Discussion Paper Series No.138

Firm-Level Relationship between
Technological Capability and Foreign Direct Investment

Eiichi Tomiura
July

Kobe University
2003

Discussion papers are a series of manuscripts in their draft form.
They are not intended for circulation or distribution except as
indicated by the author. For that reason Discussion Papers may
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Firm-Level Relationship between
Technological Capability and Foreign Direct Investment

Eiichi Tomiura*
Research Institute for Economics and Business Administration, Kobe University,
2-1 Rokko-dai, Nada-ku, Kobe City 657-8501 JAPAN.
4 July 2003

Abstract— This paper reexamines the relationship between FDI and R&D by exploiting firm-level
data for 118,300 Japanese manufacturers with no threshold. Our study confirms that the positive
association between FDI and R&D is robust even if firms undertaking no FDI and/or no R&D are
included. The inclusion of such firms, however, substantially attenuates the relationship. Higher
technological capability is positively related with more extensive FDI, especially FDI in industrial
countries by firms that have invested in Asia. Firms rich in human skills tend to prefer majority
ownership in FDI, as predicted by the internalization FDI theory.

JEL Classifications: F23; L22* 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 were
helpful in arranging this access. An earlier version of this paper was presented at the International
Conference on New Development in Asia Pacific Region: Economic Analysis using Micro Data at
Yokohama and RIEB Policy Research Workshop at Kobe University. The author acknowledges
valuable comments from participants, especially Rene Belderbos, Jong-Wha Lee, Fukunari Kimura,
Koji Tanabe, and Ryuhei Wakasugi. The research for this paper is partly supported by the
Grant-in-Aid for Scientific Research Nos. B14330012 and C13630056 from Ministry of Educationand Science. All remaining errors are mine.

1

I. Introduction
As described in Caves (1996), previous studies have repeatedly shown that the intensityof research and development (R&D) is positively related with foreign direct investment (FDI).1
Although accumulated studies have contributed immensely to our understanding of FDI, theyoften depend on FDI data, which limit our focus to the comparison of firms that have alreadyinvested in foreign countries.2 However, since the vast majority of firms have no foreign
affiliates at all, and since firms not investing abroad at all are on average as R&D active as firms
investing abroad, we need to investigate a comprehensive sample including firms with no FDI.
As the inclusion of such domestic firms may attenuate the impact of R&D on FDI, this paper
examines whether or not the sample selectivity significantly affects the robustness of thepositive association between FDI and technological capability.
This paper uses previously unavailable firm-level data, derived from a comprehensive
survey for more than 118 thousand Japanese manufacturers of all sizes, in all manufacturingindustries, includin g firms with no FDI and/or no R&D. Both R&D and FDI data areconsistently drawn from the same survey. The survey’s wide coverage and large sample size
ensure that it provides an accurate overall representation of manufacturing firms.
This paper not only reexamines the relationship between R&D and FDI using a newly
available firm-level data set, but also investigates two hypotheses inspired by the previous
studies. Firstly, FDI in Asia and FDI in industrial countries are compared. Previous studies
based on 1980s data, such as Belderbos and Sleuwaegen (1996), found that R&D of a Japanese
firm tends to have a positive effect on the firm’s FDI in industrial countries, but no significant

1

Both Caves (1996) and UNCTC (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.
2 As an exceptional previous study, Belderbos and Sleuwaegen (1996) include 65 no-FDI firms intheir analysis of the firm’s choice among domestic, FDI in West, and FDI in Asia.
2effect on FDI in Asia. Recent dramatic development of Asian economies induces us toreexamine this issue with more recent data because FDI in Asia may now require Japanese firms
to accumulate technological capability. Utilizing firm-level data, this paper also investigates
whether or not the relationship between R&D and FDI in industrial countries is affected by thefirm’s experience with FDI in Asia.
Secondly, inspired by recent empirical studies of FDI such as Asiedu and Esfahani (2001)
and Barbosa and Louri (2002), this paper examines the relationship between the R&D of a firm
and the firm’s FDI ownership structure preferences. The difficulty in defining and monitoring
the proprietary rights associated with intangible assets induces firms to prefer majority
ownership shares in FDI in order to securely internalize the gains from these costly-to-market
assets. Consequently, this paper examines whether or not the effect of R&D on FDI is stronger
for majority-owned FDI than for minority-owned FDI. Using firm-level characteristics included
in our dataset, this paper also evaluates the impact of management skills, which is anotherintangible asset as important as technology capital accumulated by R&D spending, on theownership structure of FDI.
The rest of this paper is organized as follows. Section II describes our data. Section III
explains empirical specifications and estimation methods. Section IV reports empirical results.
Section V concludes.

II. Description of Data
All the data used for this paper are derived from the firm-level data of the Basic Survey ofCommercial and Manufacturing Structure and Activity (Sho-Kogyo Jittai Kihon Chosa inJapanese).3 This survey, including firms of all sizes and in all manufacturing industries,

3

Although the data for commercial industries are also available in the same survey, this paper3contains a range of data on firm characteristics at 1998, such as sales, capital, R&D spending,and industry classification. 4 The survey captures FDI in the number of foreignsubsidiaries/affiliates for each firm, although no data is available for FDI size.5 Regarding
regional destinations of FDI, Asia is considered separately from the rest of the world. 6
Regarding ownership structure, the survey identifies foreign subsidiaries with majority
ownership (kogaisha in Japanese) and those with holding shares of no less than 20 percent but
no more than 50 percent (kanrengaisha in Japanese).7 A large number of firms with no FDI or
no R&D at all are included in our sample of 118,300 manufacturers. The survey’s large size andwide coverage ensures that it provides an accurate overall representation of manufacturing inJapan. The published aggregate data from this survey confirm the relatively strong positive
correlation between R&D intensity (R&D spending divided by sales) and FDI at theindustry-level. 8 Averaging over heterogeneous firms, however, inevitably contaminates
industry-level values.
Table 1 classifies firms by whether or not the firm is involved in R&D or FDI.9
Noteworthy findings from our micro-data are as follows. Here, we will refer to firms withstrictly positive FDI (R&D) as “FDI firms” (“R&D firms”) and to firms with zero FDI (R&D)
as “no-FDI firms” (“no-R&D firms”), respectively.10

focuses on manufacturing firms.
4
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.
5 Sales branch offices are not distinguished from manufacturing affiliates in the survey.
6 “Asia” in this survey includes not only ASEAN, but also countries such as China and India.
7 Foreign offices with ownership less than 20% are not recognized with destination disaggregation.
8 The correlation between the percentage R&D intensity and the industry’s share in total number offoreign subsidiaries is 0.731 among two-digit industries.
9 Since all the firms with no response to questions on R&D spending values and on FDI counts
explicitly answered that they conduct no R&D and no FDI at all in the binary question, we treat
them as zero. For physical capital and human capital, however, it is implausible to assume that firms
with no response to the question as firms with zero capital.
10 Although the survey is collected from parent firms, R&D figures may include R&D expenditures4

First, as shown in (A) of Table 1, 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 ismerely around three percent of all firms. 11 This implies that previous studies based on samples
solely from FDI firms do not replicate the whole manufacturing at all. On the other hand, nearly
17 percent of firms conduct R&D.12 The share of R&D firms is substantially higher among FDI
firms, compared with among no-FDI firms (66%>15%). By covering these no-FDI firms andno-R&D firms, our sample is suited for evaluating the impact of R&D on FDI decision in theuniverse of all firms.
Second, as reported in (B), R&D firms appear more active in FDI than no-R&D firms inall categories of FDI, irrespective of destination and ownership structure.13 Since most FDI by
Japanese firms go either into Asia, U.S., or E.U. regions, and since no further regional
disaggregation is available in the survey, le t us approximately interpret the rest of the world
other than Asia as industrial countries.14 The contrast between R&D firms and no-R&D firms isparticularly evident in FDI with majority ownership in industrial countries.
Third, however, among firms undertaking positive R&D, the average R&D intensity of
FDI firms is approximately the same as that of no-FDI firms, as demonstrated in (C). Thisobservation indicates that previous results based only on FDI firms may overestimate theby foreign subsidiaries. Although overseas R&D is practically limited in Japanese firms, thetheoretical possibility of including overseas R&D requires us not to interpret results as a causal
relation from R&D to FDI.
11 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.
12 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.
13 This kind of cross-aggregation is not released in the published aggregated statistics.
14 We can confirm this approximation by alternative data sources. For example, the recent figure
from FDI statistics by the Ministry of Finance shows that 87% of aggregate Japanese FDI into
non-Asian countries is either in U.S., Europe, or Oceania.
5magnitude of the effect of R&D on FDI. Consequently, this paper examines in the next section
whether or not this sample selectivity is significant by including no-R&D firms as well as
no-FDI firms into regressions.
Finally, as shown in (D) of Table 1, while the average R&D firm is five to seven 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 in terms of sales. This may suggest some forms of fixed sunk entry costs
associated with R&D and FDI, especially with FDI. This implied difference in fixed costs
between FDI and R&D is also consistent with our previous finding of the asymmetry that theshare of FDI firms is much lower than that of R&D firms in the total number of firms.

III. 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.

 R & Di K + β2 ln Qi + β3 ln i + β4 ln HS i + γDUM + u i . (1)FDI i = α + β1 ln  1 +
Qi 
Qi

The suffix i indexes the firm. FDI denotes foreign direct investment either in Asia or industrial
countries, either with majority ownership or minority ownership. The variables Q, K, HS, andR&D are the output (sales), the capital (tangible fixed asset), the human skill intensity(measured by per-capita overhead expenditure), and the R&D spending, respectively. 15 The
vector of industry dummy variables is expressed by DUM.16 The error term is denoted by u.
Adding one to R&D intensity before taking logarithm substantially increases the numbers of

15

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.
Data of advertising expenditure and business group affiliations are not available in the survey.
16 The two-digit classification is used, since including dummies for all three-digit industries
considerably loses the degree of freedom in regressions.
6

observations available for regressions, since more than eighty percent of firms in our sample
have zero R&D.17 Although this specification is flexible in incorporating a wide range ofcontrol variables, we should not interpret the results as suggesting that more active R&D causes
more extensive FDI because they are simultaneously determined. 18
According to the standard theory of FDI, as surveyed by Caves (1996) for example, a firm
invests directly in foreign countries if the firm has advantage, compared with local rival firms,
in intangible assets, such as technology or human capital. Unless a firm possesses these assets
costly to transact at arm’s length, the firm is not likely to establish their own subsidiaries inforeign countries.19 Taking account of the technology gap between developed and developing
countries, we expect that more active R&D is required for firms directly investing in industrial
countries, compared with Asia.20 Similarly, regarding ownership structure, FDI with majority
ownership, rather than FDI with minority ownership, is expected to more strongly relate with
R&D because the gains from intangible assets are not easily traded in markets.21 Since humancapital is also suited for internalization, the coefficient on HS is expected larger for FDI inindustrial countries than FDI in 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 in any region andwith any ownership structures because FDI incurs fixed sunk costs for establishing subsidiaries.

17

R&D is measured in flow (spending during the previous year), while FDI is in stock terms(number of foreign subsidiaries). To overcome this inconsistency, we will later use patent data.
18 Since we depend on a cross-section data as in many previous studies of FDI, finding appropriate
instrumental variables is practically difficult.
19 Host country factors are not considered here due to the limit of data availability. Kogut andChang (1991) examine the motivation to source advanced local technology at the aggregatedindustry level. Asiedu and Esfahani (2001) consider local costly-to-market inputs into FDI projects.
20 If the FDI competition among various industrial countries is intense, this difference depending on
destination is not obvious. Since no data on American or European firms investing in Asia isavailable in our survey of Japanese firms, however, this aspect is omitted from this paper.
21 Asiedu and Esfahani (2001) and Barbosa and Louri (2002) focused on the effect on the ownershipchoice of individual FDI project, based on affiliate data. This paper investigates the effect on parent
firm’s FDI extent in respective ownership structure, based on a survey of parent firms including
no-FDI firms. These two approaches should be viewed as complementary.
7The capital-output ratio is supposed to act as an inverse proxy for richness in intangible assets.22
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 FDI and R&D both strictly positive. This can be served as the benchmark of our analysis,comparable with previous studies. Second, taking account that our FDI measure is a limited
dependent variable, this paper estimates the equation not only by OLS, but also by Poisson,
negative binomial regressions, or Tobit. 23 Third, this paper replaces R&D spending by patentdata. Although evaluating technology stock is very difficult, the flow of R&D expenditureduring the previous year is far from the perfect measure of the firm’s technological capability.
Consequently, this paper estimates the following:

PAT 
K
FDI = α + β1 ln 1 + + β2 ln Q + β3 ln + β4 ln HS + γDUM + u . (2)K QThe number of patents owned by the firm is denoted by PAT. 24 This specification allows us torelate FDI stock with the firm’s technological capability also in stock terms.
Finally, this paper investigates whether or not the relation between R&D and FDI
decisions is affected by the firm’s experience with FDI in other regions.25 Firms that havealready invested in industrial countries may not need additional R&D spending at the decision22

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.
23 Although it is 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 in West and FDI in Asia by 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) use the negative binomial
regression for Japanese FDI counts into U.S. industries.
24 Since the survey contains no other patent data, we cannot adjust differences in values ordepreciations across patents. To control for the firm size consistently in stock term, PAT isdivided by K, but our principal results are robust even if the patent-sales ratio is used instead.
25

This comparison is inspired by the analysis of the firm’s choice among investing only in Asia,
only in West, or both by Belderbos and Sleuwaegen (1996).
8

of FDI in Asia because those firms must have already acquired high technological capability
before investing in industrial countries. On the other hand, firms that have invested in Asia mayneed additionally active R&D when they decide to invest in industrial countries because thetechnological capability competing with rival firms in advanced countries is the final criticalfactor for those firms, which have satisfied other necessary requirements for overseas operations.
Consequently, we expect that the coefficient on R&D intensity in the regression is insignificant
or very small for FDI in Asia by firms that have invested in industrial countries, and relatively
large for FDI in industrial countrie s by firms that have invested in Asia.
Our regressions have so far compared coefficients estimated from separate regressionsof different measures of FDI. However, it remains to be known whether the destination of FDI(to industrial countries than Asia) or the ownership structure (majority than minority) relatively
dictates the results. Therefore, to discriminate the destination effect from the ownership effect,
this paper replaces the dependent variable of absolute level of FDI by the relative share as
follows.26

 R&D
FDI _ Ind _ Maj
K= α + β1 ln 1 + + β2 ln Q + β3 ln + β4 ln HS + γDUM + u
FDI _ World _ Maj
Q 
Q

(3)
This equation is estimated by Tobit because no share can be beyond zero and one. The positive
β1 in (3) indicates that the destination effect is stronger than the ownership effect. Similar
regressions are also conducted for the share of industrial countries in all minority-owned FDI,
the share of majority-owned FDI in all FDI to industrial countrie s, and the share ofmajority-owned FDI in all FDI to Asia. These regression results will distinguish whether
destination or ownership of FDI is more strongly affected by R&D intensity.

26

The simultaneity problem is less serious in the specification (3), which is on the relative shares,
compared with previous specifications (1) or (2).
9

IV. Estimation Results
Summary Statistics
This section reports empiric al results from our sample. Before discussing regression results,
descriptive statistics of variables used for estimation is informative. Table 2 clearly shows
remarkable variations across firms. On average, each FDI firm has two or three foreignsubsidiaries, while each R&D firm spends around three percent of their sales on R&D.27
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 (0.47 to0.52), also clearly positive with R&D intensity (0.24 to 0.35) and lower but also positive withhuman skills (0.14 to 0.23). However, if all firms with no R&D or no FDI are included, FDI
becomes much less correlated with R&D and other variables. The correlation of FDI is now atmost 0.2 with sales and far less than 0.1 with other variables. This contrast suggest theimportance of including firms with no FDI or no R&D in investigating whether or not theestimates obtained only from FDI firms are biased.

OLS Results from the Restricted Sample
Table 4 reports the OLS results for the specification (1) from the sample restricting tofirms with FDI and R&D both strictly positive. Although it is larger than those used in previous
studies, the sample size is limited to around one thousand firms. The main findings from thisbenchmark case are as follows.
First, for majority-owned FDI, the effect of R&D on the extent of FDI in industrial
countries is substantially larger than FDI in Asia. This finding is consistent with previous results,
27

The published aggregated statistics from the same survey report similar average FDI and slightly
higher R&D intensity.
10

including Belderbos and Sleuwaegen (1996) and Fukao et al. (1994).
Second, the effect of R&D is found statistically significant on FDI in Asia at 1998,
contrary to the previous results from 1980s data by Belderbos and Sleuwaegen (1996) andFukao et al. (1994). Although Belderbos and Sleuwaegen (1996) cited the relative absence of
local competing firms in the region in discussing their results, this paper confirms that relative
R&D strengths in Japanese and Asian firms have drastically changed in the last decade.
However, we must note that the gap in the magnitude of the R&D coefficient between Asia andindustrial countries still remains remarkably 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 in industrial countries and Asia, suggesting fixed sunk entry
costs for FDI. The physical capital intensity is found insignificant in this case. Firms with richer
accumulation of human capital are more likely to directly invest significantly in industrial
countries with majority ownership. Thus, as expected, intangible assets in technology and in
human capital work similarly for FDI.
Finally, to the contrary, the significant contrast in the effects of R&D 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 is likely to
be induced by factors omitted in our regressions, such as the proximity to markets, or low
production costs in the host country.

Alternative Estimation Results
This section reports regression results with alternative methods or data, focusing on thecomparison within majority-owned FDI. First, Table 5 reports results from larger samples

11

including no-FDI firms as well as no-R&D firms. As a result, the sample size increases to95,143 firms, reported in the columns (1) and (2). Besides, by dropping explanatory variables
other than R&D and output, we cover exactly all the firms in our data set: 118,300 firms in thecolumns (3) and (4). In estimations, we consider that FDI cannot be negative.28 Substantiallylarger effect of R&D on FDI in industrial countries than that in Asia is confirmed robust.
In addition to the confirmation of our main finding, the comparison of regression results
reported in Tables 4 and 5 reveals the following interesting points. First of all, although it
remains consistently larger than that on FDI in Asia, the impact of R&D on FDI in industrial
countries is found to be less remarkable if no-FDI or no-R&D firms are included. The
coefficient on R&D intensity is estimated to be as small as half of the OLS estimate from therestricted sample. This indicates that results dependent on restricted samples composed only of
FDI firms may considerably overestimate the effect of R&D on FDI. Our estimates from thecomprehensive sample indicate that a ten-percent rise in R&D expenditure relative to sales,
evaluated at the mean, results in the opening of approximately merely one (1.498) new
majority-owned foreign affiliate in an industrial country, as opposed to approximately three(3.056) affiliates as previously estimated from the restricted sample.29 Such an overestimation
may not be enormous, but should not be negligible in discussing the impacts of various policies
in the real world. For example, R&D subsidy is supposed to enhance international
competitiveness of individual firms, but its effect on overseas business operations of the country
as a whole will be limited if large numbers of no-FDI firms are considered.

28

Since the survey has no information on exits from FDI or closure of foreign subsidiaries, all FDI
figures are non-negative. Since maximum likelihood calculations for count data models did notconverge within reasonable numbers of iterations, this paper employs Tobit model, which takes
account of the corner solution constraint. Since our sample is now larger than 118 thousand,neglecting the integer constraint may not be serious. We will discuss regressions for count data later.
29 This calculation is based on the estimated coefficient reported in the column (1) in Tables 4 and 5
and the average shown in Table 2 for the R&D-sales ratio.
12

Other noteworthy findings from the comparison across different samples are as follows.
First, the capital-output ratio, which was insignificant in OLS on the restricted sample, becomes
precisely estimated in larger samples. Second, the impact of managerial skills on FDI in Asia
gains statistical significance if no-FDI or no-R&D firms are included. 30 This second change isplausible because the comparison within FDI firms ignores that those firms tend to be
noticeably richer in human capital than domestic firms.
This paper further checks the robustness of our results as follows. First, since ourmeasure of FDI is the number of foreign subsidiaries, the results from regressions appropriate
for count data are shown in Table 6. 31 This table confirms that the impacts of R&D and of
human skills on FDI in industrial countries remain substantially larger than FDI in Asia.
Consequently, the integer constraint does not affect our main findings.
Second, this paper uses patent data to check the robustness of our results based on R&D
flow data. As reported in Table 7, we again find larger effect of technological capabilit y on FDIin industrial countries than FDI in Asia, both from the restricted sample composed only of thefirms with FDI and PAT both strictly positive and from the whole sample including no-FDI
firms and no-patent firms. Furthermore, the inclusion of domestic and no-patent firms decreases
the coefficient estimate on technological capability to around the half of that from the restricted
sample. Therefore, the use of R&D expenditure data, as employed in most previous studies,
does not affect our principal results.

30

Belderbos and Sleuwaegen (1996) find significantly positive effect of human capital intensity on
FDI both in West and Southeast Asia in the 1980s.
31 Since the assumption of equality between mean and variance is rejected, the negative binomial
regression is employed over the restricted sample. 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. Since maximum likelihood calculation for count data regressions did not converge inlarger samples including no-R&D firms, we use the left-censored Tobit model, as were reported
previously.
13

Impact of FDI experience in other regions
Table 8 presents the regression results investigating the effect of the firm’s FDI
experience in other regions. Previous regressions have ignored whether or not the same firm
simultaneously invested in other regions, but accumulated experiences in global business arelikely to affect a firm’s FDI decisions. The most notable finding in Table 8 is that R&D intensity
is statistically insignificant in determining the extent of FDI in Asia for firms that own theiraffiliates in industrial countries.32 This result is exactly as expected because the presence of
these firms in industrial countries proves their already high technological capability. In contrast,
firms that have their affiliates in Asia tend to require remarkably active R&D in order to makethe move to FDI in industrial countries. Somewhat surprisingly, the magnitude of the effect of
R&D on FDI in industrial countries by firms that have affiliates in Asia is substantially larger
than even that by firms with no overseas affiliates in Asia at all. This finding, however, isplausible because, in deciding on FDI in an industrial country, their technological capability islikely to be the final critical determinant for firms that already have experienced with foreign
affiliate operations, but may be just one of many important factors for firms that have no FDI
experience. Combined with our previous findings, this additional evidence indicates that thepositive association between R&D and FDI is not universal across all firms, but rather
concentrated on the extent of FDI in industrial countries by firms that have invested in Asia.
Although we should be cautious in interpreting a cross-section result, our use of comprehensive
firm-level data reveals that the effect of R&D on FDI is heterogeneous depending on the firm’s
FDI experience.

32

The sample in this case consists of all firms with at least one affiliate in industrial countries. As aresult, firms with no affiliates in Asia at all are included. Therefore, these firms are different fromthe “global firms” investing in both regions, as defined by Belderbos and Sleuwaegen (1996).
14

Regression of FDI Shares
The results from the regressions of relative FDI shares are reported in Table 9. The shareof FDI to industrial countries significantly increases with R&D intensity of the firm, while theshare of majority-owned FDI has insignificant relationship. Thus, R&D tends to more strongly
affect the destination rather than ownership preference of FDI. Since our survey, however, does
not distinguish FDI with full ownership in majority-owned FDI, this 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.33
The same regression of FDI shares also shows that human skill intensity significantly
raises the share of majority-owned FDI in total FDI to industrial countries. Since technology or
gains from R&D, compared with human managerial skills, are supposed to be less difficult totransact 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 ownership appears rather
plausible. Since these regressions record very low R 2 due partly to a large number of censoreddata, however, additional investigations will be required before the final conclusion
discriminating destination vs. ownership effects.

V. Concluding Remarks
This paper has investigated the relationship between technological capability and FDI, using
firm-level data for 118,300 Japanese manufacturers. The effect of R&D on FDI is confirmed
sizably stronger for FDI in industrial countries than FDI in Asia. However, the inclusion of
33

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 R&D on
FDI is significant for FDI into new plants, not for FDI in joint ventures by Japanese FDI into U.S.
15

no-FDI and/or no-R&D firms reduces the coefficient estimate on R&D intensity approximatelyby half in the case of majority-owned FDI in industrial countries. Therefore, the positive
relationship between R&D and FDI is non-negligibly overestimated if those large numbers offirms are neglected. This finding has important implications for discussing various policies in
the real world, such as the impact of R&D incentive measures on globalization of firms.
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 FDI and to further disaggregate regional destinations of FDI.
These developments will certainly enrich our findings.

REFERENCES
Asiedu, Elizabeth, and Hadi Salehi Esfahani, “Ownership Structure in Foreign Direct
Investment Projects,” Review of Economics and Statistics 83 (2001), 647-662.
Barbosa, Natalia, and Helen Louri, “On the Determinants of Multinationals’ Ownership
Preferences: Evidence from Greece and Portugal,” International Journal of Industrial
Organization 20 (2002), 493—515.
Belderbos, Rene, and Leo Sleuwaegen, “Japanese Firms and the Decision to Invest Abroad:
Business Groups and Regional Core Networks,” Review of Economics and Statistics 78(1996), 214—220.
Caves, Richard, Multinational Enterprise and Economic Analysis, second edition, (Cambridge:
Cambridge University Press, 1996).
Fukao, Kyoji, Toshiya Izawa, Morio Kuninori, and Toru Nakakita, “R&D Investment andOverseas Production: An Empirical Analysis of Japan’s Electric Machinery Industry
Based on Corporate Data,” Bank of Japan Monetary and Economic Studies 12 (1994),
1—60.
Kogut, Bruce, and Sea Jin Chang, “Technological Capability and Japanese Foreign Direct
Investment in the United States,” Review of Economics and Statistics 73 (1991),
401—413.
United Nations Center on Transnational Corporations (UNCTC), The Determinants of Foreign
Direct Investment: A Survey of the Evidence, (New York: United Nations Publications,
1992).

16

TABLE 1. — DESCRIPTIVE SUMMARY OF FDI AND R&D(A) Number of Firms
FDI>0

FDI=0

TOTAL

R&D>0

2,489

17,15019,639

R&D=0
TOTAL

1,292
3,781

97,369114,519

98,661
118,300(FDI>0)World_Majority

R&D>0
4.09

R&D=0
1.52

R&D/No-R&D
2.68

Asia_Majority

2.53

1.31

1.94

World_Minority
Asia_Minority

2.42
2.13

1.52
1.48

1.59
1.43

(R&D>0)
(%)

FDI>0
2.60

FDI=0
2.73

TOTAL
2.71

(Mil. ¥)
R&D>0

FDI>0
70,034

FDI=0
3,403

FDI/No-FDI
20.58

R&D=0

9,517

678

14.04

R&D/No-R&D

7.36

5.02

(Av. Sales=2,629)(B) FDI(C) R&D intensity(D) Sales

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 VARIABLESNUMBER OF
OBSERVATIONSAVERAGESTANDARD
DEVIATION

FDI(World_Majority)

2,468

3.32

7.29

FDI
(Asia_Majority)
FDI(World_Minority)

1,862

2.14

3.19

1,831

2.15

3.16

FDI
(Asia_Minority)

1,528

1.93

2.60

R&D/Q (%)
Q

19,314
118,300

2.71
2,629

15.35
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

0.349

0.336

0.249

0.239

1.000

Q

0.505

0.518

0.466

0.476

0.358

1.000

K/Q

0.012

−0.005

0.056

0.077

0.086

0.132

1.000

HS

0.231

0.232

0.140

0.179

0.214

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
−0.046
1.000
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. For R&D/Q, the logarithm
is taken after adding one.

19

TABLE 4. — OLS RESULTS FROM THE RESTRICTED SAMPLE
(1)

(2)

(3)

(4)

Industrial_Maj

Asia_Maj

Industrial_Min

Asia_Minln( 1 + R & D / Q )43.343(12.530)

9.763(4.331)8.559(2.816)

12.678(3.699)

ln Q

1.486
(0.134)0.185(0.157)

0.943(0.092)0.107(0.102)0.235(0.035)0.031(0.036)0.505(0.053)0.066(0.065)

ln HS

0.472
(0.198)0.122(0.112)0.077(0.059)0.106(0.095)

R2

0.273
1,266

0.276
1,266

0.186
1,056

0.232
1,056

ln K Q

Number ofobservations

Notes: This table covers only firms with R&D and FDI both strictly positive. Estimatedheteroskedasticity-consistent standard errors are in parentheses. Industry dummies are included.

20

TABLE 5. — RESULTS FROM LARGER SAMPLESln (1 + R & D / Q )ln Q

ln K Qln HS

Statistics

Number ofobservations(1)
Industrial

(2)
Asia

(3)
Industrial

(4)
Asia

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

10.245(0.987)3.974(0.094)1.397(0.143)1.956(0.042)0.516(0.057)4.257(0.097)
-----

2.025
(0.042)
-----

1.791
(0.157)0.513(0.063)

-----

-----

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

22

2

Pseudo R =0.370

Pseudo R =0.285

Pseudo R =0.374

Pseudo R 2 =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. Industry dummies are included.

21

TABLE 6. — REGRESSIONS FOR COUNT DATA

ln (1 + R & D / Q )ln Q

ln K Qln HS

Statistics(1)
Industrial(FDI firms)

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

(4)
Asia
(including
no-FDI firms)

7.395(1.582)0.731(0.905)3.505(0.343)−1.155(1.149)0.731(0.027)0.290(0.070)0.336(0.016)0.056(0.038)0.877(0.025)0.402(0.059)0.803(0.021)
0.247(0.046)0.273(0.073)0.031(0.038)0.298(0.077)0.183(0.048)Log likelihood

Log likelihood

Log likelihood

Log likelihood= −1758.842= −2186.070= −5462.011= −5530.298

2

2

2

Pseudo R 2= 0.537

Pseudo R= 0.249

Number ofobservations

1,266

Pseudo R= 0.160

1,266

Pseudo R= 0.646

19,101

19,101

Notes: The columns (1) and (2) are from negative binomial regression only for FDI firms, while thecolumns (3) and (4) are from Poisson regression including no-FDI firms. In all four cases, no-R&D
firms are excluded. Industry dummies are included.

22

TABLE 7. — REGRESSIONS WITH PATENT DATA

ln (1 + PAT K )ln Q
ln K Qln HS

Statistics

Number ofobservations(1)
Industrial

(2)
Asia

(3)
Industrial

(4)
Asia

(FDI and PAT(FDI and PAT(All firms)(All firms)

both positive)

both positive)

13.563(4.635)6.600(2.265)7.904(1.383)3.347(0.751)1.973(0.215)0.712(0.253)1.144(0.130)0.298(0.162)

4.052(0.096)1.585(0.146)1.987(0.042)0.565(0.058)0.762(0.330)0.169(0.163)1.892(0.158)0.541(0.063)Log likelihood

Log likelihood= − 6214.596= − 9002.170

2R =0. 266974

2

R =0. 285

974

2

Pseudo R 2= 0. 284

Left-censored

Left-censoredobs. = 93,860

obs. = 93,285

95,143

95,143Pseudo R= 0. 366

Notes: The columns (1) and (2) are from OLS regressions over the firms with FDI and PAT bothstrictly positive, while the columns (3) and (4) are left-censored Tobit results from all firms. Industry
dummies are included.

23

TABLE 8. — IMPACT OF FDI IN THE OTHER REGION

ln (1 + R & D / Q )ln Q
ln K Qln HS

Statistics

Number ofobservations(1)
Ind. FDI(Firms with
Asia FDI)(2)
Ind. FDI(Firms with No
Asia FDI)(3)
Asia FDI(Firms withInd. FDI)(4)
Asia FDI(Firms with No
Ind. FDI)

56.229(9.581)

10.212(1.229)5.945(5.639)3.551(1.124)3.732(0.176)0.953(0.357)1.997(0.083)0.821(0.102)2.226(0.110)0.094(0.225)1.040(0.035)0.294(0.039)1.647(0.434)0.874(0.110)0.121(0.289)0.209(0.043)Log likelihood =Log likelihood =Log likelihood =Log likelihood =−2602.048−3271.671−2283.674−6311.211

22

2

Pseudo R =0.183

Pseudo R =0.289

Pseudo R =0.121

Pseudo R 2 =0.189

Left-censored

Left-censored

Left-censored

Left-censoredobs. = 1,181

obs. = 92,679

obs. = 606

obs. = 92,679

1,858

93,285

1,283

93,860Notes: The dependent variable in the column (1) is majority-owned FDI in industrial countries by
the firms that have invested in Asia, while (2) is from the firms that have not invested in Asia. The
column (3) is from firms that have invested in industrial countries, while (4) is from firms that havenot invested in industrial countries. The model is estimated by left-censored Tobit in all cases.
Industry dummies are included.

24

TABLE 9. — REGRESSION OF FDI SHAREln (1 + R & D / Q )ln Q
ln K Qln HS

Statistics(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)0.174(0.018)0.180(0.038)0.185(0.036)0.177(0.082)0.058(0.030)0.114(0.070)0.0892(0.026)0.137(0.055)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

2

Number ofobservations2

2

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

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.
Industry dummies are included.

25