Innovation Capacity in Europe:
A
Cross-National Comparison of Twenty-Eight European Countries
By: Jelle de Bruin
10246487
Abstract:
This thesis constructs an innovation capacity index by scoring countries in five innovation pillars: R&D, Academic System, Technological Adaptation, Economic Environment and Regulatory Framework. Using this index, the European countries were ranked for each year during 1996-2013. By doing this, we tried to explain why some countries have been leading the charts, why some countries have increased in ranking and why other countries have failed in maintaining or growing a high innovation capacity.We found that overtime Sweden, Denmark and Finland were the best performers, and Bulgaria and Romania the worst. Additionally we found that Luxembourg, Slovenia, Estonia and Lithuania were the most improved countries during our time interval, while Slovakia, Italy, and Poland were the most declined.
From these rankings and their changes we learned that in order to achieve a high innovation capacity a country must have a government that supports innovation, an academic system that is able contribute to scientific knowledge, a considerable expenditure in R&D, a strong economy, and must be quick to adapt itself to the latest technology. Moreover, we found that joining the EU, in general, had a positive effect on the innovation capacity of countries who were late to join.
Index
Introduction 5 Background 5 Relevance 5 Hard Data 6 Over Time 6 Research Question 7 Structure 7 Literature Review 8 Joseph Schumpeter 8Inventions and Innovations 9
Innovation Capacity 9
Countries and Systems 10
National Innovation Systems 11
Factors Determining a Nation’s Innovation Capacity 12
An Index 19
Methodology 20
Measuring Innovation Capacity 23
Data Collection 23
Evaluating Innovation Capacity Index 24
Variables 25
Missing Variables 26
Index Structure and Formulation 26
Analysis 29
The Rules andRegulations Pillar 29
The Academic System Pillar 31
The Technological Adaptation Pillar 32
The Economical Environment Pillar 33
The Research and Development Pillar 34
Producing an Index 36
Results 38
Rules and Regulations Pillar 39
Academic System Pillar 41
Technological Adaptaion Pillar 43
Economical Environment Pillar 46
Research and Development Pillar 48
Innovation Capacity Index 50
Scores per country 52
Discussion 63
Limitations and Further Research 66
Conclusion 68
Practical Implications 68
References 69
Appendix 76
Detailed Definition of Variables 76
Each of the Pillars and their Sub-Index Scores and Ranking 82
Rules and Regulations 82
Academic System 88
Technological Adaptation 93
Economic Environment 98
Research and Development 103
Innovation Capacity Index Scores per year 108
1.Introduction
1.1 BackgroundIdeas about what drives the economic welfare of a region have evolved over time. “Natural resources, population growth, industrialization, geography, climate, and military might have all played a role in the past” (Lopéz-Claros a Mata, 2002). However, as of late there has been a relative newcomer to this debate - one could even go as far as to claim that it’s the most important ‘engine of productivity and growth’(Lopéz-Claros a Mata, 2002). This newcomer is the innovation excellence of a region.
Sustainable competitiveness is the main source for the success of an economic actor (Tura and Hamaakorpi, 2005). The competitiveness of an economic actor is strongly related to its adaptability to the emerging techno-economic environment (Schienstock and Hämäläinen, 2001 citing Abramovitz, 1995 and Lipsey, 1997); or in other words, a region’s capability to innovate is the driver of progress that creates disparities in the productivity and growth of different regions (David and Foray, 2002). Burda and Wyplosz confirm this by saying that “innovation is one of the most important drivers of economic growth” (2009, p.93 - 94).
The European Union is aware of this as they have said “innovation is vital to European competitiveness in the global economy” (EC, 2016). But how do the European countries compare to each other in their capacity to innovate, and how did this change over time, and why? Answering these questions is important as there is still a lot of disparity between the European countries and it will be of great value to know how great that disparity is and what the reason is why some countries are more successful than others.
Innovation has been a hot topic amongst academics in recent years, because it remains something rather intangible and hard to measure. Which has lead to a lot of discussion around what influences and stimulates innovation.
1.2 Relevance
Currently, there are multiple indexes which measure the innovation capacity of countries, e.g. “Summary Innovation Index (SII, it rates 27 countries for years 2009/2010), National Innovative Capacity Index (NICI, it rates 71 countries in 2001),
Innovation Capacity Index (ICI, it rates 131 countries for years 2009/2010), Global Innovation Index (GII, it rates 125 countries in 2011)” (Kovacic, 2012, p.8) and the innovative capacity index by Porter and Stern in 2000. However, they all differ in which factors they are composed of and the methodology of calculation. That is why this thesis will compose its own index based on the consensus about innovation proxies in current literature. Moreover, all of the indexes are looking at a fixed point in time, and are at the mercy of surveys which harms the objectiveness of their results.
1.2.1 Hard Data
The ICI of this paper is constructed from variables that can be seen as hard data indicators. Most of the data are parameters that can be directly measured (Research and Development expenditure, government spending on education, etc.). Because of this, the data is not susceptible to opinions, business and society perceptions. Think of a hypothetical example where the inhabitants of country A think they are really innovative, but in reality this proves to be false. Surveys would then provide flawed results, whereas hard data cannot lie, and will indicate the true situation. Over the last decade a considerable amount of international organizations have developed indicators for factors that were previously measured by surveys. The world bank has done a great deal of good work in this area. They are performing a lot of fieldwork to examine actual- as opposed to perceived- factors that influence the business world. World bank is the best example, but they are not the only one. Organizations such as the IMF and the Telecommunications union have also broadened the scope of variables with which they try to measure the use of the latest technology (Lopez-Claros, 2011).
1.2.2 Over Time
Like stated before, most of the indexes which are already existing look at a fixed point in time (Lopez-Claroz, 2001; Global Innovation Index, 2015; Porter, 2000; WIPO, 2014; etc.). Usually, this is because those indexes are constructed every year, but it’s only since recently that most of them started to do this. Take for example the index created by Lopez-Claros and Mata, their first annual index only started in 2011. Limited time spans restraint us from fully understanding the shifts in international rankings over time. It’s not solely interesting to look at which countries
made it to the top ten, it’s also of great value to look at the fastest climbers. Because, understanding which individual elements, that contribute to innovation, were the reason of this climb provides an immense contribution to our knowledge of how to boost innovation.
1.3 Research Question
The facts from previous paragraph raise the following question: “Which European countries have the best innovation capacity when compared to each other during the time interval 1996-2013?” In order to answer this question we will use our self-constructed ranking to explain results, changes, and reasons that accompany a country’s innovation capacity. The process of this thesis naturally lead to several sub-questions, namely: “Why are some countries consistently scoring high, and others low?” and “What is the reason that certain countries were able to rise in ranking, while others declined?” and “How did joining the European Union influence the innovation capacity for countries who joined during our time interval?”
1.4 Structure
This thesis will continue with a literature analysis, in which a short history and explanation of innovation will be provided, the different variables that influence innovation will be discussed. The literature analysis will also explain regional innovation systems, and how innovation capacity can be measured through the use of indexes. The 9 hypotheses formed in the conceptual framework will be tested during the analysis apart from hypothesis 1, which is answered in the discussion section of this thesis. Furthermore, we will discuss how our innovation capacity index is constructed in the methodology section.
In the results section of this thesis we will interpret the rankings of our index over the years, and touch shortly on each country separately. The implications and what it means for our research question will then be considered in the discussion section, introducing multiple interesting ideas for future research. In the conclusion, a summary of the most important finds is given together with practical tips on how to boost a country’s innovation capacity.
2. Literature Review
2.1.1 Joseph Schumpeter on Innovation and the Entrepreneur
As this thesis is researching the innovation capacity of 28 European countries over the years, it is helpful to discuss what innovation is and what we mean by innovation capacity. Naturally, we start by discussing one of the founding fathers of the theory surrounding innovation and entrepreneurship: Joseph Schumpeter. He “is regarded as one the greatest economists of the first half of the twentieth century,” (Sledzik, 2013) his theories revolve around entrepreneurial innovations and their role as key drivers of economic growth. According to Schumpeter, “innovations are essential to explaining economic growth, and the ‘entrepreneur’ is the central innovator.” As Schumpeter described in The Theory of Economic Development “the entrepreneur’s main function is to allocate existing resources to ‘new uses and new combinations’” (Sledzik, 2013; Schumpeter, 1912). From this we learn that Entrepreneurs function as shift agents and are a crucial factor for turning innovation capacity into actual innovation.
Schumpeter beliefs that innovation acts as a “wind of creative destruction” (Schumpeter, 1934), which means that in order for something new to grow, something old has to be destroyed. So, in order for innovation to thrive it’s important to maintain an enabling environment for entrepreneurs, in which change is easy and possible (Schumpeter, 1934).
The doctrines of Schumpeter are still influential today “in fact the definition of innovation formulated in the Oslo Manual by the Organization for Economic Cooperation and Development, OECD, in 2005 is built upon Schumpeter’s theory:
“ ‘Innovation activities are all scientific, technological, organizational, financial
and commercial steps which actually, or are intended to, lead to the implementation of innovations. Some innovation activities are themselves innovative, others are novel activities but are necessary for the implementation of innovations. Innovation activities also include R&D that is directly related to the development of a specific innovation.’ “( Holmgren, 2012; OECD, 2005).
2.1.2 Inventions and Innovations
Most scholars normally make an important distinction between invention and innovation (Lewis et. al, 2014; Nauta, 2009; Hartley, 2006; Fagerberg, 2006; Schumpeter,1912). “Invention is the first occurrence of an idea for a new product or process, while innovation is the first attempt to carry it out into practice” (Fagerberg, 2006). The implementation part doesn’t have to be complicated, successful or fully developed, but it does have to be more than just an idea. This is important as it is often the innovation capacity of a region that promotes the turning of inventions into actual innovations, but more on that later.
A fundamental characteristic of innovation is “that every new innovation consists of a new combination of existing ideas, capabilities, skills, resources, etc.” (Fagerberg 2006; Diamond 1998). So, to turn an invention into an innovation one “normally needs to combine several different types of knowledge, capabilities, skills, and resources” (Fagerberg, 2006). Logically, it follows that the more of these factors a nation provides “the greater the scope for them to be combined in different ways, producing new innovations which will be both more complex and more sophisticated,” (Fagerberg 2006; Diamond 1998).
All of the above led us to support the following definition of innovation by Lewis et al. (2014) “Innovation means producing or working on something new; that is, doing things differently or in a new form. It is the process of working with inventions; whether that is a product, a technology, a service, a new type of production, a new process or a new form of collaboration.”
2.1.3 Innovation Capacity
Innovation capacity, as a concept is quite close to our previously defined concept of innovation, however it’s not as much about realized innovation as it is about the possibilities and opportunities to innovate available to the entrepreneur (Furman and Porter, 2002; Romijn, 2000; Insead, 2007). Wonglimpiyarat argues that innovation capacity “refers to the possibility to make major improvements and modifications to existing technologies, and to create new technologies” (2010). The task of exploiting the possibilities at hand and turning innovation capacity into actual innovation is the task of the previously introduced “entrepreneur” (Schumpeter, 1912).
When looking at country level innovation capacity, or ‘national innovation capacity’ we refer we define that as a the ability of a country –as both a political and economic entity-
“to produce and commercialize a flow of new-to-the world technologies over the long term. National innovative capacity is not the realized level of innovative output per se but reflects more fundamental determinants of the innovation process. Differences in national innovative capacity reflect variation in both economic geography, as well as cross-country differences in innovation policy (e.g. the level of public support for basic research or legal protection for intellectual property) (Furman et al, 2002).
Technological opportunities are most likely to be exploited in regions that are most conducive to developing innovative technology and are actively trying to boost innovation capacity (Furman et al, 2002). Even though, it’s possible that innovative advances occur in regions with a lower level of innovation capacity, they are much more likely to occur in regions with higher levels of innovation capacity.
“Long lags between invention and innovation may have to do with the fact that, in many cases, some or all of the conditions for commercialization may be lacking. There may not be a sufficient need (yet!) or it may be impossible to produce and/or market because some vital inputs or complementary factors are not (yet!) available.” (Fagerberg,2006)
One needs only to look at Leonardo da Vinci for an example. He made inventions as accomplished as helicopters and submarines, lacking the resources and technology needed to turn this into commercialized innovations (Fagerberg, 2006). In this example there was insufficient innovation capacity to allow Da Vinci to enact on any of his inventions and turning them into innovations.
2.2 Countries and Systems
Innovation is very much a concept that is influenced and defined by the geographical location the entrepreneur or firm is situated in. This is due to the fact that, Innovation consists of “combining the existing ideas, capabilities, skills, resources, etc.” at hand in a certain sector (Fagerberg 2006; Diamond 1998). On top of that, “Firms do not normally innovate in isolation, but in collaboration and interdependence with other organizations in their spatial proximity” (Fagerberg, 2006; Edquist, 2006). Because similar firms can create a great deal of positive externalities in the form of knowledge spillovers, transactional efficiencies, etc. which are all enhanced when firms are geographically close together (Furman et al., 2002; Porter, 1990; Porter, 1998; Niosi, 1991; Carlsson and Stankiewicz, 1991; Audretsch and Stephan, 1996; Mowery and
Nelson, 1999)
Ever since Schumpeter’s contribution on the business cycles (1939) there has been a large discussion on the relationship between innovation and economic development of regions (Arc et al., 2016). Within this discussion Lundvall (1992) and Nelson (1993) have argued that national and geographical setting have a major impact upon how economic agents behave and how firms perform (Arc et al., 2016). Lundvall (1992) recognized the fact that “narrow” organizations “are embedded in a much wider socio-economic system in which political and cultural influences as well as economic policies help to determine the scale, direction and relative success of all innovative activities” (Freeman, p.195). Asheim and Gertler (2004) went even further and claimed, “geography is fundamental, not incidental, to the innovation process itself: that one simply cannot understand innovation properly if one does not appreciate the central role of spatial proximity and concentration in this process” (p. 292). All of these sources stress the importance of understanding the geographical location and innovation. In this light researching the innovation capacity of European countries when compared to each other could be very valuable.
2.3 National Innovation Systems
Innovation is the main source of growth for regions in OECD countries (OECD, 2016) and “economic output is no longer mainly a function of capital and labor but, increasingly, of knowledge and the acquisition of new knowledge” (Lopez-Claros, 2011). Thus, countries are actively seeking to promote and innovation. In order to do so governments and policy makers must have a good understanding of what influences innovation and in what way. They, combine this knowledge in a “national innovation system,”(Freeman, 1987; Lundvall, 1992; Nelson, 1993; Cooke and Morgan, 1994) which is “a way of describing and analyzing the set of institutions that generate and mould economic growth, to the extent that one has a theory of economic growth in which technological innovation is the key driving force” (Nelson, 1993, p.34). Therefore, this thesis made use of the literature on this subject in helping to identify influential factors to innovation capacity.
National innovation system literature “has its roots in the innovation system approach introduced by Freeman (1987) in a study on the economy of Japan, and later developed by Lundvall (1992) and Nelson (1993)” (Cooke et al., 2011). This system made it possible to research the “nonlinear and path-dependent nature of the
innovation system” (Cooke et al. 2011). This means that it enabled researchers to look at the interconnectedness between different organizations and circumstances, such as universities, R&D expenditure, firms etc., and see how they, together, influence the regional innovativeness. “Important aspects of innovation systems include linkages between universities and firms, the role of universities in providing public spaces for economic actors to meet, relationships of firms with their customers and suppliers” (Asheim and Gertler, 2004). This will later help this thesis in constructing a framework which constructs a nation’s innovation capacity.
However, a generally accepted definition of what is part of an innovation system and what is not is non-existing. Both Nelson and Lundvall define national innovation systems in terms of factors influencing the innovation process (Edquist, 2005), but they propose different definitions of the concept.
2.4 Factors determining a nation’s innovation capacity
Innovation is not at all easy to measure, so it’s not surprising that Nelson and Lundvall disagree on their definitions. Because, as Edquist (2005, p.201) pointed out, our systematic knowledge about the determinants of innovation is still limited. Moreover, “the literature emphasizes the difficulty and the weakness of the use of individual indicators to measure the global concept of innovation (like patents, R&D expenditures, percentage of sales related to new products, etc.). Each of those indicators—although highly correlated— give a different view of apparently the same subject” (Buesa et al. 2010). However, according to other scholars benchmarks such as patents are the best measures currently available to us (Porter, 2000; Stern, 2002; OECD, 2004). This stresses once more what we have established before in this literature review: national innovative capacity depends on a great deal of cross- cutting factors which contribute broadly to innovativeness throughout the economy (Furman et al., 2002). It’s nigh impossible to create a system that accounts for every aspect of innovation. Nonetheless, in order to be able to come to a sensible measure of innovation capacity, the best thing one can do is to combine as many of the most important and influential factors as possible. We will talk about several of these factors below (Edquist, 2005).
2.4.1 Education and Human Capital
In general, education and a good public health contribute to a more effective participation in the economy and politics of a country or region (Sen 1999; Lopez-Claros, 2011).
The biggest threat when the education of a region is lacking is illiteracy. When people are not able to read and write properly, it’s very hard for them to take part in the society and contribute to developments and innovation. It’s also easier for them to fall prey to manipulations. Porter recognized this is in his most renowned work Competitive Advantage of Nations and said:
achieving more sophisticated competitive advantages and competing in advanced segments and new industries demands human resources with improving skills and abilities. The quality of human resources must be steadily rising if a nation’s economy is to upgrade. Not only does achieving higher productivity require more skilled managers and employees, but improving human resources in other nations sets a rising standard even to maintain current competitive positions…Education and training constitute perhaps the single greatest long-term leverage point available to all levels of government in upgrading industry. Improving the general education system is an essential priority of government and a maker of economic and not just social policy (Porter, 1990, p. 628).
Education allows actors within a system to access more information by being able to understand it, and build forth upon it. When people are continuously trained in new processes and the operation of the latest technologies, they will be able to focus on what is new, and which contributions they can make (Lopez-Claros, 2011). A measure that behaves synchronized with this ability to contribute to innovation through education is the number of scientific articles published in journals per country. In order for a scientific article to be published in a journal, it must contribute to our knowledge, it must add something new to the already existing body of literature. In such manner, when a country scores high in the amount of scientific articles published in journals this is proof of a successful academic system which is strengthening that country’s capacity to innovate (Huang, Huang & Chen, 2013).
H1: the higher the amount of scientific articles published in journals by a given country, the more innovation occurs in a country.
Higher education seems the be the most fruitful form of education. “Countries which have invested heavily in creating a well-developed infrastructure for tertiary education have reaped enormous benefits in terms of growth” (Mata, 2011). As soon as countries move beyond the stage in which their main source of growth is imitating other countries, it becomes crucial to invest in higher education to foster innovation (Aghion, 2008). We would be able to measure the quantity of a nation’s higher education system by utilizing the gross enrollment rate for higher education.
H2: the higher the gross enrollment rate for secondary and tertiary education, the more innovation occurs in a country.
2.4.2 Regulatory Framework
Innovation is heavily influenced by the rules and rights of a certain region. A few questions one could ask are: Is it easy to pay taxes or get licenses? Is it easier or more difficult to enforce contracts? Is there any protection for investors? Etc. (Lopez-Claros, 2011). If a region scores positive on many of those questions it’s likely that they support entrepreneurship and, as we’ve learned above, an entrepreneurial environment is, by nature, an environment with a high capacity for innovation. In nearly all of their reports on innovation, the OECD also stresses the importance of a favorable regulatory framework if regions (OECD, 1997). The World Bank has combined the numbers on nearly all of the questions surrounding the regulatory framework to create a score representative for the quality of a nation’s regulatory framework. Scores can be found in the World Bank’s Doing Business Report (DBR), which is available free of charge on their website. Perhaps the most important variable which measures the degree to which people of a country are free to choose their own government is the Voice and Accountability measure in the DBR. When people are free to choose their own governments they are more willing to take risk as there will be more stability (Lopez-Claros, 2011). Additionally, a country’s regulatory framework is able to change much more rapidly conforming to what is needed by latest technological innovations, because people are able to chose their
own governments accordingly (Furman et al, 2002).
H3: the higher the voice and accountability score of a given country, the more innovation there will be in that country.
The extent of intellectual property protection plays a great part in the extent to which innovation is possible and attractive in a nation’s innovation system (Merges and Nelson, 1990). When it’s easy and fast for people to register their intellectual property and to protect that property, it becomes much more interesting to innovate (Furman et al, 2002). Because, this will greatly reduce the risk that people take by investing in their innovations. Also, it will increment the rewards involved for the innovator as it will eliminate the possibility of idea-theft (Sakakibara and Porter, 2000). We can measure the quality of a country’s policy on this topic by looking at the average time it takes to register intellectual property in days, which can again be found on WorldBank for free.
H4: the shorter the time to register intellectual property, the more innovation will thrive in a given country.
2.4.3 R&D Expenditures
“During the 1950s and 1960s, our understanding of the economy was advanced by developing measures of research and development (R&D), an input measurement, as a proxy for innovative output” (Acs et al., 2002). Ever since, expenditures in R&D are a common proxy for determining a region’s innovative capacity and attitude (OECD, 2009). R&D is defined in the Fruscati Manual (2002) as “a creative work undertaken on a systematic basis in order to increase the stock of knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications.” The reason why R&D expenditures are often used as a proxy for measuring the innovation capacity of a region is because it’s often assumed that greater investment in basic R&D will lead to greater applied research, which will lead to more innovation.
share of innovating firms and a relatively high level of R&D output” (Fritsch, 2002).
H5: a higher R&D expenditure leads to a higher innovation capacity
Using this proxy brings with it the advantage that it’s very linear and straightforward to use, so it’s less susceptible to errors. At the same time, however, one shouldn’t solely focus on R&D, because this would lead to completely overlooking other important factors which determine how regional innovation is generated (OECD, 2009).
Further, it’s argued by Porter and Stern (2002) and Porter (2000) that the number of researchers in R&D often coincides with the innovation capacity of a region, since they form the basis of ongoing research contributing to innovation. They explain that the more researchers in R&D are present in a region, the more likely it will be that major innovations are made. Without skilled scientists “operating in an environment with access to cutting-edge technology, it is unlikely that a country will produce an appreciable amount of new-to-the-world innovative output “ (Furman et al, 2002).
H6: when a region has more researchers in R&D, they will also have more innovation.
2.4.4 Patent Data
During the 1970s, people started to use patent data as an intermediate measure of innovation in the economy (Acs et al., 2002). Patent data measures the knowledge flow of an area. That is to say, if knowledge is not easily accessible at every point in space, the location where knowledge is created and shared becomes very important in understanding the economic development of that location (Acs et al., 2000). It has been proven that innovation and economic growth in cities is directly related to “the inter industry knowledge flows” (Acs et al., 2000, p. 4; Glaeser, Kallal, Scheinkman and Shleifer, 1992). It has also been proven that these knowledge flows can be measured by patent data (Acs and Audretsch, 1989; Acs, Audretsch and Feldman, 1991). All in all, patent data could be a very good parameter to measure the
innovation capacity of a region.
Since patent data has been used as the most used proxy for innovation there has been a steady flow of criticism. Critics argue that, although, this proxy can function as a good indicator of innovation in a certain region, it falls short in measuring the economic value of these innovations (Hall et al., 2001). According to Griliches (1979) and Pakes and Griliches (1980, p. 378), “patents are a flawed measure (of innovative output) particularly since not all new innovations are patented and since patents differ greatly in their economic impact.” Despite the negativity surrounding patents as innovation measure, scholars have yet to come up with a better proxy that is as widely accepted. According to Porter (2000) patents are still the best measure for the innovative output of a region, and he argues that they come as close to reality as currently possible. Thus, patent data can certainly be used as, and is currently the best available, proxy of innovation (-capacity), in contempt of all the criticism it has received.
2.4.5 Economical Environment
GDP per capita has a double connection to innovation, a feedback effect (Gelindo & Mendez, 2013). First off, more innovation generally leads to a higher GDP per capita. So, when a region has a high average in this aspect it can be a sign of successful innovation. Additionally, regions with a higher GDP per capita have more options to invest in innovation and R&D, “as entrepreneurs need financial resources to carry out their activities and to finance innovations” (Gelindo & Mendez, 2013, p. 827). Moreover, more innovations and able entrepreneurs will result in an even bigger capacity to innovate (Ulku, 2004). GDP per capita can function as an indication of a country’s capability to translate its knowledge stock into a realized state of economic development (Furman et al., 2002). Furthermore, when people have more to spend, that will raise the region’s demand for advanced goods and the presence of “sophisticated and demanding local customers” (Porter, 1990). Which will then increase the incentive for firms and entrepreneurs to innovate.
H7: the higher a country’s GDP per capita, the higher that country’s ability to innovate.
Furman et al. (2002) have found in their studies that the level of a nation’s innovative capacity affects the share of high-technology exports in the total of manufactured exports. When the innovative capacity rises so does the share of high-technology exports. This is because the percentage of high-high-technology exports signifies the level of technology orientation in a country’s economy. When a region’s economy is focused on high-technology, that means firms and entrepreneurs have to be very innovative in order to stay competitive (Clarysse, 1999). In theory, the higher this incentive to innovate, the higher that region’s innovation capacity will be.
H8: the higher the share of high-technology exports in a country the larger its innovation capacity.
2.4.6 Technological Adaptation
Where the challenges of a decade ago were mainly to restructure, lower the costs, and raise the quality, today the advantage of regions over others is the ability to shift the frontier of technological innovation as fast as their rivals can catch up (Porter and Stern, 2002). In order to be capable of technological innovations, however, regions must have adapted to and must be able to apply the newest technology up until that point in time (Furman et al, 2002).
Besides this aspect, the availability of a fast and widely accessible internet connection greatly boosts the innovative capacity of a region. By being able to access the immense knowledge base that is formed by the internet, actors within a region are enabled to learn about the latest technological advancements and will be empowered to contribute to a more innovative nation (Lopez-Claros, 2011). In 2003 Spain’s science minister recognized the importance of becoming and maintaining to be such an information society when he argued that things such as telecommunications and internet access help in “weaving new technologies into the social fabric” (Bosch, 2003). With which he meant to say that the more people are adapted to the latest technologies, the better they will be in thinking about and being ready for further innovation.
One is able to measure this ‘technological adaptation’ by looking at the use of services like internet, mobile cellular subscriptions, and fixed broadband subscriptions.
H9: the higher the amount of internet users, the higher a nation’s innovation capacity.
H10: the higher the amount of mobile cellular subscriptions, the higher a nation’s innovation capacity.
H11: the higher the amount of fixed broadband subscriptions, the higher a nation’s innovation capacity.
2.4.7 There are Other Influencing Factors
There exist a great deal of other factors which contribute to an enabling national environment for innovation, however, we chose to discuss in detail the variables above, as those are the ones used throughout this thesis. Later, in the variables part of this thesis, we will go into a more comprehensive explanation as to why we picked the variables we did. In short, many of the other variables lacked reliable or internationally comparable data, or data was missing all together. This simply made the comparison of those variables over our time span impossible, and it would not be very contributing to this thesis to talk about them all in detail.
2.5 An Index
How does one combine this many factors that are potentially influencing the innovation capacity and come to a sensible indicator? According to Buesa et al (2010), “it’s impossible to use the traditional econometric models based on individual variables in a system where all factors and agents influence each other.” So we need a different approach. Oftentimes, researchers and policymakers make use of an “index” or “composite indicator” in such a case (OECD, 2008).
Indexes or “composite indicators are based on sub-indicators that have no common meaningful unit of measurement and there is no obvious way of weighting these sub-indicators” (Composite Indicator Research Group, 2014). In other words, it’s a way of incorporating large volumes of data into a scoring system. “The practice of synthesizing large volumes of information into a scoring system which can be translated into an index and an associated set of rankings can provide considerable value-added in measuring innovation” (Lopez-Claros, 2011).
on the nature of competitive behavior. In essence, Porter provided a framework of five forces “enumerating the characteristics of the environment in a nation’s industrial cluster” (Furman et al, 2002). Since nations strive for competitive advantage, the first four forces at work help to assess the fifth: a nation’s level of competitiveness (Morningstar, 2015). It’s the combination of these 4 different elements in order to form a fifth one which measures the intangible concept you are trying to asses that makes Porter’s framework important. Porter found a way to make a sensible evaluation by combining sub-elements in order to help assess the main concept. The framework can be seen in figure 1.
An index is constructed by first selecting the possible variables and their data sets. Afterwards one must examine their empirical relationships, one must be sure that the variables selected actually say something about the concept one is trying to measure. Once this is done, the various sub-indicators, are assigned a weight depending on their relative importance to the concept one is trying to measure, in this case innovation capacity. Values of different data sets and their weights are then combined and the sum of it produces a score, which will allow one to create an index that gives an overall impression of the situation. This index-score must then be revalidated against the concept one is trying to measure to make sure it’s indeed statistically significant (Crossman, 2016). Creating such an index, which would be able to identify the innovation capacity of a region is exactly what this thesis needs in order to compare the innovation capacity in the 28 European countries.
Figure 1. The innovation orientation of national industry clusters (CGM, 2016; Porter, 1980).
This thesis will need a framework like Porter’s. However, not build around competitiveness, but around innovation capacity. Augusto Lopez-Claros and Mata (2011) have created such an innovation capacity index. They argue that there is “no unique way to do it”, and that it’s kind of up to the researcher to decide how many pillars are created and how the index is designed, as long as it makes sense and is coherent. Their framework and the five pillars which they created can be seen in figure 2.
Figure 2. Innovation Capacity Index (Lopez-Claros and Mata, 2011).
One of the crucial points for building an index is the application of proper weights (Acs, 2010). To avoid the arbitrary use of methodology some indexes avoid weighting altogether. Examples would be the Doing Business Report and the Index of Economic Freedom (Acs, 2010; Worldbank, 2015; Heritage, 2015). However weighting is very useful when pillars consist of multiple sub-components. It could be that one sub-element has a larger effect on innovation than another sub-element, so the latter should have a smaller weight or be omitted altogether because of not being significant in this situation. According to Porter and Stern (2002), the best way to go about weighting, is to create a regression model. Choose an innovation proxy, in their eyes the amount of patents granted is the best, and test all the separate measures against the baseline regression. Doing this will test both the significance of the measures and will help the researcher in choosing weights based on the coefficients.
3. Methodology
3.1 Measuring National Innovative Capacity
Using the country-level data retrieved from databases such as WorldBank, the world Governance Index, WIPO and the International Telecommunications Union this thesis tries to identify elements of the innovation environment that have a statistically significant relationship to innovation. These elements will be grouped into five pillars, consisting of: R&D, Regulatory Framework, Academic System, Technological Adaption, and Economical environment. The elements are then used to calculate how each of the euro-countries perform along each of the pillars of innovation capacity, in order to construct a ranking (i.e. index). This ranking will be calculated for each of the years from 1996 through 2013, in order to find any major changes and to explain why those happened.
It is important to realize that this index, like every other existing innovation index, is merely an approximation of the truth. Because, it is extremely difficult to measure innovation capacity for a number of reasons. First, “measures of innovative output are imperfect (only certain types of innovation can be measured) and subject to some random fluctuations” (Porter and Stern, 2000, p.6). Secondly, through the use of traditional data sources it’s impossible to measure certain nuanced aspects of the innovation capacity, such as cluster-specific innovation environment and innovation policy (Porter, 2000). Third, the elements that construct the innovation capacity are intertwined and cooperative to each other. Naturally, it then follows that the elements can be highly correlated when performing statistical tests. Regression analyses can become very meticulous when exploring a country’s innovation capacity.
3.1.1 Data Collection
Because the intention of constructing this index lies within being able to make relevant cross-national comparisons this thesis makes use of sources which have gathered their data on a comparable basis, using a common methodology. These include: the World Bank’s World Development Indicators, which provides data on over 800 indicators including aspects of social and economic development (Worldbank, 2015); the International Telecommunications Union, which maintains a database on the most up-to-date ICT and telecommunication statistics (ITU,2015);
the World Intellectual Property Organization who preserves a database on all patent and trademark applications and grants (WIPO, 2015); and the World Bank/International Finance Corporation’s Doing Business Report, which consists of objective measures of business regulations and their enforcement (Worldbank 2015a).
3.1.2 Evaluating the Innovation Capacity index across Europe
To examine the linkage between realized innovation and the variables which are associated with the innovative capacity of a nation this thesis went through various steps.
First, most of the data such as the amount of researchers and the amount of patents granted came in the total numbers per country. So, we had to control for population as to make it a fair cross-national comparison. This was done by using historical population levels recorded at the same date as the original data. With this information we were able to convert the totals to per capita values, which gave a more realistic impression of the situation in a particular nation.
Secondly, In order to be able to perform a regression analysis we had to choose a proxy for innovation that showed us how much innovation there had been in a country in a given year. We chose to use the measure available, namely, the “international” patents granted by the European Patent Office (EPO). These were measured by the WIPO statistics database, and were last updated in 2015. We chose for the EPO patents as an innovation parameter for several reasons. First and foremost, this is the best available parameter that is consistent across time and space. Secondly, there is quite some cost involved in the patent process, so for companies to have gone through with this, and to have it be granted shows the potential of the innovation’s economical value. Lastly, the European patent office maintains a standard of technological excellence in order for innovations to be granted a patent, so one can be assured that the patents qualify as contributing to the latest technological developments (Porter and Stern, 2002).
Lastly, to calculate the relationship of the European patents granted to the variables that measure the innovation capacity, this thesis tests the different measures in a regression analysis. This allows us to assign relative weights to the individual variables in our index. Like this, we can be sure that the relationship between our innovation capacity index and the data on international innovation
performance is statistically significant. This also helped us in determining which elements or variables had to be discarded from further analysis.
3.2 Variables
When we started our research there were 14 variables (see figure 3a) that were closely related to innovation according to existing literature, and we collected data on. A detailed definition of each of the variables can be found in the appendix.
Academic System Rules and Regulations Tech Adaptation Economical Environment R&D Scientific and Technical Journal Arti cles (per 100 people) Procedures to Register Intellectual Property Voice and Accountability Mobile cellular subscriptions (per 100 people) High-technology exports (% of manufactured exports) Research and development expenditure (% of GDP) Secondary Gross Enrollment Rate Cost of Business Start-up Procedure (in % of GDP) Internet users (per 100 people) GDP per capita (current US$) Researchers in R&D (per million people) Tertiary Gross Enrollment Rate Time Required to Start a Business Fixed broadband
subscriptions (per 100 people)
Figure 3a. 5 Pillars of Innovation Capacity and their elements.
Each of the variables were put into the baseline regression, one at a time. The results of each of the elements when used in the regression analysis can be found in the analysis, which closely follows this part. Elements of our model were tested in the baseline regression, in which we set our significance level at 𝛼𝛼 =0.05. Out of the 14 variables we started with, 9 remained as they proved to be positively correlated and statistically significant.
To build our innovation capacity index it was not viable to create a multivariate regression analysis out of the 5 pillars for the simple reason that nearly all of the components are highly correlated with each other. Therefore, rather than unraveling all the specific effects associated with the pillars, this thesis will create a parsimonious specification using only a few variables in each sub group. However, within the pillars themselves, we did account for correlation and avoided measures that accounted for the same effect twice. This left us with 6 variables which were grouped along the five pillars that collectively form the innovation capacity of a nation. This can be seen in Figure 3b.
Rules and Regulations
Academic System
Tech Adaptation Economical Environment R&D Procedures to Register Intellectual Property Voice and Accountability Scientific Articles (per 100 people) Fixed broadband subscriptions (per 100 people) GDP per capita (current US$) Research and development expenditure (% of GDP)
Figure 3b. 5 Pillars of Innovation Capacity and their elements.
3.2.1 Missing Variables
One constraint faced by us, and many other researchers when constructing such an index is the lack of reliable or internationally comparable data. (Porter and Stern, 2002; Mata, 2011; Global Innovation Index, 2015). The absence of such data has limited us greatly in including variables which, a priori, were suggested to be relevant by theory or factual considerations. This was the case for measures such as, for example, for number of high&new-tech industrial zones and local government budgetary expenditure for culture, education, science and technology (or weight in local GDP). Most countries maintained a very limited database that was publicly accessible for these measures. Oftentimes, information would only be administered for a few years, which would be different years from other countries. This, simply, made comparison of those variables impossible.
3.2.2 Index Structure and Formulation
In constructing the index we strived to create a parsimonious indication of a region’s innovation capacity and compare those among the 28 European countries. Once we had identified which variables were going to help us accomplish this, an early priority was to organize them in a sensible way. Undoubtedly, there is no unique or exclusive way to such a thing, nor is there any right amount of pillars or sub-indexes to be created. We are convenient with our formulation in which we identify 5 pillars:
1. Rules and Regulations 2. Academic System
3. Technological Adaptation 4. Economical Environment 5. Research and Development
A more detailed portrayal can be seen in figure 4, and each of the variables will be shortly discussed underneath in the analysis part. The selection of pillars and variables is based on our theoretical analysis which can be read in the literature analysis part of this thesis
3.3 Analysis
Before we constructed our index, we performed a regression analysis to find out if the variables we chose had any effect on innovation, and if the variables were statistically significant enough to be used. We took a base year to perform a regression analysis, in this case the year 2013. In which, the amount of patents granted per million people was the dependent variable against which the numerous parameters were tested for their significance and correlation. For our significance level we maintained an 𝛼𝛼 = 0.05.
3.3.1 The Rules and Regulations Pillar
To determine a nation’s rules and regulations system two variables were selected, each with a strong and sound relationship to European patenting: number of procedures to register intellectual property and the voice and accountability of the country’s inhabitants
To test the relationship of this pillar to innovation the the two measures were first added separately to the baseline regression to establish their individual relationship to innovation. The results are given below in figure 5a and 5b.
Dependent variable = EPO patents 2012-2013
Independent variable
Coef. P- value Adj.𝑹𝑹𝟐𝟐
Nr. of procedures to register intellectual property
1,65 0,014 0,21
Figure 5a. Regression analysis with nr. of procedures to register intellectual property as independent variable
Dependent variable = EPO patents 2012-2013
Independent variable
Coef. P- value Adj.𝑹𝑹𝟐𝟐
Voice and Acc. of country’s inhabitants
1,82 0,015 0,21
Both of them met the significance standard of P<0,05 which we set. Additionally both of our independent variables proved to have an 𝑅𝑅2 above 0,21 showing that they account for a large part of the innovation in a given country. This means we can accept our third and fourth hypotheses:
H3: the higher the voice and accountability score of a given country, the more innovation there will be in that country.
H4: the shorter the time to register intellectual property, the more innovation will thrive in a given country.
Before we could merge the two variables in order to test our entire pillar in the regression analysis we first had to check for the interrelatedness of the two variables and make sure that the two are not highly correlated with each other. We want to make sure that the two variables account for a unique effect on innovation. The results of this test can be seen in figure 6. One can clearly see that the correlation is very low and almost negligible, which means that we can safely combine these two measures in our rules & regulations pillar.
Dependent variable = Voice and acc. of country’s inhabitants
Independent variables
Coef. P- value Adj.𝑹𝑹𝟐𝟐
Nr. of procedures to register intellectual property
-0,09 0,627 0,00924
Figure 6. Correlation of the two variables
We then combined the 2 variables in a multivariate regression to establish their relative weights. The weights were established using the coefficients from each variable in the regression analysis which can be seen in figure 7.
Dependent variable = EPO patents 2012-2013
Independent variables
Coef. P- value Adj.𝑹𝑹𝟐𝟐 Sig. F
Anova 0,45 0,000619
Nr. of procedures to register intellectual property
1,90 0,003
Voice and Acc. of country’s
inhabitants
1,82 0,002
Figure 7. Regression rules & regulations pillar
The overall regression model for this pillar was significant, F(2, 25) = 10,07, p<0,001, 𝑅𝑅2 = 0,45.
In the regression voice and accountability had a coefficient of 1.902736 and number of procedures to register intellectual property had a coefficient of 1.821464. We used these coefficients to determine the weights of our two variables, which were used in calculating the weighted sum in order to create the rules & regulations sub-index score. The coefficients translated to voice and accountability receiving a weight of 0.51 and number of procedures to register intellectual property receiving a weight of 0.49.
From the literature there were two other measures which we theorized to be part of this pillar, namely: Cost of business start-up procedure (in %GDP) and Time required to start a business. However when we performed the statistical test on these two measures it resulted in them being not significant. Cost of business start-up procedure (in %GDP), F(1,26) = 1.02, p= n.s., 𝑅𝑅2 = 0,04 and Time required to start a business, F(1,26) = 0,044, p=n.s., 𝑅𝑅2 = 0,002. They proved to be statistically too insignificant to say anything about European patents granted and thus concurrently too insignificant to make claims about innovation. This lead us to drop those two measures from the index. The results can be seen in figure 8.
Dependent variable = EPO patents 2012-2013
Independent variable
Coef. P- value Adj.𝑹𝑹𝟐𝟐
Cost of business start-up procedure (in %GDP)
-2,18 0,32 0,04
Time required to start a business
-0,24 0,84 0,002
Figure 8. Regression analysis results on cost of business start-up procedure, and time
required to start a business.
3.3.2 The Academic System Pillar
For our academic system pillar we theorized three measures to be significant and have a positive influence on innovation capacity. Those three measures were the number of scientific articles published in journals per 100 people, secondary gross enrollment rate, and tertiary gross enrollment rate. One proxy showed excellent statistical significance in relationship to European patenting. Namely, the number of scientific articles published per 100 people. This measure showed a P-value of <0,05, thus it was well below our set 𝛼𝛼 = 0,05, moreover, the 𝑹𝑹𝟐𝟐 shows us that this measure accounts for a rather large part of our dependent variable. The results of the regression analysis can be seen below in figure 9.
Dependent variable = EPO patents 2012-2013 Independent variable P- value Adj.𝑹𝑹𝟐𝟐 Nr. of scientific articles published (per 100 people) 0,0183 0,20
Figure 9. Regression results nr. of scientific articles published
So we could accept our first hypothesis:
H1: the higher the amount of scientific articles published in journals by a given country, the more innovation occurs in a country.
This proxy allowed us to compare not only the size of a country’s academic system but especially the quality of that system. As we have learned earlier in this thesis, it’s mainly the efficiency, excellence, and its ability to add something new of an academic system that proves to be the biggest positive influence on the number of patents granted (Porter, 1990; Mata, 2011).
It may be because of the same reason that our quantitative variables, secondary enrollment rate and tertiary gross enrollment rate, showed almost no significance at all. This disproved our hypotheses that either of the gross enrollment rates would boost innovation considerably. The results can be seen in figure 10.
H2: the higher the gross enrollment rate for secondary and tertiary education, the more innovation occurs in a country.
Dependent variable = EPO patents 2012-2013 Independent variable P- value Adj.𝑹𝑹𝟐𝟐 Secondary gross enrollment rate 0,44 0,023 Tertiary gross enrollment rate 0,94 0,00
Figure 10. Regression results of enrollment rates
Due to their low significance we were unable to use these two measures in the calculation of our index, as they would have given flawed results in our calculation of the innovation capacity.
3.3.3 The Technological Adaptation Pillar
For this pillar we started out with three measures that were potentially suitable for measuring the level of innovation of a country. Those measures were: Mobile cellular subscriptions per 100 people, Internet users per 100 people and Fixed broadband subscriptions per 100 people. All three of them measure the degree to which inhabitants of a country have adapted themselves to new technologies. From these three only the fixed broadband subscriptions per 100 people proved to have a robust relationship with innovation and be statistically significant enough to pass the
boundaries we set. The amount of internet users per 100 people, came very close to meet the confidence level but fell just short. The results from the regression analyses can be seen in below in figure 11.
Dependent variable = EPO patents 2012-2013 Independent variable P- value Adj.𝑹𝑹𝟐𝟐 Mobile cellular subscriptions (per 100 people) 0,43 0,024
Internet users (per 100 people) 0,051 0,16 Fixed broadband subscriptions (per 100 people) 0,043 0,15
Figure 11. Regression results of technology adaptation
This proves our 11th hypothesis and disproves our 9th and 10th hypotheses, in the sense we cannot use the measures of internet users per 100 people and mobile cellular subscriptions per 100 people in our index. So the technological adaptation pillar will consist of fixed broadband subscriptions per 100 people.
H9: the higher the amount of internet users, the higher a nation’s innovation capacity.
H10: the higher the amount of mobile cellular subscriptions, the higher a nation’s innovation capacity.
H11: the higher the amount of fixed broadband subscriptions, the higher a nation’s innovation capacity.
3.3.4 The Economical Environment Pillar
To determine the economical environment of a nation we hypothesized two measures to have a meaningful effect, specifically: GDP per capita and % of high-new tech products in manufactured exports. As can be seen in the literature part of
this thesis. However, when performing our regression analysis we found that the significance level of % of high-new tech products in manufactured exports was extremely low , F(1, 26) = 0,01, P= n.s., 𝑅𝑅2 = 0,0004. This measure is, thus, very unfit to make any claims about innovation capacity, consequently we couldn’t make use of this measure in this pillar. GPD per capita, on the other hand, proved to be highly significant as well as having a very robust relationship to innovation, F(1,26) = 8,66, P < 0,05, 𝑅𝑅2 = 0,25. So hypothesis 7 was proven and hypothesis 8 was disproven.
H7: the higher a country’s GDP per capita, the higher that country’s ability to innovate.
H8: the higher the share of high-technology exports in a country the larger its innovation capacity. Dependent variable = EPO patents 2012-2013 Independent variable P- value Adj.𝑹𝑹𝟐𝟐 GDP per capita 0,007 0,25 % of high-new tech products in manufactured exports 0,92 0,00
Figure 12. Regression results economical environment
GDP per capita will allow us to compare the economical strength of the European countries and its inhabitants and its influence on innovation .
3.3.5 The Research and Development Pillar
From the literature part of this thesis we hypothesized that both researchers per million people and Research and Development expenditure (% of GDP) would have a significant effect on the amounts of European patents granted to a nation. When we tested the measures against the baseline regression we found that, indeed, both of the measures were significant and correlated to innovation. researchers per million people F(1, 26) = 4,73, P<0,05., 𝑅𝑅2= 0,15, and research and development
expenditure (%GDP) F(1, 26) = 12,09, P<0,05., 𝑅𝑅2 = 0,32. This proved both of our hypotheses
H5: a higher R&D expenditure leads to a higher innovation capacity
H6: when a region has more researchers in R&D, they will also have more innovation. Dependent variable = EPO patents 2012-2013 Independent variable P- value Adj.𝑹𝑹𝟐𝟐 Researchers (per million people) 0,039 0,15 R&D expenditure (% GDP) 0,002 0,32
Figure 13. Regression results Research & Development pillar
However, the two measures are also highly correlated with each other, which is a problem as they would both account for the same effect on innovation.
Dependent variable = Researchers (per million people) Independent variable P- value Adj.𝑹𝑹𝟐𝟐 R&D expenditure (% GDP) 3,21E-09 0,75
Figure 14. Correlation between Researchers (per million people) and R&D Expenditure (% GDP)
This meant we could only incorporate one of the two measure in our index. We chose to use R&D expenditure (%GDP), as this measure had the most significant relationship to innovation of the two. Thus, allowing us to make the most realistic and accurate claims with our index.
3.3.6 Converting values into scores to produce an index
In the methodology part of this thesis we have already explained the framework around which we built our index, and now that we have put our variables through a statistical test we are ready to turn our raw data into an index.
One of the main advantages of an index is enabling comparison between values, which are otherwise incomparable, due to having no comparable meaningful unit of measurement (Composite Indicator Research Group, 2014).
So, in order to combine all of the separate variables into our index and rank the European countries, we must find a way to turn the values from our raw data into comparable numbers. We chose to normalize all the data values on a scale from 0-100. This was done by equalizing the lowest respective value of a certain year to 0, and the highest to 100. All of the scores between those two values were then given a value that corresponds with how far along the scale they are. This was calculated by taking the value, subtract the lowest value from it (the one equalized to 0 index points), and then divide it by 1% of the difference between the highest (the one equalized to 100 index points) and lowest value. A list of all values converted to index-rankings can be seen in the appendix.
In this way we were able to produce a list of comparable sub-index scores for every year and pillar. The actual Innovation capacity index was then composed by taking the average from the weighted sum of all sub-indexes.
To illustrate we will calculate for the hypothetical situation in which Sweden has the highest GDP per capita , namely 89, and Croatia the lowest, namely 44. In this hypothetical situation Germany then has a GDP per capita of 68. The calculation would then be the following:
3.3.7 Testing our Index to the Baseline Regression
Lastly, we had to test our newly created index in the same baseline regression to see if it was actually significant. It proved to be highly significant, which confirms the claim we make by saying our index is indicative of the innovation capacity in a country. The Results can be seen in figure 15
Dependent variable = EPO patents 2012-2013
Independent variable
Coeff. P- value Adj.𝑹𝑹𝟐𝟐
Innovation Capacity Index Scores
1,18 0,007 0,43
Figure 15. Regression results Innovation Capacity Index
Country Calculation
Sweden This is the highest scoring country so their score is equalized to the maximum.
89 = 100 index points
Croatia This is the lowest scoring country so their score is equalized to the minimum.
44 = 0 index points
Germany Other countries are then ranked along the set scale by taking the difference between their value and the lowest value and dividing that by 1% of the difference between the highest and the lowest value (1% of the set scale).
(68−44)(89−44
100 ) = 53,33
4. Results
For the results part of this thesis we will first examine each of the pillars separately, this will give the reader a good impression of which countries are the well performing countries in each aspect, and which are the lesser performing countries. Each discussion of the sub-indexes will be preceded by a large graph with the respective sub-index scores and ranks from 1996 – 2013, to illustrate rankings and movement over the years. We have tried to list all the rankings in numeric form as to give the reader the precise values for each year separately, but this was not feasible as the lists are very extensive and would take up incredible amounts of space per sub index in a very cluttered manner. However, if the reader wants to see a more detailed version of the sub indexes, the index scores and rankings can be found per pillar in the appendix. Following the analysis of each pillar separately, we will discuss our Innovation Capacity Index scores and ranking in the same way.
Following this general comparison of results we will look at each of the 28 European countries separately, and specifically analyze the first year of their ranking, the last year of their ranking and the best year of their ranking. Using these rankings we will try to find logical explanation as of why certain shifts did or did not happen.
As one can see the Scandinavian countries are by far the best performing countries with Sweden leading them by a length. This is not entirely surprising as those countries have a well established track record of innovation and highly-developed and sophisticated tech-sectors. Additionally, their governments have recognized that this is one of their international competitive strengths so they have provided in rules and regulations which are stimulating of innovation. The Economist even goes as far as saying “The Nordic countries are the best governed in the world” (Feb 2nd, 2013). The Nordic countries for the honesty and transparency of their countries, in Sweden, for example, everyone has access to all official records.
It’s also not very surprising to see the Eastern-European countries, mainly the Balkan, performing the worst out of the 28 countries. This can be explained by the Bosnian wars just coming to a closure at the begin of our analysis (the Bosnian wars were ended in 1995). Additionally most of the Eastern-European countries didn’t join the European Union until much later, so before they didn’t have any particular governmental standards that needed to be met. Furthermore, where the Scandinavian governments pride themselves for their transparency, the Eastern European countries still struggle with some depravity.
Despite this, Slovenia being at the constant bottom of the charts is a bit flawed, as they have a quite decent Voice and Accountability sub-index score (82 on average), but the time required to register intellectual property is disproportionately long compared to other countries, even up to 130 times as long as in Sweden in the first year of our data.
A striking observation, however, is that conflicting results can be observed for countries which were late in joining the European Union. Countries such as Slovakia (2004), Hungary (2004), Czech Republic (2004) etc, didn’t necessarily get better after joining, and some of them even dropped in ranking. However, other countries such as Lithuania (2004), Malta (2004), and Estonia (2004) significantly improved.
