentage of exporters in all active ﬁrms ishigher in industries with a larger dispersion of logarithm of sales in a crosssection of 57 manufacturing industries. The X-axis measures the standard
deviation of the logarithm of sales, and the Y-axis measures the percentage of
non-MNE exporters. Fig. 2 plots the percentage of MNEs across industries.
The X-axis again measures the standard deviation of the logarithm of sales.
Fig. 2 reveals that industries with a larger dispersion of sales have higher
percentages of MNEs. Fig. 3 shows how the percentage of MNEs varies withthe ratio of R&D expenditures to sales and demonstrates the third strong
pattern: the percentage of MNEs is higher in R&D intensive industries.
Finally, Fig. 4 shows the percentage of exporters and MNEs is higher inindustries with a lower import tariﬀ.

3

Model

To explain why the percentage of exporters and MNEs systematically varies,
we use a framework based on Helpman et al. (2004) and establish therelationship between intra-industry ﬁrm heterogeneity and the percentageof exporters and MNEs. We specify the model, which is a simpliﬁed version
of Helpman et al. (2004), and extend it to generate predictions about thepercentage of exporters and MNEs.

3.1

Set-upJ countries are indexed by j and S industries are indexed by s. A continuum of heterogeneous ﬁrms produces diﬀerentiated goods in each country
and sector. The preferences are identical everywhere and given by a CobbDouglas aggregate over industry-speciﬁc CES consumption indice Cjs :

uj =

∏
s

[∫
θs
, Cjs =Cjs

]1αxjs (ω)α dω, 0<α<1

(1)

ω∈Ωjs

where xjs (ω) is the quantity of good consumed, Ωjs is the set of goods
available in industry s in country j, and tha parameter α determines theelasticity of substitution across products, which is σ = 1/(1 − α) > 1.
Parameter θs indicates the total expenditure share of each industry and4satisﬁes∑

s θs = 1. Then country j ’s demand for product in industry s is

xjs (ω) =pjs (ω)−σ θs Yj
1−σ
pjs

(2)

where Yj is gross national expenditure in country j, pjs (ω) is the price ofgood ω in industry s in country j, and Pjs is the price index in industry s
in country j, given by
[∫

] 1

1−σ

pjs (ω)1−σ dω

Pjs =

(3)

ω∈Ωjs

Next we temporarily consider particular industry s and drop index s.
Each ﬁrm is capable of producing a single good using a single input calledlabor, whose price in country j is wj . Firms are heterogeneous in terms of
their productivity ϕ. The empirical distribution of ϕ in each country F (ϕ)
is assumed to be Pareto with shape parameter k, i.e.:( )kb
F (ϕ) = 1 −, ϕ≥b≥0
ϕ(4)

where b is a minimum value in an industry’s productivity distribution. We
assume that k > σ + 1, which ensures that the distribution of productivity
draws have ﬁnite variances. k is an inverse measure of variance, since thevariance of Pareto distribution is given by
V (ϕ) =b2 k
, for k > 2(k − 1)2 (k − 2)

The smaller parameter k is, the larger the variance of productivity is. The
Pareto assumption is consistent with the evidence (see Helpman et al. 2004;
Wakasugi et al. 2008). Note that we assume that productivity distributions
diﬀer among industries.
After a ﬁrm observes a productivity draw from distribution F (ϕ), a
ﬁrm bears the ﬁxed costs of domestic production rf D if it chooses to enter
the market. These are costs of setting up production facilities including a
research institute in home country. r is an indusrty-speciﬁc measure of R&D
intensity and r > 1. A ﬁrm in R&D intensitive industry must incur larger
ﬁxed cost due to R&D expenditure.
In serving foreign markets, a ﬁrm faces a proximity-concentration tradeoﬀ. If the ﬁrm chooses to export, it bears additional ﬁxed costs f X per
5

foreign market, faces domestic wage wh , and incurs iceberg transport cost
τi > 1. On the other hand, if it chooses to serve a foreign market by FDI,
it bears additional ﬁxed costs f I in every foreign market. In this case, theﬁrm may avoid transport cost and face local labor cost wi . These ﬁxed costs
are industry-speciﬁc.
A ﬁrm from country h that sells its product will face marginal costs of zwh ϕ if it sells in home country hzτi whif it exports to a foreign country ic(ϕ) =(5)ϕ
 zwi if it produces in a foreign country iϕwhere z is a industry-speciﬁc inverse measure of R&D intensity i.e., z ′ (r) <0, and z ∈ (0, 1). We assume that R&D reduces marginal costs.
A ﬁrm facing demand curve (1) will optimally charge a price of p(ϕ) =c(ϕ)/α. The proﬁt from the domestic market isπ D = (zwh )1−σ Ah ϕσ−1 − rf D

(6)where Ah = (1 − α)ασ−1 θYh Phσ−1 is the mark-up adjusted demand level in
an industry and in country i. We regard ϕσ−1 as a productivity index, sinceσ > 1.
Setting π D = 0, we deﬁne the entry cutoﬀ for domestic production as
(
D

ϕ =

rf D
(zwh )1−σ Ah

1
) σ−1

(7)

Firms with productivity below this cutoﬀ (ϕ < ϕD ) do not enter the industry, but ﬁrms with productivity above this cutoﬀ (ϕ ≥ ϕD ) do enter and sell
their products in their home countries.
Similarly, the additional proﬁt from exports to country i isπ X = (zτi wh )1−σ Ai ϕσ−1 − f X(8)and the additional proﬁt from FDI in country i isπ I = (zwi )1−σ Ai ϕσ−1 − f I(9)Setting π X = 0, we deﬁne the export cutoﬀ:
[

ϕ

X

fX
=
(zτi wh )1−σ Ai
6

1
] σ−1

(10)

We also deﬁne the FDI cutoﬀ as[

fI − fX
[ 1−σ
]
ϕI =
Ai z 1−σ wi − (τi wh )1−σ

] 1

σ−1

(11)

where setting π X = π I . Following Helpman et al. (2004), we assume( )σ−1wi
f I > τiσ−1 f X > rf D , which ensure ϕD < ϕX < ϕI if Ah = Ai .
wh
The optimal strategy of internationalization depends on each ﬁrm’s productivity. First, ﬁrms with productivity levels between entry cutoﬀ and export
cutoﬀ ( ϕ ∈ (ϕD , ϕX )) only supply their products to domestic markets andneither export nor conduct FDI. These ﬁrms are “purely domestic.” Second,ﬁrms with productivity levels between the export cutoﬀ and FDI cutoﬀ (ϕ ∈ (ϕX , ϕI )) are “exporters,” who supply their products to domestic markets and export them to foreign markets. Firms with productivity levels
above the FDI cutoﬀ (ϕ > ϕI ) are “MNEs,” who invest in a foreign country.
Therefore, exporters are more productive than purely domestic ﬁrms, and
MNEs, in turn, are more productive than exporters.

3.2

Prevalence of internationalized modes

In this section we consider the relationship between the inter-industry variation of the percentage of internationalized ﬁrms and productivity dispersion. Helpman et al. (2004) derived the relationship between the relative
magnitude of exports and local FDI sales and productivity dispersion andpredicted that industries with higher dispersion levels of ﬁrm productivity
have lower ratios of exports to FDI sales. They tested this prediction using
American data with European ﬁrm-level data. Their results support thetheoretical model’s predicted link between intra-industry ﬁrm-level heterogeneity and relative export sales. However, except their own study, little
evidence supports their prediction at the industry level.
Our approach is slightly diﬀerent from Helpman et al. (2004) and moreclosely resembles Antras and Helpman (2004, 2008). We establish the relationship between the inter-industry variation of the percentage of internationalized ﬁrms and intra-industry productivity dispersion. While Helpman
et al. (2004) focused on the relative magnitude of export sales, we focusedon the percentage of each internationalization mode of ﬁrms for two reasons. First, we do not have the data of FDI local sales per country, which
is necessary to construct the relative magnitude of export sales. Second, we
can easily derive richer predictions than Helpman et al. (2004) by deriving
predictions not only about the relative percentage of exports over FDI but
7also about the percentage of MNEs and exporters and MNEs. Given the
Pareto assumption (2), the number of purely domestic ﬁrms in all activeﬁrms can be written( D )kF (ϕX ) − F (ϕD )
ϕ
D
δ =
=1−
(12)
D
1 − F (ϕ )ϕXwhere we exclude exit ﬁrms. Hence, the percentage of the sum of exporters
and MNEs is( D )kϕ
1 − F (ϕX )
N
δ =
=
(13)
D
1 − F (ϕ )ϕXSince ϕD < ϕX , an increase in this percentage is driven by a decrease in k,which is generated by an increase in the dispersion of productivity. Next,
the percentage of MNEs is( D )kϕ
1 − F (ϕI )
I
=
(14)
δ =
1 − F (ϕD )ϕISince ϕD < ϕI , a decrease in k increases the percentage of MNEs. Similarly,
the percentage of exporters equals( D )k ( D )kϕϕ
F (ϕI ) − F (ϕX )
X
=
−
(15)
δ =
D
X
1 − F (ϕ )ϕϕI
The ﬁrst term means the percentage of internationalized ﬁrms (exporters and
MNEs), and the second term means the percentage of MNEs. Both increase
when k decreases. Therefore, the eﬀect of the increase in the productivity
dispersion on the percentage of exporters is ambiguous. However, we canderive the eﬀect of an increase in productivity dispersion on MNEs perexporters. This relative percentage of MNEs over exporters is
δ IX =

δI
1= ( )k
I
δX
ϕ
ϕX

(16)
−1

This relative percentage increases when k decreases.
In addition, we examine the change of R&D intensity, which is relevantin the next section’s empirical analysis. From (10), (11), and z ′ (r) < 0,∂ϕX
< 0 and
∂r

∂ϕI
< 0.
∂r

(17)

∂δ I
>0
∂r

∂δ N
> 0.
∂r

(18)

Therefore, we get
and

8

4

Empirical speciﬁcations

Our aim is to empirically analyze the eﬀect of our measure of ﬁrm-size dispersion, R&D intensity, and other variables on the following: (i) the percentage of exporters, (ii) the percentage of MNEs, (iii) the relative percentage of
MNEs over exporters, and (iv) the percentage of the sum of exporters and
MNEs. We clarify the eﬀect of the productivity dispersion on the percentageof exporters in our empirical analysis, although the model predicts that theeﬀect can be either positive or negative.
We estimate the following reduced-form speciﬁcation:
δsrt = µ + χsr + λr · yeart + β1 ln DISP ERSEst(19)+β2 ln RDIN Tst + β3 ln SKIN Tst + β4 ln KAP IN Tst+β5 ln ADIN Tst + ϵsrt
where µ is constant, δsrt ∈ (δ X , δ I , δ IX , δ N ), and s, r, and t are indexesof industries, regions, and years, respectively. For each ﬁrm in our sample,
we observe its value of export sales per region (Asia, North America, andEurope) and its number of foreign aﬃliates per region. Using these data,
for each region we can identify each ﬁrm as one of three types: “purely
domestic,” “Non-MNE exporter,” or “MNE.” We approximate δ IX as MNEs
/ (non-MNE exporters +1) because some pairs of industries and regions haveno exporters. DISP ERSEst is our measure of the extent of productivity
dispersion across ﬁrms within industry s in year t. We use the standard
deviation of the logarithm of ﬁrm sales across all ﬁrms within an industry
as a measure of the dispersion of ﬁrm productivity, following Helpman et al.
(2004) and Yeaple (2006). RDIN Tst is the ratio of R&D expenditures tosales (R&D intensity).
χsr is a ﬁxed eﬀect of the pair of industry s and region r, λ is an indicatorvariable for region r, and yeart is an indicator variable for year t. Since
cutoﬀs are functions of trade costs*1 , wages, and market sizes, these variables
also aﬀect the percentages of internationalized ﬁrms that we estimate. Since
these factors are speciﬁc to country or a country and industry pair, proxying
them is diﬃcult because we do not have the number of internationalizedﬁrms per country. We, therefore, added the ﬁxed eﬀects of an industry andregion pair and the interaction of region dummies with year dummies to theestimation equations.
*1

While we have import tariﬀ data, we do not have any data on variable trade costs
faced by Japanese ﬁrms when they export their goods.

9Finally, we included capital intensity (KAP IN Tst ), the number of skilledworkers per total employment (skill intensity, SKIN Tst ), and the ratio ofadvertisement expenditures to sales (advertisement intensity, ADIN Tst ) inregression to the control for the omitted industry characteristics. All of thesevariables were constructed from the METI survey. The descriptive statistics
for all variables are shown in Table 1.
In this section, we also empirically examine the eﬀect of a decline inimport tariﬀs applied to foreign goods on the percentage of exporters and
MNEs. Although our partial equilibrium model does not capture the link
between import tariﬀs and the percentages, Melitz (2003) showed that adecline in variable trade costs forces low-productivity ﬁrms to exit and results in increases in the average productivity in an industry. If this so-called
reallocation eﬀect exists, as Bernard et al. (2006) empirically show, lower
import tariﬀs encourage more low-productivity ﬁrms to exit and increase
the percentage of exporters and MNEs. We estimate the following equation:
δsrt = µ + χsr + λr · yeart + γ1 ln T ARIF Fst−1(20)+γ2 ln RDIN Tst + γ3 ln SKIN Tst + γ4 ln KAP IN Tst+γ5 ln ADIN Tst + ϵsrt
T ARIF Fst−1 , which is an import-weighted average tariﬀ applied to theimport of foreign goods in industry s in year t − 1 in Japan, is taken from
Nicita and Olarreaga (2007)*2 where the data are described in more detail.
This variable is lagged by one year to avoid reverse causality.

5

Results

We ﬁrst discuss the results shown in Table 2 where we estimated the coeﬃcients by the random eﬀect model. The dependent variables in columns (1),(2), (3), and (4) are the following percentages: non-MNE exporters, MNEs,
MNEs per non-MNE exporters, and the sum of exporters and MNEs, respectively. Since , the coeﬃcient estimates in column (4) equal the sum of
the coeﬃcients in columns (1) and (2). First, the coeﬃcients on the log ofdispersion are positive in all four columns and statistically signiﬁcant in allcolumns except column (3). Although the coeﬃcient in column (3) is not
signiﬁcant, the estimated sign is consistent with the theoretical implications
derived in Section 3 that predicted that industries with a higher level of productivity dispersion have larger percentages of internationalized ﬁrms. In an*2

We make a concordance to match the 3-digit ISIC industries to the METI code industries.

10

unreported regression of restricted sample which excludes Asia, we obtainan estimation resultus that the log of dispersion has a signiﬁcantly positiveeﬀect on the relative percentage of non-MNE exporters over MNEs. Since
these estimated signs show that industries with higher dispersion of productivity have a larger percentages of MNEs, MNEs over non-MNE exporters,
and exporters and MNEs, all the results support the theoretical predictions.
The positive coeﬃcient of dispersion in column (2) provides evidence thatindustries with larger dispersion of productivity have a larger percentage of
non-MNE exporters.
Second, the coeﬃcients on R&D intensity are positive and signiﬁcant incolumns (2) and (4). This implies that R&D plays an important role indoing FDI, as predicted by the theory.
Third, such control variables as skill intensity and advertisement intensity are signiﬁcant in some columns. In particular, the skill intensity coeﬃcients are positive in columns (2)-(4) and signiﬁcant in columns (2) and (4).
This indicates that MNEs need more skilled workers in their home countries
and is consistent with earlier ﬁndings by Head and Ries (2002).
Next, Table 3 reports estimation results where we included import tariﬀ
rather than dispersion as an explanatory variables. The result reveals thatimport tariﬀs signiﬁcantly decrease all percentages except the relative percentage of non-MNE exporters over MNEs in line with Melitz (2003). Thissuggests that government protection discourages low-productivity ﬁrms fromexiting and lowers the percentage of exporters and MNEs.

6

Concluding remarks

In this paper, we examined the link between ﬁrm heterogeneity and theprevalence of exporting and FDI. In addition, we extend the standard heterogeneity model of Helpman et al. (2004) to explain the roles of R&D inexport and FDI, while Helpman et al. (2004) model cannot capture it. In
particular, we develop a model where R&D reduces marginal costs, while aﬁrm which invests in R&D incurs larger ﬁxed costs.
Our model yields two testable implications. First, industries with larger
productivity dispersion have (i) a larger percentage of ﬁrms that conduct
FDI, (ii) a larger relative percentage of MNEs over exporters, and (iii) alarger percentage of the sum of exporters and MNEs. Second, R&D intensive industries have an advantage in conducting FDI. The empirical results
accord with both implications of the model and additionally revealed that(iv) the percentage of exporters is larger in industries with a larger degree11of productivity dispersion and that (v) R&D intensity has positive eﬀect on
the fraction of non-MNE exporters as well as the fraction of MNEs.
In addition, we empirically examined whether highly protected industries
have a smaller percentage of internationalized ﬁrms. The result conﬁrms
that lower import tariﬀs increase the percentage of exporters and MNEs.
This suggests that government protection may discourage ﬁrms from supplying their products to foreign markets. Our results also shed light on theindustry characteristics associated with export and FDI. In particular, we
revealed that FDI is prevalent in skill intensive industries.
We conclude that within-industrial heterogeneity as well as R&D intensity and government trade policies play crucial roles in the structureof foreign trade and investment. Greater dispersion in productivity acrossﬁrms within a single industry is associated with more FDI, as predicted in
our model, and also with more exporting. In addition, R&D intensity is
associated with a larger percentage of MNEs. Furthermore, lower importtariﬀs positively aﬀect both the percentage of exporters and MNEs.

Acknowledgment
I thank Professor Taiji Furusawa, Professor Jota Ishikawa, Associate Professor Naoto Jinji, Professor Se-il Mun, Associate Professor Jee-Hyeong
Park, Associate Professor Naomitsu Yashiro, Professor Ryuhei Wakasugi,and other participants in seminars at Kyoto and Hitotsubashi Universities
for their helpful comments and suggestions.

References[1] Antràs, Pol and Elhanan Helpman. (2004) “Global Sourcing.” Journalof Political Economy, 112(3):552-580.
[2] Antràs, Pol and Elhanan Helpman (2008). “Contractual Frictions andGlobal Sourcing.” In Helpman E, Martin D, Verdier T (eds), The Organization of Firms in a Global Economy, Harvard University Press,
pp 9-54.
[3] Bernard, Andrew B. and J. Bradford Jensen. (1995) “Exporters, Jobs,
and Wages in U.S. Manufacturing: 1976-87.” Brookings Papers on Economic Activity: Microeconomics, 67-112.

12

[4] Bernard, Andrew B., J. Bradford Jensen, Stephen J. Redding and PeterK. Schott. (2007) “Firms in International Trade.” Journal of Economic
Perspectives, 21(3):105-130.
[5] Bernard, Andrew B., J. Bradford Jensen, and Peter K. Schott. (2006)“Trade Costs, Firms, and Productivity,” Journal of Monetary Economics, 53(5): 917-937.
[6] Bernard, Andrew B., Stephen J. Redding, and Peter K. Schott. (2007)“Comparative Advantage and Heterogeneous Firms,” Review of Economic Studies, 74(1): 31-66.
[7] Brainard, S. Lael. (1997) “An Empirical Assessment of the ProximityConcentration Trade-oﬀ between Multinational Sales and Trade. American Economic Review, 87(4):520-544.
[8] Dixit, Avinash K. and Joseph E. Stiglitz. (1977) “Monopolistic Competition and Optimum Product Diversity.” American Economic Review,
67:297-308.
[9] Head, Keith and John Ries. (2002) “Oﬀshore production and skill upgrading by Japanese manufacturing ﬁrms,” Journal of International
Economics, 58: 81-105.
[10] Head, Keith and John Ries. (2003) “Heterogeneity and the FDI versus
Export Decision of Japanese Manufacturers. Journal of the Japaneseand International Economies, 17:448-467.
[11] Helpman, Elhanan, Marc Melitz and Yona Rubinstein. (2008) “Estimating Trade Flows: Trading Partners And Trading Volumes. Quarterly Journal of Economics, 123 (2):441-487.
[12] Helpman, Elhanan, Marc J. Melitz and Stephen Ross Yeaple. (2004)“Export Versus FDI with Heterogeneous Firms. American Economic
Review, 94(1):300-316.
[13] Kimura, Fukunari and Kozo Kiyota. (2006) “Exports, FDI, and Productivity of Firm: Dynamic Evidence from Japanese Firms. Review ofWorld Economics, 142(4):695-719.
[14] Krugman, Paul R. (1980) “Scale Economies, Product Diﬀerentiation,and the Pattern of Trade.” American Economic Review, 70(5):950-959.

13

[15] Melitz, Marc J. (2003) “The Impact of Trade on Intra-Industry
Reallocations and Aggregate Industry Productivity. Econometrica,
71(6):1695-1725.
[16] Nicita, Alessandro and Marcelo Olarreaga. (2007) “Trade, Production,
and Protection Database, 1976-2004,” The World Bank Economic Review, 21(1): 165-171.
[17] Tomiura, Eiichi. (2007) “Foreign Outsourcing, Exporting, and FDI: A
Productivity Comparison at the Firm Level,” Journal of International
Economics, 72: 113-127.
[18] Wakasugi, Ryuhei, Yasuyuki Todo, Hitoshi Sato, Shuichiro Nishioka,
Toshiyuki Matsuura, Banri Ito, and Ayumu Tanaka. (2008) “Internationalization of Japanese Firms: New Findings Based on Firm-Level
Data,” RIETI Discussion Paper, 08-E-036.
[19] Yeaple, Stephen Ross. (2006) “Oﬀshoring, Foreign Direct Investment,
and the Structure of U.S. Trade,” Journal of the European Economic
Association, 4(2-3): 602-611.

14

.3

329

321

209

Fraction of Non−MNE Exporters
.1
.2

291

292
219

149 289

251
319
239
302
301
272

262

0

123 192
142
281
182
152
170
129
132
193
169122
141
143
161
151
121
252

1

211

231

304
305 323
293

261
220

.5

202

309
204
271
340

201 259
240

205

322

299

303
131

311
181
191

1.5
S.D. of ln Sales

2.5

Figure 1: Dispersion and Percentage of Non-MNE exporters

.4

231

Fraction of MNEs
.2
.3

323 202

211
304

0

.1

204 322209
239
311
271 305
291
340
201
309
293 303292
149 329 299
321302272
219
220
261
289
205
301319
251
259
142
240
151
131
170141
152
181
132
169 161
192
262
129
182
123
122
143
281
191
193
252
121

.5

1

1.5
S.D. of ln Sales

2

Figure 2: Dispersion and Percentage of MNEs

15

.4

231

323

211

202

Fraction of MNEs
.2
.3

322
204209239
311
271 291305
304
340
201 309293
303
329
299 302
272
292
149
321
219
220
261
289
319
301
251
259
142

205

.1

240
151
170 131
141
152
181
132
169 161
192
262
129
182
123143122281
193
121 252

0

191

−4

−2

ln R&D intensity

02Figure 3: R&D intensity and Percentage of MNEs

.6

231

209

Fraction of Exporters and MNEs
.2
.4

329
322
321
291
309
304
305
302
303

202
211
204

323
292
299

271
205340
219

239

201

293

272
289
261

301

170

192
191
193

149
251

259

220

262
181
132
182
281

240
131

142

152
151

141
169
161
143

252

123
129
122

0

121

−4

−2

0
ln Import Tariff in Japan

2

4

Figure 4: Import Tariﬀ and Percentage of the sum of exporters and MNEs

16

Table 1: Descriptive Statistics
Variable
Non-MNE exporters / All
MNEs / All
MNEs / Non-MNE exporters
Exporters and MNEs / Allln S. D. of ln Sales
ln Capital intensity
ln R ＆ D intensity
ln Skill intensity
ln Ad. Intensity
ln Tariﬀ

Mean
0.143
0.202
1.851
0.345
1.247
2.859
0.236
-2.205-5.397-0.780

17

S. D.
0.100
0.098
1.438
0.180
0.280
0.761
1.210
1.076
1.111
2.046

N
513
513
513
513
513
513
512
505
513
492

Min
0.000
0.031
0.375
0.047
0.626
0.968
-5.688-8.442-7.893-3.912

Max
0.417
0.556
12.500
0.889
2.270
5.511
2.484
-1.066-2.7552.806

Table 2: The share of internationalized ﬁrms (Japan, 1997-2005): Dispersion

Dep. Var.
ln Dispersion (Sales)ln Capital intensity
ln R ＆ D intensity
ln Skill intensity
ln Ad. Intensity
Observations
Number of Clusters
R-squared
p-value
BPL test(1)
Non-MNE
Exporters
0.044*[0.027]-0.006[0.009]0.009***[0.003]0.00[0.001]-0.002[0.003]
1512
171
0.274
0.00

(2)
MNEs
0.084***[0.026]0.001[0.008]0.015***[0.003]0.003***[0.001]-0.009***[0.003]
1512
171
0.576

(3)
MNEs per
Non-MNE Exporters
0.546[0.350]-0.056[0.114]-0.048[0.053]0.026[0.023]-0.027[0.037]
1512
171
0.194

(4)
Exportersand MNEs
0.115***[0.028]-0.004[0.012]0.019***[0.005]0.003***[0.001]-0.011***[0.003]
1512
171
0.474

0.00

0.00

0.00

Notes: Coeﬃcients are estimated by random eﬀect model. Robust standard
errors in brackets. ∗∗∗ Signiﬁcant at 1%. ∗∗ Signiﬁcant at 5%. ∗ Signiﬁcantat 10%. Dependent variables in column (1), (2), (3), and (4) are the share of
nonmultinational exporters, the share of multinationals, multinationals per
nonmultinational exporters, and the share of exporters and multinationals,
respectively. The interaction of region dummies with year dummies andconstant are suppressed.

18

Table 3: The share of internationalized ﬁrms (Japan, 1997-2005): Tariﬀ

Dep. Var.
ln Capital intensity
ln R ＆ D intensity
ln Skill intensity
ln Ad. Intensity
ln Tariﬀ
Observations
Number of Clusters
R-squared
p-value
BPL test(1)
Non-MNE
Exporters
0.000[0.008]0.010***[0.004]0.001[0.001]0.001[0.003]-0.011***[0.003]
1449
165
0.379
0.00

(2)
MNEs
0.012
[0.007]0.015***[0.003]0.003***[0.001]-0.005**[0.002]-0.011***[0.002]
1449
165
0.476

(3)
MNEs per
Non-MNE Exporters
0.034[0.083]-0.053[0.056]0.025[0.023]0.003[0.035]0.011[0.027]
1449
165
0.182

(4)
Exportersand MNEs
0.010[0.012]0.021***[0.005]0.004***[0.001]-0.005[0.003]-0.023***[0.005]
1449
165
0.486

0.00

0.00

0.00

Notes: Coeﬃcients are estimated by random eﬀect model. Robust standard
errors in brackets. ∗∗∗ Signiﬁcant at 1%. ∗∗ Signiﬁcant at 5%. ∗ Signiﬁcantat 10%. Dependent variables in column (1), (2), (3), and (4) are the share of
nonmultinational exporters, the share of multinationals, multinationals per
nonmultinational exporters, and the share of exporters and multinationals,
respectively. The interaction of region dummies with year dummies andconstant are suppressed.

19

Table 4: Industry Description and Classiﬁcation
Code
121
122
123
129
131
132
141
142
143
149
151
152
161
169
170
181
182
191
192
193
201
202
204
205
209
211
219
220
231

Description
Meat and meat products
Fish and ﬁsh products
Grain mill products
Other food products
Beverages and tobacco products
Prepared animal feeds
Spinning
WeavingDyeingOther textiles
Knitted and crocheted fabrics and articles
Other wearing apparel
Sawmilling and planing of wood
Other products of wood
Furniture
Paper and paper products
Corrugated paper and paperboard
Publishing of newspapers
Publishing
Printing
Chemical fertilizer and inorganic chemistry
Organic chemistry
Soap and detergents
Pharmaceuticals and medicinal chemicals
Other chemical products
Reﬁned petroleum products
Other petroleum products
Plastic products
Rubber tyres and tubes

20

Code
239
240
251
252
259
261
262
271
272
281
289
291
292
293
299
301
302
303
304
305
309
311
319
321
322
323
329
340

Description
Other rubber products
Leather and fur
Glass and glass products
Cement, lime and plaster
Other non-metallic mineral products
Basic iron and steel
Casting of iron and steel
Non-ferrous metals
Casting of non-ferrous metals
Structural metal products
Other fabricated metal products
Machinery for metallurgy
Other special purpose machineryOﬃce machinery
Other general purpose machinery
Industrial electricity machinery
Household electrical appliances
Communication equipment
Applied electronic apparatus
Electronic components
Other electrical equipment
Motor vehicles
Other transport equipment
Medical equipment
Optical instruments
Watches and clocks
Other precision instruments
Other manufacturing