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Master programme in Economic Growth,
Innovation and Spatial Dynamics
The multiple relationships among innovation, human capital and economic
growth
An empirical study of Sweden
Chao Li
[email protected]
Abstract:The impact of innovation and human capital on economic performance has
drawn more and more attention. This is greatly due to the strong trend of globalization
and technology development, which characterize the economy as technology or
innovation oriented developing pattern. Human capital and innovation are considered
as the endogenous factors for supporting the long run economic growth in the new
growth theories. In the thesis, we emphasized the multiple relationships between
innovation, human capital and economic growth. In the quantitative analysis, the
econometric test basically supports the conventional theories about human capital and
innovation’s significant positive influence on economic performance in the long run.
Thus promoting innovation’s development would be the top priorities for both policy
makers and individual firms. In the short run analysis, the Swedish empirical case
implies that economic level and old knowledge’s impact on innovation’s creation is
tremendous. What’s more, we should also notice that there is an obvious time lag for
human capital and R&D investment to significantly affect innovations’ development.
Key words: Innovation, human capital, economic growth
EKHR22
Master thesis (15 credits ECTS)
June 2011
Supervisor: Anders Nilsson
Examiner: Mats Olsson
Website www.ehl.lu.se
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List of Contents
1. Introduction...........................................................................................................................4
2. Literature review ...................................................................................................................5
2.1 Analysis of the three key concepts .............................................................................5
2.1.1 Definition of Innovation ..................................................................................5
2.1.2 Innovation system and innovation capability: innovation’s
formation ......................................................................................................7
2.1.3 Human capital: the measurement and properties ............................................9
2.1.4 Concept of economic growth ........................................................................10
2.2 The relations between the concepts .......................................................................... 11
2.2.1 Innovation and economic performance ......................................................... 11
2.2.2 Human capital and economic growth ............................................................14
2.2.3 Human capital and innovation performance .................................................17
2.2.4 Approaches for studying multiple relationships between human
capital, innovation and economic growth.....................................................18
3. Empirical study ...................................................................................................................19
3.1 Model construction ...................................................................................................20
3.1.1 The long-run relationship: cointegration .......................................................20
3.1.2 The second model of multiple regression......................................................21
3.2 Data ..........................................................................................................................22
3.3 The analysis of long-run relationship .......................................................................25
3.3.1Economic performance and human capital (hypothesis 1).............................25
3.3.2 Economic performance and innovation (hypothesis 2) .................................29
3.3.3 Human capital and innovation (hypothesis 3) ...............................................31
3.4 Short-run dynamic regression with distributed lags .................................................31
3.4.1dynamic model with distributed lags..............................................................31
3.4.2 Residual test ..................................................................................................33
3.4.3 Interpretation of the regression .....................................................................34
4. Conclusion and policy implications ....................................................................................35
Reference ................................................................................................................................38
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List of tables and graphs
Graph 1- Productivity in Sweden from 1959 to 2000............................................. 26
Graph 2- Graph 2 ln(TFP) of Sweden from 1959 to 1990 ..................................... 30
Graph3- Normality test ......................................................................................... 34
Table 1- Cointegration result .................................................................................... 28
Table 2- Cointegration report for tertiary education gowth ........................................ 28
Table 3- Cointegration report for aggregated education growth ................................. 29
Table 4-Cointegration report for TFP and economic performance ............................. 30
Table 5- Cointegration report for TFP and human capital .......................................... 31
Table 6- The regression of the patent development ................................................... 32
Table 7- The modified version of patent development regression .............................. 33
Table 8- White test for the heteroskedasticity............................................................ 33
Table 9- LM test for autocorrelation ......................................................................... 34
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The multiple relationships among innovation,
human capital and economic growth
An empirical study of Sweden
1. Introduction
The fact that innovation has a decisive role in determining an economy (both at firm’s
and aggregate’s level)’s competitiveness and long-run sustainable development has
been gradually recognized by scholars and policy makers. The reasons for this
agreement can be mostly attributed to the globalization (Research and innovation in
Sweden: an international comparison, 2008). According to the report, the globalization
on the one hand promotes the integration of economic actors around the whole world
creating a more competitive environment which requires strong innovation abilities for
both individual firms and countries; on the other hand, the pressure in form of limited
resource and fragile ecology become to be more serious as the globalization
strengthened and rapid economic development, which also needs the innovation to ease
such pressure. From the perspective of economic growth, Augusto López-Claros et al
in their book “The innovation for development report2010-2011:innovation as a driver
of productivity and economic growth” argues that the tradition production input factors
like labor, raw material and capital goods do not play the dominant role in production
as before, on the contrary, they give way to some intangible capital, such as intellectual
resource, new knowledge and innovation capability.
Similarly, there also exists
consensus about human capital’s contribution on economic growth. Different studies
have offered evidences that the education attainment, school enrollment rate growth
and literacy rate etc have the significant influence on the economic performance and
there is also clear causality relationship between education’s quality and successful
economic development (Leandro Prados de la Escosura & Joan R. Rosés, 2010). The
impact can be realized through different channels: the high level of human capital can
promote labor productivity, spill over and diffuse new knowledge and create good
social atmosphere. The importance of human capital lies in it improves the quality of
labor which makes the production function more efficient given certain amount of
factor input, and at the mean time, since the useful skills can also be valuable in other
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firm besides the original workplace (Gary Becker, 1975), the labor mobility would also
improve the general productivity level of the whole region. Because both innovation
and human capital are crucial to the endogenous economic growth, it is quite intuitive
to assume there is correlation between innovation and human capital. Obviously,
human capital is the prerequisite for innovation, since knowledge and skills nested in
human would be the basis for creating the new one; on the other hand, innovation’s
appearance would bring the human capital to a higher level due to the new
knowledge’s input.
This thesis will try to present the three closely-related concepts: innovation, human
capital and economic growth. My objective is to explore the causality relationship and
mechanism between each two of them. There are two key issues in the thesis: firstly,
the important role of human capital and innovation in the endogenous economic
growth; secondly, elements in the innovation system and their contributions to the
innovation capability. Beside the theoretical analysis, I will also make the quantitative
investigate to test if the empirical case follows the theoretical hypothesis.
The thesis is organized as follows: the first part is literature review and theoretical
analysis including two sections: section one will introduce key concepts of the paper
and present related studies; section two will focus on the relation between them and the
mechanism of constructing their causality relationship; the second part will apply the
econometric approach to empirically analyze the Swedish case in both short and long
run perspective. In the long run, the cointegration model will be built to analyze three
pairs of relationships between economic performance and human capital growth as
well as innovation, and technology development and human capital growth; in the
short run, the multiple linear regression with distributed lags will be applied to
investigate different contributions of the input factors to the innovation. Finally, the
third part will summarize both qualitative and quantitative analysis in favor of getting
the main points of the thesis as well as suggestions for the policy makers.
2. Literature review
2.1 Analysis of the three key concepts
2.1.1 Definition of Innovation
The concept of innovation can be defined from different perspectives: Anna Sandström
et al (2000) define the innovation in the Schumpeter classical way which focus on its
property of commercial value and industrial application and classifies it in four
dimensions: a new product; a new process; a new market and a new form of
organization. From the perspective of function and purpose, “Innovation was
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intrinsically about identifying and using opportunities to create new products, services
or work practices” (Hsing-Kuo Wanga et al 2008: p1191), therefore, the process of
achieving innovation is “equated with the continuing pursuit of harnessing new and
unique knowledge” (Hsing-Kuo Wanga et al 2008: p1191). To be more in detail, the
difference between innovation and invention indicates the crucial properties of
innovation: the innovation is not only a new idea or knowledge, but also successfully
transformed into the real application and commercial use (Rajiv S. Narvekar & Karuna
Jain, 2006). There might exist a long time lag between the invention and innovation,
reflecting the process from proposing the new idea, implementing them and finally
commercializing them. The strong economic implication and market-oriented
properties would be main difference between innovation and invention, which also
marks if the innovation is a successful one. Different types of innovation will also have
diversified impacts on the economy and social development, for example: the product
innovation, which means the product with new function and better quality, may have a
more clear effect on the economic performance and the effect will emerge soon after
new products entering market; on the other hand, the process innovation may have
ambiguous effect on economy which would be observed in a long term since it does
not directly provide new products but focus on improving the efficiency during the
production process (Jan Fagerberg et al, 2004). Besides these two common types, the
intangible innovation, such as new market or new organization, would be even more
usual and important for firms. Since on one hand developing product innovation is
actually very risky requiring great amount of financial and capital resource, however a
new organization or institution would be easier implemented without much cost; and
on the other hand, the new product’s ability of making profit and achieving higher
productivity is also hard due to the strong competition especially in its initial
developing period, but new organization and market would realize better resource
allocation and provide more effective incentives to the actors in a comparatively short
run.
According to the impact of innovation on the technological developing trajectory,
innovation can also be classified into incremental and radical change. As Hsing-Kuo
Wang et al (2008) argue, the difference between the two types lies in if it reinforces or
transforms the existing knowledge. The incremental one will maintain the basic
framework of the original knowledge and improve and reinforce it; on the contrary, the
radical innovation will lead to fundamental change to the old knowledge, and
sometimes even treat the old knowledge as obsolete and destroy it. Without a doubt,
the two forms of innovation will have quite different impact on the labor market,
unemployment rate and the general economic development. From this perspective, it
can be deduced that it is not always the situation that innovation promotes economic
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development, since sometimes radical change will directly lead to some industry’s
decadency and serious unemployment as well as great structural change. Therefore
such transition period has negative influence for the old industry and labors working in
it and its effect on economy would be ambiguous.
2.1.2 Innovation system and innovation capability: innovation’s formation
It is quite clear that the concept of innovation presents the properties of the final
product rather than the process of creating it. A lot of studies have shown that
innovation’s formation and development is not depended on single factor but a
complicated systematic interaction (D. Doloreux, 2002). It requires that the individual
actors like education institution, firm and public sector have efficient and wellorganized cooperation to achieve this goal. Therefore, in this part the system consisted
of different actors and correspondingly its capability to innovate will be analyzed.
The innovation system, which is always discussed in form of regional innovation
system or national innovation system (here the regional innovation system and national
innovation system are treated as they have similar properties but different in size, as D.
Doloreux pointed out that the former one can be seen as the subset of the latter one), is
defined as “in which firms and other organizations are systematically engaged in
interactive
learning
through
an
institutional
milieu
characterized
by
embeddedness”(Cooke P et al 1998: p1581). Three key factors of this definition are
highlighted: the “interactive learning” indicates that knowledge exchange and
transform in an innovation system is in a multiple and collective way; “milieu” means
that the environment for the innovation system is complicated and consists of
regulations and institutions; “embeddedness” points to knowledge and economic
achievement produced by the innovation system (D. Doloreux, 2002). Since the system
contains multiple actors, there are different views for indicating the key elements in the
innovation system. Doloreux (2002) summarizes six important elements in the national
innovation system: (1) “the internal organization of firms”, (2) “the inter-firm
relationship”, (3) “the role of the public sector”, (4) “the institutional set-up of the
financial sector”, (5) “R&D intensity and R&D organizations” as well as (6)
“knowledge infrastructures” ((Doloreux 2002: p 246), p 247). Generally, the business
actor, i.e. the firm, is the main protagonist for outputting innovation; education and
financial institutions offer the necessary financial and human resource to the process of
creating new knowledge; and government is the policy makers in favor of the
innovation system’s operation. Another factor that closely related to innovation system,
which recently has drawn much academic attention, is the geography. The most famous
concepts from the line of geography might be cluster and agglomeration. As Jaakko
Simonen (2008) argued, the two concepts “emphasize the role played by interactions
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between firms and their local environments” (Jaakko Simonen et al 2008: p147).
Extending from the two concepts, the newly proposed components closely related to
geography are factors such as local labor market and local entrepreneurs (Jaakko
Simonen, Philip McCann, 2008). The reason for emphasizing factor of geography is
that small firms or the start ups are the main actors that produce innovations and since
small firms have limited financial and social resource and normally are more
vulnerable, the cooperation with local actors like other firms and institutions would be
crucial for their growth.
The quality of the system would be reflected by its capability of innovating. Since the
essential part of innovation system is creating new knowledge through interactive
learning, exchanging knowledge and information as well as full access to the useful
resource, etc, both the internal and external factors that can improve the situation of
interactive-learning and problem-solving skills would be helpful for the innovation
capabilities. Chen Tiejun et al (2006) argue that effective interaction with suppliers,
customers as well as other business-related organization can form the complement
effect of providing the “missing input” which can enhance the firm’s innovation
capability; Rajiv S. Narvekar et al (2006) have argued that the internal driver like “inhouse technology developed by R&D department, top management vision to initiate
change or even employee’s personal initiatives” (Rajiv S. Narvekar et al 2006: p178) as
well as external driver like “market requirements, the degree of market competition,
and the geographical location of the firm” (Rajiv S. Narvekar et al 2006: p178) both
can influence and characterize the system’s innovation capability; Aija Leiponen has
argued that the “interaction and feedback processes among activities within the firm as
well as between the firm’s internal and external sources of knowledge” (Aija Leiponen
2005: p304) is decisive to the creation of innovation; according to Anne Kallio et al
(2009), the “absorptive capability” and “social capital” would be the key factors
determining if it is a successful innovation story. The former one points to the ability to
learn, assimilate and apply the new knowledge. Zahra and George (2002) further
classify this ability into two levels: the “potential absorptive capacity” which focus on
assimilating and imitating; “realized absorptive capacity” will be useful for
transforming the knowledge to the system or region’s own need. The later one, social
capital, refers to some privilege to the certain groups. It helps the group to access some
useful and important social resource and at the meantime limit other groups to access
to it. A common example of it is the privilege based on the family or friend relationship
that can be beneficial to the close-related firms and form the competitiveness based on
the exclusive resource. Such social capital will promote and reinforce these specific
groups’ innovation ability, however, it may also harm other networks’ benefit since the
block of accessing social resource. Therefore, Anne Kallio et al (2010) proposed to
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create “bridging social capital” to open the network allowing information and resource
flow, which not only benefit the individual network but also create a good environment
for others.
All these arguments emphasizes greatly on the importance of accessing the resource
and the efficiency to utilize these resource. The former one seems quite straightforward
by establishing social capital with other actors in the system to acquire amount of
information, knowledge and financial resource. However, the latter one is implicit to
achieve though a lot of scholars propose through strengthening the firm’s absorptive
ability and problem-solving skill. The problem is what factors would determine firm’s
capability of absorbing and transforming? I assume that the firm’s internal human
capital stock and its quality would be dominant for these capabilities though necessary
financial resource like R&D investment is also crucial. The successful cluster such as
Silicon Valley in the US and Zhongguan Cun in China both provide the evidences for
this argument. The former one’s success is due to the close cooperation with Stanford
University and the latter one also has similar situation due to the proximity with
Beijing University and Tsinghua University.
2.1.3 Human capital: the measurement and properties
As Theodore W. Schultz (1961) argued, for quite a long time, the human’s skill and
knowledge were not treated as some form of capital. The priority of assuring human’s
freedom without any bondage makes it difficult to consider human resource as capital
goods and asset. Such a bias may lead to the problem of incorrectly underestimate the
human’s impact on the economic development. However, the truth is that “an
appropriate accounting of the capital in society would show that human capital
accounts for a much greater portion of the total capital stock than does physical
capital” (Lazear Edward 2002: xviii). Human capital has different terms of definitions:
Epj Kleynhans describe it as “those elements in humans that enhance the quality of
labor, such as skills, knowledge and wisdom, which make it worth more in the
production process” (Epj Kleynhans 2006: p55). Roos et al. (1998) defines human
capital in a more broad way which not only includes the conventional elements like
skill and knowledge but also attitude and agility like personality, motivation and
adaptation. Beside the general view of human capital, some scholars consider it within
the scope of an organization, such as an individual firm. Therefore the concept is
expanded to “the human capital of an organization”, which will be “firm-specific”,
“employee-related” and “job-related” knowledge and ability stock within the
organization. The employees’ knowledge, skills, personality and staff construction
would reflect the general character of the firm’s human capital quality.
Carmen Cabello-Medina et al summarize two properties of human capital: the value
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and uniqueness. The former one is necessary factor input for production function,
emphasizing the impact of knowledge and skills on creating market values and profits
through the normal production process; the uniqueness points to the employees’
“irreplaceable” and “idiosyncratic” characters since each individual has specialties and
unique personalities. The divergence of human capital characters is basis of generating
new product.
Like physical capital, the human capital is formalized by investing in human. And for
most of the cases, the investment is in the form of education. The investment is urged
by education’s returns. A lot of studies have acquired global evidences that labor with
more qualified education background would earn more than others in the long run.
Similar to the approach of evaluating the physical capital, we may also measure human
capital by estimating its cost of investment and correspondingly benefits to the
education. As Schultz pointed out, the cost of education includes fees for tuition,
facilities of school and school’s operation, and most importantly for the individuals, the
opportunity cost, which is the foregone income accounting big proportion of the whole
cost expenditure; as for the return, which is normally positively associated with
productivity and level of education, is calculated by the benefit offset by the cost.
Although it is hard to estimate the opportunity cost, the rate of returns is still
outstanding. According to Schultz’s estimates, even the lower limit of return to the
education is close to physical capital investment. To sum up, the knowledge and skills
acquired by investing in human though “can’t be sold”, it affects this individual’s
income, and the increase of earning would be closely correlated with yield of education
investment (Schultz, Theodore W, 1961)
2.1.4 Concept of economic growth
In the The Oxford Encyclopaedia of Economic History vol2, the economic growth is
defined as the “increasing ability of a society to produce goods and services and to
satisfy consumer wants” (Crafts, N.F.R.2003: p137)
And the author explains the two main factors that affect such ability: one is the
accumulation of labor and capital and the other is the advanced technology, economies
of scale as well as organization efficiency improvement (Crafts, N.F.R.2003). From the
long-run and historical perspective, the economic growth is often with the economy
structure change from low productive sector to the high one. For example: from the
agriculture sector to industry and finally shift to the service-based economy. Besides,
economic growth is also characterized by better education level, technological progress
and more sophisticated product.
Entering the 1990s onward, one of the most important characters of international
economy pattern is the globalization, which is unprecedented and intensified.
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Evidences for globalization have been collected from different perspectives, such as
the increase of foreign direct investment and international capital mobility, as well as
more and more integrated capital and commodity market (Paul W. Rhode and Gianni
Toniolo, 2006). The strong trend of globalization is generated on one hand through
more flexible and open regulations, and on the other hand, the transportation progress
which greatly saves the time and reduces the cost for transporting. Under such
circumstance, each country’s economic growth is no more an independent phenomenon,
but affected by the global atmosphere. The sources of economic growth, such as capital
investment, technology diffusion, and better qualified labors, etc are more diversified.
Another important feature in the modern economic growth is the technology’s
appearance represented by the information and communication technology (ICT). The
surge of US productivity from mid 1990s has been greatly attributed to the information
technology’s development (Paul W. Rhode and Gianni Toniolo, 2006). Economists
have reached the agreement to some extent that the ICT is gradually evolve to be a
“General Purpose Technology” (GPT) since it is pervasively adopted by all kinds of
business sectors not only within the IT industry, and it effectively reduces the cost and
improves the productivity, and most importantly, it may promote innovation in the
related industry and it has quick self-updating speed, which would stimulate the labor
market and new market demand. From the macro or global perspective, the impact of
ICT is so called “death of distance”, which means the spread of information technology
connects all the regions in the world and further strengthened the trend of globalization.
Compared with economic growth in the previous period, the modern economic growth
is more world-integrated and technology based requiring the country’s capability of
innovating as well as attracting external resources.
2.2 The relations between the concepts
2.2.1 Innovation and economic performance
The relation between innovation and economic activity can be traced from Adam
Smith’s famous example of pin factory. He pointed out that labor division and
invention of the new machines would be the main reasons attributing to productivity
growth in the pin industry, as he stated in the book of The Wealth of Nations, “This
great increase in the quantity of work, which, in consequence of the division of
labor, …is owing to three different circumstances; first, to the increase of dexterity in
every particular workman…and lastly, to the invention of the great number of
machines which facilitate and abridge labor, and enable one man to do the work of
many”(Adam Smith The Wealth of Nation: P 14). The basic principles of the argument
is that: firstly, labor division will reduce the worker’s scale of business and make them
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focus on one special field of the production process, which will improve their working
skills and dexterity; secondly, under the circumstance of labor division, workers can
more easily to invent and implement innovations on the basis of the old machines and
technologies. Just as Adam Smith observed, “a great part of the machines made use of
in those manufactures in which labor is most subdivided, were originally the inventions
of common workman, who, being each of them employed in some very simple
operation, naturally turned their thoughts towards finding out easier and readier
methods of performing it” (Adam Smith The Wealth of Nation: P 17). The
sophisticated skills and progress acquired as the consequence of labor division improve
the productivity, which thus contribute a lot to the pin industry. Smith’s empirical
observation also implies one important approach, or the prerequisite, of achieving
innovation, i.e. accumulation of the working experience on one special field. From this
perspective, it does not require formal knowledge or even literacy since innovation is
from human’s learning and inference ability. Joel Mokyr (2005) argues that the premodern economic growth in Europe before 1750 was oriented from the institution
change such as regulations and law through which “commercial relations” was
established in favor of reinforcing business trust and credit and the technology progress
was not obvious and limited within a narrow “localized” scope. The character of such
economic growth is it is quite unstable and vulnerable to external shock; however, the
modern economic growth driven by the Industrial Revolution brings new type of
technology-driven growth which is more sustainable and strong. It could be said that
the radical innovation, i.e. the First Industrial Revolution totally changed world
economy pattern making the west Europe the richest region (Joel Mokyr, 2005).
The pioneer of innovation theory, Joseph Schumpeter, constructs the relation between
innovation and economic performance through the process of “creative destruction”,
which means “…incessantly revolutionizes the economic structure from within,
incessantly destroying the old one, incessantly creating a new one” (Schumpeter 1942:
p 83). He points out that the entrepreneurs, who are willing to afford the risk to
propose new idea, develop new product and invest and explore the commodity within
the existing technology and knowledge scope, are the fundamental actors as well as
force to stimulate the economy’s long-run development. The term “creative
destruction” indicates that such process is trying to fight against the “prevalence of
inertia” (Jan Fagerberg et al 2004: p 21), or break the equilibrium in order to create the
new opportunities for the economic growth. Erik Dahmén proposed the concept of
development blocks which plays the role of changing original economic structure and
guide entrepreneurs’ choice. Schön (2008) further illustrate the detailed process of such
“creative destruction”. He defines transformation and rationalization to present process
with different economic structural characters. During the transformation process, it is
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“resource demanding” and investment profit is also quite uncertain. From the
perspective of new technology, during this process, the technology has not been widely
spread with imperfect function and high price; during the latter period of
rationalization, the new structure of industry and labor has formed representing a more
advanced production level and the technology is becoming more standardized with
lower price. Philippe Aghion and Peter Howitt explored the endogenous growth model
from the perspective of Schumpeter’s innovation theory. The model introduces the new
concept, which emphasizes the process that new technology replaces the old one,
namely, the obsolescence. The technology becomes to be obsolescence when it lost the
advantage of holding monopoly rent due to the new generation of technology
developed by the new turn’s research. According to Philippe Aghion and Peter
Howitt’s theory, the amount of research oriented from market will determine the
period’s economic growth, and since “the relationship between the amount of research
in two successive periods can be modelled as deterministic” (Philippe Aghion & Peter
Howitt 1992: p 324), the economic growth can be functioned by one period’s research
and such relationship.
Alfred Marshall proposed the theory of spillover and externalities to help understand
innovation’s impact on economy. He argues that process of interactive learning may
generate positive externalities for other firms involving in the industry and thus
improve the general level of whole industry’s productive level. Similar arguments
mentioned by Stokey and Griliches also show that process of creating innovation will
expand the knowledge stock and enhance knowledge flow through the spillover effect,
which will strengthen the region and industry’s competitiveness. The externalities are
generated since business secret is hardly kept and imperfect patent system thus
spillover is unavoidable; taking one step back, even without creating the real new
product, the learning and exchanging process will also be helpful to the knowledge
transfer.
Bengt-Åke Lundvall et al further illustrate the new product’s importance for both firms
and whole economy. New product offers new function, better quality or cost-reduction
which will stimulate new market demand. The growth of demand will not only
maintain and enhance the firms’ position and competitiveness but also promote the
employment and thus increase general income level, and sometimes radical innovation
will also bring about the structure change or industry shift which will totally change the
old economy framework and resource allocation. Bengt-Åke Lundvall et al also
emphasized the importance of process innovation, since without innovated producing
process, economy will step into stagnation and fall into the trap of “technological
unemployment”. From the perspective of macro economics, Kenneth Arrow argues that
because the new technology has greater productivity than the old one, investment in
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more advanced capital will lead to the higher productivity at the aggregate level
(Bernard Marr, 2005). Not only limited within the scope of production innovation or
process innovation, Kirchhoff et al argues that new business or organization brought by
innovation will also promote employment and new industry’s development, which may
spur the economy development.
2.2.2 Human capital and economic growth
Human capital’s impact on economic performance has been confirmed by numerous
studies. Barbara Sianesi et al summarized that “increasing school enrolment rates by
one percentage points leads to an increase in per capita GDP growth of between one
and three percentage points every year” and “Increasing average education in the
population by one year would raise the level of output per capita by between three and
six percent” (Barbara Sianesi and John Van Reenen 2003: p159) .This result is
estimated by adopting augmented Solow model and the approach of new growth model
will lead to a even more greater result.
Mokyr argued that the fundamental reason for the Western Europe’s rapid economic
growth lies in the “useful knowledge”, as he stated, it is because they “know more”.
The amount of knowledge expanded rapidly at the aggregated level during industrial
revolution. The expansion brings about “Greater specialization, professionalization,
and expertization” (Mokyr Joel, 2005), which is considered as the initial engine for the
modern economic growth. The author further points out that the reduction of access
cost is the main reason for the great knowledge expansion. Since if the access to
knowledge is confined to limited groups of people, the knowledge diffusion and
creation would be inhibited; on the contrary, open access will lead to more
opportunities of new technologies and invention’s emergence.
David Mitch (1990) emphasizes the importance of formal education by arguing two
basic points: firstly, technology has been more and more important for underlying
modern economic growth, and secondly the technology progress is essentially pushed
by the basic science research. Therefore, the inference can be made that the
fundamental factor to promote modern economic growth lies in investing in education
to fuel the “science-based technology”. Besides the formal education, David Mitch also
emphasized the importance of on-job training and points out that it has the substitute
effect with the formal education. Anders Nilsson also points out that the vocational
education and on-job training has positive effect on promoting labor productivity and
comparing to the formal education it has a more direct and specified effect on
productivity. However, the paper also points out the difficulties to establish clear model
between education and total economic growth due to the nonlinearity and complexity
although evidences about the productivity enhancing effect by the vocational education
are observed. The different components of education such as vocational training and
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formal education have different mechanisms for promoting economic development: the
former one focus on the short run and direct influence with firm and field specific
character, and the latter one will have the long-run impact and for most of cases the
effect might be indirect and more general.
The most famous and tradition economic growth theory is the Solow growth
(neoclassical growth model) theory proposed by Robert Solow. The model is based on
the Cobb-Douglas production function: Y=AKαL1-α, in which Y is the total output, L
and K are input factors of labor and capital respectively, α is the weight for the factor
of capital input. Since α is less than 1, there are diminishing returns as the capital
increases and accumulates. The function can be further rewritten as the growth
accounting form of ΔY/Y=α*ΔK/K+(1-α)*ΔL/L+TFP (Y, L and K have the same
meaning as the formula above, TFP is the total factor productivity pointing to the
residual after accounting all the other input factors and it is usually used as the
measurement of technology progress). The advantage of neoclassical model is of
course it decomposes the economic growth into different components, which will be
easily for analyzing different factors’ contribution to the output growth. In order to
diminish the proportion of residual in the growth accounting there are several studies
making progress by improving quality of the collected data and refine the input factors.
Barbara Sianesi et al argued that the neoclassical growth model can’t explain the
causality between education and economic level and there is no “potential indirect
effects” of the education on economic performance reflected in the model. However,
since it is good for making the cross-country analysis, Robert J. Barro argues that it can
be used as the complementary when applying the new growth theory.
.
Paul M. Romer (1986) proposed the endogenous growth model “in which long-run
growth is driven primarily by the accumulation of knowledge by forward-looking,
profit-maximizing agents” (Paul M. Romer 1986: p 1003). The progress made by this
new endogenous model is it solves the puzzle of diminishing returns as the capital
stock increases in conventional theory. The main problem of the diminish return is it
can’t explain what has happened in the real economy life. According to the tradition
theory, countries with different capital and income level should be gradually converged,
since diminishing returns will lead the capital flowing to the more profitable regions,
such as the low capital-stock regions (Lazear, Ed (ed.), 2002). Continuous capital flow
and investment will stop unless no imbalance existing among these regions. However,
there does not exist an obvious convergence trend from the global perspective; on the
contrary, there are sustained and increasing-returns to the capital investment in the
long-run in some of advanced regions. Therefore the conventional exogenous theories
have the flaw to interpret such long-run economic growth. Two key points of the
endogenous theory can be summarized as: 1. “knowledge may have an increasing
16
marginal product…knowledge will grow without bound” (Paul M. Romer 1986: p
1003); 2. Positive externalities would be generated during the knowledge production
process. It could be said that the mistake of diminish returns to capital accumulation
lies in it exclude the human resource or knowledge as the capital, and much of the
increasing returns might be attributed to intellectual capital. The advantage of
endogenous theory is it not only treats human capital and innovation as endogenous
engine for economic growth which are determined within the market-oriented
framework but also reflects the very important externalities of education to economy.
Anders Björklund and Mikael Lindahl (2005) analyzed education’s effect on economic
performance. According to them, the effect can be divided from the perspectives of
internal and external. The internal effect mainly includes the income/production effect
and effect on the life expectancy and children. The internal emphasizes the effect
within the individual. Through investing in human capital, formal education or on-job
training will enhance people’s working skill and professional knowledge thus
representing higher production ability and income. For most of the time, the income
effect will also combined with the so called “signaling effect”, which means that
investment in human capital is not purely for improving productivity but also signals
the people’s capacity, intelligence and social background etc since sometimes the
education is not directly linked to work. Effect on life expectancy and children is easy
to understand since higher education will bring more knowledge about health to people
and generally improves people’s living standard as well as more probabilities of
bringing good education and family atmosphere to the children. All these advantages
would contribute to the economy from the individual’s perspective. For the external
effect, there are different forms and mechanisms. Anders Björklund et al point out that
people with high education level can transfer their knowledge to the coworkers, who
will then improve the whole workplace’s productivity, and sometimes even innovation
will be created during the interaction process, which opens opportunity for new
streams of economic growth. Besides, outside production sector, the authors argues
that higher education will “lower crime rate” and promoting democracy. In a more
broad sense, education may help to form the “social capital” which is would benefit to
the long-run society development (Anders Nilsson, 2010). This kind of “social capital”
would be useful for creating democratic political institution and effective legal system
for the country; and also can improve individual’s ethics and personality. It is difficult
to measure such effects since they do not have direct economic consequence, however,
my assumption is that these indirect effects on society constitute great part of the social
returns. As I mentioned above, for a lot of cases, education does not have close relation
to the future career, and knowledge and skill developed during the education may also
be irrelevant to job. But highly educated population would help improve the quality of
17
economic and social development in the long run. It might also be the reason that the
social returns of education are larger than aggregated individual returns.
2.2.3 Human capital and innovation performance
Although human capital is conventionally considered to be crucial to the innovation
development currently, there are some controversial issues about it in the early time.
One of the typical issues raised by the commentators is the effect of de-skilling brought
by capital substitution. As advanced machines substitute labor to a larger extent, there
is less demand to the professional and skillful workers and it seems that new
technology will have little to do with human capital accumulation; however, after
1980s, the wage gap between the skillful workers and non-skillful one is gradually
enlarging which indicates that the demand of higher quality of labor increases (Jonas
Ljungberg, 2004). Jonas argues that the relationship between skill and new technology
experienced from substitution to the complementary since more complicated
technology and work require more professional knowledge and skills. From the
perspective of firm level, the firm’s ability to develop new product and service has
close connection to the quality of the firm’s human capital stock since “employees with
valuable knowledge and skills are positively associated with innovative capacity,
because they contribute to the identification of new market opportunities, and
employees with such knowledge are willing to experiment and apply new procedures”
(Carmen Cabello Medina et al 2011: p 809). At the mean time, employees with
excellent human capital usually have the better receiver competence and are “more
flexible” in leaning and transforming the new knowledge into its own need (Carmen
Cabello Medina et al, 2011). Carmen Cabello-Medina et al also emphasize that
employees with special knowledge and skill will contribute the unique and precious
market value for the firm and since it is rare and hard to imitate and transfer, it
enhances the competitiveness of the firm. Aghion and Howitt (1998) point out that the
relationship between human capital and innovation is a two-way causal process.
Human capital is one of the decisive factors affecting innovation’s development, and at
the mean time, market opportunities brought by the successful innovation will promote
the next turn’s human capital investment and accumulation. This means we should
“integrate innovation and capital accumulation into a single framework” (Jinli Zeng
2002: p 542). Brunello Giorgio et al (2007) empirically study the case of US to explore
to what extent the talent contributes to the innovation performance from 1970 to 2000.
And the result shows that in the US increasing the proportion of PHD by 3% will lead
to 1% increase in innovation (measured as the weighted patent). As the thesis argued in
the former part, innovation is operated in a systematic way involving multiple actors in
it, human capital have to interact with other factors very well in order to promote the
18
innovation. Anker Lund Vinding emphasized the “complementarity between internal
capability and external collaboration” (Jesper L Christensen et al 2004: p 157),
indicating the necessities of combining the internal human capital and R&D investment
as well as the external resource and communication. This assumption implies non
linear relationship between human capital and innovation.
To sum up, we can simply interpret the relationship between human capital and
innovation through three stages: firstly during the poor technology period, they have
the substitute relation, and then it gradually evolve to be a complementary process, and
finally the modern technology and economy environment lead to an interaction and
interdependence relationship between them. The more and more complex relationship
is established as the technology becomes to be more sophisticated and knowledge
stock expands to an unprecedented level.
2.2.4 Approaches for studying multiple relationships between human capital,
innovation and economic growth
There are several models proposed by the economists to illustrate the multiple
relationships between the three factors. The first generation of the model is the “black
box” model. The feature of the model is that it recognizes the importance of innovation
to the economic performance, but it ignores what exactly happened in that black box,
i.e. people will not care much about the mechanism and process for creating innovation
(Larisa V. Shavinina, 2003).
The second model is called “linear model”, which indicate that great investment in
R&D will promote the applied and theoretical research thus creating more possibilities
for the invention’s come. And the more inventions created the more chance for the
innovation’s emergence and sustained long-run economic growth. The linear model
emphasize that R&D investment is the initial engine for the whole relationship, just as
Andrés Rodríguez-Pose argued it is “at the heart of technological progress and,
eventually, economic growth” (Andrés Rodríguez-Pose et al 2008: p 54). The linear
model has a straightforward and simple-form interpretation to the multiple
relationships with great focus on the R&D’s impact. According to the dynamic
direction, the model can be further classifies into two types: “technology push” and
“market driven”. They have opposite causality in interpreting the mechanism of
generating innovation. For the former one, the mechanism develops along “Basic
Science→ Applied Science and Engineering → Manufacturing→ Marketing → Sales”
and for the latter one it behaves like “Market Place → Technology Development →
Manufacturing → Sales” (Larisa V. Shavinina. 2003: p 46 ).
The third one, developed by Lundvall et al, is called innovation system which has been
introduced in the former part. Comparing to the linear one, the new model consider
19
innovation is the “territorially embedded process” (Andrés Rodríguez-Pose & Riccardo
Crescenzi 2008: p 54). As the thesis introduced, essential actors will interact with other
components during the process and their relationship is not linear. This approach offers
a more vivid picture of innovation and deepens our understanding about the
mechanism of the innovation system. However, the flaw of the model is also obvious.
The model is more descriptive and qualitative and it is hard to transform some of the
mechanism into the quantitative estimates. For example, how to construct the
interactive learning or the institution’s impact in the econometric model? And how to
test that internal interaction plays a significant role in innovation development? It may
require more advanced econometric tool or better indicators to improve this kind of
model.
The fourth model, according to Andrés Rodríguez-Pose et al, is the spillover effect
model. According to my understanding, this approach also emphasizes the importance
of actors’ interaction, but it simplifies and weakens the detailed process of it and only
focus on the patterns of communicating and transforming the knowledge and
information, such as how to acquire valuable information about market as well as
competitors and collaborators, and how to absorb new knowledge from both internal
and external sources, etc. Different approaches of contact would be the fundamental
for the spillover effect and the model. The main problem of this approach is it has a
careful analysis in the part of innovation creation, but lacks interaction between
innovation and market, i.e. we do not know if the product is commercially successful
and what economic effect (e.g. profit of new product or new market demand) would it
bring.
The fifth one is the evolutionary model. The model mainly decomposes the cycle of the
product innovation from innovation “generation” to “selection” to “Reproduction and
inheritance” and finally to the “Fitness and adaptation” (Larisa V. Shavinina 2003: p
49). This approach challenges the tradition view that treats market as perfect and
information symmetry. Since in the real economy, firm would have great risk for
introducing the new product to the market due to the numerous competitors and
information asymmetry, and it may fail even when its quality is satisfied just because it
does not fit the external environment (Tisdell, 1995). It implies the conditions for a
successful innovation is actually very strict and innovation would have to experience
tortuous process to commercialization. The model contains the other two core elements
of the thesis: human capital would be presented in the “generation” step and economic
effect would be the basic criteria to determine if it is fit or adapted.
3. Empirical study
20
In this section, I will choose Sweden as the empirical case to study the multiple
relationships between economic performance, innovation and human capital. The basic
approach is the time series analysis which mainly contains two aspects: 1. Test if there
exists long-run relationship between the key concepts such as economic performance
and human capital as well as innovation; 2. Run the multiple linear regression
intending to explore what factors will significantly affect the innovation’s development.
The following parts will carefully illustrate the model, data, hypothesis and the
econometric analysis.
3.1 Model construction
As I explained in the former introduction, two types of models will be applied for
analysis. The first model is the so-called cointegration approach since the essential of
this approach explores the long run relationship between the variables; the second one
is the short run analysis putting different independent variables into the dynamic
regression to test which one is statistically significant for the dependent variable. The
reason for choosing this multiple-variable regression aims to identify each factor’s
contribution and analyze if their performance fit the qualitative hypothesis.
3.1.1 The long-run relationship: cointegration
The concept of cointegration is firstly proposed by Engle and Granger. The basic idea
is that if there are two time series xt and yt, which have the same integration order I(d),
for most of cases their linear combination will still be I(d). However, if “there exists a
vector β, such that the disturbance term from the regression (εt = yt — βxt) is of a lower
order of integration, I (d — b), where b > 0” (Richard Harris & Robert Sollis 2003 : p
34), then the two time series xt and yt are cointegrated of order I (d, b). The indication
of cointegration is that although time series may behave like a “random walk”, there
might be some internal power controlling them. Just as R. Carter Hill et al pointed out,
if two time series are cointegrated, they will “share similar stochastic trends, and, since
the difference et is stationary, they never diverge too far from each other” ( R. Carter
Hill et al 2007: p 339). It offers an effective and simple way to detect if there is longrun relationship between the two variables. According to the theoretical analysis above,
the human capital or innovation, which are considered as the endogenous engine for
the sustained long-run economic growth, should have the long-run relationship with
economic performance. Besides, since human capital is one of the fundamental factors
in generating innovation, the paper will also assume that human capital positively
correlates with innovation. Therefore the three hypotheses are proposed:
Hypothesis 1
21
There exists positive long-run relationship between human capital and economic
performance.
Hypothesis 2
Similarly, there is also positive long-run relationship between innovation and economic
performance.
Hypothesis 3
Positive long-run relationship should also be observed between human capital and
innovation.
Based on the hypothesis, the long-run model will be constructed as: Yt=α0+α1t+α2Ht+εt,
Yt=β0+β1t+β2It+ut and It=γ0+γ1t+γ2Ht+ξt, in which, Yt, Ht, and It represent indicators of
economic performance, human capital and innovation respectively; α0, β0 andγ0 are the
constants of the long-run equation; α1, β1 and γ1 are the coefficients of the trend, and t
points to the trend; εt, ut and ξt are the residuals of the regression. Then if the two time
series are cointegrated, the residuals from the regression should be stationary process.
This could be tested by the Engle-Granger approach.
3.1.2 The second model of multiple regression
According to the innovation models mentioned in the theoretical section, the linear
model has clear and simple mathematical form to interpret the multiple relationships
among variables. In the short-run analysis, the thesis will adopt this type of model. The
model is based on the “knowledge production function” (Riccardo Crescenzi et al,
2007) and the form of model is consistent with the Cobb-Douglas Function.
Considering our specific case, it can be extended as i=αhβrγyλmθeε (Soogwan Doh et al
2010& Wei Chi et al 2010), in which i, h, r, y and m represent variables of innovation,
human capital, expenditure on R&D, economic level as well as immigrants population
respectively. For transforming the function into the linear model, it can be rewriten as
lni=lnα+βlnh+γlnr+λlny+θlnm+ε. R&D has an essential role in the “linear model” for
promoting innovation; human capital is also the basis for creating and diffusing new
knowledge, besides, income level should also have some indirect effect on innovation
since higher economic level usually implies good education quality and larger
investment in human capital and R&D, and finally immigrants with qualified education
background as source of human capital and knowledge spillover will also positively
affect innovation. In addition, beside current term of variables’ impact, the distributed
lags of these variables will also be added into the model. Therefore, the fourth
hypothesis can be proposed as:
22
Hypothesis 4
Human capital, R&D expenditure, income level and immigrants with good education
(also with approximate number of lags of these variables) have the significant impact
on innovation.
However, one drawback of the linear model, as I mentioned before, is it lacks some key
elements for the system of innovation. There is no variable indicating the interactive
learning process, positive externalities, knowledge spillover and some important social
capital like trust, which is quite crucial for the small business and start-up
entrepreneurs. Therefore, according to my understanding, the function mainly reflects
the impact of quantity of the factors rather than the quality of them.
3.2 Data
In this part, I will illustrate the indicators of the variables, the source of the variables
and how they are constructed. According to the model mentioned above, there are five
variables in our models: innovation, human capital, economic performance, immigrants
and R&D expenditure. Each of them will be explained as below:
Economic performance
The thesis will adopt three dimensions to reflect economic performance: GDP, GDP
per capita and productivity as the proxy for economic performance and income level.
The first two indicators can be directly collected from total economy database of The
Conference Board, which is an independent research association. The data is from
1950 to 2010 which is converted at 1990 US dollar. The productivity is calculated as
p=y/l, where p, y and l represent productivity, total output (GDP) and labor (person
employed) respectively. All of them can be found from the same source as the first two
indicators.
Human capital
There are many ways to measure a country’s human capital, such as average education
level, school enrollment, average years of schooling, literacy level, and human
development index, etc. (Soogwan Doh et al, 2010; Leandro Prados de la Escosura et
al, 2010; Hans-Jiirgen Engelbrecht, 1997; Mourad Dakhli et al,2004). Of course, these
indicators have their special advantages and may especially emphasize some dimension
of human capital characters. For example, the vocational education and on-job training
will reflect the professional and practical skills but lack the ability to explain the
development of basic science; literacy level is a good indicator for measuring the
general education level of a country but it seems more effective for the developing
countries; the proxy for formal education like education attainment, school enrollment
23
and amount of PHD are mostly adopted by the scholars but it is limited for explaining
the dimension of working experience and skills.
In this sturdy, I will use two types of indicators to measure human capital: the first one
is the average years of schooling in the long-run analysis. This type of proxy is
“superior measure of human capital … compared to the frequently used proxies of
school enrolment ratios and adult literacy rates” (Hans-Jiirgen Engelbrecht 1997: p
1482). The data is collected from the paper Human Capital and Economic Growth:
Sweden, 1870-2000 by J. Ljungberg and A. Nilsson (2009) which can be found from
Lund University Macroeconomic and Demographic Database. It measures the average
schooling years for population from 15 to 65 years old from 1870 to 2000, which
basically covers all the population that involved into different levels of education. It is
an aggregated measuring which contains average schooling years of primary education,
lower and upper secondary school education, tertiary education and vocational
education. The greatest advantage is that it includes all most every level of education
which can be used both at the aggregated level and broken down and it has a long span
for making the cointegration analysis.
The second proxy for human capital, which is used in the short run regression, is the
number of people among 290 municipalities of Sweden aged from 16 to 74 who has
participated in the two main types of formal education: three years’ post secondary
education or more; and post graduate education. The data is collected from the
Statistics Sweden, with the time span from 1985 to 2009.
Innovation
There also several proxies can be used for measuring innovation, like the patent growth
rate, R&D expenditure, and “technology-based export” (Mourad Dakhli et al, 2004).
However, as Soogwan Doh et al argued, “R&D is an input and not an output in the
innovation process” (Soogwan Doh& Zoltan J. Acs 2010: p 246), the R&D investment
only reflects a limited dimension of innovation. The export information of technologybased firm is an efficient approach for measuring innovation. It can present the
competitiveness of the firm and correspondingly the aggregated level would reflect the
whole country’s innovation ability and competitiveness. From my point of view, this
approach quite fits the important property of innovation, i.e. the market-oriented and
commercialization. Since the success of innovation will greatly depend on if it can be
recognized by the market and widely accepted by the customers, the ability of firm to
export their hi-tech based product can greatly reflect their innovation capabilities.
However, it is only one significant aspect of innovation but ignores domestic demand
and the small and medium enterprises since most of export oriented high-technology
companies would be the large companies.
24
In this paper, I will use two types of proxies: first one is the most common
measurement, the amount of patent granted by the Swedish patent office including
Swedish patent, public patent as well as European patents which are validated in
Sweden, as the indicator of innovation in the short-run regression analysis. The data is
collected from the Swedish patent database with the span from 1985 to 2009. However,
the view of treating patent as innovation indicator has been questioned by economists.
Some commentators consider that patent only “focuses on a rather narrow aspect of
innovative activity, excluding product modifications as well as process innovation or
activities such as fashion design”(Mourad Dakhli & Dirk De Clercq 2004: p 120).
Another dimension which is often been challenged is the confusion between invention
and innovation (Soogwan Doh et al 2010; Mourad Dakhli et al 2004). For most of the
cases, the new knowledge that is applied for the patent can only be treated as an
invention rather than innovation since there is no accurate evaluation for exact
commercial value and market potential of the new knowledge. As I mentioned above,
successful innovation is actually quite rear which means a large proportion of the
invention would be failed and eliminated by the market, therefore, the patent amount
would not be a very accurate measurement of innovation. However, since it is the most
comprehensive indicators reflecting a country’s new technology development, I will
apply it as the indicator of innovation in the short-run analysis.
The second one that I will adopt in the long run analysis is the TFP (total factor
productivity) index. As N.F.R Crafts (2003) has pointed out, the TFP can be
approximately represented as “the state of technology” though it would be interfered
by other factors. The advantage of applying TFP is it avoids the problem of confusing
about invention and innovation, since the TFP is calculated from the Cobb-Douglas
production function directly which sufficiently confirms the innovation or technology’s
impact on economic development. One difficulty is how to calculate the TFP.
According to the production function “Y=AKβL1-β”, A is the TFP representing the
efficiency of transforming input factors into output and it is calculated as the ratio
between output and product of weighted capital and labor. It can also be calculated as:
lnA=lnY-βlnK-(1-β)lnL. The key problem of it is how to determine the value of β.
David T. Coe and Elhanan Helpman defined β as “the average share of capital income”
and according to their estimate the β equals to 0.338 for Sweden; Nehru and
Dhareshwar (1993) argues that for most of cases the ratio between weights of capital
and labor range from 3/7 to 4.5/5.5 and he chooses 4/6 as the benchmarking, For
simplicity, I will choose β=0.3 for approximately estimating the weight of capital.
Besides, for the other factors: the labor, which is measured as person employed (in
thousands of persons), is collected from the same source to economic performance
with time span from 1959 to 2010. The data of capital stock is collected from "A new
25
database on physical capital stocks: sources, methodology and results" (Nehru and
Dhareshwar, 1993), with a time span from 1950 to1990. The disadvantage of the TFP
measurement is it might be interfered by other factors and the calculation approach is
not very accurate.
R&D expenditure
The Swedish R&D expenditure can be found from Eurostat’s database. It is measured
by the euro per inhabitant from 1985 to 2009, which covers all the sectors including
business enterprise sector, government sector, higher education sector as well as private
non-profit sector. One serious flaw of the data is that it misses the data for every second
year from 1981 to 2002, and unfortunately this is the best data source that can be found.
Immigrants
The data of immigrants is collected from the Statistics Sweden with time span from
1985 to 2009. The immigrants cover 100 countries around the world with education
level of post secondary education (three years or more) and post graduate. Their
specialties include social science, natural science, technology, manufacture, agriculture,
health care, etc.
3.3 The analysis of long-run relationship
As I mentioned in the section of model construction, the cointegration analysis will be
applied for testing the three hypothesizes. After testing, a brief comment would be
given for summarizing the test report.
3.3.1Economic performance and human capital (hypothesis 1)
For testing this hypothesis, I will firstly choose growth of average schooling years for
tertiary education in Sweden as the indicator of human capital and productivity as the
indicator of economic performance. There are two reasons that I only choose the
dimension of tertiary education as the indicator of human capital. Firstly, there is lag
effect for the average schooling years’ growth to impact on productivity. It is
reasonable to assume that no matter formal education or the vocational training, it will
take some time for people who are participating the current education to perform and
apply their knowledge in the future period; especially, for young people who take
primary and secondary education, there would be quite a long time for them to bring
significant influence to productivity improvement. Therefore, tertiary education can
bring a comparatively more immediate impact on economic performance than other
levels of education; secondly, the aggregated indicator itself has some drawbacks.
Since different education levels and types do not have the same level of impact on
26
improving productivity, for example, higher education would have more significant
influence on productivity, and education that below secondary schooling would be very
limited and much less important for modern economic growth. Therefore, the
aggregated statistic would bring some bias to the test when giving each education
component the same weight and thus weaken human capital’s influence on productivity
growth. For avoiding the long time lag effect and present the most significant part of
human capital, tertiary schooling years growth would be firstly tested. Afterward, the
aggregated statistic would be tested.
According to cointegration test procedure, there are basically three steps: 1.testing if
the two series are integrated in the same order; 2. running the long run equation
between the series and acquire the residuals from it; 3 using the Engle-Granger
approach to test if the residual is stationary thus concluding the cointegration test.
Step1 Testing the integration order
I will adopt the Augmented Dickey-Fuller test to do this step. The complete procedure
for testing unit root is from the most general form Δyt=β0+γyt-1+β1t+∑λiΔyt-i+εt to the
restricted model Δyt=γyt-1+∑λiΔyt-i+εt. The plot of the productivity is as below:
Graph 1 Productivity in Sweden from 1959 to 2000
44
40
36
32
28
24
20
16
50
55
60
65
70
75
80
85
90
95
00
PRODUCTIVITY
.
Source: Total economy database of The Conference Board
The scale of the productivity is thousand of 1990 US dollar per labor
From the Graph 1, it is quite clear that from 1959 to 1973, there is a rapid productivity
27
growth in Sweden, and from 1973 to 1990 it entered into a stagnation period with a
slow increase, after 1990 onward, there is a dramatic growth again. It is clear
productivity goes in a non-stationary way.
According to the Augmented Dickey-Fuller (ADF) test, the p value of the unrestricted
model with a constant and trend is 0.90 which is larger than 0.05 thus null hypothesis
can’t be rejected initially indicating there is a unit root in the model. Then moving
forward to the F test, the RSS of the unrestricted model is 9.317046, on the other hand,
the RSS of restricted model is 9.547187. The number of restrictions is two (trend and
the lag term), and the number of the variables in the unrestricted model is three. The
sample length is 41. According to the function F
(in which RSS represent the sum of squared residual, r points to the number of
restrictions, T means the sample length and K indicates variables in the unrestricted
model), we can calculate the F=0.47. Since the critical value of sample size equal to 50
is 6.73 at 5% significant level, the null hypothesis of β1=γ=0 can’t be rejected leading
us to estimate the more restrict one: Δyt=c+γyt-1+∑λiΔyt-i+εt, and again p value is
0.9721, which is much larger than 0.05. Therefore, another F test is required to
determine if the constant would be excluded or not. Calculated in the same way, the
RSS of unrestricted model is 9.361590, with the RSS of restricted one 26.08015, and
r=2, K=2, T=41. The F statistic is 34.8, which is larger than the critical value. The test
result indicates that we need to evaluateΔyt=c+γyt-1+∑λiΔyt-i+εt, by comparing the t
statistic (the t statistic is 0.244 in this ADF test) with the standard normal distribution,
and it is quite obvious that γ=0 can’t be rejected at 5% significant level, indicating the
productivity is a non-stationary process.
Then we make the first difference of the productivity, and again test its stationarity.
Still we start from the most general model with both constant and trend, and p value is
0.0126, which is less than 0.05, therefore, the null hypothesis of unit root in the
difference term of productivity can be rejected, stating it is a stationary process.
Therefore, we can conclude that productivity is an I(1) process.
Similarly, we can do the same procedure to test the integration order of the growth of
the average schooling years for tertiary education, and the same results can be
concluded. These tests confirm that the time series of productivity and tertiary
education growth are both integrated into order one.
Step 2 Running the long-run equation
Since
both
two
variables
are
I(1)
series,
the
long-run
function
of
productivity=c+βt+α(dh3)+e, in which dh3 is the tertiary education growth, will be
estimated. For excluding the effect from the deterministic components on the residual,
28
two sets of deterministic components would be added in the long-run equation
(constant and trend; constant with no trend). This can help to present the accurate
relationship between the variables eliminating other external influence. After running
the long-run equation, the residual would be saved for the next step.
Step3 Engle-Granger test
The last step is using the Engle-Granger approach to test if the residual is a stationary
process. Since the two sets of deterministic components are added into the long-run
equation, none will be added into the augmented Dickey-Fuller model. The critical
value of cointegration is different from the normal ADF critical value, which is
calculated through C(P)=Ф∞+
+
, and the result output is as followings:
Table 1 Cointegration result
Residuals
Τ statistic
Critical value(5%)
Constant and trend
-2.033222
-4.1751
Constant and no trend
-5.093061
-4.0199
The result shows that under the deterministic component of constant and no trend, the
null hypothesis of non stationary can be rejected in favor of cointegration between the
two variables. Though it fails in the other set including both constant and trend, it
basically supports the conclusion that productivity and tertiary education growth has
positive long-run equation.
I also apply the same approach to test the tertiary education growth with other
economic indicators such as total GDP and GDP per capita, and since their t statistics
are less than the critical one, both of the two experiments indicate there is such long
run relationship.
Combining all these test, the total output is as following:
Table 2 Cointegration report for tertiary education gowth
Long-run
T statistic
Critical value(5%)
Conclusion
-5.093061
-4.0199
Cointegration
-4.056206
-3.467
Cointegration
-3.734139
-3.467
Cointegration
relationship
Productivity and growth
of
average
years
for
schooling
tertiary
education
GDP and growth of
average schooling years
for tertiary education
GDP per capita and
29
growth
of
schooling
average
years
for
tertiary education
For further exploring the relationship between economic performance and aggregated
human capital stock growth, the same tests are applied. The test output is as below:
Table 3 Cointegration report for aggregated education growth
Long-run
T statistic
Critical value(5%)
Conclusion
-0.789966
-4.0199
Non
relationship
Productivity
aggregated
and
human
Cointegration
capital growth
GDP and aggregated
human
-2.775677
-3.9834
capital
Non
Cointegration
growth
GDP per capita and
aggregated
-2.948205
-3.9834
human
Non
Cointegration
capital growth
Obviously, it shows that none of these experiments gets conclusion of cointegration
which means there is no long run relationship between the aggregated human capital
growth and economic performance. And I also test other levels of education such as
primary education and secondary education and vocational education, however, the
same result as aggregated level.
Considering both table 2 and table 3, the statistic output on one hand confirms that
education, as most important form of human capital investment, has positive impact on
economic performance in the long run, on the other hand, such relationship can only be
observed at the tertiary education level, which partly confirms the first hypothesis.
Comparing tertiary education to primary and secondary, the tertiary education has a
current impact on economic performance without waiting for long time; in contrasting
to vocational education, it is more stable and standardized than vocational education,
which is more flexible and complex due to the firm-specific character. These
advantages make the tertiary education the main engine for the long-run economic
growth.
3.3.2 Economic performance and innovation (hypothesis 2)
Since in this section each year’s innovation is measured as the technology state, I will
firstly calculate the TFP from 1959 to 1990 using the function and weight mentioned
30
above. The trend of ln(TFP) is as following:
Graph 2 ln(TFP) of Sweden from 1959 to 1990
10.6
10.5
10.4
10.3
10.2
10.1
50
55
60
65
70
75
80
85
90
95
00
TFP
Source: Total economy database of The Conference Board and "A new database on physical capital
stocks: sources, methodology and results" (Nehru and Dhareshwar, 1993)
It is quite clear that Sweden enjoys a rapid technology progress during the whole
period, especially from 1959 to 1970. The testing approach is the same as the first
hypothesis. Both ln(TFP) and economic indicators (productivity, GDP and GDP per
capita) are belong to the I(1) series. And the Engle-Granger test is as follow:
Table 4 Cointegration report for TFP and economic performance
Relationship
T statistic
Critical value
Conclusion
-4.158861
-3.7789*
Cointegration
TFP and GDP
-3.977437
-3.7789*
Cointegration
TFP and GDP per
-4.282903
-4.1116**
Cointegration
TFP
and
productivity
capita
* represents the critical value at 10% significant level
** represents the critical value at 5% significant level
All the t statistics are less than the critical value indicating the conclusion of
cointegration. The results confirm the hypothesis 2 that ln(TFP) and economic
indicators share a similar stochastic trend. The technology or innovation level
correlates with economic development in the long run.
31
3.3.3 Human capital and innovation (hypothesis 3)
The last long-run analysis is between human capital and innovation. Repeating the
previous approaches, firstly I test the cointegration between TFP and tertiary education
growth. The t statistic is -4.293922 with critical value equals to -3.5367 concluding
thus there is cointegration relationship between them. Then similarly to hypothesis 1, I
also test it at the aggregated level and for this time the general growth of average
schooling years is positively cointegrated with innovation (the t statistic is -3.408,
which is less than the critical value of -3.1822 at 10%significant level), implying that if
average schooling years has a more rapid increase, the current year’s technology will
be in a more advanced state in the long run. To be more in detail, I also test each
component’s relationship with TFP, and surprisingly, all of them have positive long run
relation with TFP. The result is as following:
Table 5 Cointegration report for TFP and human capital
Relationship
TFP
and
tertiary
T statistic
Critical value
Conclusion
-4.293922
-3.5367**
cointegration
-3.218980
-3.1822*
cointegration
-3.351140
-3.1822*
cointegration
-3.408
-3.1822*
cointegration
education growth
TFP and secondary
education
TFP and primary
education growth
TFP and aggregated
education growth
* represents the critical value at 10% significant level
** represents the critical value at 5% significant level
The result output confirms the third hypothesis at both aggregated and individual level,
though they pass the cointegration test at the different significant level. Comparing it to
the hypothesis one, we may find that human capital has different impact on economy
and innovation. For the former one, only tertiary education has significant impact
implying lower education with long time lag would be not important for current
economic growth; however, for innovation, it seems that lower education are still
important for innovation. In other words, we may say that economic performance may
be more sensitive to the level and timeliness of education than innovation.
.
3.4 Short-run dynamic regression with distributed lags
3.4.1 dynamic model with distributed lags
In this section, I will test different factors’ contribution to innovation’s development.
32
Before running the multiple linear regression, for avoiding the spurious regression
which may lead to the fake relationship between variables, the unit root test will be
firstly conducted. According to Augmented Dickey-Fuller test, except ln(patent) is a
stationary process, all the other variables such as log term of human capital, R&D
expenditure, immigrants and GDP are all having a unit root. One of the common ways
of getting rid of unit root is generating the difference term of original variables by
running the calculation of Δy=yt-yt-1. After taking the first difference, all the rest
variables turn to be stationary except growth of ln(GDP). Thus another difference
operator is made transforming it into stationary. Therefore, we have one I(2) series, one
I(0) series and three I(1) series. Since we have clear causation in the hypothesis, the
regression can be directly operated without diagnosing the causality direction.
Furthermore, I consider there might be lag effect on dependent variable, therefore 1 or
2 lag terms of both dependent and independent variables are added into the regression
(due to the limited sample size, the maximum amount of lag is two). However, since
the form of regression is changed due to the difference term, the original hypothesis is
also correspondingly changed in favor of studying the impact of independent variables
‘change on dependent variable. The following is the regression output:
Table 6 The regression of the patent development
Independent
Coefficient
T statistic
P value
5.531048
2.801284
0.0150**
GDP
3.922205
4.237143
0.001***
Lagged term 1 of
0.420443
2.055417
0.0605**
-1.735483
-3.709153
0.0026***
1.0962
1.740131
0.1054
R&D increase
-0.205017
-0.825826
0.4238
Lagged term 1 of
-0.673655
-2.139082
0.0520*
0.885380
2.588805
0.0225**
-0.064454
-0.651966
0.5258
Variables
Constant
Growth
of
increase
patent
Human
capital
increase
Lagged term 1 of
human
capital
increase
R&D increase
Lagged term 2 of
R&D increase
Immigrant increase
Note: all these variables points to the log term
33
* Significant at 10% significant level
** Significant at 5% significant level
*** Significant at 1% significant level
The dependent variable in this regression is the patent. The R2 of the regression is
0.832349, which is quite high. Observed from this regression output, one may notice
that the coefficient of immigrant increase and R&D increase in current term are very
poor with extreme insignificant t statistic. In order to further improve the general
interpretation of the regression, both of the two will be excluded, which is leading to
the new regression as below:
Table 7 The modified version of patent development regression
Independent
Coefficient
T statistic
P value
5.160723
3.092428
0.0074***
GDP
3.452274
4.348933
0.0006***
Lagged term 1 of
0.459062
2.651746
0.0181**
-1.851453
-4.188902
0.0008***
1.050423
1.981334
0.0662*
-0.610358
-2.068552
0.0563*
0.742833
2.455210
0.0268**
Variables
Constant
Growth
of
increase
patent
Human
capital
increase
Lagged term 1 of
human
capital
increase
Lagged term 1 of
R&D increase
Lagged term 2 of
R&D increase
* Significant at 10% significant level
** Significant at 5% significant level
*** Significant at 1% significant level
The general R2 of the new regression is 0.817297 and every variable is significant at
10% significant level at least, indicating the whole model and every variable has a
good fitting and interpretation ability.
3.4.2 Residual test
In this section, the heteroskedasticity, autocorrelation and normality of the residual will
be tested in order to know how good the model is. For heteroskedasticity, the White
test would be adopted, and the Eview test result is:
Table 8 White test for the heteroskedasticity
34
Since the p value of the nR2 is more than 0.1, therefore the null hypothesis of
homoskedasticity can’t be rejected. Thus the model does not have problem of
heteroskedasticity.
Then I will apply Lagrange-Multiplier (LM) approach to test if there is autocorrelation
in the residuals, and the result is:
Table 9 LM test for autocorrelation
Since p value of the nR2 is less than 0.05, thus the null hypothesis of no autocorrelation
would be rejected. Therefore, there is the problem of autocorrelation in this model.
Finally, Jarque-Bera approach is used for testing the normality of the residuals. And
according to graph 3,
Graph 3 Normality test
7
Series: Residuals
Sample 1988 2009
Observations 22
6
5
4
3
2
1
0
-0.10
-0.05
-0.00
0.05
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
-7.20e-16
-0.006866
0.106314
-0.112653
0.060266
-0.039375
2.486601
Jarque-Bera
Probability
0.247299
0.883690
0.10
p value of JB test is much larger than 0.05, thus the null hypothesis can’t be rejected.
So the residual is normal distributed.
Since the residual is normal distributed and there is no heteroskedasticity, the residual
can be approximately treated as white noise process
3.4.3 Interpretation of the regression
35
According to the regression result, several points can be summarized: firstly, the lagged
term of patent has the positive impact on current term’s patent amount, indicating that
new knowledge’s production is on the basis of old one; secondly, the growth rate of
GDP growth has a quite strong positive impact on innovation development. It may
imply the process of interacted reinforcement: on one hand innovation would promote
economic growth in the long run, and on the other hand, growth rate of economic
growth would enhance a society’s innovation ability; thirdly, the core elements of
innovation system, the human capital and R&D expenditure, have quite unexpected
effect on innovation. It is quite clear that the current human capital increase and the
first lagged term of R&D increase negatively affect innovation, which is quite
suspicious; however, the first lagged term of human capital increase and second lagged
term of R&D increase have the positive effect. Therefore, it can be assumed that there
is a time effect in the relationship since the current R&D investment or human capital
growth will promote innovation in a few years. This also confirms that innovation
formation is a time-consuming process and there is clear time lag between input and
output. However, the negative coefficient of current term’s human capital growth and
first lagged term of R&D increase is hard to interpret. The regression supports part of
the hypothesis 4 (since the form of model has been changed, the interpretation of
regression would be different from original hypothesis), such as it confirms impact of
GDP as well as lagged R&D and human capital on innovation, however, both of
human capital and R&D’s current impact is suspicious.
4. Conclusion and policy implications
The essential part of the thesis is analyzing the multiple relationships among the three
key elements: economic growth, human capital and innovation. The endogenous
growth theories have pointed out that innovation and human capital are the engines for
supporting economy’s long run development. Both of them are functioned as
promoting technology progress, improving productivity, reinforcing social capability
and environment of absorbing and diffusing new technology, and enhancing country,
firm as well as individual’s competitiveness, etc. Since human capital and innovation
have similar impact on economic performance, an intuitive assumption is there must be
close correlation between them. However, according to the modern theory of
innovation system, the innovation is created through a learning and problem-solving
process consisting of numerous factors and their relationships are nonlinear and
complicated. Human capital is one of the decisive factors in the system and innovation
can be treated as the final product of it. For testing such multiple relationships among
the three factors and exploring innovation’s formation process, the thesis studied the
empirical case of Sweden. The empirical study basically supports the theoretical
36
argument, but there are several implications are useful for practical policies and
academic research:
First, tertiary education should be highlighted in the human capital stock, since
empirical study in this thesis has confirmed its close correlation with both technology
and economic development in the long run. This is consistent with the theory of human
capital: the tertiary education can improve the quality of the labor stock in term of
providing professional skills and general knowledge. What’s more, since the time lag
between tertiary education and future career is much shorter than other formal
education, the impact is quite obvious. Therefore, reinforcing the quality of tertiary
education, improving the university enrollment rate, investing university infrastructures
and facility, etc are crucial for promoting long run economic growth and technology
development.
Second, other levels of formal education such as primary and secondary education do
not show such pattern with economic development. This is mostly attributed to the
large span of time between the study and work. Besides, the trend of global and
knowledge-based economy requires higher levels of education thus primary and
secondary education would be limited. However, the cointegration result only reflects
the linear relation which is focus on the direct impact such as improving productivity
and GDP, it does not include the indirect effect like external and non-economy
influence, e.g. promoting the democracy and lowering crime rate, which is indirect and
nonlinear. Therefore, it would still be reasonable to assume that even primary and
secondary education has such indirect impact although it can’t be concluded from the
cointegration result.
The first and second points imply that my first hypothesis is established with a strong
restriction. Different observing levels would have quite diversified test results. Thus,
the relationship between human capital and economic performance should be more
cautiously evaluated considering the specific type or level of education.
Third, technology state and economic performance have positive long run relation. In
other words, the level of technology marks the economic development stage in a
country. The conclusion fits theory that new product, new process, new market and
new organization generated by innovation can break the equilibrium in favor of
forming new economic opportunity. It could explain that Sweden makes dramatic
success on economic development greatly owing to knowledge or technology oriented
development pattern, and keep on enhancing innovation ability is still very important
for the future economic growth. This conclusion basically confirms the second
hypothesis, no matter at the macro or micro level of economic performance, the long
term relationship can be significantly observed.
Fourth, the TFP has positive long-run relationship with indicators of human capital.
37
According to the theoretical analysis, the quantity and quality of human capital would
determine the organization’s ability of absorbing and applying knowledge, which is
one of essential factors for promoting innovation. The test result basically follows the
theoretical assumptions. In this test, not only the tertiary education, but also other lower
levels of education all have significant impact, which is different from first hypothesis.
The result not only emphasizes the tertiary education’s impact, but also indicates that
primary and secondary education is the basis and precondition for the higher education
and innovation. The primary and secondary education’s indirect impact on innovation
would be more significant than it on economic performance. Therefore, the third
hypothesis has completely passed the cointegration test.
The first, second and fourth points signal that when we make the analysis of multiple
relationships among human capital, innovation and economic performance, we need to
carefully classify the explanatory variables and also a more specified analysis for the
difference of interaction mechanisms is required. In this case, the indirect impacts
from primary and secondary education on economic performance and innovation have
completely different significant level.
Fifth, with respect of generating innovation, it seems that there would be time lags for
human capital and R&D expenditure affecting innovation. It implies R&D and human
capital’s impact on innovation would be a time consuming process and it demands a lot
of resources in the initial period, which indicates the great cost and risk for generating
innovation; on the other hand, improving efficiency of utilizing the R&D investment as
well as human capital to shorten the time lag is also crucial for capability of creating
innovation, and this could be achieved by improving the innovation system efficiency
such as optimizing the allocation of human and financial resource within the
organization as well as promoting effective cooperation with external actors to get
useful information and feedback. This point partly confirms the fourth hypothesis.
Some variables such as immigrants are excluded from the regression, and some
variables also need restriction to achieve the expected assumption such as human
capital and innovation. It also from an indirect perspective confirms that the innovation
creation process is not simply investing the input factors but requires the optimal
organization form or institution arrangements to achieve the high efficiency.
38
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