21COE, University of Tokyo

MMRC Discussion Paper No. 44

MMRC
DISCUSSION PAPER SERIES

MMRC-F-44

Reconsidering the Effects of Intranational andInternational R&D Spillovers:
Firm-level Evidence from Japan
Faculty of Business Administration, Yokohama National
University
andManufacturing Management Research Center, the University of
Tokyo
July 2005

July 2005

Reconsidering the Eﬀects of Intranational and International R&D
Spillovers: Firm-level Evidence from Japan∗
Kozo Kiyota†
Faculty of Business Administration, Yokohama National University
andManufacturing Management Research Center, the University of Tokyo
Abstract
In 2000, 71.8 percent of Japanese ﬁrms do not invest in Research andDevelopment (R&D). Using ﬁrm-level longitudinal data in Japan, thispaper asks why many ﬁrms can achieve high productivity growth without
any R&D investments. The results indicate that, in addition to the ﬁrm’s
own R&D activities, international R&D spillovers through foreign direct
investment and machinery imports have signiﬁcantly positive eﬀects on
productivity growth. The eﬀects of intranational R&D spillovers are
generally positive but not signiﬁcant. My results thus imply that the
majority of ﬁrms do without R&D investment because of international
R&D spillover eﬀects. (96 words)
Key words: Productivity, R&D Spillovers, Foreign Direct Investment, International Trade, Patents
JEL classification code: F10 (International Trade, General); F23 (Multinational Firms; International Business); O3 (Technological Change; Researchand Development)∗This research is a part of the project “A study on structural changes in machine industries in Japan
based on the current trends in major production items,” March 2005. The database used in this paper
was prepared and analyzed in cooperation with the Economic Research Institute of Japan Society for the
Promotion of Machine Industry (JSPMI), Japan Center for Economic Research (JCER), and the Researchand Statistics Department, the Ministry of Economy, Trade, and Industry (METI), the Government ofJapan. I wish to thank Fukunari Kimura, Hiroyuki Odagiri, Robert Stern, Eiichi Tomiura, and Ryuhei
Wakasugi for helpful comments and Judith Jackson for editorial assistance. The analysis and conclusion
are those of the author and do not necessarily reﬂect those of Yokohama National University, JSPMI,
JCER, the METI, or the Government of Japan.
†
Contacts: Kozo Kiyota, Faculty of Business Administration, Yokohama National University, 79-4,
Tokiwadai, Hodogaya-ku, Yokohama 240-8501, Japan; Phone/Fax: +81-45-339-3770; E-mail: kiyota “at”
ynu “dot” ac “dot” jp.

1

Introduction

Research and development (R&D) is regarded as a key factor of productivity growth.
However, most ﬁrms do not conduct R&D investment even in developed countries. Figure
1 indicates the share of ﬁrms that invest in R&D (hereafter, R&D ﬁrms) and ﬁrms that donot (hereafter, non-R&D ﬁrms) in Japan. In 2000, 71.2 percent of ﬁrms are non-R&D ﬁrms.
More than half are non-R&D ﬁrms in manufacturing while 87.8 percent are non-R&D ﬁrms
in non-manufacturing. Why are so many ﬁrms doing without R&D investments?
=== Figure 1 ===One possible reason is technology diﬀusion, or R&D spillovers. Indeed, the link between R&D spillovers and productivity growth is central to many questions in endogenous
growth theory.1 If the imitation and implementation of new technology are cheaper thaninnovation, and if new technology made in leading countries spill over, follower countries
could rapidly catch up to the leaders without their own R&D investment. The same is trueat the ﬁrm-level. That is, R&D spillovers make it possible for follower ﬁrms to catch up
to leading ﬁrms without conducting R&D. An analysis of R&D spillovers at the ﬁrm-levelshould contribute in clarifying the micro-foundation of technology diﬀusion, accordingly.
Several empirical studies have investigated the eﬀects of international as well as intranational R&D spillovers, whose approach is divided into two groups.2 The ﬁrst group focuseson national-level spillovers. There are three types of national-level studies. One investigates
the R&D spillovers through international trade. Coe, Helpman, and Hoﬀmaister (1997)
conﬁrmed the positive eﬀects of R&D spillovers on total factor productivity (TFP) growththrough machinery and equipment trade. Similarly, Lee (1995) found positive impacts of
R&D spillovers through capital goods trade on per-capita income. The underlying argument is that the large variety of intermediate products and capital equipment embodies
foreign knowledge. Hence, the imports of these products enable countries to boost theirproductivity growth.3
1

For analysis technology diﬀusion theory, see Grossman and Helpman (1991) and Barro and Sala-iMartin (2004, Chapter 8).
2
See Barba Navaretti and Venables (2004, Chapters 7 and 9) and Keller (2004), for an extensive surveyof the related literature.
3
Coe and Helpman (1995) also examined the eﬀects of R&D spillovers through international trade using
overall imports rather than machinery and equipment imports as a weight of explanatory variable and foundpositive eﬀects of international R&D spillovers through imports on productivity growth. However, Keller(1998) later questioned the results of Coe and Helpman, showing that the international spillover eﬀects on
productivity growth using randomly generated trade shares can lead to similar or even higher eﬀects thanthose using actual trade shares. On the other hand, Kiyota (2005) has conﬁrmed the importance of R&D
spillovers through machinery trade, showing that the R&D spillovers embodied in Japanese merchandise
exports amounted to 27.0 billion US dollars in 1995, 84.3 percent of which was channeled through machinery
exports.

1

Second type investigates the spillovers through foreign direct investment (FDI). Pottelsberghe de la Potterie and Lichtenberg (2001) analyzed the eﬀects of FDI on productivity
growth of home and host countries. Based on the data for 13 OECD countries from 1971
to 1990, they found that FDI to R&D intensive countries contributed to the productivity
growth of home countries. However, they could not ﬁnd any positive eﬀects on the growthof host countries.
Third type examines international patenting. Eaton and Kortum (1999) estimated therelationship among research employment, productivity levels, and international patenting
for ﬁve leading research countries: France, Germany, Japan, the United Kingdom, and theUnited States. They have found that research performed abroad is about two-thirds aspotent as domestic research. Furthermore, at least two-thirds of the growth in each of ﬁve
countries was driven by the United States and Japan together.
The second group is ﬁrm-level (or establishment-level), which mainly focused on thespillovers related to patents and FDI. For example, Branstetter (2001) examined the impacts of intranational and international R&D spillovers on the patent activities of Japaneseand U.S. ﬁrms. His results indicated that the intranational spillovers were more important
than international spillovers. In his regressions, the estimated coeﬃcients of intranational
R&D spillover variables had signiﬁcantly positive signs while the international spillover
variables had either negative or insigniﬁcant signs.
Recent ﬁrm-level studies by Aitken and Harrison (1999), Haskel, Pereira, and Slaughter(2002), Keller and Yeaple (2004), and Javorcik (2004) have focused on the role of FDI.4
Although Aitken and Harrison (1999) and Haskel, Pereira, and Slaughter (2002) did not ﬁndany evidence to support the spillovers from foreign-owned ﬁrms to domestic ﬁrms, Keller
and Yeaple (2004) and Javorcik (2004) conﬁrmed that FDI led to substantial productivity
gains for domestic ﬁrms.5 Keller and Yeaple also examined the eﬀects of international tradeas well as FDI. They conﬁrmed that the positive impacts of FDI-related R&D spillovers
lasted far longer than those of import-related R&D spillovers. They concluded that importrelated R&D spillovers accelerated productivity growth, but the eﬀects of import-related
R&D spillovers were weaker than those of FDI.
This paper examines the eﬀects of intranational and international R&D spillovers on
productivity growth at the ﬁrm level and builds upon the previous research just noted. The
contribution of this study is twofold. First, this paper examines spillovers through FDI,
imports, and patents, at the same time. Previous studies recognize these three channels as
the primary channels for international R&D spillovers but they are examined diﬀerently.
My results suggest that, in addition to the ﬁrm’s own R&D activities, international R&D
spillovers through FDI have positive eﬀects on productivity growth in both manufacturing
and non-manufacturing industries. Besides, R&D spillovers through imports have signiﬁcantly positive eﬀects in manufacturing. On the other hand, intranational R&D spillovers
4Görg and Strobl (2001) provided extensive survey on spillovers through FDI.
Keller and Yeaple (2004) pointed out several reasons why their results are diﬀerent from those ofprevious studies and found that their results could be attributed to improved measurement of foreign
multinational activity. On the other hand, Javorcik (2004) found that R&D spillovers were originatedfrom shared domestic and foreign ownership but not from fully owned foreign aﬃliates.
52

have positive eﬀects, but they are not signiﬁcant.
Second, this study provides a detailed robustness check and identiﬁes reasons why theexisting literature presents diﬀerent results. I examine the eﬀects of endogeneity, time lag,the eﬀects on longer-term growth, measurement issues, omitted variable bias, and limited
coverage of the data. The robust results are obtained in the R&D spillovers through tradeand ﬁrm-level FDI except when the coverage of the data is narrow. The industry-level
spillovers through FDI are sensitive to its measurement, and the coeﬃcients of patentvariables are sensitive to the omission of variables. The results of the analysis based on thelimited sample could be diﬀerent from the results obtained from the broader sample.
The analysis is based on the conﬁdential ﬁrm-level data that are collected by theJapanese Ministry of Economy, Trade, and Industry (METI). In addition to its high reliability, these data have three advantages. First, ﬁrm-level data make it possible to measureﬁrm productivity more correctly. Firm-level data can capture not only the activity of production plants but also that of headquarters, sales branches, and research institutions thatbelong to the same ﬁrm. Since non-manufacturing establishments are not covered in theplant-level manufacturing census, the ﬁrm-level data are more appropriate than plant-level
data to capture the overall activities of the ﬁrm. Second, the large sample can provide morereliable econometric analysis, insofar as the data cover more than 22,000 ﬁrms annually.
Finally, the data can provide an overall picture of Japanese ﬁrms, covering manufacturing
and a large part of non-manufacturing sectors. The broad coverage of the data enables me
to examine why my results are diﬀerent from those of previous studies.
In what follows, Section 2 presents the model. Section 3 discusses the data and themeasurement issues of productivity and R&D spillovers. Presentation of econometric results follows in Section 4. Section 5 examines the robustness of the results. I conclude in
Section 6 with a summary of the major ﬁndings and a discussion of their potential policy
implications.

2

Baseline ModelI begin with setting up a simple model of productivity convergence, following Bernard andJones (1996). The strength of this approach is that the speciﬁcation does not depend on
the form of production function, which is summarized as follows.
Denote TFP for a ﬁrm i in year t by θit . The TFP growth is assumed to be describedas:ln θit = γi + λ {ln θ1t−1 − ln θit−1 } + ln θit−1 + ln it ,(1)

where ln θ1t−1 − ln θit−1 represents a catch-up variable, which represents the distance inproductivity between the most productivity ﬁrm, denoted by 1, and ﬁrm i. The speed ofcatching-up thefore is captured by λ while the asymptotic rate of productivity growth ofﬁrm i is denoted by γi . Finally, ln it represents disturbance term.
This framework yields the following catch-up path of productivity:ln θ̂it = (γi − γ1 ) + (1 − λ) ln θ̂it−1 + ln ˆit ,3

where θ̂it = θit /θ1t and ˆit = it /1t , respectively. In the long-run, the annual average TFP
growth rate of ﬁrm i relative to ﬁrm 1 between year 0 and year T is written as:T1
1 − (1 − λ)T1(ln θ̂iT − ln θ̂i0 ) = −ln θ̂i0 +(1 − λ)T −τ (γi − γ1 + ln ˆiτ ).
TTT τ =1This is the basis of familiar convergence model of long-run average growth on initial level
and I specify a baseline model as follows.
∆ ln θ̂iT =1(ln θ̂iT − ln θ̂i0 ) = β0 + β1 ln θ̂i0 + µiT ,T(2)

where catch-up is denoted by a negative coeﬃcient of β1 = −{1 − (1 − λ)T }/T . I assumeµiT ∼ N(0, σ).
Several models have been proposed to explain intranational and international R&D
spillovers, but there is no consensus on the existence of the spillovers. Following Keller andYeaple (2004), I take a broad view on how R&D spillovers might aﬀect the productivity ofﬁrms. That is, instead of modeling a particular mechanism of R&D spillovers, my approach
is simply to ask whether ﬁrms can achieve higher productivity growth when there are R&D
spillovers, adding spillover eﬀects to equation (1) as control variables:∆ ln θ̂iT = β0 + β1 ln θ̂i0 + σfi0 + ωgi0 + φhi0 + µiT ,(3)

where fi0 , gi0 , and hi0 represent ﬁrm i’s own R&D activity, intranational, and the international R&D spillover variables, respectively.

3

Data, Measurement, and Empirical Speciﬁcation

3.1

Data

I use the micro database of Kigyou Katsudou Kihon Chousa Houkokusho (The Results of
the Basic Survey of Japanese Business Structure and Activities) prepared by the Researchand Statistics Department, Ministry of Economy, Trade and Industry (METI) (1996-2002).
This survey was ﬁrst conducted in 1991, again in 1994, and annually afterwards. The main
purpose of the survey is to capture statistically the overall picture of Japanese corporateﬁrms in light of their activity diversiﬁcation, globalization, and strategies on R&D andinformation technology.
The strength of the survey is its sample coverage and reliability of information. The
survey is comprised of all ﬁrms with more than 50 employees and with capital of more than
30 million yen, covering both manufacturing and non-manufacturing ﬁrms, although someindustries such as ﬁnance, insurance, and software services are not included. Industry
is available at a 3-digit level. The list of industries is presented in Appendix Table 1.
The limitation of the survey is the lack of some information on ﬁnancial and institutional4

features such as keiretsu. Some information on ﬁnance, location, and intermediate inputs
is not available, either.
From this survey, I have developed a longitudinal (panel) data set for the years from1994 to 2000. I drop the ﬁrms from my sample set for which the age data (the year of thesurvey minus the year of establishment), total wages, tangible assets, value-added (sales
minus purchases), or the number of workers are not positive and in cases with incomplete
replies. The number of ﬁrms exceeds 22,000 for each year.6

3.2

Measurement of TFP

To make comparisons of productivity across ﬁrms and time-series, I employ the multilateralindex method in computing TFP developed by Caves, Christensen, and Diewert (1982) andextended by Good, Nadiri, Roller, and Sickles (1983).7 The advantage of multilateral index
is that I do not assume any speciﬁc production function, which is in line with the baselinemodel described in Section 2.
This multilateral index uses a hypothetical ﬁrm that has the arithmetic mean values oflog output, log input, and input cost shares over ﬁrms in each year. Each ﬁrm’s output
and inputs are measured relative to this hypothetical ﬁrm. The hypothetical ﬁrms arechain-linked over time. Hence, the index measures the TFP of each ﬁrm in year t relative
to that of the hypothetical ﬁrm in year 0 (initial year).
Speciﬁcally, the TFP index for ﬁrm i in year t is deﬁned as:ln θit ≈

t

 


ln Qit − ln Qt +ln Qτ − ln Qτ −1
τ =1

−
+

J




1
(sijt + s̄jt ) ln Xijt − ln X jt
2
j=1

t 
J

1

2
τ =1 j=1(s̄jτ + s̄jτ −1 ) ln X jτ − ln X jτ −1 ,(4)where ln Qit , ln Xijt , and sijt are the log output, log input of factor j, and the cost shareof factor j for ﬁrm i, respectively. ln Qt , ln X jt , and s̄jt are the same variables for the
hypothetical ﬁrm in year t and are equal to the arithmetic mean of the corresponding
variable over all ﬁrms in year t.
6

In the questionnaire, the deﬁnitions of “imports” (and “exports”) are slightly changed after 1997. The
sales and purchases of aﬃliates abroad are included before 1996 while they are excluded after 1997. The
average ratio of the latter value to the former value between 1997 and 1999 is 0.621 for imports (and0.658 for exports). I use the product of trade values and these shares before 1996 so that trade values are
consistent throughout the period.
7
There is an alternative method that is based on the econometric estimation of production functions,which is proposed by Olley and Pakes (1996) and extended by Levinsohn and Petrin (2003). However, thisframework has to specify a production function, which is not consistent with our baseline model. Moreover,because of the limited availability of intermediate inputs, their method was not feasible. Therefore, I employ
a multilateral index method in the present study.

5

The ﬁrst term of the ﬁrst line indicates the deviation of the ﬁrm i’s output from theoutput of the hypothetical ﬁrm in year t. The second term means the cumulative change
in the output of the hypothetical ﬁrm from year 0 to year t. The same operations areapplied to each input j in the second and the third lines, weighted by the average of thecost shares. Outputs are deﬁned as value-added while inputs are capital and labor. As
for other additional data and their manipulation, I adopt the methodology described in
Nishimura, Nakajima, and Kiyota (2005).

3.3

Measurement of Intranational and International R&D Spillovers

Several channels of intranational and international R&D spillovers are examined, controlling the ﬁrm’s own R&D activity and observable ﬁrm speciﬁc characteristics.
Firm’s own R&D investment
The ﬁrm’s own R&D investment, R&DitO , is measured the by R&D expenditure-sales ratio:
R&DitO =

R&Dit
,
P Qit

where R&Dit represents ﬁrm i’s R&D expenditure and P Qit represents ﬁrm i’s sales. The
use of R&D ﬂows, however, could be a problem if investments in R&D take some time tobear fruit. I will discuss this matter later in Section 5.
Intranational R&D spillovers
To capture the eﬀects of intranational R&D spillovers, I employ two variables. One isindustry-level R&D that captures the domestic R&D spillovers within a given industry,8and this is deﬁned as the sum of R&D expenditures divided by the sum of sales in thesame industry:R&Dktk=i,k ∈M /,R&DitD =k∈N P Qkt
where M and N represent a set of foreign-owned ﬁrms and all ﬁrms in the same industry,
respectively. The ﬁrm’s own R&D expenditure and the foreign-owned ﬁrm’s R&D activity
are excluded from intranational R&D spillovers and are included as international R&D
spillovers. A foreign-owned ﬁrm is deﬁned as a ﬁrm where more than 50 percent of theequity is foreign-owned (majority-owned ﬁrms).

8

Regional spillover is also important (Aitken, Hanson, and Harrison, 1997; Keller, 2002). However,
the regional R&D spillovers are important at the plant or establishment level rather than ﬁrm level, andthe headquarters do not have to locate near production sites. In fact, the headquarters of ﬁrms in Japan
concentrate in Tokyo. Besides, the information on location is not available in my data set because of
conﬁdentiality. Hence, the focuses here are on industry lines of R&D spillovers.

6

The other is ﬁrm i’s payments to domestic patents, P atentD
it . The interpretation ofpatent payments is fairly straightforward because the use of patents means the direct purchase of technology through market transaction. Indeed, several studies such as Branstetter(2000) focused on the role of patents as a channel of technology diﬀusion. I measure patentvariable as patent payments scaled by sales.
International R&D spillovers
The eﬀects of international R&D spillovers are captured by machinery imports, the patent
payments to foreign ﬁrms, and FDI. Machinery imports, IMPit , are ﬁrm i’s imports ofmachinery products scaled by sales. Since ﬁrm-level machinery imports are not available
in the survey, I ﬁrst calculate the share of machinery inputs by each industry using input
coeﬃcients from the Japanese Input-Output table,9 and then multiply the ﬁrm’s imports bythis share. Firm i’s payments to foreign patents, P atentFit , represents the patent payments
to foreign ﬁrms divided by sales.
Two FDI variables are used. One is to capture the ﬁrm-level eﬀect and the other is tocapture the industry-level eﬀect. The ﬁrm-level eﬀect is captured by the degree of foreign
ownership, or foreign equity share, F DIitf irm . As for industry-level eﬀect, various measures
are used in the previous studies. While the previous studies mainly used the employment offoreign-owned ﬁrms as the activity of foreign-owned ﬁrms in a given industry (e.g., Aitken
and Harrison, 1999; Keller and Yeaple, 2004; Javorcik, 2004),10 this paper directly tries tocapture the R&D activity of foreign-owned ﬁrms. Speciﬁcally, the R&D activity of foreignowned ﬁrms is measured by the R&D expenditures of foreign-owned ﬁrms divided by thesum of sales in the same industry:

R&Dkt
.
F DIitind = k∈M
k∈N P Qkt
I also include ﬁrm characteristics, Zit , to control for observable ﬁrm speciﬁc factors andindividual ﬁrm eﬀect, ηi , to control for unobservable ﬁrm heterogeneity. In sum, the baselinemodel between year t and t + 1 is speciﬁed as follows.
∆ ln θ̂it+1 = β0 + β1 ln θ̂it + σR&DitO + ω1 R&DitD + ω2 P atentDit+ψ1 IMPit + ψ2 P atentFit + ψ3 F DIitf irm + ψ4 F DIitind+κZit + ηi + µit+1(5)

I use the number of workers, ﬁrm age, and capital-labor ratio as ﬁrm characteristics, Zit . In
the estimation, I use θit instead of θ̂it for simplicity. This does not cause any problems, since
it simply shifts β0 . Machinery imports are used only for manufacturing ﬁrms because theeﬀects of R&D spillovers through machinery imports are limited to manufacturing sectors.
910

Ministry of Internal Aﬀairs and Communication (2004).
Alternative measures of FDI are examined in the Appendix.

7

4

Results

4.1

Basic Facts

Before going into the dynamic productivity growth analysis, let me review the data andthe static diﬀerence of ﬁrm performance between R&D ﬁrms and non-R&D ﬁrms. Previous
studies have examined the relationship between industry and R&D. But R&D ﬁrms are infact quite spread out across industries, especially in the manufacturing sectors.
Figure 2 illustrates the distribution of industries by the shares of R&D ﬁrms. Eachof the 117 3-digit industries is placed in one of ten bins according to the percentage ofR&D ﬁrms in that industry. The fraction of R&D ﬁrms that lies between 10 and 50
percent is 47 percent in all industry, 55.9 percent in manufacturing, and 37.9 percent in
non-manufacturing, respectively.11 Therefore, it is diﬃcult to know to which industry R&D
ﬁrms belong, especially for manufacturing industries.
=== Figure 2 ===Table 1 presents the R&D premium for TFP and labor productivity.12 The premiummeans the productivity gap between R&D ﬁrms and non-R&D ﬁrms in the same industry
and the same year. The beneﬁt of R&D is clear in non-manufacturing but not in manufacturing ﬁrms. In the manufacturing sector, though the coeﬃcients of TFP and laborproductivity of R&D ﬁrms show positive signs, they are not statistically signiﬁcant. On theother hand, in the non-manufacturing sector, I observe a signiﬁcant diﬀerence in productivity between R&D ﬁrms and non-R&D ﬁrms. Both TFP and labor productivity indicate
positive and signiﬁcant signs. The productivity gaps between R&D ﬁrms and non-R&D
ﬁrms are about 2 percent.
=== Table 1 ===Table 2 presents the trends of TFP and R&D variables. The diﬀerence between manufacturing and non-manufacturing is clear, especially for R&D variables. The ﬁrm’s own
R&D in manufacturing is eight times as much as that in non-manufacturing. Similarly,
all R&D spillover variables in manufacturing are larger than those in non-manufacturing.
This means that R&D activity in manufacturing is diﬀerent from R&D activity in nonmanufacturing. Therefore, in the following section, I conduct separate regressions for manufacturing and non-manufacturing.
=== Table 2 ===11

The fractions of industries that are equal to zero for R&D ﬁrms are 0 percent in manufacturing and24 percent in non-manufacturing, respectively.
12
R&D premium is computed using the same framework as the export premium in Bernard and Jensen(1999). The premium is the coeﬃcients on an R&D ﬁrm dummy in a regression of the form: ln Zit =α0 + α1 Yit + it , where Z is productivity (either TFP or labor productivity), Y is R&D ﬁrm statusthat takes value one if the ﬁrm is an R&D ﬁrm, and  is an error term. Year and industry dummies arealso included. A ﬁxed-eﬀects model is employed for the estimation (based on the results of the Hausman
speciﬁcation test).

8

4.2

Eﬀects of R&D Spillovers on Productivity Growth

Table 3 shows the results of equation (4) estimated by a maximum likelihood random-eﬀectmodel. I employ random-eﬀect rather than ﬁxed-eﬀect because, in my baseline model, ﬁxedeﬀect implies that each ﬁrm has a diﬀerent asymptotic rate of productivity growth (i.e.,
γi = γ1 ) even if they belong to the same industry. This makes it diﬃcult to interpret theresults in the context of productivity convergence. Hence I assume that asymptotic rate of
TFP growth is the same within the narrowly deﬁned (3-digit level) industries and include
industry dummies instead of ﬁxed-eﬀect. The basic indicators of variables are summarizedin Appendix Table 2.
=== Table 3 ===Table 3 presents two types of results. One includes ﬁrm characteristics and the other
does not. Adding ﬁrm characteristics improves both Akaike Information Criterion (AIC)and Schwarz (Bayesian) Information Criterion (BIC), suggesting that the part of productivity growth is explained by ﬁrm characteristics.
Three ﬁndings are evident in this table. First, the ﬁrm’s own R&D activity has signiﬁcantly positive eﬀects on productivity growth in both manufacturing and non-manufacturing.
The coeﬃcients of a ﬁrm’s own R&D activity indicate positive and signiﬁcant signs. Second,
I conﬁrm the positive eﬀects of intranational R&D spillovers only in the non-manufacturingsector. In manufacturing, both industry R&D and patents do not have any signiﬁcanteﬀects on productivity growth, as the coeﬃcients of intranational R&D spillovers are positive but not signiﬁcant. In non-manufacturing, domestic patents contribute to productivity
growth. The coeﬃcients of patent payments to domestic ﬁrms show positive and signiﬁcantsigns.
Third, international R&D spillovers generally have positive eﬀects on productivity
growth. Among the spillover variables, trade and FDI are particularly important. The
R&D spillovers through machinery imports have positive eﬀects in manufacturing. Besides, the impacts of ﬁrm-level R&D spillovers through FDI, or foreign ownership, havepositive and signiﬁcant eﬀects in non-manufacturing as well as in manufacturing.13 However, industry-level R&D spillovers through FDI exist only in manufacturing. Foreign
patents do not have signiﬁcant eﬀects on productivity growth in manufacturing and nonmanufacturing.
I also apply instrumental variable (IV) methods to remove the possible eﬀects of endogeneity. I use lagged spillover variables and all other exogenous variables as instruments,
treating all spillover variables as endogenous variables. Because of the use of lagged variable, I lose 14,475 ﬁrms for manufacturing and 13,834 ﬁrms for non-manufacturing. The IV
estimation results presented in Table 1 suggest that the eﬀects of endogeneity do not cause
serious problems. The results are relatively robust, especially for manufacturing ﬁrms. The
next section examines the robustness of these results in more detail.
13

The positive eﬀects of foreign ownership on productivity growth are also conﬁrmed in other studies.
See, for instance, Kimura and Kiyota (2005).

9

5

Alternative Speciﬁcations: Comparisons of Resultswith Other Studies

The detailed results of a robustness check are reported in the Appendix below. The main
conclusions are summarized as follows. First, the ﬁrm’s own R&D and international R&D
spillovers, especially ﬁrm-level spillovers through trade (machinery imports) and FDI (foreign ownership), are signiﬁcant channels of productivity growth both in manufacturing
and non-manufacturing sectors. I conﬁrm that these results are robust. On the other
hand, intranational R&D spillovers do not present robust results on productivity growthin general.
Second, the measurement aﬀects the results of industry-level R&D spillovers. While thispaper captures R&D spillovers through FDI by foreign ﬁrms’ R&D expenditures, previous
studies used the employment of foreign ﬁrms. When I follow the measures employed in previous studies, the industry-level R&D spillover variables through FDI no longer show positive eﬀects. In the manufacturing sector, my measurement presented in Section 4 performs
better than the measurement used in previous studies. In the non-manufacturing sector,
however, the measurement of previous studies performs better than the measurement usedin my study. Since the previous studies used employment to capture the eﬀects of FDI, myresults might indicate that the spillover channels are diﬀerent between manufacturing andnon-manufacturing. While R&D activity is an important channel in manufacturing sector,
employment is more important than R&D activity as a channel of the spillovers.
Finally, the coeﬃcients of patent variables are sensitive to omitted variables. The
coeﬃcients of foreign patents become signiﬁcantly positive once I drop some of the otherinternational spillover variables. However, the baseline model in Section 4 performs better
than the models without some variables. This indicates that to control various channels is
important in analyzing spillover eﬀects. This result also suggests that it matters a great
deal how FDI is measured and which variables are included in inferring the eﬀects of R&D
spillovers.
The results represented above conﬁrm that international R&D spillovers are importantchannels compared with intranational R&D spillovers. Although my results partly support
the results of Keller and Yeaple (2004), they contradict the ﬁndings of other previous
studies such as Branstetter (2001), Aitken and Harrison (1999), and Haskel, Pereira, andSlaughter (2002). I will discuss why my results are diﬀerent from those of other studies.
There are a couple of things to be noticed. First, as Keller and Yeaple (2004) claimed,
the measurement of FDI matters a great deal. As conﬁrmed in the Appendix, the coefﬁcients of industry-level FDI are aﬀected by measurement. Indeed, if I follow their measurement, my results become consistent with the ﬁndings of previous studies.
Second, the quality and coverage of data are also important. My data cover all manufacturing and relatively small ﬁrms. Studies that focus on some speciﬁc industries and/or
large ﬁrms may cause a sort of sample selection bias. For instance, if high-tech ﬁrms face
more foreign exposure, the analysis that selects high-tech sectors does not fully capture
the relative importance of high-tech sectors in the economy. Such analysis can reveal the10relative importance of international spillovers within high-tech industries but not acrossindustries. To check the eﬀect of sample selection, I ran regressions for high-tech industries
according to the diﬀerent levels of employment scale. High-tech industries are deﬁned as
chemicals, machinery, electronics, transportation, and precision instruments manufacturingindustries, following Branstetter (2001). Large ﬁrms are deﬁned by the size of employment,
and I examine the ﬁrms with more than 300 workers, 500 workers, 1000 workers, and 2000
workers.
Table 4 presents the regression results for high-tech sectors. The larger the ﬁrms chosen,
the more the eﬀects of intranational R&D spillovers appear, which is consistent with theﬁndings of Branstetter (2001). The coeﬃcients of domestic patents are signiﬁcant for ﬁrms
with more than 1,000 workers and 2,000 workers. On the other hand, the eﬀects of a ﬁrm’s
own R&D activity and international R&D spillovers weaken. Since most large ﬁrms in
high-tech sectors face more foreign exposure than ﬁrms in other sectors, regression analysis
that is based only on these ﬁrms might mask the beneﬁts of international R&D spillovers.
My results strongly suggest that the discussion based on the analysis with limited samples
cannot be generalized, and the broad coverage of samples is important.
=== Table 4 ===Third, country speciﬁc factors also play important roles in international R&D spillovers.
For instance, in Japan, the amount of inward FDI is still extremely small compared withother OECD countries. The share of inward FDI stock positions compared to GDP in
1999 is only 1.4 percent, which is far below the OECD average.14 The small amount of
FDI might have positive eﬀects at the ﬁrm-level but not enough to aﬀect results at theindustry-level. Similarly, the eﬀects of import-related R&D spillovers could be small in
the countries with lower trade intensities. The relative scales of trade and FDI could be
important factors to get the beneﬁt of international R&D spillovers.
Finally, the speed of intranational R&D spillovers could be faster than international
spillovers. As Branstetter (2001) pointed out, the time required for new innovation tospill over is short.15 If the intranational technology diﬀusion occurred within one year, thestudies based on annual data might not fully capture such eﬀects.

6Concluding Remarks

The majority of ﬁrms apparently do without R&D investment because of international R&D
spillover eﬀects. In the present study, ﬁrm-level longitudinal data from 1994 to 2000 in
Japan were used. I found that ﬁrm-level FDI had positive impacts on productivity growth in
14

The OECD average is 12.1 percent, which is deﬁned as the sum of inward FDI positions divided by
the sum of GDP for OECD countries. For more detail, see OECD (2001).
15“Empirical research suggests that the time required for new innovation to “leak out” is quite short. ...
70% of new product innovation “leak out” within one year and only 17% take more than 18 months. ...
some 71% of Japanese ﬁrms and 69% of U.S. ﬁrms receive useful information about the R&D activities of
their competitors on a monthly basis.” (Branstetter, 2001, p. 60)

11both manufacturing and non-manufacturing industries. In manufacturing, import-related
R&D spillovers also have signiﬁcantly positive eﬀects on productivity growth. On theother hand, intranational R&D spillovers were not signiﬁcant in general. The results thus
suggest that international R&D spillovers through trade and FDI have signiﬁcant eﬀects
on productivity growth.
The results of R&D spillovers through trade and ﬁrm-level FDI appear robust. However, other variables are sensitive to the measurement issues and omitted variables. My
measurement of industry-level R&D spillovers through FDI, which directly captures the
R&D activity of foreign-owned ﬁrms, performs better than the measurement used in theprevious studies in analyzing the manufacturing sector. In the non-manufacturing sector,
however, the measurements by Aitken and Harrison (1999) and Keller and Yeaple (2004)
perform better than the measurement used in my study. Further, patent variables are
sensitive to the omission of other variables.
The broad coverage of the data is important. The analysis based on the limited sample
produces diﬀerent results from the analysis based on the broader sample. The domestic patents do not aﬀect the R&D spillovers, which is inconsistent with the results ofBranstetter (2001). However, once I limit the sample to large ﬁrms in high-tech sectors as
Branstetter (2001), my results become consistent with those of Branstetter (2001): domestic patents have signiﬁcantly positive eﬀects on spillovers. The empirical studies of R&D
spillovers therefore must pay more attention to the measurement issues, omitted variable
bias, and the coverage of the data.
Three policy implications may be suggested from our analysis. First, tightening intellectual property rights should be discussed more carefully. While the intellectual property
rights could promote innovation (through R&D), the lack of technology diﬀusion prevents
followers from catching up a leading ﬁrm. Since followers are likely to be new entrants andsmall- and medium-sized ﬁrms, strong protection on intellectual property could be harmful
for their growth.
Second, policy makers should recognize that trade and FDI can be alternative avenues
to the productivity growth. Even small countries that cannot invest in R&D have opportunities to achieve higher productivity growth through international R&D spillovers.
Imports of merchandise and inward FDI can be the engine of growth. The promotion of
innovation is an important policy but we do not have to invent everything by ourselves.
The combination of spillover as well as innovation enables us to use limited resources more
eﬀectively.
Finally, the construction of reliable data is important to capture the spillover eﬀects
correctly. In the present study, the broad coverage of observations removes the possible
problems caused by limited samples, and the high availability of various items enables me
to check how the results are sensitive to the measurement.
In closing, I may note some directions for future research. If spillovers occur easily,ﬁrms do not have any incentive to innovate new technologies by themselves. It is important
therefore to estimate the speed of spillovers. It is also desirable to clarify the backward
and forward linkages of technology diﬀusion, which have not been covered in the present
analysis.
12References
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Appendix Robustness CheckTime Lag of R&D Spillovers
I ﬁrst consider timing. As discussed earlier, the use of R&D ﬂows could be a problem if
investments in R&D take some time to bear fruit. There are two ways to remedy thisproblem. One is to include lagged values of past R&D investments, and the other is toconstruct an R&D stock. Since my sample period covers only seven years, it is diﬃcult
to construct the appropriate R&D stocks at the ﬁrm-level. As a compromise, I lagged all
R&D spillover variables by one year to check robustness.
Model 2 in Appendix Table 3 presents the estimation results for one year lagged independent variables. Note that lagging independent variables by one year means that I
lose 13,547 observations for manufacturing and 11,398 observations for non-manufacturing.
By and large, the results are similar to those obtained in Table 3 although some variables lose their signiﬁcance level. In manufacturing, robust results are obtained in a ﬁrm’s
own R&D activity, machinery imports, ﬁrm-level FDI, and industry-level FDI. In nonmanufacturing, robust results are obtained in a ﬁrm’s own R&D activity and ﬁrm-level
FDI. Positive spillovers through domestic and foreign patents are no longer conﬁrmed in
non-manufacturing ﬁrms.
=== Appendix Table 3 ===Next, I consider the eﬀects on longer-term growth. The beneﬁt of considering longerterm growth is that it reduces the inﬂuence of noise. The cost of doing so reduces thenumber of observations. As a compromise, I experimented with three-year productivity
growth. Model 3 of Appendix Table 3 shows the results of longer-term growth. Note that I
lose 27,013 ﬁrms for manufacturing and 24,631 ﬁrms for non-manufacturing if I use threeyear growth as a dependent variable. Independent variables except the inital TFP level arenow 3-year average between year t and year t + 2.
In manufacturing, the results are almost the same as those of Model 1 in Table 3,
indicating positive and signiﬁcant signs in a ﬁrm’s own R&D activity, machinery imports,
and both ﬁrm-level and industry-level FDI. On the other hand, in non-manufacturing,robust results are obtained only in a ﬁrm’s own R&D and imports.
15

Alternative Measurement of FDI
Next, I turn to the issue of FDI measurement. As Keller and Yeaple (2004) argued, measurement matters. The mismeasurement of independent variables will tend to bias thecoeﬃcient estimate toward zero. It is not easy to determine the appropriate measure of
R&D spillovers through FDI, but I could investigate how the results are sensitive to themeasurement. I test three alternative measures of techonology spillovers through FDI, focusing on employment of foreign-owned ﬁrms.16 The ﬁrst measure is the deﬁnition adopted
by Keller and Yeaple (2004). I deﬁne FDI as the employment share of foreign-owned ﬁrms
and represent it as F DI KYitind :

Lkt
ind
.
F DI KYit = k∈M
k∈N Lkt
The second measure is the same deﬁnition of Aitken and Harrison (1999), which isdeﬁned as foreign equity share over plants in the ﬁrm, weighted by each ﬁrm’s share insectoral employments.

Lkt × F Skt
ind,F DI AHit = k∈Nk∈N Lkt
where F Sit is the foreign equity share of ﬁrm i.17 Third measure examines the diﬀerence
between shared and fully owned ﬁrms. As Javorcik (2004) found, the R&D spillovers could
take place not by fully foreign-owned ﬁrms but by partially foreign-owned ﬁrms.

Lkt × F Skt × F Oktk∈N Lkt × F Skt × P Okt
ind
ind

and F DI Jfit = k∈N ,
F DI Jpit =k∈N Lktk∈N Lkt
where P Oit and F Oit are dummy variables for partially and fully foreign owned ﬁrm i,
respectively.
Models 4-6 in Appendix Table 3 present the results of alternative measures of FDI. In
both manufacturing and non-manufacturing, the results are generally the same as those of
the baseline model (Model 1) except for the eﬀects of industry-level R&D spillovers throughFDI. In manufacturing, the coeﬃcient indicates positive sign though it is not signiﬁcant.
In non-manufacturing, negative and signiﬁcant eﬀects of FDI are observed.
These results become generally consistent with the ﬁndings of Keller and Yeaple (2004)
and Aitken and Harrison (1999). However, I could not ﬁnd positive eﬀects of partial foreign ownership. In terms of AIC and BIC, Models 4-6 perform worse than the baselinemodel (Model 1 in Table 3) in manufacturing but not in non-manufacturing. In manufacturing, both AIC and BIC in Models 4-6 are much higher than those which obtained in thebaseline model. In non-manufacturing, however, AIC and BIC obtained in Models 4-6 are
16

For a recent discussion of the relationship between FDI and spillovers through workers, see Fosfuri,
Motta, and Ronde (2001).
17
Since my data set cannot distinguish FDI from portfolio investment, I aggregate the foreign equity
share for ﬁrms with more than 10 percent equity share by foreign ﬁrms to remove the eﬀects of portfolio
investments.

16

slightly lower than those which in the baseline model. Thus my results might indicate thatthe spillover channels are diﬀerent between manufacturing and non-manufacturing. R&D
activity is an important channel in manufacturing sector. On the other hand, employment
is more important than R&D activity as a channel of the spillovers.
Omitted Variable Bias
Finally, I examine the eﬀects of omitted variables. So far I have examined several channels
of intranational and international R&D spillovers, some of which have not been examinedin previous studies. If the regression equation is estimated without relevant variables,
there will be a so-called omitted variable bias problem. To examine the eﬀects of omitted
variables, I estimate the baseline model, dropping some of the spillover variables from thebaseline model.
Appendix Table 4 presents the estimation results, which indicate how sensitive thecoeﬃcients are when some variables are omitted. I obtain relatively robust results for all
but patent variables. Although the coeﬃcients of machinery imports and FDI presentchange slightly, the results are almost the same as those that obtained in the baselinemodel, retaining high signiﬁcance levels. On the other hand, the coeﬃcients of domestic
and foreign patent variables are sensitive to the omission of other variables as both ofthe patent variables become signiﬁcantly positive in manufacturing while foreign patentvariables become signiﬁcantly positive in non-manufacturing. My results thus suggest thatthe eﬀects of omitted variable bias exist: the coeﬃcients of patent variables are sensitive
to the omitted variable while those of trade and FDI are not. Furthermore, both AIC and
BIC presented in Appendix Table 4 are higher than those which obtained in the baselinemodel, implying that the baseline model performs better than the models without somevariables.
=== Appendix Table 4 ===17

Figure 1. R&D Firms Versus Non-R&D Firms

Figure 2. Industry Intensity of R&D Firms

A) All industry

A) All industry
Non-R&D firms

40

28.8%
(6,235 firms)

71.2%(15,426 firms)

Number of industries

R&D firms

35
30
25
20
15
10
5
0
0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

90-100

70-80

80-90

90-100

70-80

80-90

90-100Percentage of R&D firms in the industryB) Manufacturing
R&D firms

Non-R&D firms

45.3%(4,921 firms)

16
Number of industriesB) Manufacturing

14
12
10
8
6
4
2
0
0-10

54.7%
(5,947 firms)

10-20

20-30

30-40

40-50

50-60

60-70

Percentage of R&D firms in the industry

C) Non-manufacturing

C) Non-manufacturing
Non-R&D firms

12.2%(1,314 firms)

40
Number of industries

R&D firms

35
30
25
20
15
10
5
0

87.8%
(9,479 firms)

0-10

10-20

20-30

30-40

40-50

50-60

60-70

Percentage of R&D firms in the industry

Table 1. Performance Gap between R&D Firms and Non-R&D Firms

coefficient

standard
errors (s.e.)[0.004][0.004]All industry
TFP
Labor productivity

0.01*
0.01*

Manufacturing
TFP
Labor productivity

coefficient
s.e.
0.01
[0.005]0.01[0.005]

1995

1996

1997

1998

1999

2000

-0.0620.868
1.361-0.0420.842
1.743-0.0200.848
1.757-0.051
0.858
1.685

0.013
0.946
1.550

0.059
0.951
1.344

0.023
0.914
1.389

0.017
0.064
0.014

0.014
0.082
0.014

0.012
0.107
0.016

0.012
0.140
0.014

0.014
0.153
0.014

0.016
0.169
0.012

0.014
0.189
0.013

FDIfirm Foreign equity share (%)
1.754
0.929
0.901
0.901
1.455
FDIindR&D by foreign-owned firms (% of sales, industry average)
0.680
0.416
0.270
0.454
0.740
Non-manufacturing
1994
1995
1996
1997
1998
TFP
lnTFP
-0.092 -0.034
0.006
0.030
-0.016
0.108
0.118
0.107
0.111
0.121
R&DO Firm's own R&D (% of sales)
R&DD Industry R&D (% of sales, industry average)
0.111
0.170
0.153
0.175
0.163
PatentD Patent payments to domestic firms (% of sales)
0.002
0.001
0.003
0.008
0.003
IMP
Machinery imports (% of sales)
0.004
0.004
0.005
0.006
0.006
PatentF Patent payments to foreign firms (% of sales)
0.001
0.002
0.002
0.002
0.002
FDIfirm Foreign equity share (%)
1.447
0.980
0.901
0.772
1.699
R&D by foreign-owned firms (% of sales, industry average)
0.105
0.093
0.072
0.106
0.127
FDIindNote:
TFP is a chain index from the hypothetical firm in 1994 (equal to zero: TFP=1 and therefore lnTFP=0).
Source: METI Database.

1.645
0.944
1999
0.009
0.126
0.134
0.006
0.007
0.002
1.869
0.149

1.594
1.111
2000
0.068
0.138
0.232
0.002
0.007
0.002
1.748
0.136

N

R2

153,147
153,147

0.02
0.01

TFP
R&DO
R&DD

lnTFP
Firm's own R&D (% of sales)
Industry R&D (% of sales, industry average)

N
82,04382,043

R2
0.02
0.02

PatentDIMP
PatentF

Patent payments to domestic firms (% of sales)
Machinery imports (% of sales)
Patent payments to foreign firms (% of sales)

Non-manufacturing
coefficient
s.e.
N
R2
TFP
0.02*
[0.007]
71,104
0.02
Labor productivity
0.02*[0.008]
71,1040.00
Notes: 1) Fixed-effect model is used for estimation. All regression includes
year and industry dummies (not reported).
2) * indicates level of significance at 5%.
3) Estimated coefficients indicate the gaps of each variable between
R&D firms and non-R&D firms.
4) Labor productivity is defined as per-capita value-added.
Source: METI Database.

Table 2. Total Factor Productivity (TFP) and R&D Spillover Variables(Mean)
Manufacturing
1994

Table 3. Effects of R&D Spillovers on Productivity Growth
Dependent variable: TFP growth
Model 0-0.290**[0.003]

Manufacturing
Model 1-0.297**[0.003]Model 1-0.285**[0.003]Model 0-0.217**[0.003]Non-manufacturing
Model 1-0.239**[0.003]Model 1-0.239**[0.004]0.892**[0.084]0.445**[0.087]0.728**[0.135]

1.105**[0.188]1.216**[0.188]1.041**[0.373]0.058[0.397]1.297[0.729]0.390[0.396]1.363[0.727]

4.833[3.844]3.909[2.067]0.605[0.784]2.019*[0.951]0.468[0.779]1.887*[0.945]-0.544[3.535]0.278[2.486]

1.109**[0.212]2.178*[0.895]0.170**[0.019]0.976*[0.423]0.996**[0.212]1.701[0.893]0.134**[0.019]1.238**[0.422]0.850**[0.297]1.094[1.648]0.154**[0.029]10.198*[4.287]6.344*[3.122]0.189**[0.016]0.977[1.085]6.308*[3.101]0.173**[0.016]1.088[1.077]7.986[6.692]0.181**[0.022]
4.002[3.773]-0.024-0.183**-0.189**0.022[0.012][0.018][0.032][0.104]
Firm characteristics
No
Yes
Yes
No
Year dummies
Yes
Yes
Yes
Yes
Industry dummies
Yes
Yes
Yes
Yes
Estimation method
ML
ML
IV
ML
Observations
64,61764,61750,14244,397
Number of firms
14,205
14,205
12,875
12,379Log-likelihood-32419.8-32227.5---17508.3
Akaike Information Criterion (AIC)64985.764607.0--35156.6
Schwarz (Bayesian) Information Criterion (BIC)
65648.2
65296.8--35765.7
R-squared----0.339--Notes:
1) ML: Maximum likelihood random-effect model is used for estimation. IV: Instrumental variable method is used for estimation.
2) All regressions include year and industry dummies (not reported).
3) ** and * indicate level of significance at 1% and 5% respectively, and figures in brackets indicate standard errors.
4) Firm characteristics are the number of workers, firm age, and capital-labor ratio.
Source:METI Database.

0.080[0.104]
Yes
Yes
Yes
ML
44,397
12,379-17204.2
34554.435189.5---

0.088[0.119]
Yes
Yes
Yes
IV
30,563
10,613------0.340

lnTFP
Firm's own R&D activity
R&DO Firm's own R&D
Intranational R&D spillovers
R&DD Industry R&D
PatentD Payments to domestic patents
International R&D spillovers
IMP
Machinery imports
PatentF

Payments to foreign patents

FDIfirm

Foreign equity share (Firm-level FDI)

FDIindR&D by foreign-owned firms (Industry-level FDI)

Constant

Table 4. Effects of R&D Spillovers on Productivity Growth
Dependent variable: TFP growth
All workers
lnTFP
Firm's own R&D activity
R&DO Firm's own R&D
Intranational R&D spillovers
R&DD Industry R&D
PatentD Payments to domestic patents
International R&D spillovers
IMP
Machinery imports
PatentF Payments to foreign patents
FDIfirm

Foreign equity share (Firm-level FDI)

FDIindR&D by foreign-owned firms (Industry-level FDI)

Constant

High-tech manufacturing industry
more than 300
More than 500 More than 1000
workers
workers
workers

More than 2000
workers-0.332**[0.004]-0.346**[0.008]-0.338**[0.010]-0.328**[0.014]-0.236**[0.019]0.618**[0.101]0.307[0.159]0.195[0.194]0.295[0.245]-0.007[0.347]0.389[0.424]
1.648*[0.798]0.180[0.763]3.095[2.196]-0.361[1.036]5.400[2.807]-1.739[1.373]7.201*[3.475]-1.051[1.721]
14.172**[5.051]

1.123**[0.215]1.572[0.999]0.136**[0.022]0.644[0.458]0.223[0.368]1.844[1.406]0.105**[0.033]0.806[0.821]-0.098[0.479]2.858[1.852]0.081*[0.037]0.166[1.081]-0.396[0.578]2.218[2.094]0.063[0.043]-2.028[1.454]-1.209[0.654]3.606[2.285]0.087[0.047]-2.903[1.751]-0.012-0.052-0.0620.047-0.160[0.027][0.056][0.080][0.121][0.222]
Firm characteristics
Yes
Yes
Yes
Yes
Yes
Year dummies
Yes
Yes
Yes
Yes
Yes
Industry dummies
Yes
Yes
Yes
Yes
Yes
Observations
29,497
8,948
5,654
2,801
1,282
Number of firms
6,530
1,872
1,157
584
260
Log-likelihood-14708.6-4229.1-2805.9-1277.2-313.6
Akaike Information Criterion (AIC)29495.2
8536.25689.92632.3703.2
Schwarz (Bayesian) Information Criterion (BIC)
29818.6
8813.15948.82863.9899.1
Notes:
1) Following Branstetter (2001), we define 'high-tech industry' as the chemicals, machinery, electronics, transportation, and precision instruments
manufacturing industries.
2) For other notes and sources, see Table 3.

Appendix Table 1. List of Industries
Industry
Manufacturing
1 Livestock products
2 Seafood products
3 Flour and grain mill products
4 Miscellaneous foods and related products
5 Soft drinks, carbonated water, alcoholic, tea and tobacco
6 Prepared animal foods and organic fertilizers
7 Silk reeling plants and spinning mills
8 Woven fabric mills and knit fabrics mills
9 Dyed and finished textiles
10 Miscellaneous textile mill products
11 Textile and knitted garments
12 Other textile apparel and accessories
13 Sawing, planting mills and plywood products
14 Miscellaneous manufacture of wood products, including bamboo and
15 Manufacture of furniture and fixtures
16 Pulp and paper
17 Paper worked products
18 Newspaper
19 Publishing industry
20 Printing and allied industries
21 Chemical fertilizers and industrial inorganic chemicals
22 Industrial organic chemicals
23 Chemical fibers
24 Oil and fat products, soaps, synthetic detergents, surface-active agentsand paints
25 Drugs and medicines
26 Miscellaneous chemical and allied products
27 Petroleum refining
28 Miscellaneous petroleum and coal products
29 Plastic products, except otherwise classified
30 Tires and inner tubes
31 Miscellaneous rubber products
32 Manufacture of leather tanning, leather products and fur skins
33 Glass and its products
34 Cement and its products
35 Miscellaneous ceramic, stone and clay products
36 Iron and steel
37 Miscellaneous iron and steel
38 Smelting and refining of non-ferrous metals
39 Non-ferrous metals worked products
40 Fabricated constructional and architectural metal products, including
fabricated plate work and sheet metal work

Industry
41 Miscellaneous fabricated metal products
42 Metal working machinery
43 Special industry machinery
44 Office, service industry and household machines
45 Miscellaneous machinery and machine parts
46 Industrial electric apparatus
47 Household electric appliances
48 Communication equipment and related products
49 Electronic data processing machines, digital and analog computer,equipment and accessories
50 Electronic parts and devices
51 Miscellaneous electrical machinery equipment and supplies
52 Motor vehicles, parts and accessories
53 Miscellaneous transportation equipment
54 Medical instruments and apparatus
55 Optical instruments and lenses
56 Watches, clocks, clockwork-operated devices and parts
57 Miscellaneous precision instruments and machinery
58 Ordnance and accessories
59 Miscellaneous manufacturing industries
Non-manufacturing
60 Wholesale trade (textile products except apparel, apparel accessories andnotions)
61 Wholesale trade (apparel, apparel accessories and notions)
62 Wholesale trade (metal mining)
63 Wholesale trade (coal and lignite mining)
64 Wholesale trade (crude petroleum and natural gas production )65 Wholesale trade (non-metallic mineral mining)
66 Wholesale trade (agricultural, animal and poultry farm and aquatic
67 Wholesale trade (food and beverages)
68 Wholesale trade (building materials)
69 Wholesale trade (chemicals and related products)
70 Wholesale trade (minerals and metals)
71 Wholesale trade (recovered material)
72 Wholesale trade (machineries)
73 Wholesale trade (furniture, fixtures and house furnishings)
74 Wholesale trade (drugs and toiletries)
75 Wholesale trade (agents and brokers)
76 Miscellaneous wholesale trade
77 Retail trade (dry goods, apparel and apparel accessories)
78 Retail trade (food and beverage)
79 Retail trade (motor vehicles and bicycles)
80 Retail trade (furniture, household utensil and household appliance)

Industry
81 Household appliance stores
82 Drug and toiletry stores
83 Fuel stores
84 Miscellaneous retail trade85 General eating and drinking places
86 Agriculture
87 Forestry
88 Fisheries and aquaculture
89 Construction
90 Gas, electricity and water supply
91 Road freight transport
92 Warehousing
93 Travel agency
94 Miscellaneous transport
95 Telecommunications
96 Finance and insurance
97 Real estate agencies
98 Real estate managers
99 Laundries
100 Automobile parking
101 Photographic studios
102 Hotels, boarding houses and other lodging places
103 Legitimate theatres and performances and theatrical companies
104 Sports facilities
105 Automobile repair services
106 Machinery and furniture repair services
107 Rental and leasing services
108 Computer programming and other software services
109 Data processing and information services
110 Advertising
111 Engineering
112 Commercial and engineering design services
113 Building maintenance services
114 Display services
115 Education
116 Research and development
117 Amusement parkAppendix Table 2. Summary Statistics
Basic Statistics
Variable
TFP
lnTFP

ManufacturingNMean64,617
0.001

S.E.
0.588

Non-manufacturingNMean
44,397
0.012

S.E.
0.592

R&DO

Firm's own R&D

64,617

0.009

0.021

44,397

0.001

0.010

R&DD

Industry R&D

64,617

0.016

0.013

44,397

0.002

0.003

PatentD Payments to domestic patents

64,617

0.000

0.002

44,397

0.000

0.002

IMP

Machinery imports

64,617

0.001

0.008

44,397

0.000

0.000

PatentF Payments to foreign patents

64,617

0.000

0.002

44,397

0.000

0.001

FDIfirm

Foreign equity share

64,617

0.013

0.089

44,397

0.016

0.111

FDIindR&D by foreign-owned firms

64,617

0.006

0.010

44,397

0.001

0.003

Correlation Matrix
R&DO
R&DD
PatentDIMP
PatentF
FDIfirm
FDIind
lnTFP
1
0.079
0.089
-0.004
0.098
0.018
0.167
0.102
Firm's own R&D
0.136
1
0.056
0.036
0.052
0.126
0.053
0.117
Industry R&D
0.100
0.315
1
0.014
0.018
0.010
0.016
0.209
Payments to domestic patents
0.020
0.086
0.035
1
0.045
0.266
0.046
0.024
Machinery imports
0.057
0.096
0.094
0.011
1
0.045
0.247
0.035
Payments to foreign patents
0.052
0.114
0.072
0.045
0.068
1
0.249
0.050
Foreign equity share
0.107
0.134
0.080
0.009
0.145
0.054
1
0.042
R&D by foreign-owned firms
0.070
0.206
0.246
0.010
0.139
0.019
0.060
1
1) Shaded cells indicate non-manufacturing sector.
2) Number of observations is 64,617 for manufacturing and 44,397 for non-manufacturing.
TFP

TFP
R&DO
R&DD
D
PatentIMP
PatentF
FDIfirm
ind
FDI
Note:

Appendix Table 3. Robustness Check: Effects of R&D Spillovers on Productivity Growth, Manufacturing
Dependent variable: TFP growthlnTFP
Firm's own R&D activity
R&DO
Firm's own R&D
Intranational R&D spillovers
Industry R&D
R&DD
D

Payments to domestic patents

PatentInternational R&D spillovers
IMP
Machinery imports
PatentF

Payments to foreign patents

firmForeign equity share (Firm-level FDI)

FDI

FDIindFDI_KYR&D by foreign-owned firms (Industry-level FDI)ind

FDI_AKind
FDI_Jpind
FDI_JfindConstant

Employment by foreign-owned firms (Industry-level FDI)
(Keller and Yeaple, 2003)
Foreign equity share (Industry-level FDI)
(Aitken and Harrison, 1999)
Foreign equity share, partial ownership (Industry-level FDI)
(Javorcik, 2004, partial ownership)
Foreign equity share, full ownership (Industry-level FDI)
(Javorcik, 2004, full ownership)Non-manufacturing
Model 4Model 5
Keller and
Aitken andYeaple, 2003
Harrison, 1999
-0.239**-0.239**[0.003][0.003]Model 5
Aitken andHarrison, 1999
-0.298**[0.003]Model 6
Javorcik, 2004

Model 2
lagged-variables

Model 33-year growth-0.259**[0.002]

Manufacturing
Model 4
Keller andYeaple, 2003
-0.297**[0.003]-0.298**[0.003]-0.222**[0.004]-0.173**[0.003]0.573**[0.097]0.433**[0.070]0.446**[0.087]0.444**[0.087]0.445**[0.087]0.877**[0.216]

0.593**[0.129]1.208**[0.188]1.210**[0.188]1.217**[0.188]-0.004[0.286]1.651*[0.770]0.207[0.190]0.047[0.068]-0.041[0.369]1.358[0.727]-0.609[0.337]1.371[0.727]-0.567[0.340]1.370[0.727]-0.204[0.533]0.276[1.365]0.734[0.395]0.068[0.080]0.236[0.782]
1.864*[0.944]0.261[0.777]1.862*[0.944]0.385[0.781]1.865*[0.944]

0.991**[0.262]1.261[0.956]0.126**[0.021]
1.153**[0.314]0.611**[0.168]1.182[0.732]0.103**[0.028]1.886**[0.210]1.001**[0.212]1.690[0.893]0.134**[0.019]0.999**[0.212]1.672[0.893]0.136**[0.019]1.000**[0.212]1.673[0.893]0.136**[0.019]4.952[3.283]0.159**[0.018]1.000[0.829]5.204*[2.345]0.100**[0.032]
1.417*[0.673]6.323*[3.101]0.174**[0.016]6.320*[3.101]0.174**[0.016]6.322*[3.101]0.175**[0.016]

Model 2
lagged-variables

Model 33-year growth-0.286**[0.003]0.048[0.030]Model 6
Javorcik, 2004
-0.239**[0.003]-0.067[0.046]-0.055[0.049]-0.210*[0.092]-0.389[0.393]-0.046[0.050]-0.164**-0.177**-0.182**-0.175**-0.173**[0.020][0.014][0.018][0.018][0.018]Firm characteristics
Yes
Yes
Yes
Yes
Yes
Year dummies
Yes
Yes
Yes
Yes
Yes
Industry dummies
Yes
Yes
Yes
Yes
Yes
Observations
51,07037,60464,61764,61764,617
Number of firms
12,992
11,747
14,205
14,205
14,205Log-likelihood-24811.4
18737.3-32230.5-32231.2-32230.8
Akaike Information Criterion (AIC)49774.8-37326.5
64613.064614.3
64615.6
Schwarz (Bayesian) Information Criterion (BIC)
50446.7-36694.965302.865304.1
65314.5
Notes:
1) All independent variables except lnTFP and firm characteristics are lagged one-year in Model 2. The dependent variable is the annual average of three
years rather than one year in Model 3. Models 4 and 5 test the alternative measure of industry-level FDI.
2) For other notes and sources, see Table 3.

-0.441*[0.174]-0.132[0.105]0.080[0.111]
Yes
Yes
Yes
32,998
10,905-12028.824201.524806.7

0.084[0.070]
Yes
Yes
Yes
19,766
9,079
11501.0-22861.9-22309.5

0.079[0.104]
Yes
Yes
Yes
44,397
12,379-17203.634553.2
35188.4

0.081[0.104]
Yes
Yes
Yes
44,397
12,379-17202.1
34550.2
35185.4

0.081[0.104]
Yes
Yes
Yes
44,397
12,379-17200.934549.735193.6

Appendix Table 4. Effects of Omitted Variables
Dependent variable: TFP growthlnTFP
Firm's own R&D activity
R&DO Firm's own R&D
Intranational R&D spillovers
R&DD Industry R&D
PatentD Payments to domestic patents

Without
patents-0.297**[0.003]-0.297**[0.003]-0.296**[0.003]-0.296**[0.003]0.464**[0.087]0.464**[0.087]0.465**[0.087]0.394[0.396]1.474*[0.727]0.387[0.396]1.020**[0.211]

International R&D spillovers
IMP
Machinery imports
PatentF

Payments to foreign patents

FDIfirm

Foreign equity share (Firm-level FDI)

FDIindR&D by foreign-owned firms (Industry-level FDI)

Constant
Firm characteristics
Year dummies
Industry dummies
Observations
Number of firms
Log-likelihood
Akaike Information Criterion (AIC)
Schwarz (Bayesian) Information Criterion (BIC)
For notes and sources, see Table 3.

Manufacturing
Without FDI Trade only Patents only

Without
trade

1.814*[0.893]0.146**[0.019]1.258**[0.422]0.143**[0.018]1.232**[0.422]-0.186**[0.018]Yes
Yes
Yes
64,617
14,205-32238.664627.1
65307.9-0.183**[0.018]Yes
Yes
Yes
64,617
14,205-32231.2
64610.3
65282.0

Non-manufacturing
Without FDI Patents only

FDI only

Without
patents-0.296**[0.003]-0.297**[0.003]-0.239**[0.003]-0.234**[0.003]-0.234**[0.003]-0.239**[0.003]0.494**[0.087]0.490**[0.087]0.484**[0.087]1.277**[0.186]

1.270**[0.188]1.270**[0.188]1.277**[0.186]-0.423[0.287]1.476*[0.727]-0.424[0.287]-0.435[0.287]1.623*[0.727]0.390[0.396]0.511[0.779]0.438[0.776]
1.822[0.946]0.438[0.776]
1.822[0.946]0.511[0.779]1.197**[0.210]3.102**[0.871]1.256**[0.209]7.782*[3.103]

7.782*[3.103]-0.179**[0.018]Yes
Yes
Yes
64,617
14,205-32258.0
64663.965335.5

3.383**[0.870]-0.179**[0.018]Yes
Yes
Yes
64,617
14,205-32266.6
64677.265330.6-0.182**[0.018]Yes
Yes
Yes
64,617
14,205-32274.2
64694.4
65357.0

0.155**[0.018]1.251**[0.422]0.175**[0.016]1.050[1.077]-0.185**[0.018]Yes
Yes
Yes
64,617
14,205-32242.864631.665294.2

0.079[0.104]
Yes
Yes
Yes
44,397
12,379-17209.734561.435179.2

FDI only

0.175**[0.016]1.050[1.077]0.080[0.104]
Yes
Yes
Yes
44,397
12,379-17265.834673.535291.3

0.080[0.104]
Yes
Yes
Yes
44,397
12,379-17265.834673.535291.3

0.079[0.104]
Yes
Yes
Yes
44,397
12,379-17209.734561.435179.2