2. Technological Forecasting & Social Change 173 (2021) 121169
2
BRICS reflects the objective revival of new world actors, developed and
developing countries (Sadovnichiy et al., 2016). Most developing
countries do not have innovation and strong policies to provide the
necessary economic results. However, the BRICS economy has proven
itself effective in competing with developed countries such as the G-7
countries. Over the next few years, the growth generated by BRICS
through innovation will probably become a more important force in the
world economy (World Bank, 2011). Although the BRICS recently
described strong economic growth, they continue to experience a sig
nificant trend of their economic stability towards innovation. Thus,
BRICS has the prospect of forming an economic bloc that influences the
current separation of G-7 status (Chang and Caudill, 2005).
Several good empirical studies have shown that there is a positive
relationship between innovation and economic growth, and innovation
has now become a major component of economic growth worldwide
(Sesay et al., 2018; Castano, Méndez, and Galindo, 2016; Dittrich and
Duysters, 2007; Schumpeter, 1934). Literature shows the relationship
between economic growth, innovation consisting of R&D expenditures,
trademarks, and patents making references to developed and developing
countries, using macroeconomic and microeconomic data (Sesay et al.,
2018; Franco, and de Oliveira, 2017; Rabiei, 2011). There is the accel
eration of BRICS economic growth, however, whether the relationship
between innovation and economic growth in BRICS is comparable to G-7
countries is still unclear (Stiglingh, 2015). Most of these studies
employed mean-based econometric estimators including vector
error-correction (VEM) model, pooled ordinary least square (POLS) and
fully modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary
Least Squares (DOLS) estimators. These estimators use averages to
predict outcomes but do not permit the relationship to be witnessed over
the time frame. In many cases, inconclusive findings may emerge from
different periods and mean-based approaches used by these authors.
Therefore, this study used a panel vector autoregressive model (PVAR)
that accounts for heterogeneity and endogeneity as well as
time-invariant non-observed fixed effects. Unlike the mean-based esti
mators, the PVAR, through its variance decomposition and impulse
response function allows the behavior of the variables to be observed
over a while. Hence, detail and trend results could be revealed and that
will be useful for policy decisions. Again, though, BRICS and G7 coun
tries have been widely studied, there is limited research that compares
these two country blocks which creates a gap hence this research.
Therefore, comparative studies on the subject through a systematic and
analytical research approach will contribute significantly to the current
body of knowledge in this field (Mostafa and Mahmood, 2015).
The overall objective of this study is to investigate the impacts of
R&D, trademark, and a patent on the economic growth among BRICS
and G7 countries. This objective has been separated into two sub-
objectives. (1) to examine how the economic growth of BRICS and G7
react to shocks from R&D, trademark, and patent by applying a PVAR
approach over the period 2000–2017. (2) to investigate whether the
reaction of economic growth to shocks from R&D, trademark, and patent
differ between BRICS and G7 countries.
The contribution of this study to literature and knowledge is as fol
lows. First, this study adds to the literature on innovation and growth by
giving empirical data on how R&D, trademark, and patent impact eco
nomic growth. This research, on the other hand, presents a comparative
analysis for the G7 (advanced group) and the BRICS (emerging group)
during a given period. This allows for a comparison of the behavior of
innovation in terms of growth results in the BRICS and G7 nations.
Second, this study implemented the PVAR model in the Generalized
Method of Moments (GMM) style to investigate the topic. Unlike other
mean-based estimators (OLS, POLS, DOLS, VCM) commonly employed
in innovation-growth studies, the PVAR estimator gives both mean-
based and trend findings for a better understanding of the issue. For
instance, through the variance decomposition and impulse response
function of the PVAR estimator, this study revealed that the marginal
impact of R&D for G7 and BRICS for the early 2000s. However, the G7′
s
marginal influence stayed steady from the mid-2000s forward, whereas
the BRICS’ marginal impact dropped during the same period. This
finding suggests that the impact of R&D on G7 economic growth is likely
to be stable in the future while that of BRICS is likely to reduce in the
future. Unlike mean-based estimators, which may have produced a
mean result, the PVAR yields fascinating results and allows the series’
behavior to be monitored (Love and Zicchino, 2006).
The rest of the study was in this manner: Section 2 presents the
literature review related to the study, Section 3, deals with the meth
odology. It covers variables and data, a test of normality, econometric
modeling, and endogeneity check. Section 4 deals with results and dis
cussions. It presents the results based on the objectives of the study.
Finally, Section 5 present the conclusion, potential policy recommen
dations and limitations, and further research directions.
2. Literature review
Innovation is essential for sustainable growth and economic devel
opment (Gerguri and Ramadani, 2010), as a result, the connection be
tween economic growth and innovation has become a great interest for
researchers, this concept is well-debated literature. This concept has its
origin in the research realized by (Solow, 1956), who pointed out the
existence of a long-term relationship between economic growth and
innovation. (Schumpeter, 1912, 1939) makes the distinction between
economic growth and economic development. Thus, from his point of
view, the economic growth represents a slowly and progressive change
of the economic system, resulting from exogenous factors of the eco
nomic system and on the other hand, the economic development which
is generated by discontinuous internal changes caused by economic in
novations, coming from the economic system. The empirical studies
pointed out the relationship between economic growth, innovation, the
research, and development expenditures in both developed and
emerging markets.
Ulku (2004) has investigated the relationship between economic
growth, research and development expenditures, innovation for 20
OECD countries and 10 non-OECD member countries from the period
1981-1997 by using a panel model, built on the GMM methodology. The
finding indicated that research and development expenditures increase
the level of innovations and the latter lead to permanent growth of GDP/
capita. The results obtained provide evidence that innovations have a
positive impact on GDP/capita, both for developed and emerging
economies.
Pessoa (2007) investigates the connection between economic growth
and innovation was developed by (who has focused on the role of the
research and development expenditures in the relationship between
innovation and economic growth in the case of Sweden and Ireland. The
findings suggest that there is not a strong link between research and
development expenditures and economic growth, and the innovation
policy must take into consideration the complexity of the economic
growth process, by including other indicators, in addition to research
and development expenditures.
On the other hand, Samimi and Alerasoul (2009) investigated the
impact of R&D on economic growth in developing countries. They used
a sample of 30 developing countries for which the necessary data is
available for the period 2000 to 2006. According to their analyses, the
low R&D expenditures of developing countries have no significant effect
on economic growth.
Vuckovic (2016) investigate the influence of innovation activities on
S. Gyedu et al.
3. Technological Forecasting & Social Change 173 (2021) 121169
3
economic growth in emerging markets during the period of 1991 to
2013. A multiple regression model was used, which ultimately showed
that there is no statistically significant relationship between innovation
and economic growth.
Czarnitzki and Toivanen (2013) analyzed the link between economic
growth and research and development investments in the case of Ger
many and Belgium. The results indicated that public investments in
research and development stimulate private investments and the effects
vary based on experience in corporate innovation activity and the level
of labor productivity from the past.
Bayarcelik and Taşel (2012) examine the relationship between
innovation and economic growth in Turkey by using endogenous eco
nomic growth theory. The results indicated a positive and significant
relation between R&D expenditure and the number of R&D employees
as an innovation variable and economic growth in Turkey by using
endogenous economic growth theory.
Pece et al. (2015) provide evidence of a positive relationship be
tween economic growth and innovation among the Central and Eastern
European (CEE) countries, namely Poland, Czech Republic, and
Hungary using innovation variables such as number of patents, number
of trademarks, R&D expenditures.
Long et al. (2017) also analyze the impact of environmental inno
vation on the economic and environmental performance of
Korean-owned firms in China. They find a greater effect of environ
mental innovation behavior on environmental performance than eco
nomic performance.
Busu and Trica (2019) analyze the sustainability of the CE indicators
and elaborate a multilinear regression model with panel data for
determining the dependency of the main CE factors on EU economic
growth. Six statistical hypotheses were validated through a multiple
regression model with the use of the statistical software EViews 11. The
study was conducted for 27 EU countries from 2010 to 2017. Based on
econometric modeling, the paper highlights that a circular economy
generates sustainable economic growth across the EU.
Westmore (2013) investigated the determinants of R&D expendi
tures and patents and the link between innovation and economic
growth, by using a panel model, based on a sample of 19 OECD coun
tries. The empirical results show that tax incentives and public support
for R&D and patent rights encourage innovation activities.
Petrariu et al. (2013) examined the connection between economic
growth and innovation, by using a panel model. Their findings indicated
that the level of development of an economy, reflected in the allocation
of resources for research and development is the main support for
innovation. The results indicated that Central and Eastern European
economies recorded fast economic growth, but it was not based on the
innovation process. Compared with the growth rate, innovation is seen
as a catch-up process.
2.1. Gaps in the literature
Theoretically, taking into account empirical findings from different
scholars, the effect of innovation on economic growth has led to mixed
outcomes. Although some studies have found that innovation does not
influence economic growth (Vuckovic, M. 2016)., other studies have
found that innovation influences economic growth (Pece et al., 2015).
Methodologically, to analyze the topic, many diverse approaches were
used. Most of these researches, however, focus on either patent or P&D
or trademark as the only innovation index. Using them separately only
represents a subset of innovation that does not completely capture all
aspects of innovation. Again, though, BRICS and G7 countries have been
widely studied, there is limited research that compares these two
country blocks which creates a gap hence this research.
3. Materials and methods
3.1. Variables
Our variables of interest are economic growth and innovation. This
research proxied economic growth as GDP per capita and innovation as
research and development (rd), trademark (tradmk), and patent. This
follows the methodologies of Vuckovic (2016), Pece et al. (2015),
Sandner (2009), Greenhalgh et al. (2005) that broadly proxied innova
tion as R&D, patent, and trademark. In addition, this satisfies the
theoretical arguments surrounding economic growth (Vuckovic, 2016;
Pece et al., 2015). Literature has also established the impact of R&D on
economic growth (Blanco et al., 2016; Bayarcelik and Taşel, 2012). Also,
trademark and patent have been established as the determinants of
economic growth (Pece et al., 2015).
3.2. Data
Our sample comprises annual data from G7 countries which are
Canada, France, Germany, Italy, Japan, United Kingdom, and the United
States, and BRICS countries which also comprise Brazil, Russia, India,
China, and South Africa. This paper employed panel data from
2000–2017. Data were retrieved from the World Development In
dicators database (World Bank 2019). This paper used natural log
transformations for all the variables. This is a common practice in
econometric analysis to eliminate the problem of excessive intra-group
variance and prevent the occurrence of heteroscedasticity (Charfed
dine and Ben Khediri, 2016). Tables 1 and 2 present the descriptive
statistics of the data.
The results in Table 1 show G7 countries have higher GDP per capita
(M=10.570; SD=0.205) than BRICS countries (M=8.389; SD=1.023. In
addition, all the variables have high mean among the G7 countries
accept trademark where BRICS countries have high mean than G7
countries. This is a result of more non-residents trademark registration
in the BRICS countries. The correlation matrix shows intercorrelations
among the variables. The matrix revealed there is no multicollinearity
among the explanatory variables (see Table 2).
3.3. Economic metric model
This paper employed firstly the econometric panel vector autore
gressive (panel VAR) model, secondly the Generalized Method of Mo
ments (GMM) framework implemented by Love and Zicchino (2006) to
investigate the impact of innovation on economic growth among G7 and
BRICS countries.
3.3.1. Panel vector autoregressive (PVAR) model
The panel VAR provides a model for endogenous and exogenous
shocks, which is the most important source of macroeconomic dynamics
for an open economy. The panel VAR model is neutral against certain
theories of economic growth. In contrast to VAR, the panel VAR model
contributes to the dynamic heterogeneity of our data which increases
coherence and measurement of consistency, especially where there is
heterogeneity in GDP per capita, R&D, patents, and trademarks among
G7 and BRICS countries (Canova and Ciccarelli, 2013). The general
formula for panel VAR implemented by Love and Zicchino (2006) is
represented as:
Yit = μi + A(L)Yit + αi + δt + εit i = 1, 2, …, N t = 1, 2, …, T (1)
Where Yit is the vector of endogenous stationary series variables of
GDP per capita (gdppc), R&D (rd), patent (patent), and trademark
(tradmk). μi captures individual heterogeneity or fixed-effects between
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4. Technological Forecasting & Social Change 173 (2021) 121169
4
different cross-sectional units. A(L) represents the matrix polynomial in
the lag operator with A(L)=A1L1
+…+ Ap-1Lp-1
+ ApLp
. ai indicates the
vector that determines the specific effects of the country found in this
regression, δt represents the dummy variables for the country’s specific
time, and εi,t denotes idiosyncratic errors, with E( εi,t) = 0, E(ε
′
i,tεi,t) =
∑
and E( ε
′
i,t) = 0for t>j. Eq. (1) can be specified to reflect systems of
equations involving the variables of the study as:
Where lngdppc represents the log of economic growth, lnrd repre
sents log of research and development, lnpatent represents log of patent
application, and lntradmk represents log of trade marks.β, η, ϕ, and φ are
parameters to be estimated. μi, i, t, ai, δt, and εit remain as defined in Eq.
(1). It is important to estimate equations for variables to build their
reactions in the estimation process. The Schwarz information criterion
(SIC) was used to determine the optimal autoregressive lag length, j, j∈
(1 ..., p). The PVAR model state fixed effects are determined by µi which
cover all time-constant factors that are not observed at the country level.
Although our interest in on how innovation affects economic growth,
the introduction of multiple fixed effects variables μi, ai, and δt, in the
PVAR model, helps to neutralize unobserved effects (including other
Table 1.
Statistical description.
G7 countries BRICS countries
Variable Obs Mean Std. Dev. Min Max Obs Mean Std. Dev. Min Max
lngdppc 125 10.570 0.205 10.015 11.001 90 8.389 1.023 6.098 9.681
lnrd 120 0.729 0.324 0.005 1.224 78 0.046 0.278 -0.478 0.746
lntradmk 126 11.282 0.679 10.430 13.013 87 11.590 1.124 9.922 14.557
lnpatent 119 11.052 1.398 9.128 13.316 90 10.456 1.358 8.100 14.152
Table 2.
Correlational matrix.
G7 countries BRICS countries
lngdppc lnrd lntradmk lnpatent Lngdppc lnrd lntradmk lnpatent
lngdppc 1 1
Lnrd 0.104 1 0.205 1
lntradmk 0.297 0.708 1 -0.266 0.701 1
lnpatent 0.252 0.783 0.797 1 -0.113 0.848 0.899 1
Δlngdppcit = μ1i +
∑p
j=1
β1jΔlngdppcit− j +
∑p
j=1
φ1jΔlnrdit− j +
∑p
j=1
ϕ1jΔlnpatentit− j
+
∑p
j=1
η1jΔlntradmkit− j + α1i + δ1t + ε1it
(1.1)
Δlnrdit = μ2i +
∑p
j=1
β2jΔlngdppcit− j +
∑p
j=1
φ2jΔlnrdit− j +
∑p
j=1
ϕ2jΔlnpatentit− j
+
∑p
j=1
η2jΔlntradmkit− j + α2i + δ2t + ε2it
(1.2)
Δlnpatentit = μ3i +
∑p
j=1
β3jΔlngdppcit− j +
∑p
j=1
φ3jΔlnrdit− j +
∑p
j=1
ϕ3jΔlnpatentit− j
+
∑p
j=1
η3jΔlntradmkit− j + α3i + δ3t + ε3it
(1.3)
Δlntradmkit = μ4i +
∑p
j=1
β4jΔlngdppcit− j +
∑p
j=1
φ4jdlnrdit− j +
∑p
j=1
ϕ4jΔlnpatentit− j
+
∑p
j=1
η4jΔlntradmkit− j + α4i + δ1t + ε4it
(1.4)
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5. Technological Forecasting & Social Change 173 (2021) 121169
5
determinants of economic growth determinants) that may affect eco
nomic growth (Acheampong et al. 2019; Ntarmah et al. 2020).
4. Results and discussions
4.1. Unit roots tests
In other to implement the PVAR model, there is the need to establish
the stationarity of the variables by performing unit roots tests. Among
the proposed panel unit roots test, the authors used Im–Pesaran–Shin
(IPS) (2003) and Fisher-type (Maddala and Wu, 1999; Choi, 2001) tests
to check for stationarity of the series. These tests are suitable for our
dataset for two main reasons. First, unlike the other panel unit roots
tests, these tests handle unbalanced datasets. Second, they allow for
heterogeneous panels. The unit-roots results is presented in Table 3.
The panel unit roots tests in Table 3 show that, at level, panels
contain unit roots for all variables. This implies that using the ordinary
least square regression to investigate the phenomenon will lead to a
spurious result. However, at the first difference, the null hypothesis of
unit roots is rejected at a 1% significance level indicating that all the
variables are stationary at first difference. Thus, providing a prerequisite
for estimating the panel VAR model based on the first differencing of the
variables. Thus, the authors proceeded to estimate the optimal lag using
Andrews and Lu (2001) three model selection criteria. The result is
presented in Table 4.
Based on the result in Table 4, the first-order lag is selected as the
optimal lag for the PVAR model in absolute terms, its overall coefficient
of determination (CD) is the highest with the MBIC, MAIC, and MQIC
being the smallest.
4.2. Panel VAR estimates
the authors estimate the first-order lag PVAR model based on the Eqs.
(1.1)-(1.4) and the optimal lag selected. Table 5 presents the estimates of
the first-order lag of panel vector autoregressive equations. Since the
unit root results revealed that the series is stationary at the first differ
ence (d), the PVAR model must be implemented in their stationary form
(Love and Zicchino, 2006). Following the publications of Abrigo and
Love (2016), Acheampong (2019), and Charfeddine and Khediri (2019),
Ntarmah, Kong, and Manu (2021), we implemented the PVAR model
through GMM style in their stationary form (at the first difference for all
the variables).
This subsection describes the results of the estimation of the PVAR
model reported of GDP per capita in Table 5. For the R&D equation, the
results reported show that R&D, trademark, and GDP per capita are
statistically significant for the G7. Contrary R&D, patents, and GDP per
capita are statistically significant for BRICS. In particular, the results
show that first lags of R&D, trademark, and GDP per capita are positively
correlated with R&D for the G7, while R&D, patent, and GDP per capita
are positively correlated with R&D. This indicates that an increase in the
initial values of R&D, trademark and GDP per capita for G7 will induce
an increase in the current level of R&D. In the same vain an increase in
the initial values of R&D, patent and GDP per capita for BRICS will cost
an increase in the current level of R&D.
Secondly, for the patent equation, the results reported show that
patent, trademark, and GDP per capita are statistically significant for the
G7 but all the four variables are statistically significant for the BRICS. In
particular, the results show that the first lags of patent and trademark
are positively correlated but the only GDP per capita is negatively
correlated with a patent for the G7, while the first lags of all the variables
are positively correlated with a patent for the BRICS. This shows that for
the G7 countries, an increase in the initial values of patent and trade
mark will induce an increase but GDP per capita will cost a decrease in
the current level of patent, while an increase in the initial values of all
the variables in the BRICS will cost an increase in the current level of the
patent.
Thirdly, for the trademark equation, the results reported show that
R&D, trademark, and GDP per capita are statistically significant for G7
countries but the only trademark is statistically significant for BRICS
countries., In particular, the results show that the first lags of a trade
mark is positively correlated with the current level of trademark for both
Table 5.
Panel vector autoregression estimates result of GDP per capita
G7 countries BRICS countries
dlnrd dlnpatent dlntradmk dlngdppc dlnrd dlnpatent dlntradmk dlngdppc
dlnrd (t-1) 0.446***
(0.115)
-0.190 (0.152) -0.844***
(0.259)
-1.095***
(0.304)
0.114**
(0.052)
0.226**
(0.121)
0.109
(0.138)
0.604***
(0.129)
dlnpatent (t-1) 0.014
(0.065)
0.251***
(0.090)
-0.124
(0.159)
-1.075***
(0.192)
0.380***
(0.050)
0.356***
(0.075)
-0.126
(0.093)
0.001
(0.086)
dlntradmk (t-1) 0.066**
(0.031)
0.107***
(0.032)
0.272***
(0.066)
-0.157**
(0.074)
-0.033
(0.030)
0.148***
(0.051)
0.300***
(0.073)
-0.059
(0.049)
dlngdppc (t-1) 0.076***
(0.028)
-0.073*
(0.040)
-0.297***
(0.069)
0.337***
(0.083)
0.063**
(0.030)
0.167***
(0.034)
-0.005
(0.048)
0.679***
(0.056)
***, **, * indicate significant at 1%, 5% and 10% level respectively, ‘d’ represents first difference operator
Table 3.
Unit roots tests.
Test at level(no trend) Test at first difference
G7 countries BRICS countries G7 countries BRICS countries
IPS Fisher IPS Fisher IPS Fisher IPS Fisher
lngdppc 0.507 -2.422 *** -0.284 -0.431 -3.532*** -4.072*** -2.218** -2.035**
lnrd 1.959 2.489 -0.029 -0.180 -4.262*** -2.969*** -3.219*** -3.500***
lntradmk 1.917 1.869 1.940 1.776 -4.869*** -7.355*** -3.190*** -5.071***
lnpatent -0.344 0.779 -2.330*** -0.439 -3.527*** -2.623*** -3.190*** -2.203***
***, **, * indicate significant at 1%, 5% and 10% level respectively
Table 4.
Optimal lag.
lag CD J J pvalue MBIC MAIC MQIC
1 0.999995 48.44129 0.455032 -163.663 -47.5587 -94.2029
2 0.999994 35.1347 0.321852 -106.268 -28.8653 -59.9615
3 0.999965 21.84979 0.148108 -48.8517 -10.1502 -25.6983
S. Gyedu et al.
6. Technological Forecasting & Social Change 173 (2021) 121169
6
G7 and BRICS but R&D and GDP per capita are negatively correlated for
G7 with the current level of trademark, This indicates that increasing
trademark will cost an increase in the current level for both G7 and
BRICS and decrease R&D and GDP per capita for only G7 countries.
Finally, for the GDP per capita equation, the results reported show
that all the four variables are statistically significant for the G7 but only
R&D, GDP per capita are statistically significant for the BRICS., In
particular, the results show that the first lags of GDP per capita is pos
itive but R&D, patent, and trademark are negatively correlated with the
current level of GDP per capita for the G7 while R&D and GDP per capita
are positively correlated with GDP per capita. This indicates that an
increase in GDP per capita and a decrease in the R&D, patent, and
trademark for G7 and R&D and GDP per capita for BRICS will cost an
increase in the level of GDP per capita.
These results seem to confirm the logical argument that the impact of
innovation on economic growth for the G7 countries is greater than
BRICS. This could be a result of high investment in R&D and flexible
government procedures in registering and filling trademarks and patents
among the G7. However, BRICS countries have complicated government
procedures in terms of patent filing and trademark registration which
prevent people from registering for their innovative work (Hatemi et al.,
2016). For example, India has the highest transaction costs. The cost of
registering businesses and patents in India is the highest among the 7%
BRICS countries, 7.4 times that of Brazil, 7.9 times that of China, and
27.3 times that of Russia, which is far more expensive than those of other
countries. G7 countries. (Ata Ur Rehman et al., 2015; Bingwen and
Huibo, 2010). In addition, the number of patent and trademark regis
trations in the year 2016 for G7 countries was greater than BRICS
countries, and also the amount invested in R&D in G7 countries is more
than BRICS countries over the years (Blanco, Gu, , and Prieger, 2016).
Once again it is suggested that inputs and outputs from the global
innovation index are affected by the 2008-2009 economic crisis, which
may have caused a drastic decline that affected most developing coun
tries until 2013 in which BRICS countries could be part (Franco and de
Oliveira, 2017).
4.3. Granger causality tests
After the PVAR estimation and its stability check, the authors
continue to perform the Granger causality test (Abrigo and Love, 2015),
based on the Wald test. The null hypothesis shows the absence of cau
sality. the authors confirmed the presence of endogeneity by the blocks
of exogeneity analysis (ALL). The results of the Granger causality test are
exhibited in Table 6.
According to Table 6, there is a bidirectional relationship between
R&D and trademark, R&D and GDP per capita, patent and GDP per
capita, trademark and GDP per capita among G7 countries, on the
contrary, a bidirectional relationship exists between R&D and patent,
R&D and GDP per capita among BRICS countries. This implies that
variables granger cause to each other within the model. This result is
similar to Long, et al. (2015) which showed the bidirectional relation
ship between capital and GDP while CO2 emission and economic growth
have a positive bidirectional influence on each other. Also. there is a
unidirectional relationship between patent and trademark and no
directional relationship between R&D and patent among G7 countries,
while unidirectional relationship exists between patent and trademark,
patent and GDP per capita among BRICS countries, there is no direc
tional relationship between trademark and GDP per capita and R&D and
trademark among BRICS countries. These results validate the results of
(Hatemi et al., 2016; Ntuli et al., 2015; Inglesi-Lotz and Pouris, 2013)
These result seems to suggest that not the only GDP per capita of the
countries of our sample be affected by changes in the R&D, patent and
trademark, but also R&D, patent and trademark can be affected by GDP
per capita. Moreover, our finding indicates that the causality also runs in
the opposite direction and that GDP per capita may have also increased
the countries R&D, patent, and trademark. Relative to the causality that
seems to run from R&D, patent, and trademark to GDP per capita, our
findings indicate that R&D, patent, and the trademark could have led to
an increase in GDP per capita in these countries. The relationship be
tween R&D, patent and trademark, and GDP per capita has been
extensively studied given the importance of this issue to the current
economic debate, but the conclusions are far from being consensual
(Pece et al., 2015). In our specific case, that is, G7 and BRICS countries,
the fact that R&D, patent and trademark may foster economic growth is
not a surprise to these countries as a result of good economic policies
regarding the investment made in R&D. However the G7 countries can
increase their innovation more by investing in effective R&D sectors as
compared to the BRICS countries which seem to promote their innova
tion through technology spillovers, therefore, economic growth can also
increase the level of innovation in the G7 and BRICS countries devel
opment process. This shows bidirectional causality between innovation
and economic growth (Maradana et al, 2017; Pradhan et al., 2016;
Hatemi et al., 2016; Ulku, 2004).
4.4. Impulse response
To estimate PVAR in which a sequence of causal variables is
imperative, it is very important to perform the impulse response func
tion. According to Sims (1980) variables that appear earlier in a more
exogenous sequence and affect the following variables simultaneously
or even with lag, whereas variables that appear later in the system are
more endogenous and affect the previous variable only with a lag. Based
on the literature, the authors estimate the impulse response function by
following the order of R&D, patents, trademarks, and GDP per capita.
Thus, R&D is considered more exogenous while GDP per capita is more
endogenous in the model. Following this sequence, the authors estimate
orthogonal IRFs from shocks as suggested by Sims (1980) based on
Cholesky’s decomposition.
The results show that the effect of one standard deviation shock in
the growth of trademark on GDP per capita was instantaneously positive
but decreasing from the first year to year 2 and increase from year 2 to
year 4 and equal to zero from year 5 to 10 for the G7 countries, while, it
was positive but decreasing from the first year to the year 3 and equal to
zero from year 4 to 10 for the BRICS countries. Also, the results show
that the effect of one standard deviation shock in the growth of R&D on
GDP per capita was instantaneously negative but increasing from the
first year to year 3 and equal to zero from year 4 to 10 for the G7
countries, while, it was negative but increasing from the first year to the
year 2 and equal to zero from year 3 to 10 for the BRICS countries. The
results show also that the maximum negative impact occurs in the first
Table 6.
Granger causality tests.
G7 countries BRICS countries
dlnrd dlnpatent dlntradmk dlngdppc dlnrd dlnpatent dlntradmk dlngdppc
dlnrd 1.561 10.612*** 12.999*** 3.508** 0.623 22.022***
dlnpatent 0.043 0.609 31.312*** 56.694*** 1.851 0.001
dlntradmk 4.657** 10.876*** 4.488** 1.215 8.577*** 1.458
dlngdppc 7.226*** 3.296* 18.599*** 4.307** 23.772*** 0.012
ALL 22.433*** 14.739*** 27.538*** 47.808*** 69.333*** 37.306*** 3.238 24.449***
***, **, * indicate significant at 1%, 5% and 10% level respectively, ‘d’ represents first difference operator
S. Gyedu et al.
7. Technological Forecasting & Social Change 173 (2021) 121169
7
year with G7 countries but maximum positive for the BRICS countries.
Moreover, the results show that the effect of one standard deviation
shock in the growth of patent on GDP per capita was instantaneously
negative from the first year but increase from the year 2 to 10 years for
the G7 countries, while, it was positive but decreasing from the first year
to year 10 for the BRICS countries. This seems to suggest that the overall
impact of R&D, patent, and trademark on economic growth are very
high, and it increases quickly, indicating that the contribution of inno
vation efficiently improve economic growth. Comparatively the G7
countries are very stronger than the BRICS countries. This result is ex
pected since most of the G7 countries’ innovation is well invested than
the BRICS. In addition, the R&D, patent, and trademark policies
implemented by these countries are also characterized by their effi
ciency at both markets and legal frameworks as well as an appropriate
institutional structure (Hatemi et al., 2016). The efficiency of Innovation
among the BRICS countries is lower as compared to that of the G7
countries. Therefore it is better to expand knowledge spillover of inno
vation technology through supply chain collaboration and R&D
cooperation among different countries (Long et al., 2019; Pece et al.,
2015). Innovation policy has frequently relied on a model of the impact
of science and technology on economic development. The main
assumption underlying this model is that R&D carried out by research
ers/scientists leads to a new idea, which becomes a new product, for
which a production process is developed by industrial engineers, and for
which a marketing plan is then set up, conducting to its increasing de
mand in the market. Thereby increasing the economic growth of that
particular country (Hatemi et al., 2016; Pessoa, 2007).
4.5. Variance decomposition
Although impulse responses can provide details about the effect of
variations in one variable on another, they do not determine the
magnitude and extent of this effect. As a result, the authors performed
variance decomposition techniques to determine this. Variance decom
position provides information about variations in percentages in the
dependent series that are caused not only by their shocks but also by
Table 7.
Error variance decomposition impulse response function.
Forecast Impulse variable
G 7 countries Brics countries
dlnrd dlnpatent dlntradmk dlngdppc dlnrd dlnpatent dlntradmk dlngdppc
dlnrd
0 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
1 1.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000
2 0.929 0.005 0.024 0.042 0.729 0.259 0.001 0.011
3 0.908 0.009 0.031 0.052 0.631 0.321 0.009 0.039
4 0.902 0.015 0.031 0.052 0.580 0.341 0.014 0.064
5 0.899 0.017 0.031 0.052 0.550 0.351 0.016 0.083
6 0.898 0.017 0.031 0.053 0.529 0.358 0.017 0.096
7 0.898 0.018 0.031 0.053 0.515 0.362 0.017 0.106
8 0.898 0.018 0.031 0.053 0.504 0.366 0.017 0.113
9 0.898 0.018 0.031 0.053 0.497 0.368 0.017 0.118
10 0.898 0.018 0.031 0.053 0.491 0.369 0.017 0.122
dlnpatent
0 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
1 0.125 0.875 0.000 0.000 0.061 0.939 0.000 0.000
2 0.154 0.810 0.016 0.020 0.055 0.875 0.036 0.034
3 0.156 0.780 0.018 0.047 0.051 0.837 0.045 0.066
4 0.153 0.769 0.018 0.060 0.049 0.814 0.047 0.090
5 0.151 0.766 0.018 0.066 0.048 0.799 0.046 0.107
6 0.149 0.764 0.019 0.068 0.047 0.788 0.046 0.119
7 0.149 0.763 0.019 0.070 0.046 0.781 0.045 0.128
8 0.149 0.762 0.019 0.070 0.046 0.775 0.045 0.134
9 0.148 0.762 0.019 0.071 0.046 0.771 0.045 0.138
10 0.148 0.762 0.019 0.071 0.045 0.768 0.045 0.142
dlntradmk
0 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
1 0.131 0.124 0.745 0.000 0.021 0.080 0.899 0.000
2 0.176 0.100 0.622 0.103 0.020 0.074 0.906 0.000
3 0.166 0.107 0.572 0.155 0.020 0.074 0.905 0.000
4 0.159 0.126 0.549 0.166 0.020 0.075 0.905 0.001
5 0.156 0.136 0.539 0.169 0.020 0.075 0.904 0.001
6 0.154 0.140 0.534 0.171 0.020 0.075 0.904 0.001
7 0.153 0.142 0.532 0.172 0.020 0.075 0.903 0.001
8 0.153 0.143 0.531 0.173 0.020 0.075 0.903 0.002
9 0.153 0.144 0.530 0.173 0.020 0.076 0.903 0.002
10 0.153 0.144 0.530 0.174 0.020 0.076 0.903 0.002
dlngdppc
0 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
1 0.106 0.000 0.019 0.875 0.128 0.168 0.033 0.672
2 0.120 0.167 0.016 0.697 0.092 0.172 0.028 0.708
3 0.109 0.222 0.032 0.638 0.079 0.209 0.024 0.688
4 0.105 0.232 0.039 0.623 0.072 0.242 0.022 0.664
5 0.104 0.235 0.041 0.620 0.068 0.264 0.022 0.647
6 0.103 0.238 0.041 0.618 0.065 0.279 0.022 0.634
7 0.103 0.239 0.041 0.617 0.063 0.289 0.022 0.625
8 0.103 0.240 0.041 0.616 0.062 0.297 0.022 0.619
9 0.103 0.240 0.041 0.616 0.061 0.302 0.022 0.615
10 0.103 0.241 0.041 0.616 0.060 0.306 0.023 0.612
‘d’ represents the first difference operator.
S. Gyedu et al.
8. Technological Forecasting & Social Change 173 (2021) 121169
8
shocks generated by other variables. The results of the variance
decomposition obtained from the orthogonalized impulse response co
efficient matrix are presented in Table 7. For this study, the authors
interpret the decomposition of the error variance by focusing on the
10th period in which most variables have the highest explaining power
than the others.
The results variance decomposition obtained from the orthogonal
ized impulse response coefficient matrices is presented in Table 7. The
result shows that patent, trademark, and GDP per capita approximately
explains 2%, 3%, and 5% respectively of the variance in R&D for the G7
countries, while patent, trademark, and GDP per capita approximately
explain 37%, 2%, and 12% respectively of the variance in R&D for the
BRICS countries. However, the influence of patent and GDP per capita is
higher for the BRICS countries than the G7 but the influence of trade
mark is higher for the G7 countries than the BRICS countries. In the same
vain R&D, trademark, and GDP per capita explains approximately 15%,
2%, and 7% respectively for the G7 countries of the variance in patent,
while R&D, trademark, and GDP per capita explains approximately 5%,
5%, and 14% respectively for the Brics countries of the variance in the
patent. However, the influence of R&D is higher for the G7 than the
BRICS but trademark and GDP per capita are higher for the BRICS than
the G7 countries. Also R&D, patent, and GDP per capita approximately
explain 15%, 14%, and 17% respectively for the G7 countries of the
variance in trademark but R&D, patent, and GDP per capita approxi
mately explains 2%, 8%, and 0.2% respectively for the BRICS countries
of the variance in trademark. However, the influence of R&D, patents,
Fig. 1.. Impulse -Response Results. ‘d’ means first difference of the variable. Source: Authors’ Production.
Fig. 2.. Module Stability. Source: Authors’ Production.
S. Gyedu et al.
9. Technological Forecasting & Social Change 173 (2021) 121169
9
and GDP per capita is higher for the G7 countries than the BRICS
countries. Similarly, R&D, patent, and trademark approximately explain
10%, 24%, and 4% respectively for the G7 countries of the variance GDP
per capita, while in the BRICS countries R&D, patent and trademark
approximately explains 6%, 31%, and 2% respectively of the variance in
GDP per capita. The influence of R&D and trademark are higher for the
G7 than the BRICS countries, while the patent is higher for the BRICS
than the G7 countries.
The results show that R&D explains approximately 10% of the var
iations in GDP per capita while patent and trademark explain approxi
mately 24% and 4% respectively of the variations in GDP per capita for
the G7 countries, similarly, R&D explains approximately 6% of the
variations in GDP per capita while patent and trademark explain
approximately 31% and 2% respectively of the variations in GDP per
capita for the BRICS countries. The bulk of the variations in GDP per
capita is explained by itself approximately 62% and 61% for G7 and
BRICS countries respectively. The results confirm that trademark weakly
explains GDP per capita and even decreases further for BRICS countries
(Hatemi et el., 2016; Stiglingh, 2015; Pessoa, 2007).
4.6. Model stability
It is in our interest to determine the reaction of each endogenous
variable to the shock of exogenous changes in other variables in the
panel VAR system, it is appropriate to check the stability conditions of
the PVAR estimation results. Fig. 1 illustrates the results of the PVAR
stability conditions. As illustrated in Fig. 1. Modulus calculated from
each eigenvalue of the estimated model is less than one (or located in
side the outer circle. This implies that the model is stable (Lutkepohl,
2005; Hamilton, 1994). Therefore, the authors continue to IRF estimates
and error-estimate variance decomposition (Fig. 2).
5. Conclusion, possible policy recommendations and limitation,
and further research directions
It has been realized that innovation is essential for economic growth
and development in this globalized economic (Castano et al., 2016;
Schumpeter, 1934). The objective of this study is to examine the impacts
of innovation on economic growth among G7 and BRICS countries using
the GMM style panel vector autoregressive (panel var) approach over
the period 2000-2017. the authors conclude that conditioning on
innovation determinants, an increase in the initial values of R&D,
trademark, and GDP per capita for G7 will induce an increase in the
current level of R&D but an increase in the initial values of R&D, patent,
and GDP per capita for BRICS will cause an increase in the current level
of R&D. Also in the G7 countries, an increase in the initial values of
patent and trademark will induce an increase but GDP per capita will
cost a decrease in the current level of patent, while an increase in the
initial values of all the variables in the BRICS will cost an increase in the
current level of the patent. Moreover, the increasing trademark will cost
an increase in the current level for both G7 and BRICS and decrease R&D
and GDP per capita for only G7 countries.
Also, the authors found out that the overall impact of R&D, patent,
and trademark on economic growth are very high, and it increases
quickly among the G7 and BRICS countries, indicating that the contri
bution of innovation efficiently improve economic growth. Compara
tively the G7 countries are very stronger than the BRICS countries. This
result is expected since most of the G7 countries’ innovation is well
invested than the BRICS.
In addition, the findings also suggested that not only GDP per capita
of the countries of our sample be affected by changes in the R&D, patent,
and trademark, but also R&D, patent, and trademark can be affected by
GDP per capita. Moreover, our finding indicates that the causality also
runs in the opposite direction and that GDP per capita may have also
increased the country’s R&D, patent, and trademark among the G7 and
BRICS countries.
5.1. Policy recommendations
Based on the findings of this study, the following potential policy
recommendations are provided. First, the fact that innovation has varied
impacts on economic growth among the BRICS and G7 countries implies
that policymakers should revisit their economic growth policies and
align them with various aspects of innovation. In addition, the authors
have recommended some persuading indicators for BRICS economies to
explore the development of innovation most especially in R&D, patents,
and trademarks as a potential opportunity to speed up their economic
growth. Again, BRICS countries should not only push for the adoption of
stronger levels of patent and trademark enforcement on the adoption of
stronger laws per se but also align with the shift in the attention of policy
negotiations. This will help produce more findings that will make a
positive contribution to the country’s economic growth. In the same
futility, the Governments of the G7 countries must maintain the amount
(if it cannot be increased) allocated for research and development but
must pay more attention to patents and trademark registration. As it has
been concluded in our findings that conditioning on innovation de
terminants in the G7 countries, an increase in the initial values of patent
and trademark will induce an increase but GDP per capita will cost a
decrease in the current level of the patent.
5.2. Limitation and further research directions
Although this paper offers in-depth knowledge on the understanding
of the impact of innovation on economic growth among BRICS and G7
countries, it has certain limitations. The first, limitation is that the
sample is limited to only BRICS and G7 countries over the period 2000-
2017 due to data unavailability for some countries. Although the sample
period (18 years) is relatively long, the period before 2000 was not
studied in this study. Therefore, the dynamic association between the
variables was not established in this study. Therefore, the generalization
of the findings of this study should be limited to the period from 2000
and above. Also, the study examined the link between innovation and
economic growth while conditioning on other growth determinants.
Although the implementation of the PVAR model helped to condition
other growth determinants, it is to state that further studies should
compare innovation and other growth determinants to help unravel
their contributions in future growth.
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