G-SEC WORKING PAPER No.1
How Enforcement of Intellectual Property
Rights Affects the International Technology
Transfer: Evidence from Japanese MNCs
Ryuhei Wakasugi* and Banri Ito**
June, 2005

Abstract
How stronger intellectual property rights affect technology transfer is one of important
issues of international microeconomics. This paper examines empirically their effect ontechnology transfer in the world by using the panel data of Japanese multinational firms. The
results of our estimation reveal that the technology transfer measured by royalty payments ofaffiliate to parent firms is substantial in the countries where the enforcement of IPRS is strict,and that it increases in the countries where IPRS are strengthened. Those results are
consistent with the previous studies based on the US and European firm data.

(96 words)

Keywords: Intellectual property rights; Technology transfer; Multinational firms; Firm-level
data
JEL classification: C23, F23, O31, O34*

Faculty of Economics, Keio University, Mita 2-15-45, Minato-ku, Tokyo, 108-8345, Japan.
Phone/Fax: +81-(0)3-5427-1074, E-mail: wakasugi@econ.keio.ac.jp**
Global Security Research Institute, Keio University. Phone/Fax: +81-(0)3-5427-1372/1871,
E-mail: banri@2002.jukuin.keio.ac.jp

1. Introduction
It is remarkable that, according to the Balance of Payment Statistics, the transaction oftechnology takes a significant part of international trade. This is caused by an increase in theinternational transfer of technology evolved through foreign direct investment (FDI) and theinternational fragmentation of production process, in addition to a changing structure of trade in goods
to trade in software. It is also notable that a number of developing countries have been strengthening
the enforcement of intellectual property rights (IPRS) in their countries in these years. Such an
enforcement of IPRS causes a question how it affects the international transfer of technology.
Although this issue is one of important research subjects of law and international economics, few
empirical analyses have been undertaken thus far. There are some reasons for this. The first is thelimited availability of data. Few countries disclose firm-level data on the international transfer oftechnology. The United States exceptionally performs detailed surveys on the activity of multinationalfirms. Thus, almost all the literatures in this field have so far been dependent either on data from the
U.S. or original surveys tailor-made by researchers. The second is that the reform of IPRS indeveloping countries has only been implemented since the late 1990s after the conclusion of TRIPs
agreement under the WTO framework, and thus, the coverage of data appears to be insufficient.
Consequently, empirical analyses have been hampered by the limited size of samples.
There are several avenues to technology transfer - the license fee accompaniment, thetransfer pricing thorough trading of goods, and the distribution of dividends. This paper focuses on1

intra-firm technology transfer between a parent firm and its overseas affiliates since we can explicitly
observe the transaction of technology between parent and affiliate firms from statistical data, incontrast to other channels of transfer.
In this paper, we at first present a simple analytical framework based on imperfectcompetition to explain how the strengthened IPRS might affect the technology diffusion from aforeign country to a domestic market. The central idea is that the enforcement of IPRS is to the cost of
a local firm that imitated the foreign technology in a low cost and its disadvantage to lose the share of
the production of the imperfectly competitive goods since the enforcement of IPRS will raise theapprorpiability of the technology. On reflection, it is to the advantage of a foreign firm to capture alarger share of the production and raise the value of transferred technology. As a consequence, it ispossible that the strengthened IPRS expands the technology flow of a foreign firm to the local market.
It, however, is not obvious that this framework well explains the real technology diffusion.
In the part of empirical examination of the paper, we test how significantly the enforcementof IPRS affects the technology flow from Japan to the rest of the world along our theoreticalframework. For this purpose, we construct original panel-data by matching firm-specific factors ofJapanese multinationals with the market-specific factors including the enforcement index of IPRS. As
long as the authors know, this is the first attempt to test the effects of IPRS by using Japanesefirm-level data.
The findings of this paper are twofold. First, technology transfer will prevail in a country thathas a high level of IPRS. Second, IPRS reform will stimulate technology transfers. These results are2

consistent with our theoretical hypothesis that strengthened IPRS would raise imitation costs, which asa result would increase the royalty fee, thus accelerating technology transfers. This paper relates to
Maskus and Penubarti (1995), Lee and Mansfield (1996), Smith (1999, 2001), Branstetter, Fisman andFoley (2004), and Smarzynska (2004) which empirically examined the issue of the impact ofstrengthened IPRS on the transaction of technology.
This paper is organized as follows: Section 2 presents a theoretical background to examine
the effect of the strengthened IPRS on technology transfer. Section 3 describes the framework for
empirical examination. Section 4 describes the sample statistics and the data used for the estimation.
Section 5 discusses the results of estimation. The last section concludes this paper.

2. Theoretical BackgroundWe use the simplest structure capable of presenting the effect of the enforcement of IPRS.
Firm behavior is modeled as a simple Cournot-Nash duopoly, with one local firm and one
foreign-affiliated firm, who produce identical products in the local market. We assume that both firms
produce only for the local market. We let x and x * denote the supply of the local firm and that of
the foreign affiliate, respectively. The local firm imitates foreign technology with a cost per unit
output, τ , which varies according to the degree of enforcement of IPRS. Along with the discussion by
Helpman (1993) and Glass and Saggi (2002), we assume that the higher the degree of enforcement of
IPRS the higher the imitation cost. The foreign affiliate uses the technology developed by its parent
firm with royalty payment per unit output τ * paid to its parent firm.
3For analysis, we consider the following multi-stage one-shot game. In stage 0, the naturechooses the situation in which both the local firm with a low imitation cost of foreign technology andthe foreign-affiliate with royalty payment to the genuine foreign technology supply their products forthe local market. In stage 1, the government of the local market announces to strengthen theenforcement of IPRS. It raises the imitation cost of local firm. In stage 2, the parent firm of foreigncountry decides the level of royalty fee. In stage 3, given the degree of IPRS and the royalty fee, thelocal firm produces quantity x and the foreign affiliate produces x * under their profit
maximization. For the simplification, we assume the inverse market demand for the good in the local
market is given by:p( x + x * ) = a − ( x + x * ) ,(1)

where p is price in the local market. The local firm maximizes variable profit π :π ( x, x * , c,τ , ) = xp( x + x * ) − cx − τx ,(2)

where c is marginal cost for production except for the imitation cost of foreign technology. We assume
that both the variable costs c and τ are constant regardless the quantity of production for thereason of simplification. While there may be some additional sunk costs which explain the existenceof imperfect competition in this market, they are omitted because they play no role in this analysis.
The technology flow from parent firm in foreign country to its affiliate is expressed as thetotal amount of royalty payment defined by the function of quantity x * and the royalty fee τ * . We
assume the total revenue of royalty payment TF is defined by:

TF = τ * x * .
4The parent firm determines the royalty fee to maximize its total revenue of royalty payment.
The optimal royalty fee and the total revenue of technology flow are solved by backward induction.
The first-order condition for profit maximization of the local firm is:

π x = a − 2 x − x* − (c + τ ) = 0 ,(3)where derivative is denoted by subscript.
Similarly, the variable profit of the foreign affiliate, π * , is given by:

π * ( x, x * , c,τ , ) = x * p( x + x * ) − c * x − τ * x , (2*)
leading to first- order condition:

π x* = a − 2 x * − x − ( c * + τ * ) = 0

(3*)

*

First-order conditions (3) and (3*) show the best response of each firm to any particular output chosen
by its rival. The solution to two equations is the non-cooperative solution that leads to the optimal
supply of two firms:

x=

[

]

(4)

]

(4*)

1
a + c* + τ * − 2(c + τ ) ,
3

x* =

[

1
a + c + τ − 2(c * +τ *) ,3

For the simplification, we assume that the marginal cost is same between foreign affiliate and local
firm, c = c* = w . The total amount of royalty payment, TF , is expressed by:

TF =

[

]

1
a − w + τ − 2τ * τ * ,3(5)

leading to first-order condition for maximization of royalty payment:

5

TFτ * =()

1
a − w + τ − 4τ * = 0 ,3(6)

The solution to equation (6) with respect to τ * is the optimal royalty fee under the given
parameters: the enforcement of IPRS, market size and other variable costs. The optimal royalty fee andthe total amount paid for the technology diffusion are denoted by:τ* =1[a − w + τ ] ,
4

(7)

1
[a − w + τ ]2 ,
24

(7*)

TF =

Equation (7) and (7*) state that the amount of royalty payment from affiliate to its parent
firm is determined by the enforcement of IPRS τ and other market- and firm- specific conditions
included in a and w .
As mentioned earlier, we assume that the strengthened enforcement of IPRS causes anincrease of imitation cost. By taking derivatives of τ * and TF with respect to the imitation cost andother variable costs for production, we derive xτ < 0 , xτ* > 0 , τ τ* > 0 , TFτ > 0 , τ w* < 0 , andTFw < 0 1. They lead to the propositions as follows:
Proposition 1. A rise of the enforcement of IPRS
(1) reduces the supply of local firm;(2) increase the supply of foreign affiliate;(3) raises the optimal royalty fee of the transferred technology;(4) increases the amount of royalty payment from foreign affiliate to its parent firm.

1

We assume that the supply quantity of goods of both firms is positive.
6

Proposition 2. A rise of variable costs for production decreases the optimal royalty fee and the amountof royalty payment.

3. Empirical Framework
Taking into account the difficulty to observe the royalty fee of each technology transaction,
our empirical examination focuses on the amount of payment for transferred technology. In order totest empirically how the enforcement of IPRS and other market- and firm- specific conditions affectthe international technology diffusion, we use the following equation:ln TFilt = β 0 + β1 ln τ jt + β 2 ln Pit + β 3 ln Ailt + β 4 ln H jt + ε ilt + uil ,(8)where l is the index for affiliates, i is the index for the affiliate’s parent firm, t is an index indicating
the year, and j denotes the affiliate’s host country. The dependent variable TFilt is the proxy for thevolume of transferred technology from parent firm i to affiliate firm l at the year t. τ jt denotes theimitation cost in country j. Pit represents a vector of characteristics of the parent firm, such as R&D
expenditure. Ailt represents a vector of characteristics of the affiliates, including the type oftransaction with the parent firm. H jt represents a vector of characteristics of the host country. β 0 is
a constant, uil denotes the time-invariant fixed effects for the affiliates, representing their individual
characteristics, and ε ilt is the error term.
As explained in the above framework, the imitation cost of local firm would depend on thelevel of IPRS. Thus, we introduce the level of IPRS in the host country (IPR) as the proxy for τ jt . With
regard to the characteristics of the parent firm Pit , R&D expenditure (P_R&D) is employed,7considering the parent’s R&D size. Although this parameter should be measured in terms of the R&D
stock in order to take into account the accumulated knowledge over a long time, we use R&D flow
because of the unavailability of stock data. Regarding the characteristics of the affiliates, in addition to
the number of employees (EMP) as a proxy for firm size, capital goods (INVIMJP) and intermediate
products (BUYJP) which affiliates imported from Japan are considered in order to control the firm
specific relationship with their parent firm in the transactions. Most affiliates manufacture products byimporting capital goods such as machinery and its parts from Japan. In this case, these imported goods
from Japan may be accompanied by the technology involved in their usage. Thus, it is expected thatthe technology that complements such imported goods is simultaneously transferred in the transactionof capital and intermediate goods.
Concerning the characteristics of the host country, we note that previous studies haveempirically demonstrated the importance of the openness of the host country’s market. We introduce
the parameters of market openness, FDI openness, and corporate tax rate along the lines of precedent
research. The effect of market size is not directly estimated since the market size of each production is
not observable and is thought to be subsumed in the time-invariant term uil in equation (8).
The equation for estimation takes the following form:ln TFilt = β 0 + γ 1 ln( IPR jt ) + γ 2 ln( P _ R & Dit ) + γ 3 ln( EMPilt )+ γ 4 ln( INVIMJPilt ) + γ 5 ln( BUYJPilt ) + γ 6 ln(OPEN jt )(9)

+ γ 7 ln( FDI jt ) + γ 8 ln(TAX jt ) + ε ilt + uil
where the coefficients γ k (k = 1, 2,L8) indicate the elasticity of technology transfer to the change ofeach variable.
8In addition to equation (9), we examine the difference in the growth of technology transfer
between 1995 and 2001 using the following equation to determine whether IPR reforms impact thegrowth of technology transfers. The constant term and fixed effects for the affiliates are eliminated in
the difference approach of equation (10),⎛ τ jt ⎞⎛ H jt ⎞⎛ TFilt ⎞
⎛ P ⎞
⎛ A ⎞
ln ⎜
⎟⎟ + δ 2 ln ⎜ it ⎟ + δ 3 ln ⎜ ilt ⎟ + δ 4 ln ⎜⎜⎟⎟ + ε ilt .
⎟ = δ1 ln ⎜⎜⎝ TFilt −1 ⎠⎝ Pit −1 ⎠⎝ Ailt −1 ⎠⎝ τ jt −1 ⎠⎝ H jt −1 ⎠(10)Equation (9) is estimated using the fixed effects and random effects models. Various specification tests,
such as F test, Breusch-Pagan Lagrangian multiplier test, and Hausman test, were used to select theappropriate model for the estimation. Equation (10) is estimated by ordinary least square regression
method (OLS).

4. Sample Statistics and Data Description
4.1 Sample Statistics
This paper uses firm-level data collected from referred to as “Kaigai Jigyo Katsudo Kihon
Cyousa” issued by Japanese Ministry of Economy, Trade and Industry, which is a survey of activities
of Japanese multinational companies. The data are obtained from a survey of each year; a full survey
is carried out every three years, while complementary surveys are carried out between the full surveys.
The survey examines those affiliates that are capitalized over 10% by Japanese investors, sub-affiliates
capitalized over 50% by their affiliates, which are in turn capitalized over 50% by Japanese investors,
and the parent firms that own these affiliates. The data of royalty payments is available for the year of
1995, 1998, and 2001, in which a full survey was carried out. The number of affiliates was 10,420,
9

13017, and 13,693 in 1995, 1998, and 2001, respectively. We constructed the complete panel datacomprising 5,764 affiliates in order to avoid fluctuation in the number of affiliates over time. The
sample size is restricted due to the limited availability of Index of Patent Right (IPR) data provided byPark and Wagh (2002). The IPR is surveyed every five years; thus, the points of time considered in our
analysis are 1995 and 2001, and samples that are not merged with the IPR are omitted from the data
set. As a result, 4,872 and 4,866 affiliates remained in the data sets of 1995 and 2001, respectively.
Consequently, the maximum sample size was 9,738. Table 1 tabulates this data under the headings ofhost country name and number of affiliates in 1995 and 2001.
The dependent variable denotes the royalty payments made to parent firms in Japan. The
total payment directed to the Japanese investors comprises the dividend and royalty payments. We
checked for consistency in these payments and proceeded to refine the data. However, with regard to
the distinction between the “zero” and “missing” values, a missing value is set to zero only if zero isentered as the total payment for the Japanese investors. After the data cleaning, one value is added forall variables representing the characteristics of parent firms and their affiliates and logarithm is taken
for them.
4.2 Data Description
As for explanatory variables, R&D expenditures are considered to be characteristic of theparent firm because technology transfers are expected to increase with the R&D capability ofaffiliate’s parent firms. Data regarding the R&D expenditures of the parent firm are also collected in
the survey, and it is possible to merge this data with that of the corresponding affiliates by using a10parent code and an affiliate code. In case of the affiliates, the total number of employees and theimport of capital and intermediate goods from Japan are considered. These data also are obtained fromthe survey data.
Every five years, the key variable - the IPR - was surveyed for 63 countries by Park andWagh (2002). They allot scores based on the following five criteria: (1) Does the protection of patentright cover major industries such as pharmaceutical, chemical, foods, etc.? (2) How long is theprotection of the patent right valid? (3) Is there provision for legal enforcement? (4) Is the country amember to international treaties? (5) Do restrictions exist on patent rights? The score ranges from 0 to
5; a high score implies a stronger IPR system. We combined the IPR data with the affiliate data using
the host country code. As a result, the panel data comprising the IPR data of 49 countries is obtainedfor estimation as seen in Table 1.
The characteristics of the host country include the openness of the market determined by theformula “(export + import)/GDP” (OPEN), the FDI openness determined by the formula “(inflow of
FDI)/GDP” (FDI), and the difference between the corporate tax rate in the host country and that inJapan (TAX). It is indicated that firms sometimes incorrectly report the amount of royalty payments inorder to avoid taxation. To cope with this problem, Hines (1995) and Grubert (1998), who examined
the impact of taxation on declarations by firms, demonstrated that the impact is controlled by the
corporate and withholding tax rates in the host country. With regard to the source of these data
pertaining to different countries, the tax data is obtained from the “Corporate Tax Rate Survey” carriedout by KPMG and other data is obtained from the “World Development Indicator” by the World Bank.
11

Table 2 presents the descriptive statistics of variables used in estimating equation (2). The descriptive
statistics of variables used in estimating equation (3), which adopts a difference approach, arepresented in Table 3.

5. Estimation Result
The results of estimation using the panel data for equation (2) are presented in Table 4,
while those of the difference approach are presented in Table 5. The three combinations of explanatory
variables deserve particular attention and are selected and numbered in the tables as [1]-[3].
5.1 Estimation for Fixed Effects
The natural logarithms for the parent firm’s R&D expenditures (P_R&D), total number of
employees (EMP), and IPRS of the host country (IPR) are used for all estimations, while theremaining explanatory variables, such as the characteristics of the host country and the measures ofrelationships between affiliates and their parent firms, are considered over time in order to examine
their effect on technology transfers.
The procedure for the specification of the model is as follows. First, by using F test, it isexamined whether all the estimated individual effects uil are equal to zero. If all values are zero, thepooling estimation model is more appropriate for the data set than the fixed effects model.
Breusch-Pagan test, which examines whether the variance of the individual effects is zero, is used tocompare the pooling and random effects models. Rejecting that the variance is zero implies that the
random effects model should be used. Next, we proceed to examine the Hausman test, which tests the12validity of the random effects model against the fixed effects model.
With regard to model [1], the result of the F test leads to the rejection of the null hypothesisthat individual effects equal zero; thus, the fixed effects model is adopted. Moreover, the result of theBreusch-Pagan test rejects the pooling estimation model and supports the random-effects model. In the
Hausman test, the random effect model is rejected at 5 percent of statistical significance. Thus, thefixed effects model is judged to be the most appropriate method for our estimation. In models [2] and[3], specification test are carried out in the same manner, supporting the fixed effects model.
The impact of IPRS is a major concern of this study. As expected, the signs of all
coefficients of ln(IPR) are positive, and all the estimates are significantly greater than zero, except forthe case of Random[2] and Fixed[3]. The value of the estimated elasticity of technology transfers withregard to IPR determined by Fixed[1] is 1.06, implying that the rate of increase of technology transfers
exceeds that of the strengthening of IPRS. Estimates of the coefficient of ln(P_R&D), which is a proxy
for the R&D capability of the parent firm, are statistically insignificant for all fixed effects models.
Although the R&D expenditures of the parent firm are larger, its technology may not always betransferred to its affiliates. In contrast, estimates of the coefficient of the number of affiliate employees(EMP) are always significantly positive, irrespective of the combination of the explanatory variables.
In addition, as seen from the value of the standard error, the coefficients are highly significant. With
regard to the characteristics of the host country, while trade openness and FDI openness is entirely
insignificant, with the exception of Random[2], the coefficients of ln(TAX), which represents thedifference in the corporate tax rate between Japan and the host country, are always significantly
13negative.
Even after certain characteristics of a country, such as trade openness, FDI openness, anddifferential corporate tax rate are controlled, the coefficient of ln(IPR) remains significantly positiveand its magnitude appears to be almost unchanged (see Fixed[1], Fixed[2] in Table 4). However, thecoefficient of log of (IPR) becomes insignificant with the introduction of both imported capital goods
and intermediate goods from Japan which are proxies for the linkage of transactions between affiliates
and their parent firm and are considered as explanatory variables.
To summarize, we observed a positive effect of IPRS on technology transfers after
controlling the firm and market specific factors such as trade and FDI openness and taxation in thehost country. The impact is attenuated by controlling the business relationships between affiliates andtheir parent firm.
5.2 Estimation Result of Difference Approach
Another fundamental concern of this analysis is to examine whether the reinforcement of
IPRS induces technology transfers. The strengthened IPRS are expected to be positively correlated tothe increase in technology transfers as well as a positive correlation between the level of the IPRS andthe volume of transferred technology. The results of the estimation of the difference approach arepresented in Table 5. The combination of explanatory variables displayed is identical to that of thepanel data. The constant term and fixed effects for affiliates on account of adopting the difference
approach are eliminated.
The coefficients of ln( IPRt IPRt −1 ) are positive with a statistical significance and range
14

from 0.22 to 0.29. The coefficients of the growth rate of R&D expenditures by the parent firm become
significantly positive, while these were insignificant when the level of the variables was considered.
The growth rate of affiliate’s employees is also significantly positive. Even when the characteristics ofthe host country and the proxies for the linkage of transactions between affiliates and their parent firms
are considered as explanatory variables, the change in IPR remains positive in high statisticalsignificance. These results clearly indicate that the IPR reforms from 1995 to 2000 contributed toward
increasing technology transfers of Japanese multinational firms to their affiliates in foreign countries.

6. Conclusions
Whether stronger IPRS accelerate the international transaction of technology is an issue ofparticular interest. The recent reinforcement of IPRS provides a basis to investigate this issue. Until
date, although various empirical analyses have observed the positive impact of IPRS on technology
transfer, only few studies use firm-level data. In particular, while the transaction of technology bymultinational companies has been considered as a crucial channel for technology transfer, few
empirical investigations have been conducted on this channel. This paper examines whether thestrengthening of IPRS increases technology transfers by using firm-level data of Japanesemultinational companies. Results of the estimation indicate that technology transfer would prevail in
those countries that have a high IPR level. It is remarkable that IPRS have positive effects ontechnology transfers even at the firm level. Further, the difference approach revealed that IPR reforms
stimulate technology transfers. These results are consistent with the theoretical hypothesis that15

strengthened IPRS would raise imitation costs, bring a higher incentive to transfer the technology, andincrease the royalty fee and the total payments of transferred technology. The results obtained in thispaper support the results of previous studies that the technology transfer by multinational companies
could play an important role.
In closing the paper, we briefly note remaining subjects for further study on this issue. The
enforcement of IPRS often is evaluated from the aspect of income distribution between the donor andrecipient countries of technology. While this paper did not address the issue of how technology
transfer affects economic welfare because of the difficulty to abstract statistically the effect oftechnology transfer by Japanese MNCs on economic welfare, it is an interested issue. It is also notedthat technology transfer between affiliates and their parent firms is not a unique channel. While we did
not discuss it because of the limited availability of data, it is certain that the licensing to arm’s length
parties is also a major channel of technology transfer. A variety of technology transfer channels
including this type could be examined. As for data set, we utilized the IPR data provided by Park andWagh (2002) as a proxy for the strength of intellectual property rights. It is possible that the indices do
not present the timing when IPRS in the host country really were strengthened. This problem can be
mitigated by using data that identify IPRS reforms for every country. It should also be taken into
account that there may be a large variation in the effects of IPRS on technology transfer for different
firms. For example, knowledge-intensive firms, such as a firm holding a large number of patents, may
benefit from the effects of IPRS. Combining the micro data and patent data could make the analysis
possible.
16

Acknowledgement
The authors acknowledge the Statistics Department of Japanese Ministry of Economy, Tradeand Industry, which permitted them to use the firm level data of “Kaigai Jigyo Katsudo Kihon
Cyousa”

References
Branstetter, Lee, Fisman, Raymond and Foley, C. Fritz (2004) “Do Stronger Intellectual Property
Rights Increase International Technology Transfer? Empirical Evidence from U.S.
Firm-Level Panel Data”, World Bank Policy Research Working Paper No. 3305.
Glass, A.J., Saggi, K. (2002) “Intellectual Property Rights, Foreign Direct Investment and Innovation,”
Journal of International Economics, Vol. 56, pp. 131-153.
Grubert, H. (1998) “Taxes and Division of Foreign Operating Income among Royalties, Interest,
Dividends, and Retained Earnings,” Journal of Public Economics, Vol. 68, pp. 269-290.
Helpman, E. (1993) “Innovation, Imitation and Intellectual Property Rights,” Econometrica 61, pp.
1247-1280.
Hines, J. (1995) “Taxes, Technology Transfer, and R&D by Multinational Firms,” in M. Feldstein, J
Hines, and R. G. Hubbard, eds. Taxing Multinational Corporations, University of Chicago
Press.
Lee, Jeong-Yeun and Mansfield, Edwin (1996) “Intellectual Property Protection and U.S. Foreign
Direct Investment,” The Review of Economics and Statistics, Vol. 78, Issue 2, pp. 181-186.
17

Maskus, K. and M. Penubarti, (1995) “How Trade-Related Are Intellectual Property Rights?,” Journalof International Economics 39, pp. 227-248.
Park, Walter G. and Smita Wagh (2002) “Index of Patent Rights” in Economic Freedom of the World:
2002 Annual Report, Chapter 2, pp. 33-43.
Smarzynska Javorcik, Beata（2004） “The Composition of Foreign Direct Investment and Protectionof Intellectual Property Rights: Evidence from Transition Economies,”

EuropeanEconomic Review, vol. 48, issue 1, pp. 39-62.
Smith, Pamela J. (1999) “Are Weak Patent Rights a Barrier to U.S. Exports?,” Journal of International
Economics 48, pp. 151-177.
Smith, Pamela J. (2001) “How Do Foreign Patent Rights Affect U.S. Exports, Affiliate Sales, andLicenses?” Journal of International Economics 55, pp. 411–439.

18

Table 1: The Distribution of Affiliates
Country
Argentina
Australia
Austria
Bangladesh
BelgiumBrazilCanadaChili
China
Colombia
Czech Republic
Denmark
Ecuador
Egypt
Ethiopia
FranceGermany
GreeceGuatemalaHong Kong
Hungary
India
Indonesia
IrelandIslael
Italy
Kenya
Korea
Madagascar
MexicoNetherlands
New Zealand
NorwayPakistan
Peru
PolandRomania
RussiaSingapore
South Africa
Spain
Sri Lanka
SwedenSwizerland
ThailandTurkeyUnited Kingdom
United States
Venezuela
Total

1995
20
199
16
3
61
101
120
26
532
12
8
10
4
3
1
115
224
3
2
341
7
28
226
9
2
64
1
136
1
60
135
42
10
9
9
8
2
12
338
3
56
3
24
20
364
10
291
1,188
13
4,872

19

2001
20
199
16
3
61
101
122
25
534
12
8
10
4
3
1
114
224
3
2
341
7
29
224
10
2
64
1
134
1
61
136
42
10
9
9
8
2
12
336
3
56
3
24
20
367
10
286
1,184
13
4,866

Total
40
398
32
6
122
202
242
51
1,066
24
16
20
8
6
2
229
448
6
4
682
14
57
450
19
4
128
2
270
2
121
271
84
20
18
18
16
4
24
674
6
112
6
48
40
731
20
577
2,372
26
9,738

Table 2: Descriptive Statistics for Panel Data

Variable
Royalty
IPR
P_R&D
EMP
INVIMJP
BUYJP
OPEN

Mean
64.06
3.46

1995
Std. Dev
Min
750.52
1

Max
39241

1.19

0

4.86

34401.04 79516.54

147743044339

Mean
168.37

2001
Std. Dev
Min
3413.97
1

3.79

Max
1619391.01

1552368.73 97420.45

1527360

1

48189

1

12204

287.371064.301

324.891323.7896.31

601.40

-254

18740

4065.34 17671.36

140985581.64

506.27

17

345

93.48

93.45

22

341

5883.65 60376.11

1 2893831

86.91

97.26

FDI

2.64

3.42

0

13.7

4.65

5.51

-2.32

32.36

TAX

17.69

7.46

-1.56

36.64

8.85

6.84

-0.1

27

1995
Std. Dev
Min
1.90
0

Max
10.58

Mean
1.28

2001
Std. Dev
Min
2.10
0

Max
11.99

1.58

1.29

Variable (log)ln(Royalty )ln(IPR )

Mean
1.01
1.16

0.42

0

0.29

0

1.61

ln(P_R&D )

6.88

4.25

0

13.08

9.01

2.43

0

13.18

ln(EMP )

4.03

1.82

0

10.70

3.90

2.10

0

10.78

ln(INVIMJP )

1.05

2.10

0

9.84

0.97

2.00

0

9.41

ln(BUYJP )

5.60

2.82

0

12.92

5.49

3.11

0

14.88

ln(OPEN )

4.04

0.84

2.83

5.84

4.16

0.83

3.09

5.83

ln(FDI )

0.52

0.98

-4.61

2.62

1.17

0.90

-1.77

3.48

ln(TAX )

2.81

0.40

1.95

3.60

1.88

0.87

0.56

3.30

Table 3: Descriptive Statistics for Difference Data
Variable

Mean

Std. Dev

Min

Max

0.06

0.57

-6.21

6.46

-0.02

0.50

-5.10

8.87

ln ( P _ R & Dit P _ R & Dit −1 )

0.12

0.56

-3.17

3.78

ln ( EMPilt EMPilt −1 )

0.05

0.93

-6.57

7.94

ln ( INVIMJPilt INVIMJPilt −1 )

0.45

0.64

-2.38

3.76

ln ( BUYJPilt BUYJPilt −1 )

-0.78

0.61

-2.38

0.48

0.12

0.95

-8.56

8.48

0.10

0.16

-2.08

1.80

0.11

0.17

-0.80

1.39

ln ( Royaltyilt Royaltyilt −1 )ln ( IPR jt IPR jt −1 )ln ( OPEN jt OPEN jt −1 )ln ( FDI jt FDI jt −1 )ln (TAX jt TAX jt −1 )20Table 4: Estimation for Panel Data

Fixed[1]Random[1] Fixed[2]Random[2] Fixed[3]Random[3]ln(IPR : IPR level of host country)

1.065**(0.265)0.326**(0.088)0.942*(0.415)0.187(0.116)0.584(0.814)0.445**(0.144)ln(P_R&D : R&D expenditures of parent firm)

0.035(0.034)0.065**(0.009)-0.017(0.038)0.059**(0.010)-0.020(0.085)0.050**(0.014)ln(EMP : the number of affiliate's employee)

0.521**(0.057)0.551**(0.018)0.513**(0.062)0.552**(0.019)0.779**(0.135)0.508**(0.032)ln(INVIMJP : Import investment goods fromJapan)

0.039(0.050)0.217**(0.020)ln(BUYJP : Procurement from Japan)

0.081(0.053)0.048**(0.016)ln(OPEN : Trade openness)-0.069(0.437)0.206**(0.064)0.201(0.794)0.084(0.084)ln(FDI : FDI openness)-0.072(0.136)-0.084(0.047)-0.244(0.274)0.013(0.062)ln(TAX : The difference of host corporate tax
rate and Japan corporate tax rate)-0.278**(0.085)-0.160**(0.044)-0.394*(0.157)-0.196**(0.062)

Constant

The number of observations
The number of groups-2.565**(0.423)-2.065**(0.155)-0.974(1.665)-2.222**(0.289)-2.896(2.869)-2.315**(0.395)

3692
2664

3692
2664

3410
2517

3410
2517

1749
1433

1749
1433

F test that all uil = 0 (pooling vs fixed effects)

F = 2.46 Pr>F = 0.000 F = 2.37 Pr>F = 0.000 F = 1.88 Pr>F = 0.001

Breusch-Pagan Lagrangian multiplier test
(pooling vs random effects)

chi-sq = 412.28
Pr>chi-sq = 0.000

chi-sq = 347.23
Pr>chi-sq = 0.000

chi-sq = 90.74
Pr>chi-sq = 0.000

Hausman test (random effect vs fixed effect)chi-sq = 9.04
Pr>chi-sq = 0.028

chi-sq = 19.01
Pr>chi-sq = 0.004

chi-sq = 29.08
Pr>chi-sq = 0.000

Note: The numbers of parentheses present standard errors.
* and ** indicate the statistical significance with 5 percent and 1 percent, respectively.

21

Table 5: Estimation for Difference

OLS[1]

OLS[2]

OLS[3]ln ( IPRt IPRt −1 )0.244** 0.217*(0.072) (0.108)

0.294*(0.145)ln ( P _ R & Dt P _ R & Dt −1 )0.168** 0.141** 0.094**(0.023) (0.025) (0.034)ln ( EMPt EMPt −1 )0.101** 0.097** 0.086**(0.016) (0.017) (0.022)ln ( INVIMJPt INVIMJPt −1 )0.044
(0.030)ln ( BUYJPt BUYJPt −1 )0.074**(0.020)ln ( OPEN t OPEN t −1 )-0.000(0.107)-0.094(0.139)ln ( FDI t FDIt −1 )-0.004(0.029)-0.015(0.036)ln (TAX t TAX t −1 )-0.087** -0.083**(0.020) (0.024)

The number of observations

1897

1649

780

Note: The numbers of parentheses present standard errors.
* and ** indicate the statistical significance with 5 percent and 1 percent, respectively.

22