ble of Contents
ASSESSMENT OF POSSIBLE EFFECTIVE STRATEGIES
IN THE TRANSITION PROCESS TO A KNOWLEDGE-
BASED ECONOMY: THE CASE OF TURKEY
Umut Gir, Sibel Giiven
Sibel Giiven ( contact author)
Middle East Technical University,
Department of Industrial Engineering,
Ynonii Bulvary, 06531
Ankara - Turkey
Phone : + 90 312 210 22 86
Fax: +90 312210 12 68
e-mail: maktav@ie.metu.edu.tr
Umut Gir
State Planning Organization - Turkey
Devlet Planlama Tebkilaty Misteparlyéy,
Necatibey Cad. EMSAGM No:108, 06100
Ankara - Turkey
Phone : + 90 312 230 87 20 / 6004
Fax: +90 312231 17 89
e-mail: ugur@ dpt.gov.tr
The aim of this study is to introduce a framework which can be used for the
assessment of possible strategies in the transition to a knowledge-based economy
(KBE). For this purpose, a mixed integer programming (MIP) model is developed to
determine the required levels of human resources and information and communications
technology (ICT) investments for given levels of government R&D investment of the
country shown to be the most significant determinant of the phenomenon. The model is
solved for the case of Turkey. The results indicate that: (i) government R&D in Turkey
should increase to considerable levels in order to trigger the transition to a KBE and
(ii) transformation towards a KBE with an inefficient innovation system may require
considerable amounts of additional resources compared to transformation with a more
efficient system. The results illustrate that any improvement in the innovation system for
Turkey may lead to considerable savings from the required human resources and ICT
investments given same levels of R&D investment.
Keywords: Knowledge-Based Economy, Research and Development, Mixed Integer
Programming
1. INTRODUCTION
The term knowledge-based economy (KBE) has been the subject of debate
among many researchers during the last decade due to the rapid and simultaneous
structural changes observed in economic and social activities within and among
societies. It was the 8-year long continuous growth of the US economy with high GDP
growth rate, low inflation and low unemployment that fuelled the debate among
economists. The US experience led to some contradictions with respect to predefined
concepts and relations set by economic theory.
The main concern of some of the arguments within the literature have been
related to the institutional and organisational structures and the way they are to be
configured (i.e. organisational diversity, knowledge diffusion within and among the
economic actors, regulatory bodies... etc.). The remaining part of the literature deals
with the more tangible aspect of knowledge-based economies. It is observed that in
many countries some industries and service sectors have raised their share in total
economic activity, gained importance in international trade and became critical due to
being effective in the way they affect the operations of other sectors (i.e. information
and communications technologies -ICT).
Although efforts have been made to establish frameworks for analysing different
aspects of the KBE concept, a comprehensive analytic study to cover the systemic
interactions among the entities and institutions of the KBE is still lacking. It is only
through the development of such a framework that it may be possible to assess the
effect of different strategic options towards becoming a KBE.
For this purpose in this study, a definition is proposed for the KBE phenomenon.
It is defined to be the system in which there exist high levels of incentives to exploit
intellectual effort and to disseminate the knowledge created with the associated
mechanisms and resources supporting, sustaining and developing the attained level.
Discussing the capabilities of countries from the perspective of the knowledge-
based economy requires analysing the national innovation systems of the countries
comparatively. Since there does not exist normative guides to assess any nation’s
system of innovation, many institutions and researchers prefer to adapt best policy
approaches. What is generally accepted is that, any system of innovation should
enhance and promote the creation and the diffusion of knowledge. What is also argued
within the literature is that there exist a synergistic interaction between the creation and
the diffusion process in such a way that one stimulates the other.
In this study it is argued that the capabilities achievable by a nation’s innovation
system is reflected in the relations between the resources allocated to the creation and
the diffusion activities. Making such an analysis is appropriate because it does not
require normative guides for comparison. Rather, it will allow making comparisons
among the countries which somehow intend to adapt the best policy approach.
When analysing various indicators, it is observed that the system forming the
knowledge-based economy exhibits a coherent behaviour requiring to perform well in
most of the aspects rather than focusing on only some subsets. Hence, a kind of
simultaneity in the states of various aspects is worth mentioning. However, the
efficiency levels (with respect to the utilisation of resources which require investment)
at which that simultaneity pervades may differ.
It is argued that, due to this simultaneity aspect, by considering only a subset of
the related factors, all the remaining related factors will be considered.
There are alternative indicators for knowledge creation and diffusion. Regarding
the knowledge creation activity, total R&D expenditure made by govemment and
private sectors can be considered to be the indicators of the level of knowledge created
in a country. Diffusion of knowledge, on the other hand, can be measured by the level
of investment in ICT. Though ICT are utilised in the diffusion of codified knowledge,
the unavailability of indicators in this area imposes the use of ICT as a proxy to reflect
the direct means of knowledge diffusion. What can indirectly be utilised to measure the
diffusion activity is the quantity of university graduates (UG) in a country. The
accumulated knowledge in the universities and institutions are transmitted to these
people who are expected to take part in R&D activities. The portion of UGs who are
occupied in R&D activities should also be considered. These people are referred to as
researchers, scientists and engineers (RSE).
Utilising these arguments a framework is introduced that would enable the
identification of the associations between some of the basic aspects of the phenomenon.
The basic factors considered are the expenditure in research and development (R&D),
expenditure in information and communications technologies (ICT), researchers,
scientists and engineers (RSE) employed in R&D and the portion of population who are
university graduates (UG). Based on these factors a framework for analysing a country
from the perspective of its transformation into a KBE is introduced. The fact that the
efficiency of utilisation of these basic factors differs from country to country is also
taken into account. It is hoped that the framework developed will constitute a useful
analytic tool in assessing some related strategic schemes. The framework and related
measures are explained in Section 2.
Based on the arguments cited above, two issues are thought to be significant for
identifying the structural differences between the countries. The first one is related with
the state of the R&D activities (i.e. knowledge creation) and the other is the efficiency
of a country in utilising its resources in conducting its knowledge-based economic
activities.
It is observed that countries are in either of the two possible phases with respect
to the levels of private R&D expenditure and govemment R&D expenditure: the phase
in which private R&D of the country is dormant (i.e. insufficient private R&D) and the
phase in which private R&D is at a self-sustaining state. In the study, the former phase
is referred to as 1 phase and the latter as 2"™ phase. In order to identify in which phase a
country is, it is assumed that if government R&D expenditure of that country is higher
than its private R&D expenditure the country is in 1° phase, and the reverse holds for
the 2" phase.
With respect to the utilisation of resources in conducting knowledge-based
activities, the basic argument is as follows: in order to support the creation of a given
amount of knowledge (measured by R&D) some level of ICT investment has to be
made. It can also be inferred that without attaining a certain level of RSE, the given
amount of R&D cannot be achieved. The required amount of RSE can only be obtained
from the stock of UGs in the population.
Thus, in this study, using the data for OECD countries over the years a
hypothetical thin boundary enveloping all the points (denoting countries) is derived and
interpreted as the frontier of utilisation of RSE in R&D activities and is referred to as
envelope function. It can be said that any country is not expected to achieve a given
level of R&D activity with less than the amount of RSE indicated by the envelope
function. In this sense, the points on the envelope function always reflect the best case
performances for varying levels of R&D activities. Such an approach allows comparing
the performances of countries in the utilisation of RSE in R&D activities with the best
possible case.
It can also be observed from OECD data that there is positive correlation
between RSE per population and UG per population indicating that, on the average, as
the required level of RSE increases, the requirement for UG increases. Thus a country is
not expected to have a given amount of RSE per population if it has UG per population
less than the amount mapped by the envelope function.
Also there exists a positive correlation between per capita R&D expenditure and
per capita ICT expenditure. This indicates that to support increasing levels of R&D
activity, the country has to invest more in ICT. The development and interpretation of
the respective envelope functions are explained in Section 3.
In order to assess the effects of alternative strategies in the transition to a KBE
for the case of Turkey and to apply the framework outlined above, a mixed integer
programming (MIP) model is developed. The model is presented in Section 3.
In applying the framework and the model to the case of Turkey an investment
scheme for government funded research and development is set. Section 4 summarises
the scheme and the results obtained by using the model. Concluding remarks are
outlined in Section 5.
2. THE FRAMEWORK
Knowledge As The Basis of Analysis And Its Consequences
Currently, the widely accepted definition of the knowledge-based economy is
put forward by OECD (1996) as “...economies which are directly based on the
production, distribution and use of knowledge”. It is argued that the term “results from
a fuller recognition of the role of knowledge and technology in economic growth”.
Similar to the definition of the OECD, Dyker and Radosevic (1999), articulate
the activities underpinning the knowledge-based economy as:
¢ knowledge creation (knowledge investments),
¢ knowledge diffusion (knowledge distribution).
According to both these definitions, it can be said that knowledge is considered
as the basic object of the phenomenon an4d it is to be placed in the center of the analysis.
Hence, understanding the basic characteristics of knowledge and the processes
associated with it will help in understanding the related arguments existing in the
literature.
The second inference that can be drawn from the definitions is that because the
terms creation/production, diffusion/distribution and use are directly related with the
concepts of ‘innovation’ (in specific) and ‘national innovation systems’ (in general), the
knowledge-based economy phenomenon requires considering both concepts.
Though not mentioned explicitly, the tawonomy of knowledge which is proposed
by Lundvall and Johnson (1994) forms a common basis for most of the arguments
existing in the innovation literature. Their work basically distinguishes four types of
knowledge which are in continual interaction (at some pace) with each other: know-
what, know-why, know-how, know-who. Lundvall and Johnson (1994) grouped them in
two on the basis of the similarities of their forms in which they may be available to
individuals. Consequently, know-what and know-why were regarded as codified
knowledge (information) while leaving the tacit part to include know-how and know-
who.
Tacit knowledge is harder to code and lies mainly in skills which are developed
through time by performing some type of activity in a repeated manner. Consequently,
transmitting this kind of knowledge requires a considerable amount of social interaction
with the right actors possessing the skills of interest (eg. mobility of researchers,
education, master-apprentice relations ... etc can be considered as the means of transfer
of codified knowledge). Codified knowledge, as the name implies, can be coded
physically (ie. on paper, CD... etc) and hence is referred to as information as well. As
compared to tacit knowledge, codified knowledge is easier to transmit when considered
the capabilities and effectiveness of ICT. However, producing and interpreting codified
knowledge (especially know-why) is hard due to the fact that it requires highly
specialized labour with sophisticated equipment and this process requires applying
scientific and technical procedures (patents, technical papers, utilisation of cellular
phones... etc can be considered as the means of transfer of codified knowledge).
“That codified and tacit knowledge are complementary is ... indisputable”
(Foray and Lundvall; 1996). That is, observing the four categories of knowledge
throughout the activities of life will lead to the conclusion that they hardly exist in their
pure individual forms and any process includes combinations of these categories with
differing weights.
Note that, identifying whether a process requires tacit-intensive or codified-
intensive knowledge will help in deciding in what way it is better to transfer (anywhere,
anybody) how that process is conducted. (i.e. diffusion of economically useful
knowledge)
The transfer of knowledge is also promoted through the codification process.
The term codification can be explained as the process of transforming tacit part of
knowledge to information (codified knowledge). However, it should be mentioned that
codification is not the only means of creation of codified knowledge. Codified
knowledge brings about its associated tacit knowledge. The dynamic nature of the
codification process is explained by Foray and Lundvall (1996) as “... the spiral
movement where tacit is transformed into codified knowledge, followed by a movement
back to practice where new kinds of tacit knowledge are developed.”
One other point worth noting is that the need for handling codified knowledge is
enhancing developments in ICT and that the advances in ICT are, in tum, accelerating
and enhancing the codification process. Thus, via ICT, the accessibility -and hence the
diffusion- of knowledge is improved. Given the accelerated rate of change driven by the
enhancements in the ICT, it is implicitly required that the associated workforce has the
capability of adapting and using advanced technology.
Classifying knowledge in this way brings about the concepts of knowledge stock
of an economy and knowledge flows within an economy. It is clear that new knowledge
is continuously added to the knowledge stock while obsolete knowledge is dropped off.
Knowledge flow is an important factor in determining a country’s innovative
capability which is also emphasized implicitly in Matthew's (1996) taxonomy (with the
associated concept learning) and within the concept of National Innovation Systems.
Initially, they attempted to explain the new phenomenon with the growing importance
of the process of learning which is a phenomenon highly dependent on the knowledge,
its classification, and its place in various kinds of transactions.
Up to this point it has been intended to analyse the ongoing processes with
respect to their knowledge content. It has also been illustrated that most of the processes
contain a mixture of different knowledge types and does not consist of only a single
type and identifying the tacit/codified content of required knowledge for any process
will have a determining effect on how it is created and transmitted. Based on these
arguments, it may be claimed that the activities mentioned above have been existent
since the beginning of humanity; however, in the recent past the increased pace of
knowledge creation and diffusion, the spread of knowledge utilisation and consequently
the weight of knowledge within the routine economic activities emphasised the
importance of knowledge. Hence, as will be shown and discussed further a significant
number of countries (either intentionally or unintentionally) began to take into
consideration the characteristics and requirements implied by knowledge-based
activities in determining their policies.
Having mentioned the literature on knowledge related transactions, it seems
appropriate to assess related strategies and policies on the basis of the extent that they
promote the creation and diffusion of economically and socially useful knowledge.
Such an idea will facilitate comparison of national innovation systems of different
countries on common grounds.
Discussing the capabilities of countries from the perspective of the knowledge-
based economy requires analysing the national innovation systems of the countries
comparatively. Since there does not exist normative guides to assess any nation’s
system of innovation, many institutions and researchers prefer to adapt best policy
approaches. What is generally accepted is that, any system of innovation should
enhance and promote the creation and the diffusion of knowledge. What is also argued
within the literature is that there exist a synergistic interaction between the creation and
the diffusion process in such a way that one stimulates the other.
Clarifying The System
Based on the arguments discussed above and the findings which are to be
illustrated in the following, a definition is proposed for the KBE phenomenon. It is
defined to be the system in which there exist high levels of incentives to exploit
intellectual effort and to disseminate the knowledge created with the associated
mechanisms and resources supporting, sustaining and developing the attained level.
Once the definition of the KBE is introduced, and the ongoing basics within
KBE’s are illustrated, the factors involved in the system comprising KBE can be
reduced to two broad sets:
* Tangible factors - the resources which require investment,
* Contextual issues - the institutional, cultural and regulational settings in
which tangible factors are utilised.
In this study it is argued that the capabilities achievable by a nation’s innovation
system is reflected in the relations between the resources (tangible factors) allocated to
the creation and the diffusion activities. Adopting such an approach is appropriate
because it does not require normative guides for comparison. Rather, facilitating a best
policy approach, it will allow making comparisons (among the countries) on common
grounds.
The framework which will be illustrated in the following section attempts to
handle the combined behaviour of the tangible factors and contextual issues reflecting
both the relations among the tangible factors and between tangible factors and
contextual issues.
Establishing The F ramework
Firstly, an OECD-wide analysis with the following indicators was conducted to
get an initial inference about the common features of the KBE. The analysis was not
conducted on a systematic basis and during the analysis most recently available values
were utilised: Number of granted patents per 10,000 population (1998), gross domestic
expenditure on research and development as a percentage of GDP (2000), direct public
expenditure for educational institutions as a percentage of GDP (1995), ICT expenditure
as a percentage of GDP (1997), ratio of the population with university level education to
total population aged between 25-64 (1996).
The reason that these indicators were used can be explained as that these remain
as the most commonly cited indicators and also reflect the states of many other issues
which are directly related with them.
Though the indicators mentioned above comprise only a limited portion of the
cited indicators within the literature it can be observed that any of the indicators tend to
assume high values when the others are high. That may lead to the argument that the
system forming the knowledge-based economy exhibits a coherent behaviour requiring
to perform well in most of the aspects rather than focusing on only some subsets.
Hence, a kind of simultaneity in the states of various aspects is worth mentioning.
However, the efficiency levels (with respect to the utilisation of resources which require
investment) at which that simultaneity pervades may differ. These, in fact, were
expected due to the arguments discussed in the previous section.
It may be argued that, due to this simultaneity aspect, by considering only a
subset of the related factors, all the remaining related factors will be considered.
There are alternative indicators for knowledge creation and diffusion. Regarding
the knowledge creation activity, total R&D expenditure made by govemment and
private sectors can be considered to be the indicators of the level of knowledge created
in a country. Diffusion of knowledge, on the other hand, can be measured by the level
of investment in ICT. Though ICT are utilised in the diffusion of codified knowledge,
the unavailability of indicators in this area imposes the use of ICT as a proxy to reflect
the direct means of knowledge diffusion. What can indirectly be utilised to measure the
diffusion activity is the quantity of university graduates (UG) in a country. The
accumulated knowledge in the universities and institutions are transmitted to these
people who are expected to take part in R&D activities. The portion of UGs who are
occupied in R&D activities should also be considered. These people are referred to as
researchers, scientists and engineers (RSE).
The relation between these factors are assumed to prevail as illustrated in
Figure-1.
R&D
RSE IcT
f:
UG
Figure-1 The Relations Between R&D, ICT, RSE And UG
Figure-1 illustrates that in order to support the creation of a given amount of
knowledge (indicated by R&D) some level of ICT investment has to be made. It can
also be inferred that without some level of RSE the given amount of R&D cannot be
achieved. The required amount of RSE can only be obtained from the stock of UGs
within the population as well. Finally, the lines connecting each factor to other indicate
the relation between pairs (UG and RSE, R&D and RSE, R&D and ICT) so as to
identify the magnitude of the requirement of one factor in order to achieve a given
amount of the other.
The relations between these factors, which are set by lines 1, 2 and 3 in Figure-
1, cannot be considered independent from each other. They are expected to reflect the
capability of the country in utilising these factors.
At this point it should be noted that, the availability of the data (in terms of
continuity) imposes a restriction on the attempts to perform a regression analysis based
on time series data, the results of which may be utilised for making a cross-country
comparison with respect to the capabilities of their national innovation systems.
However, even though the familiar analysis techniques may not be sufficient for the
existing data, there exists a sufficient amount of data to get inferences on performances
of countries. The concem is how to obtain the information hidden in them.
As a consequence of the restrictions mentioned, the indicators are paired with
respect to their corresponding years. Once the data pairs are obtained, they are plotted
on a scattergram regardless of the country which they belong to and the year of
observation. It should also be noted that, to be able to handle and compare different
countries and various years the data were corrected to per capita (per population) and
constant $ purchasing power parity (PPP) terms.
First the association between the RSE per population and per capita R&D
expenditure is seeked (Refer to Figure-2).
§ scooo
io
9 i000
q
§ e000
£
ASE Per Populaton
Figure-2 The Association Between RSE per population and per capita R&D
Expenditure
As observed from Figure-2, there exists a positive correlation between RSE per
population and per capita R&D expenditure which can be interpreted as follows: for
increasing levels of R&D expenditure (indicating knowledge creation) the requirement
of qualified human resources also increases. Also, the variation in the requirement of
RSE per population increases as the level of per capita R&D expenditure increases. This
may be because;
* The number of observations for levels of per capita R&D expenditure may
be less than the number of observations for higher levels of per capita R&D
expenditure.
+ As the level of R&D expenditure increases, the national system of
innovation may require further enhancements which possibly makes the
system harder to manage.
Referring to Figure-2 again, the thin boundary enveloping all the plots can be
observed. This piecewise linear line (i.e. a piecewise linear function) can be interpreted
as the frontier of utilisation of RSE in R&D activities which will be referred to as
envelope function within the following. That is to say that, for a given level of per capita
R&D expenditure, there is no RSE per population figure that is smaller than the figure
mapped by the envelope function. Such an argument may lead to the following
interpretation: Any country is not expected to achieve a given level of R&D activity
with less than the amount of RSE indicated by the envelope function; and because the
plots were constructed regardless of the country and year of observation, the points on
the envelope function may belong to different years of different countries. In this sense,
the points on the envelope function always reflect the best case performances for
varying levels of R&D activities. Such an approach allows comparing the performance
of countries in the utilisation of RSE in R&D activities with the best possible case. It
should be noted that such an argument is not contradicting with the best policy
approach which was mentioned earlier. In order to illustrate these arguments Figure-3
will be utilised.
P |
E oo i aad
+
e =
ru
SE per Population
Figure-3 The Association Between RSE per population and per capita R&D
Expenditure - Comparing Iceland And Japan With The Envelope
Function
In Figure-3, the past performances of Japan and Iceland in utilising RSE for
increasing levels of their R&D activities are observed. Referring the plots of Japan and
Iceland (in Figure 3.2.2.3) as utilisation paths of RSE for R&D activities, one can
observe the relative efficiency of each country both with respect to the envelope
function (i.e. best case) and with respect to each other. Note that though Japan could
have performed some PPP $ 285 (per capita) of R&D activity with 0.0016 RSE per
population, it performed this amount of R&D with 0.0026 RSE per population which is
higher than 0.0016 RSE per population (which has been the best possible case). The
same finding can be observed all the way through the utilisation path of Japan. This
may mean that, while increasing its R&D activity, Japan has been inefficient with
respect to the utilisation of RSE in R&D activities. However, it will be, misleading at
this stage, to make comments about the degree of inefficiency of that country.
Comparing the utilisation path of Iceland in Figure-3 with the envelope function
will lead to the same findings as obtained in the case of Japan (i.e. inefficiency).
However, comparing the utilisation paths of Japan and Iceland will lead to the result
that Iceland has been more inefficient in utilising its RSE for its R&D activities as
compared to Japan.
The reason for the difference in the efficiencies of Japan and Iceland with
respect to the utilisation of RSE in R&D activities may be due to many factors (e.g. the
quality of R&D projects chosen within the country, the quality of RSE with respect to
their competencies in their areas of concern ... etc.). Though the analysis of these factors
are beyond the scope of this study, that the possible range of factors affecting the
patterns of utilisation paths is directly related with the concepts involved in national
innovation systems should be kept in mind.
0.008500
.900000
wa o \
0.038000
0.003000
o
1 secs $ z
Q oi el
0.002000
2
4 °
0.001000
0.001000
0.000500
0.000000
UG Per Population
Figure-4 The Association Between RSE per population and UG per Population
With respect to Figure-4, it can be said that all the arguments put forward in
Figure-3 also hold for Figure-4 which reflects the relation between RSE per population
and UG per population. That is, the same arguments with respect to the efficiency
comparisons (i.e. utilization paths) are also valid for them. The same is said for Figure -
5 which attempts to illustrate the relation between the per capita ICT expenditure and
per capita R&D expenditure.
700.00 2
: ~~ ee
i we *
; a
in x
i 0.00 | a epi tases
jae Lag!
so, bo?
7
0.00 had
er Capt i Expenditure (Constant PP 4s)
Figure-5 The Association Between per capita ICT investment and per capita
R&D investment
While adapting the approach discussed in this part, it has been implicitly
assumed that the size of the country (i.e. with respect to its economy, population and
area) has no effect on the observed performance of the country in utilising its RSE, UG
and ICT in R&D activities. However, it should be noted that the effects of the size of
the country on the performance of its utilising UG, RSE and ICT in R&D activities
require extensive further research.
In the arguments cited above, there exist one more issue which remains to be
clarified: the state of the R&D activities (i.e. knowledge creation).
R&D
Kim (2000) states that “where demand for technology from the private sector is
still dormant, ... government R&D expenditure has to be made for a certain period of
time, as a necessary condition for indigenous private R&D...” to take off.
Kim (2000) illustrates the relation between the development pattern of demand
for technology with respect to the pattern of supply of technology for the case of Korea.
It is supposed that the economy’s total demand for and supply of technology is
negligible until a certain time T: at which government starts allocating national
resources to the supply of technology. Assuming that appropriate market environment is
satisfied, at time T2 > Ti, demand from firms is realised. Finally, at time T >T1>T1
both the demand and supply reach a point where they are equal which is claimed to be
the “... starting point for the economy to be self sustaining”.
In investigating his arguments for the case of Korea, Kim (2000) used
govemment R&D expenditure to measure supply of technology and private R&D
expenditure to measure demand for technology. The investment level at which private
R&D equals government R&D is defined to be the self-sustaining point.
Adapting Kim's (2000) approach, the relation between government and private
R&D expenditure is analysed for OECD countries. The scatter plots of goverment and
private R&D of OECD countries are formed. Three basic patterns were observed:
+ There exist countries whose private R&D expenditure has been higher than
their government R&D expenditure for the period analysed, which are said
to be in a self-sustaining state. These countries are Austria, Belgium, Czech
Republic, Denmark, Finland, Germany, Japan, Netherlands, Norway, Spain,
Sweden, Switzerland and UK.
* Greece, Mexico, Poland and Turkey are the countries in which private R&D
expenditure has remained negligible for the period analysed compared to the
countries mentioned above. Also in these countries, it is observed that
government R&D expenditure is stable at relatively low levels which can be
interpreted as insufficient supply for stimulating firms to invest in R&D.
+ In the figures for Australia, Canada, France, Iceland, Ireland and the US, the
point at which the countries start to be self-sustaining can be observed.
Based on the discussions outlined above, it can be claimed that countries may be
in two possible phases with respect to the levels of private and government R&D
expenditures: the phase in which private R&D expenditure of the country is dormant
(ie. insufficient government R&D) and the phase in which private R&D expenditure is
at a self-sustaining state. Within the following, the former phase will be referred to as 1°
phase and the latter as 2" phase. In order to identify in which phase a country is, it will
be assumed that if government R&D expenditure of that country is higher than its
private R&D expenditure the country is in 1° phase and the reverse holds for the 2°?
phase.
3. THE MODEL
Incorporating the abstractions and generalisations mentioned above, a model is
constructed with the idea that there exists a minimum required level for each of the
main factors (of the KBE phenomenon) in order to achieve a given level of the other.
Let RSE be the number of researchers, scientists and engineers per population ,
and UG be the number of university graduates aged between 25-64 per population, and
ICT be the per capita expenditure on information and communications technology, and
PRIRD be per capita private expenditure on R&D, and GOVRD be per capita
government expenditure on R&D. Suppose that f is a function where f(RSE) = R&Dase
and R&Dasz is the maximum amount of R&D that can be achieved by RSE; g isa
function where g(ICT) =R&Djcr and R&Dicr is the maximum amount of R&D that can
be achieved by ICT; and h is a function where h(UG) = RSE and RSEvyg is the
maximum amount of RSE that can be achieved by UG. Then the model in compact
form can be expressed as:
Max. R&D
subject to
PRIRD; =m(GOVRD;, PRIRD:1) (a)
h'(RSEue) <UG (b)
g)(R&Dict) <ICT (c)
f'(R&Dpse) <RSE (d)
Constraint (a) determines the relation between R&D expenditure of government
and private sectors. Constraint (b) determines the minimum required level of UG per
population in order to achieve a given level of RSE per population. Constraint (c)
reflects the minimum required amount of ICT expenditure per population in order to
supply the corresponding amount of R&D expenditure per population. Hence, in order
to achieve some level of R&D investment the constraints force the country to invest at
least that amount in ICT. Constraint (d) determines the minimum required RSE per
population for a given amount of R&D expenditure per population. Constraints (b), (c)
and (d) are constructed with the help of utilisation paths methodology. In addition, the
model outlined above covers a period of 23 years in each of which the values of RSE,
UG, ICT, PRIRD and GOVRD are determined in order to maximise the terminal year
total R&D value.
The variables used in the model are as follows:
GRD; : Amount of per capita GOVRD in year t (in constant 1995 PPP $’s);
PRD; : Amount of per capita PRIRD in year t (in constant 1995 PPP $’s);
GRD1; : The amount of GRD; if GRD; > PRD, (i.e. phase 1);
GRD2, : The amount of GRD; if GRD; < PRD, (i.e. phase 2);
PRD1; : The amount of PRD; if GRD; > PRD; (i.e. phase 1);
PRD2; : The amount of PRD; if GRD; < PRD; (i.e. phase 2);
PRD1A; : Value of PRD 1; if t is not the period just before transition to phase 2;
PRD1B; : Value of PRD 1; if t is the period just before transition to phase 2;
vt =1 if GRD; > PRD;
0 otherwise ;
ICT; : Percapita ICT expenditure in year t ;
RSE; __: Share of RSE in total population in year t;
UG; _: Share of UG in total population in year t;
The parameters are as follows:
PRD init : Amount of PRIRD in year 2000 if the country is in phase 1 in that year;
PRD2init_ : Amount of PRIRD in year 2000 if the country is in phase 2 in that year;
G1 : Coefficient of contribution of GRD; to PRD; in phase 1;
G2 : Coefficient of contribution of GRD; to PRD; in phase 2;
Pl : Coefficient of contribution of PRD;.; to PRD; in phase 1;
P2 : Coefficient of contribution of PRD; to PRD; in phase 2;
CON1 : Constant term in the estimation of PRD; in phase 1;
CON2 : Constant term in the estimation of PRD; in phase 2;
M : Any number quite larger than the maximum value which GRD; or PRD; can
assume;
B_ICT : Coefficient determining the increase in R&D when ICT is increased by 1;
B_RSE: Coefficient determining the increase in R&D when RSE is increased by 1;
B_UG : Coefficient determining the increase in RSE when UG is increased by 1;
C_ICT : Constant term in the relation between ICT and R&D;
C_RSE: Constant term in the relation between RSE and R&D;
C_UG : Constant term in the relation between UG and RSE;
POP, : The projected population of the country in year t;
GDP, _: The projected gross domestic product of the country in year t;
GDP2000: The gross domestic product of the country in year 2000;
SRD;: Maximum allowed share of GDP, allocated to government R&D (i.e. GRD,);
KGDP*: Annual average growth rate of GDP;
Currently the model is as follows:
Max. GRD 2023 + PRD 2023
subject to
GRD1,; > PRD1; forV t (1)
PRD2; > GRD2; forV t (2)
GRD1, M*y ; forv t (3)
GRD2; M*(1 -y) forV t (4)
PRD1: M*y ¢ forV t (5)
PRD2, M*(1 -y) forV t (6)
GRD; = GRD1,+GRD2, forV t (7)
PRD; = PRD1,+GRD2, forV t (8)
PRD1, = PRD1A;+PRD1B; forV t (9)
PRDIB; < M* (y- yun) for t <2023 (10)
ye = Yun for t <2023 (11)
PRD; = CON1* y, +CON2 * (1-y,) +G1 * GRD1,+G2* GRD2, +P1*
PRD1A;.; +P2* PRD1By1 +P2* PRD2;1 fort = 2002,..,2023 (12)
PRD; =CON1 * y; +CON2* (1-y,) +G1* GRD1,+G2* GRD2, + Pl *
PRD1,1 + P2* PRD2:1 for t = 2001 (13)
PRD, +GRD; =B_ICT * ICT; + C_ICT for V t (14)
PRD; +GRD; =B_RSE* RSE, + C_RSE forv t (15)
RSE; =B_UG * UG; +C_UG for t (16)
GRD; = (SRD * GDP,) / POP; (17)
GDPi+1 = KGDP * GDP; forv t > 2001 (18)
GDP; = KGDP * GDP2o00 forV t =2001 (19)
and
GRD, PRD: GRD1; GRD2;, PRD1;, PRD2,, PRD1A;,
PRD1B, ICT, RSE, UG;0 andy, = 0/1 (20)
The model maximises the terminal year (year 2023) total R&D expenditure for
Turkey given annual government R&D expenditures throughout the planning horizon
and determines the required levels of human resources and information and
communications technology (ICT) investments for the given levels of government R&D
expenditures.
4, APPLICATION OF THE FRAMEWORK: TURKEY
Setting The Scheme
In applying the framework and the model to the case of Turkey an investment
scheme for government funded research and development is set by us. The reason for
doing this is that Turkey is a country whose R&D expenditures are the least among the
OECD countries and have been stable at the level of 20.61 PPP $ per capita until 1997.
Since in the model private R&D expenditures at time t are assumed to be a function of
government R&D expenditures in t and private R&D expenditures in t-1, when
government R&D expenditures are held constant at their 1997 level or slightly
increased, the model projects increasing levels of total R&D expenditures. This imposes
a requirement for the level of ICT expenditures and number of RSE and UG to increase.
The natural pace of increase of these factors in Turkey results in an infeasible solution.
In the developed scheme, the annual govemment R&D expenditure is set as a
constant percentage of annual GDP of Turkey. In obtaining a meaningful scheme, the
government investment levels of OECD countries as a percentage of their GDPs were
traced for the available years. It was observed that 0.6 % has been the highest share of
GDP devoted to R&D by the OECD governments since 1981. Assuming that the GDP
of Turkey will increase 4 % annually and the percentage of GDP devoted to R&D will
be 0.6 %, the pattern that government R&D and private R&D expenditure will follow is
illustrated in Figure-6, and used as such in the model.
Figure-6 The Projected Government And Private Per Capita R&D Expenditures With
4% Annual GDP Growth and 0.6 % of GDP Allocated To Govemment R&D
It should also be noted that with such a scheme, Turkey is projected to achieve
PPP $ 138.2 worth of total per capita R&D activity while remaining in phase 1. Then
the requirements imposed by that scheme are analysed considering various scenarios in
which Turkey exhibits an efficient utilisation of RSE, UG and ICT, as well as the case
in which factors are assumed to be used inefficiently (derived by using the envelope
functions explained above).
Because determining the efficient and inefficient requirement patterns of RSE,
UG and ICT are topics which require further research, past performance of OECD
countries, whose development patterns with respect to R&D expenditure have been
similar to Turkey’s projected R&D expenditure pattem were analysed. The efficient and
inefficient countries were identified with respect to their performances in utilising each
of the RSE, UG and ICT. These countries are identified as follows:
¢ Ireland is supposed to reflect the efficient utilisation of RSE in performing R&D
activities whereas Poland and Iceland are supposed to reflect the inefficient
utilisation.
¢ Ireland is supposed to reflect the efficient utilisation of ICT in performing R&D
activities whereas New Zealand is supposed to reflect the inefficient utilisation.
¢ Ireland is supposed to reflect the efficient utilisation of UG in deriving RSE whereas
Spain is supposed to reflect the inefficient utilisation.
In order to use the findings summarised above in the model, the utilisation paths
should be defined mathematically. Note that, the slope of the function (i.e. utilisation
path) defining the additional requirement of the independent variable (e.g. ICT) in order
to increase the dependent variable by one unit (e.g. R&D) can be used for defining the
related utilisation paths. Simple linear regression is utilised for estimating these slopes
using the data of the countries noted above.
Table-1 The Estimated Slopes For Utilisation Paths
R&D vs. RSE R&D vs. ICT RSE vs. UG
Efficient Case 109,794.40 0.280505 0.000145
Inefficient Case | 50,849.12 for 0<R&D<100 0.131871 0.000107
95,657.35 for 100<R&D<200
In constructing the constraints (14), (15) and (16), the utilisation paths will be
assumed to start from the most recent RSE, UG and ICT values of Turkey while having
the slopes illustrated in Table-1. The equations of utilisation paths for corresponding
cases are given in Table-2.
Table-2 The Estimated Equations For Utilisation Paths
Efficient Case Inefficient Case
R&D vs. RSE | R&D = (109,794.40) * RSE - 12.5168 20081 0) ; (100 ; 0.001864) ; (200 ;
0.002909)
R&D vs. ICT | R&D =(0.280505) * ICT - 25.0391 R&D =(0.131871) * ICT - 0.84996
RSE vs. UG RSE = (0.000145) * UG - 0.00057 RSE = (0.000107) * UG - 0.00034
It should be mentioned that, while comparing the efficient and inefficient cases,
the only monetary costs (due to being inefficient) which can be identified are the costs
associated with ICT expenditure.
With respect to the utilisation of human resources, it can be claimed that
education constitutes the most important issue in the quality of human resources (which,
in tum, is supposed to affect the utilisation of UG and RSE in knowledge-based
activities). So, the level of education expenditures can be said to reflect the efficiency of
utilising human resources in performing R&D activities. Considering this argument, it is
proposed that the monetary costs of realising an inefficient utilisation of UGs in
deriving RSEs is the additional expenditure made per university student. Because these
costs cannot be determined by the model, they are calculated exogenously.
In order to handle this situation, the model assumes inefficient utilisations of
RSE in performing R&D. Based on the resulting RSE’s the inefficient utilisation of UG
in deriving RSE will be compared with the efficient utilisation of UG. This difference
can be interpreted as the increased amount of educational expenditure per university
student starting from 2001. However, because the effects of well-trained students who
start education in 2001 can be realised only after they are introduced to the market the
system will be assumed to be efficient after 2006. So, the model is adjusted in such a
way that until 2006, the UG will be utilised inefficiently and after 2006 they will be
utilised efficiently. This case is referred to as educational enhancement case.
Analysing The Results
In order to compare the levels of projected requirements of RSE, UG and ICT
expenditure of efficient and inefficient cases, Table-3 will be utilised.
Table-3 The Results of Model Runs For The Efficient And Inefficient Cases
R&D NUMBER OF RSE | NUMBER OF UG ICT EXPENDITURE
Exp. (Constant PPP $)
eunion Efficient | Inefficient] Efficient |Inefficient] Efficient —_ [Inefficient Case
PPP s) Case Case Case Case Case
2001) 3,499] 39,434) 61,636] 5,323,871] 7,899,317 18,388,595,225] 26,957,886,450|
2002! 3,753] 41,844) 66,869] 5,531,186] 8,389,616] 19,384,714,950] 28,893,753,500|
2003 4,016) 44,377| 71,960] 5,743,125] 8,895,972) 20,412,807,724} 30,897,082,696|
2004| 4,289] 46,957| 77,223] 5,961,036] 9,418,395) 21,471,365,160] 32,968,054,644|
2005] 4,570] 49,649 82,655] 6,183,311] 9,957,203) 22,560,975,075] 35,108,426,950|
2006] 4,860) 52,361 88,287] 6,408,471] 10,510,519) 23,677,267,014} 37,314,690,420|
2007 5,160} 55,173} 94,069] 6,638,821] 11,080,788] 24,824,378,172] 39,592,206,984|
2008 5,469] 58,095] 100,081) 6,873,716] 11,669,280] 26,005,022,812] 41,945,118,264|
2009) 5,789} 61,131} 106,261) 7,115,269] 12,276,402] 27,221,858,855] 44,377,702,505|
2010/ 6,120] 64,279) 112,675] 7,362,296] 12,902,847] 28,475,151,060] 46,891,554,820
2011) 6,462] 67,451] 119,377] 7,614,050] 13,548,231] 29,763,301,796] 49,485,899,928)
2012, 6,815} 70,751] 126,195) 7,873,266] 14,215,746] 31,095,058,599] 52,172,989,693)
2013 7,182) 74,182] 133,359] 8,140,831) 14,907,287] 32,472,213,634] 54,956,729,846|
2014| 7,561] 77,747! 140,722) 8,416,890] 15,622,482] 33,896,603,542] 57,840,813,614|
2015) 7,955} 81,369} 147,422) 8,701,585) 16,276,103] 35,370,109,443] 60,829,491,254|
2016 8,363] 85,206] 152,403] 8,995,059] 16,760,418] 36,894,736,269] 63,926,800,519|
2017 8,785] 89,184] 157,455) 9,298,256) 17,259,948] 38,472,536,608] 67,137,352,076|
2018 9,224) 93,224) 162,657) 9,611,343) 17,773,338] 40,105,691,628] 70,465,625,012
2019 9,678] 97,490] 168,094] 9,933,670] 18,303,254] 41,796,433,328] 73,916,281,800|
2020| 10,149) 101,820] 173,606] 10,267,030] 18,849,156] 43,547,127,050| 77,494,502,313
2021] 10,638) 106,383) 179,359] 10,611,614) 19,412,972] 45,360,108,841) 81,205,253,925
2022| 11,145] 111,097) —185,359| 10,967,611) 19,994,172] 47,237,933,781| 85,053,948,431
2023[ 11,620) 115,387) 190,321] 11,266,170] 20,458,409] 48,933,914,547| 88,661,376,725
While comparing the results with respect to the differences between efficient
and inefficient cases, it should be kept in mind that, in each year, the efficient and
inefficient case values of each factor (e.g. RSE, UG, ICT) correspond to the same R&D
expenditure. That is, PPP $ 6,120 millions worth of R&D expenditure can be conducted
with 64,279 RSE and 112,675 RSE depending on the efficient or inefficient utilisation
of the RSE resources in performing R&D activities.
In order to compare the significant differences between the requirements of both
cases, the results which were tabulated in Table-3 are represented as bar charts in
Figures-7, 8 and 9.
200,000
180,000
160,000
140,000
20,000
woop IL
40,000
20,000
SEES EESESELE EEE ELL SS ESEEEOSOS
Figure-7 The Comparison of Efficient and Inefficient Cases With Respect to RSE
Requirement
As observed from Figure-7, the additional requirement of RSE due to
inefficiency assumes a range of values between 22,202 (in year 2001) and 74,934 (in
year 2023). Also, the calculations with the figures obtained from Table-3 suggest that
due to being inefficient in utilising RSE resources of the country in performing R&D
activities, Turkey should create, on the average, 5,000 RSE per year (assuming that the
rate of death of RSE is negligible compared to its rate of birth). This figure appears to
be (on the average) 3,000 RSE for the efficient case.
errr
aati
0
soe ¢ SF PP > Sr SP GP GP Pg? cP cP Pr
SILI IEF III IIHF FEES PLP SSS
Yeats
Figure-8 The Comparison of Efficient and Inefficient Cases With Respect to UG
Requirement
The additional UG requirement due to being inefficient rises up to 9,192,239 in
year 2023 from 2,575,446 in year 2001. Also, as can be calculated from Table-3 if
Turkey were efficient, on the average, it would require 250,000 graduates (also post
graduates) each year from universities in order to support the annual requirement of
RSE due to the annual increase in R&D activities whereas in the inefficient case this
figure rises up to some 500,000 (on the average) which will possibly require extra fixed
costs (building, equipment ...etc.) beside the variable costs (instructor, overhead
. etc.).
In fact the inefficiency of the country with respect to utilising UG may be due to
several reasons. One of the reasons may be the lack of sufficient level of training such
that 1 RSE can only be obtained from 2 UGs though the same level of RSE can be
obtained from only 1 sufficiently trained UG. One other reason may be that the
economic conditions may not be appropriate to invest in R&D activities and hence
existing UG can hardly be converted to RSE.
100,000,000,000,
90,000,000,000 =
0,000,000,000
70,000,000,000
60,000,000,000
50,000,000,000
40,000,000,000
FEE FEFS IES SIP POSSESS
veans
oe
8 oe
Figure-9 The Comparison of Efficient and Inefficient Cases With Respect to The
Requirement of ICT Expenditure
It can be seen in Figure-9 and calculated from Table-3 that through the year
2023 in the efficient case, the country gradually doubles the ICT expenditure to support
the same level of R&D expenditure when compared with the efficient case. The total
amount of additional spending (from 2001 to 2023) sums up to PPP $ 511 billions due
to being inefficient in utilising information and communications infrastructure in R&D
activities. This amount is more than the current GDP of Turkey even though the
calculation does not represent the net present value of annual expenditures. Note that
PPP $ 511 billions means, on the average, PPP $ 22 billions of yearly savings, part of
which can be allocated to R&D. However, allocating some amount of Savings from ICT
expenditures will lead to increases in R&D which in tum will require more ICT
expenditures. That is, improving the system will possibly require investing some
amount to various aspects of the system leaving only some portion of the savings to
allocate to R&D.
After illustrating the individual direct effects of improving the relation between
per capita ICT expenditure and per capita R&D expenditure, the individual effect of
improving RSE per population and UG per population is illustrated considering the
educational enhancement case explained above.
The projected UG requirements for the educational enhancement case that
correspond to number of RSE’s in the inefficient case are illustrated in Table-4.
Table-4 The UG Requirements For Inefficient Case And Educational Enhancement
Case
‘Required Number of UG
Inefficient |Educational
Case Enhancement
Case
2001| 7,899,317| 7,899,317
2002| 8,389,616] 8,389,616)
2003| 8,895,972 8,895,972|
2004| 9,418,395 9,418,395)
2005| 9,957,203] 9,957,203}
2006/ 10,510,519) 10,510,519)
2007| 11,080,788] _ 10,966,263)
2008/ 11,669,280] 11,433,839)
2009] 12,276,402 11,914,333
2010) 12,902,847] 12,409,194)
2011] 13,548,231 12,915,925
2012/ 14,215,746] _ 13,439,690)
2013/ 14,907,287] 13,980,006)
2014/ 15,622,482 14,538,689
2015] 16,276,103] 15,052,431
2016] 16,760,418] 15,440,275)
2017/ 17,259,948] 15,839,248)
2018/ 17,773,338] 16,249,539)
2019] 18,303,254 16,671,340)
2020] 18,849,156 17,104,841
2021| 19,412,972 17,551,066)
2022/ 19,994,172 18,011,070)
2023| 20,458,409 18,353,361
The results of inefficient case and educational enhancement case with respect to
UG requirements are illustrated in Figure- 10.
25,000,000
20,000,000
5,000,000
SSK HK SK SL SKS
YEARS
Figure-10 The Comparison of Inefficient And Educational Enhancement Cases With
Respect to UG Requirement
As can be observed from Figure-10, the UG requirement starts to differ for the
two cases in year 2007. The additional UG requirement (due to not investing in
education) appears to be 114,525 in 2007 (i.e. calculated using Table-4) and rises up to
2,105,048 in 2023. Also, in the educational enhancement case, starting from 2007, the
additional required UG drops from some 460,000 to 342,000 in 2023. However, in the
inefficient case, this requirement is sustained around the value of 500,000. This may be
interpreted as follows: investing in education increases the quality of human resources
in such a way that supporting increasing levels of R&D requires less amount of UG
compared to the case of poorly qualified human resources.
In order to estimate the levels of educational expenditures per university student
associated with the inefficient case, and educational enhancement case Table-5 will be
used.
In Table-5 it can be observed that the higher the per capita R&D expenditure,
the higher the expenditure per university student. So, it can be inferred from Table-5
that attaining high levels of R&D activity requires considerable investments in
education which means qualified human resources. In order to make a rough calculation
to get inference about the cost of the trade-off between improving the efficiency of the
system and leaving it on its own, the expenditure per university student of a country
which is around the level of Turkey’s projected per capita R&D expenditure will be
considered as the cost per student to have an efficient system with respect to the relation
between UG and RSE. Spain seems to be appropriate for such an assumption, which on
the average spend some PPP $ 5300 per university student. Mexico which invests PPP $
4,600 can also be assumed to represent the current situation of Turkey.
Table-5 Expenditures Per University Student For Various OECD Countries
(Constant PPP $s)
Expenditure Per Per Capita R&D
University Student Expenditure (C onstant
(Constant PPP $s) PPP $s)
ICOUNTRY 1993} 1994) 1995} 1993} 1994) 1995
iceland 6,252 o «| 265.73} 284.59] 301.12
[US _ ..| 20,946] 644.64] 637.04] 671.43
Switzerland ..{ 18,913} 18,998 % * vs
Canada 11,688] 12,328] 13,311] 269.85] 283.97] 281.54
Australia 11,529] 12,199} 12,866 »| 259.86 a
apan 8,078] 9,500] 9,747] 525.26] 517.68] 549.24
Netherlands| 9,082] 8,824] 9,550] 255.96] 268.82] 275.65
(Germany «| 9,050] 9,399 “ «| 411.33
Czech R. «(| 7,772] 9,151 .{ 119.41
IN. Zealand 7,654] 8,157] 8,340] 123.90 «| 124.74
Austria 8,951} 8,616) 7,954} 206.90) 216.21] 216.04
Finland OS «| 7,733] 325.15 «| 359.98
Hungary | 10,346| 8,459 6,344) i 7
[Korea «{ 6,300} 6,271 DS ® a
Spain «| 5,326] 90.23] 84.89] 92.77
Italy «| 5,020] 5,038 ES ws és
Mexico 6,150} 7,098} 4,698 8.43] 12.94] 12.86
Greece {3,118} 3,334] 28.78 «| 32.14
In order to calculate the costs of being inefficient with respect to utilising UG in
deriving RSE, it will be assumed that each additional UG (i.e. AUG = AUG; - AUG:1)
added to the stock of succeeding year will be the product of an educational system in
which PPP $ 4,600 is spent per university student. This figure is realised as PPP $ 5,300
additional UG in the educational enhancement case, however, it will be assumed for this
case that this amount will have been spent for the AUG who started education in 2001
and introduced to the market in year 2006.
The cumulative number of AUG from 2007 to 2023 appears to be 7,842,842 for
the educational enhancement case and 9,947,890 for the inefficient case. Assuming that
these people have been educated for 5 years, the associated costs for each case are PPP
$ 229 billions for the inefficient case and PPP $ 208 billions for the inefficient case. The
difference appears to be PPP $ 21 billions for the total of 23 years which results in PPP
$ 912 millions per annum which can also be allocated to R&D activities or utilised for
the betterment of educational system.
5. CONCLUSION
Though not proved appropriately, due to the insufficiency of the data, two basic
characteristics of the knowledge-based economy phenomenon have been illustrated in
this study. These characteristics are simultaneity and differences in efficiency. Being a
knowledge-based economy imposes on a country the requirement to perform well
simultaneously at a broad set of aspects, noting that the relations between these aspects
in terms of efficiency of utilisation of them may differ from one innovation system to
the other.
Also, it is implicitly put forward that, for the countries which are at the very
initial step of being knowledge-based, government expenditure in R&D forms the vital
element in stimulating the entities within the economy for taking part in the production
of knowledge. It should be noted that, knowledge creation is a process which goes
hand-in-hand with knowledge-diffusion. Hence, in this sense, stimulating the creation
also means stimulating the diffusion. Both these points are illustrated in applying the
model for the case of Turkey. It is observed, for projected R&D levels of the country,
that the required ICT investment rose in relation to the level of R&D investment.
Envelope functions, constituting a reference point for comparing the countries
with respect to the capabilities of their innovation systems allowed the researcher to
distinguish various OECD countries with respect to their utilising RSE, UG and ICT in
performing knowledge creation activities. They were assumed, in fact, to reflect the best
policy approach which the researchers prefer to adapt when considering national
innovation systems. However, it was noted that, in analysing the efficiencies of
countries with respect to the envelope functions in utilising RSE, UG and ICT, slight
deviations of utilisation paths from the envelope function due to the differences in the
populations, geographical dispersions and size of the economies should be taken into
consideration which may mislead the researcher. These effects on the utilisation paths
should also be considered while comparing countries with each other with respect to the
efficiencies in utilising RSE, UG and ICT.
Related with the envelope functions, the utilisation paths remain as the issue
which needs extensive research. Basically, the factors which affect the behaviour of the
functions for different levels of R&D and RSE, R&D and ICT, RSE and UG should be
investigated by the researcher who aims to test different development scenarios in the
transition process to a knowledge-based economy. Because the aim of the current study
is mainly to introduce a framework which is to be used in the assessment of various
strategies in transforming to a knowledge-based economy, Section 4 should be treated
as a first application of the framework proposed. However, with respect to the
application efforts there exist much to be studied. One important area closely related
with this issue is analysing the trade-offs between enhancing the system of innovation
(ie. the shape of the utilisation paths) or investing in R&D.
The initial results indicate that: (i) government R&D in Turkey should increase
to considerable levels in order to trigger the transition to a knowledge-based economy
and (ii) transformation towards a knowledge-based economy with an inefficient
innovation system may require considerable amounts of additional resources. It is
illustrated for the case of Turkey that any improvement in the innovation system
(moving towards the enveloping frontier) may lead to considerable savings in the
required human resources and ICT investments, given the same levels of R&D
investment.
The costs of transforming the country to an efficient state from an inefficient
state with respect to the utilisation of ICT infrastructure and human resources are also
investigated. It is found that due to inefficient utilization of ICT infrastructure in the
creation of knowledge (i.e. R&D activities) Turkey has to pay PPP (purchasing power
parity) $ 20 billions every year for 23 years on the average over the period analysed. On
the other hand, with respect to the utilization of human resources, it is assumed that the
investment in university level education has been the determining factor in the
utilization of UGs in knowledge creation activities, and the results indicate that Turkey
could have yearly savings of PPP $ 900 millions if she diverts her resources to obtain
qualified UGs. In addition to this, Turkey might have the capability of conducting the
same level of R&D activities with less UG if they are sufficiently well trained.
The results of the analysis also lead to the conclusion that the possible savings
arising from efficient utilisation of human resources and ICT infrastructure can be used
to increase investments in R&D. This may lead to the policy implication that efficient
utilisation of ICT infrastructure should be encouraged. Further policy implications may
be derived considering the fact that investing additional amounts in R&D would lead to
increased ICT expenditure, noting the fact that possible savings arising from efficient
utilization of ICT can only be partly allocated to R&D, the remaining of which should
be allocated to ICT again and to other aspects of the innovation system (such as
betterment of educational system in order to support the increased level of knowledge
creation activities). This is an outcome of the assumption of simultaneity reinforcing the
point that if Turkey is to increase her knowledge-based activities, she should perform
well in all of a broad set of areas rather than in only a part of these. So further research
is required in order to decide on which resources the estimated savings should be
allocated to. Also, the obstacles in projecting the utilisation patterns of UG, RSE and
ICT are topics which require further investigation.
Nevertheless, it can be concluded from the study that, Turkey, if she intends to
transform to a knowledge-based economy, should allocate much higher amounts of her
resources to R&D activities compared to its current level. While doing so, she should
prefer to invest in enhancing her innovation system rather than wasting money and time
to support an inefficient system.
REFERENCES
Dyker, A.D., S. Radosevic; 1999; “Building The Knowledge-Based Economy in
Countris In Transition - From Concepts to Policies”; SPRU Electronic Working Paper
Series; No:36; Brighton; UK.
Foray, D.; B. A. Lundvall; 1996; “The Knowledge-Based Economy: From the
Economics of Knowledge To The Learning Economy” in Employment and Growth in
the Knowledge-Based Economy; OECD; Paris; p.22.
Kim, S., G.; “Is Government Investment in R&D and Market Environment Needed For
Indigenous Private R&D in Less Developed Countries?: Evidence From Korea”; in
Science and Public Policy, V.26, N.1, pp.13-22.
Lundvall, B.A.; 1996; “The Social Dimension Of The Leaming Economy”; DRUID
(Danish Research Unit for Industrial Dynamics) Working Paper; No:96-1; Aalborg;
Denmark.
Lundvall, B.A., B. Johnson; (1994); “The Learning Economy”; Journal of Industry
Studies; V.1, N.2, December; pp.23-42.
Matthew, J. (1996): “Organisational Foundations Of The KBE”, in OECD, Employment
and Growth in The KBE; Paris, p161
OECD, 1996; “The Knowledge-Based Economy”; OECD; Paris; 1996; p.9
OECD, 1999; “OECD Science, Technology and Industry Scoreboard 1999 -
Benchmarking Knowledge-Based Economies”; Paris; p.7.
OECD, 1999; “The Impact of Public R&D Expenditure on Business R&D”; Paris.
OECD, 2000; “Information Technology Outlook 2000”; Annex 3, Definitions and
Methodology; Paris.
Vonortas, S., N.; 2000; “Technology Policy In The United States and The European
Union: Shifting Orientation Towards Technology Users”; in Science and Public Policy,
V.27, N.2; pp. 97-108.
Back to the Top
