mueid al raee - understanding innovation systems - as contributors to economic growth - through the...

Understanding “Innovation Systems” – as

contributors to economic growth – through the use of Indicators

Prepared by

Mueid Al Raee

Paper submitted to Professor Bart Verspagen in partial fulfillment of the course titled Innovation and Growth in the Global Economy

Understanding “Innovation Systems” – as contributors to economic growth – through the use of Indicators

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Contents

Abstract ........................................................................................................................................... 3

Introduction .................................................................................................................................... 4

Science, Research and Innovation .................................................................................................. 6

Interaction of Management and Quality Standards in Innovation Systems .................................. 9

Role of ICT in Effective Innovation Systems.................................................................................. 10

Primary, Secondary and Tertiary Education ................................................................................. 11

Financial Systems .......................................................................................................................... 13

Synopsis and Conclusion ............................................................................................................... 14

References .................................................................................................................................... 17


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Abstract

Through factor analysis Fagerberg and Srholec are able to match indicators to each of the

capabilities and conclude the importance of “innovation systems” in economic development. In

this paper we are reviewing the literature to establish a theoretical basis for Fagerberg and

Srholecs use of indicators that defines “innovation systems”. It is observed that there is

reasonable theoretical congruence to the work of Fagerberg. In doing this analysis we have

reiterated the important question of what indicators are most suitable to define an “innovation

system”. In this regard our understanding is that Education, ICT, Financial Systems and

Management Systems define the health of an innovation system where as science and

technology indicators such as R&D expenditure, patent counts, scientific and technical articles,

and innovation counts are the inputs, intermediate outputs and direct outputs of the

“innovation system”


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Introduction

Jan Fagerberg, Mowery and Nelson open the book “A Handbook of Innovation” with an

interesting preface addressing the concerns that innovation studies are plagued by researchers

in various disciplines looking at innovation through their own limited perspectives (Fagerberg,

et al., 2005). In addition to this argument, the multi-faceted nature of Innovation itself makes

the task of achieving comprehensive understanding of the concept of innovation and defining it

somewhat complicated. Innovation exists everywhere, whereas we are adept at thinking of

innovation in the physical realm; the term innovation captures much more than that. Even

minor changes in how various steps of a management and production process are organized

are an example of innovation in non-physical realm. In addition to development of new physical

products and non-physical changes in production processes and management processes,

Schumpeter includes new sources of supply and exploitation of new markets in the definition of

innovation (Fagerberg, 2003).

Innovation has been discussed at length as being a catalyst to growth, contributor to growth

and as the most important input into growth. Schumpeter considered economic development

as contingent upon the innovation that took place during that period of time (Fagerberg, 2003).

In the context of modern economic growth the concept of total factor productivity has been

introduced to account for gaps in explanation of productivity growth that were observed while

using only capital and labour as inputs (Solow, 1956). In concerns of improving the standard of

living across the world through economic growth, the phenomenon of catch-up has been

deemed of critical importance. It has been discussed that such a catch-up is possible through


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having robust innovation systems. Having adequate “intrinsic learning capabilities” allows

follower countries to benefit from the innovations of leaders and subsequently catalyse the

catch-up of their economies (Verspagen, 1991). Not having these capabilities can lead to these

nations falling behind in the race of prosperity. Various authors have discussed the catching up

and falling behind phenomenon on similar basis, using terms like “social capability”

(Abramovitz, 1986), and “absorptive capacity” (Cohen & Levinthal, 1990), etc. Fagerberg and

Srholec identify “innovation system”, “governance”, “political system” and “openness” as four

different types of capabilities. Through factor analysis they are able to match indicators to each

of the capabilities and conclude the utmost importance of “innovation systems” and

“governance” in economic development (Fagerberg & Srholec, 2008). In their factor analysis

“innovation system” is mainly explained by technology, knowledge, ICT and management

related indicators; USPTO Patent, science and engineering articles, ISO9000 certifications, fixed

and mobile phone subscriber, internet users, personal computers, primary school teacher-pupil

ratio, secondary school enrolment, and tertiary school enrolment. Financial health indicators

like market capitalisation of listed companies and domestic credit to private sector are found to

be marginally defining “innovation systems”. In this context it is important to understand

theoretically whether these indicators can be used consistently to define “innovation systems”

for research on the role of innovation in economic development and also for measurement of

policy results in efforts for boosting “innovation systems” within countries to achieve

accelerated socio-economic growth.

In the following we are going to lay out a theoretical discussion on innovation indicators and

analyse their relevance to the role of innovation systems in economic growth based on


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literature review. Section 2 will discuss technological indicators of innovation systems –

Patents, and science and technology paper, Section 3 will try to look at why efficient

management systems are critical in defining innovation systems, the fourth section will look at

ICT prevalence and its role in the same, the fifth section addresses what can perhaps be

referred to as the base of any well functioning innovation system and that is the role of

primary, secondary and tertiary education. Section 6 will try to look at the importance of

financial health for functioning of innovation systems. We will try to conclude by commenting

on discussion in the context of the role of innovations systems in economic development.

Science, Research and Innovation

Among the many indicators that define a country’s innovative system the most intuitive

contributors may be number of patent, total research and development, and number of

scientific publications generated in the country. Another measure that is commonly used is

innovation counts and is the only one that directly measures innovative output (Acs, et al.,

2001). Total R&D is an input measure, whereas total number of inventions patented and

number of scientific publications generated can be considered intermediate outputs.

The research and development data thus can be used as a proxy for innovative output.

However the utility of research and development data is constrained because of three main

factors; first R&D measures only resources that are budgeted (Acs, et al., 2001), secondly

measurements of R&D do not consistently reflect the use of R&D embodied in capital goods

(Smith, 2005) as is assumed under the knowledge externality ideas of the “new” neo-classical


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models (Verspagen, 1992); third creation and access of knowledge, and innovation is not

necessarily through R&D (Smith, 2005) (Fagerberg & Srholec, 2008). The advantage of R&D data

as an indicator lies in its availability and harmonization across countries and time, and the

detailed sub-classifications that can be exploited for research in innovation and productivity

growth (Smith, 2005). However, Smiths observation of availability of R&D data may not

encompass the developing countries as Fagerberg and Srohlec excluded R&D data from their

factor analysis due to unavailability for many developing countries (Fagerberg & Srholec, 2008).

Moving from input to intermediate outputs we discover that the two measures being

considered here i.e. articles published in scientific and technical journals, and patent data do

not give a complete picture of the capacity to innovate for a country. For articles published in

scientific and technical journals they are an indicator of research activity. Patents are an

indicator of invention rather than innovation (Smith, 2005) (Fagerberg & Srholec, 2008). The

criticism of patents as an incomplete source of information of total innovation output and as

such innovation capability is given by Kleinknecht et al. in the following words;

“It is obvious that the patent indicator misses many non-patented inventions and innovations.

Some types of technology are not patentable, and, in some cases, it is still being debated

whether certain items (e.g, new business formulae on the internet) can be patented. On the

other hand, what is the share of patents that is never translated into commercially viable

products and processes? And can this share be assumed to be constant across branches and

firm size classes? Moreover in some cases patent figures can be obscured by strategic

behaviour: a firm will not commercialize the patent but use it to prevent a competitor patenting


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and using it. Moreover, two other (minor) shortcomings should be mentioned. First, patents are

not always easily classified by economically relevant industry or product lines. Second, some

patents can reflect minor improvements of little economic value, while others prove extremely

valuable and the question is whether such differences are adequately captured by citation

analyses.” (Kleinknecht, et al., 2002)

The advantages of patent data lie in their availability, detailed classification and perhaps

relative importance by means of citation analyses.

Innovation counts offer the promise of being the most relevant measure of innovation systems

output – a resultant of the successful utilization of the learning capacity. They represent direct

measures of innovation and are of two types; “object” and “subject” approach (subject

approach covers both input and output). According to the Oslo Manual the surveys for “object”

approach utilize the total expenditure for specific innovations implemented in a given year of

during a given period regardless of the year regardless of the year in which the expenditure

occurs (OECD & EUROSTAT, 2005). The importance in linking innovation to economic growth

may be expanded by the nature that they cover only technological and/or economically

significant innovations; however this might be biased due to experts’ perception of relevance of

the innovation (Archibugi & Pianta, 1996). Internationally comparable databases are difficult to

develop and surveys differ in design, sample definition and implementation. Since the most

important innovations are being reported there are lot of incremental, minor or “routine”

innovations that are not big enough, however they may have incremental effects on firm


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productivity and subsequently country growth. As such a portion of important innovations is

not a part of “object” based innovation counts (Smith, 2005). The “subject” approach covers

expenditures for implemented, potential, and abandoned innovation activities, and is the

recommended approach (OECD & EUROSTAT, 2005). The main advantage of the “subject”

approach is that is gives information on both firms generating and using innovations as such it

allows for the inclusion of service industries in innovation surveys. However, it is in its infancy –

internationally comparable data is not available and time-series comparisons are not yet

possible (Archibugi & Pianta, 1996).

Interaction of Management and Quality Standards in Innovation Systems

In the context of management, innovation systems are driven through developing ideas that

transform into implementable or cashable products or processes, managing attention towards

an idea and during the innovation process, ensuring that the whole is in sight while many part

ideas are being turned into a whole innovation, and institutional leadership where relevant for

ensuring the successful development of ideas into innovations (Van De Ven, 1986).

Management of innovation systems and defining what type of management system provides a

satisfactory environment for innovation are both challenging tasks. The indicator used by

Fagerberg in this regard is the adoption of ISO9000 certification. Whereas it is discussed in

literature that quality and systems management standards are of a signalling nature (Terlaak &

King, 2006). Fagerberg acknowledges its procedural nature, yet he argues that the ISO9000

certification is seen a sign of quality (Fagerberg & Srholec, 2008). Whereas this argument is

correct, the reasons behind diffusion of ISO9000 have not been unanimous as far as literature


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in this field shows (Frederic M. Viadiu, 2006). There is no denying in the role of appropriate

management systems in creating robust innovation systems however using quality

management systems has its disadvantages. The lack of any better alternative makes the use of

ISO9000 certifications per capita important to link the effect of quality management systems on

innovation systems.

Role of ICT in Effective Innovation Systems

Intuitively the presence of good ICT systems within a region is important for the ability to

acquire technology and innovate. Communication plays a key role in innovation systems and

ICT related total factor productivity growth has been discussed in literature in comparison with

ICT Capital having direct impact to growth. The evidence points out that the contribution of ICT

capital has been a more important source of growth than ICTs contribution to growth through

TFP growth (Ark & Piatowski, 2004). Work by Lee and Kahtri for an ICT growth accounting study

for Asia also lead to the same conclusion (Lee & Khatri, 2003). A quarter of a percentage point

of the acceleration in labour productivity is attributed to TFP growth through ICT sector and half

a percentage point to capital deepening (accumulation of ICT capital) by (Oliner & Sichel, 2000).

Is the representation of impact of ICT growth to innovation systems, by considering; fixed and

mobile phone subscriber, internet users, and personal computers as relevant indicators

appropriate? From literature evidence and the intuitive argument made earlier it is clear that

ICT has an impact on the overall economic development directly and perhaps through providing

adequate communication channels as a contribution to the innovation systems. However, a


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recommendation that can be looked into in the future is to work with the ICT Development

Index (IDI) and its relationship with innovation systems.

The IDI is an index published by the United Nations International Telecommunication Union

based on internationally agreed information and communication technologies (ICT) indicators.

This makes it a valuable tool for benchmarking the most important indicators for measuring the

information society. The IDI is a standard tool that governments, operators, development

agencies, researchers and others can use to measure the digital divide and compare ICT

performance within and across countries. The ICT Development Index is based on 11 ICT

indicators, grouped in three clusters: access, use and skills. The access sub-index captures ICT

readiness, and includes five infrastructure and access indicators (fixed-telephony, mobile

telephony, international Internet bandwidth, households with computers, and households with

Internet). The use sub-index captures ICT intensity, and includes three ICT intensity and usage

indicators (Internet users, fixed (wired)-broadband, and mobile broadband). The skills sub-index

captures ICT capability or skills as indispensable input indicators. It includes three proxy

indicators (adult literacy, gross secondary enrolment and gross tertiary enrolment) and

therefore is given less weight in the computation of the IDI compared with the other two sub-

indices (ITU, 2012).

Primary, Secondary and Tertiary Education

Educated employees are required to understand and apply new production techniques. More

educated populations are more innovative and this speeds up the adoption of new technologies


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(Nelson & Phelps, 1966). This is not only limited to the manufacturing sector but also in the

agricultural sector where adoption of new techniques leads to higher productivity. Nelson has

argued separately on the importance of education in technological development; concluding

that the rate of change of technological change is directly proportional to the investment in

education. In terms of the importance of education in developing countries it can be said that it

is more important than in advanced countries. The pace of technological development is

increasing and new production techniques require more knowledge and understanding. A well

trained labour force can benefit from education as educational investment has shown to be an

important contributing factor to building technological capabilities (Evenson & Westphal, 1994).

Educational enrolment indicators tell us something about the efforts made by the society to

educate its people. For primary education Fagerberg has used the pupil-teacher ratio, and for

secondary and tertiary the gross enrolment has been used. These indicators offer estimation

toward how education affects the innovation system. However they are not the best choice as

pupil-teacher ratio by itself does not say anything about the quality of education. The same

stand for secondary and tertiary enrolment in regard to the quality comment in addition to that

it does not cover completion. The quality of data itself on secondary and tertiary education is

deteriorating and lesser amount of data is available for these levels globally (Szirmai, 2005).

Measures of educational attainment may better represent the innovative capability of the

society. The measures of year of schooling in labour force obtained through labour force

surveys and population census even though more representative as indicator of the robustness

of innovation systems are not available across a large number of countries. Similar benefit and


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disadvantages exist for the use of percentage of labour force than has completed a given

educational cycle in ratio to the total length in years of that educational cycle (Szirmai, 2005).

Financial Systems

In National Systems for Financing Innovation two main group of parameters are identified as

critical for technological investment capabilities (OECD, 1995); namely macroeconomic and

microeconomic parameters. The national and global economic and financial context, which

includes macroeconomic conditions such as resource and market access, and national

competitiveness, can exert influence to determine to determine investment costs and as

subsequently influence a firm’s ability to identify investment opportunities. These are the

macroeconomic parameters. The firm’s own profile, its self-financing capability ties with

financial institution etc. are some of the microeconomic parameters. Fagerberg includes the

macroeconomic factors within the concept of openness and the microoeconomic parameters

are adequately covered by the indicators of market capitalisation of listed companies, and

domestic credit to private sector (Fagerberg & Srholec, 2008). In functioning of healthy

innovation system it is critical that finance is available for new economically viable ideas. The

issue that thus arises in national financial systems is related to the question; Is there a lack of

finance for economically viable ideas? Or – Is there a lack of economically feasible ideas for the

finance that is available in the market? In any case it is clear that the existence of healthy

financial system is of critical importance to a healthy innovation system and as such the

indicators impact as identified by Fagerbergs factor analysis can be considered as suitable for

estimating the relationship with the innovation systems in a country or region.


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Synopsis and Conclusion

In the previous sections we have presented a discussion on indicators that may define an

innovation system. This encompasses Science, Research and Innovation indicators,

Management and Quality Standards, ICT Indicators, Educational Indicators, and Financial

Indicators. The discussion shows the shortcomings and suitability of all of these indicators

depending on the intent of use.

Within the Science, Research and Innovation indicators the most suitable for degree of

innovativeness of a country might be considered innovation counts – “object” approach or

output data in “subject” approach. However, we must keep in mind that even though – due to

the nature of the literature being reviewed – we diverge into the realm of looking at the

indicators as measure of innovativeness. However, our discussion originates from trying to

measure capability of innovation and understanding the characteristics of a robust innovation

system. There are two utilities of being able to achieve the mentioned target; provide

researchers with better measures to carry out research, secondly provide policy executers with

better indicators to measure success or failure of policy. Whereas input measures such as R&D

expenditures, or intermediate output such as scientific article produced do not provide a

complete picture of the innovation system yet they are adequate for cross-country comparison.

This is owing to the fact the development or growth of an innovation system in its capability to

grow further will be reflected in the growth of the parameters associated with these indicators.

For policy makers however, patent count, journal articles and input measures are not adequate.

The requirement in the policy realm is to be able to measure final output. Most governments


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are either subsidising research and innovation activities or funding R&D. The interest there is

not only to know what has been invested or what inventions (patents, journal articles) have

been made possible but to know what has actually come out as innovations. For policy makers

the innovation count in “subject” approach likely carries most value as it accounts for input and

output, and successful and unsuccessful attempts at radical and incremental innovations.

Quality management standards such as that of ISO9000 certifications are good proxies for the

role of quality management in innovation systems. However a good innovation system may

exist without any quality management certification. Even though ISO9000 certifications have

been shown to be linked with growth within organizations however it has been found that

these changes do not come from changes in functioning of an organization. The signalling

concept might thus be playing a role in marketing the organization as robust. Thus we conclude

here that certification may not have the value that has been assigned to it in defining the

capacity of innovation systems. Saying this it must be reminded that quality management is

crucial to processes that lead to successful innovation – we here are only criticising the use of

certifications as an indicator of the capability to success indicate. With no other data available

on management systems certifications might be the only choice. However, more research

should go into understanding the relationship of Management in general and Quality

Management in particular and the robustness of national innovation systems.

ICT systems are important in technological catch-up because by nature they make the

possibility of acquiring technology easier. Simply by this nature of ICT systems it can be argued

that the greater the number of computers, internet user and mobile and fixed line telephone


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users the greater the freedom of information flow for learning new technologies and advancing

innovation. The positive effect of ICT on innovation systems has been confirmed in literature

and is critical for a well functioning innovation system. It is also important to understand that

an innovation system cannot produce innovations simply through communication of new

technologies or ideas but require human capital and finance for the conversion of the

communication or ideas into economically viable innovation.

Education as such, plays a crucial role in defining an innovation system. The proxies of

educational attainment generally used are pupil-teacher ratio, secondary and tertiary

education. Even though literature criticises such proxy in the development field, it might be

suitable to use these indicator to account for the role of education in innovation system. In the

financial indicators – market capitalisation of listed companies and domestic credit to private

sector may be augmented by capital available for R&D and Innovation. Since the earlier two

only cover private financial resources and health.

In conclusion we would like to remark that management systems, ICT, education and financial

indicators provide information about the robustness of an innovation system, i.e. the learning

capability of a country or region. The science and technology indicators only look at the inputs

into this innovation system and the output of this innovation system. Also the research into

understanding on what indictors best relate to innovativeness and innovation systems is both

very limited and as such empirical studies on the impact of innovation systems on growth are

also scarce. It is recommended that future research should focus on understanding on

innovation systems and how we can best define them through use of indicators or proxies.


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