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FDI Inflows and R&D Activity in Developing Countries

Authors:
  • STIKes Karsa Husada Garut
  • Xiamen University Malaysia

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This study investigates the impact of FDI inflows on R&D activity in 48 developing countries for the 1996-2013 periods. The results based on the system Generalized Method of Moment (GMM) estimator show that FDI inflows discourage R&D activity in developing countries. This finding is consistent with the view that foreign R&D investment is a substitute for domestic R&D efforts. This suggests that firms in developing countries are more inclined toward imitation of the existing products rather than innovation of a new technology. However, domestic R&D activity appears to benefit from imports of machinery and equipment, stronger legal protections, better human capital and higher economic growth.
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509
IJEM
International Journal
of
Economics
and
Management
Journal homepage: http://www.ije m.upm.edu.my
FDI Inflows and R&D Activity in Developing Countries
Faculty of Management Multimedia University, 63100 Selangor, Malaysia
ABSTRACT
This study investigates the impact of FDI inflows on R&D activity in 48 developing
countries for the 1996-2013 periods. The results based on the system Generalized Method
of Moment (GMM) estimator show that FDI inflows discourage R&D activity in developing
countries. This finding is consistent with the view that foreign R&D investment is a
substitute for domestic R&D efforts. This suggests that firms in developing countries are
more inclined toward imitation of the existing products rather than innovation of a new
technology. However, domestic R&D activity appears to benefit from imports of machinery
and equipment, stronger legal protections, better human capital and higher economic
growth.
JEL Classification: F21, O30,
Keywords: Foreign Direct Investment; Research &Development; Generalised Method of
Moment; Developing Countries
Article history:
Received: 5 Jun 2018
Accepted: 21 November 2018
* Corresponding author: Email: wazman@upm.edu.my
D
W.N.W. AZMAN-SAINIa *, M.Z.M. FARHANa , CHEE-LIP TEEb AND YIN-LI TUNa
Faculty of Economics and Management Universiti Putra Malaysia, 43400 Selangor, Malaysia
a
b
Int. Journal of Economics and Management 12 (S2): 509-521 (2018)
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International Journal of Economics and Management
INTRODUCTION
Foreign direct investment (FDI) by multinational corporations (MNCs) is viewed as an important channel for
host countries (especially the developing ones) to access new technologies that are available at the world’s
frontier.
1
MNCs have always been linked to superior technologies, patents, trade secrets, brand names,
management techniques and marketing strategies (Dunning, 1993). MNCs are known for their huge investment
in research and development (R&D) activities and they also hire a large number of professional and technical
employees (Markusen, 1995). In addition, they invest substantially to improve the quality of their workforce
through extensive trainings (Fosfuri et al., 2001). Since knowledge cannot be completely internalized, some of
the benefits linked to FDI may be transmitted to local firms once MNCs have established their subsidiary in
host countries. This is expected to enhance the productivity of local firms, leading to the expansion of local
business activities. Given that MNCs has many benefits to offer, policymakers believe that FDI should be an
integral part of development strategies for counties that wish to improve their economic performance.
Since the 1980s, many countries have liberalized their policies on FDI by relaxing the restriction on
foreign firms and adopting FDI-enhancing policies. According to UNCTAD (2013) an annual average of 102
changes in FDI regulation were made during the 1991-2012 period. Of these changes, 84% changes were made
on liberalization, promotion and facilitation to create a more favourable environment for investment prospect.
As a result of policy changes that encourage more investments by MNCs, FDI inflows into both developed and
developing countries have increased significantly over the past few decades, especially in developing countries.
Specifically, FDI flows into developing and transition economies have increased from around US$3.8 billion in
1970 to around US$690 billion in 2010. For the first time in history, FDI inflow to developing and transition
countries accounted for more than half of the global FDI inflow in 2010. Over the periods, the average growth
of global FDI is 13% per year with the highest growth rate of 55% was recorded in 1999. In fact, the performance
of FDI is much better than the growth of world’s output which was recorded only 2.67% per year.
Given that FDI flows have increased significantly in the past few decades, several studies have examined
the impact of FDI on host country economic performance. However, most studies have mainly focussed on the
impact of FDI on domestic output growth (see for example Borensztein et al., 1998; Alfaro et al., 2004; Azman-
Saini et al 2010, among others). The FDI-growth link has been tested using different procedures, data sets and
time periods, and the findings show mixed results. While there is a plethora of research on the influence of FDI
on output growth, the potential impacts of FDI on other local activities such as research and development (R&D)
activity has been largely ignored. However, ignoring the impact of FDI on R&D activity may lead to a
significant underestimation of the overall impact of FDI on the economy.
There are several reasons to expect that domestic innovation activity such as R&D may benefit from FDI
inflows, thus allowing domestic firms to improve their technological base. First, competition introduced by
MNCs may encourage local firms to make a more efficient use of existing resources and technology or even to
adopt new technologies (Markusen & Venables, 1999; Wang & Blomstrom, 1992). MNCs presence may also
promote backward linkages between MNCs and their local suppliers by means of technological know-how
transfer, staff training, and so on. These vertical spillovers can then enhance the innovation capability of local
suppliers (Rodrıguez-Clare,1996). It should also be noted that FDI inflows may also have a negative impact on
local R&D activity as MNCs presence will allows domestic firms to adopt and internalise foreign technology
at lower cost. Second, MNCs presence may has demonstration effects on local R&D activity. MNCs may inspire
local firms to develop new products and processes because every successful innovation by MNCs will allow
local firms to study the attributes of the newly invented product and improve upon it. This allows local firms to
begin their R&D activity from a higher level of technology. Finally, technology spillovers may take place
through labour mobility (Fosfuri et al., 2001; Glass &Saggi, 2002). Local firms may hire workers who were
trained by MNCs with latest technology and this is expected to improve local firm’s innovation capability.
MNCs are known to be among the most technologically advanced firms, as they are responsible for a large part
of the world's R&D expenditures (Borensztein et al., 1998). They also hire a large number of technical and
professional workers and provide extensive trainings for their workforce (Markusen, 1995). However, this
spillover channel may have negative impact as MNCs always attract the best workers from local firms by
offering higher wages (Sinani & Meyer, 2004).
1
Apart from new technology, MNCs presence is also viewed as a source of new capital injection and additional investment in both human
and physical capital. It also contributes to foreign exchange earnings for local economies and employment creation (de Mello,1999).
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International Journal of Economics and Management
This paper examines the impact of FDI on R&D activity in developing countries by employing a system
generalised method of moment (GMM) estimator proposed by Arellano and Bond (1991), Arellano and Bover
(1995), and Blundell and Bond (1998). The choice of this estimator over other alternatives is because of its
ability to control for country-specific effects, dynamic effects, as well as endogeneity problem. The findings
show that FDI has a negative impact on R&D activity. Meanwhile, import, protection of property right, human
capital and income growth appear to have positive impacts on domestic R&D activity.
The rest of the paper is organised as follows. Next section summarizes the findings on past literature. The
following section highlights the empirical model. Then, the descriptions of methodology and data are provided.
After that, empirical results are presented. The final section concludes.
LITERATURE REVIEW
FDI is widely accepted as an important ingredient for development strategy in many countries (especially the
developing ones). The adoption of FDI-stimulating policies and provision of incentives (i.e., tax incentives
and/or subsidies) by many countries are based on the expectation that MNCs presence will bring significant
benefits to the local economy. MNCs have been linked to superior technologies, patents, trade secrets, brand
names, management techniques, and marketing strategies (Dunning, 1993). Besides that, MNCs are known for
huge spending in R&D activity and they are technologically far superior compared to local firms (Borensztein
et al., 1998). Additionally, they employ a large number of technical and professional workers (Markusen, 1995).
Through FDIs, the recipient countries are granted instant access to advanced technology available at the world’s
frontiers that may benefit local firms.
A large body of the existing literature on FDI spillovers has focussed on the growth-effect of FDI with
inconclusive findings. In a review of firm-level studies on FDI spillovers Gorg and Greenway (2004) find that
only six out of 25 studies find some positive evidence of FDI spillovers. Meanwhile, Herzer et al (2008) re-
examines the FDI-led growth hypothesis for 28 developing countries using cointegration techniques on a
country-by-country basis. They find that there is no effect of FDI on growth (both long-term and short-term) in
most countries. In fact, there is not a single country where a positive unidirectional long-term effect from FDI
to GDP is found. However, several recent studies suggest that the growth-effect of FDI is dependent on local
conditions. Several factors have been put forward in the literature such as human capital (Borensztein et al.,
1998), financial market (Hermes and Lensink, 2003; Alfaro et al., 2004; Durham 2004) and quality of institution
(Azman-Saini et al., 2010; Algualcil et al., 2011), among many others
Several studies examine the impact of technology transfer embodied in FDI on domestic productivity.
For instance, van Pottelsberghe and Lichtenberg (2001) extend Coe and Helpman’s (1995) work by
incorporating inward and outward FDI as channels for technology transfer.
2
They analyse 13 countries find that
foreign R&D spills over across borders via imports and outward FDI channels but not through inward FDI.
However, several recent studies reveal that inward FDI is an important channel for enhancing domestic
productivity (see for example, Bitzer and Kerekes, 2008; Zhu and Jeon, 2007; Savvides and Zachariadis, 2005;
Ang and Madsen, 2013).
Apart from the impact on domestic output and productivity, FDI may also affect domestic innovation
performance. However, empirical studies of FDI spillover effects on local innovation performance are rare and
mainly focus on micro level. FDI inflows may increase competition in the domestic market by offering similar
products that have been locally produced, but with better quality and at cheaper prices. This puts pressure on
local firms to produce better products and encourage them to engage in R&D activity. However, some may
argue that FDI discourages R&D activities when local firms merely imitate newly introduced imported products
which eventually diminish the creativity and innovation in the long run. Generally, the findings reveal mixed
evidence. For instance, one of the earliest studies by Co (2000) compare the effect of greenfield FDI and non-
greenfield FDI on domestic R&D activities in the United States. Using industry-level data, the author find a
significant positive impact only when there is a continuous flow of non-greenfield FDI. This finding is
consistent with Cheung and Lin (2004) who also find positive effects of FDI on the number of domestic patent
2
Coe and Helpman (1995) is the pioneering work on R&D spillovers. The authors assess R&D spillovers across 21 OECD countries plus
Israel and provide empirical evidence of a positive relationship between R&D expenditures and total factor productivity They find that not
only domestic R&D contributes significantly to productivity growth but also (trade-embodied) foreign R&D.
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International Journal of Economics and Management
applications in China. This finding was further supported by Fan and Hu (2007) who find that FDI has a positive
impact on the R&D effort by Chinese firms only in sectors with more foreign presence. However, the overall
impact of MNCs presence on R&D activity for all firms (firms in all sectors with or without foreign presence)
is negative. Several studies find that the impact of FDI on innovation is dependent on other conditions. For
instance Kathuria (2008) examines Indian firms in the high-tech industries during the post-reform period. The
author found that the effect on R&D is negative during the earlier phase of liberalisation. In the later phase, the
effect is found to be not significant. More recently, Crescenzi et al. (2015) examine the U.K firms and find that
domestic firms in sectors with greater investments by MNCs show a stronger innovative performance.
Furthermore, they find that the internationalization of both their market engagement and ownership structure is
the main driver of this effect.
Although evidence using micro-level data are voluminous, studies at macro-level are relatively limited.
Alvi et al. (2007) examines if patent protection and technology transfer facilitate R&D in a sample of 21
countries (developed and emerging countries). The results suggest that there is threshold effect such that FDI
has a positive effect only if the country depends heavily on FDI inflows. Specifically, they find that the threshold
level of FDI to be three per cent (of GDP). Moreover, they find that patent protection has a positive effect on
R&D which weakens at high levels of protection. In a similar study, Wang (2010) examines the determinants
of R&D investment in 26 OECD countries using Extreme Bound Analysis approach and find that the transfer
of foreign technology via trade and FDI had a robust negative impact on R&D. Moreover, human capital (i.e.
tertiary education and the proportion of scientific researchers) appear to be robust in explaining R&D
investment. Meanwhile, in a study of 44 countries (OECD plus developing) Ang (2011) find that the
implementation of financial reform policies is negatively associated with accumulation of new ideas. However,
the impact of financial development is found to be positive.
Several studies examine the impact of import on R&D activity and many of them focus on the micro
level analysis. Lee (1996) investigate the Korean manufacturing firms and find that the firms utilizing imported
technology are more willing to engage in R&D only when there is a formal R&D institution. Funk (2003) find
that the U.S manufacturing firms which are not involved in foreign sales are affected by the increased
competition induced by imports, hence reducing their investment in R&D.
3
However, the author cautions that
this result may be biased as it does not consider the embedded research or knowledge in imported goods. In the
case of Chinese firms, Li et al. (2011) find that public R&D subsidies and disembodied technology imports
positively impact on firms' private R&D, while nonhightech product exports and embodied technology imports
do not have positive effects. Moreover, they find that hightech product exports have no significant impact on
R&D investment. Meanwhile, Katrak (1989) find evidence of a positive relationship between technology
importing firms and their decision to engage in R&D using data from India. However, R&D investment
allocation depends on the cost of importing the technology. Recently, Parameswaran (2010) reveal that export,
in general, encourages investment in innovation by Indian firms. Moreover, the impact of import competition
depends on domestic market structure. It promotes investment in R&D only when domestic market is highly
concentrated, otherwise the effect is negative.
A patent law or other intellectual property right (IPR) protections can provide an incentive for the firms
to allow temporary technological rents of knowledge (Edquist and Johnson, 1997). Thus, protections laws may
encourage firms to engage in R&D. Varsakelis (2001) examine the impact by using a cross country analysis for
selected 50 countries. The empirical results show that countries with a strong patent protection framework are
willing to invest more in R&D. These findings were further supported by Falk (2006) and Wang (2010).
However, Alvi et al. (2007) find that strict protection laws tend to demotivate R&D activities and can encourage
imitation of imported products.
MODEL SPECIFICATION
This study utilizes a model which is similar to Wang (2010). The model can be expressed as follows:
3
Import-induced competition arises as more imported products penetrate the local market and compete with local products. Funk (2003)
argue that import-induced competition tends to reduce the R&D efforts by domestic firms by increasing the first mover advantages.
Domestic firms are less to engage in research activities due to lack of information on embedded knowledge in imported products.
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International Journal of Economics and Management
RDi,t1RDi,t-1+ β2FDIi,t + β3Zi,t + ηi+ it (1)
where i is country index, t is time index, RD is R&D intensity (gross R&D expenditure over GDP), FDI is
foreign direct investment, Z is a vector of conditional variables which are believed to affect R&D activity, ηi is
country-specific effect and it is the usual error term. The group of conditional variables includes human capital,
import of high technology products, investment in physical capital, intellectual property right and income
growth.
METHODOLOGY
This study employs the generalized method-of-moments (GMM) panel estimator which was first proposed by
Holtz-Eakin et al. (1990). This method was then extended by Arellano and Bond (1991), Arellano and Bover
(1995), and Blundell and Bond (1998). This estimator has several advantages. It can control country-specific
effects, dynamic effects and simultaneity bias caused by the endogenous explanatory variables. This
methodological procedure has been used in the of finance-growth link (Levine et al., 2000; Beck et al., 2000),
FDI-growth link (Alguacil et al 2011), R&D spillovers (Chee-Lip et al., 2015), among many others. Arellano
and Bond (1991) suggested that the country-specific effect to be eliminated by transforming Equation 1 into
first differences, as follows:
(RDi,t−RDi,t-1) = β1(RDi,t-1−RDi,t-2) + β2(FDIi,t−FDIi,t-1) + β3(Zi,t−Zi,t-1) + (i,ti,t-1) (2)
Furthermore, Arellano and Bond (1991) proposed the use of lagged levels for the regressors to identify
the possible simultaneity bias of explanatory variables and the correlation between (RDi,t−1−RDi,t−2) and
i,t−εit−1). However, this is only valid under the condition that the error terms are not serially correlated.
According to Arellano and Bond (1991), the following moment conditions are applied:
           (3)
           (4)
             (5)
Alonso-Borrego and Arellano (1999) and Blundell and Bond (1998) argued that the lagged levels of the
variables can be inefficient when the explanatory variables are persistent. This may lead to biased parameter
estimates in small samples and a larger asymptotic variance. Blundell and Bond (1998) developed a procedure
that transforms these instruments to become exogenous to the fixed effects. Under this procedure, it is assumed
that changes in any instrumenting variable are uncorrelated with the fixed effects in Equation 1. Therefore,
according to Arellano and Bover (1995), additional moment conditions for the second part of the system (the
regression in levels) are to be set as follows:
           (6)
            (7)
           (8)
There are two specification tests to determine the consistency issue of the GMM estimators. First, the
Hansen Test (1982) overidentifies the joint validity of the instruments. The null hypothesis is that the
instruments are not correlated with the residuals. Under the null hypothesis of joint validity in all instruments,
the empirical moments have zero expectations and the J-statistic is distributed as a χ2 with degrees of freedom
equal to the degree of overidentification. Secondly, in order to identify autocorrelation besides the fixed effects,
the Arellano-Bond test is applied to the residuals of the first difference. The Arellano-Bond test for
autocorrelation examines the hypothesis of no second-order serial correlation in the error terms of the first
difference. Failure to reject the null hypotheses in both tests provides support to the estimated model.
There are two variants of GMM estimators, namely one- and two-step estimators (Arellano and Bond,
1991). The one-step GMM estimator utilises weighting matrices that are independent of estimated parameters,
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International Journal of Economics and Management
while the two-step estimator employs optimal weighting matrices
4
. This adjustment makes the two-step
estimator asymptotically more efficient than the one-step estimator. Consequently, this paper uses the moment
conditions presented in Equation 3 to Equation 8 and employs the two-step estimator.
DATA DESCRIPTION
The aim of this study is to examine the impact of FDI inflows on local R&D in developing countries. In 2013,
there were 76 developing countries listed by the World Bank. After omitting countries with missing data, small
island economies and outliers, our final data set consists of 48 countries. This balanced panel data set covers
the period from 1996 to 2013, where the average data are taken for every three years. To measure the R&D
intensity, this study uses the annual ratio of gross expenditure on R&D (GERD). This indicator is widely used
in the literature (see for example, Alvi et al., 2007; Wang, 2010; Ghazalian, 2012). The data were retrieved from
the UNESCO Institute of Statistics (UIS) database. We employ a ratio of FDI inflows to GDP as a proxy for
FDI and the data were collected from the World Development Indicators database. Additionally, we include
import of machinery and equipment expressed as a ratio to GDP and the data were retrieved from the World
Trade Organization (WTO) database.
Based on the endogenous growth theory and production function theory, human capital stock and
scientific researcher are important for R&D activity. This study employs the human development index (HDI)
as a proxy for human capital. The index is calculated by taking the average of two indicators, the schooling
years and the return on education. The data were obtained from the Penn World Table (PWT) database. The
data on scientific researcher proportion is measured by taking the total researchers to the total employment ratio,
available from the UIS database. Furthermore, we include the protection property right index compiled and
published by the Fraser Institute (Gwartney et al., 2013). The data are collected based on a survey on 150 partner
institutes of recognized departments of economics in national universities, independent research institutes, or
business organizations. This inclusion of this variable is based on the fact that protection measures related to
intellectual property rights is expected to reduce the uncertainty that surrounds the possibility of
misappropriation of new invention. They also serve as an incentive for firms to engage in R&D because it allows
firms to enjoy temporary technological rents. In addition, we include income growth based on the prediction of
R&D-driven growth model which predict that incentives to invest in R&D is strongly tied to the size of the
economy. Larger market implies stronger incentive to invest in R&D, which in turn result in faster growth. The
data were taken from the WDI. Finally, we also include gross fixed capital formation to GDP as a proxy for
investment in physical capital and the data were taken from the World Development Indicator database. Physical
capital formation is widely known for their contribution for national output. Investment in physical capital could
either complements R&D investment (from the viewpoint of aggregate production) or substitutes R&D because
they compete for limited national resources (Bebczuk, 2002). Table 1 provides a summary of variables used in
this study.
Table 1 List of Variables
Variable
Proxy
Research and development
Gross expenditure on R&D (GERD) to GDP
FDI
FDI inflows to GDP
Import
Total import of machinery and equipment to GDP
Human capital
Human Development Index
Scientific Researcher
Total researchers to total employment
Property Right
Protection of Property right index
Income growth
GDP per capita growth rate
Investment
Gross fixed capital formation to GDP
Figure 1 displays R&D spending and FDI inflows for the sampled countries using data averaged over the
entire period (19962013). The fitted line shows a weak positive relationship between the FDI and growth
(R2=0.074). This observation shows that countries with higher FDI inflows tended to have higher level of R&D
activity. However, this simple correlation analysis does not imply any causal effect between R&D and FDI
which is precisely the type of relation that we are interested in this study.
4
Specifically, the moment conditions are weighted by a consistent estimate of their covariance matrix.
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International Journal of Economics and Management
Figure 1 Scatterplot of FDI versus R&D
EMPIRICAL FINDINGS
This section presents the empirical findings of this study. Table 2 shows the mean, median, standard deviation,
minimum and maximum values of all variables. For the dependent variable (RD), the mean value is 0.40 per
cent and the standard deviation is 0.363, while the maximum value of intensity is 2.66 per cent and the lowest
is 0.01 per cent. Our main variable, FDI, has a mean value of 3.74 per cent with a standard deviation of 3.13.
The minimum value of FDI intensity is 0.003 per cent, while the maximum value is 25.118 per cent. Similar to
R&D and FDI, the rest of the variables show considerable variation in data across countries.
Table 2 Descriptive statistics
Variable
Mean
Median
Std. Dev.
Min
Max
R&D intensity
0.40
0.29
0.36
0.01
2.66
FDI
3.74
2.99
3.13
0.003
25.11
Import
0.30
0.29
0.11
0.02
0.66
Human Capital
23.96
24.89
5.17
5.48
33.06
Scientific Researcher
2.24
1.05
3.10
0.04
25.25
Property Right Index
44.44
45
15.40
3.00
99.66
Investment
21.00
20.53
6.21
2.91
46.47
Income Growth
4.59
4.67
3.49
-4.99
35.45
Table 3 present the results of estimating the impact of FDI and other variables on domestic R&D activity.
Results in Column 2 are based on the one-step estimator, while results in Column 3 are obtained from the two-
step estimator which is our preferred estimator. The result of one-step estimator does not pass the speficication
test and therefore unreliable. Interestingly, our preferred equation pass the Hansen and AR(2) specification tests
which suggest that the models are adequately specified and the instruments used are valid. The results reveal
that all variables are significant in both one-step and two-step estimations, except for the investment, which is
only found to be significant in the one-step estimation.
Looking at the core variable, FDI intensity shows a negative effect on R&D activities in host countries
with the elasticity range between 0.7185 and 0.7685. This finding complements Fan and Hu (2007) and Kathuria
(2008) who find the negative impact of FDI on R&D. In addition, Wang (2010), also find that foreign technology
inflows (which include import and FDI) exert a negative impact on R&D activity in OECD countries. This
finding is consistent with the view that FDI inflows and domestic R&D activity are substitutes as MNCs
presence will allows domestic firms to access foreign technology at lower cost. Given that firms in developing
countries have limited resources for R&D activity, they may improve their technological base by interacting
with R&D leaders through licensing, cooperation, and so on. Another possible reason for this finding is that
local firm in developing countries poses poor technological absorption and innovative capability. Consequently,
domestic firms are discouraged from engaging in R&D activities as they are more inclined towards imitation of
newly introduced products.
Argentina
Armenia
Bolivia
Brazil
Burkina Faso
Burundi
China
Colombia
Costa Rica
Congo
Ecuador
Egypt
El Salvador Gambia
Honduras
Hungary
India
Indonesia
Iran
Iraq Jamaica
Jordan
Kazakhstan
Kyrgyzstan
Madagascar
Malaysia
Mexico
Morocco
Myanmar
Pakistan Panama
Paraguay
Peru
Philippines
Moldova
Romania
South Africa
Sri Lanka
Sudan
Tajikistan
Thailand
Macedonia
Tunisia
Turkey
Uganda
Ukraine
Tanzania
Zambia
3.5 44.5 5
3.4 3.5 3.6 3.7 3.8
FDI
RD
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International Journal of Economics and Management
Meanwhile, our finding on imports of machinery and equipment shows a positive and significant effect
on local R&D activity. Interestingly, this finding appears to be contradicting to the finding on FDI which has a
negative impact on R&D. However, our finding is consistent with the view that trade liberalisation leads to
greater competitive pressure on domestic firms. Specifically, openness to imports will force domestic firms to
improve the quality of the products, to reduce management inefficiencies, and most importantly, to increase the
technological base by investing more on R&D activity in order to stay competitive.
Protection of property right appears to have the biggest impact on R&D activity in developing countries
with the elasticity of about one. This finding is consistent with the view that protection of property right,
especially protection of intellectual property, serves as an effective tools for promoting inventions by providing
inventors with a limited monopoly over a technological solution. The finding is consistent with Hu and Mathews
(2005), Wu et al. (2007) and Alvi et al. (2007). The results on human capital and scientific researcher reveal
that both variables are found to be positive and statistically significant in both models. This finding is consistent
Wang (2010) who find that both education and scientific researchers are robust determinants of R&D intensity
with positive impact in OECD countries. Investment in physical capital is found to be significant only in model
using one-step estimator with elasticity of 0.3379. This finding is in line with the view that investment in
physical capital complement R&D activity in developing countries. In the case of income growth, the result
indicate that the variable is an important determinant of R&D activity as the estimated coefficients turn out to
be positive and significant in both models. Specifically, the elasticity ranges from 0.1267 to 0.1455. This is in
line with the view that larger market implies stronger incentive for investors to generate new knowledge. This
finding is consistent with Braconier (2000) and Hartman (2003).
Table 3 Results of GMM estimation
System GMM
Variables
One-Step
Two-step
Lag R&D
0.9031a
(0.0573)
0.8424a
(0.0280)
FDI
-0.7185a
(0.1850)
-0.7685a
(0.1096)
Import
0.2485b
(0.0997)
0.2094a
(0.0361)
Property Right
1.0369c
(0.1922)
1.0017a
(0.1742)
Investment
0.3379c
(0.1923)
0.1082
(0.0925)
Human Capital
0.1717c
(0.0882)
0.0748c
(0.0361)
Scientific Researcher
0.8191a
(0.3100)
0.6365a
(0.2347)
Income Growth
0.1455a
(0.0527)
0.1267a
(0.0222)
T3
-0.04741
(0.0294)
-0.0474
(0.0292)
T4
-0.1057a
(0.0334)
-0.1057a
(0.0402)
T5
-0.0508
(0.0319)
-0.0508
(0.0405)
T6
-0.0282
(0.0303)
-0.0282
(0.0455)
Hansen test (p-value)
0.0000
0.7426
AR (1) test (p-value)
0.0695
AR (2) test (p-value)
0.6930
Observations
235
235
Notes: a, b, c indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Figures in parentheses are standard errors. All
variables are in logarithmic form. T3, T4, T5 and T6 are time dummies for 2002-2004, 2005-2007, 2008-2010 and 2011-2013 periods,
respectively.
As a robustness check, we identify potential outliers in our sample and to ensure that the negative link
established between FDI and R&D is robust and not driven by outlier observations. In order to test for outlier
presence, this study employs the DFITS statistics as suggested by Belsley et al. (1980).
5
The test shows that
5
The DFITS test identifies observations with high combination of leverage and residual. The test is computed as
)1/( jjjj hhrDFITS
,
where
j
r
is studentized residual given by
)1/( )( jjjj hser
with
)( j
s
refer to the root mean squared error (s) of the regression equation
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International Journal of Economics and Management
Jordan and Ukraine are true outliers as the absolute DFITS scores for these countries are 1.9933 and 1.2220,
respectively, which is greater than the threshold value of 0.8433. This means that Jordan and Ukraine have high
combinations of residuals and leverage points and they fall relatively far from the rest of the observations. This
result suggests that the negative link between FDI and R&D documented earlier may be influenced by outliers.
Figure 2 illustrate the distributions of leverage point and residual for all countries in our sample. Clearly, the
figure shows that Jordan and Ukraine have high combinations of residual and leverage.
Figure 2 Scatter plot of leverage versus residual squared
We re-estimate a new sample with the exclusion of Jordan and Ukraine. The results are presented in
Table 4. Interestingly, the results show that the impact of FDI on R&D remains intact as the p-value for the
estimated coefficient on FDI is less than one per cent for both one-step and two-step estimators. Therefore, our
interpretation on the negative impact of FDI inflows on local R&D activity is unchanged. In addition, almost
all explanatory variables are found to be significant at the 10 percent level. However, the coefficient on
investment is found to be significant only in model utilizing one-step estimator. More importantly, the
specification tests indicate that the preferred model (i.e. two-step estimator) is adequately specified and the
result is not affected by simultaneity bias. However, the one-step estimation does not pass the Hansen test as its
p-value is less than 0.05. Generally, this supports our previous interpretation regarding the impact of FDI inflows
in discouraging R&D investment in the host countries. The result also shows that the link is robust and not
driven by outlier observations.
Table 4 Results of GMM estimation with exclusion of outliers
Variables
One-step
Two-step
Lag R&D
0.9268a
(0.0577)
0.8496a
(0.0344)
FDI
-0.6312a
(0.2001)
-0.7205a
(0.1111)
Import
0.1990c
(0.1045)
0.2066a
(0.0347)
Property Right
1.0259b
(0.4013)
0.7207a
(0.2655)
Investment
0.3375c
(0.2006)
0.0569
(0.1014)
Human Capital
0.1889c
(0.0965)
0.1198b
(0.2655)
Scientific Researcher
1.1771a
(0.4233)
0.7613a
(0.2152)
Income Growth
0.1467b
(0.0603)
0.1426a
(0.0271)
T3
-0.0546c
(0.0324)
-0.0416a
(0.0152)
T4
-0.1277c
(-0.0389)
-0.7663a
(0.0206)
with jth observation removed, and h is leverage statistic. Following Belsley et al. (1980), an observation is considered as outlier if the
absolute DFITS statistic is greater than
nk /2
, where k denotes the number of explanatory variables and nthe number of countries.
Argentina
Armenia
Bolivia
Brazil
Burkina Faso
Burundi
China
Colombia
Costa Rica
Ecuador
Egypt
El Salvador
Gambia
Honduras
Hungary India
Indonesia
Iran Jamaica
Jordan
Kazakhstan
Kyrgyzstan
Madagascar
Malaysia
Mexico
Morocco
Pakistan
Panama
Paraguay
Peru
Philippines Moldova
Romania
South Africa
Sri Lanka Sudan
Tajikistan
Thailand
Macedonia
Tunisia
Turkey Uganda
Ukraine
Tanzania
Zambia
0.2 .4 .6 .8
Leverage
0 .02 .04 .06 .08 .1
Normalized residual squared
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International Journal of Economics and Management
Table 4 Cont.
T5
-0.0634c
(-0.0359)
-0.0234
(0.0229)
T6
-0.0495
(0.0358)
0.0006
(0.0276)
Hansen test (p-value)
0.0001
0.9498
AR (1) test (p-value)
0.0751
AR (2) test (p-value)
0.7968
Observations
225
225
Notes: a, b, c indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Figures in parentheses are standard errors. All
variables are in logarithmic form. T3, T4, T5 and T6 are time dummies for 2002-2004, 2005-2007, 2008-2010 and 2011-2013 periods,
respectively.
CONCLUSIONS
Developing countries view FDI as an important channel for them to tap advance technology developed by R&D
leaders. Therefore, many countries adopt FDI-stimulating policies by offering various incentives to MNCs. This
paper examines the impact of FDI inflows on R&D activity using a data set comprising 48 developing countries
for the 1996-2013 periods. The results reveal that FDI inflows tend to discourage domestic R&D activity which
suggests that foreign R&D investment is a substitute for domestic R&D efforts. Therefore, developing countries
with limited resources for R&D activity should focus on R&D activity on areas with a comparative advantage
and imports other technologies from foreign countries at lower costs. Moreover, this study reveals that import,
protection of property rights, human capital (both education and number of scientific researchers), and income
growth are important for local R&D performance. Therefore, developing countries should embrace trade
liberalization by reducing tariff and non-tariff barriers, and strengthen the legal protection policies (such as
protection of intellectual property and patent law).They should also improve the quality of education system
and accumulate more human capital to engage in R&D activity. Finally, they should also adopt growth-
enhancing policies as higher growth is expected to promote R&D activity.
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ACKNOWLEDGEMENT
The first author acknowledges financial support from Universiti Putra Malaysia research grants
(#GP/2018/9651600 and #GP-IPB/20149440903).
... For example, Vaona and Pianta (2008) investigated the effects of import of technology on innovation for some developing countries. Also, Azman-Saini et al. (2018) examined the impact of FDI on innovation for the selected European countries. In addition, some studies have employed nonlinear panel data models such as Probit, Tobit, and Logit at the firm level depending on the structure of the dataset in recent years in the literature (e.g. ...
... and panel SGMM in the area (e.g. Salomon & Shaver, 2005;Liu & Buck, 2007;Ramani et. al., 2008;Sun & Du, 2010;Golovko & Valentini, 2014;Rao et. al., 2024). However, the panel SGMM has not been sufficiently considered in most studies. To the best of our knowledge, there are only a few studies in the literature that use SGMM (e.g. Hsu & Chuang, 2014;Azman-Saini et. al., 2018;Wang & Choi, 2023). ...
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This is the second edition of the celebrated volume by Professor John H. Dunning, first published in 1993, which has now been not only updated but also enriched with the addition of a number of new topics. This addition was not least due to the expertise of the co-author, Sarianna Lundan, in the institutional aspects of international business and the internal governance of transnational corporations (TNCs). It is a comprehensive synthesis of all the theories in International Business based on extremely rich data evaluation in almost all fields of TNC activities and their environment. It is a “creative masterpiece which unbundles the DNA of the field of international business” as described by Alan Rugman in his assessment of this volume.
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