084

Exchange Rates, FTAs and Intra and Inter
Firm Trade

Atsuyuki KATO

akatoecon@staff.kanazawa-u.ac.jp
05 April 2024

Exchange Rates, FTAs, and Intra- and Inter-firm Trade*Atsuyuki KATO†
Kanazawa University and RIETI
Abstract
This paper examined the effects of exchange rate changes and FTAs on intra- and interfirm trade using data of the overseas affiliates of the Japanese manufacturing firms during
the period 2009-2016. We estimated procurement of overseas affiliates of Japanesemanufacturing firms from their parent firms and other firms in Japan, applying the
Poisson pseudo maximum likelihood method to firm level export data. Our results
revealed that procurement of the overseas affiliates from the parent firms increases as theJapanese yen appreciates while that from non-parent Japanese firms are not responsive to
exchange rate changes. FTAs are negatively related to import from Japan. These findings
give us some implications in discussion about relations between exchange rates, FTAs
and supply chains networks.
Keywords: Exchange Rate Changes, FTAs, Intra-firm Trade, Supply Chains
JEL Classification Code: F14, L11

1. Introduction
Exchange rates changes have been thought to have considerable impacts on trade. In
practice, a sharp appreciation of the Japanese yen significantly reduced Japan’s export
following the global financial crisis. On the other hand, its export resurged as the yen
depreciated because of quantitative ease in Japan since 2013. It means that large
fluctuation of exchange rates seems to significantly affect performance of exporting firms.
Many people such as businesspersons, policy makers, and academic researchers have,*This study was supported by JSPS KAKENHI Grant Number 19K01624.

†

akatoecon@staff.kanazawa-u.ac.jp
1therefore, paid attention to the effects of exchange rate changes on trade.
In general, a depreciation of a currency is thought to increase in exports because it
improves the price competitiveness of exporters. Some believe that exchange rate
manipulation is used as a policy tool to encourage exporters to increase in their exports,
and amplified trade imbalances. Thus, exchange rates have provoked trade conflicts
between the countries with trade surpluses and deficits, such as Japan and the US in the
1980s, or China and the US since the early 2000s. On the other hand, some believe thatsuch a traditional view on the relations between exchange rates and export has been lessexplainable as the multinational enterprises (MNEs) have their cross-border supply chain
networks to mitigate external demand and price shocks and make their production
processes more efficient. In addition, the Bank of Japan (BOJ) mentioned that exchange
rate changes have less affected real export because ‘exports of Japanese firms have shiftedto higher value-added goods, and thus they are less likely to get involved in pricecompetition to secure market shares’ 1 . So, there are still some different views how
exchange rate changes affect trade.
As well as exchange rates changes, free trade agreements (FTAs) have also been paid
attention to as an important factor to affect trade. In general, policymakers promote FTAs
to encourage firms to increase in export to the FTA partner countries. But the actual
statistics unveiled that FTAs do not always increase in export in the short run even though
FTAs possibly stimulate the activities of MNEs in the FTA partner countries. So, it isstrongly required to examine what effects FTAs have on export, import and foreign direct
investment (FDI).
In this paper, we would like to provide additional statistical evidence on such issues. We
1

The BOJ (2018).
2

examine the effects of exchange rate changes and FTAs on intra- and inter-firm import ofthe overseas affiliates of the Japanese manufacturing MNEs. It is important to separately
examine both intra- and inter-firm import because it gives some insights on the supply
chain networks where intra-firm trade has got important as global value chains of MNEs
developed2. We also examine if the BOJ’s report is true, including markup of the parent
firms in our estimation. It may shed light on the role of the parent firms in the supply
chain networks.
Many existing papers examined the effects of exchange rate changes on trade.
Thorbecke and Kato (2012) found that an appreciation of yen reduced Japan’s export ofconsumption goods while Kato (2015) revealed that Japan’s export of the medium skill
and technology intensive products is not responsive to exchange rate changes. Thorbecke
and Kato (2018) found that exchange rates don’t affect Swiss exports, particularly,
pharmaceuticals and watches because Switzerland exports most sophisticated products
all over the world. It looks supportive to the BOJ’s view that high value-added exports
are less influenced by exchange rate fluctuations.
Some papers examined the effects of exchange rate changes on firm level trade3. But
empirical papers discussing the effects of exchange rate changes on intra-firm trade arefew because of data availability. Among those papers, Ando and Kimura (2013) foundthat exchange rate changes affect intra-firm exports of the Japanese manufacturing MNEs
more. Song (2015) also examined the effects of external environment changes on Korean
MNEs. On the other hand, Kato (2019) concluded that an appreciation of yen increases
in intra-firm trade of the Japanese MNEs unlike previous papers. Thus, the results are

2

Lanz and Miroudot (2011).

3

For Example, Berman et al. (2012), and Guillou (2008).
3

mixed both in aggregate data and firm level data.
The effects of FTAs have also been widely examined 4 . Urata and Okabe (2014)
examined trade creation and trade diversion effects of FTAs using the product-level datain 67 countries. They found FTAs among developing countries were likely to cause trade
diversion while there was trade creation for many products. Murakami (2023) the
analyzed the causal effects of FTAs on the production networks trade in Latin American
and Caribbean countries and found that the depths and breadths of FTAs have positiveeffects on intra regional parts and components exports. Using the firm level data, Urataand Kato (2017) also found positive effects of FTAs on imports of the overseas affiliatesfrom Japan and the parent firms in some industries. The gravity models were applied inthese papers.
Compared to those existing papers, this paper gives further discussion about the relations
between the exchange rate changes, FTAs and the supply chain networks adding themarkup of the parent firms and the depths and breadths of FTAs. In addition, we departfrom the gravity model because it is not always reasonable to apply it to an analysis of
the supply chain networks using the firm level data. So, the value-added of this paper is
not to provide a novel finding of this issue, but to examine if the findings in the existing
papers and implications from them are robust even in a different approach.
The outline of this paper is as follows. Section 2 describes the methodology and thedetails of the data that we used. Empirical results are discussed in Section 3. And the lastsection gives a concluding remark.

4

In this paper, we denote regional trade agreements (RTAs) as FTAs as well because RTAs

are considered as parts of FTAs.
4

2. Methodology and Data
In this section, we explain the empirical model and data used in this paper. We estimateda simple import model to examine the effects of exchange rate changes and FTAs on both
intra- and inter-firm trade of overseas affiliates of the Japanese manufacturing firms.
Imports from the parent firms and non-parent Japanese firms are intra- and inter-firm
imports, respectively. Our model also includes markup of the parent firm because it isreasonable to add this variable to discuss the behavior of the overseas affiliates. In ourmodel, we also add several variables to control firm characteristics and market conditions
because those factors varying across the overseas affiliates are considered to give large
effects on their behaviors. Our estimation models are, therefore, represented as follows,

Import ipt = 𝛽0 + 𝛽1 𝑙𝑛𝑀𝑎𝑟𝑘𝑢𝑝𝑝𝑡 + 𝛽2 𝑙𝑛𝐸𝑋𝑅𝑗ℎ𝑡 + 𝛽3 𝐹𝑇𝐴𝑗ℎ𝑡 + ∑ 𝛿𝑋ht + ∑ 𝜌𝑍𝑖𝑡 +𝑢𝑖𝑡

Import int = 𝛾0 + 𝛾1 𝑙𝑛𝑀𝑎𝑟𝑘𝑢𝑝𝑝𝑡 + 𝛾2 𝑙𝑛𝐸𝑋𝑅𝑗ℎ𝑡 + 𝛾3 𝐹𝑇𝐴𝑗ℎ𝑡 + ∑ 𝜑𝑋ℎ𝑡 + ∑ 𝜃𝑍𝑖𝑡 + 𝑣𝑖𝑡

(1)

(2)

where EXR is the exchange rate index that a rise of this index means an appreciation of
yen and vice versa, and X is a control variable. Subscripts i, and t demote the individual
overseas affiliates and time where h, j, o and n are respectively the host country of theoverseas affiliates, Japan, the parent firm, and the non-parent firm. So, Import ipt andImport int are imports from the parent firm and non-parent firms. X is the set of market
conditions consisting of GDP and the level of human capital in the country where theoverseas affiliates locate. Z is the set of the characteristics of the overseas affiliates. They
are the firm age, labor productivity, the 1st generation subsidiary dummy, the firm size,
and the finished goods dummy.
5

In this paper, we estimated equations 1 and 2 by the Poisson pseudo maximum likelihood
method (PPML) developed by Santos and The Log of Gravity Tenreyro (2006) to control
lots of zeros in import data. Many existing papers assume that an appreciation of yen
decreases in export of Japan. It means that an appreciation of yen decreases in imports
from the parent firm and the non-parent firms in Japan. On the other hand, Kato (2019)
found that an appreciation of yen increases in import from the parent firms. We, therefore,
examine which EXR is estimated positively or negatively without a prior expectation. We
can expect both positive and negative estimates for FTA indices as well. In general, FTA
are expected to have a positive estimate because FTA is a policy tool to encourage firms
to export their products to the trade partners. But Japan’s FTAs are not just FTAs, but
Economic Partnership Agreements (EPAs) that largely support activities of MNEs in thepartner countries. Thus, FTAs may increase in the local procurement of the overseas
affiliates rather than imports from Japan. We, therefor, examine which FTA is estimatedpositively or negatively as well.
In control variables, the market size and the level of human capital are expected to havepositive coefficients because the overseas affiliates need and use the differentiatedproducts from Japan more if the market size is large and the level of human capital is high.
In the firm characteristics, we expect that the firm size has a negative estimate because
the order the overseas affiliates are, the more localized they are. The other variables are
expected to be positive. The overseas affiliates of high labor productivity possibly use
imported differentiated parts and components from Japan more. The 1st generation
subsidiaries are directly connected with their parent firms in the supply chain networks.
The larger the overseas affiliates are, the more imports from Japan they use. The finishedgoods produced in the overseas affiliates are considered to more rely on the differentiated6products imported from Japan.
The data used in this paper are as follows. The panel data of overseas affiliates of theJapanese manufacturing firms are constructed from the Survey of Oversea Business
Activities collected by the Ministry of Economy, Trade and Industry (METI). Markup of
the parent firms is estimated by de Loecker (2012) using and the data of the Basic Surveyof Japanese Business Structure and Activities, and then integrated with the panel data5.
Exchange rates and the local GDP are downloaded from the website of the United Nation
Conference on Trade and Development (UNCTAD) statistics. Both data are deflated by
GDP deflators. As the FTA indices, we construct the depths and the breadths of FTAs
following Limão (2016) as well as Jinji et al. (2022) and Murakami (2023)6. As Murakami(2023) briefly explains, ‘the depth evaluates the level of economic policy cooperation,
while the breadth evaluates the width of its coverage’. Comparing the degrees of thedepths and breadths seems to be important because the differences of the characteristics
of FTAs may give different effects of the procurement behavior of the overseas affiliates.
The data of the METI are compiled following the Japanese fiscal year (April-March)while the data from the other sources are recorded by the calendar year (JanuaryDecember). We think that this one quarter gap partly captures the time of decision
marking for firms. Our data covers the period between 2009 and 2016.
The descriptive statistics of our data are in the following figures and tables. Figure 1

5

These two surveys share the firm ID numbers.

6

The World Bank provides the dataset of Regional Trade Agreements (RTAs) as WTO

plus (WTO+) and WTO extra (WTO-X) that are used to construct the depths and thebreadth indices. The WTO+ covers the policy areas falling under the current mandate of the
WTO while WTO-X covers issues falling outside the current WTO mandate. The list of
WTO+ and WT-X is presented in Horn et al. (2010). The details of calculation of the depth
and the breadth are in their papers.
7illustrates the number of the overseas affiliates in our data. It is obvious that the overseas
affiliates of the Japanese manufacturing MNEs are concentrated to the countries
concluding the bilateral FTAs with Japan, the neighboring countries, and the US.
ASEAN in the countries with the bilateral FTAs and China in the neighboring countries
are particularly the larger (Appendix presents Japan’s FTAs before 2017). It means that
our estimation actually examined the effects of FTAs on imports from Japan by theoverseas affiliates in ASEAN.
Figure 2 shows the procurement behavior of the overseas affiliates of the Japanesemanufacturing firms and export from Japan to each region during the period 2015-2017.
It is clear that import from Japan by the overseas affiliate (it is equivalent to export fromJapan to the overseas affiliates) is much smaller than total export from Japan to eachregion7. Thus, our estimation examined the effects of exchange rate changes and FTAs on
only a limited part of Japan’s export. But this analysis is still meaningful, to discuss
industrial policies based on the cross-border supply chain networks.
To further discuss effective industrial policies in the supply chain networks, Table 1
presents the average markup of the parent firms by region and industry. It says that themarkup of the parent firms is not always high for the overseas affiliates in ASEAN. Rather,
it is relatively lower in machinery and transportation industries that account for a lion’sshare of Japan’s export. On the other hand, markup of the parent firms is relatively higher
in the other countries concluding bilateral FTAs with Japan. Thus, it seems to be important
to examine the effects of exchange rate changes and FTAs on import of the overseas
affiliates from Japan and consider the results of estimation.
Figure 3 is the structure of the sales of the overseas affiliates by region. It reveals that
7

The mean might be affected by leakage of data for large overseas affiliates.
8the structure of the sales of the overseas affiliate vary across regions. Within the countries
with bilateral FTAs, ASEAN and the other countries are very different. In ASEAN,
exports to Japan and the third countries account for relatively larger shares while the local
sales account for more than 60 percent of the sales in the other bilateral FTA countries.
Thus, the roles of the overseas affiliates in the supply chain networks are very different
between ASEAN and the other bilateral FTA countries. It should also be considered ininterpreting our empirical results.

3. Empirical Results
In this section, we show our empirical results and discuss them8. Table 2 present theresults of estimation of equations 1 and 2 for both the depths and the breadth. It reveals
that the different features of FTAs do not always give different effects on the procurement
behavior of the overseas affiliates. Unlike expectations by policy makers or economists,
FTA indices have statistically significantly negative estimates in all regressions. Thus, thedeeper and the wider FTA is, the less import from Japan the overseas affiliates of theJapanese manufacturing firms rely on. This result possibly coincides with the findings by
Hayakawa (2020) which revealed that the FTAs of Japan increased in the local
procurement of the overseas affiliates. Note that this result does not always mean that
FTAs decrease in Japan’s exports to the partners at the aggregate level. As we mentioned
above, trade with the overseas affiliates account for a part of Japan’s total trade. In
addition, MNEs often have various choices among the schemes which help their

8

Some suggest that we include the earthquake dummy in 2011 because the Great East

Japan Earthquake is thought to give heavy negative impacts on the supply chains of Japanesefirm. We examined the regression with that dummy and found similar results. Therefore, werely on the results without that dummy in this paper.
9international transactions in developing countries, for example, special economic zones9.
It means that FTAs are not always the first choice for MNEs in constructing their supply
chain networks. It might affect the results of estimation.
Exchange rate changes have statistically significantly positive coefficients for the intrafirm trade while statistically insignificant for inter-firm trade. It implies that anappreciation of yen increases in export from the parent firms to their overseas affiliates
unlike usual expectations of the relations between exchange rates and trade. The reasonof it is considered as follows. The overseas affiliates possibly import some differentiatedand irreplaceable products from the parent firms and their transactions are recorded interms of yen. If so, an appreciation of yen doesn’t reduce the volume of imports from theparent firms while the value of those imports increases. This result may reveal that theparent firms in Japan still play important roles in their supply chain networks although
the hollowing out have been seriously concerned.
The different results between intra- and inter-firm import may reflect the difference ininvoice currencies. Unlike intra-firm trade, the Japanese MNEs may use the local
currency or the USD as the invoice currency in inter-firm trade, to mitigate the risks ofexchange rate fluctuations10.
The markup is statistically positively estimated both in intra- and inter-firm import. Itmeans that it is important for the parent firms to produce differentiated goods to increasein import of the overseas affiliates from Japan. It also supports the view that the parent
firms in Japan sill play important roles in the supply chain networks of the Japanese

9

For example, Japanese overseas affiliates in Vietnam use the license of the export

processing enterprises (EPE), to reduce their trade cost with their parent firms.
10

Ito et al. (2016) revealed that the Japanese firms have largely use the yen and the USD as

their invoice currencies.
10

MNEs, regardless the networks are more diversified or not.
The estimated coefficients for control variables also give various insights on import ofthe overseas affiliates. Among the market conditions, the levels of human capital arestatistically significantly positively estimated for the intra-firm import while statisticallyinsignificant for inter-firm import. It possibly means that the overseas affiliates need highlevel human capital to use more differentiated products imported from the parent firms
while they use more standardized products imported from the non-parent Japanese firms.
For the latter, the overseas affiliates may prefer to lower wages of workers than higher
skills. The estimates of real GDP say that the market size is negatively related to importfrom Japan with the statistical significance. It implies that the overseas affiliates increasein their local procurement as the local economies are large.
The estimated results of the control variables of the firm characteristics are as follows.
Labor productivity and the firm size are positively estimated at the one percent
significance level in all regressions. It simply means that the overseas affiliates increasein import from Japan as their capacity of production enlarges. On the other hand, the othercontrol variables of the firm characteristics give somewhat complicated results. The firm
age is statistically insignificant for the intra-firm import while statistically significantly
positive for inter-firm import from Japan. It implies that the overseas affiliates reduce
their dependence on the parent firms as their history becomes longer. The 1st generation
subsidiary dummy is statistically significantly positive for intra-firm import while
statistically insignificant for inter-firm import. The finished goods dummy is statisticallysignificantly positive for intra-firm import while statistically significantly negative forinter-firm import. These results seem to coincide with the view that the earlier and theshorter supply chains are highly dependent on the parent firms while more developed and11

longer supply chains are more open to other firms.
We also estimated the model with one-year lag in explanatory variables as a robustness
check of our results. The results of this estimation are very similar as Table 3 presents.
So, our findings are robust even though we consider a longer time for the overseas
affiliates to response to external issues such as exchange rate fluctuations or FTAs. The
effects of exchange rate changes and FTAs on the supply chains may last longer because
the estimated coefficients for those variables are larger in regression with one-year lag.
According to our estimation results, we obtain some policy implications. First, the BOJ’sview on the relations between exchange rates and trade is possibly true for intra-firm trade
while not confirmed for inter-firm trade. It is difficult to say that an appreciation of yen
is not harmful for Japan’s export because intra-firm export to the overseas affiliates
explains only a small part of total export of Japan. Second, FTAs do not increase in export
to the overseas affiliates. It means that FTAs don’t work as effective policy tools toincrease in export from Japan within the supply chain networks of the Japanese MNEs.
Thus, we should carefully consider aims and effects of FTAs in the further study. It isuseful to encourage firms to make their products more differentiated in the cross-border
structural changes through constructing supply chain networks.

4. Concluding remarks
This paper examined the effects of exchange rate changes and FTAs on intra and interfirm import using the data of the overseas affiliates of the Japanese manufacturing firms.
Our estimation confirmed the findings in Kato (2019) that an appreciation of yen
increased in intra-firm imports of the overseas affiliates (=intra-firm exports of the parent
firms), using an alternative approach. On the other hand, inter-firm import of the overseas
12affiliates is not responsive to exchange rate changes. The FTAs are negatively related toimport from Japan. So, it is difficult to think the FTAs are effective policy tools to increasein Japan’s export through the supply chain networks of the Japanese MNEs. Ourestimation also found that markup of the parent firms is positively related to both intra
and inter-firm import of the overseas affiliates. These findings imply that the parent firms
providing differentiated products have certain roles in the supply chain networks. Fromthese findings, we conclude that we should carefully revise industrial policies. If the
purpose of the policy is to increase in export from the parent firms to their overseas
affiliates in the supply chain networks, a depreciation of yen and FTAs are not always
helpful. It is helpful for that purpose to help the parent firms to provide differentiatedproducts. But our results don’t mean that a depreciation of yen and FTA reduce Japan’s
export at the aggregate level because the weight of intra-firm trade has decreased as thestatistics shows. FTAs may be useful for encouraging the MNEs to explore foreign market
through their overseas affiliates. We need further investigation of the effects of exchange
rate changes and FTAs on the supply chain networks both at the firm and aggregate levels.

13

Appendix: Japan’s FTAs
Partner(s)Enter into ForceSingapore

2002.Sep

EPA

2008.Sep

Mexico2005.Apr

EPA

2007.Apr, 2014.Apr

Malaysia

2006.Jul

EPA

Chile

2007.Sep

EPA

Thailand2007.Nov

EPA

Indonesia

2008.Jul

EPA

ASEAN

2008.Dec

EPA

Philippines

2008.Dec

EPA

Switzerland

2009.Sep

EPA

Vietnam2009.Oct

EPA

India

2011.Aug

EPA

Peru

2012.Mar

EPA

Australia

2015.Jan

EPA

Mongolia

2016.Jun

EPA

TPP11

2018.Dec

Trade AgreementsEU

2019.Feb

EPA

United States

2020.Jan

Trade Agreements

Type

Amendment

United Kingdom 2021.Jan

EPA

RCEP

EPA

2022.Jan

Data source: Free Trade Agreement (FTA) / Economic Partnership Agreement (EPA) and
Related Initiatives | Ministry of Foreign Affairs of Japan (mofa.go.jp)
Note: The green cells are in our data period while the yellow cells are out of our data period.

14

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Guillou. S. 2008. Exports and Exchange Rate: A firm-level investigation. OFCF Working
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Table 1. Markup of the parent firms by industry and region of the overseas affiliates
Location

Light

Heavy

MachineryTransport

Miscellaneous

Neibours

2.799

1.575

2.271

2.535

1.978

AEAN

2.772

1.570

2.227

2.474

1.978

NAFTA

2.559

1.456

2.500

2.578

2.227

EU

2.678

1.316

2.516

2.611

2.257

Bilateral FTA

2.857

1.548

2.630

2.569

2.238

Note: author’s own estimation and calculation.
ASEAN is not included in Bilateral FTA countries in this table.

16

Table 2. Estimation Results
(1)

(2)

(3)

(4)

Import from Parent

Import from Others

VARIABLES

DepthsBreadths

DepthsBreadths

Markup0.135***

0.134***

0.321***

0.320***(0.0220)(0.0220)(0.0221)(0.0221)0.498***

0.484**-0.243-0.270(0.190)(0.192)(0.310)(0.306)

0.372***

0.372***-0.780***-0.780***(0.0545)(0.0544)(0.141)(0.142)-0.0638**-0.0647**-0.268***-0.268***(0.0305)(0.0305)(0.0419)(0.0410)

0.105

0.104

0.509***

0.510***(0.0771)(0.0775)(0.0816)(0.0817)0.842***

0.844***

1.255***

1.259***(0.0376)(0.0379)(0.0493)(0.0491)0.221***

0.222***-0.00269

0.00185(0.0677)(0.0678)(0.127)(0.127)0.707***

0.708***

0.932***

0.934***(0.0158)(0.0158)(0.0345)(0.0349)0.207***

0.208***-0.737***-0.735***(0.0612)(0.0610)(0.118)(0.118)-0.863***-1.379***-1.898***-3.072***(0.229)(0.347)(0.426)(0.642)-12.60***-12.56***-13.63***-13.53***(1.187)(1.186)(1.588)(1.572)

Observations

34,076

34,07636,35536,355R-squared

0.268

0.269

0.438

0.439

EXR
Human Capital
Real GDP
Firm AgeLabor Productivity
1st SubsidiaryFirm SizeFinished Goods
FTA
Constant

Note: Robust standard errors in parentheses***, and ** represent 1%, and 5% significance levels, respectively

17

Table 3. Esetimation Results with one-year lag (Robustness Check)
(5)

(6)

(7)

(8)

Import from Parent

Import from Others

VARIABLES

DepthsBreadths

DepthsBreadths

Markup0.130***

0.129***

0.336***

0.335***(0.0262)(0.0260)(0.0311)(0.0310)1.140***

1.103***-0.289-0.355(0.165)(0.165)(0.410)(0.405)0.251***

0.249***-0.431***-0.430***(0.0448)(0.0449)(0.122)(0.121)-0.0371-0.0368-0.213***-0.220***(0.0263)(0.0260)(0.0545)(0.0535)

0.286***

0.287***

0.705***

0.706***(0.0780)(0.0781)(0.127)(0.127)0.742***

0.744***

0.987***

0.986***(0.0352)(0.0351)(0.0688)(0.0684)

0.117

0.116

-0.104-0.104(0.0729)(0.0727)(0.157)(0.158)0.661***

0.661***

0.782***

0.784***(0.0182)(0.0182)(0.0488)(0.0492)

0.335***

0.337***-0.669***-0.667***(0.0660)(0.0658)(0.148)(0.149)-1.150***-1.797***-1.421***-2.424***(0.210)(0.311)(0.533)(0.779)-14.18***-14.05***-10.81***-10.42***(1.159)(1.163)(2.071)(2.070)Observations

25,952

25,952

27,846

27,846R-squared

0.299

0.301

0.149

0.151

EXR
Human Capital
Real GDP
Firm AgeLabor Productivity
1st SubsidiaryFirm SizeFinished Goods
FTA
Constant

Note: Robust standard errors in parentheses***, and ** represent 1%, and 5% significance levels, respectively

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Figure 1. The number of overseas affiliates by region

Figure 2. Procurement behavior of the overseas affiliates of the Japanese MNEs

Figure 3. The components of the sales of the overseas affiliate by region

19