Innovation and Economic Growth: An Empirical Investigation of European Countries

Vetsikas, Apostolos (1); Stamboulis, Yeoryios (2); Markatou, Maria (1) 1: Department of Planning and Regional Development, University of Thessaly, Greece; 2:

Department of Economics, University of Thessaly, Greece

Abstract

We examine the relationship between innovation outcomes and economic growth in a

group of European countries for the period 1980-2015. More specifically, three

different types of intellectual property rights are used as indicators of innovation

outcomes:

patent applications, industrial design applications and trademark

applications. Using the Johansen cointegration technique, we investigate possible

long-term relationships between these three innovation output indicators and GDP per

capita. We employ Granger causality analysis to check for causal relationships. The

empirical findings show that the long-run relationships between innovation and

economic growth are country and type of protection specific. The Granger causality

test results indicate that patent applications have a stronger causal effect on economic

growth rather than industrial design and trademark applications. The results show a

unidirectional causal link from economic growth to intellectual property rights in

Northern, mainly Nordic, European countries, while in Southern European countries

the causal relationship is reverse. The implication of this study is that empirical

analysis with emphasis on the combination of innovation output and input indicators

may produce more insightful findings on the relationship between innovation and

economic growth.

Keywords: Innovation, Economic Growth, European Countries, Cointegration,

Granger Causality

JEL codes: C22, O34, O40, O52

[2]

1. Introduction

Europe maintains lofty ambitions for building its future growth and prosperity and

safeguarding its social model through innovation (Veugelers et al., 2015). Innovation is far

from being a recent phenomenon, and is inherent to human development. In recent years, the

need to investigate and measure the relationship between innovation and economic growth has

been expressed in the field of innovation economics. The use of econometric methodologies is

an important tool to examine their relationship mainly at level of countries.

In existing literature, research and development (R&D) expenditure (input indicator) and

patents (output indicator) are widely used as a proxy for innovation partly because of the

availability of data (Hassan and Tucci, 2010). Most researchers examine the possibility of

cointegration and Granger causality relationship between innovation and economic growth for

individual or multiple countries, but results are inconclusive. However, there is an evidence of

long-term relationship in developed countries usually unidirectional causality relationship

from economic growth to innovation. This means that a strong economy is a favorable

condition for innovative activities, as developed countries have the opportunity to allocate

financial resources in this direction.

The aim for this research is to contribute to the investigation of the relationship between

innovation and economic growth, with emphasis on innovation output indicators. Using the

cointegration technique, we investigate possible long-term relationships between three

innovation output indicators (patent applications, industrial design applications and trademark

applications) and GDP per capita as a proxy of economic growth. Cullet (2005) argues that

technology and knowledge, contained in these types of intellectual property, are important

factors for economic growth and development and the contribution of technological

innovation, with the protection of intellectual property rights, in economic growth is well

established in the literature both theoretically and empirically (Nadiri, 1993; Gould and

Gruben, 1996; Park, 2008). We employ Granger causality analysis to investigate causal

relationships.

The paper is organized as follows. We outline a review of relevant empirical studies in Section

2. We present the data used and the methodology of analysis in Section 3 and the empirical

results in Section 4. In Section 5, we summarize, discuss and conclude.

2. Literature Review: R&D expenditure, Patents and Economic Growth

The relationship between innovation and economic growth has emerged quite recently as a

central and topical research theme in innovation economics. According to Maradana et al.

(2017), studies on this subject may be categorized in four groups:

Supply-leading hypothesis (SLH) assumes unidirectional causality from innovation

activities to economic growth (see, for instance, Yang, 2006; Guloglu and Tekin, 2012;

Cetin, 2013; Pradhan et al., 2016);

[3]

Demand-following hypothesis (DFH) assumes unidirectional causality from economic

growth to innovation activities (see, for instance, Sinha, 2008; Cetin, 2013; Sadraoui et

al., 2014; Pradhan et al., 2016);

Feedback hypothesis (FBH) assumes bidirectional causality between economic growth

and innovation activities (see, for instance, Guloglu and Tekin, 2012; Cetin, 2013;

Pradhan et al., 2016);

Neutrality hypothesis (NLH)assumes no relationship between economic growth and

innovation activities (see, for instance, Cetin, 2013; Pradhan et al., 2016)

Modern econometric and statistical methods are used in order to investigate mostly the

possibility of long-run relationship between growth (GDP per capita as the most commonly

measure) and innovation (whether mainly measured by expenditure on R&D or patents data)

both in developed and developing countries

2.1 Research and Development (R&D) expenditure and Economic Growth Literature

Most empirical literature studies use R&D as measure of innovative activities. Sylwester

(2001) examines the association between research and development (R&D) and the growth

rate of output per capita at a national level in 20 OECD countries using a multivariate

regression. The empirical findings show that there is not a strong association between the two

but there is reported to be a positive relationship between industry R&D expenditure and

economic growth.

Ulku (2004) investigates the main postulations of the R&D based growth models that

innovation is created in the R&D sectors and it enables sustainable economic growth. The

analysis employs various panel data techniques and uses patent and R&D data for 20 OECD

and 10 Non-OECD countries for the period 1981–1997. The results suggest a positive

relationship between per capita GDP and innovation in both OECD and non-OECD countries,

while the effect of R&D stock on innovation is significant only in the OECD countries with

large markets.

Yu-Ming et al. (2007) investigate the cointegration and causal relationship between R&D

expenditure and economic growth and examine the causality pattern in the R&D expenditure

and economic growth in China from 1953 to 2004. The results suggest that there is a long-run

cointegration relationship between the R&D and GDP, and a bidirectional causal relationship

running from R&D to GDP and vice versa in the long-run also exists.

Samimi and Alerasoul (2009) examine the impact of R&D on economic growth of 30

developing countries for the period 2000-2006. The share of government expenditure on

research in GDP, the number of researchers in each one million population and the scientific

output of the countries are used as three different proxies for R&D. Their findings based on

panel data regression models indicate that in general no significance positive impact exists in

the countries under consideration.

[4]

Cetin (2013) examines the causal relationship between R&D expenditure and economic

growth, using the standard Granger (1969) and Toda-Yamamoto (1995) tests for causality, for

9 European countries for the period 1981-2008. In consideration of standard Granger causality

test, the empirical findings clearly exhibit that R&D expenditure cause GDP in the cases of

Finland, France and Spain. The results also indicate that GDP causes R&D expenditure in

Denmark and there is no causality between variables in other countries. On the other hand, the

results of Toda-Yamamoto test imply that there is no causality between R&D expenditure and

GDP in Holland, Ireland and Italy. However, there is bidirectional causality in Finland and

France. The empirical findings also indicate that there is a causal relationship between

variables running from R&D expenditure to GDP for Austria, while the direction of causality

is from GDP to R&D expenditures for Denmark, Spain and Portugal. Consequently, this study

provides further evidence supporting the hypothesis for some European countries.

Sadraoui et al. (2014) investigate the Granger causality between R&D cooperation and

economic growth in 32 industrial and developing countries from 1970 to 2012. They use an

econometric method which is based on a panel test of the Granger non causality hypothesis.

Using a new method to evaluate causality in a heterogeneous panel, they find that the causal

relationship from R&D cooperation to economic growth is homogeneous among the panel.

However, they find strong evidence of a heterogeneity of the causal relationship from

economic growth to R&D cooperation in their sample.

Santos and Catalao-Lopes (2014) investigate the causal relationship linking R&D and growth

in a sample of 8 European Union (EU) countries, with an emphasis on Portugal. Specifically,

they use annual OECD data for GDP and R&D, covering 22 observations, from 1987 to 2008.

The empirical results, which are based on cointegration analysis, show that there is a stable

long-run relationship between GDP and R&D only in the case of the United Kingdom.

Moreover, they use Granger causality test to investigate the causal relationship linking R&D

and growth in these countries. A causal relationship from growth to R&D can only be proven

for France and Spain, whereas the inverse causality only seems to exist for the Netherlands. In

addition, increased economic growth does not seem to necessarily mean increased R&D

investment either.

Pradhan et al. (2016), using a panel vector auto-regressive model, study interactions between

innovation, financial development and economic growth in 18 Eurozone countries between

1961 and 2013. They focus on whether causality runs between these variables both ways, one

way, the other way or not at all. The empirical results show that development of the financial

sector and enhanced innovative capacity in the Eurozone contributes to long-term economic

growth in the countries in the region.

Empirical studies differ greatly in terms of level of analysis (companies, industries or

countries), sources of data (time periods, countries) and measurements of key variables

(stocks, flows or differences). Their results are not comparable; however, in general, the

empirical results confirm theoretical assumptions that R&D expenditure has a positive and

persistent effect on growth (Freimane and Balina, 2016).

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2.2 Patents and Economic Growth Literature

In recent studies, researchers use patents as a measure of innovation activity to investigate the

link between innovation and economic growth. In contrast with R&D expenditure, patents

represent innovative output, indented to be commercialized (Hassan and Tucci, 2010).

Sinha (2008) examines the relationship between patents and economic growth in Japan and

South Korea using both individual country and panel data for 1963-2005.

The empirical findings show that for Japan the logarithms of real GDP and the

number of patents are cointegrated. In addition, he finds a two-way causality between

the growth of real GDP and growth of the number of patents in Japan. For South

Korea, he does not find any evidence of cointegration and causality. For panel data,

the logarithms of real and the number of patents are cointegrated. Panel causality tests find

some evidence that the growth of real GDP Granger causes the growth of the number of

patents.

Hassan and Tucci (2010), using global patent data, empirically investigate the importance of

both the quantity and quality of innovation on economic growth, controlling for past measures

of inventive inputs. Moreover, their research examines how innovation inputs can be

translated into per capita growth under the various economic structures and stages of

economic development. The empirical results, based on a sample of 58 countries for the

period 1980–2003, indicate that countries hosting firms with higher quality patents also have

higher economic growth. Furthermore, there is some evidence that those countries that

increase the level of patenting also witness a concomitant increase in economic growth.

Saini and Jain (2011) examine the correlation between patent applications filed and financial

growth of 9 selected Asian countries for the period of ten years (2000-2009). The results

concluded that it was a mixed result in case of Asian countries. Only, technology based

countries’ economies were affected by patent applications filed. The countries having positive

correlation (namely, Singapore, Thailand, Japan, Vietnam) depicts, leaving all other factors of

affecting GDP, innovations are the major factor affecting GDP growth rate.

Josheski and Koteski (2011) investigate the dynamic link between patent growth and GDP

growth in G7 economies for the period 1963-1993. Using ARDL methodology they show that

there is a positive relationship in the long run between quarterly growth of patents and

quarterly GDP growth. In the short run however at one or two lags there exist negative

relationship between quarterly patents growth and quarterly growth of GDP. Granger causality

test shows that patent growth Granger causes GDP growth in G7 countries and unrestricted

VAR shows that there exists positive relationship between patent growth and GDP growth at

two or three lags.

Guzmán et al. (2012) examine the long-run relationship between economic activity in Mexico,

measured by real GDP, and the number of patents granted to Mexican holders by the United

States Patents and Trademark Office (USPTO) during the period 1980-2008. Empirical

[6]

evidence suggests that the marginal change in the patents affects the GDP growth rate but this

does not have a significant effect on the number of patents change, that is, the number of

patents is an exogenous variable. Moreover, the analysis of the impulse-response functions

shows that shocks in the patents have length negative effects on real GDP, so as the effects of

the real GDP shocks on the patents do.

Maradana et al. (2017) examine the long-run relationship between innovation and per capita

economic growth in the 19 European countries over the period 1989–2014. They use six

different indicators of innovation: patents-residents, patents-nonresidents, research and

development expenditure, researchers in research and development activities, high-technology

exports, and scientific and technical journal articles to examine this long-run relationship with

per capita economic growth. Using cointegration technique, the study finds evidence of long-

run relationship between innovation and per capita economic growth in most of the cases,

typically with reference to the use of a particular innovation indicator. Using Granger

causality test, the study finds the presence of both unidirectional and bidirectional causality

between innovation and per capita economic growth. Their econometric results vary from

country to country, depending upon the types of innovation indicators that they use in the

empirical investigation process.

The literature findings indicate that patents have impact on economic growth in developed

countries. The causal effects vary from country to country, depending also to the choice of

sample period. We have to stress that the existing literature is restricted for European

countries, in the case of patent indicators and economic growth. The majority of studies use

R&D expenditure as innovation proxy.

In general, the literature empirical findings indicate the existence of a positive and strong

impact of innovation on economic growth in developed countries. The cointegration

methodology is widely used in empirical studies to identify long-term relationship between the

variables under consideration, while the Granger causality test for possible causal

relationships. The econometric results indicate the existence of long-term relationship in

developed countries usually unidirectional causality relationship from economic growth to

innovation. In this paper, we use innovation output indicators to capture this relationship. We

examine the causal links between three types of IPRs and economic growth (in pairs) in a

group of European countries, which constitutes the originality of this study.

3. Intellectual Property Rights, Data and Methodology

We investigate the relationship between innovation and economic growth in a sample of

European countries1 for the period 1980-2015. The period is selected according to availability

of appropriate data. Here we use three different types of intellectual property as proxies of

innovation activity, namely:

1 Austria, Belgium, Bulgaria, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, The

Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom.

[7]

Total patent applications (direct and PCT national phase entries),

Total industrial design applications (direct and via the Hague system) and

Total trademark applications (direct and via the Madrid system).

A patent is a document, which contains structured and detail information regarding an

invention and the probable application or use; it is accessible to the general public through

national or international authorized agencies (Huang et al., 2003). The high inventive activity

of patents is prominent among the other types of intellectual property rights (Jaffe et al., 1993;

Jaffe and Trajtenberg, 1999).

An industrial design is a document that describes in a concrete way products, services and

systems, and as such links creativity to innovation (Hollanders and van Cruysen 2009). Moody

(1980) originally highlighted the importance of the role of industrial design in technological

innovation and Rothwell (1992) emphasized that industrial designs play a crucial role for

successful industrial innovation. Despite the growing recognition of industrial design, few

studies have attempted to quantify the contribution of good industrial design to company

performance and economic growth (Hertenstein et al., 2005; Micheli and Gemser, 2016).

Trademarks are words, signs, symbols of combination thereof that indentify goods and

services as produced by a particular person or a company, therefore allowing consumers to

distinguish between goods originating from different sources (Centi and Rubio, 2005). They

play a crucial role in the process of marketing innovations, being instrumental in

differentiating the attributes of goods and services in the marketplace (Mendonça et al., 2004).

Trademarks rarely appear in public policy discussions as they are not considered to constitute

particular problems for competition and innovation policy (Harhoff, 2006).

The data are drawn from the database of the World Intellectual Property Organization

(WIPO). We use the total applications for intellectual property rights to capture aggregate

activity. GDP per capita is used as a proxy for growth (constant prices, $ 2010); it is derived

from the database of the World Bank2.

Figure 1 illustrates the trends in total patent applications as to the total population in European

countries under consideration. Norway presents by far the highest patent applications as to

total population followed by Germany and United Kingdom. Belgium, Denmark, Ireland,

Luxembourg decrease since the early 1990s. Southern European countries present a downward

trend during the last decade except Italy where the decrease is less steep.

2 PAT denotes the number of applications for patents, DES, the number of applications for industrial designs

and TR, the number of applications for trademarks. GDP per capita is symbolized by GDP.

[8]

Figure 1: Trends in total patent applications as to total population

Figure 2 shows the evolution of applications for industrial designs as to the total population3.

There is a remarkable growth trend in the cases of Switzerland and Norway during the last ten

years and a notable decline in Austria since the early 2000s. In Southern European countries

there is a remarkable increase during 1998-2003. Germany, France and United Kingdom have

a stable course for the period under consideration.

Figure 2: Trends in total industrial design applications as to total population

3 There is not available data for industrial design and trademark applications for Belgium, Luxembourg and the

Netherlands. Also there is lack of data for industrial design and trademark applications in the case of Greece.

0

0,0005

0,001

0,0015

0,002

0,0025

0,003

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

Austria Belgium Bulgaria Denmark

Finland France Germany Greece

Ireland Italy Luxembourg Netherlands

Norway Portugal Spain Sweden

Switzerland United Kingdom

0

0,0002

0,0004

0,0006

0,0008

0,001

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

Austria Bulgaria Denmark Finland

France Germany Greece Ireland

Italy Norway Portugal Spain

Sweden Switzerland United Kingdom

[9]

Figure 3 illustrates the trends in total trademark applications as to the total population. During

the last 20 years there has been a surge in national trademark applications in Europe. This

increase in filings has been interpreted as a sign of increased innovative performance (Herz

and Mejer, 2016). Switzerland has the highest growth rate followed by Norway.

Figure 3: Total trademark applications as to total population

Observing econometric methodology we first examine the stationary properties of the

univariate time series. Stationarity tests in time series analysis help to avoid misleading

results, as the problem of “spurious regression” (Granger and Newbold, 1974). Augmented

Dickey-Fuller (ADF) test was used to test the unit roots of the concerned time series variables

(Dickey and Fuller, 1979). For multivariate time series analysis involving stochastic trends,

augmented Dickey– Fuller (ADF) unit root tests were calculated for individual time series to

provide evidence as to whether the variables are integrated. This was followed by multivariate

cointegration analysis.

If the hypothesis of a unit root is not rejected, then a test for cointegration is performed. The

hypothesis tested is the null of non cointegration against the alternative of cointegration, using

Johansen’s maximum likelihood method. A vector autoregression approach was used to model

each variable (which is assumed to be jointly endogenous) as a function of all the lagged

endogenous variables in the system. We used the technique of cointegration according to the

procedure of Johansen and Juselius (1990) to identify possible long-term economic

relationships between the variables. In the Johansen framework, the first step is the estimation

of an unrestricted, closed p-th order VAR in k variables. Johansen (1988) suggested two tests

statistics to determine the cointegration rank. The first of these is known as the trace statistic:

0

0,0005

0,001

0,0015

0,002

0,0025

0,003

0,0035

0,004

0,0045

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

Austria Bulgaria Denmark Finland France

Germany Greece Ireland Italy Norway

Portugal Spain Sweden Switzerland United Kingdom

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, (1)

where, are the estimated eigenvalues λ1> λ2> λ3> … >λk and r0 ranges from zero to k-1

depending upon the stage in the sequence. This is the relevant test statistics for the null

hypothesis r ≤ r0 against the alternative r ≥ r0+1. The second test statistic is the maximum

eigenvalue test known as λmax; we denote it as λmax (r0). This is closely related to the trace

statistic, but arises from changing the alternative hypothesis from r ≥ r0+1 to r = r0+1. The λmax

test statistic is:

, (2)

The null hypothesis is that there are r cointegrating vectors, against the alternative of r + 1

cointegrating vectors. It is common practice to present the results of both tests while

monitoring the trace is more powerful when considering two variables (Lutkepohl et al.,

2001). We present the results of the trace test in Table 1.

The basic assumption of this study is the possibility of strong long-term relationship in the

case of patent applications and economic growth in the northern and more developed

countries, while in the southern countries a long-run relationship is expected between the

weaker intellectual property rights (industrial designs and trademarks) and GDP per capita.

Also we assume that the impact of intellectual property rights on economic growth is more

effective in upper middle countries compared to that in lower middle income countries (Janjua

and Samad, 2007; Sattar and Mahmood, 2011).

We employed Granger causality analysis to investigate causal relationships for one to six lags.

According to Granger (1969), Y is said to “Granger-cause” X if and only if X is better

predicted by using the past values of Y than by not doing so with the past values of X being

used in either case. In short, if a scalar Y can help to forecast another scalar X, then we say

that Y Granger-causes X. If Y causes X and X does not cause Y, it is said that unidirectional

causality exists from Y to X. If Y does not cause X and X does not cause Y, then X and Y are

statistically independent. If Y causes X and X causes Y, it is said that feedback exists between

X and Y. Essentially, Granger’s definition of causality is framed in terms of predictability. To

implement the Granger test, a particular autoregressive lag length k (or p) is assumed and

Models (3) and (4) are estimated by OLS:

(3)

(4)

Furthermore, an F-test is carried out for the null hypothesis of no Granger causality; H0: bi1

bi2 ... bik 0,i 1, where, the F statistic is the Wald statistic of the null hypothesis. If the F

[11]

statistic is greater than a certain critical value for an F distribution, then we reject the null

hypothesis that Y does not Granger-cause X, which means Y Granger-causes X.

4. Empirical Results

All data are transformed into logarithmic returns in order to achieve mean-reverting

relationships, and to make econometric testing procedures valid4. The Augmented Dickey-

Fuller test (ADF test) reveals that the hypothesis of a unit root test in LPAT, LDES, LTR and

LGDP cannot be rejected even at the 5% significance level. The hypotheses of a unit root in

DLPAT, DLDES, DLTR and DLGDP are rejected at least at the 5% level of confidence5,

indicating that all the variables in question are I(1).

Table 1 summarizes the trace test results for cointegrating relationships between economic

growth and each type of intellectual property rights6.

Table 1: Johansen Cointegration Test Results

Country

LGDP -

LPAT

LGDP -

LDES

LGDP -

LTR

Austria

Belgium

n.a

n.a

Bulgaria

Denmark

Finland

France

Germany

Italy

Luxembourg

n.a

n.a

The

Netherlands

n.a

n.a

Norway

Portugal

Sweden

Switzerland

United

Kingdom

4 Table I (see Appendix) displays the estimates of the Augmented Dickey – Fuller (ADF) test in levels and in first

differences of the data with an intercept and trend. The ADF test results indicate that time series of LGDP are I(2)

in the cases of Greece, Ireland and Spain. 5 LPAT is integrated of order one in Germany, Portugal and United Kingdom at the 10% level of confidence.

Moreover, LGDP is integrated of order one in Bulgaria and Norway at the 10% level of confidence. 6 The analytic trace test results are presented in Table II to IV (in the Appendix). Variables LPAT, LDES, LTR,

LGDP, Maximum lag in VAR=2.

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The findings show that there are long run relationships between GDP per capita and patent

applications in the cases of Austria, Denmark, France, Germany, Italy, the Netherlands,

Sweden and United Kingdom at the 5% level of confidence. In Belgium there is a long run

relationship at the 10% level of confidence. In the case of GDP per capita and industrial

design applications, there are long run relationships in Austria, Finland, France, Italy,

Portugal, Sweden and United Kingdom; in Italy and Sweden results are more significant. The

last column (Table 1) indicates long run relations between GDP per capita and trademark

applications in Austria, Finland, France, Italy, Norway, Portugal and Switzerland; the findings

are more significant in Austria, Italy and Norway. Consequently, the econometric results vary

from country to country, depending upon the types of innovation indicators that we use in the

empirical investigation process.

In Table 2 we summarized the results of the Granger causality test between GDP per capita

and patents applications7.

Table 2: Granger Causality Test Results between LPAT and LGDP

Direction of Causality Lags

Country 1 2 3 4 5 6

LPAT → LGDP

Austria ** *

Belgium *** *

Bulgaria ** ** **

Denmark *

Finland * **

Italy ** **

Luxembourg **

The Netherlands ** ** ** *

Switzerland * *

LGDP → LPAT

Bulgaria *** *

Germany *** * ** *** *** ***

Norway * ** * ** * **

Portugal *

Sweden *** ** ** ** ** *

Switzerland * * **

United Kingdom *

LGDP ≠ LPAT France

*, **, *** denote significance at 10%, 5% and 1% respectively. This note also applies to the subsequent tables.

There is a unidirectional causal link from patent applications to GDP (LPAT→LGDP) in

Austria, Belgium, Denmark, Finland, Italy, Luxembourg and the Netherlands. A reverse

causal relationship (LGDP→LPAT) is indicated in Germany, Norway, Portugal, Sweden and

United Kingdom; the causal links are stronger in Germany, Norway and Sweden.

7 The analytic Granger causality test results for the three pairs of variables are presented in Table V to VII (see

Appendix).

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Bidirectional relations are indicated in the cases of Bulgaria and Switzerland (LGDP↔LPAT).

In France, there is no evidence of bidirectional or unidirectional causal links between patent

applications and GDP per capita. Table 3 reports the causal relationships between GDP per

capita and industrial design applications.

Table 3: Granger Causality Test Results between LDES and LGDP

Direction of Causality Lags

Country 1 2 3 4 5 6

LDES → LGDP

Austria *

France ***

Italy **

United Kingdom *

LGDP → LDES

Austria *

Denmark *** * **

Finland * *** ** ** *

France **

Portugal * ** ** *

Sweden *** ** *** *** *** ***

United Kingdom **

LGDP ≠ LDES

Bulgaria

Germany

Norway

Switzerland

The empirical findings indicate a unidirectional causal link from industrial design applications

to GDP (LDES→LGDP) in Italy for one lag. A reverse causal link (LGDP→LDES) is found

in Denmark, Finland, Portugal and Sweden. Bidirectional causal relations are indicated in

Austria, France and United Kingdom but only for one lag. Regarding the rest four European

countries (Bulgaria, Germany, Norway and Switzerland) Granger causality test finds no

evidence of causal links. Table 4 presents the causal relationships between GDP per capita and

trademark applications.

Table 4: Granger Causality Test Results between LTR and LGDP

Direction of Causality Lags

Country 1 2 3 4 5 6

LTR → LGDP

Austria **

France **

Italy *

Portugal ***

LGDP → LTR

Austria ** *** ** ***

Denmark *** *** **

Finland ** *** ** ** ** **

Norway * ** * *

[14]

Sweden ** * *

LGDP ≠ LTR

Bulgaria

Germany

Switzerland

United Kingdom

Table 4 indicates that there is a unidirectional causal link from trademark applications to GDP

(LTR→LGDP) in France, Italy and Portugal for one lag. The relationships between LGDP and

LTR for the four Nordic countries are unidirectional running from LGDP to LTR. In Austria

the causal link is bidirectional but is stronger from the variable of economic growth to

trademark applications. Regarding the rest four European countries (Bulgaria, Germany,

Switzerland and United Kingdom) Granger causality test finds no evidence of causal links. In

Table 5 we summarize the Granger causality test results.

Table 5: Summarized causalities (time lags in parentheses)

Direction of Causality Intellectual Property Rights Type

Patents Industrial Designs Trademarks

From IPR type to GDP

Austria (1,3)

Belgium (1,2)

Bulgaria (4,5,6)

Denmark (3)

Finland (1,2)

Italy (1,2)

Luxembourg (5)

The Netherlands (1,3,4)

Switzerland (1,2)

Austria (1)

France (1)

Italy (1)

United Kingdom (1)

Austria (1)

France (1)

Italy (1)

Portugal (1)

From GDP to IPR type

Bulgaria (5,6)

Germany (1,2,3,4,5,6)

Norway (1,2,3,4,5,6)

Portugal (6)

Sweden (1,2,3,4,5,6)

Switzerland (4,5,6)

United Kingdom (5)

Austria (1)

Denmark (1,2,6)

Finland (2,3,4,5,6)

France (1)

Portugal (1,2,3,4)

Sweden (1,2,3,4,5,6)

United Kingdom (1)

Austria (2,3,4,5)

Denmark (1,2,3)

Finland (1,2,3,4,5,6)

Norway (1,2,3,4)

Sweden (1,2,5)

No causality France

Bulgaria

Germany

Norway

Switzerland

Bulgaria

Germany

Switzerland

United Kingdom

Bold characters denote significance at 5% or 1%.

The main econometric results may be summarized as follows:

The cointegration approach shows that the long-run relationship between

economic growth and intellectual property rights is country and type of

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protection specific. The mixed results confirm empirical findings of recent studies

which investigate the possibility of cointegration between innovation and economic

growth for European countries (Santos and Catalao-Lopes, 2014; Maradana et al.,

2017).

Patent applications have a stronger effect on economic growth rather than

industrial design and trademark applications. The economic effects of industrial

design and trademark applications are immediate in contrast to patent applications

where their effects become visible after at least two lags in several countries under

consideration. Probably, certain sectors where patents are more significant and

common (e.g. pharmaceuticals, nanotechnology, etc.) are relatively stronger in these

countries, hence they have causal effects on growth in the long run.

In general, there is a powerful causal relationship running from economic growth

to three different types of innovation for a significant number of time lags. The

results are quite distinct in the cases of Austria, Denmark, Finland, Norway and

Sweden. This means that a strong economy may provide more incentives for

innovation activity and for vesting of intellectual property rights.

There is evidence for a different direction of causality between Nordic (Denmark,

Finland, Norway and Sweden plus Germany which fits the pattern only in the

case of patents) and Southern “Latin” European countries (mainly Italy and

France). Specifically, the empirical results indicate a one-way causal relationship

from economic growth to intellectual property rights in Northern European countries.

These countries are able to allocate more resources to R&D expenditure. On the

contrary, in Southern European countries and mainly in Italy, there is evidence of

reverse causal link, from intellectual property rights to economic growth, which

requires further analysis. However, the problems which were identified in the time

series of GDP (in logarithms) in Greece and Spain restrict the generalization of

empirical findings and possible differences between Northern and Southern European

countries.

It is important to highlight that results may be sensitive to the choice of sample period,

selection of variables and methodology adopted. This also indicates the sensitivity of Granger

causality and that is why results based on Granger causality should be interpreted with care.

5. Concluding Remarks

The relationship between innovation and economic growth has emerged quite recently as a

central and topical research theme in innovation economics. The empirical studies investigate

mostly the possibility of long-run relationship between innovation (whether mainly measured

by expenditure on R&D or patents data) and economic growth. The majority of literature

[16]

studies use R&D as measure of innovative activities and the results indicate that R&D

expenditure has a positive and persistent effect on economic growth. However, in recent

studies, researchers use patents as a measure of innovation activity. The empirical findings

indicate the existence of long-term relationship in developed countries usually unidirectional

causality relationship from economic growth to innovation. In this paper, we examined the

relationship between innovation and economic growth in a group of European countries for

the period 1980-2015. We used three different types of intellectual property rights to capture

innovation activity output: patent applications, industrial design applications and trademark

applications. The empirical results reported herein suggest that:

The long-run relationship between economic growth and intellectual property rights is

country and type of protection specific.

Among the three types of IPRs, patent applications have a stronger causal effect on

economic growth than industrial design and trademark applications.

There is a strong causal relationship running from economic growth to all types of

IPRs for a significant number of time lags, which indicates that a strong economy is a

favorable condition for IPR activity.

There is evidence for a different direction of causality between Northern (especially

Nordic) and Southern “Latin” European countries. In the first ones, the causal

relationship seems to be from economic growth to innovation output indicators while

in Southern countries the causal link is reverse.

The empirical studies focusing on R&D expenditure as innovation proxy indicate a

unidirectional causal relationship from innovation indicator to economic growth in

developed countries. In this paper, the causal links seem to be different in the case of

Southern European countries. However, there is a shortage in literature for IPRs and

economic growth for European countries. As a result, the comparison with literature

studies cannot be accurate.

The empirical findings and possible differences between Northern and Southern European

countries create the need for discussion about measures of innovation activity (Edquist and

Zabala-Iturriagagoitia, 2015). The European Innovation Scoreboard (EIS) places Northern

European countries (mainly Sweden, Denmark and Finland) on top, as “innovation leaders” in

the most recent reports. These countries, by-and-large due to their strong economy, are able to

allocate significant resources to R&D expenditure (public and business). The statistical

information provided by the EIS and the results of their analysis based on it should be

supplemented with other more contextual and qualitative information regarding the innovation

system under study (Foray and Hollanders, 2015). The approaches with input indicators (e.g.

R&D expenditure, number of researchers in R&D, venture capital as % of GDP etc) as

innovation proxies are likely to generate misleading results about the countries' “innovation

performance”, as they do not take into account the innovation performance indicated from the

transformation of inputs to outputs (Edquist and Zabala-Iturriagagoitia, 2015).

The “innovation performance” of an innovation system should be understood from two

perspectives: (i) the delivery-production of innovation outputs; and (ii) the innovation

[17]

efficiency of the system as a whole (Edquist and Zabala-Iturriagagoitia, 2015). Alternative

methodologies with clear separation of inputs and outputs can provide a completely different

picture (Havas, 2015). Southern European countries significantly fall behind Europe 2020

goals (in terms of R&D expenditure). These countries, which are described as moderate

innovators (European Union, 2013; 2014; 2015; 2016) may have higher performance ratios

(output/input) in terms of innovation (Edquist and Zabala-Iturriagagoitia, 2015). Still, in

qualitative terms, which are not and possibly cannot be derived from this type of quantitative

analysis, their innovation activity may be lacking in strategic significance.

In future research, innovation indicators available from the European Innovation Scoreboard

(EIS) could be exploited by using panel data techniques. A separation of European countries

in two or more groups (e.g. Northern and Southern European countries) may provide quite

different results about the efficiency of individual national innovation systems. The use of

panel data techniques and the recent panel causality tests of Dumitrescu and Hurlin (2012),

Tervo (2009) and Hartwig (2010) could be applied to investigate possible causal relationships

between innovation indicators and economic growth. The econometric approaches in the

literature, as presented in Section 2, use either R&D expenditure or patents as innovation

indicators and therefore they fail to capture the full range of innovation activities and the

sectoral expertise in each country. Possible broadening of the range of (structural) indicators

explored could yield a richer picture. Further empirical analysis with emphasis on the

combination of innovation output and input indicators may produce more insightful findings

on the relationship between innovation and economic growth.

[18]

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[23]

Appendix

Table I. Augmented Dickey Fuller Unit root test results

Augmented Dickey-Fuller

Levels

1

st differences

Intercept and Trend

Intercept and Trend

Austria

LPAT

-2,84

-6,19***

LGDP

0,09

-5,01***

LDES

-1,56

-6,89***

LTR

-1,32

-8,04***

Belgium

LPAT

-1,39

-7,08***

LGDP

-0,08

-4,85***

LDES

n.a

n.a.

LTR

n.a

n.a.

Bulgaria

LPAT

-2,38

-5,49***

LGDP

-0,95

-3,23*

LDES

-1,16

-5,18***

LTR

-0,5

-4,46***

Denmark

LPAT

-1,18

-3,83**

LGDP

-0,37

-5,00***

LDES

-2,13

-5,51***

LTR

-0,78

-5,82***

Finland

LPAT

-2,73

-7,02***

LGDP

-0,75

-3,56**

LDES

-1,24

-6,95***

LTR

-0,67

-6,50***

France

LPAT

-3,47*

-5,08

***

LGDP

-0,02

-4,24**

LDES

-1,98

-4,48***

LTR

-1,99

-5,32***

Germany

LPAT

-1,92

-3,26*

LGDP

-1,87

-5,46***

LDES

-1,80

-4,88***

LTR

-1,35

-4,91***

Greece LPAT

-1,09

-5,41

***

LGDP

-0,16

-2,56

[24]

LDES

n.a

n.a.

LTR

n.a

n.a.

Ireland

LPAT

-2,21

-3,97**

LGDP

-1,24

-0,82

LDES

-1,85

-3,97**

LTR

-1,62

-5,31***

Italy

LPAT

-3,18

-6,27***

LGDP

0,62

-4,34***

LDES

-1,81

-4,44***

LTR

-1,64

-4,73***

Luxermbourg

LPAT

-1,05

-5,38***

LGDP

-0,20

-4,96***

LDES

n.a

n.a.

LTR

n.a

n.a.

The Netherlands

LPAT

-3,50*

-5,73

***

LGDP

-0,33

-3,67**

LDES

n.a

n.a.

LTR

n.a

n.a.

Norway

LPAT

-1,09

-4,04***

LGDP

0,31

-3,21*

LDES

-1,21

-5,74***

LTR

-1,13

-4,34***

Portugal

LPAT

-0,47

-3,27*

LGDP

-1,53

-5,12***

LDES

-2,01

-4,74***

LTR

-1,67

-4,71***

Spain

LPAT

-1,48

-5,11***

LGDP

0,23

-2,74

LDES

-1,30

-3,82**

LTR

-1,41

-4,54***

Sweden

LPAT

-1,79

-3,55**

LGDP

-1,61

-4,22**

LDES

-2,27

-4,42***

LTR

-1,17

-6,17***

Switzerland

LPAT

-2,41

-8,05***

LGDP

-1,99

-4,02**

LDES

-2,41

-5,14***

[25]

LTR

-1,91

-6,73***

United Kingdom

LPAT

-1,82

-3,30*

LGDP

-0,54

-3,90**

LDES

-1,90

-4,04**

LTR

-2,22

-6,57***

*, **, *** denote significance at 10%, 5% and 1% respectively. This note also applies to the

subsequent tables.

Table II. Johansen Cointegration Test Results for LPAT and LGDP

Country Null Hypothesis Trace 5%

Result

Austria r=0 23 15,49

Cointegration r<=1 5,78 3,84

Belgium r=0 14,98 15,49

Cointegration r<=1 4,97 3,84

Bulgaria r=0 12,01 15,49

No Cointegration r<=1 0,05 3,84

Denmark r=0 17,61 15,49

Cointegration r<=1 4,86 3,84

Finland r=0 9,16 15,49

No Cointegration r<=1 0,07 3,84

France r=0 16,75 15,49

Cointegration r<=1 5,68 3,84

Germany r=0 15,88 15,49

Cointegration r<=1 3,31 3,84

Italy r=0 27,91 15,49

Cointegration r<=1 8,71 3,84

Luxembourg r=0 11,56 15,49

No Cointegration r<=1 0,62 3,84

The Netherlands r=0 22,21 15,49

Cointegration r<=1 8,01 3,84

Norway r=0 12,26 15,49

No Cointegration r<=1 3,65 3,84

Portugal r=0 7,67 15,49

No Cointegration

[26]

r<=1 3,47 3,84

Sweden r=0 17,95 15,49

Cointegration r<=1 0,44 3,84

Switzerland r=0 9,82 15,49

No Cointegration r<=1 4,15 3,84

United Kingdom r=0 16,78 15,49

Cointegration r<=1 5,22 3,84

Table III. Johansen Cointegration Test Results for LDES and LGDP

Country Null Hypothesis Trace 5%

Result

Austria r=0 15,07 15,49

Cointegration r<=1 5,43 3,84

Bulgaria r=0 9,29 15,49

No Cointegration r<=1 1,90 3,84

Denmark r=0 12,33 15,49

No Cointegration r<=1 3,12 3,84

Finland r=0 14,40 15,49

Cointegration r<=1 2,19 3,84

France r=0 16,26 15,49

Cointegration r<=1 3,36 3,84

Germany r=0 11,02 15,49

No Cointegration r<=1 1,84 3,84

Italy r=0 18,98 15,49

Cointegration r<=1 4,66 3,84

Norway r=0 10,02 15,49

No Cointegration r<=1 0,27 3,84

Portugal r=0 15,79 15,49

Cointegration r<=1 4,48 3,84

Sweden r=0 18,24 15,49

Cointegration r<=1 0,64 3,84

[27]

Switzerland r=0 9,09 15,49

No Cointegration r<=1 2,30 3,84

United Kingdom r=0 13,76 15,49

Cointegration r<=1 4,87 3,84

Table IV. Johansen Cointegration Test Results for LTR and LGDP

Country Null Hypothesis Trace 5%

Result

Austria r=0 24,01 15,49

Cointegration r<=1 9,31 3,84

Bulgaria r=0 10,68 15,49

No Cointegration r<=1 0,40 3,84

Denmark r=0 9,66 15,49

No Cointegration r<=1 0,29 3,84

Finland r=0 16,19 15,49

Cointegration r<=1 2,34 3,84

France r=0 16,03 15,49

Cointegration r<=1 4,25 3,84

Germany r=0 11,73 15,49

No Cointegration r<=1 1,96 3,84

Italy r=0 18,95 15,49

Cointegration r<=1 4,71 3,84

Norway r=0 19,29 15,49

Cointegration r<=1 8,18 3,84

Portugal r=0 15,58 15,49

Cointegration r<=1 4,28 3,84

Sweden r=0 10,95 15,49

No Cointegration r<=1 0,43 3,84

Switzerland r=0 15,47 15,49

Cointegration r<=1 0,68 3,84

United Kingdom r=0 7,78 15,49

No Cointegration r<=1 2,42 3,84

[28]

Table V. Granger Causality Test Results for LPAT and LGDP

F-Statistic

Country Null Hypothesis Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6

Austria LPAT does not Granger Cause LGDP 5,39** 0,84 2,38* 1,96 1,60 1,48

LGDP does not Granger Cause LPAT 0,34 0,72 1,11 1,72 1,83 1,54

Belgium LPAT does not Granger Cause LGDP 11,11*** 2,72* 1,49 1,03 0,77 0,74

LGDP does not Granger Cause LPAT 0,88 0,77 0,68 0,76 0,54 0,49

Bulgaria LPAT does not Granger Cause LGDP 0,33 0,27 1,14 3,82** 3,28** 4,32**

LGDP does not Granger Cause LPAT 2,14 1,48 1,14 1,87 8,30*** 2,15*

Denmark LPAT does not Granger Cause LGDP 2,18 1,47 2,88* 2,07 1,58 1,24

LGDP does not Granger Cause LPAT 0,23 0,66 1,02 1,07 0,78 0,87

Finland LPAT does not Granger Cause LGDP 3,42* 3,55** 2,06 2,17 1,86 1,61

LGDP does not Granger Cause LPAT 2,55 0,97 0,90 0,81 0,61 0,84

France LPAT does not Granger Cause LGDP 2,23 2,01 1,65 1,60 1,22 1,21

LGDP does not Granger Cause LPAT 0,02 0,09 0,65 0,42 0,76 0,63

Germany LPAT does not Granger Cause LGDP 0,29 0,30 0,89 0,55 0,30 1,05

LGDP does not Granger Cause LPAT 8,14*** 2,94* 3,42** 7,29*** 5,74*** 5,47***

Italy LPAT does not Granger Cause LGDP 6,05** 4,13** 2,06 1,24 1,03 0,73

LGDP does not Granger Cause LPAT 0,002 0,13 0,28 0,29 0,73 0,89

Luxembourg LPAT does not Granger Cause LGDP 0,56 0,12 1,86 1,32 3,62** 2,12

LGDP does not Granger Cause LPAT 0,22 0,39 0,50 1,24 0,78 1,4

The Netherlands

LPAT does not Granger Cause LGDP 4,98** 0,31 3,32** 3,05** 2,15 2,43*

LGDP does not Granger Cause LPAT 0,04 0,48 0,47 0,23 0,02 0,25

Norway LPAT does not Granger Cause LGDP 1,04 0,02 0,21 0,26 0,30 0,30

LGDP does not Granger Cause LPAT 3,63* 4,58** 2,75* 4,38** 2,69* 3,56**

Portugal LPAT does not Granger Cause LGDP 0,30 1,04 0,77 0,69 1,18 1,32

LGDP does not Granger Cause LPAT 0,09 0,94 1,01 0,92 1,49 2,39*

Sweden LPAT does not Granger Cause LGDP 2,01 0,56 1,01 1,25 0,97 1,20

LGDP does not Granger Cause LPAT 10,77*** 4,48** 3,83** 3,80** 2,99** 2,53*

Switzerland LPAT does not Granger Cause LGDP 3,69* 2,86* 0,82 0,54 0,44 0,51

LGDP does not Granger Cause LPAT 0,75 0,54 0,74 2,26* 2,50* 3,76**

United Kingdom

LPAT does not Granger Cause LGDP 0,64 1,37 1,29 1,54 1,83 1,47

LGDP does not Granger Cause LPAT 0,50 1,80 1,53 1,89 2,18* 1,17

[29]

Table VI. Granger Causality Test Results for LDES and LGDP

F-Statistic

Country Null Hypothesis Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6

Austria LDES does not Granger Cause LGDP 3,14* 0,65 1,10 1,11 1,49 1,40

LGDP does not Granger Cause LDES 4,02* 2,48 2,22 1,83 1,33 0,81

Bulgaria LDES does not Granger Cause LGDP 1,30 1,99 1,12 1,06 0,99 0,80

LGDP does not Granger Cause LDES 2,06 0,67 0,59 0,43 0,65 1,11

Denmark LDES does not Granger Cause LGDP 0,97 0,62 1,11 0,91 0,65 0,95

LGDP does not Granger Cause LDES 4,37** 3,20* 1,93 1,4 1,49 3,44**

Finland LDES does not Granger Cause LGDP 0,26 0,46 0,53 0,59 0,32 0,41

LGDP does not Granger Cause LDES 2,40 2,53* 6,63*** 4,08** 2,97** 3,00*

France LDES does not Granger Cause LGDP 7,59*** 1,68 1,92 1,38 1,36 1,78

LGDP does not Granger Cause LDES 6,39** 1,79 1,23 0,82 0,33 1,95

Germany LDES does not Granger Cause LGDP 0,08 0,43 1,60 1,75 1,21 1,96

LGDP does not Granger Cause LDES 0,52 0,59 0,34 0,41 0,59 1,5

Italy LDES does not Granger Cause LGDP 4,79** 0,70 0,98 1,61 1,34 1,10

LGDP does not Granger Cause LDES 1,07 0,87 0,57 0,41 1,43 1,17

Norway LDES does not Granger Cause LGDP 0,27 0,30 0,50 1,18 1,87 1,79

LGDP does not Granger Cause LDES 0,09 1,43 1,30 0,72 1,04 0,85

Portugal LDES does not Granger Cause LGDP 1,47 0,58 0,47 0,35 1,54 1,82

LGDP does not Granger Cause LDES 3,53* 4,76** 3,10** 2,77* 1,73 1,6

Sweden LDES does not Granger Cause LGDP 0,40 0,87 0,68 0,67 1,12 0,99

LGDP does not Granger Cause LDES 11,51*** 3,83** 6,62*** 6,83*** 6,50*** 5,19***

Switzerland LDES does not Granger Cause LGDP 0,04 0,07 0,15 0,82 1,40 1,19

LGDP does not Granger Cause LDES 2,39 1,85 1,21 1,91 1,04 0,68

United Kingdom

LDES does not Granger Cause LGDP 3,73* 0,07 1,53 1,72 2,03 2,06

LGDP does not Granger Cause LDES 4,24** 1,52 1,44 1,09 0,78 0,31

[30]

Table VII. Granger Causality Test Results for LTR and LGDP

F-Statistic

Country Null Hypothesis Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Austria LTR does not Granger Cause LGDP 5,90** 1,47 2,08 1,72 0,81 1,41

LGDP does not Granger Cause LTR 2,69 3,37** 4,65*** 3,94** 9,72*** 1,06

Bulgaria LTR does not Granger Cause LGDP 0,09 0,3 0,13 0,14 1,45 1,84

LGDP does not Granger Cause LTR 1,94 0,33 0,55 0,91 1,25 0,44

Denmark LTR does not Granger Cause LGDP 0,92 0,68 0,57 0,63 1,23 1,45

LGDP does not Granger Cause LTR 10,99*** 5,81** 3,21** 1,48 1,73 1,18

Finland LTR does not Granger Cause LGDP 1,39 0,71 1,15 0,78 0,66 0,68

LGDP does not Granger Cause LTR 5,11** 5,87** 4,17** 3,68** 3,26** 2,87**

France LTR does not Granger Cause LGDP 5,43** 1,56 1,33 0,66 0,53 0,38

LGDP does not Granger Cause LTR 0,44 0,62 0,73 0,41 0,27 1,06

Germany LTR does not Granger Cause LGDP 2,47 1,15 0,41 0,37 0,31 0,11

LGDP does not Granger Cause LTR 0,002 0,94 0,73 0,68 1,10 0,99

Italy LTR does not Granger Cause LGDP 3,19* 0,71 0,27 0,25 0,61 1,64

LGDP does not Granger Cause LTR 0,01 0,22 0,44 1,55 1,86 1,39

Norway LTR does not Granger Cause LGDP 0,06 0,04 0,08 0,44 0,48 1,65

LGDP does not Granger Cause LTR 3,88* 5,13** 2,34* 2,78* 1,65 1,43

Portugal LTR does not Granger Cause LGDP 10,36*** 1,35 0,73 0,14 0,10 0,50

LGDP does not Granger Cause LTR 0,18 0,09 0,25 0,27 0,32 0,34

Sweden LTR does not Granger Cause LGDP 0,38 0,82 0,70 0,49 1,02 0,97

LGDP does not Granger Cause LTR 6,00** 2,85* 2,23 2,07 2,16* 1,14

Switzerland LTR does not Granger Cause LGDP 0,83 0,31 0,03 0,08 0,08 0,15

LGDP does not Granger Cause LTR 0,14 0,68 1,17 0,73 1,61 0,65

United Kingdom

LTR does not Granger Cause LGDP 1,31 0,42 0,24 0,21 0,17 0,50

LGDP does not Granger Cause LTR 0,83 0,81 1,07 1,26 1,75 1,84