drilling for innovation: economic diversification through
TRANSCRIPT
Drilling For Innovation: Economic Diversification Through
The Determination, Distinction and Development of
Renewable Entrepreneurship Clusters
By
Abdallah Mohammed S. Assaf (عبدالله محمد صالح العساف) , BS, MS, MA, MBA
A Dissertation
In
BUSINESS ADMINISTRATION
Submitted to the Graduate Faculty
of Texas Tech University in
Partial Fulfillment of
the Requirements for
the Degree of
DOCTOR OF PHILOSOPHY
Committee
Ronald K. Mitchell
Chair of Committee
Benaissa Chidmi
Keith Brigham
Mark A. Sheridan
Dean of the Graduate School
May, 2016
Texas Tech University, Abdallah Mohammed S. Assaf, May 2016
Copyright 2016, Abdallah M. Assaf
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ACKNOWLEDGEMENTS
“That is the bounty of Allah, which He gives to whom He wills, and Allah is the
possessor of great bounty” (The Holy Quran, Al-Jumu’ah 62:4).
First and foremost, all praises and glory are due to Allah for His countless
bounties and blessings, amongst them is the great bounty of the development of this
dissertation.
I dedicate this dissertation to my esteemed father, Mohammed, and my loving
mother, Hussah, whom I am most proud to be their son, and to whom I send my greatest
and warmest gratefulness and appreciation for dedicating their lives to us as their
children, for their endless love and prayers, and for allowing us to envision our ultimate
aspirations then paving the path for us to achieve them; and without whom, the
development of this dissertation would never have been attainable.
I would like to express my greatest appreciation and dedication to my family; my
brother, Saleh (and his wonderful family), who continues to invest generously and
selflessly into my progress throughout my life, my brothers and sisters, Haifa (and her
wonderful family), Abdulaziz, Assaf, Sara, and my wife, Deemah, for their pure love and
always being there, for filling my life with joy, for their endless support to my aspirations
and decisions, and for withstanding my various shortcomings throughout my life, to my
cousin and great friend, Mohammed, for his sincere dedication, encouragement, and
support during the development of this dissertation, and much beyond.
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I extend my warmest appreciation to those who left a great impact and a deep
touch not only into my academic progress, but also into my whole life; namely, my
beloved grandmother, Luluah, and to my uncles Abdullah and Fahad (May Allah shower
them all with mercies).
I would like to express my sincerest gratitude to my uncle, Dr. Ibrahim Al-Assaf,
for being an inspiring role model to us all, and for honoring me with his munificent
investment and his generous support and guidance throughout my personal, academic,
and professional progress.
I would like to express my deepest gratitude to the Chair of my committee, Dr.
Ronald K. Mitchell, for his unparalleled encouragement, wise guidance, and faithful
supervision throughout my Ph.D. program at Texas Tech University. I am truly honored
to be amongst his students. I would like also to extend my appreciation to my committee
members Dr. Benaissa Chidmi and Dr. Keith Brigham for their continuous support and
most valuable guidance and feedback during the development of this dissertation. I also
thank the faculty of the Area of Management at Rawls College of Business for their
dedication and for generously investing their knowledge during my development into the
Ph.D. program at Texas Tech University.
Finally, I would like to express my sincerest gratitude and honor to be a member
of, and to receive the generous financial and personal support from The Royal Court of
The Kingdom of Saudi Arabia that allowed me to purse my studies and attain this work.
It is beyond doubt that I stand on the shoulders of my family, friends, faculty, and
sponsors, whom I am in total debt to them all for allowing me this unique opportunity.
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TABLE OF CONTENTS
ACKNOWLEDGEMENTS ....................................................................................................................... ii
ABSTRACT ................................................................................................................................................ vi
LIST OF TABLES ................................................................................................................................... viii
LIST OF FIGURES ................................................................................................................................. viii
CHAPTER 1: INTRODUCTION .............................................................................................................. 1
Cluster Theory ............................................................................................................................. 8
Likely Influencers ...................................................................................................................... 11
Contributions ............................................................................................................................. 17
Map of the Dissertation .............................................................................................................. 18
CHAPTER 2: ECONOMIC DIVERSIFICATION AND RENEWABLE
ENTREPRENEURSHIP CLUSTERS .................................................................................................... 20
A. Diversification in the World Economy .............................................................................. 20
B. The Role of Governments in Economic Diversification .................................................... 31
C. The Role of Government in Stimulating Entrepreneurship ............................................... 38
CHAPTER 3: A MODEL OF RENEWABLE ENTREPRENEURSHIP CLUSTERS
(LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT) ................................................ 41
Public Policy Variables .............................................................................................................. 41
Pace and Stability Variables ...................................................................................................... 56
Economic Inductance ................................................................................................................. 68
CHAPTER 4: METHODS ....................................................................................................................... 73
Research Design ........................................................................................................................ 73
Data Gathering ........................................................................................................................... 74
Measurement .............................................................................................................................. 77
Data Analysis ............................................................................................................................. 86
CHAPTER 5: RESULTS ......................................................................................................................... 90
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Correlations ................................................................................................................................ 90
Hypothesis Testing..................................................................................................................... 92
CHAPTER 6: DISCUSSION ................................................................................................................. 109
Evaluation of Findings ............................................................................................................. 110
Theoretical Implications .......................................................................................................... 116
Practical Implications............................................................................................................... 120
Limitations ............................................................................................................................... 123
Future Research ....................................................................................................................... 126
CHAPTER 7: CONCLUSION ............................................................................................................... 129
APPENDICES ......................................................................................................................................... 132
REFERENCES ........................................................................................................................................ 140
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ABSTRACT
What a country produces matters, because diversity of production – economic
diversification – is tied to the social well-being of its population. While considerable
research in the fields of urban and regional economics has been conducted on economic
diversification, there is still a limited understanding of what macro-level variables lead to
economic diversification within a country. To address this gap, I introduce the notion of
renewable entrepreneurship: an economic system for the generation of business that is not
critically resource dependent for the continuity of its contribution to the economy,
arguing that it provides an appropriate vehicle to achieve economic diversification and
thereby, continuing economic prosperity. The primary purpose of this study is to
examine the effect of several public policy variables, institutionalization of innovation
pace and stability variables, and a newly-conceptualized Economic Inductance Index, on
the development and growth of renewable entrepreneurship clusters. I place this
argument within a New-Keynesian framework to offer the rationale for active
government monetary, fiscal, and regulatory engagement to stimulate horizontal
economic diversification. To test the hypotheses, data covering a time period of seven
years (2007-2013) were collected from multiple archival databases. The results of the
analysis suggest that all public policy variables (i.e., business environment policy
maturity, innovation policy maturity, new venture creation policy maturity), as well
economic inductance, have a direct influence on renewable entrepreneurship cluster
growth. The results also suggest the public policy variables have a significant influence
on pace and stability variables (i.e., competition intensity and knowledge spillover
effectiveness), while economic inductance partially moderates the relationships among
public policy variables, pace and stability variables, and renewable entrepreneurship
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cluster growth. Although no significant effect was found for pace and stability variables
on renewable entrepreneurship cluster growth, this relationship was partially supported
after taking into account the moderating impact of economic inductance.
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LIST OF TABLES
Table 2.1: Categories and Related Sub-indices of the Global Competitiveness Index (GCI)........26
Table 2.2: A Map of the Diversification World (Most to Least Diversified) using the Economic
Complexity Index (ECI).................................................................................................................28
Table 2.3: Relevant Best Practices in Economic Diversification...................................................30
Table 2.4: Austrian Economics and New-Keynesian economics Assumptions.............................40
Table 5.1: Means, Standard Deviations, and Intercorrelations Among Study Variables.............100
Table 5.2: Mixed-Effects Regression Results for Renewable Entrepreneurship Cluster Growth.101
Table 5.3: Overall Estimates of Direct Variables.........................................................................102
Table 5.4: OLS Regression Results for Competition Intensity.....................................................103
Table 5.5: OLS Regression Results for Knowledge Spillover Effectiveness...............................104
Table 5.6: Summary of Findings..................................................................................................105
LIST OF FIGURES
Figure 1.1: The Impact of Natural Resource Curse on Economic Growth.......................................4
Figure 1.2: Value Adding Segments as Derivatives of Crude Oil...................................................5
Figure 3.1: Renewable Entrepreneurship Clusters – Research Model............................................42
Figure 3.2: Perfect and Imperfect Competitive Market Models.....................................................62
Figure 4.1: Econometric Model......................................................................................................88
Figure 5.1: Renewable Entrepreneurship Clusters – Results Model............................................107
Figure 5.2: Interaction of New Venture Creation Policy and Economic Inductance on
Competition Intensity....................................................................................................................108
Figure 5.3: Interaction of Knowledge Spillover Effectiveness and Economic Inductance on R/E
Cluster Growth..............................................................................................................................109
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CHAPTER 1: INTRODUCTION
What a country produces matters (Rodrik, 2005). Economic diversification of that
production also matters, and is the goal of many countries as the source of long term
financial stability; because achieving it yields benefits that are more than economic. It
has been suggested that effective economic diversification is linked to various economic,
social, political, and institutional factors (Albassam, 2015; Karl, 2007; Ramcharan,
2005). Not surprisingly, it has been found that the job creation growth rate is significantly
higher in diversified economies (Brakman et al., 2001; Dissart; 2003; Glaeser et al.,
1992; Henderson, 1997; Krugman, 1991). In addition to the direct economic benefits,
economic diversification is argued to positively influence political stability, social
development, and institutional standards (Essletzbichler, 2007; Karl, 2007). Studies have
thus found that a country’s level of economic diversification, and its economic and
growth stability, decreases the uncertainty associated with the fluctuation of prices and
production in countries that heavily depend upon fewer numbers of industries in their
economic growth (Attaran, 1986; Berkes, 2007; Ramcharan, 2006).
Given all of these desirable benefits, it is not surprising that each government in
most, if not all, nations aims to develop their own diversified economy and to set forth
strategies to reach such a sustained economic condition. For example, the government of
Saudi Arabia has adopted 10 development plans since 1970, each covering a period of
five years; and each of these has highlighted economic diversification as a top priority for
the country (Ministry of Economy and Planning, 2010). Similar plans have also been
developed for many other nations, including the other Gulf Cooperation Council (GCC)
countries: Bahrain, Kuwait, Oman, Qatar, and the United Arab Emirates (Hvidt, 2013).
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And research shows that deliberate economic diversification initiatives have resulted in
evident patterns of diverse sectoral growth in these countries (e.g., Asif et al., Working
Paper). In particular, oil rich countries have sought economic diversification.
However, oil rich countries vary in their success in developing a diversified
economy. While Malaysia, Indonesia, and Mexico have been effective in diversifying
their economies away from some sole-revenue source, mainly, oil; other oil-economy
countries especially the Gulf Cooperation Council (GCC) countries, Russia, Nigeria, and
Venezuela have had limited success in their economic diversification endeavors (Callen
et al., 2014). Such economies that heavily depend upon a limited number of industries
(e.g., oil) are argued to suffer from what is known as the “curse of natural resources”
(Sachs and Warner, 2001: 827).
Natural-resource-curse theory argues that resource-rich countries tend to grow at a
lower rate compared to those that are less endowed with natural resources (Sachs and
Warner, 2001; Tsui, 2010). Such arguments have received ample empirical support (e.g.,
Auty, 1990; Gelb, 1988; Sachs and Warner, 1995; 1999); and the resulting observations
have classified the natural resources curse to be among the ten most significant variables
related to economic growth (Doppelhofer et al., 2000). Such failures to benefit from large
natural resource endowments are attributed to the tendency of resource-cursed economies
to adopt strategies based on resource-led growth instead of, for example, export-led
growth not tied to that resource. Furthermore, the wealth from natural resources tends to
drive up demand for non-traded products in such countries (e.g., real estate investments)
and away from investing in other activities (e.g., manufacturing activities) that might
produce a more diversified economy (Sachs and Warner, 2001). The impact of the natural
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resource curse on economic diversification can be seen in Figure 1.1 (adapted from Sachs
and Warner, 2001: 829). Figure 1.1 compares the percent of exports of natural resources
of a sample of countries GDP to their real per-capita GDP growth:
Figure 1.1: The Impact of Natural Resource Curse on Economic Growth
Several reasons have been suggested for the failure of resource-rich countries to
diversify their economies, most importantly, the absence of clear and detailed economic
diversification plans that guide the process of economic diversification, and more
specifically, the lack of plans that drive growth through the development of non-critically
resource dependent industries (Albassam, 2015). Unfortunately, most diversification
initiatives in resource rich/curse economics appear to concentrate in sectors that are
highly correlated with the natural resources available in such countries (Asif et al.,
Working Paper). A good example would be the large investments in petrochemical
industries in the GCC countries utilizing their comparative advantage of oil resources
(Albassam, 2015; Hvidt, 2013). Such types of investment are termed vertical
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diversification, where a country tries to further develop their already established
resource-dependent sectors by adding more value-adding segments to the process. Figure
1.2 illustrates how disposable plastic utensil manufacturing, for example, is a value-
adding use segment from crude oil.
Figure 1.2: Value Adding Segments as Derivatives of Crude Oil
Source: Saudi Basic Industries Corporation (SABIC)
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Studies claim that resource-rich countries are seeing visible success in vertical
diversification (e.g., Hvidt, 2013; Krane, 2015). However, vertical economic
diversification does not eliminate the uncertainty associated with the fluctuation of prices
and production of the major resource – or where these resources are non-renewable, the
ultimate uncertainty within their post resource-exhaustion world. In addition, benefits
from vertical diversification are restricted to the size of the associated natural resource
supply and industry itself; and it therefore follows logically that job creation in such
situations will be limited due to the limitation of the number of industries that can branch
out from the base natural resource. Therefore, resource-rich countries continue to suffer
from the essential disabilities of the natural resource curse despite diversifying the
economy vertically (Albassam, 2015; Hvidt, 2013 Sachs and Warner, 2001; Tsui, 2010).
Horizontal diversification within a country, on the other hand, which involves
establishing new industries and nurturing underdeveloped ones (Ansoff, 1957; Hvidt,
2013) that are essentially decoupled from rich/curse resources, is argued to be more
effective in building a more stable diversified economy. Unfortunately, horizontal
diversification is far more easily said than done. A variety of questions related to
horizontal diversification are therefore pertinent, including: what horizontal-type
industries should be established in each country? Is it a one-size-fits all strategy? Or
should each country specialize in a number of new industries while abandoning others?
In addition, what factors should lead to the creation and the development of new and
underdeveloped industries, especially where some studies argue that new (horizontal-
diversification) industries are created by pure luck (e.g., Rodrik, 2005; Wolman and
Hincapie, 2014)? Should the government intervene in such endeavors or let the market
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economy “take care of itself” (Rodrik, 2005:8)? Such unanswered questions add to the
ambiguity surrounding the creation of economic diversification plans, and as a result
there has been negligible success, if any, in diversifying the economies of resource-rich
countries (Albassam, 2015). To enable effective horizontal diversification to proceed, it
is now becoming evident that plans are needed that create new industries that are
minimally dependent upon rich/curse resources, and which nevertheless generate new
sources of sustained revenue.
I therefore introduce the notion of Renewable Entrepreneurship (R/E) and argue
that R/E will be required for the creation of new horizontal industries in resource
rich/curse countries. I define renewable entrepreneurship generally to be: an economic
system for the generation of business that is not critically rich/curse resource dependent
for the continuity of its contribution to the economy; and specifically as: the creation of
new private sector employment that is minimally dependent upon non-renewable
resources, conserves short-term investment, and multiplies long term value. I further
suggest, based upon this definition, that R/E is likely to be more possible in some
geographical areas than in others: that there is likely to be a phenomenon I would term to
be an “R/E cluster” due to factors, for example, that affect the development pace and the
long-term stability of the institutions of innovation (Li and Mitchell, 2009), or that
increase the potential of that cluster to transform resource injections into the creation of
ongoing businesses (i.e. low economic inductance, c.f., Mitchell, 2003). But if this is so,
then it is logical to further assert that the existence of an R/E cluster should serve as
means to identify the possible locations within countries or geographical regions for
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diversifying economies horizontally and away from the natural resources related
industries that engender vertical diversification around some few natural resources.
Renewable entrepreneurship shares some similarities with the concept of
sustainable entrepreneurship (Dean and McMullen, 2007), but is nevertheless distinct.
Just as sustainable entrepreneurship concerns the development of new businesses without
externalities (i.e., minimizing the waste of outputs), renewable entrepreneurship concerns
the development of new businesses minimizing the waste of inputs in addition to
minimizing the waste of outputs. Waste of inputs within R/E clusters is minimized due to
the within-cluster proximity of suppliers and customers; while minimization of the waste
of outputs is minimized through increasing innovation and productivity growth via the
within-cluster knowledge spillover effect, as well as the specialization of the workforce
within that cluster (Porter, 2000).
Clearly from both its general and specific definitions, R/E focuses on the types of
businesses that although not necessarily independent of natural resources, have minimum
correlation with the natural resource and therefore its resource curse. Thus, R/E can lead
to greater economic diversification and provide the economic sustainability and growth
stability once the natural resource faces the risk of price decline and/or decline of
production. In the following section I therefore connect the notion of renewable
entrepreneurship to cluster theory and argue that R/E is specifically applicable to
geographical clusters.
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Cluster Theory
The divergences in the level of innovation and economic growth among regions
are well documented within regional economics literature (North, 1955; Amin, 1999),
and these divergences are more evident when comparing locations within the same
country (Fang and Yang, 2000; Sachs et al., 2002). In order to stimulate economic growth
in least favored regions (LFRs), urban, sociological, and political economists argue that
countries should create and develop “clusters” that will stimulate innovation and
economic development in such regions (Glaeser et al., 1992; Henderson, 2003; Jacobs,
1969; Marshall, 1920; Porter, 1990; 1998; 2000). Clusters are “geographic concentrations
of interconnected companies and institutions in a particular field, linked by
commonalities and complementarities” (Porter, 1998: 78). The roots of the concept of
clusters and its antecedents can be traced back to the writings of Alfred Marshall in his
book Principles of Economics (1890), where he argues for the externalities of specialized
industrial groups (Porter, 2000). Later, Porter developed what is considered to be a “neo-
Marshallian” concept of clusters, starting in his 1980’s writings (Martin and Sunley,
2003), arguing that clusters include several industries that compete with/complement
each other (Porter, 2000).
Benefits of clustering are argued to stream from two major sources. The first
factor is cost minimization; where firms benefit from the proximity of other
complementing firms (e.g., suppliers), as well as the proximity to market within these
clusters (e.g., buyers). Such proximity is argued to lower the cost of inputs to production
of a firm located within these clusters when compared to those located within isolated
locations (Porter, 2000; Wolman and Hincapie, 2014). The second factor is innovation
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and productivity growth; where firms within a cluster benefit from knowledge spillovers
streaming from other firms, as well as benefiting from specialization of labor and means
of production. Porter (2000) argues that due to proximity of complementing firms and
markets, firms within a cluster will be able to provide their suppliers with specific
feedback allowing them to receive more specialized inputs, and to closely track
customers’ reaction to their products and receive their opinion during production. Both
will allow firms within such clusters to excel and innovate more rapidly (Li and Mitchell,
2009). Moreover, the co-location of firms will allow them to develop bonding ties with
other firms within the same cluster (Kilkenny, 2015) which will allow tacit and sensitive
knowledge to transfer among the co-located firms. Such knowledge spillover is found to
add to the level of innovation and production growth of firms within a cluster (Audretsch
and Feldman, 1996; Jaffe et al., 1993; Li and Mitchell, 2009).
Other argued benefits of clustering include access to specialized institutions and
public goods, as well as ease of evaluating incentives and measuring performance; which
results in a better corporate governance environment (Boeprasert, 2012). Due to these
benefits, studies have found that clusters stimulate a rapid rate of new venture creation
that boosts economic growth within the regions of these clusters (e.g., Porter, 2000;
Delgadoet al., 2010; Glaeser et al., 2010).
Within the field of regional economics, industries are classified into three
different types (Porter, 2003). The first type is local industries, where these industries
tend to generate goods and services for the local society where employment is located.
The local-industry type has minimum competition with other regions. The second type is
resource dependent industries, where such industries (and therefore employment) need to
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co-locate with the natural resource they depend upon. Resource dependent industries tend
to compete both in the regional and international markets as long as effective production
can be generated from the natural resource. The third type is traded industries: industries
that are not resource specific, and do not tend to locate based on natural resources
considerations, but are based instead upon competitive and employment considerations
(e.g., human capital, specialized institutions, etc.).
Due to the idea that co-located businesses within clusters tend to conserve short-
term investments by cost minimization, and multiply long term value by increasing rates
of innovation through specialization and knowledge spillover, I argue that renewable
entrepreneurship is specifically applicable to specific types of geographical clusters:
those that develop traded industries that are minimally dependent upon non-renewable
resources. I therefore argue that according to cluster theory, such R/E clusters should be
expected to lead to the proliferation of the right type of industries needed for a
horizontally diversified economy to be generated.
However, two knowledge gaps are still to be addressed for R/E cluster-based
horizontal diversification to be realistic: (1) the variables that lead to the formation (i.e.,
inception) of clusters, and (2) those that lead to the development (i.e., growth) of such
clusters. According to Wolman and Hincapie (2014) “many argue that initial location is a
matter of idiosyncratic circumstances or simply luck, followed by processes of ‘path
dependence’ and ‘lock-in’” (p. 140). And more specifically, research is limited on the
macro variables that lead to the creation and sustainability of such clusters (Wolman and
Hincapie, 2014). Consequently, my research question that guides this dissertation is:
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RQ: Is renewable entrepreneurship (R/E) cluster growth associated with identifiable
economic variables?
Due to the argument that the initial location of clusters depends on “idiosyncratic
circumstances or simply luck” (Wolman and Hincapie, 2014: 140), three sub-questions
are triggered by the research question:
Sub-RQ1: What are the components of macro variables that influence R/E
cluster growth?
Sub-RQ2: Can these variables distinguish high vs. low R/E clusters?
Sub-RQ3: How might R/E cluster growth be improved?
What this research question, and its related sub-questions, invoke will be a
discussion (Chapter 2) of the economic theory setting within which their investigation is
framed. Furthermore, the answers to these questions also invoke a relatively involved
econometric model, that, without a brief precise of the likely relationships to be
addressed (Chapter 3), might prove to be more difficult to clearly explain. Thus, in the
following section I present likely influencers of the formation and development of R/E
clusters, both direct influencers, and influencers that might moderate the direct
influencer-R/E cluster relationship.
Likely Influencers
Direct: Public Policy and Clusters
Economic diversification exists within a larger macroeconomic setting (Rodrik,
2005). Macroeconomic settings are composed of three major components: markets (i.e.,
consumer spending); investments (i.e., business spending); and public policy
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(government spending, economic policy settings) (Blanchard and Fischer, 1989). The
basic idea is that once a stable macroeconomic system has been established in a country,
with the appropriate regulatory setting, economies should grow vertically and
horizontally in that economy. The debate of whether governments should intervene in
economic markets may be traced back to the days of the Great Depression in the 1930’s.
Prior to that, Classical Economic Theory (Smith, 1776; Ricardo, 1817) argued that
economies are self-stabilizing in the absence of any government-induced distortions (e.g.,
price and wage controls, banks restrictions, etc.) with only small flections in the
economy. However, largely due to market failure (Coase, 1937; Williamson, 1979) and
the inability of economies to self-correct; Keynesian Theory was proposed wherein John
Maynard Keynes argued that due to frictions in the economic system, the economy will
not be able to self-stabilize in the short and medium terms. This condition should
therefore require the government to develop a fiscal policy that includes several fiscal-
policy mechanisms (e.g., lower taxes, increased government spending, etc.) through
which such market failures can be corrected more quickly (Keynes, 1929). This theory
gained popularity in public policy as a result of its role to stimulate economic stability
and growth in the 1930’s (Samuelson, 1988). Monetarist Theory (e.g., Friedman, 1956,
1969), however, later argued for the importance of developing monetary policy to control
other economic factors (e.g., inflation, money supply and demand, etc.). These additional
arguments resulted in the development of New Keynesian Theory that argues for the
importance of public policy in general: government intervention through both fiscal
policy and monetary policy (Blinder, 1979; Gordon, 1972; 1975; Phelps, 1968; 1972;
1978); an approach that is followed now in most economies. Public policy is generally
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defined as “a system of laws, regulatory measures, courses of action, and funding
priorities concerning a given topic promulgated by a governmental entity or its
representatives” (Evans, 2008: vii). Thus public policy mainly concerns itself with the
regulatory policy that relates to organizing the socioeconomic interactions in different
settings including those of the macroeconomic environment (Evans, 2008).
The same debate can be found when it comes to the government role in the
formation and development of economic diversification clusters. While some studies
claim that government intervention can be harmful to clusters (Brakman and Marrewijk,
2013), the majority of researchers argue that government role in cluster formation and
development is vital and inevitable (Nathan and Overman, 2013; Porter, 2000; 2009).
However, despite the largely argued role of the government, scholars within the literature
disagree on the level and the mechanisms of government intervention in cluster formation
and development (Bartik, 2009; Wolman and Hincapie, 2014). In fact, Motoyama (2008)
argues that “…a limitation of the theory is its feasibility and whether and how
government can effectively fill-in the missing components of the cluster ... how and how
well government can promote the missing components is questionable” (p. 360). As a
result, the argued government role in the literature is merely suggested, which means that
researchers provide these arguments without actual studies that measure the influence of
government intervention on the formation and development of clusters.
Based upon the New-Keynesian expectations of public policy as previously
argued, I suggest that the tasks of government intervention can be better specified
through additional theoretical and empirical analysis, to enable the matching of the
various types of public policy factors: (1) fiscal policy factors (e.g., infrastructure
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development, availability of specialized institutions, communication platform
effectiveness); (2) monetary policy factors (e.g., ease of financing, ease of inflow of
foreign direct investments [FDI]); and (3) regulatory policy factors (e.g., regulatory
complexity, effectiveness IP protection).
And based upon the argument of Motoyama (2008), that feasibility and
practicality of government intervention are both open questions, it is sensible also to
inquire how the expectations of New-Keynesian-based assessments, and the identification
of likely R/E clusters, might be more effective. In short, it seems only reasonable to
better specify the basis for policy effectiveness. Transaction Inductance Theory
(Mitchell, 2003) offers a theoretical mechanism to suggest how social receptivity vs.
resistance within a cluster might help to answer such questions.
Transaction Inductance Theory holds that the phenomenon of “inductance” –
which I apply herein to be a type of social reactivity to or from public policy – causes
waste, and impedes economic growth within an economic setting (Mitchell, 2003). Such
economic inductance might help in explaining variance in the level of innovation and
economic growth among regions. For the purposes of this dissertation, I define economic
inductance to be resistance to the conservation of economic energy, and I suggest that
Economic Inductance may have a direct influence; but also may have, as further
elaborated below, a moderating influence as well.
Mediating: Pace and Stability Variables
As previously noted, some geographical areas are likely to be better than others in
developing horizontal diversification clusters due to factors that affect the pace of
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development and the stability of the institutions of innovation (Li and Mitchell, 2009). It
is well understood that “… institutions vary widely in their consequences for economic
performance; some economies develop institutions that produce growth and development,
while others develop institutions that produce stagnation” (North, 1990: 159). And when
it comes to businesses in R/E clusters, the institutionalization of innovation therefore
becomes vital for their survival. Two major variables are argued to directly impact
process, where the development toward the institutionalization of innovation can emerge.
First, as mentioned earlier, one of the major argued benefits of businesses co-locating in a
geographical cluster is to profit from the knowledge spillover streaming from other firms,
as well as benefiting from the specialized means of production (i.e., technology and
labor) available in such clusters (Porter, 2000). The second benefit, mainly to the whole
economy, from co-existing within a cluster is the intensified competition among
businesses which guarantees that survival will be for the fittest (Porter, 1998; 2000).
Both of these variables are argued to lead toward the institutionalization of
innovation within a cluster. First, Li and Mitchell (2009) argue that as competition
intensifies among businesses within a cluster, businesses tend to be innovative in order to
survive. Hence, they suggest that mechanisms that govern local competition will control
the pace of innovation institutionalization, and therefore, I suggest, affect the productivity
growth rate within a country. Second, although intensifying competition will result in a
higher pace of innovation, competition does not guarantee the constancy of the
innovativeness rate. Thus, the role of knowledge spillover mechanisms within a cluster is
argued to impact the stability of the innovation institutionalization through the level of
specialization within that cluster (Li and Mitchell, 2009). It therefore seems logical to
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expect both types of mechanisms to be directly impacted by public policy variables; and
furthermore that it is the role of governments to ensure the effectiveness of these
mechanisms (e.g., setting antitrust laws, communication platform effectiveness) within
these clusters (Li and Mitchell, 2009; Porter, 2000).
Moderating: Economic inductance
As previously noted, the notion of economic inductance captures the phenomenon
of resistance to the conservation of economic energy. What variables might vary in ways
that affect the conservation of economic energy as it relates to public policy?
Sociologists, urbanists, and economists have hypothesized and tested the role of several
factors to explain variance in the level of innovation and economic growth among
regions, namely, the role of social structure within a society (e.g., Bourdieu, 1985; Burt,
1992; Coleman, 1988; Portes and Sensenbrenner, 1993; Putnam, 1995) in
enabling/inhibiting knowledge transfer and innovation; which has been contrasted with
the role of human capital (e.g., Becker, 1975; Jacobs, 1984; Lucus, 1988; Schultz, 1963)
in generating economic activities and regional growth. Due to mixed results found in
studies that have tested each argument, a new creative capital perspective (Florida, 2003)
was presented more recently and is gaining empirical support. This perspective combines
several factors from both previous views. In his creative capital perspective, Florida
(2003) argues that regional growth and cluster success lies in three major factors that
either will produce growth or will resist it: technology, talent, and tolerance. Technology
is defined to be the level of high-technology concentration within the cluster, talent as the
level of human education within the cluster, and tolerance as the level of openness and
acceptance others within the culture of the cluster. I argue that these factors will
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moderate the process of cluster formation and development due to their likely effect on
economic inductance.
Contributions
In this dissertation I contribute to the literatures of entrepreneurship, regional,
urban, and development economics in several respects. My first contribution is the
introduction, specifically the composition of the notion of renewable entrepreneurship
(R/E): a concept that is distinctly serviceable to the creation of employment from self-
revitalizing businesses through government engagement, where I argue that the
application of R/E within economic clusters likely serves as means to achieve regional
economic diversity, and hence, stable economic growth. Second, I extend urban and
regional economics research by providing additional understanding regarding what
economic variables influence renewable entrepreneurship cluster growth, and how
government engagement can be optimized to lead to regional horizontal economic
diversification (Motoyama, 2008).
Third, the results extend the notion of institutionalization of innovation of pace
and stability variables (Li and Mitchell, 2009) by suggesting that not all levels of
competition intensity are productive for the economy, and that for value creation from
knowledge spillover effectiveness to be amplified, favorable economic and institutional
conditions have to be in met for the stability of economic growth (Gordon and McCann,
2005; Landau and Rosenberg, 1986). Fourth, this dissertation extends transaction
inductance theory (Mitchell, 2003), by introducing the notion of economic inductance,
and arguing that such economic inductance within societies will cause waste of resource
investments and deter economic growth. Fifth, this dissertation develops theoretically
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rigorous and empirically valid means whereby high potential economic clusters can be
identified and distinguished from those with lower potential for renewable
entrepreneurship cluster growth, and hence, where government investment can be
optimized leading to regional horizontal economic diversification (Motoyama, 2008).
Finally, this dissertation answers several calls to further investigate the influence of
public policy on entrepreneurship (Zahra and Wright, 2011), by connecting
entrepreneurship research to the macroeconomic theories and research methods within
the domains of regional, urban, and development economics, where the role of
government engagement in shaping economic policy and economic growth is
productively developed (Snowdon and Vane, 2005).
Map of the Dissertation
This dissertation is divided into seven chapters. In this introductory chapter
(Chapter 1), I have introduced the notion of Renewable Entrepreneurship, where Porter’s
(2000) cluster theory serves as vehicle where Renewable Entrepreneurship can be
examined. In Chapter 2, I organize the urban and regional economics literature under the
concept of economic diversification. To achieve this goal, I synthesize three interrelated
areas: diversification in the world economy, the role of governments in economic
diversification, and the role of government in stimulating renewable entrepreneurship. In
Chapter 3, extrapolating from the New-Keynesian framework, I develop a model of
renewable entrepreneurship clusters. The model suggests nine hypotheses associating
renewable entrepreneurship cluster growth to various public policy and pace and stability
variables, and with the Economic Inductance Index. In Chapter 4, I present an empirical
study used to examine this model. Chapter 5 reports the results of the study and
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hypotheses testing. Chapter 6 evaluates the findings, discusses implications for theory
and practice, limitations and future research opportunities. Finally, Chapter 7 concludes
this dissertation.
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CHAPTER 2: ECONOMIC DIVERSIFICATION AND RENEWABLE
ENTREPRENEURSHIP CLUSTERS
As theoretical background for this dissertation, I next examine three interrelated
topics to set the stage for the research: diversification in the world economy, the role of
governments in economic diversification, and specifically, the role of government in
stimulating renewable entrepreneurship.
A. Diversification in the World Economy
Given the importance of economic diversification, several econometric indices have
been developed to measure the diversity of an economy (Albassam, 2015; Mack et al.,
2007; Pede, 2013; Wagner, 2000). In economic analysis, the use of indices to establish
the meaning of applicable constructs is commonplace. While research that depends upon
reflective indicators would not include measures in the process of construct definition;
research that utilizes formative indicators – where the index is the construct – requires the
specification of the measures as the only practical means to build theory
(Diamantopoulos and Winklhofer, 2001).
The first such economic diversification index was introduced by Rapkin (1954) who
introduced an Index of Economic Diversification that measures economic diversity using
two factors: (1) the number of different economic activities (i.e., number of industries)
within a geographical area of analysis; and (2) the employment distribution among the
industries within the geographical area, in order to take into account the concentration of
the industries in such an area. Since then econometric indices have proliferated to
measure specific elements of economic diversification (Mack et al., 2007), and to rank
countries based on their economic diversification and export-led growth strategies (e.g.,
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The Global Competitiveness Report, 2014). In the following sections I survey the leading
diversification measures, and then present a ranking of selected countries according to
some of the key indices; to compare the economy of those endowed with rich resources
with those that implemented export-led growth strategies with industries less connected
with rich/curse resources.
Equiproportional Measures:
The fundamental assumption of equiproportional measures is that the number of
industries matters more to economic diversity than does the type of industry (Siegel et al.,
1993; 1995). This assumption was derived from entropy law, where entropy measures the
economic activities (e.g., employment distribution) among industries (Wagner, 2000). An
economy that has a greater concentration of economic activities is considered to be a less
diversified economy, and therefore more likely to be subject to entropy: deterioration
over time. The most common equiproportional measures are: (1) the Ogive index (Oi):
which sums the difference between actual economic activities in each industry and equal
economic activities in those industries (Attaran and Zwick, 1987); (2) the Herfindalh
index (Hi): which sums the squares of the market shares of firms within an industry or
sector to provide an indication of competition (low) or monopoly (high); and (3) the
National Average index (Ni): which sums the difference between actual economic
activities in each industry and the national average economic activities of those same
industries (Dissart, 2003). Equiproportional measures are commonly used due to their
computational ease; and that they do not demand as much data as other measures
(Akpadock, 1996; Attaran, 1986; Deller and Chicoine, 1989; Kort, 1981).
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Type-of-Industries Measures:
As the name implies, the primary assumption of type-of-industries measures is
that the type of industries, rather than the number of industries, matters more to economic
diversification, and these measures assume that economic growth is driven by export
demand (Wagner, 2000). The leading type-of-industries measures are: (1) the Percent
Durable Good measure (Pi): which assumes that excess income leads to increasing
exports, and that demand of durable goods is sensitive to variability of income. Thus, the
percent of durable goods in exports is calculated to reflect the diversity mix in an
economy (Siegel et al., 1995); (2) the Location Quotient measure (LQs): which assumes
that the excess of either income or employment in an industry within a region, when
compared to the nation, generates greater exports due to the idea that higher
concentrations of income and/or employment will lead to higher production rates
(Shaffer, 1989); and (3) the Shift-Share measure (SSi): which compares the region’s
growth rate relative to the total (i.e., national) growth rate, by comparing the growth rate
of industries in the region to those in the nation (Wagner, 2000).
Industrial Portfolio Measure:
Influenced by portfolio theory from the finance literature (Markowitz, 1959;
Sharpe, 1970), several researchers developed a comparable measure where the principal
assumption is that policymakers should construct an industrial portfolio by selecting the
industries that the region/country should invest and specialize in; in an analogous
approach to an investor picking the financial instruments that he/she would invest in
(Brown and Pheasant, 1985; Conroy, 1974; Hunt and Sheesley, 1994). Such an industrial
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portfolio is considered mean-variance efficient if it generates the highest returns when
compared to other portfolios with the same amount of variance and where no other
portfolio with lower variance achieved the same return.
Input–Output Measures:
Input–output (I-O) models have been developed to highlight the importance of
inter-industry linkages on the structure and performance of the regional economy (Siegel
et al., 1995; Wagner and Deller, 1998). Such inter-industry linkages are expected to
intensify in more diversified economies (Wagner, 2000). The (I-O) models are
constructed using three measures: (1) the economy size (i.e., number of industries in the
economy); (2) the degree of imports; and (3) the flow of inputs produced locally between
industries in the regional economy. Each of these three measures is then compared to the
base economy (e.g., national economy) to determine the degree of diversification in the
regional economy (Wagner and Deller, 1998).
Global Ranking Measures:
The role of economic diversity mainly focuses on regional economic growth and
stability (Siegel et al., 1995), and on empirical studies and developed measures where
limited in such domains (Wagner and Deller, 1998). Nevertheless, growing interest is
being directed toward measuring economic diversification globally in order to compare
local practices and strategies of economic diversity with those in the international market
(Hofmann, 2012; Hvidt, 2011; Porter, 2011). Reflecting this interest, measures have been
developed to rank the economies of countries around the globe based on their level of
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diversification. In the following, I will briefly review the two leading measures: (1) The
Global Competitiveness Report; and (2) The Atlas of Economic Complexity.
1. The Global Competitiveness Report: Originally developed and sponsored by
the World Economic Forum, The Global Competitiveness Report has been evaluating and
ranking 144 different countries (as of the 2014-2015 report) for 35 years based on their
competitiveness performance. Competitiveness is defined as the “set of institutions,
policies, and factors that determine the level of productivity of a country” (The Global
Competitiveness Report, 2014: 4). Competitiveness performance in these countries is
measured using the Global Competitiveness Index (GCI). This index contains 12 sub-
indices (termed “pillars” in the report), which then are classified into three categories;
those that relate to factor-driven economies, efficiency-driven economies, and
innovation-driven economies. The Global Competitiveness Report argues that a country’s
economy moves from being factor-driven (the basic state) to being innovation-driven (the
most advanced state) as it becomes more competitive. The three categories and the
related sub-indices are illustrated in Table 2.1.
As shown in the table, each country is evaluated and ranked based on its summed
score on all of the sub-indices, while also ranked based on each of these sub-indices
allowing for more sophisticated analysis, then classified into one of the three types of
economies. For example, Saudi Arabia, a resource-rich country, is ranked 24th
(of 144) in
the overall index, 15th
among the factor-driven economies, 33rd
among the efficiency-
driven economies, and 32nd
among the innovation-driven economies. While Japan,
considered to be a resource-poor country, is ranked 6th
in the overall index, 25th
among
the factor-driven economies, 7th
among the efficiency-driven economies, and 2nd
among
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the innovation-driven economies (The Global Competitiveness Report, 2014). Given that
the GCI includes sub-indices that relate to economic diversification (e.g., market size), a
number of studies of economic diversification utilized the GCI or selected sub-indices
among the measures used (e.g., Chiang, 2007; Manolescu, 2011). Nevertheless, the
measure is argued to focus more on macroeconomic evaluations more than economic
diversification per se (Chiang, 2007).
Table 2.1: Categories and Related Sub-indices of the Global Competitiveness Index
(GCI)
Category
(Economy) Related Sub-indices (SIs)
Factor-Driven
Economies
SI1: Institutions: Legal and administrative framework.
SI2: Infrastructure: Detrimental for the location of the
economic activities.
SI3: Macroeconomic Environment: Vital for economic growth.
SI4: Health and Primary Education: Healthy workforce is
central for economic productivity.
Efficiency-Driven
Economies
SI5: Higher Education and Training: To carry out complex
tasks.
SI6: Goods Market Efficiency: To efficiently produce and trade
the right mix of products.
SI7: Labor Market Efficiency: To allocate workers to their most
effective use.
SI8: Financial Market Development: To allocate resources
effectively.
SI9: Technological Readiness: To leverage information
adequately.
SI10: Market Size (i.e., number and size of industries): To
benefit from economies of scale.
Innovation-Driven
Economies
SI11: Business Sophistication: To enhance economic
productivity.
SI12: Innovation: The single factor that leads to economic
growth in the long run.
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2. The Atlas of Economic Complexity: Although less globally recognized than The
Global Competitiveness Report, The Atlas of Economic Complexity is considered to be
more closely related to the concept of economic diversification (Hausmann and Hidalgo,
2014; Tacchella et al., 2012). The Atlas of Economic Complexity is an outcome of a joint
project between Harvard University and Massachusetts Institute of Technology (MIT) to
measure societal productive knowledge that each country holds through using the
Economic Complexity Index (ECI) that reflects the product mix in the country’s export
basket by using the Standard International Trade Classification (SITC) as basis of
analysis. The primary assumption that underlies this research is that countries do not
make the products they want, but make those that they can. Complex economies are
argued to be those that “can weave vast quantities of relevant knowledge together, across
large networks of people, to generate a diverse mix of knowledge-intensive products”
(The Atlas of Economic Complexity, 2014: 18). Simpler economies, on the other hand,
produce simpler and easily imitable products. To measure economic complexity, The
Atlas of Economic Complexity uses two factors: (1) Diversity: defined as the number of
products that a country produces; and (2) Ubiquity: defined as the types of products that a
country produces, and measured through calculating the number of countries that make
the same product. In other words, it is not only the quantity of products that matter, but
also the quality of these products and the value of productive knowledge they reflect. The
Atlas of Economic Complexity ranks countries based on their ECI score, and provides
data that covers the period from 1995 until 2013 in its online database that is currently
being managed by the Center for International Development at Harvard University.
Given its focus in economic diversification, several studies utilized the ECI to measure
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diversity in various settings (e.g., Farra et al., 2013; Jain, 2014; Ženka and Novotný,
2014). Table 2.2 lists the Economic Complexity Index 2013 rankings for 124 countries,
with special emphasis on selected resource-rich [italics] vs. resource-poor [bold]
countries:
Table 2.2: A Map of the Diversification World (Most to Least Diversified) using the
Economic Complexity Index (ECI)
Rank Country Rank Country Rank Country
1 Japan 42 Turkey 84 Sri Lanka
2 Switzerland 43 Russia 85 Botswana
3 Germany 44 Panama 86 Paraguay
4 South Korea 45 Philippines 87 Uganda
5 Sweden 46 Lebanon 88 Senegal
6 Finland 47 India 89 Kazakhstan
7 Austria 48 Greece 90 Uzbekistan
8 Czech Republic 49 Tunisia 91 Peru
9 United Kingdom 50 Jordan 92 Pakistan
10 Slovak Republic 51 Brazil 93 Honduras
11 Singapore 52 Uruguay 94 Venezuela
12 Slovenia 53 Colombia 95 Zimbabwe
13 United States 54 New Zealand 96 Cambodia
14 Hungary 55 Costa Rica 97 Malawi
15 France 56 El Salvador 98 Iran
16 Italy 57 United Arab
Emir.
99 Tanzania
17 Ireland 58 Saudi Arabia 100 Ecuador
18 Belarus 59 Moldova 101 Ghana
19 Belgium 60 South Africa 102 Algeria
20 Denmark 61 Mauritius 103 Nicaragua
21 Israel 62 Georgia 104 Mongolia
22 China 63 Syrian Arab Rep. 105 Angola
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Rank Country Rank Country Rank Country
23 Mexico 64 Indonesia 106 Cote d’Ivoire
24 Poland 65 Macedonia 107 Bangladesh
25 Netherlands 66 Egypt 108 Madagascar
26 Thailand 67 Argentina 109 Lao PDR
27 Spain 68 Viet Nam 110 Congo
28 Romania 69 Chile 111 Mozambique
29 Estonia 70 Jamaica 112 Bolivia
30 Croatia 71 Trinidad and
Tobago
113 Sudan
31 Hong Kong 72 Cuba 114 Turkmenistan
32 Malaysia 73 Dominican
Republic
115 Azerbaijan
33 Norway 74 Guatemala 116 Ethiopia
34 Lithuania 75 Kuwait 117 Gabon
35 Portugal 76 Oman 118 Cameroon
36 Bosnia and
Herzegovina
77 Zambia 119 Yemen
37 Bulgaria 78 Australia 120 Papua New
Guinea
38 Canada 79 Kenya 121 Libya
39 Latvia 80 Namibia 122 Nigeria
40 Serbia 81 Qatar 123 Mauritania
41 Ukraine 82 Morocco 124 Guinea
42 Turkey 83 Albania
Relevant Best Practices:
As can be seen from the table above and as discussed earlier, natural resource
endowments seem to be unrelated to economic complexity within a country. In fact, the
table echoes the argument of natural resource curse theory, as it shows that countries that
are considered resource-poor countries to be much more economically diverse and
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complex (e.g., Japan, Germany, and S. Korea) when compared to resource-rich countries
(e.g., Russia, Saudi Arabia, Kuwait, Venezuela, Iran, and Nigeria). Despite that, quite a
few resource-rich countries and cities are argued to develop plans that have helped them
to diversify away from their natural resources, namely, Malaysia, Indonesia, and Mexico
as well as the Emirate of Dubai (Callen et al., 2014; Gelb, 2011; Sachs and Warner,
2001). Table 2.3 illustrates the best practices argued to be helping resource-rich countries
to turn the natural resource curse into a blessing, by achieving more diversified and
complex economies (Callen et al., 2014).
Table 2.3: Relevant Best Practices in Economic Diversification
Best Practices Successful Examples
Investing to form and develop highly-productive
economic clusters. Malaysia, Mexico, and
Indonesia
Developing linkages (enhancing network
development) within vertical and horizontal
diversification clusters
Malaysia
Attracting Foreign Direct Investment (FDI) to
leverage the economic growth. Malaysia, Mexico, and
Indonesia
Export-led growth plans, tax incentives, and ease of
finance to promote SMEs and entrepreneurial
activities.
Malaysia
High investment in training to be able to establish
and develop diverse products and achieve higher
levels of complex economy.
Malaysia and Mexico
Business-friendly environment, light regulations,
and advanced infrastructure. Dubai
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The conclusion that can be drawn from the variety of arguments and measures
presented, is that economic growth and prosperity is tightly linked to the products a
country produces. Concentrating on few products is shown to impede horizontal
diversification-led economic stability; and producing diverse but simple products will
also limit the economic growth (The Atlas of Economic Complexity, 2014). Thus, it is
not only how many products a country produces, but also the types of products produced
(Siegel et al., 1995, Wagner, 2000). Economic clusters are considered to be among the
most prominent means to achieve economic diversification that is high in both quantity
and quality (Callen et al., 2014; Porter, 1998; 2000; 2003), because not all diversification
(namely, simple diversification) is desirable.
As I have previously argued, renewable entrepreneurship (R/E) clusters: co-located
businesses within geographical clusters within which traded industries that are minimally
dependent upon non-renewable resources are developed, are expected to conserve short-
term investments by cost minimization, and multiply long term value by increasing rates
of innovation through specialization and knowledge spillover. Thus, I further argue that
R/E clusters are the appropriate mechanism to enable a country to achieve complex
economic diversification. Additionally, I argue that this desirable state of creating a
complex economy (i.e. horizontally diversified) within the world economy can be
reached through the formation and development of R/E clusters comprised of traded
industries (Sachs and Warner, 2001), which by the R/E cluster definition presented earlier
means creating clusters that: (1) conserve short-term investments by cost minimization
through co-locating within the cluster; (2) multiply long term value by increasing rates of
complexity and innovation through specialization and knowledge spillover; and (3)
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develop traded industries that are minimally dependent upon non-renewable resources.
However, the “creation” of an economic cluster is itself a complex and difficult
undertaking – one which entrepreneurs or businesses cannot reasonably be expected to
create without public intervention, and (as I shall further argue) public leadership.
Hence, it is important to include the role of governments in this analysis.
B. The Role of Governments in Economic Diversification
In order to understand the role of governments in economic diversification, we need
to explore the various competing economic theories that argue for or against government
intervention in the economy in general. The main and the most well-known debate within
macroeconomics is the Classical vs. Keynesian debate that started in the 1930’s
(Snowdon and Vane, 2005). Both views center on their explanation of market efficiency
and economic equilibrium, and even more importantly economic disequilibrium
(Greenwald and Stiglitz, 1987). In the following subsections I review each of the
competing theories and the assumptions of the major economic schools of thought
(namely, classical, Keynesian, monetarist, neoclassical, and New-Keynesian schools of
thought) on the role of governments in the economy. This analysis is important as it lays
the groundwork for my research model, which depends for its justification upon a New-
Keynesian argument: that certain government interventions, empirically derived, are
likely to better enable – through the identification and development of R/E clusters – a
horizontally diversified economy.
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The Classical School:
The origins of classical macroeconomics are attributed to the writings of Adam Smith
in his great work in The Wealth of Nations (1776), as well as to those of David Ricardo
(e.g., Principles of Political Economy and Taxation [1817]) and John Stuart Mill (e.g.,
The History of British India [1817]) amongst others. The major assumptions of classical
economics are:
1. Economic agents (whether firms or households) are rational, and are profit/utility
maximizing seekers.
2. Markets are perfectly competitive.
3. Economic agents have perfect knowledge of markets and prices.
4. Trade takes place when prices are established.
5. Economic agents have stable expectations.
Classical economists argue that these assumptions ensure that markets always clear
and that equilibrium is achieved (Snowdon and Vane, 2005). Classical economists
recognize that market failures might occur, but they claim that these failures are only
temporary and that markets should self-correct such failures, thus invoking the notion of
Invisible Hand (Smith, 1776). Therefore, classical economists stand against any
government intervention. As a result of the absence of a government role (and thus any
fiscal and monetary policies), microeconomic theories dominated the capitalist economy
for approximately two centuries, until the purported self-stabilizing notion was
challenged due to the economic events that occurred during the Great Depression, and
Keynesian theory was introduced to accommodate them (Greenwald and Stiglitz, 1987).
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The Keynesian School:
The contribution of Keynes to economic theory is claimed to be the “the most
significant event in 20th-century economic science” (Samuelson, 1988: 32). In fact,
Keynes’ General Theory (1936) is argued to mark the birth of macroeconomic theories,
and that microeconomics is “his creation” (Snowdon and Vane, 2005: 55). Given the
increasing critiques of classical economics’ inability to adequately explain market
failures that lasted a considerable amount of time, and the inability of markets to self-
stabilize in such economic events as the Great Depression, Keynes developed his General
Theory to confront the assumptions of classical economics. The main assumptions of
Keynesian economics are:
1. Economic markets are inherently unstable.
2. If left to itself, the economy will take a lot of time to return to a near equilibrium
status.
3. The prosperity level of an economy is essentially determined by aggregate
demand, and government should intervene to influence spending and shift the
demand curve outward (where demand increases due to factors other than price:
e.g. increased expectations, increased disposable income, weather, etc.).
4. When compared to monetary policy, fiscal policy is considered to be more
effective to stabilize the economy and correct market failures.
These assumptions introduced the role of governments in stimulating economies
at the macro-level through mechanisms of fiscal policies (e.g., lower taxes, increased
government spending, etc.), and supported the argument that such a role is vital,
especially during the periods of market failure. This gave Keynesian economics
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domination of macroeconomic theorizing and policy making participation during the
1950’s and 1960’s (Greenwald and Stiglitz, 1987; Dornbusch et al., 1989; Snowdon and
Vane, 2005).
The Monetarist School:
Having its origins in the 16th
century (see Locke, 1692; Hume, 1752), the quantity
theory of money was developed in the 1950’s-1960’s mainly by Milton Friedman (see
Friedman, 1956, 1969; Friedman and Schwartz, 1963) to argue for the role that monetary
policy can play to achieve price stability. This view was labeled “monetarism” in 1968 by
Karl Brunner (Snowdon and Vane, 2005). The major assumptions of monetarist
economics are:
1. Money supply is the main factor explaining variability in money income.
2. When money demand is in a stable state, most instability is attributed to variations
in the money supply.
3. The government can control money supply through mechanisms of monetary
policy.
4. Money supply should be allowed to grow at the same rate as the economy in order
to maintain long-term price stability.
The monetarist view has had major policy implications, especially on the
development of monetary policy and its mechanisms, including: currency exchange rates,
interest rates (and corollary reserve requirements) to control inflation, money supply and
money demand in the economy (e.g. in open market operations – such as the buying or
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selling of government bonds to expand [buy] or contract [sell] the money supply)
(Snowdon and Vane, 2005).
The Neoclassical School:
Due to the Great Inflation during the 1970’s, Keynesian economics were challenged
by several economists guided by Robert E. Lucas Jr. (see Lucas, 1973; 1976) who argued
that Keynesian economics does not provide adequate guidance on the development of
monetary and fiscal policies (Lucas and Sargent, 1979). These economists argued that the
visible hand of the government should be prevented (as they assume continuous market
clearing which would only be interrupted by government). They mainly do not recognize
the existence of market failure, and argue that what are thought to be market failures are
natural responses in price shifting, and that higher unemployment rates are attributed to
changes in workers preferences to take more leisure relative to current wages (Greenwald
and Stiglitz, 1987). The main assumptions of neoclassical economics are:
1. Assumes a general equilibrium framework.
2. Economic agents (whether firms or households) are rational, and are profit/utility
maximizing seekers.
3. Agents do not have perfect information, and their decisions are based on relative
prices.
4. Wage and price flexibility will ensure the continuous market clearing process.
5. An increase of the money supply through the mechanisms of monetary policy will
only increase prices without a direct effect on economic growth.
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6. Government fiscal policy (e.g., increasing spending to stimulate aggregate
demand) will have an impact only in the short run.
The contribution of neoclassical economists dominated macroeconomic research
during the 1970’s. However, several critiques were raised regarding the assumptions of
continuous market clearing and imperfect information (Snowdon and Vane, 2005). By
the early 1980’s, the majority of research found no theoretical or empirical support for
neoclassical economics, which made it lose ground to New-Keynesian economics
(Greenwald and Stiglitz, 1987; Snowdon and Vane, 2005).
The New-Keynesian School:
As discussed earlier, due to the major limitations and critiques of Keynesian
economics during the 1970’s, it was asserted that “… It is time to put Keynes to rest in
the economists’ Hall of Fame” (Lindbeck, 1998: 178), and move to more developed
models. Such challenges made it necessary that Keynesian model and assumptions
undergo major modifications to reflect the role of monetary policy as well as the impact
of supply shocks. Such modifications were led by the efforts of Gordon (1972; 1975),
Phelps (1968; 1972; 1978) and Blinder (1979) who enabled Keynesian economics to
adapt and absorb such changes. New-Keynesian economics accepts the major
assumptions of the orthodox Keynesian model, and adds the following main assumptions:
1. In addition to fiscal policy, monetary policy contributes greatly to market
stability.
2. Markets are characterized by imperfect competition.
3. Economic agents have asymmetric information of markets and prices.
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Thus, the New-Keynesian economics is described to reflect ‘real’ macroeconomics
(Snowdon and Vane, 2005). Despite continuous debate and critiques from neoclassical
economists, New-Keynesian economics has shown a significant resilience for the past 30
years, which is mainly attributed to its ability to adapt and evolve both theoretically and
empirically (Shaw, 1988; Lindbeck, 1998; Gali, 2002).
Summary
In conclusion, due to the dominance of the Keynesian and New-Keynesian models in
economic thought, government interventions in the economy are viewed to be inevitable
(Greenwald and Stiglitz, 1987; Dornbusch et al., 1989; Snowdon and Vane, 2005). When
it comes to economic diversification, the argument is no different. Rodrik (2005), for
example, argues that “… when we look closely at the details of how successful industries
are actually generated – how they ‘get off the ground’– we find that in almost all such
cases, public intervention has played a significant role” (2005: 8). The importance of the
public policy role is even indicated in the formation and development of clusters, as
suggested by Porter: “… public policy that provides rules, mechanisms, and incentives
for capturing external economies will improve productivity and, with it, job, wage, and
innovation growth [of clusters]” (2009: 5). Thus, the question now is not whether
government should or should not intervene, but rather, how and where it should intervene
to achieve the desired state: complex (horizontal and vertical) economic diversification.
In the next section I suggest that such intervention will necessarily engage
entrepreneurship in general to help to develop R/E clusters specifically.
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C. The Role of Government in Stimulating Entrepreneurship
Since the inception of entrepreneurship as a field, the field has mainly concerned
itself with the nexus of two phenomena: individuals and opportunities (Shane and
Venkataraman, 2000). The research continues to generate inquiries on these two concepts
that mainly focus on the entrepreneur (e.g., Baum et al., 2001; Gartner, 1989; Lee and
Venkataraman, 2006; Simon et al., 2000), entrepreneurial cognition (e.g., Baron, 1998;
Haynie et al., 2010; Mitchell et al., 2000; 2002; 2007; 2011), opportunity discovery (e.g.,
Hayek, 1948; Kirzner, 1979; Shane and Venkataraman, 2000; Eckhardt and Shane,
2003), opportunity creation (e.g., Alvarez and Barney, 2007; Mitchell et al., 2008;
Shackle, 1979; Sarasvathy, 2001; Baker and Nelson, 2005), and opportunity exploitation
and venture creation (e.g., Choi et al., 2008; Hmieleski and Baron, 2008; Westerman et
al., 2006) to name a few of the primary areas of focus. Research on the impact of public
policy on entrepreneurship has remained limited for the most part to practical
implications sections of published research (e.g., Dean and McMullen, 2007; Holcombe,
2003; Shane, 2000) and has stayed away from the focus of major entrepreneurship
journals (Zahra and Wright, 2011).
Some efforts to study the way public policy influences entrepreneurship have been
developed, which are primarily led by the theoretical work of David Audretsch (e.g.,
2007; 2009; 2010), and followed by empirical applications (cf. Li and Mitchell, 2009).
Several studies measure the influence of various public policy tools (e.g., Holtz-Eakin
[2000] study of the impact of ‘tax policies’ on small businesses survival) on
entrepreneurial activity. Other studies measure the reverse effect of entrepreneurship and
new venture creation on several macro-level factors. For example, in his study,
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39
Audretsch (2010) measures the impact that Small and Medium Enterprises (SMEs) have
on economic complexity and growth. As indicated earlier, a common theme in
entrepreneurial public policy studies is that they are mostly published in economics
journals rather than entrepreneurship journals. Research in entrepreneurship is largely
based on Austrian economics perspective (e.g., Kirzner, 1972; Shane, 2000). Table 4
compares the assumptions of Austrian economics to those of Keynesian economics and
illustrates the similarity between the two economic views.
Table 2.4: Austrian Economics and New-Keynesian economics Assumptions
Category Austrian Economics New-Keynesian
Framework Disequilibrium State Disequilibrium State
Information Asymmetrically distributed Asymmetrically distributed
Market Competition Imperfect Imperfect
Equilibrating Agents Entrepreneurs Governments
Given the similarity between the assumptions of Austrian economics and New-
Keynesian economics, it is surprising that calls to connect entrepreneurship research with
public policy were not made until recently (Zahra and Wright, 2011), who argue that now
is the golden age of entrepreneurship. Also, due to the similarities between the two
economic schools, I further argue that New-Keynesian economics should be connected to
entrepreneurship research, specifically when considering the impact of public policy in
stimulating entrepreneurial activities. Such engagement will enable entrepreneurship
research to explore the impact of various mechanisms in macroeconomics taking account
of their greater potential for effects on entrepreneurial activities. So, for example, instead
of merely measuring the impact of tax policies (Holtz-Eakin, 2000) on stimulating
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entrepreneurial activities, one can envision research into the effects of the various tools of
fiscal, monetary, and regulatory policies on entrepreneurship, as further discussed in the
next chapter. Furthermore, under this logic, it might even be possible to question the
primary point of disagreement between Austrian Economics and New-Keynesian
Economics: the extent to which governments, in addition to their role in entrepreneurial
activities are also a direct force behind the movement toward economic equilibrium in
general.. Also, making such a connection may also allow scholars to identify further
types of opportunities that are created by other economic phenomena (e.g., opportunities
related to recession and financial stimulus). Nevertheless, I argue that the most important
benefit of introducing New-Keynesian economics to entrepreneurship research is that we
will better be able to account for the impact of governmental engagement in
entrepreneurship, and we can specifically identify public policy factors that
stimulate/inhibit entrepreneurship (Zahra and Wright, 2011).
Thus, in this dissertation, I utilize as a theoretical backdrop the assumptions of
New-Keynesian economics to underpin the logic for my research model: where
government-influenced macroeconomic variables are suggested to be related to R/E
cluster growth. In the next chapter I therefore present my research model and
hypotheses, based upon this theoretical foundation.
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CHAPTER 3: A MODEL OF RENEWABLE ENTREPRENEURSHIP CLUSTERS (LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT)
In this chapter I develop a model of Renewable Entrepreneurship (R/E) clusters
and the hypotheses that enable me to address my research question. As illustrated in
Figure 3.1, this model includes multiple variables that, as discussed previously, are likely
to impact R/E cluster growth, including public policy variables and pace and stability
variables. Furthermore, this model also accounts for the possible interaction of these
variables with Economic Inductance on the development and growth of R/E clusters. In
the remainder of this chapter I develop these hypotheses as I discuss each of these
variables in greater detail.
Figure 3.1: Renewable Entrepreneurship Clusters – Research Model
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Public Policy Variables
Economic clusters are found to generate regional economic growth (Delgado et
al., 2010; Glaeser et al., 2010). Nevertheless, the need for economic growth by itself does
not justify government intervention (Audretsch et al., 2007). The economic rationale
behind such intervention, however, may be derived from a New-Keynesian logic, where
the government role is seen as a key to correct market failures through effective public
policy (Snowdon and Vane, 2005). Market failures in entrepreneurial activities within a
geographical zone are argued to result from shortcomings on either the supply side of
entrepreneurship (availability of financial, human, and technological resources) or the
demand side of entrepreneurship (feasibility of business opportunities and market
growth) or both (Verheul et al., 2002; Wennekers et al., 2002). Government initiative(s)
to correct such failure(s) are argued to take several forms and to be applied at varying
levels of development/maturity; including policies related to the general business
environment (Porter, 1998; 2000; 2003; 2009), policies related to innovation and market
growth (McCann and Ortega-Argilés, 2013), and policies related to new venture creation
(Delgado et al., 2010; Stevenson and Lundström, 2007). I next discuss each of these
policies (separately), and hypothesize how they might relate to R/E cluster growth.
Business Environment Policy Maturity
The quality of the public-policy-shaped business environment within a region
constrains its economic growth rates (Porter and Kramer, 2002). Government economic
policies (e.g., relating to infrastructure development, regulatory system complexity, tax
structure, trade policy effectiveness) are argued to have a decisive role on the
proliferation rates of firms within an economic cluster (Dennis, 2011). Benign and
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supporting business environments are argued to minimize the transaction costs (TC) of
economic exchanges by substituting well-developed/mature public policies for the
otherwise diverse (and TC wasteful) safeguarding measures needed; as well as by
providing access to cost-effective resources; to thereby enable a greater number of
socioeconomic exchanges to occur (Doeringer and Terkla, 1995), and thereby enabling
economies to grow. However, in a hostile and uncertain business environment “various
kinds of external controls and supports must be devised to aid exchanges—that is, to
reduce transaction costs” (Schott, 1998: 112). Such controls and safeguards consume
what should otherwise be more-productively invested capital, and thus, limit economic
growth (Aidis et al., 2012). Therefore, it is not surprising to see governments trying to be
competitive in the development of cluster policies that aim to enhance regional business
environments (Greenstone et al., 2010); although, when it comes to policy
implementation, two main approaches have been advanced to enhance a regional business
environment: (1) place-neutral policies; and (2) place-based policies (Brakman and
Marrewijk, 2013).
Place-neutral Policies
Largely grounded in new economic geography theory (Krugman, 1991), which
promotes the advantages of economic clustering, place-neutral policies are those that
encompass instruments and initiatives that correct for market failures (e.g.,
underinvestment in infrastructure development) and provide roles and mechanisms that
incentivize business creation (e.g., tax structure). Under place-neutral assumptions, such
policy instruments do not target a specific geographical group, but all cluster participants
(Porter, 2009). Supporters of these “horizontal” polices, including the World Bank and
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EU Development entities (e.g. The European Commission’s Directorate-General for
International Cooperation and Development [DG-DEVCO]), argue that the main role for
governments within an economic cluster is “removing obstacles to the growth and
upgrading of existing and emerging clusters” (Porter, 2000: 16), and that economic
development policies should be designed “without explicit consideration to space”
(World Bank Development Report, 2009: 24) in order to guarantee equal opportunity,
and ensure economic growth in any location that encompasses condensed economic
activity. The European Union’s regional development policy also promotes the design
and implication of place-neutral policies to encourage growth in the economic activity
(Barca et al., 2012).
These place-neutral initiatives, also known as “setting the table” activities (Lerner
2009: 89), (including: tax incentive programs, advancement of public infrastructure,
enabling flexibility of corporate laws, contract enforcement, etc.) are argued to affect all
economic actors, and hence all economic activities, simultaneously, scaling the benefits
of these initiatives according to their level of maturity (Chatterji et al., 2013). According
to Porter (2000; 2009), the practice of picking winners, and favoring specific types of
industries over others by adopting place-based policies will not only limit economic
growth by abandoning vital economic activities, but also it will harm economic
productivity due to the risk of introducing market distortions through limiting
competition. Developing place-neutral initiatives, instead, recognizes the fact that “all
clusters [and economic activities] are good” (Porter, 2009: 6).
Overall, empirical evidence supports the benefits claimed by the development of
place-neutral policies, showing that the overall regulative system, tax rates, and endowed
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infrastructure are all drivers of economic activity and economic growth (Acs and
Audretsch, 1990; Quatraro and Vivarelli, 2014). For instance, Limao and Venables
(2001) used bilateral trade data among the United States and several countries to measure
the impact of the level of infrastructure in each of these countries on trade cost. The
results confirmed that the poorly-endowed infrastructure in several African countries is
driving up transportation and trade costs, and hence, has severely limited their economic
growth. Their results agree with the World Bank (2014) Doing Business Report which
shows that 7 of the 10 most difficult countries to do business in are in Africa.
Furthermore, studying the impact of macro-level governmental initiatives on new venture
creation growth rates, Kroksgård (2008) compared data from 63 different country and
found that contract enforcement, corporate governance, corporate law complexity,
government expenditure and trade rate are significant determinants of new venture
creation growth rates, and hence, economic growth rates.
Place-based policies
Place-based arguments, in contrast, suggest that space does matter, and that
depending on the context, governments should develop initiatives that are tailored to
setup a business environment that encourages specific types of economic activities (e.g.,
industries, clusters, sectors, etc.), while it overlooks (or in some cases even hinders) the
rest. Such practices are better known as “picking winners” policies (Porter, 2009: 6),
where specific types of economic activities are claimed to generate comparative
advantage for a country, and hence, better economic growth rates. These initiatives aim to
lower the transaction costs in these sectors even further through direct public investment
as well as through designing specialized infrastructure and corporate laws that fit these
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“picked” economic sectors (Porter, 2000). The argument against place-neutral policies
claims that following “one size fits all” policy initiatives does not take into consideration
the special characteristics that each economic sector requires, and thus, does not unlock
the full economic growth potential in these sectors (Barca et al., 2012). This claim has
been made by several international organizations such as the Organization for Economic
Cooperation and Development (OECD), and the Development Bank of Latin America
(CAF) (e.g., OECD, 2009a; 2009b).
Nevertheless, a large number of studies argue that place-based policies are
problematic for several reasons. First, several studies have found that special
circumstances have a significant impact on the success/failure of specific geographies
where specific economic activities are targeted; thus, making it difficult to predict which
cluster will succeed and generate higher economic growth (Brakman and Marrewijk,
2013). For example: Glaeser (2011) published a case study on the impact of hurricane
Katrina on New Orleans, showing that post hurricane, the city now has a more optimal
size which has boosted the economic growth within that area. Second, place-based
initiatives are found to promote Directly Unproductive Profit (DUP)-seeking activities
(Baldwin and Robert-Nicoud, 2007; Bhagwati, 1982), which mainly benefit from
introducing market distortions through limiting or skewing competition within the
marketplace and hence, limiting the economic impact of such special treatment that
place-based policies claim to enhance. Finally, and most importantly for the analysis in
this dissertation, place-based policies, which target specific economic activities, result in
an economy that is more vertically, instead of horizontally, diversified (see Brakman and
Marrewijk, 2013; Duranton, 2011, for a complete review).
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Thus, given that:
the development of place-neutral policies (including, but not limited to:
infrastructure development, regulatory system supportiveness, tax structure,
trade policy effectiveness) are drivers of economic activity and economic
growth;
place-neutral policies promote a business environment that is better aligned
with the notion of horizontal diversification of the economy (where the aim is
to develop new economic sectors in the economy while nurturing
underdeveloped ones (Porter, 2000);
the main argument in this dissertation is that renewable entrepreneurship
(R/E) clusters are the appropriate mechanism to enable a country to achieve
the complex (horizontal) economic diversification desired;
Then, (echoing Duranton (2011) who advises that governments should develop by
improving “… land-use planning, urban transport, provision of local public goods, etc.
[and that] … these policies … may not be as ‘sexy’ as setting up a bio-tech cluster … the
recommendation for local governments is to improve their traditional areas of
intervention rather than try to do ‘new things’” (p. 36)),
I suggest:
Hypothesis 1: Place-neutral business environment policy maturity within
an R/E cluster is positively related to R/E cluster growth.
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Innovation Policy Maturity
Innovation is argued to be the single most important component of long-term
economic growth (Rosenberg, 2004). The importance of innovation in economic growth
in the economic development literature was first realized by Solow (1956) as he, along
with Swan (1956), developed a simple economic growth model, which asserts that
aggregate output is simply a function of fixed capital and labor. In an extension to his
initial contribution, Solow (1957) conducted a study to apply his model to the US growth
data during the first half of the 20th century. In that study he was interested in calculating
the percentage of growth that was attributed to fixed capital and labor. The remarkable
discovery in this analysis was that about 90% of the US growth was neither explained by
fixed capital nor by labor.
This substantial residual (i.e., 90% of the US growth) was left unexplained, and
later was termed the Solow residual. It was not until the work of Griliches (1979), who
interpreted the residual as the accumulation of knowledge stocks, that the neoclassical-
economics-focused total factor productivity function (TFP) was introduced:
Y = AK α L
1−α
where Output (Y) is a function of knowledge (A), fixed capital (K), labor (L),
0<α<1 is the elasticity of output. Hence, aggregate output can be expanded through
either: (1) increasing the input factors used in production (i.e., fixed capital and/or labor);
or (2) increasing the amount of knowledge (i.e., to innovate) which will result in an
increase in the aggregate output for the given stocks of capital and labor. This significant
role of knowledge in economic growth has led many advanced economies to invest
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heavily in intangible assets (i.e., intellectual property, new modes of organizing, etc.),
even sometimes at rates greater than rates of investment in tangible assets (i.e., fixed
capital and labor) (Corrado et al., 2012). I note, however, that one of the major critiques
of neoclassical growth theory is that it fails to differentiate between public and private
knowledge, as it considers all knowledge to be a public good (Uppenberg, 2009).
When it comes to the regional economics literature, research has long investigated
the relationship between innovation and geography (e.g., Anselin et al., 1997; Audretsch
and Feldman, 1996; Jaffe et al., 1993; Porter, 1990). Several studies have found that
disparities exist among regions in their innovation rates, mainly due to cluster
externalities (Acs and Varga, 2002; Van Oort, 2004) and the environment for
entrepreneurship and innovation (Sternberg, 2011). The main argument is that when
firms are collocated within a cluster, innovation will be amplified through knowledge
spillovers streaming from firms within that cluster (Li and Mitchell, 2009; Porter, 2000;
Wolman and Hincapie, 2014). Hence, connectivity with other sources of knowledge (i.e.,
firms and research institutions) is argued to be the main driver of innovation, and
therefore, of the economic growth superiority found in these regions (McCann and Acs,
2011).
The role that knowledge plays in economic growth, however, is not merely
concerned with the acquisition of knowledge assets, but also with knowledge process:
“… converting new ideas into marketable outcomes” (McCann and Ortega-Argilés, 2013:
188). The introduction of this process view led to the development of the knowledge
based view (KBV) of strategy (Grant, 1996), which argues that knowledge is the most
strategically important resource within the firm. Within the KBV, the firm is
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conceptualized as an entity for integrating knowledge. In essence, where knowledge is
held and created by individuals, the main role of firms is to provide the conditions needed
for such individuals to create new knowledge (Nonaka, 1994). Knowledge created can
then be aggregated (e.g., through the creation of common language, systems, etc.),
transferred, and then applied broadly throughout the economy (Grant, 1996).
When it comes to knowledge transferability and appropriability, the literature
distinguishes between two categories of knowledge creation: explicit knowledge and tacit
knowledge. Explicit knowledge is what Grant terms “knowing about” knowledge (1996:
111), and it refers to knowledge that can be codified and transferred in a formal
systematic language (Nonaka, 1994). Tacit knowledge: “knowing how” knowledge, is
personal knowledge that has been developed through experience and rooted in action
within a specific context; as Polanyi put it, “We can know more than we can tell”
(1966:4). Tacit knowledge involves a cognitive element that centers in the concept of
cognitive scripts (Nonaka, 1994) that are gained through experience. An expert script is
defined as “highly developed, sequentially ordered knowledge germane to a specific
field” (Mitchell et al. 2000: 975); and it is acquired in a dynamic process (Glaser, 1984;
Read, 1987; Schumacher and Czerwinski, 1992), through deliberate practice (Baron and
Henry, 2010; Mitchell, 2005). Such tacit knowledge is difficult to express in formal
language, and thus, cannot be transferred easily (Nonaka, 1994), which makes it even
more difficult to appropriate (Grant, 1996).
Although knowledge is created in the minds of individuals, it is the interaction
among individuals that allows them to transfer ideas and to develop new ones (Kogut and
Zander, 1992). Nonaka (1994) refers to the individuals comprising this process as
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“communities of interaction” (p. 15), which amplify and create new knowledge. It
follows that creating communities of interaction will allow knowledge in general and
tacit knowledge specifically, to transfer among participants within such communities,
which results in the knowledge spillover effect (McCann and Ortega-Argilés, 2013). It
also follows that creating an environment where such interaction is possible will result in
higher levels of innovation, leading to higher financial growth for firms (and
aggregations of firms) (Grant, 1996; Nonaka, 1994), and to higher economic growth for
the cluster (Porter, 1990; 2000; 2009). However, when it comes to economic growth
within clusters, government intervention to develop innovation policies is deemed
essential to correct the market and institutional failures that can impede within-cluster
knowledge creation, transfer, and aggregation (McCann and Ortega-Argilés, 2013).
Innovation policy encompasses governmental initiatives that on one hand
incentivize R&D (e.g., through IP protection and through the availability of public
research institutions), but on the other hand allow for benefits of knowledge spillover
(e.g., through communities of interaction) within an R/E cluster (Delgado et al., 2010;
McCann and Ortega-Argilés, 2013). As mentioned earlier, the neoclassical growth model
argues that knowledge, and thus innovation as its derivative, is a public good. Public
goods, by definition, do not lead directly to incentives from innovation (Casson, 1982).
Therefore, issues related to the appropriability and spillover of knowledge can benefit
(often greatly) from government intervention to stimulate R&D and limit market failure
risk (McCann and Ortega-Argilés, 2013). When it comes to “appropriability,” if firms
are prevented from appropriating the benefits they can generate from their knowledge
development, investments in R&D will be discouraged; and thus it stands to reason that
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innovation rates and overall economic growth will be severely and negatively impacted.
In addition, it also stands to reason that if private benefits of knowledge are not
recognized, and all knowledge is considered to be a public good, firms will be
discouraged from participating in interaction events that enable knowledge transfer,
creation, and appropriability (e.g., due to fears of free-rider behavior, where other
economic actors can costlessly acquire the focal firm’s innovation). The expected result
is that the knowledge spillover effect dries up, and limitation of innovation and economic
growth rates ensues (McCann and Ortega-Argilés, 2013). Consequently, the need for
both knowledge appropriability and knowledge spillover provides the rationale for
government action to establish innovation policy to counter these market failures (OECD,
2010) and to develop an environment that will stimulate R&D and allow for the
amplification of the level innovation through the development of different appropriability
means (e.g., patents, secrecy, specific contracts), the subsidization of R&D institutes,
and the encouragement of knowledge spillover effects through supporting firms’
engagement in the community of interaction. Thus, I suggest that:
Hypothesis 2: Innovation policy maturity within an R/E cluster is
positively related to R/E cluster growth.
New Venture Creation Policy Maturity
Entrepreneurs are argued to be agents of change (Schumpeter, 1934; 1942) that
drive the markets toward equilibrium (Hayek, 1937; Kirzner, 1972; 1979) through
innovation. Classic theories of economic growth emphasize the role of innovation to
drive endogenous growth (Solow, 1956; Romer, 1986) [see Acemoglu (2009), for a
comprehensive review]). These theories argue that capital and labor are important factors
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in the economy, but that long-term economic growth depends on innovation and
technology. Although the entrepreneurship literature suggests generally the idea that new
venture creation is considered to be the main vehicle that entrepreneurs use to introduce
innovation, combine opportunity and resources, and to drive job creation, and therefore,
economic development (Gartner, 1985; 1990); macroeconomic theories have various
views on the role that new venture creation plays in economic growth.
According to neoclassical theory, new venture creation is the outcome where
expected profits can be maximized after adjusting for costs. The estimated profits and
costs are argued to be anticipated based on information streaming from similar activities
in the market. And since neoclassical theory argues that the market is already in
equilibrium (Snowdon and Vane, 2005), new entrants will only decrease profits available
to existing firms, which as a result decrease income available for investment (or
reinvestment). Therefore, in neoclassical theory, new venture creation is viewed to retard
economic growth. Consequently, neoclassical theory argues that economic growth needs
larger firms, instead of increasing numbers of new ones (Carree and Thurik, 2003).
In contrast, in the evolutionary school of economics, entrepreneurship is
considered to be the main driver behind economic growth (Audretsch and Keilbach,
2005). Evolutionary theories emphasize the importance that the development of
knowledge-based-economies plays, which gives rise to increases in new venture creation
rates, and the shift from large to small firms (Carree and Thurik, 2003). Under
evolutionary-school economic assumptions, the underlying mechanism is variation,
selection, and retention (Nelson and Winter, 1982). The main idea is that knowledge is
inherently uncertain and asymmetrically distributed among economic agents, which
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54
creates divergence among those agents on the expected value of commercializing new
ideas, and therefore which provides a driver for economic agents toward creating a wide
variety of new ventures. Thus, under evolutionary theories, entrepreneurship, and thereby
new venture creation as its result, is argued to be “the vehicle by which various (and
sometimes the most radical) ideas are sometimes implemented and commercialized”
(Audretsch and Keilbach, 2005: 80). Evolutionary theories also suggest the notion that
firms’ sizes within an economy are shaped by a selection process. In this selection
process, from the variety of new firms that enter the market with innovations [in the form
of new products], such innovative products as surpass those already offered in the market
by incumbent firms, are “selected for” in order for these new firms to succeed. This
selection process is what Schumpeter calls creative destruction; and he considers it “the
essential fact about capitalism” (1942: 83), where in this selection mechanism new
products are retained, to replace obsolete ones. Hence: variation, selection, and retention
as an entrepreneurial process. The evolutionary economics argument of the role of
entrepreneurship and new venture creation play on economic growth found strong
empirical support in several recent studies (Acs and Audretsch 1993; Acs and Armington,
2006; Klapper et al., 2010). These findings suggest weakness in the neoclassical
explanation and further suggest the increased credibility of the explanations offered by
growth theory and evolutionary economic theory.
Given the importance of new venture creation rates on economic growth, and
given the credibility of evolutionary economic theory, it follows that an investigation of
the conditions under which some new firms thrive in some geographical zones, but not
others is warranted. Economic performance within such geographical zones varies widely
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55
depending on the institutions and regulations in place. North (1990) emphasizes the role
of such institutions and argues that “some economies develop institutions that produce
growth and development, while others develop institutions that produce stagnation” (p.
154). Further, Gartner (1985) argues that in their creation of new ventures, entrepreneurs
do not work in isolated zones, but instead respond to their environment; an idea that is
further developed in the recent entrepreneurial cognition literature. By presenting a
socially situated view of entrepreneurship (Dew et al., 2015; Mitchell, Randolph-Seng,
and Mitchell, 2011) it is suggested that entrepreneurial knowledge depends upon the
situation and upon its distribution among persons within some definable area of influence
within the venture environment.
It follows that for increasing entrepreneurship and new venture creation growth
rates to be achieved, it is essential for an economy to develop new venture creation policy
that results in a “highly supportive regional entrepreneurial environment” (Gartner, 1985:
700). Several environmental factors that have been found to stimulate new venture
creation include: capital availability, access to suppliers, access to customers and new
markets, access to universities research institutions, access to supporting services, and
suitability of the transportation system, and most importantly, the ease of navigating
governmental complexity (Bruno and Tyebjee, 1982). In fact, Stevenson and Lundström
(2007) further argue that the single role of new venture creation policy is to correct for
“government failure” (p. 112) by eliminating the governmentally induced barriers to
entry and transacting, and by easing the difficulty of administrative and regulatory
requirements for the start-up process.
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Given that the advantages of the development of geographical clusters encompass
several environmental factors (i.e., availability of suppliers, customers, research
institutes, and supporting services within the cluster) that were found to stimulate the
growth rates of start-ups (Porter, 1998; 2000; 2003; 2009); it follows that the
development of new venture creation policy that encompasses instruments which reduce
barriers to/lower the cost of entry and allow for greater competition intensity (e.g., ease
of navigating governmental complexity, ease of financing, availability of business
incubators, etc.) within a renewable entrepreneurship cluster (Delgado et al., 2010;
Stevenson and Lundström, 2007) is essential for the growth of the R/E cluster, and thus,
for economic growth. Therefore, I suggest that:
Hypothesis 3: New venture creation policy maturity within an R/E cluster
is positively related to R/E cluster growth.
Pace and Stability Variables
The role of institutions long has been recognized by economists since the writings
of Adam Smith (1776), where specialization of labor within a society has been
considered to be an essential element for explaining the key underlying features of
economic growth, including: effective productivity through technological development,
enhanced resource allocation, and specialized production (North, 1989). Institutions are
considered to be self-regulating mechanisms (Douglas, 1986), which “consist of
cognitive, normative, and regulative structures and activities that provide stability and
meaning to social behavior” (Scott, 1995: 33). Institutions exist due to the uncertainties
involved in human interaction (DiMaggio and Powell, 1983; North, 1990).Thus,
institutions are argued to exert substantial control over human actions, including
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socioeconomic actions (Jepperson, 1991). Nonconformity with such institutions will be
met with increasing economic and social costs to correct such behavior (Phillips et al.,
2000).
Nevertheless, institutions differ across societies and in their consequences within
these societies. North (1990), for example, argues that “institutions vary widely in their
consequences for economic performance ... some economies develop institutions that
produce growth and development, while others develop institutions that produce
stagnation” (p. 154). He further argues that, for example, failure to develop low-cost
institutions for contract enforcement is considered to be the most prominent source of
stagnation across history. Such expressed concern is highly visible in the literature of
regional economics, which as previously noted, is a field that is mainly concerned with
the phenomena of variation of economic growth among different geographical areas
(McCann, 2001; Nourse, 1968; Richardson, 1970).
Earlier in this dissertation I presented the argument that both pace and
stability variables lead toward the institutionalization of innovation within a
cluster; that competition intensity influences the pace of institutionalization; and
that knowledge spillover increases its intensity (Li and Mitchell, 2009). In this
section I develop hypotheses concerning each of these variables in turn.
Competition Intensity
In its most simplistic form, competition is defined as the “independent rivalry of
two or more persons” (Stigler, 1957: 1). In neoclassical economics, markets are assumed
to be in a state of perfect competition, where no firm is able to hold market power to set
or manipulate the price of homogenous products (Snowdon and Vane, 2005). Such
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assumptions led Thompson (1989) to argue that competition is the single most effective
factor that restrains inflation rates within the economy. This assumption is clearly
reflected in the writings of Adam Smith (1776) where pure competition is seen as the
mechanism that transfers profit-maximizing behavior of rational economic agents into a
social optimum, a theoretical mechanism that is better known as the invisible hand
theorem (Makowski and Ostroy, 2001; Snowdon and Vane, 2005; Stigler, 1957). On the
role of competition, Smith argues that “if this capital is divided between two different
grocers, their competition will tend to make both of them sell cheaper, than if it were in
the hands of one only; and if it were divided among twenty, their competition would be
just so much the greater, and the chance of their combining together, in order to raise the
price, just so much the less” (1776: 126). It is such logic that leads to an argument that
competition intensity can influence the pace of the institutionalization of innovation.
In order for a perfectly competitive market to exist, the theory of perfect
competition argues that four conditions have to be met (for a comprehensive review, see
Frank, 1991):
1. Firms Sell Homogenous Products: meaning that products sold by firms operating
in a perfectly competitive market are perfect substitutes for one another.
2. Firms Are Price Takers: individual firms are unable to affect the price, (i.e., via
increasing production, sales, etc.) . This condition is likely to be met when a large
number of firms operate in the market.
3. Free Market Entry and Exit, with Perfect Mobility of Factors of Production:
under this condition, once a firm discovers a business opportunity, it is assumed
to be able to accumulate factors of production and enter the market to take
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advantage of such an opportunity. Similarly, once the benefit of the business
opportunity is extracted, the firm is free to dispose of the factors of production
and exit the market.
4. Firms and Consumers have Perfect Information.
Nevertheless, due to the impossibility of satisfying these conditions (i.e.,
heterogeneity of products, imperfect mobility of resources, and information are
asymmetrically distributed) very few [if any] markets come close to the state of being
perfectly competitive (Frank and Glass, 1991). Thus, three economic models of imperfect
competition were developed to study markets reactions under different competition
conditions. These models range from Monopolistic Competition models at the end of the
spectrum nearest to perfect competition, to Pure Monopoly at the other end (Makowski
and Ostroy, 2001). The role of these market models and the conditions under each
include (Nicholson and Snyder, 2011):
1. Monopolistic Competition:
a. Firms sell similar (but not standardized) products.
b. Large number of firms operates in the market.
c. Easy (but not free) Market Entry and Exit.
Thus, there is a strong incentive to develop differentiated and innovative products
in a knowledge-based-economy to compete with rivals and generate profits.
2. Oligopoly:
a. A few large firms.
b. Firms sell standardized and differentiated products.
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c. High barriers to entry: including large capital investments, economies of
scale, etc.
Hence, imperfect competition models argue that the structure of an oligopolistic
market leads to constant profit generating behavior, resulting in higher invested
amounts in R&D (when compared to pure monopoly markets as will be discussed
below), and thus, higher rates of technological advancement. However, such a
market structure is argued to limit innovative behavior of small firms due to the
high barriers to entry (Bonin, 1991).
3. Pure Monopoly:
a. A single firm: the firm and the industry are the same.
b. The firm sells a unique product.
c. The firm is price maker.
d. Market entry and exit is blocked.
Consequently, monopolistic firms have little incentive to invest in R&D and
engage in innovative product development. Thus, pure monopoly markets are
argued to be a destructive force within economies. Thompson argues that
“arrogance, insolence, inefficiency, and complacency are too often the hallmarks
of monopoly” (1989: 2).
The gradual shift of these market competition (perfect and imperfect) models is
illustrated in Figure 3.2.
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Figure 3.2: Perfect and Imperfect Competitive Market Models
Competition Intensity and New Venture Creation Policy Maturity
Since purely competitive markets are argued to be unachievable in practice, and
pure monopoly markets are argued to be destructive to economies, it is vital to set forth
government initiatives that enable and promote competition intensity (Thompson, 1989).
Competition intensity refers to the level of potential crowding-out effects on
entrepreneurial activities, which drives the speed of information and knowledge flows
within an R/E cluster (Delgado et al., 2010; Li and Mitchell, 2009). Thompson (1989)
differentiates between two types of competition: 1) Structural Competition; and 2)
Below-Capacity Competition. Structural competition refers to the market factors that
limit monopoly power. He argues that increasing structural competition could be
achieved through: a) increasing international trade, and thus, limiting the power of
domestic sellers to raise prices; and b) increasing the number of suppliers in the
marketplace. Below-capacity competition refers to the increased intensity of competition
due to increasing demand and/or decreasing supply in the market.
Clearly, the development of a new venture creation policy (Delgado et al., 2010;
Stevenson and Lundström, 2007), as mentioned earlier, that encompasses instruments
which reduce barriers to/lower the cost of entry and allow for greater
competition intensity (e.g., ease of managing governmental complexity, ease of
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financing, increasing domestic and international demand, etc.) can help in achieving the
benefits of both competition types referred to by Thompson (1989) and in gradually
shifting a marketplace from the state of pure monopoly toward monopolistic competition,
where innovation can grow and knowledge-based-economy development is achieved.
Hence, I suggest that:
Hypothesis 4: Within an R/E cluster, new venture creation policy maturity
is positively related to competition intensity within that cluster.
Competition Intensity and R/E Cluster Growth
When it comes to the impact that competition intensity can have on economic
growth, two different views are proposed. In the neoclassical growth model developed by
Solow (1970), economic growth is achieved via the accumulation of capital and labor,
while technological progress is treated as an exogenous factor. As advanced, this model
suggests that competition intensity, and thus the growth rates of new venture creation, is
argued to limit profit resources available for other incumbent firms, leading to a decrease
in investment spending on R&D (Carree and Thurik, 2003). Consequently, competition
intensification is expected to result in decreasing innovation and technological
development rates in the economy, thus having a negative effect on economic growth. In
other words, competition intensity and economic growth are inversely related under this
model, a proposition that did not find strong empirical support (Nickell, 1996).
In contrast, economic models under imperfect competition suggest that as market
structure shifts from pure monopoly to monopolistic competition, the single most
effective profit making behavior will be product differentiation, which will require higher
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rates of innovation, and thus economic growth (Nicholson and Snyder, 2011).
Furthermore, in the regional economics literature, cluster theory argues that competition
intensity is a major driver of innovation and technological advancement; and that as
completion intensifies, survival will be for the fittest (Porter, 1998; 2000; 2003; 2009).
Thus, firms within a market that is characterized by a large number of firms operating
and competing within the same cluster will put pressure on these firms to innovate to
survive, to enjoy a temporal monopoly (cf. Rumelt, 1987), and to harvest profit, or they
will be forced to exit.
Li and Mitchell (2009) argue that such market pressure and competition intensity
will positively influence the pace of innovation institutionalization, and therefore, the
productivity growth rate within the economy. The main argument of the
institutionalization view of innovation pace is that increasing rates of competition
intensity contribute to the overall environment by creating a more turbulent setting and
therefore a setting where market participants must be more attentive and vigilant. Such an
environment is characterized by higher rates of uncertainty and dynamism (e.g., Tan
2001; 2006). In such an environment, it has been found that firms become more
innovative and entrepreneurial (Thornhill, 2006). Thus, higher rates of competition
intensity are expected to increase innovation rates, and thereby boost the pace of
innovation institutionalization within the economy. Such an impact is expected to be
amplified within an R/E cluster due to the collocation and the proximity of other firms
and the increased rivalry rates (Porter, 2000). Thus, I suggest:
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Hypothesis 5: The level of competition intensity within an R/E cluster is
positively related to R/E cluster growth.
Knowledge Spillover Effectiveness
Traditional theories of innovation and technological change assume that the firm
is the starting point of innovation, while technological progress is treated as an
endogenous factor (Baldwin and Scott, 1987; Griliches, 1979). In an attempt to assess the
return on investment in R&D and its contribution to economic growth, Griliches (1979)
developed a model of the knowledge-production function. This model suggests that
knowledge is created and exploited within the same entity, and that firm innovation rates
(measured using patents and other forms of intellectual property creation) are the direct
result of firm investment in R&D and human capital, which can be presented
symbolically as:
I = αRD*HK+ ɛ
where I represents the rate of innovation, RD stands for R&D investments, HK stands for
investments in human capital, and ɛ is the error term. Thus, R&D is regarded as the
greatest source of economic knowledge production (Cohen and Klepper, 1991; 1992).
However, empirical results show that at the aggregate level (e.g., industry, sector,
economy, etc.), the rates of economic knowledge production have, in fact, exceeded
investment rates in R&D and human capital (Acs and Audretsch, 1990; Scherer, 1982).
In contrast to the foregoing approach, knowledge spillover theory (KST) treats
technological progress as both endogenous and exogenous (Audretsch et al., 2005). In his
introduction of the theory, Audretsch argues that “it is the knowledge in the possession of
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economic agents that is exogenous, and in an effort to appropriate the returns from that
knowledge, the spillover of knowledge from its producing entity involves endogenously
creating a new firm” (1995:179-180). KST argues that knowledge spillover from other
entities creates opportunities that can be exploited by other firms, which give rise to
higher rates of new ventures that are thereby created to exploit such an opportunity. Early
attempts were made to modify the knowledge-production-function by recognizing the
knowledge spillover effect streaming from public research institutes (Jaffe, 1989), which
can be presented as:
I = αIRD*UR*(UR*GC) + ɛ
where I represents the rate of innovation, IRD stands for R&D investments, UR is the
research expenditure conducted at universities, GC measures the geographic proximity
between universities and the firm and ɛ is the error term. These attempts where further
developed by KST theorists to include the spillover effect streaming from other firms in
the field. This view of the knowledge spillover effect has found ample empirical support
(e.g., Audretsch et al., 2005; Franklin et al., 2001; Li and Mitchell, 2009; Roberts and
Malone, 1996).
Knowledge Spillover Effectiveness and Innovation Policy Maturity
Within the regional economics literature, the impact of knowledge spillover is
highlighted due to the visibility of its impact: especially in economic clusters, where
firms operating in the same industry collocate (Proter, 2000). In order for knowledge
spillover to be fully effective through the ability of knowledge specialization to create
opportunities and promote innovation within an R/E cluster (Acs et al., 2009; Li and
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Mitchell, 2009), specific control measures need to be implemented that allow for and
further encourage knowledge creation, transfer, and appropriability (Grant, 1996), and
protect knowledge creators from the previously noted market failures that discourage
knowledge creation and/or knowledge transfer. Such market failures include the
treatment of knowledge as public good under neoclassical theory (Duranton, 2011), and
free-rider behaviors that prevail in the absence of sufficient control measures (McCann
and Ortega-Argilés, 2013). The development of an effective innovation policy
that incentivizes R&D (e.g., through IP protection and availability of public research
institutions), but yet allows for and encourages the benefits of knowledge spillover (e.g.,
through communication platforms) can facilitate the benefits of knowledge spillover
within an economy (Delgado et al., 2010; McCann and Ortega-Argilés, 2013). Therefore,
I suggest:
Hypothesis 6: Within an R/E cluster, innovation policy maturity within an
R/E cluster is positively related to knowledge spillover effectiveness within
that cluster.
Knowledge Spillover Effectiveness and Renewable Entrepreneurship Cluster Growth
Due to its major role in driving economic growth, major theorizing has been
conducted to identify drivers of entrepreneurship and new venture creation (Gartner,
1989; Low and McMillan, 1988; Rumelt, 1987; Shane and Venkataraman, 2000). In their
definition of the field, Shane and Venkataraman (2000) argue that entrepreneurship lies at
the nexus of individual and opportunity, where inquiries regarding why, when, and how
opportunities for the creation of goods and services come into existence are considered
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the most important to investigate. KST answers comport well with this assertion by
arguing that knowledge production in other entities (public and private) provides a source
of business opportunities that alert agents can recognize and exploit (Audretsch et al.,
2005). Such a benefit is argued to be bounded within the regional proximity of the
knowledge creator (Jaffe et al., 1993).
Such process reflects the Schumpeterian approach (1934), wherein he argues that
opportunities are created, rather than discovered. Holcombe (2003) considers
entrepreneurial activities, and opportunity exploitation, the most important source of
business opportunities. He argues that once an entrepreneur acts on an opportunity,
he/she creates other opportunities that allow other entrepreneurs to recognize them (e.g.,
through social network; Ozgen and Baron, 2007) and exploit these opportunities. This
process of continuous creation of business opportunities, and then entrepreneurial
exploitation is expected to lead to higher levels of economic growth. Li and Mitchell
(2009) argue that for the innovation institutionalization to stabilize, systematic episodes
of supply of innovative opportunities should be embedded within the environment.
Within an R/E cluster, where firms collocate with other complementing and
competing, firms benefits of knowledge spillover is expected to amplify due to the
proximity of these firms, their higher rates of socialization, and the higher specialization
rates of means of inputs, in addition to the proximity and similarity of their suppliers
(Porter, 2000). I argue that such characteristics should allow for the higher rates of
knowledge transfer among firms operating within an R/E cluster, leading to the growth of
the R/E cluster, and thus, to economic growth. Thus, I suggest that:
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Hypothesis 7: Knowledge spillover effectiveness within an R/E cluster is
positively related to R/E cluster growth.
Economic Inductance
In cluster theory Porter (2000) notes that “the mere presence of firms, suppliers,
and institutions in a location creates the potential for economic value, but it does not
necessarily ensure the realization of this potential ... when a cluster shares a uniform
approach to competing, a sort of groupthink often reinforces old behaviors, suppresses
new ideas, and creates rigidities that prevent the adoption of improvements” (p. 252,
264). This old-behavior momentum phenomenon may be conceptualized as a kind of
cognitive rigidity. Recently, entrepreneurial cognition research has suggested that
entrepreneurial cognitions are socially-situated dynamic cognitions (Mitchell et al.,
2011), and that entrepreneurial cognitions are much more pliable – influenced by and
influencing their outer environment (Baucus, Baucus, and Mitchell, 2014; Mitchell et al.,
2014). Thus, it seems reasonable to expect that to the extent that within-cluster
cognitions are pliable vs. rigid – i.e. they have low vs. high economic inductance
(Mitchell, 2003) – then such clusters may be more likely to be susceptible to economic
growth. This susceptibility may also affect public policy and institutionalization. Thus
the notion of economic inductance appears likely to be an explanatory addition to my
theorizing.
The notion of economic inductance – which I define herein to be a type of
socioeconomic inertia: social reactivity to economic opportunity that causes waste, and
impedes economic growth within an economic setting – was first introduced within
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Transaction Inductance Theory (Mitchell, 2003). According to the theory, “ … the level
of this reactivity is termed inductance (I) and can be computed as a function of a
reactivity constant (C) that represents the inertial characteristics of the mechanism”
(2003, p. 206). It has been suggested that “… in the entrepreneurship case, one of the
key implications of the theory … is that the level of cognitive inertia in entrepreneurship
(such as the capability to manage a startup without a lot of failure-generating waste) is
susceptible to change (entrepreneurship as transaction cognitions can be taught), and
therefore is susceptible to design” (2003, pp. 206-207). I would argue that economic
inductance may not only be malleable, but that it may also be measurable. Thus,
economic inductance, which I suggest can be conceptualized to be resistance to the
conservation of economic energy might help explaining the variance in the level of
innovation and economic growth among regions.
To explain variations in the level of innovation and economic growth among
regions; sociologists, urbanists, and economists have hypothesized and tested the role of
several variables, and more specifically, the role of social structure within a society (e.g.,
Bourdieu, 1985; Burt, 1992; Coleman, 1988; Portes & Sensenbrenner, 1993; Putnam,
1995) in enabling/inhibiting value creation from knowledge investments; and the role of
human capital (e.g., Becker, 1975; Jacobs, 1984; Lucus, 1988; Schultz, 1963) in
generating socioeconomic activities and economic growth. To complement such views,
Florida (2003) developed his creative capital perspective, within which he combines
several factors from both previous views. In his creative capital perspective, Florida
(2003) argues that regional growth and cluster success depends upon three key elements
within societies that will produce growth or will resist it, namely: technology, talent, and
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tolerance. He defines technology to be the level of high-technology concentration within
the cluster; talent is the level of human education within the cluster; and tolerance is the
level of openness and acceptance others within the culture of the cluster. Due to their
likely effect on economic inductance, I argue that these elements capture a notion of
economic inductance, where the level of economic inductance within a region is likely to
negatively influence the growth rates of the renewable entrepreneurship cluster.
Therefore, I suggest that:
Hypothesis 8: Economic inductance with within an R/E cluster is
negatively related to R/E cluster growth.
And, due to its argued longer-term institutional impact as well (Jepperson, 199;
North, 1990), and in addition to the argued direct relationship among economic
inductance and renewable entrepreneurship cluster growth, economic inductance is
expected (generally, because empirical examination has not yet been conducted) to hinder
the effectiveness of various public policy initiatives as well as the economic outcomes of
the institutions of innovation of pace and stability, especially in the short- and medium-
term (Lenihan, 2011). Therefore, I argue that economic inductance is likely to moderate
the proposed relationships among public policy variables, pace and stability variables,
and R/E cluster growth. Hence, I suggest that:
Hypothesis 9a: Economic inductance within an R/E cluster geography moderates
the relationship between business environment policy maturity and R/E cluster
growth; such that when economic inductance is high, the effect of business
environment policy maturity will be weaker on R/E cluster growth.
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Hypothesis 9b: Economic inductance within an R/E cluster geography moderates
the relationship between innovation policy maturity and R/E cluster growth; such
that when economic inductance is high, the effect of innovation policy maturity
will be weaker on R/E cluster growth.
Hypothesis 9c: Economic inductance within an R/E cluster geography moderates
the relationship between new venture creation policy maturity and R/E cluster
growth; such that when economic inductance is high, the effect of the new venture
creation policy maturity will be weaker on R/E cluster growth.
Hypothesis 9d: Economic inductance within an R/E cluster geography moderates
the relationship between new venture creation policy maturity and competition
intensity; such that when the level of economic inductance is high, the effect of the
new venture creation policy maturity will be weaker on the competition intensity.
Hypothesis 9e: Economic inductance within an R/E cluster geography moderates
the relationship between competition intensity and R/E cluster growth; such that
when the level of economic inductance is high, the effect of competition intensity
will be weaker on R/E cluster growth.
Hypothesis 9f: Economic inductance within an R/E cluster geography moderates
the relationship between innovation policy maturity and knowledge spillover
effectiveness; such that when the level of economic inductance is high, the effect
of innovation policy maturity will be weaker on knowledge spillover effectiveness.
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Hypothesis 9g: Economic inductance within an R/E cluster geography
moderates the relationship between knowledge spillover effectiveness and
R/E cluster growth; such that when the level of economic inductance is
high, the effect of knowledge spillover effectiveness will be weaker on R/E
cluster growth.
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CHAPTER 4: METHODS
This chapter describes the methods used to test the hypotheses presented within
this dissertation. The research design is presented in the first section of this chapter,
followed by the data gathering section where the various sources of archival data utilized
within this study are described. Next, is the measurement section which illustrates the
operationalization of each variable. The data analysis section, which includes a
specifically constructed econometric model to test the hypotheses, is presented at the end
of this chapter.
Research Design
The main research question within this dissertation is: Is renewable
entrepreneurship (R/E) cluster growth associated with identifiable economic variables?
In this dissertation I utilize clusters within the United States to assess the impact of such
macroeconomic variables on R/E cluster growth. Limiting the data to clusters within the
U.S. helps to control for various political, economic, and regulatory factors when
compared to cross-country (e.g., OECD countries, European Union, etc.) cluster data
(Maddala, 1999), while still providing a sufficiently large economic sampling frame
within which variation in the constructs of interest might reasonably be expected.
The formation of a cluster often depends on political factors (Ried et al., 2008).
Several states within the United States have established cluster-based economic
development programs that support cluster creation within those states (e.g., Texas
Industry Cluster Initiative, Washington State Cluster Development Analysis, Utah
Economic Cluster Program, etc.). For instance, in 2004, the governor of Texas
announced that the economic development policy within the state will focus upon the
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establishment of various industry clusters (Texas Industry Profiles, 2004). Similar
announcements were made by numerous local officials of other states as well states
(Akundi, 2003). Hence, while R/E cluster growth will be measured at the cluster level; I
suggest the usage of state level data to measure the various effects of the proposed
independent variables on R/E cluster growth. Similarly, state level data will be collected
to measure mediating and moderating variables.
Data Gathering
The goal of this dissertation is to understand, explain, and predict the degree of
growth in renewable entrepreneurship clusters associated with changes in
macroeconomic variables (namely, public policy variables, pace and stability variables,
and economic inductance) over time, which requires a longitudinal design (See Figure
3.1, Research Model). Thus, I collected and analyzed state-level and cluster-level data
over a seven-year period from 2007-2013. This time period was selected for three main
reasons. First, the seven-year period allows sufficient time for renewable
entrepreneurship cluster growth to develop and changes in macroeconomic variables to
appear, and is thus adequate for examining relationships amongst them. Second, the
selected time period reflects the current business and economic environment by capturing
the impact of the financial crisis of 2007-2008, and of the policies that followed (e.g.,
Klapper and Love, 2011; Leigh and Blakely, 2013; Porter and Kramer, 2011; Stoddard
and Noy, 2015; Wilson and Eilertsen, 2010). Third, the selected time period was limited
to overlapped years available within the various databases as the sources of the secondary
data utilized in this dissertation.
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Hence, due to the variety of variables involved in this dissertation, archival data
will be gathered; and is available from five different sources. First, I shall utilize the U.S.
Cluster Mapping Project (USCMP) database to collect cluster-level data related to R/E
cluster growth and cluster-level control variables. The USCMP employs Porter’s (2003)
definitions of traded, local, and natural resource-dependent industries, mentioned
previously, to group economic activity within the four-digit SIC codes into clusters that
follow Porter’s classification. For each cluster, the USCMP provides annual observations
of various cluster characteristics (e.g., level of employment, number of establishments,
average annual wage, cluster specialization, etc.) from 1998 onward. The database
allows users to obtain measures of these characteristics at county, economic area (EA),
and state levels. Given that the goal of this dissertation is to reflect the importance of
renewable entrepreneurship clusters, I will focus on clusters that are closely related to the
research question and the hypotheses, namely, traded clusters. Under this category, the
database identifies 51 types of traded clusters (e.g., aerospace, environmental services,
information technology, etc.) including 778 subclusters.
Second, I shall employ the Small Business Policy Index (SBPI) which is created
by the Small Business & Entrepreneurship Council (SBE Council). The SBE Council
has published the SBPI from 1985 onward, and rates and scores each of the 50 states
based on a wide variety of policy measures (42 total), including tax (e.g., personal
income taxes, corporate income taxes, property taxes, sales taxes, etc.), regulatory (e.g.,
energy regulations, state minimum wage, regulatory flexibility status, etc.), and
governmental (e.g., government spending & debt, education reform, highway cost
efficiency, etc.) measures. Using this rating, the SBPI ranks states from the friendliest to
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the least friendly business environment. Third, I shall use the State New Economy Index
(SNEI) which is structured by The Information Technology & Innovation Foundation
(ITIF), and sponsored by Kauffman Foundation. Since 1999, the ITIF has published
seven editions of the Index, which tracks economic transformation in each of the 50
states. To assess the state of economy evolution in states, the SNEI uses 5 broad
categories (i.e., knowledge jobs, globalization, economic dynamism, the digital economy,
and innovation capacity), which are constructed from 26 different indicators (e.g.,
immigration of knowledge workers, initial public offerings (IPOs), industry investment in
R&D, etc.).
Fourth, I will gather data using the Economic Freedom of North America Index
(EFNAI) that has been created by the Fraser Institute. Starting in 2002, the Fraser
Institute has published 10 editions of the EFNAI to measure the extent to which
economic policies in each state/province in the U.S., Canada, and Mexico enable
economic freedom, defined as “the degree to which persons are free individually and
collectively to undertake economic activities of their choice, regardless of political
structure” (Wright, 1982: 51-52). The EFNAI evaluates economic freedom in each
state/province based on 5 areas (e.g., size of government, legal system and property
rights, sound money, etc.) and 53 indicators and sub-indicators (e.g., legal enforcement of
contracts, business costs of crime, black-market exchange rates, etc.). Fifth, I shall utilize
the General Patent Statistics Reports published by the Patent Technology Monitoring
Team (PTMT). The PTMT publishes periodic reports that reflect patenting activity
within the U.S. Patent and Trademark Office (USPTO). Within this dataset, I shall focus
primarily on the U.S. Colleges and Universities Utility Patent Grants Report, where it
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reflects the total patenting activity of colleges and universities within each state since
1969.
Measurement
Dependent Variable
Renewable Entrepreneurship Cluster Growth
Growth in various segments of the economy (i.e., sector, cluster, industry) has
been measured by both employment growth (e.g., Acs and Armington, 2004; Glaeser et
al., 1992) and entry of new firms (e.g., Hause and Rietz, 1984; McDougall et al., 1994).
Although both measures have been utilized within cluster and economic agglomeration
studies (e.g., Delgado et al., 2010) , employment growth remains as the most popular
measure due to its alignment with the notion of knowledge spillover introduced within
cluster and economic agglomeration theories (Van Soest et al., 2002). Endogenous
growth theory highlights the role of knowledge held by economic agents, and argues that
knowledge spillovers among such agents are a crucial factor leading to production and
economic growth (Romer 1986, Lucas 1988). Within cluster and economic
agglomeration theories, economic growth has been used to argue for and against
economic clusters. While Glaeser et al. (1992) and Feldman and Audretsch (1999), found
that diversity across a broad range of sectors (i.e., economic clustering) enhanced
employment growth, Henderson et al. (1995), Black and Henderson (1999a), and
Beardsell and Henderson (1999), found the same effect on employment growth when
economic activities are concentrated within a single industry.
Following the most commonly used conventions, I measured renewable
entrepreneurship cluster growth using employment growth, through utilizing the U.S.
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Cluster Mapping Project (USCMP) database. USCMP is a national initiative that is led by
Harvard Business School’s Institute for Strategy and Competitiveness, the U.S.
Department of Commerce, and U.S. Economic Development Administration. This
project has been established based on Porter’s (2003) classification of clusters to provide
over 50 million open data records on economic clusters to support economic growth
within the United States. Following Porter (2003), the USCMP group divides economic
activity within the four-digit SIC codes into traded, local, and natural resource-dependent
clusters. For each cluster, the USCMP provides annual observations of various
characteristics, including: employment growth rates, number of establishments, average
annual wage, and cluster specialization, from 1998 onward. The database allows users to
obtain measures of these characteristics at county, economic area (EA), and state levels.
Within this data base, I gather data over a 7-year period from 2007-2013 on employment
growth of each traded cluster, due to the previously developed rationale that traded
clusters are closely related to the research question and hypotheses within this
dissertation. As also noted previously, the database identifies 51 types of traded clusters
(e.g., aerospace, environmental services, information technology, etc.) including 778
subclusters.
Independent Variables
Business Environment Policy Maturity
Several business climate indexes have been developed to measure the
effectiveness of state regional policies. These indexes are mainly classified into two
main categories: productivity-focused indexes (e.g., model of spatial equilibrium,
weighted averages of residuals from wage and rent equations, etc.); and cost-focused
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indexes (e.g., Economic Freedom of North America Index, Small Business Policy Index,
State New Economy Index, State Business Tax Climate Index, etc.). In their analysis to
both types of indexes, Kolko et al. (2013) found that cost-focused indexes significantly
predict employment and output growth, while no such results were found using
productivity-focused indexes.
The Small Business Policy Index (SBPI), published by the Small Business &
Entrepreneurship Council (SBE Council), is considered the de facto cost-focused index
used in several studies to compare business environment policies among states (e.g.,
Motoyama and Hui, 2015; Pages and Toft, 2009; Wang and Martin, 2011). The index
rates, and gives scores to, each state based on a wide array of government-related factors.
According to Keating (2004), such government-imposed factors drive up doing business
costs, resulting in a negative effect on job creation, and ultimately economic growth.
Thus, the hypothesis within the SBPI is that a lower score indicates better policies that
enhance the business environment, and thus higher job creation and economic growth
rates.
Innovation Policy Maturity
Several national foundations construct indexes and publish reports to rank
innovation policy (Pages and Toft, 2009). The frequency of these reports ranges from
semiannual reporting to reporting once every few years. Examples of such indexes
include: the State Technology and Science Index, the Best Performing Cities series,
CFED’s Development Report Card of the States, and the Innovation Capacity Index
within the Information Technology and Innovation Foundation’s State New Economy
Index. Due to differences in the level of analysis (e.g., Best Performing Cities), or the
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limitation of time coverage of an index (e.g., the State Technology and Science Index), I
intend to measure innovation policy using the Innovation Capacity indicator (ICI). ICI is
an indicator that is published within the Information Technology and Innovation
Foundation’s State New Economy Index. The ICI, which has been utilized by a number
of studies (e.g., Atkinson and Correa, 2007; Atkinson; 2013), gives scores and ranks each
state using seven measures:
1. Share of jobs in high-tech industries;
2. The share of workers that are scientists and engineers;
3. The number of patents issued to companies and individuals;
4. Industry R&D as a share of worker earnings;
5. non-industrial R&D as a share of GSP;
6. clean energy consumption; and
7. Venture capital invested as a share of worker earnings.
Higher scores in Innovation Capacity indicate higher levels of innovation
capacity, which arguably ought to lead to higher economic growth rates.
New Venture Creation Policy Maturity
Several factors have been identified as key drivers of new venture creation
(Sutaria and Hicks, 2004). These measures include per capita bank deposits (Reynolds et
al., 1994), unemployment level (Ritsila and Tervo, 2002), level of local market demand
(Reynolds, 1994), and level of technological development (Shane, 2001), among others.
When it comes to measuring the maturity of new venture creation policy Campbell et al.
(2007) argue that “state governments’ policy selection leads to more or less
entrepreneurial activity within a state; as economic freedom increases due to favorable
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government policies, entrepreneurs are more likely to start new ventures” (p. 43). Thus,
the Economic Freedom of North America Index (EFNAI) has become a popular measure
of new venture creation policy, which has been utilized in various studies (e.g.,
Bjørnskov and Foss, 2008; Campbell et al., 2007; 2011; 2013; Kreft and Sobel, 2005).
The EFNAI, published by Fraser Institute, evaluates economic freedom and gives scores
to each state based on major 5 areas (e.g., size of government, legal system and property
rights, sound money, etc.) and 53 indicators and sub-indicators (e.g., legal enforcement of
contracts, business costs of crime, black-market exchange rates, etc.). The higher score
indicates higher levels of economic freedom leading to higher levels of new venture
creation, and thus higher job creation and economic growth rates.
Mediating Variables
Competition Intensity
Regional competition intensity is often measured using business startups rates
(Boari, 2001; Decker et al., 2014; Kawai and Urata, 2002), new firm survivial and failure
rates (Falck, 2007; Mata and Portugal, 1994), and per worker firm intensity (Glaeser et
al., 1992; Li and Mitchell, 2009), among many other methods. Recent studies have used
multi-dimensional indicators to measure competition intensity. For instance, Fritsch et
al. (2006) developed a multi-dimensional index that includes industry size and regional
growth rate to measure new firm survival rates and competition intensity. Others have
measured competition intensity using the Economic Dynamism indicator (e.g., Atkinson;
2013; Malecki, 2004). Economic Dynamism is an indicator that is also published within
the Information Technology and Innovation Foundation’s State New Economy Index,
which gives scores and ranks each state using five major measures:
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1. The degree of job churning;
2. The number fast growing firms;
3. The number and value of companies’ IPOs;
4. The number of entrepreneurs starting new businesses; and
5. The number of individual inventor patents granted.
Higher scores in Economic Dynamism indicate higher levels of innovation
capacity, which arguably ought to lead to higher economic growth rates.
Knowledge Spillover Effectiveness
The challenge with measuring knowledge spillover is that it is a latent variable
and is invisible by nature (i.e., cannot be directly observed). Thus, reflective indictors are
required in order to order to indirectly measure knowledge spillover (Diamantopoulos
and Winklhofer, 2001). Within the regional economics literature, two main approaches
are utilized to identify empirically regional knowledge spillover: through its effect on
wages and on patent activity.
Lucas (1988) argues that regional level of productivity is positively associated
with the level of human capital within that region. Given that education is one of the
major aspects of human capital, many studies use education level as a measure for
regional human capital (e.g., Becker, 2009; Fleisher and Zhao, 2010; Mathur, 1999;
Rodríguez-Pose and Vilalta-Bufí, 2005). Accordingly, knowledge spillover occurs when
highly-skilled workers in a region make other workers within that region more
productive. Such an increase in productivity is argued to lead to higher wages (Ciccone
and Peri, 2006; Combes et al., 2008; Hanushek and Woessmann; 2007; 2008; Moretti,
2004).
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Nevertheless, one of the major pitfalls of wage studies is that they treat
differences in average level of education among regions as static conditions, providing a
major limitation on their association to economic growth (Carlino, 2014). In order to
overcome such limitations, studies have utilized patenting activity to measure the level of
accumulation of knowledge within a region, which is argued to provide more informative
data on the association among knowledge spillovers and growth (Jaffe et al., 1992).
Arguably, universities are among the major sources of patents, regional
knowledge spillover, and thus, regional economic growth (Audretsch et al., 2005a;
2005b; Belenzon and Schankerman, 2013; Mueller, 2006). Thus, following convention, I
will measure regional knowledge spillover using the annual total number of utility patents
granted to all colleges and universities within each state. Higher number of utility patents
(protection for new functional inventions or improvements to existing functional
inventions: dealing with a machine, a process a product or to the composition of matter)
granted to colleges and universities within a state, indicates higher levels of knowledge
spillover, and thus, the likelihood of economic growth. Such data can be obtained from
the General Patent Statistics Reports, which are available from the Patent Technology
Monitoring Team (PTMT), and which publishes periodic reports that reflect patenting
activity within the U.S. Patent and Trademark Office (USPTO).
Economic Inductance Index
Given that no empirical studies have yet been conducted on the notion of
economic inductance (Mitchell, 2003), it is to be expected that no indicators exist to
measure economic inductance. Likely measures of economic inductance would be those
which can help to specify the level of resistance to the rapid conversion of resources
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infusions into economic growth, within a given cluster. As noted previously, lower
inductance would be expected where education, worker productivity, and technology
availability are high. Using the Global Creativity Index, published by the Martin
Prosperity Institute and measures economic growth based on the 3Ts (i.e., talent,
technology, and tolerance) of the creative capital perspective (Florida, 2003), as a guide; I
utilized both the Knowledge Jobs indicator and The Digital Economy indicator published
within the Information Technology and Innovation Foundation’s State New Economy
Index. I shall construct an indicator that takes the average score of both indicators and
thereby provides overall scores for my Economic Inductance Index. Thus, as noted,
higher scores on this index indicate lower economic inductance. Combined, Knowledge
Jobs and The Digital Economy indicators, now the Economic Inductance Index, gives
scores to each state based on the following eleven aspects:
A. Components of the Knowledge Jobs indicator:
1. Employment in IT occupations in non-IT sectors;
2. The share of the workforce employed in managerial, professional, and
technical occupations;
3. The education level of the workforce;
4. The average educational attainment of recent immigrants;
5. The average educational attainment of recent U.S. inter-state migrants;
6. Worker productivity in the manufacturing sector; and
7. Employment in high-wage traded services.
B. Components of The Digital Economy indicator:
8. The use of IT to deliver state government services;
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9. The percentage of farmers online and using computers for business;
10. The adoption and average speed of broadband telecommunications; and
11. Health information technology use.
Control Variables
I included several control variables that the literature has identified as possible
contributing factors to economic growth, in order to isolate the effects of the independent
variables, mediators, and moderators on renewable entrepreneurship cluster growth.
Hence, at the state level I controlled for (1) population density: measured by each state
population size/ its land area (in squared miles); and (2) accessibility to coastlines:
measured using a dummy variable coded as 1 if the state has access to coastlines, and 0
otherwise, because both of these variables contribute to explanations for employment-
sensitive economic growth (Kolko et al., 2013). Data for both state level control
variables were obtained from the United States Census Bureau database. And at the
cluster level I propose to control for (1) cluster affiliation: measured using dummy
variables denoting a cluster USCMP classification; and (2) annual wage rate: measured
using the average annual salary within each cluster. Since cluster affiliation can affect
agglomeration and cluster-type (e.g. industry) variation (and hence employment-sensitive
economic growth), and because annual wage rates may also be correlated with
employment-sensitive economic growth (Delgado et al., 2010), both were included as
cluster level control variables. Data for both cluster level control variables were obtained
from the U.S. Cluster Mapping Project (USCMP) database.
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Data Analysis
I employed random coefficient modeling (RCM) to test the direct effects
(hypotheses 1-3; 8), mediation effects (hypotheses 4-7), and interaction effects
(hypotheses 9a-g) proposed in this dissertation. RCM is a statistical technique that is
designed to examine multilevel relationships and allows for both fixed and random
effects, where level-1 is often models time (i.e., cluster-observation), followed by
subsequent levels within which time is nested. Due to the longitudinal nature of the data,
RCM provides appropriate technique to overcome potential time-based errors (Bliese and
Ployhart, 2002; Short et al., 2006).
One other key benefit of RCM is that it allows the examination of multilevel
mediational relationships that is not easily conducted using other statistical techniques
(Mathieu et al., 2008). Within this dissertation, for example, Competition Intensity is
suggested to mediate the relationship between New Venture Creation Policy Maturity and
R/E Cluster Growth (i.e., hypotheses 4-5); while Knowledge Spillover Effectiveness is
suggested to mediate the relationship between Innovation Policy Maturity and R/E
Cluster Growth (i.e., hypotheses 6-7). Following single level mediation testing
guidelines (e.g., Baron and Kenny, 1986) reformulated for multilevel models (e.g., Krull
and MacKinnon, 2001; Mathieu and Taylor, 2007) is argued to overestimate or
underestimate the multilevel mediation effect (Zhang et al., 2009). Thus, Zhang et al.
(2009) suggested a 3-step test to overcome such errors within RCM and HLM-based
multilevel mediation models. The following econometric model, which is specifically
constructed to test the hypotheses in this dissertation, illustrates each step of the
suggested procedure.
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FIGURE 4.1: Econometric Model
Random Coefficient Modeling - Three-Level Regression Model
Step 1: Testing for the direct and related interaction effects (i.e., H1-3; 8a-c):
L1: EMPLOYMENT_GROWTHijt = α0ij + α1ij(ANNUAL_WAGE)ijt +
α2ij(CLUSTER_AFFILIATION)i + ɛijt (1)
L2: α0ij = β0j + π01(POPULATION_DENSITY)j + π02(COASTAL_ACCESS)j +λ0ij (2)
α1ij = β1j + λ1ij (3)
α2ij = β2j + λ2ij (4)
L3: β0j = γ01 + γ02(BUS_ENV)j + γ03(INNOVATION) j + γ04(NEW_VENTURE) j +
γ05(ECON_IND) j + γ06(BUS_ENV)*(ECON_IND)j +
γ07(INNOVATION)*(ECON_IND)j + γ08 (NEW_VENTURE)*(ECON_IND) j + u0j (5)
β1j = θ11 + θ12(BUS_ENV)j + θ13(INNOVATION) j + θ14(NEW_VENTURE) j +
θ15(ECON_IND) j + θ16(BUS_ENV)*(ECON_IND)j +
θ17(INNOVATION)*(ECON_IND) j + θ18(NEW_VENTURE)*(ECON_IND) j + u1j (6)
β2j = γ21 + γ22(BUS_ENV)j + γ23(INNOVATION) j + γ24(NEW_VENTURE) j +
γ25(ECON_IND) j + γ26(BUS_ENV)*(ECON_IND)j +
γ27(INNOVATION)*(ECON_IND)j + γ28 (NEW_VENTURE)*(ECON_IND) j + u2j (7)
Step 1
Model:
EMPLOYMENT_GROWTHijt = (((γ01 + γ02(BUS_ENV)j + γ03(INNOVATION) j +
γ04(NEW_VENTURE) j + γ05(ECON_IND) j + γ06(BUS_ENV)*(ECON_IND)j +
γ07(INNOVATION)*(ECON_IND)j + γ08 (NEW_VENTURE)*(ECON_IND) j + u0j) +
π01(POPULATION_DENSITY)j + π02(COASTAL_ACCESS)j) + λ0ij) + ((θ11 +
θ12(BUS_ENV)j + θ13(INNOVATION) j + θ14(NEW_VENTURE) j + θ15(ECON_IND) j +
θ16(BUS_ENV)*(ECON_IND)j + θ17(INNOVATION)*(ECON_IND) j +
θ18(NEW_VENTURE)*(ECON_IND) j + u1j) + λ1ij)*(ANNUAL_WAGE)ijt + ((γ21 +
γ22(BUS_ENV)j + γ23(INNOVATION) j + γ24(NEW_VENTURE) j + γ25(ECON_IND) j +
γ26(BUS_ENV)*(ECON_IND)j + γ27(INNOVATION)*(ECON_IND)j + γ28
(NEW_VENTURE)*(ECON_IND) j + u2j) + λ2ij)*(CLUSTER_AFFILIATION)i + ɛijt (8)
Step 2: Testing for the mediation effects (i.e., H4-7; 8d-g):
Step 2
Models:
KNOWLEDGEj = α0 + α1(INNOVATION)j + α2(INNOVATION)*(ECON_IND)j + ɛj
(9)
COMPETITIONj = α0 + α1(NEW_VENTURE)j + α2(NEW_VENTURE)*(ECON_IND)j
+ ɛj (10)
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Step 3: Testing the full model:
L1: EMPLOYMENT_GROWTHijt = α0ij + α1ij(ANNUAL_WAGE)ijt +
α2ij(CLUSTER_AFFILIATION)i + ɛijt (11)
L2: α0ij = β0j + π01(POPULATION_DENSITY)j + π02(COASTAL_ACCESS)j +λ0ij (12)
α1ij = β1j + λ1ij (13)
α2ij = β2j + λ2ij (14)
L3: β0j = γ01 + γ02(BUS_ENV)j + γ03(INNOVATION) j + γ04(NEW_VENTURE) j +
γ05(KNOWLEDGE)j + γ06(COMPETITION)j + γ07(ECON_IND)j +
γ08(BUS_ENV)*(ECON_IND)j + γ09(INNOVATION)*(ECON_IND)j +
γ10(NEW_VENTURE)*(ECON_IND) j + γ11(KNOWLEDGE)*(ECON_IND)j +
γ12(COMPETITION)*(ECON_IND)j + u0j (15)
β1j = θ11 + θ12(BUS_ENV)j + θ13(INNOVATION) j + θ14(NEW_VENTURE) j +
θ15(KNOWLEDGE)j + θ16(COMPETITION)j + θ17(ECON_IND)j +
θ18(BUS_ENV)*(ECON_IND)j + θ19(INNOVATION)*(ECON_IND) j +
θ20(NEW_VENTURE)*(ECON_IND) j + θ21(KNOWLEDGE)*(ECON_IND)j +
θ22(COMPETITION)*(ECON_IND)j + u1j (16)
β2j = γ21 + γ22(BUS_ENV)j + γ23(INNOVATION) j + γ24(NEW_VENTURE) j +
γ25(KNOWLEDGE)j + γ26(COMPETITION)j + γ27(ECON_IND)j +
γ28(BUS_ENV)*(ECON_IND)j + γ29(INNOVATION)*(ECON_IND)j +
γ20(NEW_VENTURE)*(ECON_IND) j + γ21(KNOWLEDGE)*(ECON_IND)j +
γ22(COMPETITION)*(ECON_IND)j + u2j (17)
Full
Model:
EMPLOYMENT_GROWTHijt = (((γ01 + γ02(BUS_ENV)j + γ03(INNOVATION) j +
γ04(NEW_VENTURE) j + γ05(KNOWLEDGE)j + γ06(COMPETITION)j +
γ07(ECON_IND)j + γ08(BUS_ENV)*(ECON_IND)j + γ09(INNOVATION)*(ECON_IND)j
+ γ10(NEW_VENTURE)*(ECON_IND) j + γ11(KNOWLEDGE)*(ECON_IND)j +
γ12(COMPETITION)*(ECON_IND)j + u0j) + π01(POPULATION_DENSITY)j +
π02(COASTAL_ACCESS)j) + λ0ij) + ((θ11 + θ12(BUS_ENV)j + θ13(INNOVATION) j +
θ14(NEW_VENTURE) j + θ15(KNOWLEDGE)j + θ16(COMPETITION)j +
θ17(ECON_IND)j + θ18(BUS_ENV)*(ECON_IND)j + θ19(INNOVATION)*(ECON_IND)
j + θ20(NEW_VENTURE)*(ECON_IND) j + θ21(KNOWLEDGE)*(ECON_IND)j +
θ22(COMPETITION)*(ECON_IND)j + u1j) + λ1ij)*(ANNUAL_WAGE)ijt + ((γ21 +
γ22(BUS_ENV)j + γ23(INNOVATION) j + γ24(NEW_VENTURE) j + γ25(KNOWLEDGE)j
+ γ26(COMPETITION)j + γ27(ECON_IND)j + γ28(BUS_ENV)*(ECON_IND)j +
γ29(INNOVATION)*(ECON_IND)j + γ20(NEW_VENTURE)*(ECON_IND) j +
γ21(KNOWLEDGE)*(ECON_IND)j + γ22(COMPETITION)*(ECON_IND)j + u2j) +
λ2ij)*(CLUSTER_AFFILIATION)i + ɛijt (18)
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In this model, equations (1; 11) correspond to the lower level or observation
level (for a given cluster, at a given time and a given state), allowing for the parameters
to vary across clusters and states. Equations (2-4; 12-14) correspond to the Level-2 (i.e.,
clusters within states) equation. In this equation, the intercept varies with some control
variables; while the slopes are modeled as a common average and a deviation from it.
Finally, equations (5-7; 15-17) correspond to the Level-3 (i.e., states) equation. Through
successive substitutions, we obtain equations (8-10; 18) corresponding to the full
model. The subscripts i, j and t refer to clusters, states, and time respectively; α0-2ij are the
intercepts for state j; β0-2j are the intercepts for cluster i; while ɛijt, λ0-2ij, and u0-2j are the
Level-1, Level-2 and Level-3 random shocks or disturbances, respectively. The overall
effects of the direct variables estimated using the following 3-step procedure:
Step 1: Estimating the overall effect using the following equation:
where Y is the dependent variable, X is the direct variable, is sample mean of the
moderating variable, β1 is the estimated coefficient for the direct variable, and β2 is the
estimated coefficient for the interaction effect.
Step 2: Estimating the variance of the overall effect using the following equation:
Step 3: Estimating the significance of the overall effect using the following equation:
where t is the t-value for the overall effect.
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CHAPTER 5: RESULTS
In this chapter, I present the data analyses and results of this study. An analysis of
the correlations among study variables is presented in the first section of this chapter,
followed by the results of hypotheses testing using random coefficient modeling, a 3-step
multilevel mediating test, and the specifically constructed econometric model presented
in the previous chapter. Table 5.6, which includes a summary of findings, as well as
Figure 5.1, which presents a summary of the results, are presented at the end of this
chapter.
Correlations
The general purpose of this study is to assess the relationship among the
dependent variable – renewable entrepreneurship (R/E) cluster growth; and various
direct – public policy (i.e., business environment policy maturity, innovation policy
maturity, new venture creation policy maturity) and economic inductance – variables,
mediating – pace and stability (i.e., competition intensity and knowledge spillover
effectiveness) – variables, and moderating – economic inductance – variables. In
addition, four control variables: cluster affiliation, annual wage rate, population density,
and coastal accessibility, were included in the study.
To measure these variables, archival data were obtained from multiple sources
including: The U.S. Cluster Mapping Project (USCMP) database, The Small Business &
Entrepreneurship Council (SBE Council) database, The Information Technology &
Innovation Foundation (ITIF) database, the Fraser Institute database, and The General
Patent Statistics Reports within the U.S. Patent and Trademark Office (USPTO). In total,
the sample in this study consisted of 10,200 R/E cluster-year observations for 2,550
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clusters representing each of the 50 states in the USA, generating 8,439 usable R/E
cluster-year observations with no missing values. The unusable R/E cluster-year
observations are attributed to missing values either in employment growth rate (220
missing R/E cluster-year observations), in annual wage rates (1,758 missing R/E cluster-
year observations), or both (3 missing R/E cluster-year observations). This reduction in
data should not impact the sample power as the useable sample size (8,439 R/E cluster-
year, 2,266 clusters, 50 states observations) greatly exceeds the minimum of 785
observations needed to expect a small size effect (Cohen, 1992).
Table 5.1 reports the descriptive statistics and correlations of the variables in this
study. As shown in Table 5.1, although most of the correlations among variables in this
study are below 0.70, which is considered the threshold that differentiates variables that
are highly correlated from those that are either moderately or slightly correlated (Hair et
al., 2010), several of the variables are worth noting as they were either on the edge or
exceed that threshold of being strongly correlated. The correlation between population
density and (a) costal access is (r = 0.44, p<0.01), (b) innovation policy maturity is (r =
0.43, p<0.01), and (c) economic inductance is (r = 0.56, p<0.01). The high correlation
among population density and such variables is attributed to the higher levels of
economic activities associated to increased rates of population, and hence, increasing
rates of labor (e.g., Solow, 1956). The correlation between business environment policy
maturity and new venture creation policy maturity is (r = -0.47, p<0.01). While the
correlation between innovation policy maturity and (a) knowledge spillover effectiveness
is (r = 0.48, p<0.01), (b) competition intensity is (r = 0.49, p<0.01), and (c) economic
inductance is (r = 0.72, p<0.01). The high correlation among innovation policy maturity
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and these variables is attributed to the argued role of technology that is associated within
such variables.
Given the higher degree of correlation among some of the variables, a risk of
multicollinearity might occur. Within a multiple regression model, multicollinearity
occurs when two or more independent variables are highly correlated (Keith, 2006). To
ensure that the variables are unbounded by multicollinearity, a variance inflation factors
(VIF) test was conducted. All of the VIFs were below 4.0, much lower than the generally
accepted VIF of 10 (Bowerman and O’Connell, 1990; Hair et al., 2010). Hence, the
results of the VIFs suggest negligible risk of multicollinearity among the data in this
study.
Hypothesis Testing
To test the direct and moderating hypotheses presented in this dissertation, I used
random coefficient modeling (RCM). Testing using random coefficient modeling is
appropriate when the research design includes nested data at more than one level (Bliese
and Ployhart, 2002; Short et al., 2006), as is the case in this study; where time (i.e.,
cluster-observation) is modeled as level-1, cluster is modeled as Level-2, and State is
modeled as Level-3. As noted in the previous chapter, the random coefficient modeling
analysis was conducted using the three-level econometric model that is specifically
constructed to test the hypotheses in this study, and which allows for both fixed-effects
and random-effects. The fixed-effects components are those that apply to all
observations across the dataset, regardless of the level. While in the random effects, the
parameters within this econometric model are not constant across the three levels in the
model to capture the unique variation within each level (e.g., cluster affiliation, state).
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While the mediation hypotheses where tested following a 3-step multilevel mediation test
suggested by Zhang et al. (2009); where the first step is to measure the effect of the direct
variables on the dependent variables (presented in Model 2- Table 5.2), step 2 is to
measure the effect of the direct and moderating variables on the mediating variables
(reported in Tables 5.3 and 5.4), and finally, step 3 is to measure the effect of both the
direct and mediating variables on the dependent variable (presented in Model 3- Table
5.2).
I report the results of the random coefficient modeling analysis in Table 5.2.
Model 1 in Table 5.2 reports the random coefficient modeling of the control variables in
this study. As this table shows, three of the four control variables namely cluster
affiliation, annual wage rate, and population density had significant effects on renewable
entrepreneurship (R/E) cluster growth (β = -0. 0008, p<0.01, β = 0. 0099, p<0.01 and β =
-0. 0001, p<0.01, respectively). No such effect was found for coastal accessibility (β = 0.
0013, p>0.05).
Model 2 in Table 5.2 presents the results of the three-level random coefficient
model including the control, direct, mediating and moderating variables on R/E cluster
growth. Model 2 represents a significant improvement over Model 1(L-R Test [χ 2] =
52.20, p < 0.01). Finally, Model 3 reports the effect of the full model (i.e., control, direct,
mediating, moderating, and interaction variables) on renewable entrepreneurship (R/E)
cluster growth. Model 3 also shows a significant improvement over Model 2 (L-R Test
[χ 2] = 121.00, p < 0.01). In addition, in order to estimate the pseudo R-squared for the
RCM model, further tests were conducted using codes proc mixed and %hlmrsq in SAS
(see, Recchia, 2010). The overall pseudo R-squared for the model was 0.2231 as shown
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in Model 3. Table 5.3 reports the overall estimates of the direct variables. Table 5.4
presents the results of testing Hypotheses 4 and 5, which employs ordinary least squares
(OLS) regression to test the impact of new venture creation policy maturity and the
moderation of economic inductance on competition intensity. Table 5.5 reports the results
of Hypotheses 6 and 7, which also employs OLS regression to test the impact of
innovation policy maturity and the moderation of economic inductance on the knowledge
spillover effectiveness. Finally, Table 5.6 outlines a summary of findings on all the
hypotheses in this study.
Direct Relationships: Public Policy and Economic inductance – Hypotheses 1, 2, 3,
and 8
Hypotheses 1, 2, and 3 predict direct relationships between each public policy
maturity variable, and R/E cluster growth. Model 2 in Table 5.2 presents the results of
the three-level random coefficient model including both direct (i.e., public policy) as well
as control variables. Hypothesis 1 argues that as the place-neutral business environment
policy within an R/E cluster matures, the growth of that R/E cluster will be enhanced as a
result. The results in Table 5.2 support Hypothesis 1 (β = 0.0019, p<0.01). The results of
this study also grant support for Hypothesis 2, which posits a positive relationship
between innovation policy maturity and renewable entrepreneurship cluster growth (β =
0.0081, p<0.01). The results also lend support Hypothesis 3, which posits that as the
new venture creation policy within an R/E cluster matures, the growth of that R/E cluster
will be enhanced as a result (β = 0.0203, p<0.05). Finally, the results grant support to
Hypothesis 8, which posits that as economic inductance within an R/E cluster increases,
the growth of that R/E cluster will be diminished as a result (β = -0.0087, p<0.05).
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Mediating Relationships: Pace and Stability Variables – Hypotheses 4, 5, 6, and 7
Hypotheses 4, 5, 6, and 7 predict mediating relationships among innovation policy
maturity, new venture creation policy maturity, competition intensity, knowledge
spillover effectiveness, and R/E cluster growth. These hypotheses were tested following
a 3-step multilevel mediation test (Zhang et al., 2009), which suggest utilizing ordinary
least squares (OLS) regression and random coefficient modeling depending on the level
of analysis within the suggested relationship (i.e., single-level vs. multilevel). Table 5.4
and Table 5.5 present the results of the OLS regression which tests the relationships
among innovation policy maturity, new venture creation policy maturity, competition
intensity, and knowledge spillover effectiveness; while Model 2 in Table 5.2 presents the
results of the three-level random coefficient model which tests the relationships among
competition intensity, knowledge spillover effectiveness, and R/E cluster growth, as well
as includes the direct (i.e., public policy), moderating (i.e., economic inductance) and
control variables.
Hypothesis 4 argues that as the new venture creation policy within an R/E cluster
matures, the competition level within that cluster will be intensified as a result. Based on
the results in Table 5.4, Hypothesis 4 was supported (β = 0. 2955, p<0.01). However,
Model 2 in Table 5.2 shows that no such support was found for Hypothesis 5, which
posits a direct relationship between the level of competition intensity and renewable
entrepreneurship cluster growth (β = -0.0040, p>0.05). The results in Hypotheses 6 and 7
are similar to those in Hypotheses 4 and 5. Hypothesis 6 argues that as the innovation
policy within an R/E cluster matures, it will positively influence the level of knowledge
spillover effectiveness within that cluster as a result. The results in Table 5.5 grant
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support to Hypotheses 6 (β = 14.4945, p<0.01). Model 2 in Table 5.2 shows that no such
support was found for Hypothesis 7, which posits a positive relationship between the
level of knowledge spillover effectiveness and renewable entrepreneurship cluster growth
(β = -0.0001, p>0.05).
Moderating Relationships: Economic Inductance Variable – Hypotheses 9a-g
Hypotheses 9a-g argue that economic inductance moderates the relationships
among public policy variables (i.e., business environment policy maturity, new venture
creation policy maturity), pace and stability variables (i.e., competition intensity and
knowledge spillover effectiveness), and renewable entrepreneurship cluster growth.
These hypotheses were tested using ordinary least squares (OLS) regression and random
coefficient modeling depending on the level of analysis within the suggested relationship
(i.e., single-level vs. multilevel). The results presented in Table 5.2, 5.4, and 5.5 show
partial support for the moderating role of economic inductance.
Hypothesis 9a suggests that higher scores in economic inductance within and R/E
cluster will weaken the positive relationship between business environment policy
maturity and R/E cluster growth. When tested using the three-level random coefficient
modeling, this hypothesis was not supported, as shown in Model 3 in Table 5.2 (β =
0.0002, p>0.05). Hypothesis 9b posits that the higher the score of economic inductance
within and R/E cluster, the weaker the relationship between innovation policy maturity
and R/E cluster growth. This hypothesis was also tested using the three-level random
coefficient modeling, and as shown in Table 5.2, Hypothesis 9b was not supported (β
=0.0001, p>0.05). Hypothesis 9c posits that higher levels of economic inductance within
and R/E cluster will weaken the suggested positive relationship between new venture
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creation policy maturity and R/E cluster growth. When tested using the three-level
random coefficient modeling, this hypothesis was also not supported, as shown in Table
5.2 (β = 0.0017, p>0.05).
Consistent with the prediction in Hypothesis 9d, the results of the OLS in Table
5.4 shows that higher scores of economic inductance within an R/E cluster will weaken
the relationship between new venture creation policy maturity and the level of
competition intensity (β = -0.1031, p<0.01). The graph of this interaction is presented in
Figure 5.2; where it shows that the influence of new venture creation maturity on
competition intensity was weaker in regions that have high rates of economic inductance.
Hypothesis 9e suggests that higher scores in economic inductance within and R/E cluster
will weaken the positive relationship suggested between the level of competition intensity
and renewable entrepreneurship cluster growth. When tested using the three-level
random coefficient modeling, this hypothesis was not supported, as shown in Model 3 in
Table 5.2 (β = 0.0013, p>0.05).
Hypothesis 9f posits that higher levels of economic inductance within and R/E
cluster will weaken the suggested positive relationship between innovation policy
maturity and the level of knowledge spillover effectiveness. The results of the OLS
regression in Table 5.5 do not lend support to this hypothesis (β = -0.0212, p>0.05).
Finally, Hypothesis 9g posits that the higher the score of economic inductance within and
R/E cluster, the weaker the relationship between the level of knowledge spillover
effectiveness and R/E cluster growth. This hypothesis was also tested using three-level
random coefficient modeling, and as shown in Table 5.2, Hypothesis 9g was supported (β
= -0.0001, p<0.01). The graph of this interaction is presented in Figure 5.3; what is
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interesting is that the figure shows that the influence of knowledge spillover effectiveness
on R/E cluster growth was not only weaker in regions that have high rates of economic
inductance, but it also had a negative effect on R/E cluster growth in such regions. I
discuss the implications of these and the other results in the next chapter.
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Table 5.1: Means, Standard Deviations, and Intercorrelations among Study Variables
Variable Mean SD N 1 2 3 4 5 6 7 8 9 10
1. Employment Growth 0.02 0.64 9980
2. Cluster Affiliation 26 14.72 10200 -.04**
3. Annual Wage 48269.55 29760.33 8442 .07** -.15**
4. Population Density 164.32 202.44 10200 -.03* -.00 .15**
5. Coastal Access 0.48 0.50 10200 -.02* .00 .09** .44**
6. Business Env. Policy Mat. 62.88 15.58 10200 -.01 -.00 .10** .38** .23**
7. Innovation Policy Mat. 9.21 3.65 10200 -.01 -.00 .17** .43** .33** .32**
8. New Ven. Policy Mat. 6.68 0.62 10200 .01 .00 -.01 -.09** -.07** -.47** -.02*
9. Knowledge Spillover Eff. 74.24 111.08 10200 -.01 .00 .15** .32** .23** .27** .48** -.09**
10. Competition Intensity 9.67 2.21 10200 -.00 .00 .10** .17** .17** .07** .49** .19** .36**
11. Economic Inductance 9.89 2.77 10200 -.03* -.00 .18** .56** .35** .32** .76** .17** .36** .48*
** Correlation is significant at the .01 level.
* Correlation is significant at the .05 level.
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Table 5.2: Mixed-Effects Regression Results for Renewable Entrepreneurship Cluster Growth
Model 1 Model 2 Model 3
Est. SE Est. SE Est. SE
Cluster Affiliation -0. 0008** 0.00 -0. 0009** 0.00 -0.0009** 0.00
Annual Wage 0. 0099** 0.00 0. 0091** 0.00 0.0089** 0.00
Population Density -0. 0001** 0.00 -0. 0001** 0.00 -0.0001** 0.00
Coastal Access 0. 0013 0.00 -0.0028 0.01 -0.0060 0.01
Business Env. Policy Mat. 0.0019** 0.00 0.0001+ 0.00
Innovation Policy Mat. 0.0081** 0.00 0.0074 + 0.02
New Ven. Policy Mat. 0.0203* 0.01 0.0017 + 0.03
Competition Intensity -0.0040 0.00 -0.0157 + 0.01
Knowledge Spillover Eff. -0.0001 0.00 0.0010 + 0.00
Economic Inductance -0.0087** 0.00 -0.0397 + 0.02
Economic Ind. X BusPol 0.0002 0.00
Economic Ind. X InnovPol 0.0001 0.00
Economic Ind. X NewVenPol 0.0017 0.00
Economic Ind. X CompInt 0.0013 0.00
Economic Ind. X KnowSp -0.0001** 0.00
Constant -0. 0179** 0.01 -0. 2224** 0.06 0.0873 0.22
N 8439 8439 8439
Log Likelihood 2036.8 2062.9 2123.4
L-R Test 52.20** 121.00**
Pseudo R-squared 0.2231
** Significant at the .01 level.
* Significant at the .05 level.
+ Significance and overall estimates presented in Table 5.3
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Table 5.3: Overall Estimates of Direct Variables
Estimates SE
Business Env. Policy Mat. 0.0020** 0.00
Innovation Policy Mat. 0.0083** 0.00
New Ven. Policy Mat. 0.0186** 0.01
Competition Intensity 0.0001 0.00
Knowledge Spillover Eff. -0.0028 0.00
Economic Inductance -0.0098** 0.01
** Significant at the .01 level.
* Significant at the .05 level.
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Table 5.4: OLS Regression Results for Competition Intensity
Model 1 Model 2 Model 3
Est. SE Est. SE Est. SE
Population Density 0.0013** 0.00 -0.0016** 0.00 -0.0016** 0.00
Coastal Access 0.5426** 0.05 0. 2515** 0.05 0.2910** 0.04
New Ven. Policy Mat. 0. 2955** 0.03 1.294** 0.11
Economic Inductance 0. 4220** 0.01 -1.105** 0.07
Economic Ind. X NewVenPol -0.1031** 0.01
Constant 9.2073** 0.03 3.6613** 0.21 -2.9247** 0.72
N 10200 10200 10200
R-squared 0.0406 0.2570 0.2636
Adj R-squared 0.0404 0.2567 0.2632
** Significant at the .01 level.
* Significant at the .05 level.
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Table 5.5: OLS Regression Results for Knowledge Spillover Effectiveness
Model 1 Model 2 Model 3
Est. SE Est. SE Est. SE
Population Density 0. 1452** 0.01 0. 0801** 0.01 0.0803** 0.01
Coastal Access 26.1878** 2.31 10.3469** 2.16 10.4106** 2.18
Innovation Policy Mat. 14.4945** 0.41 14.7273** 1.07
Economic Inductance -4.065 0.58 -3.8852** 0.95
Economic Ind. X InnovPol -0.0212 0.09
Constant 37.8185** 1.51 -37.1773** 3.79 -39.0785** 8.89
N 10200 10200 10200
R-squared 0.1112 0.2482 0.2482
Adj R-squared 0.1110 0.2479 0.2478
** Significant at the .01 level.
* Significant at the .05 level.
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Table 5.6: Summary of Findings
Hypotheses: Findings
Public Policy Variables
H1: Place-neutral business environment policy maturity within an R/E
cluster is positively related to R/E cluster growth. Supported
H2: Innovation policy maturity within an R/E cluster is positively related to
R/E cluster growth. Supported
H3: New venture creation policy maturity within an R/E cluster is positively
related to R/E cluster growth. Supported
Pace and Stability Variables
H4: Within an R/E cluster, new venture creation policy maturity is positively
related to competition intensity. Supported
H5: The level of competition intensity within an R/E cluster is positively
related to R/E cluster growth. Not Supported
H6: Within an R/E cluster, innovation policy maturity is positively related to
knowledge spillover effectiveness. Supported
H7: Knowledge spillover effectiveness within an R/E cluster is positively
related to R/E cluster growth. Not Supported
Economic Inductance
H8: Economic inductance with within an R/E cluster is negatively related to
R/E cluster growth. Supported
H9a: Economic inductance within an R/E cluster moderates the relationship
between business environment policy maturity and R/E cluster growth;
such that when economic inductance is high, the effect of business
environment policy maturity will be weaker on R/E cluster growth.
Not Supported
H9b: Economic inductance within an R/E cluster moderates the relationship
between innovation policy maturity and R/E cluster growth; such that
when economic inductance is high, the effect of innovation policy
maturity will be weaker on R/E cluster growth.
Not Supported
H9c: Economic inductance within an R/E cluster moderates the relationship
between new venture creation policy maturity and R/E cluster growth;
such that when economic inductance is high, the effect of the new
venture creation policy maturity will be weaker on R/E cluster growth.
Not Supported
H9d: Economic inductance within an R/E cluster moderates the relationship
between new venture creation policy maturity and competition Supported
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Hypotheses: Findings
intensity; such that when the level of economic inductance is high, the
effect of the new venture creation policy maturity will be weaker on
the competition intensity.
H9e: Economic inductance within an R/E cluster moderates the relationship
between competition intensity and R/E cluster growth; such that when
the level of economic inductance is high, the effect of competition
intensity will be weaker on R/E cluster growth.
Not Supported
H9f: Economic inductance within an R/E cluster moderates the relationship
between innovation policy maturity and knowledge spillover
effectiveness; such that when the level of economic inductance is high,
the effect of innovation policy maturity will be weaker on knowledge
spillover effectiveness.
Not Supported
H9g: Economic inductance within an R/E cluster moderates the relationship
between knowledge spillover effectiveness and R/E cluster growth; such
that when the level of economic inductance is high, the effect of
knowledge spillover effectiveness will be weaker on R/E cluster growth.
Supported
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H1+
Business Environment
Policy Maturity
New Venture Creation
Policy Maturity
H9d (-)
H9a (ns)
H9b (ns)
H9c (ns)
H2 +
H3+
H6+
H5 (ns)
H7 (ns) H9f (ns)
H9e (ns)
H9g (-)
H4+
H7 (ns); but moderator
significant
H8 (-)
Figure 5.1: Renewable Entrepreneurship Clusters – Results Model
Economic Inductance
Public Policy Variables
For Example: Government Budget and
Spending
Regulatory Complexity
Tax Structure
Renewable
Entrepreneurship
Cluster Growth
Technology Impactfulness
Talent/Tolerance
For Example:
Universities Patents
For Example: Firms Growth Rate
Degree of Job Churning
For Example: IP Protection
Communication Platform
Effectiveness Research Institution Accessibility
For Example: Ease of Financing
Ease of Starting a Business
Economic Freedom Competition Intensity
Pace and Stability Variables
Knowledge Spillover Effectiveness
Innovation Policy Maturity
Figure 5.2: Interaction of New Venture Creation Policy and Economic Inductance
on Competition Intensity
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
Low NVC Policy Mat. High NVC Policy Mat.
Com
pet
itio
n I
nte
nsi
ty
Low Inductance
High Inductance
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Figure 5.3: Interaction of Knowledge Spillover Effectiveness and Economic
Inductance on R/E Cluster Growth
-0.5
-0.45
-0.4
-0.35
-0.3
-0.25
-0.2
-0.15
-0.1
-0.05
0
Low Knowledge
Spillover Effectiveness
High Knowledge
Spillover Effectiveness
R/E
Gro
wth
Low Inductance
High Inductance
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CHAPTER 6: DISCUSSION
While the role of government engagement in economic policies is well
established within the New-Keynesian economics perspective (Snowdon and Vane,
2005), research within urban and regional economics provides a limited understanding of
what economic variables lead to economic diversification within a country (Motoyama,
2008). To address this gap: First, the notion of renewable entrepreneurship was
introduced within this dissertation, arguing that it provides an appropriate vehicle to
achieve horizontal economic diversification and thereby, continuing economic progress.
Second, I proposed a research model of renewable entrepreneurship clusters, where the
influences of various public policy variables (i.e., business environment policy maturity,
innovation policy maturity, new venture creation policy maturity), institutionalization of
innovation pace and stability variables (i.e., competition intensity and knowledge
spillover effectiveness), and economic inductance on renewable entrepreneurship cluster
growth were theoretically developed and empirically examined.
In this chapter, I present a discussion of the results of this study, including the
theoretical and practical implications. An evaluation of the findings of this study is
presented in the first section of this chapter, followed in the second section by a
discussion of the theoretical implications of this study. In the third section I highlight
the practical implications of the findings, and include a discussion of possible new
economic policy opportunities. Following this discussion, in the fourth section, I address
the limitations of the study, and in the last section close the chapter with a discussion of
possible future research opportunities that flow from this dissertation.
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Evaluation of Findings
This study explores the effect of various public policy variables (i.e., business
environment policy maturity, innovation policy maturity, new venture creation policy
maturity), pace and stability of the institutionalization of innovation variables (i.e.,
competition intensity and knowledge spillover effectiveness), and economic inductance
on renewable entrepreneurship cluster growth. Starting with the public policy variables,
within this dissertation I found that business environment policy maturity, innovation
policy maturity, and new venture creation policy maturity have significant direct effects
on renewable entrepreneurship cluster growth. Specifically, this study reports that,
consistent with Hypothesis 1, the maturity of the business environment policy is a critical
factor to economic growth as it positively impacts renewable entrepreneurship cluster
growth. Such a finding confirms prior research, which suggests that as business
environments mature, they not only lower the transaction costs of socioeconomic
transactions, but also provide cost-effective access to essential resources which allows
such socioeconomic transactions to flourish and economic growth to increase (Doeringer
and Terkla, 1995).
The test results also provide support for Hypothesis 2, which suggests that with
higher levels of innovation policy maturity within an R/E cluster, R/E cluster growth
increases. This finding confirms the argument that government engagement by
developing innovation policies that incentivize R&D investments and allow for
knowledge transferability is essential to correct the market and institutional failures that
might limit innovation and economic growth (McCann & Ortega-Argilés, 2013).
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Consistent with Hypothesis 3, the results also show that higher maturity in new
venture creation policy within an R/E cluster improves its growth. Such a finding
contradicts the argument of neoclassical theory that views new venture creation as only
an impediment to economic growth (Snowdon and Vane, 2005), and further confirms the
view of the evolutionary theory of economics which argues for the essentiality of
entrepreneurship and new venture creation as means to drive economic growth (Nelson
and Winter, 1982).
For the pace and stability variables that represent the institutionalization of
innovation, this study provides partial support of the mediating role of competition
intensity and knowledge spillover effectiveness. When evaluating the mediating effect of
competition intensity on R/E cluster growth, the results show that, consistent with
Hypothesis 4, the maturity of the new venture creation policy within a renewable
entrepreneurship cluster significantly increases the level of competition intensity within
that cluster. Such a finding confirms the importance of governmental measures enabling
new venture creation as a way to limit monopolistic behavior, and thereby to decrease
markets barriers of entry (Thompson, 1989). However, the results did not support
Hypothesis 5, which argues that higher levels of competition intensity are expected to
result in increasing R/E cluster growth. Such a finding might shed light on prior research
which suggests that the relationship between market competition and innovation-based
outcomes such as R/E cluster growth takes on an inverted-U shape. Aghion et al. (2005)
argue that as market competition intensifies, follower firms become less encouraged to
innovate and more oriented toward zero-sum competition behaviors (e.g., price wars,
product imitation, etc.). Further, such conditions of high competition intensity are argued
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to lead to higher degrees of necessity entrepreneurship, which refers to the
socioeconomic actions that are motivated by need rather than opportunity (Kontolaimou
et al., 2015). Taken together, the effects of zero–sum competition and necessity
entrepreneurship are argued to hinder economic progress (Acs, 2006). Thus, this latter
finding suggests that future research is needed to further investigate the impact of varying
levels of competition intensity (e.g., low, moderate, high) on renewable entrepreneurship
cluster growth, and thereby on horizontal economic diversification.
In regards to the mediating effect of knowledge spillover effectiveness, the results
are similar to those of competition intensity in that a partial mediation was supported.
Specifically, the results of this study support Hypothesis 6, which argues that the maturity
of the innovation policy within a renewable entrepreneurship cluster significantly
increases the level of knowledge spillover effectiveness within that cluster. This finding
confirms the importance of government engagement in developing measures to
incentivize investments in knowledge. Hence, it highlights the essentiality of the
development of innovation policies, as discussed above, to ensure the stability of the
knowledge creating process within societies (Li and Mitchell, 2009). However, the
results did not support Hypothesis 7, which suggests that higher levels of knowledge
spillover effectiveness within an R/E cluster is expected to lead to higher R/E cluster
growth. Nevertheless, the lack of support for this hypothesis does not mean that
knowledge spillover effectiveness is nonessential to economic growth. Prior research
argues that such an impact might be contingent on the conditions of markets and
institutions and their ability to either turn knowledge investments into the innovations
that drive economic growth (i.e., the ability to transfer knowledge into commercializable
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products), or to the unsuccessful inventions which despite their novelty, fail to be
transferred into accepting markets, and hence, fail to translate into positive economic
outcomes (Casson, 1982; Gordon and McCann, 2005; Landau and Rosenberg, 1986;
McCann and Ortega-Argilés, 2013). I note, however, that our understanding of this
partial mediation is further enabled when the impact of economic inductance as a
moderator is considered, as discussed further below.
Taken together, these findings suggest that public policy engagement (through the
development of more mature new venture creation policies, and more mature innovation
policies), significantly influences the process of invoking the institutions of innovation
within societies (cf., Li and Mitchell, 2009). However, for the institutions of innovation
to be effective, and to translate knowledge into desirable economic outcomes, certain
economic and institutional conditions have to be met. As discussed next, one set of these
conditions that has been hypothesized and tested in this study is the moderating effect of
economic inductance on certain relationships. But in addition to the desirable (low)
inductance conditions found to be significant, I also suggest that future research might
productively examine various competition levels (e.g., monopolistic competition,
oligopolistic competition, etc.) as well as the various economic and institutional
conditions (e.g., markets efficiency, culture, etc.) to investigate phenomena that may also
influence economic outcomes. Such phenomena may include the possibility of some
favorable mix of competition intensity with R/E cluster growth; as well as the economic
and institutional conditions that might operate to optimize such invoked institutions of
innovation; and thereby ensure the transfer of knowledge investments into innovations
through to the market, and hence, to economic growth.
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In terms of economic inductance, in order for the idea of renewable
entrepreneurship to be more comprehensive as a system for the development of economic
growth in a variety of horizontally diversified industries, the notion of economic
inductance has been introduced to account for the phenomenon of social reactivity to new
economic engagement activities (e.g. government intervention with stimulus resources)
that results in the waste of such new resources and thereby substandard economic
progress. I have argued that economic inductance not only has a direct effect on
renewable entrepreneurship cluster growth, but also moderates the various relationships
among public policy and pace and stability variables, and R/E cluster growth. The results
of this study provide support to the direct effect of economic inductance; and as
consistent with Hypothesis 8, the results show that higher levels of economic inductance
within an R/E cluster will hinder its growth. Such a finding highlights the significant
influence that the social, economic and cognitive conditions within a region have on in
generating socioeconomic activities and economic growth; and it has profound
implications for policy making, as further discussed below.
Also, the results lend partial support to the moderating effect of economic
inductance on certain direct and mediating relationships. Of the seven hypotheses which
predicted that higher levels of economic inductance will weaken the prior hypothesized
relationships, only two were supported. Specifically, the results lend support to
Hypotheses 9d and 9g, which argue for the moderating effect of economic inductance on
the relationships between new venture creation policy maturity and competition intensity
(Hypothesis 9d), and knowledge spillover effectiveness and R/E cluster growth
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(Hypothesis 9g). As stated, no such support was found for moderation in the remaining
relationships (i.e., Hypotheses 9a, 9b, 9c, 9e, and 9f).
Such support of these two moderating hypotheses reveals very helpful and
interesting results; and it directly connects to – and helps to further explain – the findings
of the previously discussed direct and mediating hypotheses. Specifically, the support for
Hypothesis 9d, which suggests that economic inductance moderates the relationship
between new venture creation policy maturity and competition intensity, highlights this
important qualifier: that the effectiveness of government measures that aim to incentivize
innovation pace and stability rates within societies depends largely on the economic
susceptibility of theses societies; i.e., that these societies will either efficiently transfer
such initiatives into economic progress, or that such invested resources will be turned into
waste (Mitchell, 2003; North, 1990). Finally, the support of Hypothesis 9g, which argues
that economic inductance moderates the relationship between knowledge spillover
effectiveness and R/E cluster growth, directly connects to the results of Hypothesis 7 by
confirming that the process of knowledge creation within societies does not, and likely
will not, translate into innovation and desirable economic outcomes without low social
reactivity: the supporting social, economic and institutional conditions (Landau and
Rosenberg, 1986); where without these supporting conditions, knowledge investments
will only hinder economic growth due to the higher failing rates of new ventures in such
inefficient conditions (Gordon and McCann, 2005). Such negative impact of economic
inductance on the knowledge spillover effectiveness and R/E cluster growth is clearly
presented in Figure 5.3 discussed in the previous chapter.
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As a whole, the results of this study reveal that public policy variables (i.e.,
business environment policy maturity, innovation policy maturity, and new venture
creation policy maturity) and economic inductance have a substantial influence on the
growth of renewable entrepreneurship clusters. In addition, these results reveal that
public policy variables are also vital in invoking institutions of innovation within
societies (Li and Mitchell, 2009), which results in higher pace and higher stability of the
institutionalization of innovation. However, for such knowledge investments to be
translated to innovation through markets, it appears that a favorable mix of moderate
competition intensity and low economic inductance are required to ensure long-term
economic growth (Aghion et al., 2005; Gordon and McCann, 2005; Landau and
Rosenberg, 1986).
Theoretical Implications
The theoretical development of the research model that examines the growth of
renewable entrepreneurship (R/E) clusters, the subsequent operationalization through
development of the econometric model, and the empirical testing of the research model
including the various relationships among public policy variables (i.e., business
environment policy maturity, innovation policy maturity, new venture creation policy
maturity), institutionalization of innovation pace and stability variables (i.e., competition
intensity and knowledge spillover effectiveness), economic inductance, and renewable
entrepreneurship cluster growth make several contributions to theory.
First, prior research within the fields of urban and regional economics argue that
although the rationale of government engagement in shaping economic policy is well
established and accepted within a New-Keynesian framework, the literature fails to
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identify how and where such government engagement can be optimized to horizontally
diversify an economy and achieve stable economic growth (Motoyama, 2008). Within
this dissertation, I therefore extend the literatures of urban and regional economics by the
introduction of the notion of Renewable Entrepreneurship (R/E) which I have defined as
an economic system for the generation of business that is not critically rich/cursed
resource dependent for the continuity of its contribution to the economy; and also, by
suggesting that such a notion is specifically applicable within economic clusters – an idea
based on cluster theory (Porter, 1998; 2000; 2009), which refers to the “geographic
concentrations of interconnected companies and institutions in a particular field, linked
by commonalities and complementarities” (Porter, 1998: 78). With this theorizing, I am
enabled to argue that the notion of Renewable Entrepreneurship Clusters can serve as
means to explain how horizontal economic diversification within a region can be
conceptualized, specifically, as being due to the suggested capability of R/E clusters for
conserving short-term investment and multiplying long term value.
Second, based on the results of this study, several sets of economic variables have
been found to influence R/E cluster growth. The various public policy variables specified
were found to directly influence R/E cluster growth. The findings confirm that place-
neutral business environment policies are highly likely to be associated with the
minimization of transactions costs and the driving of economic growth. Also, the results
highlight the theoretical importance of capturing, in explanations, the role of government
engagement in setting policies that incentivize knowledge investments as well as those
that will lower barriers to market entry, thereby allowing for higher economic growth.
Furthermore, government measures to invoke the pace and stability of the institutions of
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innovation within societies appear to be essential for explanations that involve the
knowledge creation process. Taken together, these findings are consistent with the New-
Keynesian economics perspective; and they confirm previous theoretical assertions that
government engagement to correct for various market failures and to stabilize economic
growth is vital. These results also extend urban and regional economics research by
providing additional understanding of how government engagement can be optimized to
lead to horizontal economic diversification within a country.
Third, through examining the pace and stability of institutionalization variables,
the results of this study extend the notion of the institutionalization of innovation (Li and
Mitchell, 2009) by suggesting that not all levels of competition intensity are favorable,
nor do they create benign environments for innovation and economic growth. The results
lead me to speculate that the relationship between competition intensity and economic
growth – based upon the elimination of a direct mediating relationship – possibly takes
on an inverted-U shape (Aghion et al., 2005). Exploration of this idea is suggested as one
of the next steps in the empirical examination of the model suggested herein. The results
further suggest that for value creation from knowledge investments to be reaped,
supporting economic and institutional conditions have to be in place for the stability of
economic progress (Gordon and McCann, 2005; Landau and Rosenberg, 1986).
Fourth, as an extension of transaction inductance theory (Mitchell, 2003), the
notion of economic inductance is introduced within this dissertation and has been defined
to be resistance to the conservation of economic energy. I have argued that such
economic inductance within societies will cause waste of invested resources and hinder
economic progress. Furthermore, based on the introduction of this notion, an index was
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constructed within this study to measure the level of economic inductance within
economic clusters. The results of my testing lend support to the argued effect of
economic inductance on renewable entrepreneurship cluster growth, the effectiveness of
public policies, and on the ability to of societies to translate knowledge investments into
desirable economic outcomes. Such a notion, I argue, provides a substantive contribution
to the literatures of urban and regional economics as well as to the research in economic
diversification, by answering the where question when it comes to identifying regions
that have higher potential for economic growth and high return for government
engagement (Motoyama, 2008).
Fifth, in addition to identifying the role of the economic inductance index
mentioned above, this dissertation empirically validates means whereby high potential
economic clusters can be distinguished from those with lower potential for renewable
entrepreneurship. A three-level econometric model is specifically constructed to allow for
the parameters to vary across clusters and states in one of the most highly (horizontally)
diversified economies in the world. Such an econometric model should provide a vital
empirical tool to researchers within the fields of urban and regional economics as well as
for research in entrepreneurship policy when comparing the impact of various economic
factors on innovation and economic growth rates among regions.
Finally, this dissertation answers recent calls to connect entrepreneurship research
to public policy. Zahra and Wright (2011) argue that entrepreneurship researchers should
capitalize on the growing interest of governments in entrepreneurship, and conduct
deeper and wider research on the influence that public policy imposes on the growth and
effectiveness of entrepreneurial activities. Within this dissertation, I attempt to connect
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the domain of entrepreneurship to some of the theories and empirical methods of the
domains in urban, regional, and development economics. I suggest that such a
connection should contribute markedly to each field due to the (now even more)
complementary nature of these fields. Hence, given the relative infancy of the subfield of
entrepreneurial policy research, future researchers interested in entrepreneurial policy
research are better able to further bridge the gap among these domains, and to connect
this research stream to the overall framework of macroeconomic theories, where
renewable-entrepreneurship-focused theories of public policy engagement in the
economy are productively established and situated (cf., Snowdon and Vane, 2005).
Practical Implications
(New Economic Policy Opportunities)
In addition to the theoretical contributions suggested within this dissertation, the
nature of the research, the development of the research model, and the empirical testing
of the relationships within the research model invoke broad sets of implications for
practice, especially in regards to economic policy. First, given the rationale for
government engagement inherent within New-Keynesian Economic Policy – i.e., to use
various fiscal, monetary, and regulatory policies to correct for macroeconomic market
failures (Snowdon and Vane, 2005) – New-Keynesian Economic Policy to this point in
time has fallen short of suggesting the economic policies that lead to economic
diversification (Motoyama, 2008). As highlighted in this dissertation, the notion of
renewable entrepreneurship suggests a solution to this shortcoming by: (1) introducing a
system that drives regional economic diversification through better understanding how to
grow renewable entrepreneurship clusters; (2) identifying the various economic variables
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that significantly influence growth rates of the R/E clusters; and (3) empirically
validating means that identify regions that have high potential of renewable
entrepreneurship growth and high return of government, and private, investment.
Therefore, I suggest that within the domain of economic policy development, that the
development of Renewable Entrepreneurship Economic Policy as a new frontier not only
complements the use of current economic policies by highlighting the policies that lead to
economic diversification, but also better ensures the stability of economic progress
through the policy-setting options made available by an empirically validated model that
specifies the outlines of possible actions that can be taken in regards to, for example,
horizontal diversification, reduction of unemployment, economic growth, etc.
Second, placing and applying the notion of renewable entrepreneurship within
economic clusters has been found to enable two major benefits: (1) cost minimization,
due to the within-cluster proximity of suppliers and customers, and (2) innovation and
productivity growth, due to the within-cluster knowledge spillover effect and the
specialization of the workforce within that cluster, as suggested by Porter (2000). Based
upon the results of my study, it appears to be likely that both benefits will lead to
reaching the R/E definitional goals of conserving short-term investment and multiplying
long-term value, which – most importantly, and most distinctly from competing
economic development approaches – is highly likely to ensure that the economic system
of renewable entrepreneurship is self-revitalizing, and that future government investment
will not be critical for the continuity of the contribution of renewable entrepreneurship to
the long-term economic growth.
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Third, it is important to note that – as suggested in the literature – economic
diversification is not reached by the mere development of a horizontally diversified
economy (i.e., in diversification quantity only), but it is also reached through
development of the level of complexity of that economy (i.e. in diversification quality)
(The Atlas of Economic Complexity, 2014). In terms of this dissertation, it is therefore
not only the quantity of diverse renewable entrepreneurship clusters that is important, but
also the quality of these clusters. Based on the analysis, then, due to the continuous
innovation and productivity growth that is expected to occur within the system of
renewable entrepreneurship clusters, vertical diversification within each new R/E cluster
is therefore suggested to complement the horizontal diversification initiatives stimulated
by the application of Renewable Entrepreneurship Economic Policy. (For example, this
would mean that a highly-complex vertically diversified oil-based economy can therefore
be conceptualized as a special case of one-among-many industries in an ever broader
horizontally diversified economy). Such complementarities among vertical and horizontal
diversifications likely ensures further regional benefits by the accumulating effect of the
more value-adding segments within each cluster, and hence, results in more complex and
advance economies that are enabled to reap larger benefits of economic diversification.
Fourth, in addition to the suggested roles of public policies in driving R/E cluster
growth, government engagement should complement those efforts by creating policies
that indirectly incentivize R/E cluster formation. Such R/E cluster policies might be
termed “demand-pull” policies where, through creating market demand within an area
using the resources of fiscal policy, for example, economic agents are enabled to
recognize such opportunities and exploit them, and hence drive regional innovation and
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production growth rates (Fabrizio et al., 2015). In my view, this complementary
interaction among fiscal and R/E economic policy, when combined with further
integrations among monetary and regulatory policy, can shed new light on the pathway of
transition from a less-horizontally diversified to more horizontally diversified economy.
Then, in addition to incentivizing R/E cluster formation through market demand,
government measures are argued, where applicable, to be essential to lower market
barriers resulted from inefficiencies in property rights: for example, the “representation”
problem where in economically underperforming economies, “…the poor inhabitants…
have houses but not titles; crops but not deeds; businesses but not statutes of
incorporation” such that “without representations their assets are dead capital” (DeSoto,
2000: 6-7). The inability to acquire, or the complexity encountered to gain the rights to
ownership that underpin capital formation is argued not only to impede economic growth,
but even to limit any economic activities from occurring in the first place (DeSoto, 2000;
Greenhalgh and Rogers, 2010). However, the further exploration of these public policy
initiatives, and their future impact on economic growth and diversification, while
essential, now begins to extend beyond the scope of this dissertation, and so is left to
future research to address.
Limitations
Within this section I highlight several limitations of this study; and therefore, it is
important to interpret the results of this study in the lights of its limitations. The first
possible limitation in this study is in regards to the scope of the external validity of this
study’s findings. Specifically, to assess the impact of various economic variables (i.e.,
public policy, pace and stability, and economic inductance) on R/E cluster growth, data
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gathered for this study were limited to clusters within the United States, a country that
has one of the most highly (horizontally) diversified economies in the world (The Atlas
of Economic Complexity, 2014). Limiting the data to clusters within the U.S. might
impose a limitation on the generalizability of findings to other economic settings, where
different types of clusters and other idiosyncratic characteristics are not accounted for
within the data. Nevertheless, when compared to cross-country cluster data (e.g., GCC
countries, European Union, OECD, etc.), I argue that limiting the data to clusters within
the U.S. will have higher benefits due to controlling for various political, economic, and
regulatory factors (Maddala, 1999), while at the same time – due to the large economic
sampling frame within the dataset – not risking the loss of the variation in the constructs
of interest. However, as discussed in the next section, there thus appears to be an
opportunity to explore in other contexts the extent to which the findings reported herein
might hold or be further amplified.
Second, another possible external validity-related limitation is the time frame of
the data gathered within this study. As mentioned earlier, the primary objective of this
dissertation is to understand, explain, and predict the degree of renewable
entrepreneurship cluster growth associated with changes in various economic variables
over time. Therefore, data gathered for this study consisted of state-level and cluster-level
data that covered a time period of seven-years: from 2007-2013. This selected time
period was limited by the necessity to have an overlapping time-period among the
various databases used within this dissertation. Therefore, I acknowledge that differences
between time periods might result in different relationships among public policy, pace
and stability, economic inductance, and R/E cluster growth. However, I also contend that
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this time period allows adequate time for changes in these variables to develop, and is
thus sufficient for examining such relationships. Due to capturing the impact of the
2007-2008 financial crisis within this time-frame, as well as of the policies that followed,
I also argue that the selected time period adequately represents the current business and
economic environment (e.g., Klapper and Love, 2011; Leigh and Blakely, 2013; Porter
and Kramer, 2011; Stoddard and Noy, 2015; Wilson and Eilertsen, 2010). I therefore see
an opportunity to broaden the time period of investigations using econometric models
such as the one utilized in this dissertation, as a way to better understand the nuances of
various economic environments across history.
Finally, the notion and conceptualization of renewable entrepreneurship (R/E) are
first introduced into the literature within this dissertation. Nonetheless, a limitation is
that the R/E term has been used previously in the literature, but (as discussed previously)
in different ways and with different meanings. For example, an Internet search on
Google and Google Scholar returns 163 differential uses of the term “renewable
entrepreneurship.” An examination of these search results reveals that prior uses of the
R/E term can be categorized either as a synonym for sustainable entrepreneurship (e.g.,
Gelderen and Masurel, 2012; Robb, 2005), or for renewable energy (e.g., Wüstenhagen
and Wuebker, 2011). Such uses are distinct from and should not be confused with the
notion of R/E introduced within this dissertation. In this sense I see an opportunity to
distinguish the definition utilized herein from the other uses of R/E that are, for example,
environmental sustainability related; and also to expand the concept of entrepreneurship
more generally by adding this conceptualization of R/E and its application to the
economic diversification to the literature. And although the pseudo R-squared of 0.2231
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in the RCM model explains substantial variance, it also indicates open opportunities to
pursue investigations that – through the explanation of additional variance – may make
possible the more-comprehensive utilization of this conceptualization of R/E. I discuss
such opportunities in the next section.
Despite the foregoing limitations, this study demonstrates that various economic
variables (i.e., public policy, pace and stability, and economic inductance) have
significant influence on R/E cluster growth.
Future Research
The theoretical development of the research model that introduces and
conceptualizes the notion of renewable entrepreneurship (R/E), including the various
relationships among public policy variables (i.e., business environment policy maturity,
innovation policy maturity, new venture creation policy maturity), institutionalization of
innovation pace and stability variables (i.e., competition intensity and knowledge
spillover effectiveness), economic inductance, and renewable entrepreneurship cluster
growth, the subsequent development of the econometric model, and the empirical testing
of the hypotheses provides several opportunities for future studies.
First, in addition to the various public policy variables, pace and stability of the
institutionalization of innovation variables, and economic inductance variable included in
the research model of this study, various other variables are argued to influence and be
influenced by economic diversification; including political stability (e.g., Albassam,
2015; Dunning, 2005), social development (e.g., Ramcharan, 2005), and various
institutional variables (e.g., Karl, 2007; North, 1990). Hence, future research could refine
the renewable entrepreneurship system introduced within this dissertation to include
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additional variables, including perhaps those at additional levels of analysis, and examine
their contribution to the growth of R/E clusters, and thus overall economic
diversification.
Second, the discussion of the role of public policy on entrepreneurship and
economic growth within entrepreneurship research is still limited (Zahra and Wright,
2011), and the more macro role of entrepreneurship research is (for the most part) left to
practical implications sections of published research (e.g., Dean and McMullen, 2007;
Holcombe, 2003; Shane, 2000). Within this dissertation I have attempted to bridge this
gap by connecting the research in entrepreneurship literature with some of the theories
and empirical methods of the domains of regional, urban and development economics,
where the role of government engagement is productively developed. Hence, I suggest
that for future work, researchers interested in the nexus of entrepreneurship and public
policy further bridge this gap by exploring the research in such these research domains,
which will allow further extrapolation of the subfield of entrepreneurial public policy
research, as well as the redirection of economic development research, including research
in regional and urban economics.
Third, within this dissertation, an index of economic inductance was developed,
including three main components: technology readiness, talent, and tolerance. I do not
claim that these components capture economic inductance comprehensively. Hence, I
invite researchers to conduct extrapolations of diverse (general, and economic setting
specific) economic inductance indexes that cover different aspects of social resistance to
the conservation of economic energy. For instance, various cultural dimensions have
previously been found to influence significantly rates of economic growth (e.g., Hofstede
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and Bond, 1988; Yeh and Lawrence, 1995). I also expect that behavioral/ individual
level factors might also be found to be relevant; and should therefore be investigated (cf.
Baumol, 1968)
Finally, as discussed earlier, data gathered for this study were limited to one
economic setting (i.e., clusters within the United States). In addition, due to the different
reasons discussed above, the data covers a time period of seven years (2007-2013).
Regardless of the benefits of such an approach, extending the time period and examining
renewable entrepreneurship cluster growth within other economic settings (e.g., GCC
countries, European Union, OECD, etc.), is likely to expand our understanding of the
concept of renewable entrepreneurship and its impact on economic diversification and
economic growth. In my view, it is certainly possible that different relationships exist due
to differences in time periods as well as the various economic, political, social,
institutional, and educational forces within such settings.
CHAPTER 7: CONCLUSION
The primary objective of this dissertation has been to identify economic variables
that lead to renewable entrepreneurship cluster growth, and therefore, lead to economic
(primarily horizontal) diversification. While the role of government engagement in
economic policies has been productively established within the New-Keynesian
economics perspective (Snowdon and Vane, 2005), research within urban, regional, and
development economics is limited when it comes to what economic variables lead to
regional economic diversification (Motoyama, 2008). To address this gap, the notion of
renewable entrepreneurship was developed within this dissertation, to support the
argument that when applied within economic clusters, R/E provides an appropriate means
to achieve horizontal economic diversification, and thereby, continuing economic growth.
Also, a research model of renewable entrepreneurship clusters was developed, where the
influences of various public policy variables (i.e., business environment policy maturity,
innovation policy maturity, new venture creation policy maturity), institutionalization of
innovation pace and stability variables (i.e., competition intensity and knowledge
spillover effectiveness), and economic inductance on renewable entrepreneurship cluster
growth were theoretically derived and empirically examined.
The results of this study suggest that government engagement through a range of
policies is essential for renewable entrepreneurship cluster growth and for effective
horizontal economic diversification. Also, the results highlight the significance of the
economic inductance influence on renewable entrepreneurship cluster growth, where it
has been shown empirically (with what might be characterized as an exploratory
operationalization), that societal resistance to economic energy transfer (e.g. resistance to
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energy-transfer intervention through government funding of innovation and/or
entrepreneurship) will hinder regional economic diversification and economic growth.
The findings also highlight the essentiality of public policy in invoking the institutions of
innovation within societies (Li and Mitchell, 2009). However, the results also confirm
that the process of knowledge investments within societies is not sufficient by itself to
translate into desirable economic outcomes as indicated by renewable entrepreneurship
cluster growth, without low rates of economic inductance (i.e., low resistance) being
present through various supporting social, economic and institutional conditions (Gordon
and McCann, 2005; Landau and Rosenberg, 1986).
Within this dissertation, I have tried to connect entrepreneurship research to
macroeconomic theories and research methods developed within the domains of regional,
urban, and development economics, where the role of government engagement in shaping
economic policy is well established (Snowdon and Vane, 2005). Such an attempt to
bridge the gap among these domains should begin to answer calls to better understand the
influence of public policy on entrepreneurship (Zahra and Wright, 2011), and contribute
productively to each field due to the evidence provided herein for an even more
complementary conceptualization of these fields. Overall, I hope that within this
dissertation I have provided in some small measure a possible answer to how and where
government engagement can be optimized to productively lead to economic
diversification, and thereby, to economic prosperity.
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Business Environment
Policy Maturity
H1
H9a
H9b
H8
H9c
New Venture Creation
Policy Maturity
H3
H5 H9d H9e H4
H7 H9g H6 H9f
FIGURE 3.1: Renewable Entrepreneurship Clusters – Research Model – Editable Version
Economic Inductance
Public Policy Variables
APPENDICES
Technology Impactfulness
Talent/Tolerance
For Example: Government Budget and
Spending
Regulatory Complexity
Tax Structure
Innovation Policy Maturity
For Example: IP Protection
Communication Platform
Effectiveness Research Institution Accessibility
Renewable
Entrepreneurship
Cluster Growth
For Example: Ease of Financing
Ease of Starting a Business
Economic Freedom
Pace and Stability Variables
Competition Intensity
For Example: Firms Growth Rate
Degree of Job Churning
Knowledge Spillover Effectiveness
For Example:
Universities Patents
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APPENDICES
Appendix A: Indices and Related Sub-indices
The Small Business Policy Index (SBPI)
Measures: Business Environment Policy Maturity.
Score Calculation: Based on 42 tax, regulations, and government related sub-indices.
o High Score: Friendly Business Environment Policy (e.g., Texas: 111.438).
o Low Score: Hostile Business Environment Policy (e.g., California: 31.546).
Related Sub-indices:
1. Personal Income Tax.
2. Individual Capital Gains Tax.
3. Individual Dividends and Interest Tax.
4. Corporate Income Tax.
5. Corporate Capital Gains Tax.
6. Additional Income Tax on S-Corporations.
7. Additional Income Tax on LLCs.
8. Average Local Personal Income Tax Rate.
9. Individual Alternative Minimum Tax.
10. Corporate Alternative Minimum Tax.
11. Indexing Personal Income Tax Brackets.
12. Personal Income Tax Progressivity.
13. Corporate Income Tax Progressivity.
14. Property Taxes.
15. Sales, Gross Receipts and Excise Taxes.
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16. Death Taxes.
17. Unemployment Tax Rates.
18. Tax Limitation States.
19. Internet Taxes.
20. Remote Seller Taxes.
21. Gas Tax.
22. Diesel Tax.
23. Wireless Tax.
24. Health Savings Accounts.
25. Energy Regulation Index.
26. Workers’ Compensation Costs.
27. Total Crime Rate.
28. Right to Work.
29. State Minimum Wage.
30. Paid Family Leave.
31. E-Verify Mandate.
32. State Tort Liability Costs.
33. Regulatory Flexibility Status.
34. Number of State and Local Government Employees.
35. Trend in State and Local Government Spending.
36. Per Capita State and Local Government Spending.
37. Per Capita State and Local Government Debt.
38. Level of State and Local Revenue from the Federal Government.
39. Protecting Private Property.
40. Intrastate Equity Crowdfunding.
41. Highway Cost Efficiency.
42. Education Reform.
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The Innovation Capacity Indicator (ICI)
Measures: Innovation Policy Maturity.
Score Calculation: Based on 7 related sub-indices.
o High Score: Advanced Innovation Policy (e.g., Washington: 19.3).
o Low Score: Stagnate Innovation Policy (e.g., Louisiana: 4.3).
Related Sub-indices:
1. Share of jobs in high-tech industries.
2. The share of workers that are scientists and engineers.
3. The number of patents issued to companies and individuals.
4. Industry R&D as a share of worker earnings.
5. Non-industrial R&D as a share of GSP.
6. Clean energy consumption.
7. Venture capital invested as a share of worker earnings.
The Economic Freedom of North America Index (EFNAI)
Measures: New Venture Creation Policy Maturity.
Score Calculation: Based on major 5 areas and 53 related sub-indices.
o High Score: Enabling New Venture Creation Policy (e.g., Texas: 7.8).
o Low Score: Disabling New Venture Creation Policy (e.g., Maine: 5.2).
Related Sub-indices:
Area 1: Size of Government:
a. General Consumption Expenditures by Government as a Percentage of GDP.
b. Transfers and Subsidies as a Percentage of GDP.
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c. Social Security Payments as a Percentage of GDP.
d. Government Enterprises and Investment.
Area 2: Takings and Discriminatory Taxation:
a. Total Tax Revenue as a Percentage of GDP.
b. Top Marginal Income Tax Rate and the Income Threshold at Which It Applies.
c. Top Marginal Income and Payroll Tax Rate.
d. Indirect Tax Revenue as a Percentage of GDP.
e. Sales Taxes Collected as a Percentage of GDP.
Area 3: Regulation:
a. Labor Market Freedom.
b. Regulation of credit markets.
c. Business regulations.
Area 4: Legal System and Property Rights:
a. Judicial independence.
b. Impartial courts.
c. Protection of property rights.
d. Military interference in rule of law and the political process.
e. Integrity of the legal system.
f. Legal enforcement of contracts.
g. Regulatory restrictions on the sale of real property.
h. Reliability of Police.
i. Business costs of crime.
Area 5: Sound Money:
a. Money growth.
b. Standard deviation of inflation.
c. Inflation: most recent year.
d. Freedom to own foreign currency bank accounts.
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The Economic Dynamism Indicator
Measures: Competition Intensity.
Score Calculation: Based on major 5 areas and 53 related sub-indices.
o High Score: Intense Competition (e.g., California: 14.2).
o Low Score: Mild Competition (e.g., West Virginia: 5.8).
Related Sub-indices:
1. The degree of job churning.
2. The number fast growing firms.
3. The number and value of companies’ IPOs.
4. The number of entrepreneurs starting new businesses.
5. The number of individual inventor patents granted.
Economic Inductance Index
Measures: Economic Inductance.
Score Calculation: The average weight of Knowledge Jobs indicator and The Digital Economy indicator, including 11 related sub-indices.
o High Score: Elevated Economic Inductance (e.g., Mississippi: 17.45).
o Low Score: Mild Competition (e.g., Massachusetts: 2.86).
Related Sub-indices:
The Knowledge Jobs indicator:
a. Employment in IT occupations in non-IT sectors.
b. The share of the workforce employed in managerial, professional, and technical occupations.
c. The education level of the workforce.
d. The average educational attainment of recent immigrants.
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e. The average educational attainment of recent U.S. inter-state migrants.
f. Worker productivity in the manufacturing sector.
g. Employment in high-wage traded services.
The Digital Economy indicator:
a. The use of IT to deliver state government services.
b. The percentage of farmers online and using computers for business.
c. The adoption and average speed of broadband telecommunications.
d. Health information technology use.
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138
Appendix B: Differential Uses of the Term “Renewable Entrepreneurship”
Year Reference Title Excerpt
“Renewable Energy”
2006 DuBay Five ways to make money in
renewable energy
Not only are CU and the NREL big into renewable entrepreneurship, a
business group called CORE promotes environmental and socially
responsible practices and has a renewable energy effort that major
employers take part in
2009 EU Monitor Sectoral specialization of
manufacturing – Romania
The sluggish restructuring of the industrial base efficiency and
renewable energy should be a key which, prior to 1989, was
characterized by a high priority in Romania.
2011 Loock
How do business models impact
financial performance of
renewable energy firms?
... financing is one of the most important bottlenecks for the diffusion of
renewable energy
2012 Solar Plaza
Dutch Government to allocate €3
billion for renewable
entrepreneurship
Renewable energy is not only important for a healthier economy, but
also provides entrepreneurial opportunities.
2014 Climate
Parliament
Encouraging renewable
entrepreneurship in India
... there is tremendous potential for replacing the use of fossil fuels in the
MSME sector with more sustainable renewable energy alternatives.
“Sustainable Entrepreneurship”
2003 UNEP Finance
Initiatives
Sustaining Value: A Meeting on
Finance and Sustainability
Mr. Bart Jan Krouwel, Managing Director Sustainable Development and
Social Innovation, Rabobank
2004 Robb
Corporate Entrepreneurship and
the
Ethic of Continuous Value
Creation
... if sustainable growth is the goal, the ethic of continuous value creation
is actually intensely practical.
2005 Robb
Renewable Corporate
Entrepreneurship: The Path to
Sustainable Growth
... renewable entrepreneurship is the source for continuous generation of
"disruptive innovations" - products and services that alter the rules of the
competitive landscape - in your favor.
2007 Dhliwayo Entrepreneurial strategic The methods of creating a sustainable entrepreneurial environment the
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139
Year Reference Title Excerpt
leadership chapter presents are structural factors, entrepreneurial politics and
strategic leadership.
2009 Houses of the
Oireachtas
Sustainable Food Development:
Discussion with Irish Farmers
Association
The policy response must ensure sustainable beef production in Ireland
2012 Kouwenhoven et
al.
Reducing Food Waste: An
opportunity for innovative
catering opportunity
A new dimension of innovative and renewable entrepreneurship is a
change in the attitude/behavior of the entrepreneur toward carrying out
his/her business activities in a sustainable and environmentally friendly
manner.
2014 Antolin-Lopez et
al.
GRONEN Research Conference
2014: Preliminary Programme
How to move established industries to
sustainability?
2015 MIDWEST 2020 Regional Mission
Building together on a sustainable transformation of the Mid-West-
Flanders socio-economic regional model, by strategically focusing on
smart specialization and social anchoring.
Other Uses
2014 FMK Spring Fatigue
By the arrival of spring we should feel as the waking nature charges us
up with energy and instead of that these early spring days we feel jaded,
sleepy, many people complain about headaches, dizziness and they feel
tense.
Texas Tech University, Abdallah Mohammed S. Assaf, May 2016
140
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