KEO Discussion Paper No. 137

Impact of Extensive and Intensive Margins of Foreign Direct
Investment on Domestic Corporate Performance:
Evidence from Japanese Automobile Parts Suppliers*

Toshiyuki Matsuura,†

Keio Economic Observatory, Keio University*

This research is conducted as part of the Research Institute of Economy, Trade and Industry (RIETI) research
project, “Global Markets and Japan’s Industrial Growth.” The data utilized is microdata pertaining to the Basic
Survey of Overseas Business and the Basic Survey of Japanese Business Structure and Activities conducted by the
Ministry of Economy Trade and Industry. The author acknowledges the helpful comments of Deborah Swenson,
Ryuhei Wakasugi, Masahisa Fujita, Adrian Wood, Masayuki Morikawa, Ingrid Dallman, Lorenzo Rotunno, Kozo
Kiyota, Tomohiko Inui, Shuhei Nishitateno, Kazunobu Hayakawa, Kazuyuki Motohashi, Iichiro Uesugi, and theseminar participants at the Asian Economic Panel, RIETI, University of Bari, Hitotsubashi University, and Keio
University. I acknowledge financial support from the MEXT-Supported Program for the Strategic Research
Foundation at Private University, 2013–2017 and JSPS Grants-in-Aid for Scientific Research 26380311 and
26285058.
†
2-15-45, Mita, Minato-ku, Tokyo, 108-8345, Japan. E-mail: matsuura@sanken.keio.ac.jp1Abstract

This study investigates the impact of foreign direct investment (FDI) on domestic corporate performance,
using firm-level data of Japanese automobile parts suppliers. While previous studies used the propensity
score matching method and focused mainly on the impact of the extensive margin of FDI, this study uses
data on automobile makers’ FDI as an instrumental variable for suppliers’ FDI and estimates the impact ofboth extensive and intensive margins of FDI on domestic corporate performance. We find that while theintensive margin of FDI does not significantly impact corporate performance, the extensive margin positively
impacts sales and total factor productivity. This effect is more profound in vertical FDI. Furthermore, theimpact of the first flow of FDI is more profound than that of subsequent flows.

Key words: Foreign Direct Investment, Extensive and Intensive margins, Keiretsu network
JEL classification: F21, F23, L62

2

1. Introduction
In recent years, the impact of foreign direct investment (FDI) on domestic
corporate performance has been a central concern among business circles and policy
makers. As many firms from developed countries relocate their production sites to
low-cost countries, there is concern expressed regarding “hollowing out,” aphenomenon that refers to the potential negative employment impact of FDI on thedeveloped economies.2 On the other hand, firms that relocate labor-intensive
production to low-cost countries tend to specialize in skill- or knowledge-intensive
production in the home country. Thus, FDI might indirectly improve domestic
productivity and promote the home country’s exports of skill- or knowledge-intensive
goods to its foreign subsidiaries.
Several studies have empirically examined the causal effect of FDI on domestic
output, employment, and productivity. Debaera et al. (2010) examined this impact on
Korean multinational enterprises (MNEs), Navaretti et al. (2010) on Italian and French
MNEs, and Hayakawa et al. (2013) on Japanese MNEs. These studies used thepropensity score matching method to tackle the endogeneity problem. This is because,

2

Ando and Kimura (2015) and Kodama and Inui (2015) decompose the macro-level employment
growth rate using job creation and destruction measures and documents the contribution of FDI
firms to aggregate-level employment growth rate.
3

as Helpman et al. (2004) suggested, FDI has a selection effect: high-productivity firms
are more likely to become multinationals; thus, to identify the causal effect of FDI, asimple comparison between MNEs and non-MNEs suffers from endogenous bias. Using
the propensity score matching method has an advantage because it is applicable evenwhen the appropriate instrumental variables are not available. However, this method hassome drawbacks. First, while matching can eliminate selection-induced bias associatedwith observable firm characteristics, it cannot do so with unobservable characteristics.
Second, the previous studies compared the impact of newly investing firms with theircounterfactual impact in combination with a difference-in-difference (DID) estimator. In
other words, the previous studies focused on the impact of the extensive margin of FDI,
namely the change in firms’ domestic performance before and after their first foreign
investment.
Investigating the extensive margin of FDI restricts our attention to relatively small
or young firms since large, established firms have relatively longer experience in FDI.
Furthermore, the sales and procurement patterns of foreign subsidiaries change overtime. For example, Belderbos et al. (2001) and Kiyota et al. (2008) demonstrated thatthe local procurement ratio of MNE subsidiaries increases as they accumulate
experience in local operations. It means that an MNE subsidiary decreases imports from4

its home country as the years of operation increase. Thus, the effect of FDI on
production activities in the home country might change as a company’s production size
increases, suggesting that the impact of extensive margins of FDI might differ from thatof intensive margins of FDI, namely in terms of an increase in production in the foreignsubsidiaries.
Against this backdrop, the present study investigates the impact on corporate
performance of not only the extensive margin of FDI but also the intensive margin,using transaction network data of the Japanese automobile parts industry. Many
automobile parts suppliers in Japan are members of an automobile manufacturers’
vertical Keiretsu. The automobile Keiretsu foster long-term relationships between
assemblers and suppliers3. Thus, once automobile manufacturers relocate production
sites abroad, some auto parts suppliers follow these manufacturers and also invest
abroad. We use the transaction relationship of auto parts suppliers with automobile
manufacturers as an instrument of auto parts suppliers’ FDI4. An FDI decision by
3

Keiretsu is a business group composed of companies with supply chain partners or an associationof companies formed around a bank. The automobile manufacturers’ vertical Keiretsu is different
from the horizontal Keiretsu, which consists of the groups within the sphere of a main bank, such as
Mitsubishi or Mitsui. The automobile Keiretsu members collaborate to carry out R&D projects toensure stable supply. In the case of the horizontal Keiretsu, cross-shareholding among firms within
the same Keiretsu is prevalent. However, this is not the case with automobile manufacturers in thevertical Keiretsu. For example, among 180 Toyota Keiretsu suppliers (members of the suppliers’
association for Toyota), there are only 11 suppliers whose shareholding percentage by Toyota wasgreater than 50 percent in 1991.
4
Previous studies, such as Head et al. (1995) and Yamashita et al. (2014), demonstrate that the FDI
made by a transaction partner is one of the determinants of the decision to invest abroad. For
5automobile manufacturers primarily depends on market size or trade costs, and it isunlikely that suppliers’ performance affects automobile manufacturers’ FDI decisions.
In this study, we focus on the Keiretsu relationship between suppliers andassemblers in the 1990s. This is because Nissan Motor, which ranks second in Japanafter Toyota, began to redefine its Keiretsu relationship following the merger of Renault
and Nissan Motor in 1999. Before the 1990s, the long-term supplier-assembler Keiretsu
relationship was relatively stable; therefore, it is also less likely that automobile makers
chose good suppliers and added them as Keiretsu members during our sample periods.
Moreover, the timing and size of FDI in automobile production have varied among
Japanese auto manufacturers. Thus, we believe that automobile makers’ FDI is a goodcandidate for use as an instrumental variable.
Furthermore, there are two reasons why we focus on Japanese automotive
industries. First, the automobile manufacturing industry has been actively investing
abroad in the past 30 years, and it has the most developed international production
networks. Thus, our research is a good case study for assessing the impact ofinternationalizing a production network. Second, firms in the automobile industry haveexample, while Head et al. (1995) investigated the location choice of Japanese FDI in the U.S.,
focusing on the Keiretsu, Yamashita et al. (2014) use comprehensive sales-procurement transaction
network data.
6

a broad transaction network both within the industry and across industries. This meansthat the restructuring of a production network associated with FDI will affect otherfirms through the intra- and inter-industry transaction networks. Accordingly, exploring
the impact of FDI on corporate performance of Japanese automotive parts suppliers is
an interesting case study.
The rest of this paper is organized as follows: Section 2 explains the pathway forthe impact of FDI on corporate performance by introducing the conceptual frameworkand summarizing the previous studies. Section 3 provides the empirical methodologies
and the treatment of the data used. The empirical results are presented in Section 4, andSection 5 discusses the interpretation. Finally, Section 6 summarizes and concludes thispaper.

2. Pathway for the Impact of FDI on Corporate Performance
2.1 Conceptual framework
Before proceeding to the empirical investigation, we introduce the theoreticalconsiderations regarding the impact of FDI on domestic corporate performance. Since
the impact of FDI differs substantially depending on its type, we begin by explaining
the nature of two types of FDI: horizontal FDI (HFDI) and vertical FDI (VFDI). HFDI
7

is motivated by the inclination to avoid broadly defined trade costs by establishingproduction facilities in a foreign market rather than exporting from the home country. In
contrast, VFDI seeks to exploit the factor price gap between the home and the foreigncountries. Therefore, in VFDI, firms relocate production activities that has acomparative advantage.
The impact of FDI on output and employment in the home country might beeither negative or positive depending on whether activities at home and in the foreigncountry are complements or substitutes. In the case of HFDI, foreign investment
substitutes exports from the home country. Therefore, output and employment in thehome country decreases. Also, an increase in the number of foreign production sites
raises the demand for certain services in the company headquarters at home. In the caseof VFDI, a certain portion of the production activities shifts from one country to another,
reducing domestic production and employment. However, these negative effects can beoffset if the MNEs gain market share due to the cost savings induced by VFDI. In
addition, VFDI firms often relocate labor-intensive production to a low-wage country.
This stimulates intra-firm trade of intermediate goods from the home country to theforeign country. 5 As a result, domestic production of intermediate goods and the5

Many previous studies investigated the complementary relationship between FDI and8associated labor demand increase.
The impact on productivity also differs according to the type of FDI. Since HFDI
reduces domestic production, productivity might decline due to the loss of economies ofscale. However, if firms invest in locations with a high density of high-tech activities,
foreign affiliates can be effective channels for transferring technological knowledge tothe home country. Such knowledge transfer from the foreign country has a positive
impact on productivity in the home country. In the case of VFDI, investing firms canimprove productivity by sharing production across borders..

2.2 Previous studies
In this subsection, we briefly summarize the results of previous studies. Most recent
studies classify FDI in developed countries and developing countries as either HFDI or
VFDI, as summarized in Table 1. In regard to the effect on productivity, while Navaretti
et al. (2010), in their study of Italy and France, and Hijzen et al. (2011), in their study ofFrance, demonstrated that HFDI improves domestic productivity, Hayakawa et al.
(2013), in their study of Japan, found that VDFI has positive significant effects on
productivity. As for the impact on sales and employment, Navaretti et al. (2010) foundtrade. For more details, see Fung et al. (2010), and Nishitateno (2013).
9

that both HFDI and VFDI had positive impacts on sales and employment. Hijzen et al.
(2011) and Hayakawa et al. (2013), using French and Japanese firm-level data,
respectively, also found that both HFDI and VFDI by French and Japanese firms
increase domestic employment. In contrast, Debaera et al. (2010), in their study of
Korea, did not find any positive impact on employment through either FDI type.

== Table 1 ==The abovementioned previous studies used propensity score matching in
combination with a DID estimator; therefore, these studies focused only on theextensive margin of FDI. However, there are some studies that investigated the impactof the intensive margin of FDI by estimating the labor demand function. For example,
Harrison and McMillan (2011), in their study of the U.S.; Kambayashi and Kiyota(2015), in their study on Japan; and Muendler and Becker (2010), in their study ofGermany, estimated the labor demand function for MNEs’ domestic employment andexamined whether or not FDI substitutes for domestic employment. These studies
estimated the unconditional labor demand function derived from the translog costfunction for MNEs and used the factor price in the foreign country as an independent
10

variable. As a result, the latter studies focus only on the labor substitution caused by thefactor price gap between the home and foreign countries.6 Moreover, these studies
restrict their attention to labor demand only for MNEs. They do not compare the impactof the first flow and the subsequent flow of FDI on corporate performance variables7.

3. Empirical Investigation
3.1. Methodology
We estimate the impact of FDI on firm-level outcome variables, such as sales,
employment, value added and productivity using the following regression equation:

療
専=象+ 管∬∵≅
療
専+

橋 療
専+ 療
専,

(1)

where Yit is the outcome for firm i in year t, FDIit is the dummy variable for FDI or theindicator for the scale of foreign production, Xit represents other firm characteristics,

6

There are some studies that try to capture the overall picture of the contribution of FDI firms tomacro-level employment growth. For example, Ando and Kimura (2015) and Kodama and Inui(2015) decomposed the macro-level rate using job creation and destruction indicators anddocumented the contribution of FDI firms to aggregate-level employment growth rate. However,these studies do not consider endogeneity issues.
7
Muendler and Becker (2010) investigate the impact of both extensive and intensive margins of
FDI on employment for German MNEs. However, their sample firms were restricted to non-MNEs
that were to become MNEs during the sample period, implying that it might suffer from selection
bias.
11

and εit is an error term. In this study, we focus on FDI in the U.S., Canada, the UK,
Australia, Thailand, Malaysia, the Philippines, China, and Taiwan because these are themajor destination countries for Japanese auto assemblers. Following Kambayashi andKiyota (2015), in our estimation we use pairs of each firm’s FDI to each destination
country and corporate performance variables and estimate equation (1) by pooling nine
FDI destination countries. Therefore, our sample has three dimensions: parent firm (i),FDI destination countries (j), and time (t)8. The equation to be estimated is represented
as follows:療
専=王
象+ 管∬∵≅
療
専+

王
橋 療
専
注管+ 療
専.

(2)

When estimating equation (2) via ordinary least squares, the coefficient for FDI
need not represent the causal effect of FDI on the outcome variables because ofendogenous bias. To isolate a source of variation in a firm’s FDI, we use the Keiretsu
automobile manufacturers’ FDI (AUTO-FDI) as an instrumental variable. In addition, as
8

One might question why FDI variables in the different countries are not placed as independent
variables in one equation. There are two reasons. First, if we place several FDI variables as
independent variables in one equation, we’ll face multicollinearity problems. Second, aggregating
country-level FDI indicators at a regional level might be one solution. However, the relationshipbetween automobile manufacturers’ FDI and suppliers’ FDI should be identified at the country level,
not at the regional level. Furthermore, it is hard to find an appropriate weight when aggregating FDI
indicators at the regional level. Actually, we present the estimation results at regional-level in Table
12. However, estimation results do not have enough explanatory power.
12other instrumental variables, we use the logged GDP for each destination country. In
other words, we estimate the following first-stage regression equation:

王∬∵≅療専=

管+橋√王ℵ̲∬∵≅療専+王

失≢∵ℏ専 +

王
療
専
注管+ 療
専 (3)

To distinguish the impact of the extensive margin and the intensive margin of
FDI, we use two different measures of FDI: a dummy variable for FDI and the level of
employment in the foreign affiliates (affiliate size). The former is the dummy variable,
which takes the value 1 if firms have a foreign affiliate and 0 otherwise, and it is used tocapture the extensive margin of FDI. Because our sample includes the decisions toinvest in nine countries, represented by pairs of variables of domestic corporate
performance and FDI in each destination country, the extensive margin of FDI in thisstudy includes both the decision to engage in FDI for the first time and increases in in
the number of affiliates’ employees in destination countries for FDI. Affiliate size is thelogged number of employees of the foreign affiliates and is used to capture the impactof the intensive margin of FDI.
When estimating the impact of the extensive margin of FDI, the endogenous
variable in equation (2) is binary; we use an endogenous treatment-effect model. More
13

formally, the endogenous treatment-effect model is composed of two equations: one isthe outcome variable y, and the other is the endogenous variable D,y = ≒β + γɐ + ε(4)1,if ̺+ > 0ɐ =震,0,otherwise(5)

where x is the covariate that affects the outcome y and ̺
is the variable used to model
treatment D. The error term and u are bivariate normal with mean 0 and have thefollowing covariance matrix:
普

橋

1

双

Here, ≒ and ̺
are assumed to have no correlation with the error terms. Since thismodel cannot consider the nature of the panel data, we convert the outcome variables
into the log-difference from year t-1 to year t (Δ 宅久). We estimate the model focusing on
samples that have no investment in year t-1. As for the intensive margin, since anendogenous variable is a continuous variable, we estimate equation (2) by a fixed-effectinstrumental variable model.

3.2. Data source
In this study, we combine four types of firm-level data. The first one is thefirm-level data acquired from the Basic Survey of Japanese Business Structure and14

Activities (BSJBSA) compiled by the Ministry of Economy, Trade and Industry (METI),
Japan. This survey began in 1991 and was conducted annually after 1994.9 The
BSJBSA statistically captures an overview of Japanese corporate firms that includes thediversification and globalization of corporate activities and strategies for R&D. As
discussed in the introduction, we use this firm-level data for the automobile partsindustry for 1991 and for the 1994–2000 period. We exclude suppliers whose
shareholding ratio of automobile assemblers is greater than 50 percent since these
suppliers might jointly decide with the automobile assemblers to invest abroad.
The other firm-level data are from the Basic Survey of Overseas Business
Activities (BSOBA), also compiled by METI. These data are used to link theinformation on outward FDI to the abovementioned automobile suppliers’ firm-level
dataset. The BSOBA contains data on Japanese overseas affiliates, including thelocation, year of establishment, number of employees, and industry classification. The
BSOBA also has some qualitative questionnaires that ask each affiliate the primary
purpose of FDI or whether an affiliate engages in international division of labor withparent firms. Since microdata for the foreign affiliates are available only after 1995, we

9

All firms with more than 50 employees and capital of more than 30 million yen are covered in thesurvey.
15

complement it with the information we have on the foreign affiliates for the years 1991
and 1994 from the Directory of Japan’s Automobile Parts Industry, which is compiledby the Japan Automobile Parts Industry Association (JAPIA). In this study, we exclude
affiliates that do not engage in manufacturing activities.10
The third firm-level dataset comprises the list of members of Keiretsu supplierassociations.11 The first-tier suppliers of each Japanese automobile manufacturer are
included in the list of supplier associations.12 The list is provided by the Directory ofJapan’s Automobile Parts Industry, compiled by JAPIA. The fourth dataset comprises
the level of automobile overseas production by Japanese assemblers by country and year,
which is used as an instrumental variable. This dataset is obtained from the World
Motor Vehicle Statistics compiled by the Japan Automobile Manufacturers Association.
We match these four firm-level datasets, referring to the name and location of each firm.
Some of the automotive parts suppliers belong to more than two automobile
manufacturers’ Keiretsu groups. For example, in our sample, 120 automotive parts
10

One might be interested in the exit rate of Japanese affiliates because our sample covers the Asianfinancial crisis period. However, according to Toyokeizai’s Japanese Overseas Companies, the exit
rate for the automobile industry was around 1 percent during the 1990s. It increased in 1999, but it
was still only 2 percent.
11
An alternative data source for the transaction network is the firm-level transaction network datacollected by the credit survey companies, Teikoku Data Bank and Tokyo Shoko Research. Recent
studies, such as Yamahsita et al. (2014), used this dataset to investigate Japanese FDI. While thesedatasets are more comprehensive, they do not identify the transaction tie that is more important.
Therefore, we use the member lists of the Keiretsu supplier associations in this study.
12
Member firms of associations have meetings from time to time, and they exchange businessinformation. As of 1991, Toyota had 180 suppliers in its network and Nissan Motor had 193.
16

suppliers belonged to more than two Keiretsu groups in 1991. In such cases, we sum up
the oversea production volume of the Keiretsu automobile manufactures to which an
automotive parts supplier belongs.
As for firm characteristics, we use sales deflated by the industry-level output
deflator, the number of employees, value added (VA), and total factor productivity(TFP) as outcome variables. In this study, we obtain TFP by estimating the production
function with the Wooldridge (2009) modification of the Levinshon and Petrin
methodology. This method considers the potential co-linearity in the first stage of the
Levinsohn and Petrin (2003) estimator.13 For other firm characteristics, we include thelogged age of firms (Firm age), the logged capital–labor ratio (K-L ratio), R&D
intensity (R&D sales ratio), and firm size as dummy variables. We also control thelogged Keiretsu automakers’ domestic production in year t-1 (Keiretsu-prod) and thelogged number of overseas employees that belong to the same firms and are located inother countries in year t-1 (FDI-other). The former variable is used to control domestic

13

Variables for output and input are constructed as follows: real gross output is measured as sales
deflated by the output deflator, while intermediate input is the cost of materials deflated by the input
deflator. Labor input is measured by the total number of employees. We constructed the net capitalstock by deflating the nominal book values of tangible assets with the capital stock deflator. The
capital stock deflator is defined as the ratio of the net stock by industry to the book value of theindustry-level tangible assets. The net capital stock by industry values are obtained from the Japan
Industrial Productivity (JIP) Database 2013, while the book values of capital by industry are
obtained by aggregating the individual data obtained from the BSJBSA. All output and input
deflators are obtained from the JIP Database 2013.
17

demand conditions. While the logged level is used for the regression of intensive
margins, for the estimation of the impact of extensive margins, the log-difference ofKeiretsu automakers’ production is used as the control variable. Since some firms have“0” value both in Keiretsu automakers’ domestic production and the number of overseas
employees, we add “1” and then take the log when constructing Keiretsu-prod andFDI-other.14

3.3. Data overview
Figure 1 indicates the volume of foreign production by Japanese automobile
assemblers. This chart suggests a large heterogeneity of foreign production among
Japanese automobile assemblers. While Toyota, Nissan Motor, and Honda own large
foreign production sites in North America and Europe, foreign production for theremaining assemblers is relatively small. We use this variation as an instrumental
variable for our identification.

=== Figure 1: Size of Foreign Production ===14

Basic statistics are presented in Table A1.
18

To explore the characteristics of FDI by Japanese automobile assemblers andautomobile suppliers, we look closely at the procurement pattern of their foreign
affiliates. Table 2 reports the ratio of procurement origin for foreign affiliates owned by
Japanese automobile assemblers and auto parts suppliers. Two things are noteworthy.
First, the ratio of procurement from Japan both for auto manufacturers and auto
suppliers ranges from 35 percent to 50 percent. One might argue that the FDI by parts
suppliers is more horizontal than vertical because the suppliers are relatively small andthey invest abroad to maintain their transactions with the Keiretsu assemblers rather
than continuing to export. However, this result indicates that auto suppliers import asizable amount of intermediate goods from Japan. Second, for auto manufacturers, theratio of procurement from third countries is quite small. Specifically, the ratios foraffiliates in Asia and North America are almost “0”. The ratio for auto suppliers’
affiliates in Asia and North America is slightly higher than that for auto manufacturers.
However, it is still less than 5 percent. Affiliates in Europe procure more from third
countries both for auto manufacturers and auto suppliers. The ratios of procurementfrom third countries for affiliates are 10 percent and 20 percent for auto assemblers andauto parts suppliers, respectively. This is probably because cross-border transactions
within European countries are much easier than those within other regions due to the19EU’s Single Market Program.

=== Table 2: Ratio of procurement origin by region and industry ===Table 3 presents basic statistics for firm characteristics in our sample. The data
show that MNEs have higher sales, a higher number of employees, and higher TFP. Thismight reflect the fact that only firms with higher productivity are able to invest abroad
and become MNEs as suggested by previous theoretical and empirical studies, such as
Helpman et al. (2004) and Mayer and Ottaviano (2008). As for the difference in firm
characteristics, according to Keiretsu affiliation, similar patterns have been found:
Keiretsu firms are larger in terms of both sales and employment and have a higher K-L
ratio and TFP.

=== Table 3: Firm characteristics ===Table 4 provides the number of MNEs and Keiretsu suppliers. Among
approximately 900 sample firms, about 300 firms belong to the Keiretsu supplier20

associations. Two things are noteworthy. First, while one-third of the Keiretsu suppliers
are MNEs, the proportion of MNEs among non-Keiretsu suppliers is less than 10
percent. This is probably because Keiretsu suppliers are larger and have higher TFP than
non-Keiretsu suppliers as indicated in Table 3. Second, both Keiretsu and non-Keiretsu
suppliers have actively invested abroad from 1991 to 2000. Specifically, the number of
MNEs among non-Keiretsu suppliers has more than doubled.

=== Table 4: Number of MNEs and Keiretsu suppliers ===As a preliminary analysis, we estimate the conditional logit model for location choicefor FDI by Japanese automotive parts suppliers. Our sample consists of nine countries,
and we use automobile production by Keiretsu assemblers, non-Keiretsu Japaneseassemblers, and non-Japanese assemblers as independent variables. We also include thenumber of foreign affiliates of the Japanese auto parts suppliers and the distance fromJapan. While the former three variables are demand factors for auto suppliers, thenumber of auto parts suppliers is the proxy variable for the industry agglomeration.
Table 5 provides the estimation results for the conditional logit model. The results
suggest that while non-Keiretsu assemblers’ production has no significant impact, the21

volume of foreign production by the Keiretsu assemblers always has significant
coefficients. This suggests that the Keiretsu assembler–supplier tie is one of theimportant determinants of the location choice of suppliers’ FDI. These results do notchange even when we control the production volume of non-Japanese assemblers andthe agglomeration of Japanese auto parts suppliers as in columns (2) and (3).

=== Table 5: Location choice ===4. Estimation Results
First, to explore the determinants of FDI, we estimate equations (3) and (5).
Estimation results are presented in Table 6. While columns (1), (2), and (3) use FDI
dummy variables as dependent variables and estimate the probit model, the number ofworkers in foreign affiliates (affiliate size) is used as a dependent variable and estimate
the fixed effect model in columns (4), (5), and (6). These results correspond to thedeterminants of the FDI extensive margin and the FDI intensive margin, respectively.
The estimation results suggest that Keiretsu automobile manufacturers’ FDI(AUTO-FDI) has a significant positive impact on FDI decision by auto parts suppliersboth in terms of the extensive margin and intensive margin of FDI. We confirm that the22LR test and F-test statistics reject the null hypothesis for the joint insignificance of all
the covariates. We use the specifications in columns (4) and (6) for the impact of theextensive and intensive margins of FDI.15=== Table 6: First-stage estimation ===Next, we estimate equations (2) and (3) and equations (4) and (5) simultaneously
to examine the impact of the extensive margin of FDI. The baseline results are presented
in Panel (a) in Table 7. For all outcome variables, namely sales, employment, value
added (VA), and TFP, the coefficient of FDI is positive and significant, suggesting thatstarting to invest abroad or launching production operations in a new location has apositive impact on corporate performance at home. In contrast, Panel (b) in Table 7
presents the results of the impact of the intensive margin of FDI. The coefficients foraffiliate size are all positive but insignificant for all outcome variables. These results
might imply that while FDI has a positive impact on corporate performance at first, this15

One may be concerned that trade and investment policy might affect the decision to conduct FDI.
As for investment policy, Japan has concluded a Bilateral Investment Treaty (BIT) with somedeveloping countries. However, among our sample countries, there is no new treaty made andentered into during our sample periods. For trade policy, although Japan did not conclude any free
trade agreement in our sample period, ASEAN countries substantially reduced import tariffs in thelate 1990s. We include the import tariff rate of automobile parts and components for host countries
as an independent variable in equations (3) and (5). However, we find no significant impact on
firm-level FDI decision.
23

effect might not last long.

=== Table 7 ===As we discussed in Section 2, the impacts of FDI might differ according to the typeof FDI. Specifically, we focus on the impact of VFDI. In our FDI data from the BSOBA,
there are qualitative questionnaire items that ask each foreign affiliate whether they
engage in international division of labor or full-scale local production. We define
affiliates that engage in international division of labor as VFDI, and in this empirical
exercise, we exclude HFDI from our sample. The results are presented in Table 8. Whilethe impact of the intensive margin of FDI is again insignificant, the extensive margin of
FDI has a positive effect on corporate performance. Furthermore, compared with thesize of coefficients in Panel (a) of Table 7, the coefficients for sales, VA, and TFP areslightly larger, suggesting that FDI with international division of labor stimulates more
intra-firm trade of intermediate goods from the home country, and as a result, corporate
performance improves. However, the impact of the intensive margin of FDI is again
insignificant.

24

=== Table 8 ===We also conduct three robustness checks. First, we compare the impact of the first
FDI and the subsequent FDIs. Since we estimate equation (4) using a pair of parent
performance variables and the FDI by country and then pool the nine-country FDI pairs,
the effect of the extensive margin of FDI includes not only the impact of the first FDI
but also the impact of an increase in the number of foreign affiliates. As we mentioned,
most previous studies that used the propensity score matching methodology focused on
performance changes when firms started foreign production. To highlight the effect of
the first FDI, we estimate equation (4) by focusing on firms that have at least one
foreign affiliate in year t−1. The results are presented in Table 9. The coefficients for
FDI are significant only in case of column (2), namely the impact on employment at
home. Furthermore, the size of the coefficient for employment is smaller than that of thebaseline result presented in column (2) in Table 7. These results imply that the impact of
FDI on corporate performance is mainly driven by the first investment.

=== Table 9 ===25

Second, we exclude firms that belong to only one automobile Keiretsu group.
Since these suppliers might have strong relationships with automobile manufacturers,
they might decide to invest abroad together with the manufacturers. The results arepresented in Table 10 and the major results do not change.

=== Table 10 ===Third, we change the threshold level of the shareholding ratio of automobile
assemblers. Specifically, we exclude auto parts suppliers whose capital share of
Keiretsu auto manufacturers is greater than 20 percent and results are presented in Table
11. Again, the major results are in keeping with the baseline results.16=== Table 11 ===5. Discussion
16

For other robustness checks, first, we split our samples into Asian countries (East and Southeast
Asian countries) and Developed countries (the UK, the U.S., Canada, and Australia). However, themajor results do not differ by region. Second, we exclude FDI to China and estimate the same
regression model. While Japanese automobile manufacturers started to invest in most of the regionin our sample before 1990, FDI to China was an exception. Since Japanese automobile
manufacturers started to invest in China in the mid 1990s, some auto parts manufacturers might start
to invest in China in consultation with Keiretsu auto manufacturers in our sample periods. However,
the major results are in keeping with the baseline results.
26

Why are the effects of FDI significant only in the case of the extensive margin and
not in the case of the intensive margin? This might reflect the procurement pattern of
MNE subsidiaries. Perhaps, once firms start to invest abroad, they increase the export ofintermediate goods to their overseas subsidiaries. For example, Nishitateno (2013)
investigated the relationship between FDI and exports, focusing on Japanese automotive
parts suppliers from 1993 to 2008, and reports that automotive parts suppliers’ FDI
complements their export. Nishitateno (2013) concludes that this result might reflect thefact that Japanese auto parts suppliers have expanded overseas production and theirgrowing market penetration enables them to increase demand for some parts produced
in Japan (the market penetration effect). Thus, as they increase the export associatedwith FDI, firms expand domestic production capacity and raise their productivity.
On the other hand, according to Belderbos et al. (2001) and Kiyota et al. (2008), as
overseas subsidiaries learn more about local suppliers, some overseas subsidiaries
substitute imported supplies from the home country with local procurement (thesubstitution effect). Belderbos et al. (2001) report that this effect is more pronounced in
the case of greenfield investments. According to the BSOBA, as of 1995, 88 percent ofoverseas affiliates in the automotive parts industry have been established as greenfield
investments. Since the substitution effect offsets the market penetration effect, the27

impact of the intensive margin of FDI might become insignificant.
One might be concerned that these empirical analyses ignore the effect of the
global sourcing strategy by automobile assemblers. Affiliates of automobile
manufacturers in ASEAN, North America, and Europe might procure parts andcomponents from suppliers located in neighboring countries. Since our analysis uses
auto manufacturers’ FDI as an instrument of auto suppliers’ FDI in each destination
country, we do not consider the global sourcing strategy. This might lead to anunderestimation of the impact of auto manufacturers’ FDI on suppliers’ FDI. However,as indicated in Table 2, the ratio of auto manufacturers’ imports from a third country isless than or near 10 percent, suggesting that global sourcing is not significantly large, atleast in our sample period. Moreover, we construct the new data by pooling auto
manufacturers’ and auto suppliers’ FDI according to region, namely ASEAN and North
America, and we estimate equations (2) and (3) and equations (4) and (5).17 We include
the average GDP or the GDP growth rate by region as regional characteristics instead ofcountry characteristics. Estimation results are presented in Table 12. In the first-stage
regression, auto manufacturers’ FDI (Auto-FDI) has no significant impact on the FDI
decision. As for the size of affiliates for auto suppliers, Auto-FDI has a positive17

Since our sample contains only one European country, the UK, we do not include Europe in theanalysis at the regional level.
28

coefficient when firm characteristics are controlled. However, the estimates in thesecond-stage regression for the intensive margin of FDI are again insignificant.

=== Table 12 ===6. Concluding Remarks
This study investigates the impact of FDI on corporate performance in the homecountry, focusing on the Japanese automobile parts industry. This study differs fromprevious studies in that it uses the instrumental variable technique to deal with the issueof reverse causality and uses the supplier–customer transaction relationship as an
instrument of automotive suppliers’ FDI. This is in contrast to most previous studies that
use the propensity score matching method and focus only on the impact of the extensive
margin of FDI on corporate performance at home. Our approach enables us toinvestigate the impact of both the extensive margin and the intensive margin of FDI.
Using the Japanese automobile industry Keiretsu relationships, our empiricalanalysis reveals that the extensive margin of FDI has a positive impact on sales,
employment, value added, and TFP. Furthermore, the impact of the extensive margin of
FDI is larger when we focus on VFDI. In addition, comparing the impact of FDI
29

between the first FDI and subsequent FDIs, we find that the impact of the first FDI on
corporate performance is more profound than that of the subsequent FDIs. In contrast,
the impact of the intensive margin of FDI sometimes has positive coefficients, but they
are basically not significant. We conclude that the impacts of FDI on corporate
performance are mainly driven by the extensive margin of FDI, especially when firms
first start to invest abroad. These results might reflect the procurement pattern of MNE
affiliates. As they learn more about local suppliers, they tend to substitute imports fromtheir parent company with local procurement. Therefore, the impact of the intensive
margin of FDI might be insignificant. Finally, it should be also noted that we find no
negative impact of FDI on domestic corporate performance. “Hollowing out” is one of
the central concern about the impact of FDI. However, there is no supporting evidence
that FDI hollow out the employment at home country.
Although our study presents interesting findings, it also suggests various avenues
for future research. First, although we find evidence that the extensive margin of FDI
has a positive and significant impact on corporate performance, its mechanics and thenumerical magnitude have not yet been fully explored and remain a black box.
Identifying the source of gains in productivity requires more detailed data andsophisticated identification strategies. Second, the applicability of our results to other30

industries is at issue. As discussed, the automobile industry has the most developed
international production and distribution network. Thus, we believe that our evidence
from automotive parts suppliers has important implications. However, the nature of theinternational production network might differ from one industry to another, leading tovariation in the impact of FDI by industry. Therefore, it is also important to compare theimpact of FDI on other industries.

31Appendix
Table A1: Basic Statistics
variable
Extensive margin
ΔlnSalesΔlnEmp
ΔVA
ΔTFP
FDI dummyAuto-FDI
FDI-other
KL-ratioΔkeiretsu-prod
R&D sales ratio
variable
Intensive margin
lnSales
lnEmp
VA
TFP
Affiliate size
Auto-FDI
FDI-other
KL-ratio
Keiretsu-prod
R&D sales ratio# of obs

mean

sd

p25

p75

51920
51920
51652
51652
51920
51920
51920
51920
51920
51920
# of obs

0.002-0.012
0.037
0.047
0.003
2.881
0.723
1.370
-0.014
0.007
mean

0.159
0.107
0.345
0.339
0.057
4.795
1.941
0.128
0.070
0.015
sd

-0.075-0.051-0.113-0.100
0.000
0.000
0.000
1.288
0.000
0.000
p25

0.078
0.024
0.179
0.188
0.000
8.007
0.000
1.447
0.000
0.006
p75

1690
1690
1690
1690
1690
1690
1690
1690
1690
1690

10.714
7.230
9.181
2.149
5.331
9.650
5.139
1.317
13.081
0.027

1.267
1.166
1.315
0.356
1.309
5.031
2.931
0.068
4.996
0.025

9.927
6.604
8.423
1.955
4.554
9.473
4.111
1.271
14.0180.006

11.344
7.845
9.898
2.384
6.193
13.1207.118
1.354
15.6100.041

32

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36

Table 1: Summary of previous studies that use the propensity score matching method

Debere etal. 2010
Hijzen etal, 2011
Naveretti
et .al, 2010
Hayakawa
et al. 2013

South
Korea
FranceFranceItaly
Japan

Type of
FDI
HFDI
VFDI
HFDI
VFDI
HFDI
VFDI
HFDI
VFDI
HFDI
VFDI

Sales

TFP

Emp

n.a.
n.a.

n.a.
n.a.
+

+

+
+
+

+

+

37

+
+
+
+
+
+
+

Table 2: Ratio of foreign affiliates’ procurement origin by region in 1995
Automobile assember
location of affiliates
North America
Asia
Europe

Local
59%
57%
47%

Procurement origin
Japan
Third country
40%
0%
41%
1%
41%
11%

Auto Parts manufacturer
location of affiliates
Local
North America
59%
Asia
45%
Europe44%

Procurement origin
Japan
Third country
37%
4%
49%
6%
35%
21%

Note: Asia includes East Asia, Southeast Asia and South Asian countries. North America is

composed of the United States of America and Canada. Europe consists of Western and Eastern

European countries.

Source: Author's calculation based on the Basic Survey of Overseas Business and Activities.

38

Table 3: Firm Characteristics: MNEs and Keiretsu suppliers

non-MNEs
MNEs

non-Keiretsu
Keiretsu

Sales
8,611
(18187.0)
64,758(133341.3)# of Employees
278(471.6)1,715(3560.0)

K-L ratio
1.374(0.139)1.333(0.076)TFP
1.754(0.430)2.078(0.378)

Sales
7,022
(17124.3)
34,655(91060.1)# of Employees
236(405.8)948(2431.8)

K-L ratio
1.372(0.149)1.360(0.098)TFP
1.705(0.439)1.967(0.386)

Note: Figures in parentheses are standard deviation. Unit for sales is millions of Yen.

Source: Author’s calculation based on linked firm-level database.

39

Table 4: Number of Keiretsu suppliers and MNEs

1991
1994
1995
1996
1997
1998
1999
2000

Total
866
878
941
929
932
927
930
879

non-Keiretsu suppliers
Sub-total non-MNEs MNEs
540
523
17
541
519
22
587
564
23
580
553
27
586
556
30
586
548
38
585
546
39
546
509
37

Keiretsu suppliers
Sub-total non-MNEs MNEs
326
239
87
337
243
94
354
248
106
349
236
113
346
227
119
341
215
126
345
212
133
333
206
127

Source: Author’s calculation based on linked firm-level database.

40

Table 5: Conditional logit model for FDI location choice

Automobile production
by Keiretsu assembler
Automobile production
by non-Keiretsu assembler
Automobile production
by non-Japanese assemblers# of Japanese auto parts
suppliers
Distance from Japan

(1)
(2)
(3)
0.0858*** 0.101*** 0.0704***(0.0216) (0.0214) (0.0223)
0.000217 0.0229-0.00959(0.0187) (0.0192) (0.0201)
0.284*** 0.185***(0.0507) (0.0549)
0.561***(0.101)-1.409*** -1.514*** -0.953***(0.112)(0.108)(0.151)

Observations

3,630

3,630

3,630

Note:

1) Standard errors in parentheses.

2) ***, **, and * indicates statistical significance at 1, 5, and10 percent levels, respectively.

41

Table 6: FDI decision by auto parts suppliers

VARIABLES
Auto-FDI

(1)
(2)
(3)
ProbitProbitProbit
FDI dummy FDI dummy FDI dummy

(4)
(5)
(6)
Fixed Effect Fixed Effect Fixed EffectAffiliate size Affiliate size Affiliate size

0.00016*** 0.00001**(0.00003) (0.00001)

0.0160***(0.0059)

GDPgrowth

0.00001**(0.00001)-0.00077(0.00059)

0.0147**(0.0059)

0.0137**(0.0059)4.9066***(0.0691)0.0480***(0.0130)
3.9489***(1.3134)-1.1201*(0.6543)-0.2396(0.8818)-8.8237*(5.1303)0.1058(0.0926)
0.0464***(0.0131)4.0086***(1.3143)-1.1385*(0.6544)-0.2224(0.8818)-11.7446**(5.7318)

Yes
No
Yes
1,690
352
0.1209

Yes
No
Yes
1,690
352
0.1402

Yes
No
Yes
1,690
352
0.1411

26.16

15.43

14.49

GDP
FDI-other t-1

0.00007*** 0.00007***(0.00002) (0.00002)
0.00013*0.00013*(0.00008) (0.00007)-0.00012-0.00012(0.00024) (0.00024)
0.00202*0.00197*(0.00116) (0.00114)

Firm age
KL-ratio t-1
R&D sales ratio t-1
Constant

Year FFCountry FF
Firm-country FF
Observations
Number of id
R-squared
LR Chi2
F testYes
Yes
No
51,906

Yes
Yes
No
51,906

Yes
Yes
No
51,906

0.152
316.7

0.286
596.9

0.287
599.0

1) Standard errors in parentheses.

2) ***, **, and * indicate statistically significance at 1, 5, and 10 percent levels, respectively.

3) Coefficients in the probit model are converted into marginal effects.

42

Table 7: The impact of FDI on corporate performance: baseline results
Panel (a)(1)
VARIABLES
FDI dummy
FDI-other t-1
Firm age
KL-ratio t-1⊿ Keiretsu-prod
R&D sales ratio t-1

Year dummy
Firm size dummyCountry dummy

Extensive Margin(2)(3)

Δ lnSales

Δ lnEmp0.1082***(0.0158)-0.0004(0.0004)-0.0213***(0.0014)0.0457***(0.0052)
0.1656***(0.0103)0.2877***(0.0477)

0.0914***(0.0112)-0.0020***(0.0003)-0.0153***(0.0010)0.1076***(0.0038)
0.0667***(0.0075)-0.1952***(0.0346)

Yes
Yes
Yes

Yes
Yes
Yes

Δ lnVA

Yes
Yes
Yes

Yes
Yes
Yes

51,6520.1082(0.1521)-0.0177*(0.0092)-0.7547(0.8447)0.2332(0.3265)0.3508***(0.0959)
0.0796(0.3728)

51,920Panel (b)(1)

VARIABLES

lnSales

51,92051,652
Intensive Margin
(2)
(3)
lnEMP
VA

0.1635(0.0994)-0.0048(0.0060)-0.0081(0.5524)-0.2424(0.2135)
0.5464***(0.0627)0.1506(0.2438)

0.05910.1635(0.0654)(0.1607)0.0032-0.0145(0.0039)(0.0097)0.1671-0.5954(0.3631)(0.8926)-1.1593*** -0.6852**(0.1404)(0.3450)0.2203*** 0.5539***(0.0412)(0.1014)0.19300.2884(0.1602)(0.3939)

FDI-other t-1
Firm age
Keiretsu-prod t-1
KL-ratio t-1
R&D sales ratio t-1

Year fixed effectFirm size dummy
Firm-country fixed effectObservations
Number of id
Hansen J test

Yes
Yes
Yes
1,669
331
0.0583

Yes
Yes
Yes
1,669
331
0.206
43

Δ TFP

0.2209*** 0.2073***(0.0322)(0.0319)-0.00030.0014*(0.0009)(0.0009)-0.0331*** -0.0207***(0.0028)(0.0027)0.0604***-0.0176*(0.0105)(0.0104)
0.1988*** 0.1470***(0.0210)(0.0207)-0.2386**-0.0721(0.0968)(0.0953)

Observations

Affiliate size

(4)

Yes
Yes
Yes
1,669
331
0.652

(4)
TFP

Yes
Yes
Yes
1,669
331
0.983

1) Standard errors in parentheses.

2) ***, **, and * indicate statistically significance at 1, 5, and 10 percent levels, respectively.

44

Table 8: The impact of FDI on corporate performance: Case of VFDI
Panel (a)(1)
VARIABLES
FDI dummy
FDI_other t-1
Firm age
KL-ratio t-1⊿ Keiretsu_prod
R&D sales ratio t-1

Year dummy
Firm size dummyCountry dummy

Δ lnSales

0.1204***(0.0229)-0.0002(0.0004)-0.0213***(0.0014)0.0458***(0.0052)
0.1658***(0.0104)
0.2863***(0.0478)
Yes
Yes
Yes

Observations

51,829Panel (b)(1)

VARIABLES

lnSalesAffiliate size
FDI_other t-1
Firm age
Keiretsu_prod t-1
KL-ratio t-1
R&D sales ratio t-1

Year fixed effectFirm size dummy
Firm-country fixed effectObservations
Number of id
Hansen J test

Extensive Margin(2)(3)
Δ lnEmp

Δ lnVA

0.0901*** 0.2218***(0.0165)(0.0471)-0.0019*** 0.0017**(0.0003)(0.0009)-0.0152*** -0.0206***(0.0010)(0.0027)
0.1076***-0.0176*(0.0038)(0.0104)
0.0667*** 0.1477***(0.0075)(0.0207)-0.1963***-0.0775(0.0347)(0.0955)
Yes
Yes
Yes

(4)
Δ TFP

0.2665***(0.0525)-0.0001(0.0009)-0.0371***(0.0030)
0.0738***(0.0114)0.2471***(0.0226)-0.2465**(0.1044)

Yes
Yes
Yes

Yes
Yes
Yes

51,82951,562
Intensive Margin
(2)
(3)
lnEMP
VA

51,562(4)
TFP

0.4725(0.5048)0.0114(0.0136)-1.1300(1.5678)
0.8859*(0.4983)-1.2109(0.8729)0.2216(0.7416)-0.1443(0.2686)0.0103(0.0072)
1.3532(0.8341)0.1677(0.2651)-0.6796(0.4644)-0.1177(0.3945)0.2058(0.6704)
0.0150(0.0181)-0.1188(2.0821)0.6943(0.6617)0.0249(1.1593)0.3304(0.9849)-0.3516(0.8609)-0.0117(0.0232)
4.1357(2.6737)-0.9783(0.8498)
0.2364(1.4887)-0.5502(1.2648)

Yes
Yes
Yes
319
75
0.247

Yes
Yes
Yes
319
75
0.0406

Yes
Yes
Yes
319
75
0.601

Yes
Yes
Yes
319
75
0.282

45

1) Standard errors in parentheses.

2) ***, **, and * indicate statistically significance at 1, 5, and 10 percent levels, respectively.

46

Table 9: Robustness check; Excl. First FDI
Extensive Margin
(3)
Δ TFP

(4)
Δ lnVA-0.0073 0.0736*** -0.0407(0.0240) (0.0115) (0.0347)-0.0274(0.0379)

6,440

6,440

6,422

6,422

(1)
lnSales

(2)
lnEMP

(3)
TFP

(4)

VA

Affiliate size

0.1635(0.0994)

0.0591(0.0654)0.1082(0.1521)0.1635(0.1607)

Observations
Number of id
Hansen J test

1,669
331
0.0583

1,669
331
0.206

1,669
331
0.983

1,669
331
0.652

VARIABLES
FDI dummy

Observations
Intensive Margin
VARIABLES(1)
Δ lnSales(2)
Δ lnEmp1) Standard errors in parentheses.

2) ***, **, and * indicate statistically significance at 1, 5, and 10 percent levels, respectively.
3) Control variables, such as FDI-other, Firm-age, KL-ratio, ⊿Keiretsu-prod, R&D-sales-ratio areincluded.

47

Table 10: Robustness check; Excl. suppliers that belong to only one Keiretsu
Extensive Margin
VARIABLES
FDI dummy

Observations
Intensive Margin(1)
Δ lnSales(2)
Δ lnEmp

(3)
Δ TFP

(4)
Δ lnVA

0.1063*** 0.1233*** 0.2501*** 0.2645***(0.0208) (0.0143) (0.0410) (0.0418)
38,64938,64938,431

38,431(1)
lnSales

(2)
lnEMP

(3)
TFP

(4)

VA

Affiliate size

0.0865(0.1134)

0.0512(0.0776)0.1007(0.1988)

0.1429(0.2095)

Observations
Number of id
Hansen J test

1,224
247
0.0971

1,224
247
0.360

1,224
247
0.882

1,224
247
0.630

VARIABLES

1) Standard errors in parentheses.

2) ***, **, and * indicate statistically significance at 1, 5, and 10 percent levels, respectively.
3) Control variables, such as FDI-other, Firm-age, KL-ratio, ⊿Keiretsu-prod, R&D-sales-ratio areincluded.

48

Table 11: Robustness check; Excl. Keiretsu parent share>20%
Extensive Margin
VARIABLES
FDI dummy

Observations
Intensive Margin(1)
Δ lnSales(2)
Δ lnEmp

(3)
Δ TFP

(4)
Δ lnVA

0.1141*** 0.0826*** 0.2067*** 0.2154***(0.0191) (0.0138) (0.0389) (0.0393)
49,462

49,462

49,20349,203(1)
lnSales

(2)
lnEMP

(3)
TFP

(4)

VA

Affiliate size

0.1566(0.1231)0.0166(0.0736)0.1397(0.1837)

0.1548(0.1906)

Observations
Number of id
Hansen J test

1,170
233
0.09661,170
233
0.0522

1,170
233
0.503

1,170
233
0.978

VARIABLES

1) Standard errors in parentheses.

2) ***, **, and * indicate statistically significance at 1, 5, and 10 percent levels, respectively.
3) Control variables, such as FDI-other, Firm-age, KL-ratio, ⊿Keiretsu-prod, R&D-sales-ratio areincluded.

49

Table 12: Estimating the impact of FDI by pooling data at the regional level
Panel (a) First stage estimation results
VARIABLES
Auto-FDI

ProbitProbitProbit
FDI dummy FDI dummy FDI dummy

Fixed Effect Fixed Effect Fixed EffectAffiliate size Affiliate size Affiliate size

0.00092*** 0.00002
0.00002(0.00012) (0.00001) (0.00001)

0.0889(0.0566)

0.0689(0.0567)

0.1571**(0.0754)Country control
Firm control
Year FE
Region FE
Firm-region FE

No
No
Yes
Yes
No

No
Yes
Yes
Yes
No

Yes
Yes
Yes
Yes
No

No
No
Yes
No
Yes

No
Yes
Yes
No
Yes

Yes
Yes
Yes
No
Yes

Observations
R-squared
Pseudo-R2
LR chi2
Number of id
F test

8,320

8,320

8,320

1,050
0.1711

1,050
0.19151,050
0.1945

0.122
105.7

0.253
218.5

0.253
218.5
214
24.44

214
13.90

214
13.22

Panel (b)Second Stage: Extensive Margin
(1)
(2)
(3)

VARIABLESΔ lnSales

FDI dummy-0.0400 0.0867*** 0.1990*** 0.2115***(0.0307) (0.0182) (0.0529) (0.0527)

Year dummy
Firm size dummyCountry dummy

Observations
Panel (c)

Yes
Yes
Yes

Δ lnEmp

Δ TFP

Yes
Yes
Yes

(5)
Δ lnVA

Yes
Yes
Yes

Yes
Yes
Yes

9,672
9,672
9,620
Second Stage: Intensive Margin
(1)
(2)
(3)

9,620

VARIABLES

lnSales

lnEMP

TFP

(5)
VA

Affiliate size

-0.1561(0.1244)
Yes
Yes
Yes
1,038
202
0.0583-0.0915(0.0843)
Yes
Yes
Yes
1,038
202
0.206

0.0425(0.1728)
Yes
Yes
Yes
1,038
202
0.983

-0.0476(0.1792)
Yes
Yes
Yes
1,038
202
0.652

Year fixed effectFirm size dummy
Firm-country fixed effectObservations
Number of id
Hansen J test501) Standard errors in parentheses and ***, **, and * indicate statistically significance at 1, 5, and 10

percent levels, respectively.
2) Control variables, such as FDI-other, Firm-age, KL-ratio, ⊿Keiretsu-prod, R&D-sales-ratio areincluded in Panel (b) and Panel (c).

51

Figure 1: Volume of Japanese automobile production by region and automobile
assemblers in 2000
1,400,000
1,200,000
1,000,000
800,000
600,000
400,000
200,000
0
Overseas Production,
total

North America

Europe250,000

200,000

ToyotaHonda
Nissan150,000

Mitsubishi
Mazda

100,000

Fuji
Daihatsu

50,000Suzuki
0
ASEAN

China

Source: World Motor Vehicle Statistics (Japan Automobile Manufacturers Association)

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