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Abstract
e causality relationship of economic growth with
R&D expenditures and patent applications for the 23
OECD member countries is investigated in this study
by utilising from the data belonging the period of 1996-
2011. To that aim, GMM (Generalized Method of Mo-
ments) approach developed by Arellano-Bond (1991)
and Wald test are used. Panel causality estimation
results put forth a two-way and positive causality bet-
ween R&D expenditures and economic growth, and a
one-way and positive causality from patent applicati-
ons to economic growth. Hence, on the ground of the-
se results, it can be argued that it is important for the
countries aiming to achieve a sustainable and high rate
of growth to allocate more resources for R&D activities
and establish an eicient patent system.
Keywords: R&D Expenditures, Patent Applications,
Growth, Panel Causality
Öz
Bu çalışmada, OECD üyesi 23 ülkenin 1996-2011
dönemine ilişkin verilerinden yararlanılarak Ar&Ge
harcamaları ve patent başvuruları ile ekonomik büyü-
me arasındaki nedensellik ilişkileri araştırılmıştır. Bu
amaçla, Arellano-Bond (1991) tarafından geliştirilen
GMM (Genelleştirilmiş Momentler Metodu) yaklaşımı
ile Wald testinden yararlanılmıştır. Panel nedensellik
tahmin sonuç ları, Ar&Ge harcamaları ile ekonomik bü-
yüme arasında çi yönlü ve pozitif, patent başvurula-
rından ekonomik büyümeye doğru tek yönlü ve pozitif
bir nedensellik ilişkisinin varlığını ortaya koymuştur.
Dolayısı ile bu sonuçlardan hareketle, sürdürülebilir ve
yüksek oranlı büyümeyi hedeeyen ülkeler açısından
Ar&Ge faaliyetlerine daha fazla kaynak ayırmanın ve
etkin bir patent sistemi kurmanın önemli olduğu ifade
edilebilir.
Anahtar Kelmeler: Ar&Ge Harcamaları, Patent
Başvuruları, Büyüme, Panel Nedensellik
Introducton
Defined by the OECD as a creative work undertaken
on a systematic basis in order to increase the stock of
knowledge, including knowledge of man, culture and
society, and the use of this stock of knowledge to de-
vise new applications (OECD, 1993, p.29), R&D acti-
vities are considered among the key determinants of
economic growth in the literature on theoretical and
empirical growth. e role of the R&D in the growth
process was first discussed in the literature on endo-
genous growth theories that emerged in the 1980s
and regarded growth as a phenomenon that depends
on productivity and technological innovations (deve-
lopments). According to these theories, technologi-
cal innovations are the result of endogenous factors
and arise as a result of R&D activities that use human
The Relatonshp between R&D Expendtures, Patent Applcatons and Growth:
A Dynamc Panel Causalty Analyss for OECD Countres
Ar&Ge Harcamaları, Patent Başvuruları ve Büyüme Arasındak İlşk:
OECD Ülkeler İçin Bir Dnamk Panel Nedensellk Analz
Asst. Prof. Dr. Salih Türedi
Asst. Prof. Dr. Salih Türedi, Recep Tayyip Erdoğan University Faculty of Economics and Administrative Sciences, salih.turedi@erdogan.edu.tr
Anadolu Unversty
Journal of Socal Scences
Anadolu Ünverstes
Sosyal Blmler Dergs
40
The Relationship between R&D Expenditures, Patent Applications and Growth: A Dynamic Panel Causality Analysis for OECD Countries
capital and the existing stock of knowledge in the
economy to produce new knowledge (Romer, 1986).
As a matter of fact, Grossman and Helpman (1994)
described the technological innovations emerging as
a result of R&D activities and investments as the main
force underlying the continuous rise in the standard
of living. us, it is acknowledged that innovations
arising from R&D activities make a positive contri-
bution to economic growth by increasing the com-
petitive power of companies and countries through
reducing costs of production, improving the quality
of products, and allowing the development of new
products and production methods (Üzümcü, 2012,
p.237; Rouygari and Kızıltan, 2014, p.33).
e number of patents is another important indicator
of technological innovation (i.e., the capacity to cre-
ate technological innovations) in a country. ere is
a close relationship between R&D expenditures and
patents, which are defined as the right of the owner of
an innovation to produce, use, sell, or import the idea
or product he or she owns within a particular time
period. is relationship is clear in Figure 1, which
illustrates the emergence process of a patent. Accor-
dingly, while R&D activities are the input of tech-
nological innovation, patents are its output (Saygılı,
2003, p.89). From this perspective, while R&D acti-
vities lead to an increase in patents through creating
innovations, patents increase profitability by provi-
ding monopoly power to inventors and encouraging
R&D activities. erefore, it is possible to say that an
eective patent system enhances productivity and ac-
celerates economic growth by contributing to techno-
logy production and transfer, the spread of technical
knowledge, the expansion of economic activities, and
the rise of national and international competitive po-
wer, as well as encouraging R&D activities (Zhang,
2014, p.507-508).
Cited by Işık, 2014, p.71 from Ayhan, 2002, p.264.
Figure 1. The Emergence Process of a Patent
e number of studies related to the inuence of
R&D activities, as an input to technological inno-
vations; and patents, as an output of technological
innovations, on the growth process of the countri-
es increased in the literature with the acceptance of
technological innovations as the driving force behind
the sustainable growth by the endogenous growth
theories. e aim of this study is to investigate the
existence and direction of the causality relationship
of economic growth with the R&D expenditures and
patent applications for the OECD member countries
for the period of 1996-2011. e results derived from
the GMM estimations and Wald test applied in this
scope verify a statistically significant causality rela-
tionship among the mentioned variables. us, it is
assumed that this study will make a contribution to
the literature thanks to its dierence from the other
studies in the literature stemming from the used esti-
mation method, the period covered and the countries
examined. e structure of this paper is organised as
follows. Section two provides a brief overview of the
recent contributions to the R&D expenditures, patent
applications and economic growth literature. Section
three describes econometric methodology and data
set used analysis. Section fourth proceeds to desc-
riptive statistics and the empirical findings obtained
from analysis. e last section provides conclusion
and recommendations.
Lterature Revew
R&D Expendtures and Economc Growth Lterature
Lichtenberg (1993) investigated the relationship bet-
ween growth and R&D expenditures in both the pri-
vate and public sectors of 74 countries during 1964-
1989 and reported that there was no relationship bet-
Ide a R&D Activities Technological
Innovations
Pat ent
41
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ween economic growth and R&D expenditures in the
public sector, but R&D expenditures in the private
sector aected growth positively. Gittleman and Wol
(1995) addressed the relationship between R&D acti-
vities and economic growth by using panel data cove-
ring the period of 1960-1988 as the real GDP per ca-
pita, R&D expenditures, the number of scientists per
R&D, and the number of engineers per R&D. eir
empirical findings revealed that R&D activities acco-
unted for growth only in developed countries, but did
not account for growth in low-income underdevelo-
ped countries. Based on the panel data from 1973-
1992, Braconier (2000) conducted a study for ten
OECD member countries and determined that rise in
per capita income level led to an increase from 1.83%
to 2.93% in R&D expenditures. Yanyun and Ming-
qian (2004) performed a dynamic GMM estimation
on eight countries (Indonesia, Malaysia, Japan, Ko-
rea, ailand, Singapore, the Philippines, and China),
three of which were ASEAN countries, by using the
Cobb-Douglas production function. ey found that
R&D expenditures in the public sector made a greater
contribution to the economic growth than R&D ex-
penditures did in the private sector.
Arguing that R&D expenditures played an important
role in growth by creating an increase in innovation
and productivity, Samimi and Alerasoul (2009) made
a panel data analysis for thirty developing countries
including Turkey and found that R&D expenditures
in fact did not contribute to growth in developing
countries because such expenditures were low. Al-
tın and Kaya (2009) used time series to estimate the
relationship between economic growth and R&D in-
vestments in Turkey for the period of 1990-2005 and.
Based on an empirical analysis using the Johansen-
Juselius cointegration and error-correction techni-
que, they determined that there was no relationship
between the above-mentioned variables in the short
run, but that R&D investments were a cause of econo-
mic growth in the long run. Mehran and Reza (2011)
performed a comparative examination of the eect of
R&D expenditures on economic growth in underde-
veloped countries and OECD countries by using the
fixed eects panel data technique. ey determined
that although R&D expenditures made a positive
contribution to growth in both country groups, the
contribution was larger in OECD countries. Akcay
(2011) used the Toda-Yamamoto approach and ascer-
tained that there was a two-way causality relationship
between R&D investments and economic growth in
the United States. Gyekye et al. (2012) employed the
Cobb-Douglas production function to examine the
inuence of R&D investments on socio-economic
development in Sub-Saharan African countries. To
this end, they conducted fixed-eects panel regressi-
on estimation and found that a rise of 1% in R&D
investments contributed to economic growth in the
mentioned countries by 0.326%. Akıncı and Sevinç
(2013) conducted a study via the least-squares app-
roach and determined that R&D expenditures in the
private sector, in higher education, and in total had a
positive eect on growth in Turkey in the 1990-2011
period, but R&D expenditures in the public sector
had no positive eect on growth in that period.
Patent Applcatons and Economc Growth
Lterature
Crosby (2000) made an empirical analysis and found
that patent applications had a positive eect on labo-
ur productivity and economic growth in the Austra-
lian economy. Claiming that innovation played an
important role in economic growth, Sinha (2008)
investigated the relationship between the number of
patents granted and economic growth in Japan and
South Korea via time-series and panel data appro-
aches. e time-series analysis demonstrated that
there was no relationship between the two variables
in South Korea, but there was a two-way causality
relationship between them in Japan. e panel data
analysis, on the other hand, revealed that the above-
mentioned causality relationship was one-way from
growth to the number of patents. Ortiz (2009) perfor-
med a regression estimation based on cross-sectional
data from 23 countries covering the period of 1820-
1990 and determined that there was a strong and po-
sitive relationship between the number of patents per
person and per capita income in the long run. Kim
at al. (2009) carried out a study on the South Korean
manufacturing industry, tested the eects of patent
applications on total factor productivity (which is a
key growth determinant), and determined that non-
resident patent applications were more inuential on
the increase in productivity than resident patent app-
lications.
Josheski and Koteski (2011) used the bound test
(ARDL) and Johansen cointegration technique and
determined that there was a positive relationship bet-
ween number of patents and growth in G-7 count-
42
The Relationship between R&D Expenditures, Patent Applications and Growth: A Dynamic Panel Causality Analysis for OECD Countries
ries in the long run. ey also conducted dynamic
relationship estimation and ascertained that there
was a one-way causality relationship from number of
patents to economic growth. Arguing that economic
growth had a critical importance for sustainable de-
velopment, Saini and Jain (2011) addressed the eects
of patent applications on economic growth in nine
Asian countries. e findings obtained from the reg-
ression analysis showed that patent applications made
an insignificant contribution to economic growth in
Singapore, Japan, ailand, and Vietnam; they made
a negative contribution to economic growth in Chi-
na, Indonesia, and Malaysia; but they made a posi-
tive contribution to economic growth in India and
the Philippines. Guo and Wang (2013) carried out a
study on the Chinese economy and determined that
patent applications made a positive contribution to
growth. A rise of 1% in patent applications increased
economic growth in China by 0.26% in the period
under examination. Işık (2014) carried out a study
in Turkey, determined that patent expenditures were
a cause of economic growth, and stated that patent
expenditures should be made in an organized way to
ensure a sustainable growth.
Methodology and Data Set
Panel data, which are also referred as longitudinal
or cross-sectional time-series data, are composed by
bringing together time-series observations concer-
ning such economic units as countries, companies,
and households in a cross-sectional form. ese data
allow us to monitor the changes occurring in these
units over time (i.e. we can make multiple observati-
ons for each unit). at this method has two dimen-
sions (i.e. cross-section [I] and time-series [t]) makes
it well suited for establishing and testing quite comp-
licated behavioural models, especially in comparison
to time-series and cross-sectional analyses. erefo-
re, panel data are widely used in the applied litera-
ture (Hsiao, 2003, p.1; Baltagi, 2007, p.28-30; Hsiao,
2006, p.3-7). e model developed by Holtz-Eakin,
Newey, and Rosen (1988) is taken as the foundation
in estimating the causality relationship of economic
growth with R&D expenditures and patent applica-
tions based on panel data belonging to 23 countries.
is model is as the following:
In equation (1), y and x are the variables between
which a relationship is investigated; (i) is the horizon-
tal dimension of the model; (t) is the time dimension
of the model; and refers to the fixed eects of cross-
sectional units. Fixed eects in the model need to be
eliminated in case they lead to erroneous estimation
results. For that reason, these fixed eects are elimi-
nated through taking the dierence of the equation.
e resulting estimation model can be displayed as
follows:
In equation (2), there is a correlation between the
lagged values of the dependent variable (Δyt-j) and
error terms (Δµt). us, Holtz-Eakin, Newey and Ro-
sen (1988) and Arellano and Bond (1991) argued that
the model had to be estimated by using instrumental
variables.Accordingly, whether there is any causality
relationship between the variables is decided by esti-
mating equation (2) via the Generalized Method of
Moments (GMM), which construes all valid lagged
values of dependent and independent variables as
instrumental variables, and applying the Wald test
to all the obtained independent variable coeicients.
e validity of the created instrumental variables is
tested through the Sargan test. A balanced panel data
set covering the period of 1996-2011 is used in this
study aiming to investigate the causality relationship
of economic growth with R&D expenditures and pa-
tent applications for 23 OECD member countries1
(provided in Table 1). Detailed information regarding
the variables and data sources taken into considerati-
on in the analysis are provided in Table 2.
1 OECD has currently 34 member countries. Due to in-
adequate observation in the data set, Chile, Estonia,
Finland, Greece, Iceland, Italy, Luxembourg, New
Zealand, Norway, Sweden and Switzerland aren’t cov-
ered in the study.
(2)
(1)
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Descrptve Statstcs and Emprcal
Fndngs
Before proceeding to the econometric analysis, we
perform a correlation analysis and construct a scatter
diagram to gather some priori information on the di-
rection of the relationship between the variables. e
results of the Pearson’s correlation analysis show that
there is a statistically significant positive relationship,
as expected, between PGDP, which represents econo-
mic growth, and R&D expenditures and patent appli-
cations. According to the two-sided t-test, correlation
coeicients indicating the direction and strength of
the relationship are 0.63 and 0.24, respectively. On
the other hand, despite the irregular distribution of
the observations belonging to the variables around of
regression lines on the scatter diagrams formed sepa-
rately for the PGDP and other variables, the positive
slope of the regression lines supports the results of
the correlation analysis. Finally, we perform a simple
panel regression analysis. e results obtained in the
regression estimation are parallel to those obtained
from the correlation analysis and the scatter diagram.
Accordingly, we find that 1% increases in the share of
total R&D expenditures in the GDP and patent app-
lications increase GDP per capita (economic growth)
0.40% and 0.07% respectively (Figure 2).
Table 1. The Countries Included in the Analysis
Table 2. Data Descriptions and Sources
Turkey
South Korea
Mexico
France
USA
Netherlands
United Kingdom
Germany
Czech Republic Slovak Republic Portugal Spain
Denmark
Poland
Belgium
Austria
Finland
Slovenia
Ireland
Israel
Japan
Hungary
Canada
Variable Descriptions Source
PGDP Per Capita Gross Domestic Product (constant 2005-USD).
It is used in logarithmic values.
World Bank (WDI)
World Bank (WDI)
World Bank (WDI)
R&D
Expenditures
The Ratio of Total R&D Expenditures to GDP (% of GDP).
It is used in pure values (without calculating its logarithms).
Patent
Applications
Total Number of Patent Applications (resident and non-resident).
It is used in logarithmic values.
0
1
2
3
4
5
8.5 9.0 9.5 10.0 10.5 11.0
Pearson Correlation = 0.63 (P-value : 0.000)
Log (PGDP)=9.2902 + 0.4003 (R&D Expenditures)
Pearson Correlation = 0.24 (P-value : 0.000)
Log (PGDP)=9.3989 + 0.0723 Log (Patent Applications)
4
6
8
10
12
14
8.5 9.0 9.5 10.0 10.5 11.0
Log (PGDP ), (Economic Growth)
Log (Patent Applications)
Log (PGDP ), (Economic Growth)
R&D Expenditures (% of GDP)
Figure 2. The Relationship between R&D Expenditures, Patent Applications and Growth
44
The Relationship between R&D Expenditures, Patent Applications and Growth: A Dynamic Panel Causality Analysis for OECD Countries
Cross-Sectonal Dependence and Unt Root Test
Results
In this study investigating the causality relationship
of economic growth with the R&D expenditures and
patent applications, GMM estimator is used as one of
the dynamic panel data approaches. Since the variab-
les are assumed stationary in this approach (Jung and
Kwon, 2007, p.2), primarily, it is necessary to investi-
gate the stationary of the variables used in the analysis
via appropriate unit root tests. When the panel data
literature is considered, it is observed that the tests
developed to identify the stationary are divided into
two as the first and second generation unit root tests.
ese tests dierentiate from each other on the ba-
sis of whether there is a relationship among the units
forming the panel (cross-sectional dependence). First
generation unit root tests, such as Hadri (2000), Choi
(2001) Levin et al., Lin and Chu (2002) and Im, Pe-
saran and Shin (2003) assumes that there isn’t a de-
pendence among the cross-sectional units; second
generation unit root tests, such as SURADF [(Breu-
er et al (2002)], MADF [(Taylor and Sarno (1998)],
CADF [(Pesaran (2007)] and Bai and Ng (2004) take
into account the cross-sectional dependence. In ad-
dition, by considering the diversification among first
generation unit root tests as homogeneous and he-
terogeneous, both the homogeneity of the variables
and cross-sectional dependence shall be primarily
investigated for making a decision about which root
test is appropriate for the observation of time series
properties of the variables in the panel.
In defining whether the slope coeicients are chan-
ging across cross-sectional units, namely, in identif-
ying whether the variables are homogeneous, Delta
Tilde ( ) and Adjusted Delta Tilde ( ) tests deve-
loped by Pesaran and Yamagata (2008) are utilised.
ere are dierent approaches for estimating the
cross-sectional dependence in the panel data appli-
cations. Among the mentioned approaches, the ones
that are more frequently used in the literature are Bre-
usch and Pagan (1980) LM (Lagrange Multiplier) test
and CD test developed by Pesaran (2004). LM test is
used when cross- sectional dimension of the model
is smaller than the time dimension (N < T), CD test
is used when (N > T). In this study, since N=23 and
T=16, Pesaran (2004) CD test is preferred. e results
of homogeneity and CD test are provided in the Table
3 and Table 4.
∆̃
∆̃adj
Table 3. Results of Homogeneity Test
Test Statistics p-value
Delta Tilde Test (∆̃)
12.739
0.000
Adjusted Delta Tilde Test (∆̃adj)
14.568
0.000
H
0:
Slope parameters are homogeneous for all cross-sectional units.
According to the results provided in the Table 3, ( )
and ( ) test statistics are significant at 1%. On the
basis of these results, the null hypothesis (H0) is rejected
and it is detected that the slope coeicients are chan-
ging among the cross- sectional units, namely, variables
in the panel data set are accepted heterogeneous.
∆̃
∆̃adj
Table 4. Results of CD Test
Variables
CD Test Statistics
p-value
Log (PGDP)
58.27
0.000
R&D Expenditures
26.71
0.000
Log (Patent Applications)
2.08
0.037
H0: Series are cross-sectionally independent.
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CD test results given in the Table 4 are providing
proofs for the significant relationship among
the cross-sectional units. Hence, by rejecting the
null hypothesis (H0) assuming that the series are
independent from each other, it is accepted that
there is a cross-sectional dependence in PGDP, R&D
expenditures and patent applications series of 23
OECD member countries. is means that second
generation panel unit root tests taking into account
cross-sectional dependence shall be used in the
stationary analysis that will be conducted. erefore,
in the study, CADF (Cross-Sectionally Augmented
Dickey Fuller) test which is one of second generation
panel unit root estimators and developed by Pesaran
(2007) is used to test whether the variables are
stationary or not.
Pesaran (2007) CADF test is an extended version of
the standard Dickey-Fuller (DF) test. To put it more
clearly, this approach uses the extended version of the
standard DF regression with the first-dierences of
individual series and lagged cross-sectional averages.
is test produces valid results when (T) and (N) are
big enough under the conditions of (N > T) and (N <
T). Besides, in this approach, stationary of each units
can be searched by calculating CADF test statistic for
cross-sectional units separately, as well as, stationary
for the entire panel can be searched by means of CIPS
(Cross-Sectional Augmented IPS) statistic, which is
the arithmetic average of the CADF test statistics be-
longing to cross-sectional units. (Gengenbach et al.,
2010, p.113; Pesaran et al., 2013, p.95). e results of
the applied panel unit root test are provided in the
Table 5.
It is identified that CIPS test statistics reached as a
result of the CADF test related to three variables for-
ming the data set are higher than the table critical
values provided by Pesaran (2007). Hence, on the ba-
sis of these results, the null hypothesis (H0) arguing
Table 5. Result of CADF (CIPS) Unit Root Test
that the series are non-stationary is rejected and it is
accepted that the mentioned three variables are sta-
tionary at the level of I (0) at dierent significance
levels, namely they don’t include unit root. Aer the
application of unit root test showing the validity of
the assumption of the GMM approach regarding the
stationary of variables, causality estimations are car-
ried out.
Causalty Test Results
is section presents estimation results concerning
the causality relationship between the variables un-
der examination. Firstly, we investigate the causality
relationship between economic growth (PGDP) and
R&D expenditures in the analysis using the GMM
technique. e obtained results are presented in Tab-
le 6. e Wald test applied to all the independent
variable coeicients obtained from Model 1, whe-
re PGDP is accepted as the dependent variable, and
Model 2, where R&D expenditures are taken as the
dependent variable, demonstrates that χ2 test statis-
tics are statistically significant at 1%. Based on these
results, we can say that there is a two-way causality
relationship between economic growth and R&D ex-
penditures. In addition, the positive signs of the in-
dependent variable coeicients in both models show
that these variables aect each other positively.
Table Critical Values
CADF Test Variables CIPS Test Statistics 1% 5% 10%
Level
Log (PGDP) -2.138* -2.320 -2.150 -2.070
R&D Expenditures -2.177** -2.320 -2.150 -2.070
Log (Patent Applications) -2.610
***
-2.320 -2.150 -2.070
H
0
: Series are non-stationary. The stationary test is based on the model with a constant.
***, ** and * indicates that test statistics are 1%, 5% and 10% significant respectively.
***, ** and * indicate that test statistics are significant at 1%, 5% and 10% levels, respectively.
46
The Relationship between R&D Expenditures, Patent Applications and Growth: A Dynamic Panel Causality Analysis for OECD Countries
Table 7 presents the results of GMM testing conduc-
ted for estimating the causality relationship between
economic growth and patent applications. According
to these results, the test statistics of the Wald test (χ2)
applied to all the independent variable coeicients
are statistically significant only in Model 3, where
PGDP is accepted as a dependent variable. When
we take into consideration the sign of the sum of the
independent variable coeicients, we find a positive
one-way causality relationship from patent applica-
tions to economic growth in the analysed countries.
Concluson and Recommendatons
e present study investigates the relationship of the
economic growth with the number of patent appli-
cations and R&D expenditures in 23 OECD mem-
ber countries. e study, which employed data from
1996-2011, uses the GMM – a dynamic panel data
analysis method. According to the findings obtained
from model estimations, there is a positive two-way
causality relationship between economic growth and
R&D expenditures and a positive one-way causality
relationship from patent applications to growth in the
countries under examination. e results obtained
in the present study support the assumption of en-
dogenous growth theories that R&D activities aect
economic growth positively by creating technological
innovations and thus increasing productivity. Since
innovation is the driving force of economic growth,
countries aiming for a high rate of sustainable econo-
mic growth should allocate more resources for R&D
activities and establish an eective patent system that
enables innovations to spread across the economy
and encourages new R&D.
Table 6. Results of Panel Causality Test [Log (PGDP) and (R&D Expenditures)]
Table 7. Results of Panel Causality Test [Log (PGDP) and Log (Patent Applications)]
Dependent Variables
[Model 1]
Δ Log (PGDP) [Model 2]
Δ (R&D Expenditures)
Independent Variables
Coefficient Std. Error Coefficient Std. Error
Δ Log (PGDP)
t-1
1.1529*** [0.1601] 0.4771*** [0.1122]
Δ Log (PGDP)t-2 -0.0504 [0.1709] -0.0849 [0.0844]
Δ (R&D Expenditures)
t-1
-0.0405 [0.0451] 0.6845*** [0.2324]
Δ (R&D Expenditures)t-2 0.0369*** [0.0137] -0.0733 [0.1092]
Wald Test (χ2 statistics) 5.1720*** 9.1956***
Sargan Test (p-value) 0.88 0.35
*** implies level of significance at 1%. Log (PGDP) t-j, (R&D Expenditures) t-j, (j=2,…,6) and constant factor used as
instrumental variables in the analysis.
Dependent Variables
[Model 3] [Model 4]
Independent Variables Δ Log (PGDP) Δ Log (Patent Applications)
Coefficient Std. Error Coefficient Std. Error
Δ Log (PGDP)
t-1
1.1681*** [0.1176] 0.6246 [1.1795]
Δ Log (PGDP)
t-2
-0.1404 [0.1137] -0.3487 [0.4194]
Δ Log (Patent Applications)
t-1
0.0713* [0.0373] 0.4501** [0.2213]
Δ Log (Patent Applications)t-2 -0.0373** [0.0163] -0.0471 [0.1024]
Wald Test (χ2 statistics) 2.7091* 0.3935
Sargan Test (p-value)
0.74
0.65
***, ** and * imply levels of significance at 1%, 5% and 10% respectively. Log (PGDP) t-j, Log (Patent Applications) t-j,
(j=2,…,6) and constant factor used as instrumental variables in the analysis.
47
sbd.anadolu.edu.tr
Clt/Vol.: 16 - Sayı/No: 1 (39-48) Anadolu Üniversitesi Sosyal Bilimler Dergisi
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