To be presented at International Conference on New Development in Asia Pacific Region: Economic
Analysis using Microdata, on February 16, 2003.

Foreign Direct Investment into Asia and Domestic R&D Intensity ofJapanese Manufacturers: Firm-level Relationship

Eiichi Tomiura*
Research Institute for Economics and Business Administration, Kobe University
2-1 Rokko-dai, Nada, Kobe City 657-8501, Japan

January 31, 2003

Abstract
This paper reexamines the relationship between R&D and FDI by exploiting firm-level data of morethan 118 thousand Japanese manufacturers. The descriptive statistics and regression results confirm
that firms with higher R&D intensity tend to undertake more extensive FDI, even if large numbers of
no-FDI and/or no-R&D firms are included. The effect of R&D is more substantial on FDI toindustrial countries than FDI to Asia. Firms rich in intangible assets, such as human skills, also
appear to prefer majority ownership in FDI, as consistent with the theoretical prediction.

Keywords: Foreign direct investment; R&D; Internalization; Firm-level data.

* Eiichi Tomiura, Research Institute for Economics and Business Administration, Kobe University,

2-1 Rokko-dai, Nada-ku, Kobe City 657-8501 JAPAN. Phone: +81-78-803-7008. Fax:
+81-78-803-7059. E-mail: tomiura@rieb.kobe-u.ac.jp.
11. Introduction
As surveyed in Caves (1996), many previous studies have repeatedly showed that theintensity of research and development (R&D) is positively related with the foreign direct
investment (FDI).1 Although these accumulated results from many industries and countries
have immensely contributed to our understanding of FDI, they mostly depend on industry-level
data or on limited numbers of firms in a particular industry.2 Besides, the dependence on FDI
data inevitably excludes firms undertaking no FDI and concentrates on the comparison withinfirms who have already invested into foreign countries.3 Since the development of FDI varies
considerably across industries and across firms even within industry, and since the vast majorityof firms have no foreign subsidiaries, we need to explore more comprehensive samples toconfirm this stylized fact. Our finding that firms not investing abroad at all are on average
equally R&D active as firms investing abroad, as reported later, further motivates our use of
firm-level data covering firms with no experience in FDI.
This paper reexamines the relationship between R&D intensity and the extent of FDI by
exploiting previously unavailable firm-level data, taking account that our FDI measure is alimited dependent variable. The domestic industry survey, from which this paper derives
firm-level data, covers more than 118 thousand Japanese manufacturers, including firms in any
size, even firms with no FDI and/or no R&D in all manufacturing industries. Unlike previous
studies dependent on FDI data, this paper uses R&D and FDI data, both of which are

1

Both Caves (1996) and UN (1992) describe R&D as one of the strongest determinants of FDI
among many other firm characteristics, such as advertising, skilled managerial labor, and multi-plant
operation. Various industry characteristics, such as entry barriers and tariff protection, have also been
considered in studies at the industry level.
2 Previous studies of Japanese FDI often focused on the electric machinery industry and the sample
size is at most around two hundred firms. See Fukao et al. (1994) and Belderbos and Sleuwaegen(1996), for example.
3 As an exceptional previous study, Belderbos and Sleuwaegen (1996) include 65 no-FDI firms intheir analysis of the choice among domestic, FDI to West, and FDI to Asia.
2

consistently collected in the same survey. The coverage and sample size of our data set aresubstantially larger than those used by previous studies.
This paper not only reexamines the relationship between R&D and FDI by using a newly
available comprehensive firm-level data set, but also investigates the two hypotheses inspiredby the previous studies as follows. First, FDI into Asia and FDI into industrial countries arecompared. Previous studies based on the data of electric machinery manufacturers in the 1980s,
such as Belderbos and Sleuwaegen (1996) and Fukao et al. (1994), found that R&D of aJapanese firm tends to have a positive effect on the firm’s FDI into industrial countries, but nosignificant effect on FDI into Asia. Recent drastic development of Asian economies induces us
to reexamine this issue with more recent data because FDI into Asia may now require Japanesefirms to accumulate technological advantages in the home country. Hence, this paper uses thenewer data, which are collected at more recent 1998 and covers substantially larger numbers offirms in wider ranges of industries than were previously investigated.
Second, as inspired by recent empirical studies of FDI, including Barbosa and Louri(2002), this paper investigates the relation between intangible assets of a firm with theownership structure preference of the firm’s FDI. The research along this line suggests thatmajority ownership is preferred if the FDI is motivated by the firm’s strength in intangible
assets. The difficulty in defining and monitoring the proprietary rights associated with these
intangible assets induces firms to prefer majority ownership shares in FDI to securelyinternalize the gains from these costly-to-market assets. Consequently, this paper examines
whether the effect of R&D on FDI is stronger for majority-owned FDI, compared withminority-owned FDI. By exploiting firm characteristics collected by the survey, this paper also
evaluates the impact of human capital, which is another important intangible asset as technology
capital accumulated by R&D, on the ownership structure of FDI.

3

The rest of this paper is organized as follows. Section 2 describes our data. Section 3
explains empirical specifications and estimation methods. Section 4 reports empirical results.
Section 5 concludes.

2. Description of data
All the data used for this paper are derived from the firm-level data files of the Basic Survey ofCommercial and Manufacturing Structure and Activity (Sho-Kogyo Jittai Kihon Chosa inJapanese).4 The survey, covering all firm sizes and all manufacturing industries, includes
various firm-characteristics data at 1998, such as sales, capital, R&D spending, and industry
classification.5 The survey captures FDI in the number of foreign subsidiaries/affiliates withmajority ownership (kogaisha in Japanese) and those with share no less than 20 percent but nomore than 50 percent (kanrengaisha in Japanese), distinguishing those located in Asia from theworld total.6 A large number of firms with no FDI or no R&D at all are also included within our
sample of 118,300 manufacturers. This large size and wide coverage convince us that we caninterpret this survey as a good representation of the whole manufacturing in Japan. As plotted in
Figure 1, the published aggregate data from this same survey confirm the relatively strong
positive correlation between R&D/Sales ratio and FDI at the industry-level.7 Industry-levelvalues, however, are affected by averaging over heterogeneous firms.
4

Although the data for commercial industries are also available in the same survey, this paper
focuses on manufacturing firms.
5
Although it contains similar data, the Basic Survey of Business Structure and Activities (Kigyo
Katsudo Kihon Chosa in Japanese) covers only limited numbers of large firms (defined as those withmore than fifty employees and capital of more than thirty million yen) and is not suited forevaluating total Japanese manufacturing.
6 The region “Asia” in this survey includes not only ASEAN, but also countries such as China andIndia. Foreign offices with ownership less than 20% are not recognized with destination
disaggregation. Many previous studies also use the dichotomous measure of majority vs. minority
ownership, while Asiedu and Esfahani (2001) use continuous actual shares in FDI projects.
7 The correlation between the percentage ratio of R&D spending over sales and the industry’s sharein total number of foreign subsidiaries (majority and minority ownership, combined) is as high as
0.731 among two-digit industries.
4

Table 1 classifies firms by whether or not the firm is involved in any R&D or FDI at all.8
Noteworthy findings from our micro-data are as follows. Let us abuse abbreviations by callingfirms with strictly positive FDI (R&D) as FDI firms (R&D firms) and firms with zero FDI(R&D) as no-FDI firms (no-R&D firms), respectively, in what follows.9
First, only two percent of 118,300 firms in our sample undertake both R&D and FDI.
Even if we include no-R&D firms, the share of FDI firms is merely around three percent of all
firms.10 This implies that previous studies based on samples solely from FDI firms do not
replicate the whole manufacturing. On the other hand, nearly 17 percent of firms conduct
R&D.11 By covering these no-FDI firms and no-R&D firms, our sample is suited for evaluating
the impact of R&D on FDI decision in the universe of all firms.
Second, R&D firms appear more active in FDI than no-R&D firms in all categories of
FDI, irrespective of destination and ownership structure.12 This comparison suggests positive
relation between domestic R&D intensity and the extent of FDI. Since most of FDI by Japanesefirms go either into Asia, U.S., or into E.U. regions, and since no further regional disaggregation
is available in the survey, let us approximately interpret the rest of the world other than Asia as

8 In the survey, firms are asked whether or not the firm undertakes any R&D or FDI. Since noresponse is observed for this binary question for no firm in our sample, and since all the firms withno response to questions on R&D spending values and on FDI counts in the survey are the firms
explicitly answer that they conduct no R&D and no FDI at all, we treat them as zero. For physicalcapital and human capital, however, no binary question is set in the survey and it is implausible toassume firms with no response to the question as firms with zero capital.
9 Although the survey is collected from parent firms, R&D figures may include R&D expendituresby foreign subsidiaries.
10 Since the firms whose employees are less than fifty are sampled with probability less than one,the share of FDI firms in the whole population of all firms must be even lower if we adjust sampling
probability. Although the government does not disclose the rescaling method for different sampling
probability, the published aggregate statistics from the same survey reports that only 1.5% of all
manufacturers have foreign subsidiaries.
11 If we consider that the sampling probability is lower for smaller firms, which tend to be lessactive in R&D, the share of R&D firms must be even lower. According to the published aggregatedstatistics from the same survey, only 12% of all manufacturers conduct any R&D.
12 This kind of cross-aggregation is not released in the published aggregated statistics.

5

industrial countries.13 The gap between R&D firms and no-R&D firms is particularly evident in
FDI with majority ownership into industrial countries. Besides, the share of R&D firms is
substantially higher among FDI firms, compared with among no-FDI firms (66%>15%).
Third, however, the R&D intensity of FDI firms is found virtually the same as that ofno-FDI firms. The R&D intensity is defined by the ratio of R&D expenditure over sales. Thus,
among firms undertaking R&D, the involvement into FDI does not associate with different
levels of R&D intensity. This finding, ignored by the comparison among FDI firms, indicatesthat the comparison with no-FDI firms is necessary to evaluate the R&D impact on FDI.
Consequently, this paper controls for various factors simultaneously by regression formats in thenext section.
Finally, while the average R&D firm is three to five times larger than the average
no-R&D firm, the average FDI firm is 14 to 21 times larger than the average no-FDI firm interms of sales. This may suggest some forms of increasing returns to scale or fixed sunk entry
costs associated with R&D and FDI, especially with FDI.

3. Empirical models
This section explains the empirical models for this paper. The basic specification relating FDI
with R&D intensity, as a reduced form, is as follows.

FDI i = α + β 1 ln R & D / Qi + β 2 ln Qi + β 3 ln K i Qi + β 4 ln HS i + γDUM + u i(1)

The suffix i indexes firm in the cross-section data. FDI denotes foreign direct investment either
into Asia or into industrial countries, either with majority ownership or minority ownership. The
variables Q, K, HS, and R&D are the output (sales), the capital (tangible fixed asset), the human13

We can confirm this approximation by alternative data sources. For example, the most recent
figure from FDI statistics by the Ministry of Finance shows that 87% of aggregate Japanese FDI into
non-Asian countries is into U.S., Europe, or Oceania.
6skill intensity (measured by per-capita overhead expenditure), and the R&D spending,respectively.14 The vector of industry dummy variables is expressed by DUM.15 The error termis denoted by u. Although this specification is flexible in incorporating variables, we should notinterpret the results as suggesting that more active R&D causes more extensive FDI because
they are simultaneously determined.16
According to the standard theory of FDI, as surveyed by Caves (1996) for example, a firm
invests directly into foreign countries if the firm has advantage, compared with local rival firms,
in intangible assets, such as technology or managerial skills. Unless a firm possesses theseassets costly to transact at arm’s length, the firm is not likely to establish their own subsidiaries
in foreign countries.17 Taking account of the technology gap between developed and developing
countries, we expect that more active R&D is required for FDI into industrial countries,
compared with FDI into Asia. 18 Similarly, for ownership structure, FDI with majority
ownership, rather than FDI with minority ownership, is expected to more strongly relate with

14 Since the survey contains no data directly related with wage, this paper uses the selling, generaland administrative pay (SGA) divided by the number of employees for the index of skill intensity.
This measure can be decomposed into the wage share of non-production workers and the average
wage of the firm. Although they have been often included in the similar regressions of FDI, the dataof advertising expenditure and business group affiliations are not available in the survey. Belderbos
and Sleuwaegen (1996) is an excellent study of effect of inter-firm relations on Japanese FDI. On theother hand, although it is available in the survey, the export data is not included because FDI andexports are simultaneously determined and no appropriate instrumental variables are found in ourcross-section framework.
15 The two-digit classification is used as identification of industries, as the inclusion of dummies for
all three-digit industries considerably loses the degree of freedom in regressions.
16 Besides, R&D is measured in flow term: spending during the previous year, while FDI is in stock
terms: number of foreign subsidiaries. This inconsistent measuring is due to the limit of data
availability at the firm level.
17 Although they are another important determinant of FDI, host country factors are not consideredhere due to the limit of data availability. Kogut and Chang (1991) examine the motivation to source
advanced local technology in FDI decisions into U.S. at the aggregated industry level. Asiedu andEsfahani (2001) consider local costly-to-market inputs into jointly-owned FDI projects.
18 If the competition among multinational corporations from other industrial countries investing into
Asia is intense, this difference in R&D effect on FDI depending on destination is not obvious. Since
no data of FDI from American or European firms is available in our survey of Japanese parent firms,
however, this aspect is omitted from this paper.

7

R&D intensity because the gains from intangible assets are not easily traded in markets.19 Since
human capital is also suited for internalization, similarly as technology capital accumulated
through R&D spending, the coefficient on HS is expected larger for FDI into industrial countries
than FDI into Asia, and for majority-owned FDI than minority-owned FDI. On the other hand,the firm size is expected to have positive effect on FDI into any region and with any ownership
structures because FDI incurs fixed sunk costs for establishing subsidiaries. The capital-output
ratio is supposed to act as an inverse proxy for richness in intangible assets, or an indicator fortangibility of assets.20
This paper conducts robustness checks by using various alternative estimation methods
and alternative ranges of data. First, this paper estimates (1) by OLS over the sample of firms
with strictly positive FDI and R&D. This can be served as the benchmark of our analysis,comparable with previous studies.
Second, taking account of the fact that the survey measures FDI by counting the numberof foreign subsidiaries, this paper estimates (1) not only by OLS, but also by Poisson, negative
binomial regressions, or Tobit.21 Thus, the robustness of our findings will be confirmed byconsidering that our FDI index is a limited dependent variable.

19

Although recent studies, including Asiedu and Esfahani (2001) and Barbosa and Louri (2002),
have examined FDI ownership, they focused on the effect of parent firm’s intangible assets on theownership choice of individual FDI project, based on affiliate data, excluding by construction
no-FDI firms. This paper investigates the effect of intangible asset on parent firm’s FDI extent inrespective ownership structure, based on survey of parent firms including no-FDI firms. These two
approaches should be viewed as complementary.
20 In some previous studies, especially where direct R&D data are not available, for example in
Asiedu and Esfahani (2001), the capital-output ratio is used in this context, although our regression
simultaneously includes R&D and human skill intensity as well.
21 Although it is the most frequently used for FDI studies, the multinomial logit model forces us toconcentrates on the choice among limited numbers of alternative categories (the three-way choice
between no-FDI, FDI to West and FDI to Asia in Belderbos and Sleuwaegen (1996), for example).
Our FDI count data is more information-rich. When continuous data, such as offshore production
share (as in Fukao et al. (1994)) or joint venture share (as in Asiedu and Esfahani (2001), areavailable for FDI, Tobit model is preferable. Kogut and Chang (1991), based on counts of Japanese
FDI into U.S. at industry level, is a rare example of the negative binomial regression for FDI.
8

Thirdly, this paper includes no-R&D firms into the regression by adding one to R&D
intensity before taking logarithm as following: R&D
K
 + β 2 ln Q + β 3 ln + β 4 ln HS + γDUM + u .
FDI = α + β 1 ln1 +
Q 
Q


(2)

The above adaptation substantially increases the numbers of observations available forregressions, since more than eighty percent of firms in our sample have zero R&D value.
The regressions have so far related the absolute level of various forms of FDI with R&D
intensity and compared estimated coefficients from separate regressions of different dependent
variables. However, it remains to be known whether the destination of FDI (to industrial
countries than to Asia) or the ownership structure (majority than minority) relatively dictates theresults. Therefore, to discriminate the destination effect from the ownership effect, this paper
replaces the dependent variable of absolute level of FDI with the relative share as follows.

 R&D
FDI _ Ind _ Maj
K + β 2 ln Q + β 3 ln + β 4 ln HS + γDUM + u= α + β 1 ln1 +FDI _ World _ Maj
Q 
Q

(3)
This equation is estimated by Tobit because any share cannot be beyond zero and one. The
positive β1 in (3) provides support for the destination effect more significant than the ownershipeffect. Similar regressions are also conducted for the share of industrial countries in allminority-owned FDI, the share of majority-owned FDI in all FDI to industrial countries, and theshare of majority-owned FDI in all FDI to Asia. The comparison of these regression results will
distinguish whether destination or ownership of FDI is more strongly affected by R&D
intensity.

4. Estimation results
4.1. Summary statistics

9

This section reports empirical results from our sample. Before discussing regression results,
descriptive statistics of variables used for estimation is informative. Table 2 clearly shows
remarkable cross-sectional variations.22 The cross-sectional standard deviation is larger than
average in all the variables. On average, each FDI firm has two or three foreign subsidiaries,
while each R&D firm spends around three percent of their sales on R&D.23
The correlation between variables is summarized in Table 3. As expected, among thefirms with strictly positive R&D and FDI, the correlation of FDI is high with sales (correlation
around 0.47 to 0.52) and also clearly positive with R&D intensity and human skill (correlation
around 0.14 to 0.29). However, if all firms with no R&D or no FDI are included, FDI becomesmuch less correlated with R&D and other variables. The correlation of FDI is now at most 0.2
with sales and far less than 0.1 with other variables. Consequently, it will be fruitful toreexamine the relation between FDI and intangible asset of the firm by including large numberof firms with no FDI or no R&D to understand the more reasonable representation of the whole
economy.

4.2. Basic OLS results
Table 4 reports the OLS results for the basic specification (1) from the sample restricting
to firms with strictly positive FDI and R&D. The investigation of variations among FDI/R&D
firms facilitates the comparison with previous studies. Although it is larger than those used inprevious studies, the sample size is limited to 1,264 firms. The main findings are as follows.
First, for majority-owned FDI, the R&D effect on the extent of FDI into industrial
22

The number of firms of which R&D data are available in this table is smaller than the number offirms with positive R&D in Table I because some firms spend less than one million yen for R&D or
did not disclose their R&D spending.
23 The published aggregated statistics from the same survey report similar figures for average FDI,
but slightly higher R&D-sales ratio (3.8%). Since the relation between R&D intensity and firm sizeis U-shaped, lower representation of small-sized firms may decrease R&D intensity.
10

countries is substantially larger than FDI into Asia. The coefficient on R&D intensity is
estimated around 2.6 times larger in the regression of FDI into industrial countries, comparedwith that to Asia. Although they do not distinguish ownership structure of FDI, our finding ofsignificant R&D effect on FDI to industrial countries is consistent with the previous results,
such as Belderbos and Sleuwaegen (1996).
Second, the R&D has significantly positive effect on FDI into Asia as well. Previous
studies, such as Fukao et al. (1994), and Belderbos and Sleuwaegen (1996) based on Japanesefirm-level data in the 1980s, reported insignificant or negative effect of R&D on FDI into Asia,
but this paper finds, from the data in 1998, the significantly positive effect. Although Belderbos
and Sleuwaegen (1996) cited the relative absence of local competing firms in the region as oneof the factors for the insignificance, relative R&D strengths and labor costs in Japanese andother Asian firms/economies have drastically changed over the 1990s. Hence, now, not in the
1980s, FDI, not only into industrial countries, but also into Asia, requires active R&D forinvesting firms. However, we must note that the gap in the magnitude of the R&D effectbetween Asia and industrial countries still remains very large. We will discuss the comparison
between FDI destinations again later.
Third, other variables are also precisely estimated. The firm size is significantly positively
related with the extent of FDI both into industrial countries and into Asia. This size effectimplies the fixed sunk entry costs for FDI.24 Various factors, such as the internalization effectalready captured by other explanatory variables, may cloud the relation with physical capitalintensity. Firms with richer accumulation of human capital in the home country are more likely
to directly invest into industrial countries, not significantly into Asia.25 Thus, as expected,

24Some previous studies, such as Fukao et al. (1994), find the negative effect of firm size on FDI,
but their comparisons are within FDI firms.
25 Belderbos and Sleuwaegen (1996) find significantly positive effect of human capital intensity on
11intangible assets in technology and in human capital work similarly for FDI, particularly
strongly for FDI to industrial countries than to Asia in majority-owned FDI.
Finally, on the other hand, the significant contrast in R&D effects between industrial
countries and Asia is not found in minority-owned FDI. Since R&D-driven FDI tends to seekmajority ownership to internalize the gain from R&D, FDI with minority ownership tend to be
induced by factors omitted in our regressions, such as the proximity to markets, or low
production costs in the host country.

4.3. Alternative estimation results
Table 5 confirms that previous results are basically robust even if we consider the countdata nature of our FDI measure.26 Since the assumption of equality between mean and varianceis rejected, the negative binomial regression is employed in estimating (1) over the sample
consisting of only FDI firms. Since maximum likelihood calculation for negative binomial
regressions did not converge within reasonable numbers of iterations, we use the Poisson model,
which is another popular model for count data, for the larger sample including no-FDI firms.
This table concentrates on the comparison among FDI destination in majority-owned FDI. The
estimates reported in the table demonstrate that the R&D effect on FDI is again larger for FDI toindustrial countries than that to Asia. Even if firms with no FDI are included, the estimates for
R&D intensity and for human skills in FDI to industrial countries remain larger than that to Asia.
Consequently, measuring FDI by counts of subsidiaries does not affect our results.
Table 6 reports results from larger samples. By estimating the specification (2) instead of(1), we include no-R&D firms. As a result, the sample size increases to 95,143 firms, reported

FDI both to West and to Southeast Asia in the 1980s.
26 As in usual FDI statistics, the survey has no information on exits from FDI or closure of foreignsubsidiaries. Thus, all FDI figures are non-negative integers.
12

in the columns (1) and (2). Besides, by dropping explanatory variables other than output, we
cover all the firms in our data set: 118,300 firms, as reported in the columns (3) and (4). In
estimations, we consider that FDI cannot be negative. 27 Although the coefficient onln(1+R&D/Q) should not be directly compared with that on lnR&D/Q, larger effect of R&D on
FDI into industrial countries than that into Asia is again confirmed robust.

4.4. Regression of FDI shares
The regressions of relative FDI shares are reported in Table 7. The estimation results
show that the share of FDI into industrial countries in total FDI significantly increases with
R&D intensity of the firm both for FDI with majority ownership and with minority ownership.
On the other hand, the share of majority-owned FDI both in industrial countries and in Asia has
insignificant relationship with R&D intensity. Thus, R&D intensity tends to more strongly affectthe destination rather than ownership preference of FDI. Our survey, however, does notdistinguish FDI with full ownership in majority-owned FDI. Thus, our finding of insignificant R&D
effect on ownership may be partly affected by this mixed-up because the full ownership is supposed
to be the distinctively superior way of internalizing the gains from R&D.28

The same regression of FDI shares, however, also shows that human skill intensity
significantly raises the share of majority-owned FDI in total FDI to industrial countries. Sincetechnology or gains from R&D, compared with human managerial skills, are supposed to be

27

Since maximum likelihood calculations for count data models did not converge within reasonable
numbers of iterations, this paper employs Tobit model for the sample consisting of all firms.
Compared with OLS, Tobit takes account of the corner solution constraint. Since our sample is now
larger than 118 thousand, neglecting the integer constraint may not be serious as before.
28 Barbosa and Louri (2002) find that R&D significantly increases FDI with full ownership, while
the relation is insignificant for FDI with less-than-full majority ownership in Portugal. Although they
depend on the industry-level data, Kogut and Chang (1991) also report that the effect of domestic
R&D on FDI is significant for FDI into new plants, not for FDI in joint ventures in the case ofJapanese FDI into U.S.
13

less difficult to transact in arm’s length trade, then, our finding that R&D intensity is more
related with FDI destination and that human skill intensity is more related with FDI ownershippreference appears rather plausible. Since these regressions of relative shares record very low

R 2 due partly to large numbers of censored data in the sample, however, additional
investigations will be required before the final conclusion discriminating destination vs.
ownership effects.

5. Concluding remarks
This paper has investigated the relationship between R&D intensity and FDI by using firm-level
data of more than 118 thousand Japanese manufacturers. As expected, the effect of R&D on FDI
is stronger for FDI into industrial countries than FDI into Asia. Also consistent with the theoryof FDI, intangible assets such as human capital appear positively related with preference for the
majority ownership in FDI.
In spite of these findings, important tasks remain for future independent work. For
example, if the firm-level data of this survey is linked with other detailed FDI data, we will beable to control for the size of foreign subsidiaries and to further disaggregate regional
destinations of FDI. These developments will certainly enrich the results from this paper.

Acknowledgements
The Ministry of Public Management allowed me to have access to original micro-data files in thegovernment of Japan by No. 428 on July 22, 2002. Kazuyuki Motohashi and Junko Sakamoto of the
Ministry of Economy, Trade and Industry arranged this access. The research for this paper is partly
financed by the Grant-in-Aid for Scientific Research B No. ***and C No. 13630056. All remaining
errors are mine.

14

References
Asiedu, Elizabeth, and Esfahani, Hadi Salehi, (2001) “Ownership structure in foreign direct
investment projects,” Review of Economics and Statistics 83, 647-662.
Barbosa, Natalia, and Louri, Helen, (2002) “On the determinants of multinationals’ ownershippreferences: evidence from Greece and Portugal,” International Journal of Industrial
Organization 20, 493—515.
Belderbos, Rene, and Sleuwaegen, Leo, (1996) “Japanese firms and the decision to invest
abroad: business groups and regional core networks,” Review of Economics andStatistics 78, 214—220.
Caves, Richard, (1996) Multinational Enterprise and Economic Analysis, second edition,
Cambridge University Press.
Fukao, Kyoji, Izawa, Toshiya, Kuninori, Morio, and Nakakita, Toru, (1994) “R&D investment
and overseas production: an empirical analysis of Japan’s electric machinery industry
based on corporate data,” Bank of Japan Monetary and Economic Studies 12, 1-60.
Kogut, Bruce, and Chang, Sea Jin, (1991) “Technological capability and Japanese foreign direct
investment in the United States,” Review of Economics and Statistics 73, 401—413.
United Nations, Center on Transnational Corporations, (1992) The Determinants of Foreign
Direct Investment: A Survey of the Evidence, United Nations, New York.

15

Figure 1 R&D and FDI at the industry level
18

16

14

12

FDI

10

8

6

4

2

0
0

1

2

3

4

5

6

R&D/Sales(%)

Notes: The horizontal axis measures the percentage share of R&D expenditure in sales, while thevertical axis counts the number of foreign subsidiaries (majority-owned and minority-owned
combined, to all regions total). Each point corresponds to the average for each two-digit industry. All
manufacturing industries are plotted. All data are derived from the Basic Survey of Commercialand Manufacturing Structure and Activity 1998.

16

7

Table 1 Descriptive summary of FDI and R&D
Number of Firms

FDI>0

FDI=0

TOTAL

R&D>0

2,489

17,15019,639

R&D=0

1,292

97,36998,661

TOTAL

3,781

114,519

118,300

FDI (FDI>0)

R&D>0

R&D=0

R&D/No-R&D

World_Majority

4.09

1.52

2.68

Asia_Majority

2.53

1.31

1.94

World_Minority

2.42

1.52

1.59

Asia_Minority

2.13

1.48

1.43

FDI>0

FDI=0

TOTAL

R&D/Q (%)
(R&D>0)

2.60

2.73

2.71

Sales (Mil. ¥)

FDI>0

FDI=0

FDI/No-FDI

R&D>0

70,034

3,403

20.58

R&D=0

9,517

678

14.04

R&D/No-R&D

7.36

5.02

Av. Sales=2,629

Notes: The column or row R&D/No-R&D (FDI/No-FDI, respectively) calculates the ratio ofcorresponding value for firms with positive R&D (firms with positive FDI) over that for firms with
no R&D (firms with no FDI).

17

Table 2 Summary statistics of variables
NUMBER OF
OBSERVATIONSAVERAGESTANDARD
DEVIATION

FDI(World_Majority)

2,468

3.32

7.29

FDI
(Asia_Majority)

1,862

2.14

3.19

FDI
(World_Minority)

1,831

2.15

3.16

FDI
(Asia_Minority)

1,528

1.93

2.60

R&D/Q

19,314

2.71

15.35

Q

118,300

2,629

45,976

K/Q

96,515

0.43

3.46

HS

118,300

4.11

5.85

Notes: See text for definition of variables. FDI is counted by the number of foreign subsidiaries,
while other variables are originally measured in million yen.

18

Table 3 Correlation between variables
Firms with FDI and R&D both strictly positive (464 observations)
MajI

MajA

MinI

MinA

R&D/Q

Q

K/Q

HS

MajI

1.000

MajA

0.686

1.000

MinI

0.684

0.512

1.000

MinA

0.428

0.629

0.560

1.000

R&D/Q

0.278

0.286

0.231

0.243

1.000

Q

0.505

0.518

0.466

0.476

0.337

1.000

K/Q

0.012

−0.005

0.056

0.077

0.143

0.132

1.000

HS

0.231

0.232

0.140

0.179

0.173

0.417

−0.011

1.000

Q

K/Q

HS

Firms with no R&D or no FDI included (95,143 observations)
MajI

MajA

MinI

MinA

R&D/Q

MajI

1.000

MajA

0.666

1.000

MinI

0.569

0.463

1.000

MinA

0.429

0.554

0.478

1.000

R&D/Q

0.079

0.080

0.062

0.062

1.000

Q

0.171

0.216

0.156

0.187

0.059

1.000

K/Q

0.009

0.009

0.010

0.008

0.039

−0.072

1.000

HS
0.055
0.063
0.044
0.053
0.049
0.215
1.000
−0.046
Notes: In FDI, Maj (Min) represents majority ownership (minority ownership, respectively), and I(A) denotes industrial countries (Asia, respectively) as destination of FDI. FDI in four categories isin number of foreign subsidiaries, while other variables are in logarithm. In the table including
no-R&D firms, the logarithm is taken after adding one to R&D/Q.

19

Table 4 OLS results from restricted sample
(1)

(2)

(3)

(4)

Industrial_Maj

Asia_Maj

Industrial_Min

Asia_Minln R & D / Q

0.526(0.094)0.203(0.047)0.135(0.029)0.228(0.045)

ln Q

1.543
(0.154)0.944(0.095)0.242(0.036)0.512(0.055)ln K Q

0.249(0.150)0.085(0.102)0.023(0.036)0.047(0.066)

ln HS

0.590
(0.229)0.125(0.114)0.075(0.062)0.096(0.100)

R2
Number ofobservations

0.258

0.276

0.180

0.231

1,264

1,264

1,053

1,053

Notes: See text for definition of variables. This table covers only firms with R&D and FDI bothstrictly positive. Estimated heteroskedasticity-consistent standard errors are in parentheses. Industry
dummies are included.

20

Table 5 Results from regressions for count data
(1)
Industrial(FDI firms)

(2)
Asia
(FDI firms)(3)
Industrial(including
no-FDI firns)

(4)
Asia
(including
no-FDI firns)

ln R & D / Q

0.244(0.039)0.060(0.016)0.286(0.046)0.096(0.024)

ln Q

0.713
(0.027)0.329(0.016)0.838(0.026)0.780(0 .020)ln K Q

0.260(0.069)0.037(0.038)0.357(0 .062)0.211(0.046)

ln HS

0.268
(0.071)

0.017(0.037)0.245(0 .079)

0.134(0 .049)

Log likelihood

Log likelihood

Log likelihood

Log likelihood= −1749.886= −2178.601= − 5367.664= − 5496.162

Pseudo R 2

Pseudo R 2

Pseudo R 2

Pseudo R 2= 0.252= 0.162= 0.649= 0.535

1,264

1,264

18,536

18,536

Statistics

Number ofobservations

Notes: The dependent variable is FDI with majority ownership in all cases. The columns (1) and (2)are from negative binomial regression only for FDI firms, while the columns (3) and (4) are fromPoisson regression including no-FDI firms. Estimated heteroskedasticity-consistent standard errors
are in parentheses. Industry dummies are included.

21

Table 6 Results including no-R&D firms(1)
Industrial

(2)
Asia

(3)
Industrial

(4)
Asia

21.246(1.764)9.179(1.096)23.665(1.606)

10.245(0.987)

ln Q

3.974
(0.094)1.956(0.042)

4.257(0.097)2.025(0.042)ln K Q

1.397(0.143)0.516(0.057)

-----

-----

ln HS

1.791
(0.157)0.513(0.063)

-----

-----

ln (1 + R & D / Q )Log likelihood =Statistics

Number ofobservations

Log likelihood = Log likelihood =Log likelihood =−6181.382−8989.287−6306.346−9134.983

Pseudo R 2 =Pseudo R 2 =Pseudo R 2 =Pseudo R 2 =

0.370

0.285

0.374

0.297

Left-censored

Left-censored

Left-censored

Left-censoredobs. = 93,860

obs. = 93,285

obs. = 117,017

obs. = 116,438

95,143

95,143

118,300

118,300

Notes: The dependent variable is FDI with majority ownership in all cases. The sample covers all
firms. The equation is estimated by left-censored Tobit. Estimated standard errors are in parentheses.
Industry dummies are included.

22

Table 7 Regression of FDI share(1)
Ind _ Maj
World _ Maj(2)
Ind _ MinWorld _ Min(3)
Ind _ Maj
Ind _ All

(4)
Asia _ Maj
Asia _ All

5.482(1.111)7.570(2.633)1.377(1.865)−0.887(2.000)

ln Q

0.174
(0.018)0.185(0.036)0.058(0.030)0.0892(0.026)ln K Q

0.180(0.038)0.177(0.082)0.114(0.070)0.137(0.055)

ln HS

0.228
(0.046)0.046(0.089)0.235(0.084)−0.005(0.061)

Log likelihood

Log likelihood

Log likelihood

Log likelihood == − 2332.737= − 1419.626= − 1351.271− 2903.453

ln (1 + R & D / Q )Statistics

2

Number ofobservations

2

2

2

Pseudo R =Pseudo R =Pseudo R =Pseudo R =

0.099

0.050

0.021

0.016

Left-censored

Left-censored

Left-censored

Left-censoredobs. =1,181

obs. =1,271

obs. = 271

obs. = 979

Right-censored

Right-censored

Right-censored

Right-censoredobs.= 606

obs.=300

obs.= 999

obs.= 1,311

2,464

1,826

1,554

2,837

Notes: In the denominator, All represents all FDI to each region (both majority-owned andminority-owned FDI combined). The equation is estimated by Tobit with both sides censored.
Estimated standard errors are in parentheses. Industry dummies are included.

23