Drilling For Innovation: Economic Diversification Through ...

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


16

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


17

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


18

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


19

hypotheses testing. Chapter 6 evaluates the findings, discusses implications for theory

and practice, limitations and future research opportunities. Finally, Chapter 7 concludes

this dissertation.


20

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.,


21

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).


22

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


23

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


24

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


25

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.


26

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


27

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


29

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


30

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)


31

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.


32

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).


33

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


34

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


35

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.


36

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.


37

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.


38

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,


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


40

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.


41

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


42

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


43

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


44

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


45

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


46

“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).


47

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


49

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


50

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


51

“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


52

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


53

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


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


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.


56

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


57

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


58

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


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


66

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


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


70

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


74

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.


75

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


76

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

https://en.wikipedia.org/wiki/Ewing_Marion_Kauffman_Foundation


77

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


79

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.


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


80

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


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.


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).


93

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


98

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.


99

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




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Table 5.4: OLS Regression Results for Competition Intensity



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




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Table 5.5: OLS Regression Results for Knowledge Spillover Effectiveness



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




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Table 5.6: Summary of Findings

Hypotheses: Findings


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


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


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




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).


111

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


113

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.


116

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


117

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


118

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


119

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


120

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


121

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


123

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


125

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


126

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,



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


127

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


128

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,



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


130

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.


131

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


APPENDICES

Technology Impactfulness

Talent/Tolerance

For Example: Government Budget and

Spending

Regulatory Complexity

Tax Structure


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



For Example: Firms Growth Rate

Degree of Job Churning


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.


133

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).


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).


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.


135

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).


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).


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.


137

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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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


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.


140

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