G-SEC WORKING PAPER No.10
Intra-firm Technology Transfer and R&D in
Foreign Affiliates: Substitutes or Complements?
Evidence from Japanese Multinational Firms
Rene Belderbos * , Banri Ito **, and Ryuhei Wakasugi ***November, 2006

Abstract
R&D in foreign affiliates and technology transferred from their parent firms are important
potential drivers of productivity in host countries. In this paper we examine the simultaneous
impact of local R&D and intra-firm international technology transfer on productivity growth inforeign affiliates. We estimate a dynamic productivity model on a large sample of Japanesemanufacturing affiliates worldwide in 1996-1997 and 1999-2000. We find that both affiliate
R&D and intra-firm technology transfer contribute to productivity growth, while technology
transfer exhibits decreasing marginal returns. The two sources of technology are complements:
use of one source of technology increases the marginal impact of the other.

Keywords: R&D, technology transfer, multinational firms
JEL codes: F23, O32, O33*

Katholieke Universiteit Leuven, Department of Managerial Economics, Strategy and Innovation;
Eindhoven University of Technology, Faculty of Technology Management**
Keio University, Graduate School, Department of Economics***
Keio University , Faculty of Economics,

Intra-firm Technology Transfer and R&D in Foreign Affiliates: Substitutes
or Complements? Evidence from Japanese Multinational Firms

Rene Belderbosa, Banri Itob, Ryuhei Wakasugic

Abstract
R&D in foreign affiliates and technology transferred from their parent firms are important potential
drivers of productivity in host countries. In this paper we examine the simultaneous impact of local
R&D and intra-firm international technology transfer on productivity growth in foreign affiliates. We
estimate a dynamic productivity model on a large sample of Japanese manufacturing affiliates
worldwide in 1996-1997 and 1999-2000. We find that both affiliate R&D and intra-firm technology
transfer contribute to productivity growth, while technology transfer exhibits decreasing marginal
returns. The two sources of technology are complements: use of one source of technology increases themarginal impact of the other.

Keywords: R&D, technology transfer, multinational firms
JEL codes: F23, O32, O33aKatholieke Universiteit Leuven, Faculty of Economics and Applied Economics, Naamsestraat 69, B-

3000 Leuven, BelgiumbKeio University, Graduate School of Economics, Mita 2-15-45, Minato-ku, Tokyo, 108-8345, Japanc

Keio University, Faculty of Economics, Mita 2-15-45, Minato-ku, Tokyo, 108-8345, Japan

Acknowledgements: This research is part of the research project on “Global Innovation Systems” of
the Global Security Research Institute (G-SEC) at Keio University. Part of this research was
conducted while Rene Belderbos was visiting G-SEC at Keio University. The authors thank thestatistics office of the Ministry of Economy, Trade and Industry for its permission to access microdata
on overseas affiliates.

1. Introduction

There is increasing interest both among researchers and policy makers in international
technology transfer and overseas R&D undertaken by multinational firms (e.g. Branstetter etal., 2006). The introduction of new and improved technologies through intra-firm international
knowledge transfer and the adaptation and augmentation of these technologies through local
R&D activities are essential for the competitiveness of foreign affiliates of multinational firms.
They are expected to positively impact the host country economy through increased
productivity and potential technology spillovers to local firms.
Although a large number of studies have examined the determinants of R&D inoverseas affiliates of multinational firms (e.g. Belderbos, 2001; 2003; Kuemmerle, 1999;
Zedtwitz and Gassman, 2002; Odagiri and Yasuda, 1996; Zejan, 1990; Kumar, 1996) as wellas those of international technology transfer (e.g. Grubert, 1998; Smith, 2001; Yang and
Maskus, 2000; Smarzynska, 2004; Wakasugi and Ito, 2005; Branstetter et al, 2006), few
studies have examined the economic impact of technology transfer and R&D on host country
operations. Recent studies of international R&D have instead focused on the impact ofoverseas R&D on the productivity of home country (R&D) activities (e.g. Iwasa and Odagiri,
2004; Griffith et al, 2003; Fors, 1996; Todo and Shimizutani, 2005) 1, while the impact ofinternational technology transfer through licensing has only been examined in the context of
local firms in developing countries (Basant and Fikkert, 1996; Braga and Wilmore, 1991;
Branstetter and Chen, 2006).
1

Fors (1996) and Todo and Shimizutani (2005) also found a positive impact of parent R&D on the productivityof overseas operations, but did not examine the impact of international technology transfers.

1

In this paper, we examine the simultaneous impact of local R&D and intra-firm
international technology transfer on productivity growth in foreign affiliates, as well as thepotential complementarity or substitutability between these two sources of technology.
Consideration of both sources is important as host countries’ tax and trade policies may bedirected to reduce technology imports with the purpose of stimulating local R&D. An earlier
study at the industry level by Hines (1995) found that higher withholding taxes wereassociated with lower technology royalty payments and higher levels of local R&D, whichsuggested a possible substitutability between technology adapted or created through local
R&D and technology created and transferred by the parent. On the other hand, one may expect
a complementary relationship to exist if local R&D enhances the ‘absorptive capacity’ (Cohenand Levinthal, 1989) of affiliates to effectively introduce new parent technologies. 2 The issueof possible substitutability or complementarity between technology imports and in-house
R&D has been the subject of empirical investigation, but previous studies have focused on theperformance effects for local firms in developing countries (e.g. Deolalikar and Evenson 1989;
Braga and Willmore 1991; Basant and Fikkert, 1996) in the context of restrictive technology
import policies. 3
This paper is the first comprehensive empirical study of the productivity effects of intrafirm international technology transfer and affiliates R&D. We derive our econometric2

Complementarity (see e.g. Milgrom and Roberts, 1990) is understood in this context to exist if theimplementation of one ‘practice’ (e.g. R&D) increases the marginal return to another ‘practice’ (e.g. technology
imports).
3

Basant and Fikkert (1996) found substitution between R&D and expenditures on imported licenses for Indian

firms, while earlier work by Deolikar and Evanson (1989) had suggested complementarity.

2

specification from an augmented Cobb Douglas production function including interactions
between technology transfer and affiliate R&D in the augmentation of the knowledge stock.
The model also takes into account potential productivity convergence by including lagged
productivity levels. We estimate the dynamic productivity model on a large sample ofJapanese manufacturing affiliates worldwide in 1996-1997 and 1999-2000.
This paper is organized as follows. In the next section we present the modelingframework. Section 3 describes the data set and variable construction. Section 4 presents theestimation results and we conclude in section 5.

2. A Model of Productivity Growth

We use an augmented Cobb Douglas framework to model the manufacturing activities
of foreign affiliates:

Yit = C ita Lβit K itγ eσ it(1)where Y is value added of affiliate firm i at time t, L is the labor input, C is the physical capitalstock and K is the knowledge stock. α , β and γ are elasticities with respect to physicalcapital, labor and the knowledge stock, respectively. The parameter σ is a time variant andaffiliate-specific efficiency parameter. Dividing both sides by labor, taking the log anddifferencing the resulting equation in the two consecutive periods, we obtain the equation inits growth form:
3

Δqit = ( β − 1)Δl it + αΔcit + γΔk it + Δσ it(2)where Δqit = log(Yit ) − log( Lit ) denotes the growth in labor productivity, with lower caseletters denoting variables in natural logarithms. In equation (2) fixed firm differences in
productivity are eliminated from Δσ it , but we assume that the change in firm-specificefficiency levels is a function of past productivity:

Δσ it = θqit −1 + λt + ε it(3)

where λt is a year-specific intercept and ε it is a serially uncorrelated error term. This
specification allows for gradual convergence in efficiency levels between firms, which has
been observed to be important in the empirical productivity literature (Klette, 1996; Blundell
and Bond 2000; Belderbos et al., 2006). 4 Firms that are behind the productivity frontier are
more likely to be able to record strong productivity growth by catching up with productivity
leaders. We expect θ to fall within the interval [-1,0]. If θ is zero there is no gradualconvergence between leading firms and lagging firms; if θ is –1 complete convergencematerializes in one period.

4

Klette (1996), for instance, shows that the empirically observed persistent productivity differences between

firms require a model specification that allows for gradual convergence.

4

We transform the knowledge stock portion of the specification (cf. Griffith et al., 2003,
p.7; Jones, 2002, p. 233; Fors, 1996) as follows: 5

γΔkit ≈∂Y K t −1 ΔK
ΔK it
≈ϕ
Yt −1
∂K Yt −1 K t −1with ϕ =

∂Y
∂K

(4)

The change in the knowledge capital stock is taken as a function of both technology transferfrom the parent firm ( I itlic−1 ) and R&D investments in the affiliate ( I itR−&1D ):ΔK it = f ( I itR−&1 D , I itlic−1 )(5)

We approximate the unknown function (5) with a second-order polynomial in R&D
investment and technology transfer. 6 If the depreciation rate of the knowledge stock is small 7

we can write:

γΔk it = ϕ[η1 I itR−&1 D + η 2 I itlic−1 + η 3 ( I itR−&1 D ) 2 + η 4 ( I itlic−1 ) 2 + η 5 ( I itR−&1 D )( I itlic−1 )] / Yit −1

5(6)This assumes that the ratio of the net investment in knowledge to the knowledge stock is small:Δk it = ln(1 + ΔK it / K it −1 ) ≈ ΔK it / K it −1 .
6

See Belderbos et al. (2006). This is similar to adopting a Generalized Leontief Linear functional form (e.g. as in

Basant and Fikkert, 1996).
7

Higher depreciation rates lead to an upward bias of the estimate on the rate of return (Mairesse and Sassenou,

1991). We could expand the approximation of changes in the knowledge stock by including more lags of R&D;
findings in previous studies, e.g. Hall et al. (1986) and Klette and Johanson (1998), suggest that the mostsignificant effect of R&D on productivity occurs with a one-year lag.

5

Hence the equation includes linear terms, quadratic terms, and the interaction term between
R&D and the transferred technology. Although in previous research the quadratic terms haveoften been suppressed (e.g. Basant and Fikkert, 1996), inclusion of the quadratic terms can beessential. If the process of augmentation of the knowledge capital stock is characterized bydecreasing returns to scale and if the most R&D intensive firms engage in both internal R&D
and technology imports, the interaction term between R&D and technology transfer may be
confounded as negative as it picks up the declining marginal impact of R&D or technology
transfer. A full specification with quadratic terms is required to explore this. In the empiricalanalysis, we will estimate the productivity effects of R&D and technology transfer using (6).
In order to show the importance of using a more general specification, we will also report theresults of models with quadratic terms suppressed.
Combining equations (2), (3), and (6) and bringing the lagged productivity term to theright hand side, we arrive at the dynamic equation:

qit = (1 + θ )qit −1 + ( β − 1)Δlit + αΔcit +ϕ[η1 I itR−&1 D + η 2 I itlic−1 + η 3 ( I itR−&1 D ) 2 + η 4 ( I itlic−1 ) 2 + η 5 ( I itR−&1 D )( I itlic−1 )] / Yit −1 + λt + ε it

(7)

3. Data, Variables, and Descriptive Statistics

The data on which we estimate the model concern Japanese overseas manufacturing
affiliates and are collected by the surveys of Overseas Business Activities conducted by the6Japanese Ministry of Economy, Trade and Industry. We could access the (three-yearly) Basic
Surveys of Overseas Business Activities in 1996 and 1999 and the shorter Trend Surveys ofOverseas Business Activities carried out in 1997 and 2000 (MITI, 1997, 2000). The data
represent the accounts for previous fiscal years ending in March. Since only the Basic Surveys
contain information on technology payments and fixed capital, the data do not allow for thecreation of a full panel data set. Instead, we match the basic survey data at the affiliate levelwith the trend survey in the following year to establish productivity, employment and capitalstock growth, while we pool over the years 1996-1997 and 1999-2000. Although the surveys
include a large number of manufacturing affiliates, affiliates frequently are not included in thesurveys of consecutive years. In addition, the questions on technology payments and R&D
suffer from low response rates. We cross-checked the reliability of the data by comparing
R&D and technology payment data with other entries such as the range of functional activitiesof the affiliate (which may include R&D), the number of reported R&D employees, andanswers to similar questions for the same affiliates in earlier or later years. This to ensure thata zero was not mistaken for a missing value - a distinction which is sometimes not properly
made in the surveys. As a result, we could draw on 1,798 observations on affiliates withaccurate information on the variables of interest. The dataset includes 920 affiliates in 19961997 and 878 affiliates in 1999-2000. The affiliates are located in 38 countries.
R&D is the affiliate’s expenditure of R&D as reported in the basic survey. Our proxy
for technology transfer is the value of licensing and royalty payments to the parent firm asreported by the affiliate. The reported value of these technology transfer payments may ofcourse be biased if firms engage in transfer pricing to minimize tax payments in the host7

country, but the reported payments will be highly correlated with the real value of technology
transferred to the affiliate. 8 We calculated value added as sales minus the value of
procurement of parts and materials. 9 The capital stock in the base year is the book value of
fixed tangible assets as reported in the basic surveys. For the following years, we calculate thecapital stock as the book value in the preceding year, fixed capital investments in thefollowing year, and depreciation, with the depreciation rate set at 0.0792 10. We expressed allvalues in 1999 prices by applying the GDP deflator reported in the World Development
Indicators and the Yen-local currency exchange rate as reported in the METI surveys.
In total, the sample includes 86 billion Yen in affiliate R&D spending and 151 billion
Yen in payments for technology transfer. The ratio of R&D to value added is on average 1.6
percent while the ratio of technology payments to value added is higher at 2.7 percent(summary statistics are presented in Table 1). The distribution of affiliates over industries andcountries is shown in Tables 2 and 3. The tables also show the average R&D to value added
ratio and the technology payments to value added ratio per country and industry. Table 2
shows a concentration of both R&D and technology transfer in specific industries, in particular

8

We explored the possible bias due to transfer pricing by allowing the effect of reported technology transfer todiffer systematically with the relative effective tax rate of the host country. We did not find evidence that the
productivity impact of technology transfer was smaller for higher tax countries, which one would expect if thetransfer price in the latter countries is systematically set higher.
9This allowed for a substantially more reliable estimate for the affiliates than in case of deriving value added as

the sum of wage costs, depreciation costs and net profits. In particular profits figures are severely under-reported
in the survey.
10

We took this figure from Masuda (2000) who arrives at this rate using a perpetual inventory method.

Deprecation costs are not well reported in the basic survey and sometime aggregate tangible and intangible assets.

8

in terms of the value of R&D and technology transfer. 11 R&D and intra-firm technology
payments are concentrated in chemicals and pharmaceuticals, general machinery, electricalmachinery and transport machinery. The highest R&D intensity is however reported in the
precision machinery industry. The ranking of technology transfer intensities is slightly
different, with electrical machinery reporting the highest intensity (4.1 percent) followed bychemicals and pharmaceuticals, general machinery, transport machinery and building
materials.
The distribution of R&D and technology payments over countries is heavily skewed, asconfirmed by earlier studies of Japanese overseas R&D (e.g. Belderbos, 2003; Todo andShimizutani, 2005, Iwasa and Odagiri, 2004). The United States is responsible for half of the
R&D expenditures of the firms in the sample, and about a third of the value of technology
transfers. R&D also takes place in Asian affiliates (China, Korea, Singapore) at a scalecomparable to affiliates in Europe, but the value of R&D relative to value added is much
lower in Asian affiliates, with the exception of Korea. Affiliates in France 12 have the highest
R&D intensity (7.2 percent) followed by US affiliates (3.3) and affiliates in smaller European
countries. In terms of the importance of technology payments, US and European affiliates
report intensities that are broadly similar to R&D. Asian affiliates show substantially higher
intensities relative to R&D at 3-4 percent, again with the exception of Korea.
The concentration of R&D and technology transfer in specific industries and countries
is partly a feature of the technology intensity of industries and countries, but also suggests that
11

A similar pattern is observed for R&D and technology imports in Taiwan (Branstetter and Chen, 2006).

12

The relative high average R&D to value added ratio of French affiliates is mostly due to a high ratio for one

specific affiliate.

9multinational firms jointly ‘adopt’ the practices of technology transfer and R&D in theiraffiliates, which is indicative of a complementary relationship. We investigate this issue in thenext section.

4. Empirical Results

The estimation results for equation (7) are presented in Table 4 (robust standard errors
are given in parenthesis). Column (1) presents the estimates from a specification restricting

η 2 = η 4 = 0 , hence excluding quadratic terms, and column (2) presents the results for the fullmodel. Both models include a set of 2-digit industry dummies, country dummies, and a year
dummy. The models explain more than 85 percent of the variation in productivity. The
estimated coefficients on the lagged dependent variable imply a convergence parameter θ of -0.28, suggesting that about a fourth of the productivity lead is neutralized by the next
period. 13 The growth of employment and capital stock variables are significant and imply anelasticity of 0.26 for labor and 0.12 for fixed capital. 14 Both models show that R&D andtechnology payments add to productivity growth. In model 1, the estimated rate of return on
R&D ( ϕη1 ) is 0.54 and the return on licensing ( ϕη 3 ) is higher at 0.76. This pattern confirms
earlier results and is partly explained by the more ‘ready to use’ character of technologies

13

Previous studies using GMM techniques (e.g. Blundell and Bond, 2000; Klette, 1996) find similar values forthe lagged productivity term in production function equations.
14

These elasticities compare well with the estimates for Taiwanese firms reported in Branstetter and Chen

(2006).

10

developed by the parent firm versus the more uncertain outcome of local R&D efforts. Theinteraction effect between R&D and technology payments has a positive sign but is not
significantly different from zero. When the full model in (2) is estimated including thequadratic terms of R&D and technology payments, this changes. The interaction effectbetween R&D and technology payments is now significantly positive, demonstrating theimportance to adopt a full specification of knowledge stock augmentation. The square term oftechnology payments is significantly negative and the square term of R&D is also negative but
insignificant. 15 These results suggest that international technology transfer and local R&D arecomplements: the marginal impact of technology transfer is greater if the affiliate also engages
in local R&D and vice versa. At the same time, there are decreasing returns to technology
transfer, although the decline in marginal impact only sets in at relatively high levels of thevalue of technology transfer (close to 1.2 billion Yen). Affiliates benefit more from adding
local R&D capability to technology transferred from the parent, rather than sole reliance on
the latter. Conversely, local R&D has a smaller impact if conducted without technology
transfer from the parent.

5. Conclusions

In this paper we examine the simultaneous impact of local R&D and intra-firm
international technology transfer on productivity growth in foreign affiliates and assess the15

The insignificant coefficient for the square term of R&D may be related to limited presence in the sample ofaffiliates with higher R&D intensities.

11

potential complementarity or substitutability between these two sources of technology. It is
the first comprehensive empirical study of the productivity effects of intra-firm international
technology transfer and affiliates R&D. We derive the econometric specification from anaugmented Cobb Douglas production function including interactions between technology
transfer and affiliate R&D in the augmentation of the knowledge stock. The model also takes
into account potential productivity convergence through a dynamic specification. We estimate
the model on a large sample of Japanese manufacturing affiliates worldwide in 1996-1997
and 1999-2000. The empirical results confirm that both affiliate R&D and intra-firm
technology transfer from the parent firm contribute to productivity growth, with technology
transfer exhibiting decreasing marginal returns. Furthermore, the two sources of technology
are complements: use of one source of technology increases the marginal impact of the other.
While the empirical results are in line with earlier studies that confirmed an
independent positive impact of R&D and technology imports (e.g. Branstetter and Chen, 2006,
Basant and Fikkert, 1996), the finding of complementarity contrasts with earlier inferences
drawing on the correlation between R&D and technology exports at the industry level (Hines,
1995) and studies of productivity growth in independent firms in India (e.g. Basant and
Fikkert, 1996). Multinational firms’ affiliates benefit more directly from the two sources oftechnology, as coordination between the parent and affiliate will allow local R&D to be
specifically governed to absorb, adapt and build on parent firm technologies. The implicationis that local R&D is less efficient and less likely to be performed on a large scale if affiliates
face restrictions on the use of parent-developed technologies. Host countries tax and trade
policies directed at reducing payments for technology imports are unlikely to serve as an

12effective tool to stimulate local R&D. They may instead reduce productivity growth withnegative consequences for potential spillovers to the local economy and economic growth.
Future research could examine this issue more closely and relate technology transfer, R&D,
and productivity growth more specifically to host country policies. A parallel line of further
research is to examine whether potential complementarity between technology imports andlocal technology development exists in local firms, using the testing framework proposed inthis paper.

13

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18

Table 1. Summary Statistics (1,798 observations)
Mean

Std. Dev

Description

Productivity

1.59

1.38

Growth ( Δ log) in value added per employee

Lagged productivity

1.47

1.41

Log value added per employee in previous year

Labor growth

0.03

0.37

Growth ( Δ log) in the number of employees

Capital stock growth

0.18

0.40

Growth ( Δ log) in the value of fixed tangible assets

R&D

0.016

0.062

Ratio of R&D expenditure over value added inprevious year

Technology transfer

0.027

0.068

Ratio of license and royalty payments to parent
firm over value added in previous year

Variable19

Table 2. Distribution of R&D and Technology payments over industries

Industry

Food and drinks
TextilesWood and pulp
Chemicals and pharmaceuticals
Oil
Building materials
Steel
Nonferrous metals
Metal
General machinery
Electrical machineryTransport machinery
Precision machinery
Other manufacturing
Total# R&D / value
affiliates
added (%)
65
133
22
194
14
58
58
46
36
166
517
296
52
141
1,798

1.0
0.4
0.8
3.2
1.4
0.8
0.4
0.4
0.3
1.0
1.6
1.8
4.8
1.0
1.6

20

Technology
Total R&D payments /(mln. Yen) value added
(%)
464
1,077
423
20,928
83
2,371
204
743
103
5,410
30,024
17,841
2,417
3,883
85,971

1.4
1.4
0.5
3.2
1.3
2.6
0.5
1.5
0.6
3.1
4.1
2.9
1.1
1.2
2.7

Total
technology
payments
414
1,281
350
16,601
59
2,997
196
928
107
14,852
66,57344,440315
2,363
151,476

Table 3. Distribution of R&D and technology payments over countries

Country

North America
United States
CanadaMexicoSouth America
BrazilOther countries
Asia
China
Hong Kong
Korea
ThailandIndonesia
Singapore
Other countries
Oceania
Australia
New Zealand
EuropeBelgium
FranceGermany
Italy
Netherlands
Spain
United Kingdom
Other countries
Total# affiliates

R&D / value
added (%)

Total R&D
(mln. Yen)

Technology
payments /value added
(%)

Total
technology
payments

452
27
32

3.3
0.8
0.7

43,991
189
178

2.4
0.6
3.0

49,303
784
1,709

44
12

1.0
0.0

511
2

1.1
1.6

480
495

311
77
107
191
124
145
5

0.6
0.4
2.0
0.3
0.2
0.6
0.3

2,960
339
2,883
705
96
3,095
5

3.1
3.0
1.3
3.8
4.2
3.1
0.6

11,192
11,986
1,528
12,918
8,617
21,290
27

23
10

0.3
0.5

1,426
31

1.0
0.1

6,931
9

18
28
39
14
19
24
77
19
1,798

2.1
7.2
1.4
2.1
2.4
2.2
1.8
2.8
1.6

2,303
4,367
3,889
2,253
9,526
1,350
2,914
2,958
85,971

2.3
3.5
1.8
2.0
1.3
1.9
1.8
5.9
2.7

1,363
4,200
4,619
1,150
2,242
1,354
5,273
4,006
151,47621

Table 4. Estimation results for equation (7): productivity growth in Japanese affiliates
Model 1Model 2

Labor growth ( β − 1)

0.740
[0.013]***-0.7410.744[0.013]***-0.741

Capital stock growth ( α )[0.035]***0.121[0.034]***0.123

R&D ( ϕη1 )[0.032]***0.543[0.032]***0.752[0.222]**[0.265]***-0.0003

Lagged productivity (1 + θ )R&D² ( ϕη 2 )Technology transfer ( ϕη 3 )0.763[0.190]***Technology transfer² ( ϕη 4 )R&D * Technology transfer ( ϕη 5 )Year, Country and Industry fixed effects
Constant

Observations
R²[0.00022]
0.949[0.205]***-0.0004

0.00071[0.00055][0.00019]**0.00129[0.00063]**

Yes

Yes

0.618
[0.047]***0.603[0.047]***

1.798
0.85

1.798
0.86

Notes: robust standard errors in parenthesis; *, **, *** indicate significance at the 10, 5, and 1 percent
levels, respectively.

22