Eindhoven University of Technology

MASTER

The impact of very high capacity braodband availability on entrepreneurship in regionsevidence from the Netherlands

de Heij, C.

Award date:2019

Link to publication

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Technical University Eindhoven

Faculty of Industrial Engineering and Innovation Sciences

The Impact of Very High Capacity Broadband

Availability on Entrepreneurship in Regions: Evidence

from the Netherlands

Student Name : Christian de Heij

Student ID : 0999267

Supervisors: : B.M. Sadowski (Technology, Innovation & Society)

: E. Raiteri (Technology, Innovation & Society)

: G. Papachristos (Technology, Innovation & Society)

: M. Driesse (Dialogic Innovatie & Interactie)

Master Program : Msc. Innovation Sciences (IS)

Faculty Name : Industrial Engineering and Innovation Sciences (IE & IS)

University Name : Technical University Eindhoven (TU/e)

Publishing Date : 16-12-2019

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Abstract

While the economic progress of regions is widely believed to be influenced by the availability of

broadband, there is little empirical evidence on the importance of broadband capacity speed. The

broadband internet infrastructure has significantly improved in terms of capacity in many countries in the

last decade. In this thesis we therefore investigate the impact of very high capacity broadband availability

for businesses on entrepreneurship in regions, while controlling for a range of economic variables.

Regional level data in the Netherlands from 2011-2017 is linked to spatial information on the rollout of

fiber. We find positive significant effects of very high capacity broadband availability on entrepreneurial

activity and entrepreneurial success in regions when the policy variable is lagged for two years. We also

took heterogeneity across space into account by grouping the regions to their urbanity levels. Results

show that there exists heterogeneity and moderately urban regions seem to benefit the most from very

high capacity broadband. This study is an important step towards closing the gap in the literature on the

impact of broadband with higher capacity speeds while it also provides policy makers with much needed

empirical evidence on which to base broadband policy decisions.

Keywords: broadband, availability, regional, determinant, entrepreneurship, entrepreneurial activity,

entrepreneurial success

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Acknowledgements

I realize now how ignorant I was during my master program Innovation Sciences. I kind of expected the

master thesis to be an easy extension of the rest of the program, but this proved to be the complete

opposite for me. The amount of freedom that was given to me in terms of process and content required

a considerable amount of patience and perseverance. Many a time I felt stuck and that this thesis was

keeping me from moving to the next phase in my life. Now, in hindsight I realize that I have learned big

lessons way beyond the actual writing and research process of the thesis itself. I am therefore grateful for

the opportunity to struggle so much with myself and this master thesis that took me so much longer to

complete than I would have wanted. There are a number of people that have helped and supported me

throughout this period that I would like to thank. I am grateful for the company Dialogic that gave me the

opportunity to reconstruct fiber broadband availability for businesses in the Netherlands, in particular

Menno Driesse for his kindness, patience and guidance. I would also like to thank professor Bert Sadowski

for his willingness to be my supervisor and his invaluable expertise and considerate feedback in the field

of this study. Furthermore, professor Raiteri and professor Papachristos for their time and feedback. This

has been thought-provoking and undoubtedly improved the quality of this thesis. Finally, a big shout out

to my friends and family that have kept asking me the annoying question: “how is your thesis going?” and

that have believed in my ability to finish this. Special and honorable mention to my parents, you are my

foundation. I am very grateful, thank you.

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Summary

Economic progress is an important concern for every nation. There are large disparities between and often

also within nations. The geographic dimension is often taken for granted or overlooked, while important

to take into consideration. It is widely believed that entrepreneurship in a region is important for its

economic progress.

However, there exists much variation in entrepreneurial activity across regions (Armington & Acs, 2002;

Bosma, 2009) and this is persistent over time (Fritsch & Schmude, 2006). The regional environment is

considered as an important determinant of entrepreneurial activity and entrepreneurial success and

entrepreneurship mainly a ‘regional event’ (Sternberg, 2009).

An important aspect of the regional environment is the broadband internet infrastructure. Broadband

and related ICT technologies are considered General Purpose Technologies (GPT), because they are

pervasive throughout the whole economy, important for business productivity and opportunities, and

important for the economic progress of regions (Bresnahan & Trajtenberg, 1995; Prieger, 2013;

Richardson & Gillespie, 1996).

Prior research clearly shows that broadband internet can have a positive impact on entrepreneurial

activity in regions (Heger, Veith, & Rinawi, 2012; Kim & Orazem, 2016; Kotnik & Stritar, 2015; Mccoy,

Lyons, Morgenroth, Palcic, & Allen, 2017; Prieger, Lu, & Zhang, 2017; Whitacre, Gallardo, & Strover, 2014).

However, the broadband internet infrastructure has improved considerably in capacity speeds in the last

decades, something these studies did not take into account. The Netherlands is an example where

broadband internet can reach speeds up to 1 Gbps (Deloitte, 2017). On top of that, the goal of the

European Commission is to have a gigabit society by 2025 (European Commission, 2016), while there

exists a gap in the literature on the impact of broadband with higher capacity speeds. Insight into the

impact is therefore important for policymakers. They rely on sound empirical evidence on which to base

their policy decisions.

The purpose of this research is to make a first step towards filling up this gap and provide policymakers

with the necessary empirical evidence. This thesis therefore aims to quantitatively investigate the impact

of very high capacity broadband availability for businesses on entrepreneurship in regions, while

controlling for other economic variables considered important for entrepreneurship. The following

research questions is central:

‘To what extent is the availability of very high capacity broadband for businesses an important regional

determinant of entrepreneurship in the Netherlands?’

The focus is on municipalities in the Netherlands from 2011, when the roll-out of fiber broadband roughly

started in the Netherlands up to 2017. We combine regional level data with data on the roll-out of fiber

for businesses in one panel dataset. The empirical approach is based on a count modeling approach that

is suggested in the business establishments literature, where the attractiveness of an area depends on

multiple characteristics of the environment (Bhat, Paleti, & Singh, 2014; Mccoy, Lyons, Morgenroth,

Palcic, & Allen, 2016). Fixed effects help to control for average differences across municipalities and years

for any observable and unobservable predictors, resulting in a least squares dummy variable model

(LSDV). A model with fixed effects is not able to prove causality due to an endogeneity problem that exists

when measuring the impact of broadband (Gaasbeck, 2008; Kolko, 2012), but it provides a better

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modeling approach than a regular OLS (Whitacre, Gallardo, & Strover, 2013). The policy variable is lagged

for two years, because the roll-out year is typically not the first year when broadband is available. The

construction can take a considerable amount of time and it can also take some time before businesses

and potential entrepreneurs make use of broadband availability.

The lagged variable helps with the endogeneity problem by mitigating the possibility of reverse causality.

As a robustness check we included an instrumental variables approach that is suggested in the literature

(Kolko, 2012; Mack, 2014; Mack, Anselin, & Grubesic, 2011). Preliminary analysis showed concerns of

serial correlation and heteroscedasticity. To deal with these concerns we also adopt robust standard

errors, based on the ‘Arellano’ method, because this method is suggested for fixed effects in panel data

and deals with both serial correlation and heteroscedasticity (Arrellano, 1987).

The results show that the policy variable is positively and significantly correlated with entrepreneurial

activity as the amount of new independent firms per 1.000 business establishments and entrepreneurial

success as the amount of fast growing firms per 1.000 business establishments in the region. This was not

supported by the instrumental variable approach, but the Hausman specification test (Hausman, 1978)

showed no concern of endogeneity and the statistical power of an instrumental variable approach is

typically low (Semademi, Certo, & Withers, 2014), therefore favoring the basic LSDV model. A 10%

increase in very high capacity broadband for businesses results two years later in 0.45 independent new

firms and 0.15 fast growing firms per 1.000 business establishments. The presence of very high capacity

broadband enables new levels of advanced use of the internet, by leveraging activities such as big data

analytics, cloud services and new intensive ICT applications (Phippen & Lacohée, 2016; Ross &

Blumenstein, 2015). This can facilitate entrepreneurial activity in regions in two ways. First, advanced use

in businesses has the potential to offer new entrepreneurial opportunities in the region that can be

exploited by potential entrepreneurs that become aware of these new opportunities. Second, advanced

use offers many benefits for SME’s (Phippen & Lacohée, 2016). For entrepreneurs that are aware of the

increasingly importance of ICT in business this can influence their location decisions to favor an area with

very high capacity broadband over an area without very high capacity broadband. Advanced use can also

facilitate entrepreneurial success, because it can help SME’s survive and grow. Access to very high capacity

broadband provides SME’s with powerful tools to work faster, more efficiently and differently (Phippen

& Lacohée, 2016).

This study also looked at heterogeneity across space by grouping the regions based on urbanity levels.

Results show that there exists heterogeneity across space. Moderately urban and no urban regions

positively and slightly significantly correlated to entrepreneurial activity, while highly urban and

moderately urban regions positively and slightly significantly correlated to entrepreneurial success.

These findings are an important step towards closing the very high capacity broadband gap in the

literature. To our knowledge this is the first study to look at the impact of very high capacity broadband

with these proxies of entrepreneurship in regions. Two other advantages are the inclusion of broadband

availability specifically for businesses and the focus on regions in the Netherlands instead of regions in the

United States where most prior broadband research is conducted. This study therefore contributes to the

broadband internet literature and the literature on regional determinants of entrepreneurship. Policy

makers can take these results into consideration when making public policy decisions.

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The results should be taken somewhat lightly, because this research is unable to prove causality, although

it makes a strong case for it. It is also limited in that it was unable to look at heterogeneity across industries

and make use of spatial econometrics. Future research is needed to fill up the very high capacity

broadband gap and deal with the limitations of this study. Nevertheless, this study provides a good first

step and suggests that very high capacity broadband for businesses does make a difference on

entrepreneurship in regions.

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Table of Contents

1. Introduction .......................................................................................................................................... 9

1.1. Background ................................................................................................................................... 9

1.2. Aim and Approach ...................................................................................................................... 10

1.3. Main contributions...................................................................................................................... 11

1.4. Structure ..................................................................................................................................... 11

2. Theoretical Background ...................................................................................................................... 12

2.1. Entrepreneurship ........................................................................................................................ 12

2.1.1. Entrepreneurship conceptualized ....................................................................................... 12

2.1.2. Entrepreneurial activity conceptualized ............................................................................. 13

2.1.3. Regional determinants of entrepreneurial activity and success ........................................ 15

2.2. Broadband Internet..................................................................................................................... 17

2.2.1. Broadband internet conceptualized ................................................................................... 17

2.2.2. Technology perspective vs Policy perspective .................................................................... 18

2.3. Broadband Internet as regional determinant of entrepreneurship ........................................... 20

2.3.1. Regional perspective ........................................................................................................... 20

2.3.2. Proxies of Entrepreneurial activity ...................................................................................... 21

2.3.3. Empirical evidence .............................................................................................................. 22

2.3.4. Evolution of broadband speed ............................................................................................ 23

2.3.5. Hypotheses.......................................................................................................................... 24

3. Method ............................................................................................................................................... 26

3.1. Data ............................................................................................................................................. 26

3.1.1. Dependent variables: Entrepreneurial activity and Entrepreneurial success .................... 26

3.1.2. Policy variable: Broadband availability data ....................................................................... 27

3.1.3. Other variables: regional level economic data ................................................................... 28

3.1.4. Software utilized ................................................................................................................. 29

3.2. Empirical approach ..................................................................................................................... 30

3.2.1. Heterogeneity ..................................................................................................................... 32

3.2.2. Endogeneity ........................................................................................................................ 33

3.2.3. Robust covariance matrix estimation ................................................................................. 34

4. Results ................................................................................................................................................. 35

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4.1. Results Entrepreneurial activity (full sample) ............................................................................. 35

4.2. Results Entrepreneurial success (full sample) ............................................................................ 37

4.3. Results subsamples ..................................................................................................................... 39

5. Discussion ............................................................................................................................................ 42

5.1. Most important findings ............................................................................................................. 42

5.2. Limitations................................................................................................................................... 45

5.3. Future research ........................................................................................................................... 46

6. Conclusion ........................................................................................................................................... 47

7. Bibliography ........................................................................................................................................ 48

8. Appendix ............................................................................................................................................. 53

8.1. VIF test for multicollinearity ....................................................................................................... 53

8.2. First stage of the 2SLSDV model ................................................................................................. 53

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1. Introduction

1.1. Background

Economic progress is one of the main concerns of every nation around the world. There exist large

disparities of economic wealth between nations, but often also within. The geographic dimension is often

overlooked or taken for granted, while important to take into consideration. Research has shown that the

economic growth of regions can be influenced by entrepreneurship. New entrepreneurs create local jobs,

growth, wealth and are innovative in the way they use regional assets and resources. Acs & Armington

(2003) provide evidence of a positive relationship between entrepreneurship and economic growth. Other

research shows that entrepreneurship and small firms are important for employment, economic growth

and competitiveness (Acs, Parsons, & Tracy, 2008). Furthermore, empirical evidence on regions such as

Silicon Valley (Lee, Miller, Hancock, & Rowen, 2000), Munich (Sternberg & Tamásy, 1999) and Shanghai

(Muller, 2007) are demonstrations that a high concentration of start-ups can significantly contribute to a

regions economic progress.

Prior research also shows that there exists much regional variation in entrepreneurship. There are

pronounced regional differences in start-up activity (Armington & Acs, 2002; Bosma, 2009) and these

differences are persistent over time (Fritsch & Schmude, 2006). Sternberg (2009) provides an extensive

literature review of several theoretical arguments that support the idea that start-up activity as well as

start-up success is influenced by the regional context in which the firm was or is located. The research

concludes that the regional environment is an important context variable that cannot be ignored when

exploring the determinants of start-up activity and start-up success and therefore concludes that

entrepreneurship is primarily a ‘regional event’.

If entrepreneurship is primarily a ‘regional event’ and heavily influenced by the regional context, then

what context variables are important determinants of entrepreneurship? An important question that has

not received much scholarly attention. An important aspect of the regional environment is the digital

infrastructure, which is typically composed of broadband and related internet and communication

technologies (ICT). These technologies have become a core of the economy, shaping all kinds of economic

transactions (OECD, 2012) and can be seen as General Purpose Technologies (GPT), because they are

pervasive throughout the whole economy, very useful to businesses in terms of productivity benefits and

opportunities, and an important factor in the development of regional economies (Bresnahan &

Trajtenberg, 1995; Prieger, 2013; Richardson & Gillespie, 1996).

Research has shown that broadband internet can indeed have a positive impact on entrepreneurial

activity in regions (Heger et al., 2012; Kim & Orazem, 2016; Kotnik & Stritar, 2015; Mccoy et al., 2017;

Prieger et al., 2017; Whitacre et al., 2014). However, prior research has mainly focused on broadband with

broadband internet capacity speeds that are lower than 30 Mbps or 100 Mbps, while the broadband

infrastructure has significantly improved over the last two decades in many different countries, offering

much higher speeds. For example, the Netherlands is one of these countries where the adoption and

diffusion of fiber optic solutions and improvements on existing broadband technologies currently make it

possible to reach broadband capacity speeds up to 1 Gbps (Deloitte, 2017). These improvements are

influenced and supported by EU policy. The European Commission recognized the need for a future-proof

internet infrastructure and initially set the objective to have internet connectivity of at least 30 Mbps for

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all EU households by 2020 (European Commission, 2016). This objective was identified as the transition

towards Next Generation Access (NGA) networks, which are fixed networks capable of offering internet

connectivity of at least 30 Mbps. In the newly proposed European Electronic Communications Code, the

European Commission goes even further setting the objective of having a gigabit society by 2025. This

includes gigabit connectivity for all main socio-economic drivers with broadband capacity speeds of at

least 1 Gbps and connectivity offering at least 100 Mbps for all EU households that can be upgraded to 1

Gbps (European Commission, 2016). This new transition is regarded as a transition towards Very High

Capacity (VHC) broadband networks.

All the while, there exists almost no empirical evidence on the impact of broadband with higher capacity

speeds. Policy makers therefore have no clue about the impact of very high capacity broadband internet

on the economy, while they are pushing hard to increase it.

1.2. Aim and Approach

There is a need for empirical evidence on the impact that increasingly faster internet has on the economy.

This is important to fill up the gap that exists in the literature on broadband internet. The ICT revolution

is associated with a shift from a ‘managed economy’ towards an ‘entrepreneurial economy’ (Thurik, Stam,

& Audretsch, 2013) and previous research has shown that broadband internet can have a positive impact

on the amount of entrepreneurial activity in regions. However, the question whether very high capacity

broadband is also an important regional determinant of entrepreneurship remains and has to be

answered. Next to an important step towards filling up the gap on the impact of broadband with higher

capacity speeds, this would increase the knowledge on the importance of the regional context for

entrepreneurship. Most importantly, it provides the much needed empirical evidence on which

policymakers can base their decisions. This research therefore aims to quantitatively investigate the

impact of very high capacity broadband on entrepreneurship in regions, while controlling for a range of

other economic variables. Resulting in the following research question:

‘To what extend is the availability of very high capacity broadband for businesses an important regional

determinant of entrepreneurship in the Netherlands?’

We focus on regions in the Netherlands and combine regional level data on the percentage of houses and

offices with the availability of a very high capacity broadband network and regional level data on

entrepreneurial activity and entrepreneurial success into a single panel dataset. First, we map the

broadband availability data over the years 2011-2017 to provide initial insight into the regional variation

of broadband availability in the Netherlands. Then we proceed to perform a fixed effects regression where

the independent policy variable is lagged for two years and where we control for other economic

variables. We lag the policy variable, because the year that broadband is rolled-out is typically not the first

year that it is completely available. It can also take time before broadband availability is utilized after it is

rolled-out. Another advantage with this approach is that this deals with the endogeneity problem that

typically exists in measuring the relationship between broadband and indicators of local economic growth

(Kolko, 2012; Minges, 2016). Correlation between very high capacity broadband networks and

entrepreneurial activity does not, in itself, mean that these networks cause entrepreneurial activity. The

reverse might be true or something else might cause both to increase or decrease at the same time. The

use of a lagged policy variable can mitigate some of these concerns.

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1.3. Main contributions

This research makes two important contributions to the literature and is also practically relevant to both

policymakers and entrepreneurs or small business owners. How entrepreneurship is influenced by the

context has been a major gap in the literature for a very long time (Autio, Kenney, Mustar, Siegel, &

Wright, 2014). This research contributes to fill this gap, because we theorize how the availability of very

high capacity broadband networks in the region can influence the amount of entrepreneurial activity and

entrepreneurial success in this region and test whether it is the case. The presence of very high capacity

broadband enables new levels of advanced use of the internet, by leveraging activities such as big data

analytics, cloud services and new intensive ICT applications (Phippen & Lacohée, 2016; Ross &

Blumenstein, 2015). This can facilitate entrepreneurial activity in regions in two ways. First, advanced use

in businesses has the potential to offer new entrepreneurial opportunities in the region that can be

exploited by potential entrepreneurs that become aware of these new opportunities. Second, advanced

use offers many benefits for SME’s (Phippen & Lacohée, 2016). For entrepreneurs that are aware of the

increasingly importance of ICT in business this can influence their location decisions to favor an area with

very high capacity broadband over an area without very high capacity broadband. Advanced use can also

facilitate entrepreneurial success, because it can help SME’s survive and grow. Access to very high capacity

broadband provides SME’s with powerful tools to work faster, more efficiently and differently (Phippen

& Lacohée, 2016). Investigating this relationship can be considered as part of the context or demand-side

approach to entrepreneurship and falls under the area of economic geography of entrepreneurship

(Thornton, 1999).

How increasingly faster broadband influences different economic development outcomes is another

recent gap in the literature. There exists research on the relationship between broadband internet and

economic development outcomes, but continuous improvements and innovations in ICT technologies

allowing for increasingly higher broadband internet capacity speeds enforce the continual evaluation of

the impact that this has. Evidence of the impact and effectiveness is crucial for policymakers, because

they need empirical evidence to justify public sector interventions. This research contributes to fill up this

research gap and help policymakers make decisions based on empirical evidence.

1.4. Structure

The remainder of this research is as follows. In the next chapter the theoretical foundation of the

entrepreneurship concept (2.1) and the broadband internet concept (2.2) is first given to provide the

necessary background information that makes it easier to investigate the relationship between broadband

internet and entrepreneurship that is thereafter discussed (2.3). In chapter 3, the data that is used to

conduct this empirical research is presented (3.1) with the chosen empirical approach (3.2). The results

section in chapter 4 sums up the results based on the data and empirical approach. This is then discussed

in chapter 5. Finally, a short conclusion is given in chapter 6 that answers the research question driving

this study.

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2. Theoretical Background

Figure 2-1 below shows the structure of this chapter. First, entrepreneurship and broadband internet are

conceptualized in subsection 2.1 and 2.2 respectively, before the relationship is discussed in subsection

2.3. The purpose of the theoretical background is to build a strong theoretical foundation for the rest of

this study. At the end this chapter we hypothesize how very high capacity broadband availability relates

to entrepreneurial activity and entrepreneurial success in regions.

2.1 Entrepreneurship 2.2 Broadband internet

2.3 Broadband internet as regional determinant

of entrepreneurship

Figure 2-1: Structure of the Theoretical Background

2.1. Entrepreneurship

2.1.1. Entrepreneurship conceptualized

A start-up company is initiated by someone who is called an entrepreneur. The concept of entrepreneurial

activity is therefore closely related to the theory of entrepreneurship. To fully understand the concept it

is necessary to first conceptualize entrepreneurship. This is however not that simple, because

entrepreneurship is a widely used interdisciplinary concept that can be understood in many different

ways. It is present in traditional disciplines such as economics and business administration, but also in

psychology, sociology and economic geography. This makes it an interesting, but also complex research

field that has also grown considerably in the last decades (Sternberg, 2009). There are many different

connotations of entrepreneurship, such as start-ups, self-employment, intrapreneurship, but they do not

mean the same thing. There does not exist one single generally accepted definition of entrepreneurship

in the literature, instead there is a broad spectrum of definitions, ranging from very specific to very broad

(Sternberg & Wennekers, 2005). This makes it necessary to explore how it is defined in the literature and

to state what definition of entrepreneurship and start-up activity we adopt in this research paper.

The academic literature on entrepreneurship can be traced back all the way to the 17th century where

Cantillon (1755) first defined an entrepreneur as a person who is willing to take the personal financial risk

of creating a business venture. However, entrepreneurship as a research field has long been ignored. The

importance of the concept only really took off when Schumpeter (1942) introduced creative destruction.

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In his understanding an entrepreneur was an economic actor who challenged the status quo by moving

the production frontier forward. His view was not widely acknowledged at first, but now it is the dominant

understanding that prevails in the entrepreneurship and economics literature. Not only the creation of

new activity, but also firm exit as the destruction of obsolete economic activity is central in his work. Caves

(1988) has shown that patterns of entry and exit are indeed highly correlated.

The second influential perspective on entrepreneurship comes from Kirzner (1973) who emphasizes the

process of discovering as central in entrepreneurship, instead of the focus on disruptive forces central in

the Schumpeterian view. In his understanding an entrepreneur is an individual who discovers profit

opportunities previously unnoticed. Shane and Venkatraman (2000) share this understanding of

entrepreneurship and identified three separate phases in the entrepreneurial process: the identification

of an opportunity, the mobilization of resources, and finally the actual exploitation of the identified

opportunity.

Many more views and definitions on entrepreneurship exist in the academic literature, but not nearly as

influential as the ones of Schumpeter and Kirzner. It is possible to distinguish at least two principal and

separate meanings that are present in the broad spectrum of definitions on entrepreneurship (Sternberg

& Wennekers, 2005). The first one refers to owning and managing one’s own business at one’s own cost.

The people that own and manage their own business are called business owners, entrepreneurs or self-

employed. This is the occupational notion of entrepreneurship and within this notion there are two

different perspectives. The static perspective looks at the number of business owners, while in the

dynamic perspective the focus is on the creation of new businesses. The second meaning that can be

distinguished relates to the behavioral notion of entrepreneurship. Referring to entrepreneurial behavior

as sensing or seizing an economic opportunity. Within this notion an innovator or pioneer can be

considered equivalent to an entrepreneur, thus entrepreneurs in this perspective do not have to be

business owners, but can also be employees within an established company and are then called

intrapreneurs.

Sternberg (2009) and others (Gartner, 1989; Cooper, 2003) combine elements of the behavioral notion of

entrepreneurship with elements of the dynamic perspective of the occupational notion of

entrepreneurship, making new venture creation their preferred understanding of entrepreneurship. This

is in line with Lumpkin & Dess (1996), that identify new entry as the essential act of entrepreneurship.

New entry can be accomplished by entering new or established markets with new or existing goods or

services. New entry is the act of launching a new venture, either through an existing firm, start-up firm,

or internal corporate venturing (Burgelman, 1983). In this research we take new venture creation as the

preferred definition of entrepreneurship, because it is compatible with many different perspectives of

entrepreneurship and specific enough to give a clear conception.

2.1.2. Entrepreneurial activity conceptualized

After conceptualizing entrepreneurship it is possible to conceptualize start-up activity. As with

entrepreneurship there does not exist one single accepted definition of entrepreneurial activity in the

literature. Considering the process character of entrepreneurship can help clarify the complexity of the

concept. The Global Entrepreneurship Monitor (GEM) defines entrepreneurially active people as “adults

in the process of setting up a business they will (partly) own and or currently own and manage an operating

young business” (Reynolds, Hunt, Servais, Lopez-garcia, & Chin, 2005, p. 209). Figure 2-2 below gives a

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simplified illustration of how the GEM sees the entrepreneurial process. It consists of four phases with

three transitions between the phases (Reynolds et al., 2005). The first phase also called the conception

phase includes all adults that consider setting up their own business. These adults are then called latent

entrepreneurs. Some of them might decide to actually start a business. Once they initiate start-up

activities, then they have completed the first transition and move to the second phase, also called

gestation or start-up phase. Adults in this phase can be considered nascent entrepreneurs and are actively

involved in setting up a business. This can be either (1) an effort to create an independent new business

or (2) a new venture that is sponsored by an existing business in the form of a new branch or subsidiary.

Only individuals that are expected to have full or some ownership in the new venture are considered

nascent entrepreneurs. More than 80% of nascent entrepreneurs are developing an independent new

business (Reynolds et al., 2005). When the start-up develops into an operational business then the second

transition called the firm birth transition is completed and adults become owner-managers of a young

business. There are different aspects that can be considered as the new firm birth event, but in the case

of the Global Entrepreneurship Monitor (GEM) it triggers when there are paid salaries for at least 3

months. This is considered an indicator of the firm birth transition, but not the actual event. The final

transition to the last phase depends on the age of the firm of the entrepreneur. When the new business

is older than 3.5 years, then it transitions into the final phase and the business becomes an established

business (Reynolds et al., 2005).

Figure 2-2: The entrepreneurial process and GEM operational definitions. Source: Reynolds et al. (2005)

The GEM definition of entrepreneurial activity includes start-up efforts that are sponsored by an existing

business that may be a new branch or subsidiary next to start-up efforts that are autonomous and

independent. In this research we understand entrepreneurship as new venture creation and

entrepreneurial activity as the activity of creating a new venture either by independent and autonomous

effort or through sponsoring of an existing business. A clear distinction can be drawn however, between

entrepreneurial activity through independent start-up efforts and through subsidiaries. This is

demonstrated by Bosma (2009), who found differences in the regional determinants of the number of

independent start-ups in a region and the number of subsidiaries in a region.

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2.1.3. Regional determinants of entrepreneurial activity and success

Empirical evidence shows that there are pronounced regional differences in entrepreneurial activity

(Armington & Acs, 2002; Bosma, 2009). These regional differences are also remarkable persistent over

time (Fritsch & Schmude, 2006) . This has inspired research into the importance of the regional context to

entrepreneurship by looking at regional determinants of entrepreneurial activity and entrepreneurial

success. In entrepreneurship research, the role of the context to entrepreneurship has long been

recognized now, but mainly taken for granted when it comes to empirical studies. One decade ago

Sternberg (2009) argued that the area of economic geography of entrepreneurship was still a fertile and

mainly ignored area that needed more attention. It is therefore necessary to develop, test, and improve

theories on the spatial dimensions of entrepreneurship. Personal and firm factors alone cannot explain

the entrepreneurial activity and entrepreneurial growth event, so a wide range of contextual factors have

become more important. The analysis of contextual determinants of entrepreneurial activity as new firm

creation is part of the context or demand-side approach to entrepreneurship (Thornton, 1999). Research

on the effect that the country context can have on entrepreneurial activity has taken off in this decade,

but there remains a significant lack of studies that look into the relevance of the regional context (Autio

et al., 2014). However, Sternberg (2009) provided an extensive literature review on the role of the regional

context that we will explore in this research.

Entrepreneurial activities are to a large extent a regional or local event for multiple reasons. (1)

Entrepreneurs usually locate their new business where they were born, previously worked (Boswell, 1973)

or currently reside (Haug, 1995). However, this is mainly the case for independent new businesses, not

always for subsidiaries. (2) Regional determinants are very important for both an individual’s decision to

start a new business and for the ultimate success of the new business (Sternberg, 2009). Entrepreneurs

depend on the informal network of friends, former colleagues and bosses, relatives and first customers

for the realization of their start-up idea and this informal network is mainly regional. It is also regional or

even local institutions such as business incubators and banks that play and important role in a potential

entrepreneurs decision to go through with the start-up idea or for the growth of a start-up in the early

stages. (3) It’s not just the causes to, but also the economic effects of entrepreneurial activity that are

primarily felt locally or regionally (Sternberg, 2009). At least in the early stages of a new venture. Only

when a new venture turns into a gazelle, where it grows extremely fast and on international scale can the

effects be felt nationally (Acs et al., 2008). Regions such as Silicon Valley (Lee, Miller, Hancock, & Rowen,

2000), Munich (Sternberg & Tamásy, 1999) and Shanghai (Muller, 2007) are examples of regions where a

high concentration of start-ups significantly contribute to the economic progress of these regions.

What constitutes a region in the regional dimension of entrepreneurship is arbitrary, therefore it requires

a clear definition. For the purposes of this research, we understand regional/region as referring to sub-

national spatial units that are larger than cities. This can for example be counties or municipalities.

Therefore, a region is defined here as a sub-national territory that does not have a radius higher than 100

kilometers, but that is larger than a place of residence. We adopt this definition, because this is the

environment where the founder of a new business mainly thinks and acts according to Sternberg (2009).

His previous employment was often here, his first customers are usually here and most of his private

network comes from here.

Determinants of entrepreneurial activity can be grouped to their spatial level. As shown in figure 2-2

entrepreneurship can be seen as a process of emergence. All phases of the entrepreneurial process are

16

influenced by several determinants. Sternberg (2009) identified different determinants and made a

distinction according to their spatial level. This is shown in figure 2-3 below. The national (or macro)

environment, regional (or meso) environment and micro-environment are taken as the spatial levels. The

national factors are supra-regional and include social, political, financial, and cultural conditions as well

as the infrastructure, the educational system, and economic structure. A more explicit component of the

national level is the existence and dominance of individual industries. The regional factors are more or

less the same as the national factors, but they refer to characteristics of the individual region instead of

the entire nation. Finally, the micro-environment contains factors that relate to personal networks of the

potential entrepreneurs and their social and professional backgrounds. There are also several factors that

relate to the actual or potential entrepreneur and do not belong in one of the spatial levels, examples of

these factors are demographic factors (age and gender) and personality traits (willingness to take risk,

internal locus of control). Instead, they can have a direct influence on how the potential entrepreneur

perceives the three environments, and therefore influence entrepreneurial activity.

Figure 2-3: Determinants of entrepreneurial activity. Source: (Sternberg, 2009)

Every potential entrepreneurs decision to create a new firm is then influenced by how the environments

are perceived. The total amount of entrepreneurial activity in a particular region is determined by the

totality of individual entrepreneurs that decide to create a new firm in that region. Macro and micro

factors are operating in every nation and in all the regions of a nation, but they have an important regional

dimension, because the degree to which they are present and come into play can vary significantly per

region. In other words, these factors operate differently in different regions. Even industrial economists

agree that the presence of different geographical environments substantially impacts the variability in the

determinants and success of new firm formation (Santarelli & Vivarelli, 2007).

Regional determinants can also be grouped in the following three categories: (1) demand and supply, (2)

agglomeration effects, and (3) policy environment and culture (Bosma, 2009). Examples of demand and

supply determinants are the findings that regional population growth (Reynolds et al, 1995; Acs and

Armington, 2003) and regional income levels (Reynolds et al., 1995) can have a positive effect on the

amount of regional entrepreneurial activity. Agglomeration effects such as urbanization and localization

17

economies can also positively impact entrepreneurial activity (Bosma, 2009). Examples of the policy

environment and culture determinants are the entrepreneurial skills and perceptions of the local

population, the availability of incubators which may foster the start-up, and the existence of local

entrepreneurship support programs (Sternberg, 2009).

Regional variation in entrepreneurial activity and entrepreneurial success in this research is then

understood as the differences in the total amount of entrepreneurial activity and entrepreneurial success

between regions and determined by the totality of individual entrepreneurs that decide to create a new

venture in that region and the totality of ventures in that region that experience a high rate of growth

over a longer period.

2.2. Broadband Internet

2.2.1. Broadband internet conceptualized

Since the late 1990’s the world experienced a broadband revolution that continues to date. Increased

competition and demand forced broadband service suppliers to deliver their triple play services, with

data, voice, and video with one single connection (Spurge & Roberts, 2005). Especially in the last two

decades there has been a big increase in demands for higher broadband bandwidth from both the

consumer and the business markets (Brennenraedts, Vankan, te Velde, Veldkamp, & Kaashoek, 2014).

Higher bandwidth was initially only required for next generation TV and video services, such as Video on

Demand (VoD), and high definition TV (HDTV), but with new possibilities in big data analytics, cloud

services and new intensive ICT applications this increase is likely to keep growing. The growth in demand

and supply for digital content requires a proper digital infrastructure that can support it. This

infrastructure consists of broadband and related internet and communication technologies (ICT) that are

considered General Purpose Technologies (GPT), because they are pervasive throughout the whole

economy and very useful to businesses in terms of productivity benefits and opportunities (Bresnahan &

Trajtenberg, 1995; Prieger, 2013).

In many cases broadband internet is only considered in terms of capacity speeds and treated as one

technology, while it is actually a term used for several technologies that differ in many aspects and

compete (Spurge & Roberts, 2005). Both the diffusion and the development path of the different

technologies make up the broadband internet infrastructure together and determine the possibilities and

internet speeds available for households, businesses, and other end-users. It is therefore important to

conceptualize the concept of broadband internet.

The term broadband was initially introduced to differentiate from dial-up services and had two distinct

characteristics: speed and always on. The former simply a measure of capacity and the latter referred to

the new user experience of always having it available (Benkler, 2009). In the academic literature and public

policy reports there exists a broadly shared set of definitions and targets of policy that reflect, in different

measures, the two distinct characteristics of broadband. The primary distinction of focus is the emphasis

on (1) high capacity and / or (2) seamless connectivity. Within the focus on high capacity there is a

secondary division between (1) a focus on numeric measures of capacity, mainly download speed, and (2)

a focus on applications supported (Benkler, 2009).

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The broadband internet infrastructure consists of fixed line technologies and wireless technologies. Fixed

line solutions communicate through a physical network that provides a direct wired connection from the

service supplier to the end-user. While wireless solutions use microwave or radio frequencies as a

connection between the service supplier and end-user. The main global infrastructure is based on fixed

line technology of submarine cables between continents and a backbone infrastructure between national

and international regions. Different broadband operators connect the end-users to the backbone, this is

typically called the last mile. This broadband internet infrastructure can be conceptualized in two ways,

with a technology perspective where the focus is on the underlying technologies and with a technology

neutral perspective where the focus is instead on capacity speeds and therefore technology neutral.

2.2.2. Technology perspective vs Policy perspective

From a technology perspective there are different popular fixed broadband technologies through which

internet is offered to consumers over the last mile. These are DSL (digital subscriber line), coax cable, and

fiberglass. DSL makes use of a twisted pair copper wire connection that runs over a normal telephone

cable, while cable internet makes use of a coax cable to connect to the end-consumer. The backbone is in

many cases made up out of fiber glass. Optical fiber technology is generally seen as the go to broadband

technology, because it is the most future-proof technology due to its reliance on communication with

laser pulses instead of electricity which is the case with both DSL and cable (Deloitte, 2017). This way of

communication has a higher transmission capacity and almost no signal loss over large distances, so an

increasing portion of the DSL and coax cable networks also consists of fiber optic technology. Figure 2-4

below shows an overview of the different types of fixed internet communication networks that are

available (Xiong, 2013). Fiber to the Node (FttN) networks refer to networks where fiber optics is used to

an aggregation point, mostly a Main Distribution Frame (MDF) that serves thousands of users as is the

case with ADSL. The Fiber to the Curb (FttC) network is similar, but the aggregation point is closer to the

end-user, mostly a Subloop Distribution Frame (SDF) for VDSL or a street cabinet for Hybrid Fiber Coax

(HFC) networks that both serve hundreds of users. In Fiber to the Building (FttB), Fiber to the Home (FttH)

and Fiber to the Office (FttO) optical fiber goes all the way to the building of the end-user. However, with

FttB optical fiber stops there and internet is distributed inside the building through another technology,

while with FttH and FttO the optical fiber connection goes all the way to the end-user.

Figure 2-4: Overview of FttX communication networks. Source: (Xiong, 2013)

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The different communication networks all rely on a combination of the before mentioned technologies:

DSL, coax cable and fiber optics. These technologies have different technological development paths and

different technical capabilities. The most important broadband characteristic of the DSL technologies

(ADSL, VDSL, G.Fast) is that the speed rapidly decreases when the distance from the end-user to the MDF

or SDF increases. Therefore, FttC networks with DSL technology are already much faster than FttN,

because the aggregation point is located much closer to the end-user. Coax cable technology in Hybrid

Fiber Coax (HFC) networks (DOCSIS 3.0 and DOCSIS 3.1) suffers from overbooking. In this phenomenon

the bandwidth is not guaranteed, because the cable is shared by multiple users (Spurge & Roberts, 2005).

This can lead to underperformance in peak hours. Fiber networks where the fiber connection goes all the

way to the end-user is the most future proof, because it has the highest maximum bandwidth and it is

possible to transmit data over long distances without signal loss (Deloitte, 2017).

The policy perspective looks at the download bandwidth and is based on policy targets of the European

Union. The targets defined by the European Commission are defined in terms of coverage or availability

instead of uptake and they provide the context in which broadband internet infrastructure can be

conceptualized in terms of download bandwidth (Bourreau, Feasey, & Hoernig, 2017). The following

definitions are operationalized by the European Commission: basic broadband, fast broadband, ultrafast

broadband, Next Generation access (NGA) networks, Very High Capacity (VHC) networks and Gigabit

connectivity. The first three definitions are very straightforward and can be seen as categories of

download bandwidth. Basic broadband is defined as download bandwidth of up to 30 Mbps ( <= 30 Mbps),

fast broadband is defined as download bandwidth of at least 30 Mbps ( > 30 Mbps), and ultrafast

broadband is defined as download bandwidth of at least 100 Mbps ( > 100 Mbps). So, broadband contains

all the categories and fast broadband also contains ultrafast broadband as a subset. The next two

definitions are a bit more controversial, because the meaning of the term can vary within and between

documents of the European Commission. Next Generation Access (NGA) networks can be understood in

two ways: (1) it is defined as fixed networks with a high throughput rate of at least 30 Mbps (European

Commission, 2010), but also as (2) technologies that outperform copper ADSL (Bourreau et al., 2017). In

this perspective we consider broadband internet infrastructure in terms of bandwidth and not technology,

so we agree with the former definition and understand NGA networks as fixed networks with a download

bandwidth of at least 30 Mbps. Very High Capacity (VHC) networks can be understood in three ways: (1)

it is defined as equivalent to fiber, so essentially linked to technology, (2) it is understood as fixed networks

with bandwidth download speeds of at least 100 Mbps ( > 100 Mbps) and (3) it is defined as equivalent to

Gigabit connectivity. Gigabit connectivity refers to fixed networks with download and upload speeds of at

least 1 Gbps. In this perspective we do not take the first definition of VHC networks into consideration,

because it is linked to technology and this is a technology neutral perspective. Instead we agree with the

third definition and see very high capacity broadband networks as fixed networks with bandwidth upload

and download speeds of at least 1 Gbps.

Finally, in the literature a distinction is made with regards to the diffusion of broadband internet. The

literature tends to use a variety of terms regarding this diffusion of the broadband internet infrastructure.

They can be divided into two groups. (1) Broadband availability or provision refers to the phenomenon

that a certain broadband connection is present regardless of whether it is actually used or not. (2)

Broadband adoption or use refers instead to the phenomenon that a broadband connection is both

present and used. Most studies that measure the effects of broadband and local economic growth look

at broadband availability and the effects between availability and adoption can differ (What works centre

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for local economic growth, 2015). In regards to the debate over whether to use adoption or availability as

the key indicator of broadband penetration, Kolko (2012) made the case for availability. He pointed out

that adoption rates can be influenced by economic growth more so than availability. This is an important

consideration, because there typically exists an endogeneity problem between broadband and different

indicators of local economic growth (Kolko, 2012; Minges, 2016).

Although understanding broadband from a technology perspective gives interesting insights, in this

research we follow the policy perspective, and consider broadband in terms of capacity speed and adopt

the definitions set by the European Commission. The reason, with a policy perspective the results of this

quantitative study are more easily linked to the policy targets of the European Union and do not depend

on one or more specific technologies. This makes it possible to give better policy advice about the impact

of broadband in general and not of one or more specific broadband technologies.

2.3. Broadband Internet as regional determinant of entrepreneurship

Theoretically, the relationship between broadband and entrepreneurship is ambiguous. It depends on the

following three considerations. (1) whether the perspective is regional (local) or national (or global). (2)

The proxy that is used to measure entrepreneurship. (3) The type and capacity speed of broadband that

was available at the time of analysis. From the macro perspective, broadband and related ICT technologies

have become a core of the economy, because they shape all kinds of economic transactions (OECD, 2012)

and can be seen as General Purpose Technologies (GPT), because they are pervasive throughout the whole

economy and very useful to businesses in terms of productivity benefits and opportunities (Bresnahan &

Trajtenberg, 1995; Prieger, 2013; Richardson & Gillespie, 1996). Thurik and others (2013) identified the

shock of the ICT revolution in the late 1980’s and early 1990’s as the main factor leading to the shift of the

economy from what has been characterized as ‘managed economy’ towards the ‘entrepreneurial

economy’. It can therefore be expected that broadband has a positive impact on the total amount of

entrepreneurial activity and entrepreneurial success in nations.

2.3.1. Regional perspective

From a regional or local level, the relationship between broadband and entrepreneurship depends both

on the economy-wide effect as well as on how spatial differences in broadband availability affect the

geographic distribution of entrepreneurship. Research suggests that an entrepreneurs decision to start a

new business is influenced by how the environment is perceived and that entrepreneurship is mainly a

‘regional event’ (Sternberg, 2009). While research also suggests that the focus of new venture creation in

the last decades is motivated more than ever by ICT technologies (Thurik et al., 2013). The absence,

presence and / or quality of the digital infrastructure in a region might therefore be an important factor

in the entrepreneurs decision to create a new venture or for the possibility to grow the venture. Other

research suggests that the internet has the potential to change the geography of production by influencing

the location decisions of firms. Multiple hypotheses were introduced in the early days of internet

availability ranging from ‘the death of distance’, and ‘the death of cities’ (Negroponte, Harrington, Mckay,

& Christian, 1997) to the continued importance of cities (Steinfield, 2013). Decades later the hypothesis

that the internet has merely reduced the advantage of ‘locational economies’ by which urban companies

proximity to its customers gave it a competitive advantage seems to have been the most plausible.

Research suggests that for many activities the internet is a complement rather than a substitute (Kolko,

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1999). This means that the absence, presence, or quality of internet availability can influence the location

decision of an entrepreneur, maybe not to the extent that some researchers expected it to influence, but

it can nevertheless have an important impact on the geographic distribution of entrepreneurship. The

distribution of broadband is known to be heterogeneous across space, especially favoring urban regions

over rural regions, a phenomenon called ‘the digital divide’ (Prieger, 2013). This uneven distribution can

therefore potentially impact the geographic distribution of entrepreneurship across regions.

2.3.2. Proxies of Entrepreneurial activity

Broadband availability as an important regional characteristic to entrepreneurial activity has been

explored and identified quantitatively in previous research, but in a fractured way. One reason is that

empirical research relies on data that is as realistic as possible, therefore data of entrepreneurial activities

best suited for scientific purposes is survey data collected by the scientists themselves. However, this has

been very rare, because it is labor-intensive, expensive, and due to the broad scope of entrepreneurial

activities far from trivial (Sternberg, 2009). Therefore, researchers often choose between two different

sources of data that are readily available: (1) self-reports of individuals that are randomly selected (as

used in the Global Entrepreneurship Monitor), or (2) records from official business registers like the World

Bank and Eurostat (Stenholm, Acs, & Wuebker, 2013). Two different approaches are often used, one

taking the static perspective and the other a dynamic perspective (Audretsch, Grilo, & Thurik, 2007).

Examples of static proxies of entrepreneurial activity include self-employment and the number of business

establishments. However, self-employment is far from equivalent to entrepreneurial activity. Self-

employment might have started several years in the past, while new venture creation implicitly means

establishment in very recent past. It is also considered an ineffective measure, because it captures all

types of small business activity without differentiation (Acs et al., 2008). The number of business

establishments is also imperfect, but can be considered a better proxy, because it could be considered as

a good longitudinal measure of past entrepreneurship (Gartner & Shane, 1995; Low, 2009).

The dynamic perspective on the other hand focuses on capturing nascent and start-up activity by looking

at dynamic data on establishment flows over a certain period. Often used measures are the net entry rate,

measuring both entry and exit rates of firms, and the number of new business establishments (or start-

up rate). The start-up rate can be considered as the best measure of entrepreneurial activity, because

new business ventures are created to exploit entrepreneurial opportunities. The start-up or new firm is

either created by new and independent business owners, or by existing business owners in the form of a

subsidiary. This measure is also not without a disadvantage, because it only looks at opportunities pursued

by new firms and not at opportunities pursued inside existing firms that are pursued in a different way

than through a subsidiary. These opportunities can also be relevant for entrepreneurial activity (Dahlqvist

& Wiklund, 2011). Nevertheless, this measure has been shown to be a good proxy for the

commercialization of new knowledge through the creation of new firms and is widely used (Acs,

Audretsch, Braunerhjelm, & Carlsson, 2012).

The second reason is that most empirical studies that investigate whether broadband availability is an

important regional determinant of entrepreneurial activity do so indirectly originating from different lines

of research.

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2.3.3. Empirical evidence

Henderson et al. (2007) made the link between broadband availability and regional entrepreneurship and

found a positive relationship. They measured broadband availability as present when there were more

than three broadband providers offering services in counties in the US and used proprietorship as proxy

for entrepreneurial activity. It can however be argued that both measures are far from accurate, as

mentioned above self-employment is far from equivalent to entrepreneurial activity, because self-

employment could have happened years ago, while entrepreneurial activity has a recent component.

Using a number of 3 providers for a county to have broadband is also an arbitrary measure of real

broadband availability.

Other researchers have instead looked at the total number of business establishments as proxy of

entrepreneurial activity. Mack et al. (2011) found evidence that broadband availability is an important

factor in the location decision of knowledge intensive firms. They measured broadband availability as the

number of broadband providers in zip code areas in the US and used an instrumental variables approach

to deal with endogeneity. They also argue that spatial econometric models are an important modeling

approach when it comes to measuring the impact of broadband availability, due to possible spatial

autocorrelation. The difficulty of proving causality between broadband and indicators of economic

growth, due to endogeneity from possible reverse causality is known in the literature (Kolko, 2012;

Minges, 2016). Whitacre et al. (2014) found evidence of a positive effect between broadband and the

number of firms in rural areas. They measured broadband as county thresholds in the US and used a

propensity score matching technique to make a stronger claim towards causality. The effects of

broadband adoption where found to be stronger than those of availability and speed. Finally, Lapointe

(2015) shows that broadband speed can also be important, because they found evidence of a positive

relationship between fiber broadband availability and the total number of businesses in counties in the

US. They measure fiber broadband as the percentage of households that have access and look at panel

data with a fixed effects regression with county level individual and time fixed effects. The fixed effects

regression does not prove causality, but provides a stronger case for causality than a regular OLS model

(Whitacre et al., 2013). This line of research shows interesting considerations when measuring the impact

of broadband availability and suggests that broadband availability is an important factor for

entrepreneurial activity. However, the proxy of entrepreneurial activity that is used in those studies is not

the most accurate, because entrepreneurial activity is associated with new venture creation and not past

venture creation.

Finally, researchers have looked at entrepreneurial activity in terms of the start-up rate. It can be argued

that this is the best proxy of entrepreneurial activity, because new ventures are created to exploit new

opportunities. Different names are sometimes used in research that all refer to the start-up rate. New

firm formation, firm birth, new entry, number of new business establishments, etc. Heger et al. (2012)

use a county year panel structure and find a positive effect of broadband availability on the number of

new company foundations in US counties. They measure broadband availability as present when there is

at least one main distribution frame (MDF) and absent when there is not. It can be argued that this is not

the best way to measure broadband availability, because the presence of a main distribution frame in a

county does not necessarily mean that all households and businesses have broadband available. They also

do not control for the endogeneity properly that typically exists when measuring the impact of broadband,

because their modeling approach is a simple OLS regression. Other research looked at the impact of

23

broadband availability on the location decisions of new firms, specifically in rural counties of the US and

found that a 10% increase in broadband availability raises firm entry by 1,6% for rural counties that are

adjacent to urban counties, while this effect lowers to 0.2 % in rural areas that are not adjacent to a

metropolitan area (Kim & Orazem, 2016). Finally another study, this time in Ireland found a significant

effect between broadband availability and new firm entry, but suggests that this effect may be mediated

by the availability of human capital (Mccoy et al., 2016). They utilize a count modeling approach with fixed

effects, because the business establishment literature suggests that the attractiveness of an area should

be a function of several regional characteristics and the econometrics literature suggests the use of fixed

effects models in panel data estimations.

2.3.4. Evolution of broadband speed

The literature very clearly shows and suggests that broadband availability is an important regional

determinant of entrepreneurial activity. This indicates that broadband provision in a region can influence

an entrepreneurs decision to create a new business venture in that region, ultimately leading to regional

variation in the total amount of entrepreneurial activity across regions. It also shows important

considerations for understanding the relationship between broadband and regional variations in

entrepreneurial activity and the implications that this would have for public policy, by arguing that several

critical issues need to be addressed when measuring the impact of broadband regionally: endogeneity

(Kolko, 2012; Minges, 2016), heterogeneity across industry and space (Kim & Orazem, 2016; Kolko, 2012)

and spatial autocorrelation (Mack et al., 2011). There are however other issues that remain to a large

extent unaddressed in the literature: broadband evolution, broadband availability specifically for

businesses, and studies originating in other countries than the US.

The most important issue that remains largely unaddressed and requires attention is the broadband

evolution. The broadband infrastructure has significantly improved over the last two decades in many

different countries offering much higher internet speeds than before (Bourreau et al., 2017). For example,

the Netherlands is one of these countries where the adoption and diffusion of fiber optic solutions and

improvements on existing broadband technologies currently make it possible to reach broadband speeds

up to 1 Gbps (Deloitte, 2017). These significant improvements in the broadband infrastructure are

accelerated by the European Commission’s objective to turn Europe into a gigabit society with a new

transition towards very high capacity broadband networks (European Commission, 2018). Most empirical

studies have however focused on broadband availability (or adoption) with broadband internet speeds

that are lower than 30 Mbps or 100 Mbps, often focusing on DSL technology. The importance of

broadband capacity speed has always been a major gap in the literature, possibly because reliable data

on differences in broadband speed and reliable data on higher levels of capacity speed have not been easy

to gather for researchers. There are however a few rare studies that look at the importance of broadband

speed for regional variation in entrepreneurial activity, but only with regards to the total number of

business establishments, which is not the best proxy of entrepreneurial activity. These studies show

somewhat contradictory results. One study found no significant greater effect of higher broadband speed

compared to lower broadband speed in rural counties (Whitacre et al., 2014), another study found that

broadband speed matters for knowledge intensive firm location (Mack, 2014), and the last study found a

positive effect from the availability of fiber broadband in counties (Lapointe, 2015).

With the lack of research on the importance of broadband speed and the significant improvements of the

broadband infrastructure comes the need to empirically investigate whether these broadband networks

24

are also an important factor in the economic development of regions. This is especially relevant for policy

makers, because they need empirical evidence to justify public sector interventions. For example, if the

impact of ICT with higher capacity proves to be significantly lower or even irrelevant to entrepreneurial

activity and other regional economic development indicators, then it might make less sense for EU and

other policy makers to push hard to improve the broadband infrastructure towards a gigabit society. In

this research we therefore address this need for empirical investigation and attempt to fill a portion of

the gap by investigating whether the availability of very high capacity broadband in the Netherlands

influences regional variation in start-up activity and start-up success.

2.3.5. Hypotheses

The first hypothesis is based on the premise that the absence or presence of very high capacity broadband

in a region can influence the start-up decision in that region, therefore influencing the total amount of

entrepreneurial activity in that region. Entrepreneurship is considered in the literature as predominantly

a regional event and infrastructure is considered as an important regional determinant of entrepreneurial

activity (Sternberg, 2009). At the same time, research indicates that the motivation to start a new business

venture is heavily influenced by broadband and related ICT technologies, because these technologies

shifted the competitive advantage away from large incumbent firms to more small-scale firms (Thurik et

al., 2013). Brynjolfsson and McAfee (2012) show that there has never been a better time to be an

entrepreneur than in the ICT revolution with entrepreneurial firms like Google, Skype and Facebook as

extreme examples.

However, it is difficult to predict the influence of very high capacity broadband, because there is clearly a

difference between the presence of a broadband connection and the presence of a very high capacity

broadband connection for an entrepreneurs decision to create a new venture. The literature suggests that

there can be made a distinction between participation use and advanced use of the internet by firms

(Forman, Goldfarb, & Greenstein, 2005). Participation use refers to basic use of the internet such as

browsing, e-mail, website, file sharing, while advanced use refers to the use of internet to enhance

business operations or to offer new services. Although the presence of broadband can already be enough

for many firm activities, advanced use by businesses has greatly increased with the broadband evolution,

especially in the last two decades. Research shows that here has been a big increase in demands for higher

broadband bandwidth from both the consumer and business markets (Brennenraedts et al., 2014). This

demand seems to be fueled by new possibilities in big data analytics, cloud services, and new intensive

ICT applications. For example, Ross & Blumenstein (2015) suggest that cloud-based internet and

communication technologies can act as a facilitator for entrepreneurship.

Advanced use of the internet, by leveraging activities such as big data analytics, cloud services, and new

intensive ICT applications creates new opportunities for businesses (Phippen & Lacohée, 2016). These

new entrepreneurial opportunities inside businesses that are enabled by very high capacity broadband

can be exploited by potential entrepreneurs that become aware of the new possibilities. This can

positively influence their perception of and decision making for starting a new venture on their own and

therefore facilitate entrepreneurial activity. It is likely that if they decide to start a new venture to exploit

these new entrepreneurial opportunities that they do so in the same region, because entrepreneurship is

mainly a regional event (Sternberg, 2009).

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Next to new entrepreneurial opportunities, advanced use of the internet also offers many other benefits

to businesses (Phippen & Lacohée, 2016). For example, it can greatly reduce the cost and increase the

effectiveness of many firm activities and help SME’s stay competitive (Phippen & Lacohée, 2016). The

increased awareness of the importance and possibilities of advanced use of the internet enabled by very

high capacity broadband can influence the location decision of an entrepreneur to create a new venture.

It is likely that an entrepreneur that is aware of the advantages of very high capacity broadband prefers

to locate in an area with very high capacity broadband than in an area that does not have very high

capacity broadband.

We therefore think that the presence of very high capacity broadband in a region can positively influence

the amount of entrepreneurial activity in that region. Resulting in the following hypothesis:

Hypothesis 1: Very high capacity broadband availability for businesses is an important positive regional

determinant of entrepreneurial activity

The second hypothesis is based on the premise that the absence or presence of very high capacity

broadband in a region can influence the success of entrepreneurial activity, therefore influencing the total

amount of entrepreneurial success in that region. We already established that the literature suggests that

the regional infrastructure can be an important determinant of entrepreneurial activity, but it also

suggests that this regional infrastructure can be an important determinant of entrepreneurial success

(Sternberg, 2009). The ICT revolution seems to have shifted the competitive advantage away from large

incumbent firms to more small scale firms and inter-firm cooperation (Thurik et al., 2013).

Advanced use of the internet, by leveraging activities such as big data analytics, cloud services and new

intensive ICT applications has the potential to help entrepreneurs survive and grow. Phippen & Lacohée

(2016) looked into the benefits and business opportunities of fiber connectivity for SME’s and discovered

what they call the virtuous circle of connectivity. The idea is that the reliance on connectivity builds

dependence and reinforces benefits. As the benefits grow, new dependencies are forged, and new

innovations and benefits emerge that are again used to a greater effect. They also found that when SME’s

had access to fiber connectivity for 18 months or more, then they were able to leverage the computational

power of cloud services, enabling them to compete with larger rivals. Access to fiber based broadband

networks were providing SME’s with a set of powerful tools that they could exploit to work faster and

more efficiently, but also differently. We therefore think that the presence of very high capacity

broadband in a region can positively influence the success of entrepreneurial activities in that region,

resulting in the following hypothesis:

Hypothesis 2: Very high capacity broadband availability for businesses is an important positive regional

determinant of entrepreneurial success

26

3. Method

3.1. Data

Much of the prior literature has looked at broadband availability in the United States by utilizing data from

the Federal Communications Commission (FCC). Although this did give a relatively complete picture about

the availability, it did not provide much information about differences in broadband speed within different

regions. It is also specific for the United States and therefore restricted to regions in just one country.

Instead, to test our hypotheses we therefore looked at regions in the Netherlands. We combined regional

level data from the Netherlands with spatial information on the roll-out of fiber into a panel dataset for

the time period 2011-2017. Regional-level longitudinal data offers unique advantages to study the

regional determinants of entrepreneurial activity and entrepreneurial success. This provides the

possibility to track the performance of individuals (in this case regions) over time and it provides scope to

link to other data.

We chose municipalities as the unit of analysis, because research suggests that high levels obscure the

significant variation in broadband availability on lower levels (Kolko, 2012). Municipal politics is the lowest

form of government in the Netherlands and every municipality has a specific geographical area in the

Netherlands of which they are responsible. Together these municipalities cover the entirety of the

Netherlands. Every year the geographical boundaries and the amount of municipalities in the Netherlands

can change, therefore the data of previous years has been corrected for the geographical municipality lay-

out as it existed in 2018. In 2018 there were in total 380 municipalities in the Netherlands. Sudwest-

Fryslan was the largest municipality with 522 square kilometers of land surface and Westervoort the

smallest with only 7 square kilometers.

3.1.1. Dependent variables: Entrepreneurial activity and Entrepreneurial success

To measure entrepreneurial activity we chose to look at new firm formation (or start-up rate), because it

can be argued that this is a better proxy for entrepreneurial activity compared to the total number of

business establishments and self-employment. It has been shown to be a good proxy for the

commercialization of new knowledge through the creation of new firms (Acs et al., 2012) and it is in line

with how we understand entrepreneurial activity in this research, which is new venture creation. We only

look at the total number of independent start-ups. The firms in the collected data fulfilled the following

two criteria:

1) The company needs to be new and not an entity formed by splitting up or reconstruction of a

business already in existence.

2) There needs to be economic criteria for the company, which means there is information available

about employment or revenue.

Regional entrepreneurial activity as measured then refers to the total amount of new independent start-

ups in a specific region.

To measure entrepreneurial success we chose to look at the total number of fast growing businesses (or

the number of scale-ups) as proxy. For a firm to be considered a scale-up it has to fulfill the following

criteria:

27

1) The company needs to have at least 10 employees.

2) There needs to be autonomous growth of 10% or higher for a period of 3 years. Growth can be

expressed as the total amount of employees or total revenue.

We realize that this proxy of entrepreneurial success is not perfect, because it is possible that a company

exists for a long period before it satisfies the scale-up criteria. This also leaves out firms with

entrepreneurial success that did not make the 10% cut for 3 years. Regardless, this proxy does give insight

into the success of entrepreneurial activities. Regional entrepreneurial success as measured then refers

to the total amount of fast growing businesses in a specific region.

The data source that was utilized to get these numbers is Statistics Netherlands (CBS), an official economic

and business register offering a wealth of open data on the Dutch economy and Society.

3.1.2. Policy variable: Broadband availability data

We chose to look at availability data instead of adoption data for two reasons. (1) Kolko (2012) suggests

that it is better to look at availability data, because adoption is more prone to endogeneity when

measuring the impact of broadband on economic activity. A positive relationship between very high

capacity broadband and entrepreneurship does not, in itself, mean that very high capacity broadband

causes entrepreneurship. The reverse might be true if broadband providers choose to expand their

services in regions where entrepreneurship grows faster. Alternatively, a third variable might cause both.

Adoption is more prone to endogeneity, because higher levels of economic activity and growth raises

household incomes and business revenues, which are predictors of adoption. (2) Public policy tends to

focus more on broadband availability goals than adoption goals. The Netherlands is part of the European

Union and the goals of the European Commission are translated in availability targets. Focusing on

availability then makes it is easier to make adequate public policy recommendations based on the results

of the research.

The data source that was used is a dataset that was provided by the firm Dialogic. This is a research and

consultancy firm and one of their areas of expertise is the digital infrastructure of the Netherlands. This

dataset is a combination of the ‘Basisregistratie Adressen en Gebouwen’ (BAG), which is open data and

contains geographical and basic data on all the addresses and buildings in the Netherlands and a dataset

on the geographical locations where a fiber connection is available with the rollout date. It was possible

to make a distinction here between FttH (fiber to the home) and FttO (fiber to the office), because in the

BAG every address is coupled to a fixed object that has a specific object use purpose. This resulted in a

dataset containing all the addresses in the Netherlands with a fixed object for the period 2011-2017 of

which we knew their geographical location, object use purpose (home, business or something else),

whether or not there was a fiber network available, and if so the year in which it was rolled out. Very high

capacity broadband availability for businesses then refers to the percentage of objects with a business

purpose that have a fiber connection available in a specific region. Figure 3-1 shows fiber broadband

availability for businesses in regions in the Netherlands in the beginning and at the end of the sample

period.

28

Figure 3-1: FttO broadband availability in the Netherlands in the beginning and at the end of the sample

3.1.3. Other variables: regional level economic data

The literature on regional variation in entrepreneurship has found the following variables to be important

determinants of regional variation in entrepreneurial activity. (1) Regional income level (Reynolds, Miller,

& Maki, 1995), (2) Regional population growth (Acs & Armington, 2003), agglomeration effects (Bosma,

2009), regional knowledge creation (Armington & Acs, 2002), and the unemployment rate (Bosma, 2009).

We therefore control for these economic variables by including them in the statistical approach. We

control for regional income levels with the medium household income, for regional population growth

with the population mutation, for agglomeration effects with total employment and total number of

establishments, for regional knowledge creation with university presence, and finally for the

unemployment rate.

The data sources for these variables are two official registers in the Netherlands, namely Statistics

Netherlands (CBS) offering a wealth of open data on the Dutch economy and Society and LISA

(establishments register) a data file containing data on all the establishments in the Netherlands where

there is paid work. This data file has a geographical dimension (address data) and a socio-economic

dimension (economic activity and employment).

29

3.1.4. Software utilized

Table 3-3 shows the variables used in the empirical analysis and the corresponding data sources that

were utilized to create the final panel dataset. The data was stored and combined in PostGIS databases,

because this software works well for spatial based queries, is easy to use and manage, and is compatible

with QGIS. The data is further analyzed in R. Tables 3-1 and 3-2 show an overview of the descriptive

statistics.

Table 3-1: Descriptive statistics in the first year of the sample period

Descriptive statistics for full sample, 2011 Statistic N Mean St. Dev. Min Pctl(25) Pctl(75) Max

New entry firms 380 99.11 18.22 45.50 86.07 109.40 155.80

Fast growing firms 380 11.81 4.98 0.00 8.47 14.72 33.30

FttO % availability 380 0.04 0.13 0 0 0 1

Employment 380 583.02 166.89 190.90 463.18 682.05 1,453.00

Establishments 380 106.55 21.21 57.60 92.65 118.03 198.20

Population mutation 380 1.81 6.35 -40.00 -2.10 5.32 23.20

Household income 380 23.99 2.06 17.40 22.70 25.10 34.40

Unemployment % 380 0.04 0.01 0.03 0.04 0.05 0.08

University Presence 380 - - 0 (95.80 %) 1 (4.20 %)

Table 3-2: Descriptive statistics in the last year of the sample period

Descriptive statistics full sample, 2017 Statistic N Mean St. Dev. Min Pctl(25) Pctl(75) Max

New entry firms 380 80.77 16.18 40.90 69.80 88.93 149.40

Fast growing firms 380 14.53 5.80 0.00 10.20 18.22 40.00

FttO % availability 380 0.26 0.27 0 0 0.4 1

Employment 380 572.65 161.62 193.40 465.38 663.53 1,488.20

Establishments 380 118.95 22.65 64.90 105.57 130.88 233.80

Population mutation 380 5.19 8.57 -21.70 0.38 9.00 58.20

Household income 380 26.96 2.34 19.40 25.60 28.30 37.40

Unemployment % 380 0.04 0.01 0.03 0.04 0.05 0.08

University Presence 380 - - 0 (95.80 %) 1 (4.20 %)

30

3.2. Empirical approach

The basic empirical approach followed is based on an approach that is used in the literature on regional

determinants of firm location. This line of research suggests the use of a count model, where the

attractiveness of an area for firms over a longer time period is a function of several regional factors (Bhat

et al., 2014). Given that we are dealing with a panel dataset and that there exists an endogeneity problem

has led us to adopt an adjusted count model framework where we look at the number of new independent

firms and the number of fast growing businesses per 1.000 business establishments and include

municipality and time fixed effects. Similar models have been adopted in previous research on broadband

availability and (new) firm location (Lapointe, 2015; Mccoy et al., 2016).

In this situation the fixed effects regression is superior to the cross-sectional, pooled OLS models, and

random effects model, because by including fixed effects we are controlling for the average differences

across municipalities and years for any observable or unobservable predictors fixed over time. The fixed

effects soak up all the across group action, this leaves only the within group action and thus greatly

reduces the threat of omitted variables. This has been confirmed by performing the Hausman specification

test as preliminary analysis that helps decide whether fixed effects are necessary or not (Hausman, 1978).

The fixed effects model is effective when the outcome is not normally distributed (which is often the case

in a count model), but the distribution is known. However, this basic fixed effects count model will not be

able to prove causation, but it does provide a stronger case for causality than a cross sectional model

(Whitacre et al., 2013). This basic count model with fixed effects can be described with the following

equations:

Equation 3-1: Least squares dummy variable model with dv = new entry firms

𝑌1𝑖𝑡 = β0 + β1𝑋1𝑖𝑡 + β2𝑋2𝑖𝑡 + β3𝑋3𝑖𝑡 + β4𝑋4𝑖𝑡 + β5𝑋5𝑖𝑡 + β6𝑋6𝑖𝑡 + β7𝑋7𝑖𝑡 + 𝛼𝑖 + 𝛼𝑡 + 𝜇𝑖𝑡

Equation 3-2: Least squares dummy variable model with dv = fast growing firms

𝑌2𝑖𝑡 = β0 + β1𝑋1𝑖𝑡 + β2𝑋2𝑖𝑡 + β3𝑋3𝑖𝑡 + β4𝑋4𝑖𝑡 + β5𝑋5𝑖𝑡 + β6𝑋6𝑖𝑡 + β7𝑋7𝑖𝑡 + 𝛼𝑖 + 𝛼𝑡 + 𝜇𝑖𝑡

Resulting in two least squares dummy variable (LSDV) models one for each dependent variable, where 𝑖𝑡

refers to municipality 𝑖 at time 𝑡 , and where the rest of the variables are described in table 3-3 below.

31

Table 3-3: Variables with their descriptions and expected relationship signs

Variable Variable name Description Predicted relationship

Source

𝒀𝟏 New entry firms The total number of new independent businesses per 1.000 establishments

CBS

𝒀𝟐 Fast growing firms

The total number of fast growing businesses per 1.000 establishments

CBS

𝑿𝟏 FttO availability The percent of firms within a municipality that have access to optical fiber internet

+ Dialogic

𝑿𝟐 Employment Total employment per 1.000 inhabitants 15-64 years old

+ LISA

𝑿𝟑 Establishments Total number of firms per 1.000 inhabitants 15-74 years old

+ LISA

𝑿𝟒 Population mutation

Population mutation per 1.000 inhabitants

+ CBS

𝑿𝟓 Household income

Median Household income + CBS

𝑿𝟔 Unemployment The Unemployment rate of people between 15-74 years old

+/- CBS

𝑿𝟕 University presence

The presence of a university within a municipality

+ CBS

𝜶𝒊 Municipality Dummy variable (fixed effect)

Controls for unmeasurable and constant differences between municipalities

𝜶𝒕 Time dummy variable (fixed effect)

Controls for unmeasurable and constant differences between time periods

The policy variable FttO availability for businesses is lagged for two years, because the roll-out date is

typically not the first year when fiber internet is available. The construction of fiber broadband can take a

considerable amount of time. After the construction is done it can also take some time before the

advantages of the deployment are utilized. To have a margin of error we therefore chose to lag the policy

variable for two years. Resulting in equations 3-3 and 3-4 below.

32

Equation 3-3: Least squares dummy variable model where dv = new entry firms and FttO availability lagged for two years

𝑌1𝑖𝑡 = β0 + β1𝑋1𝑖𝑡−2 + β2𝑋2𝑖𝑡 + β3𝑋3𝑖𝑡 + β4𝑋4𝑖𝑡 + β5𝑋5𝑖𝑡 + β6𝑋6𝑖𝑡 + β7𝑋7𝑖𝑡 + 𝛼𝑖 + 𝛼𝑡 + 𝜇𝑖𝑡

Equation 3-4: Least squares dummy variable model where dv= fast growing firms and FttO availability lagged for two years

𝑌2𝑖𝑡 = β0 + β1𝑋1𝑖𝑡−2 + β2𝑋2𝑖𝑡 + β3𝑋3𝑖𝑡 + β4𝑋4𝑖𝑡 + β5𝑋5𝑖𝑡 + β6𝑋6𝑖𝑡 + β7𝑋7𝑖𝑡 + 𝛼𝑖 + 𝛼𝑡 + 𝜇𝑖𝑡

3.2.1. Heterogeneity

The literature suggests that there exists heterogeneity across space when it comes to the impact of

broadband, implying that the advantages of broadband internet can differ between regions with different

characteristics (Kolko, 2012; Mack et al., 2011). It is possible that the advantages of very high capacity

broadband also differ across space. Due to data limitations we were only capable of dealing with

heterogeneity across space and not between industries. The municipalities in the Netherlands were

subsampled according to their address density per square kilometer as they existed in 2018. Resulting in

5 groups based on urbanity levels as shown in table 3-4.

Table 3-4: Subsample of municipalities based on address density per square kilometer

Subset samples based on urbanity levels, 2011-2017

Number of municipalities

(Panel with 7 years)

% of total % FttO availability

2011

% FttO availability

2017

(1) Very strong urban

≥ 2500

addresses per square kilometer

19 (133) 5.0 %

7.2 % 25.1 %

(2) Strong urban

≥ 1500 and < 2500

addresses per square kilometer

74 (518) 19.5 %

4.8 % 32.0 %

(3) Moderate urban

≥ 1000 and < 1500

addresses per square kilometer

78 (546) 20.5 %

7.8 % 36.7 %

(4) Weak urban

≥ 500 and < 1000

addresses per square kilometer

135 (945) 35.5 %

3.3 % 25.6 %

(5) No urban

< 500

addresses per square kilometer

74 (518) 19.5%

0.5 % 11.7 %

Full set 380 (2660) 100% 4.0 % 26.4 %

33

3.2.2. Endogeneity

The relationship between broadband and different indicators of local economic growth is known to have

an endogeneity problem (Kolko, 2012; Mack et al., 2011; Minges, 2016). Does broadband infrastructure

cause regional variation in entrepreneurial activity and entrepreneurial success or is it actually the other

way around? The focus in this research on broadband availability instead of adoption and new firm

formation instead of the total number of business establishments as proxy of entrepreneurial activity

make reverse causality less of a problem, because research suggests that availability (Haller & Lyons, 2019)

and new firm formation (Mccoy et al., 2016) suffer less from endogeneity compared to adoption and the

total number of business establishments. The utilization of a lagged policy variable alleviates concerns

even further. However, other researchers suggest the use of a instrumental variable approach when it

comes to dealing with endogeneity (Kolko, 2012; Mack et al., 2011). As robustness check, we therefore

included this approach in this study with a two-stage least squares dummy variable (2SLSDV) model. It is

important to note however, that when the relationship does not suffer from endogeneity, then this model

is likely to predict worse than the LSDV model, because instrumental variables are associated with low

levels of statistical power (Semademi et al., 2014).

The percentage of Fiber to all fixed objects (FtA) in a municipality is used as instrument for the percentage

of Fiber for business objects (FttO) in a municipality. Fiber to all fixed objects (FtA) includes housing objects

and business objects. To be a good instrument, there needs to be relevance, where the percentage of FtA

should be correlated with the percentage of FttO. This is likely to be the case, because when a higher

percentage of fiber roll-out for all objects is available in a municipality then this includes business objects

and it makes the construction of fiber to businesses more accessible. Preliminary analysis with an

instrumental F-test indicates that this is the case. However, there also needs to be exogeneity where the

percentage of FtA is not independently correlated with entrepreneurial activity and entrepreneurial

success, but only through FttO. This is likely to be the case, because the vast majority of fixed objects are

houses and increased levels of fiber broadband for houses is unlikely to provide more entrepreneurial

activity and entrepreneurial success in the region, because it does not provide more entrepreneurial

opportunities within businesses that can be exploited by potential entrepreneurs, nor do potential

entrepreneurs want to locate in residential areas, nor does it help with firm survival and growth, because

there are no firms to begin with. These effects are likely to only matter when it comes to business objects.

We therefore think that this instrument is able to control for the possible endogenous relationship

between very high capacity broadband for businesses and entrepreneurial activity and entrepreneurial

success, where the existence of higher levels of entrepreneurial activity and entrepreneurial success in a

region may actually result in higher levels of Fiber broadband availability for businesses instead of the

other way around. Or where another variable influences both at the same time. The models estimated via

two-stage least squares (2SLSDV) all use this instrument.

34

3.2.3. Robust covariance matrix estimation

Preliminary analysis on the panel data of cross sectional dependence with the Pesaran cross sectional

dependence test (Pesaran, 2004) and serial correlation with the Breusch-Godfrey test (Breusch, 1978;

Godfrey, 1978), showed that there was no significant correlation between variables, but that there was

significant correlation of variables at different times. Furthermore, the Breusch-Pagan test (Breusch &

Pagan, 1979) on heteroscedasticity showed concerns of heteroscedasticity. Concerns of serial correlation

and heteroscedasticity was present in all the models. As robustness check, we therefore applied robust

covariance matrix estimation based on the ‘Arellano’ method (Arrellano, 1987). This method is

recommended for fixed effects and deals with both heteroscedasticity and serial correlation in panel data.

35

4. Results

4.1. Results Entrepreneurial activity (full sample)

The models are estimated in a sequential manner (OLS, LSDV, 2SLSDV) with statistical tests to ensure

validity. Results are shown in Table 4-1. The independent variable of concern is lagged for two years, so

the results are based on a balanced panel with: n = 380, t = 5, n = 1900. The standard OLS model shows

significance of all the variables on the dependent variable, but the F test for individual and time effects

compares the OLS model with the LSDV model that includes municipality and year effects and is highly

significant (p<0.01), therefore favoring the LSDV model. The Hausman specification test can be used to

determine if the model should include fixed or random effects (Hausman, 1978). The test is highly

significant, therefore favoring the LSDV model with fixed effects over random effects.

The results of the LSDV model show that compared to the standard OLS only three out of seven variables

are significantly correlated with the dependent variable. These are FttO availability (2 year lag),

establishments, and population mutation. The Pesaran test is not significant, indicating no concerns of

cross-sectional dependence between the regions. However, the Breusch-Godfrey test on serial correlation

(p<0.01) and the Breusch-pagan test on heteroscedasticity (p<0.01) are both highly significant indicating

the need for robust standard errors. Controlling for serial correlation and heteroscedasticity with robust

covariance matrix estimation based on the ‘Arellano’ method, shows that only FttO availability (2 year

lag) (β = 0.045 and p<0.05) and population mutation (β = 0.103 and p<0.01) remain significant. This result

is in support of hypothesis 1.

The 2SLSDV model shows a significant instrumental variable F test for the instrument FtA availability (2

year lag). The Hausman specification test can be used to determine if it is necessary to use an instrumental

variables method over a standard least squares regression method, because it detects endogenous

regressors in a regression model (Hausman, 1978). Significance of the test indicates that there are

endogenous regressors and this will cause least squares estimators to fail. In this case the test examines

the difference between the LSDV and 2SLSDV coefficients. The test is not significant indicating that there

are no concerns of endogeneity. However, this is only valid if the instrument is also valid. The results of

the first stage of the two stage least squares dummy variable model can be found in appendix 8.2. The

2SLSDV model also suffers from serial correlation (p<0.05) and heteroscedasticity (p<0.05), therefore the

need to control with robust covariance matrix estimation based on the ‘Arellano’ method. After

controlling only the variable population mutation shows significance (β = 0.104 and p<0.01). The result of

this model is not in support of hypothesis 1, because the variable FttO availability (2 year lag) is not

significant anymore.

36

Table 4-1: Results LSDV model and 2SLSDV model with full sample and dv = new entry firms

Dependent variable: New entry firms (p. 1000 establishments.)

Pooled OLS LSDV model 2SLSDV model LSDV model (rob) 2SLSDV model (rob)

FttO % (2 year lag) 0.058*** 0.045*** 0.023 0.045** 0.023

(0.013) (0.017) (0.021) (0.019) (0.022)

Employment (p. 1000 inh) 0.016*** -0.001 -0.001 -0.001 -0.001

(0.002) (0.016) (0.016) (0.019) (0.019)

Establishments (p. 1000 inh) -0.171*** 0.147** 0.146** 0.147 0.146

(0.014) (0.066) (0.066) (0.092) (0.092)

Pop mutation (p. 1000 inh) 0.469*** 0.103** 0.104** 0.103*** 0.104***

(0.042) (0.041) (0.041) (0.038) (0.038)

Household Income median 1.787*** 0.825 0.918 0.825 0.918

(0.168) (1.225) (1.227) (1.485) (1.495)

Unemployment % 7.333*** -0.096 -0.179 -0.096 -0.179

(0.299) (0.779) (0.780) (0.680) (0.677)

Uni_dum1 15.575*** -12.316 -12.493 -12.316 -12.493

(1.497) (10.397) (10.403) (11.854) (11.900)

Constant 4.969 70.369*** 69.101*** 70.369** 69.101**

(5.733) (25.276) (25.300) (30.639) (30.853)

Observations 1,900 1,900 1,900

Adjusted R2 0.457 0.732 0.732

F Statistic 229.183***(df = 7; 1892) 14.349*** (df = 389; 1510) 5,569.524***

F test (for two-way effects) 6.093***

Hausman (fixed vs random) 213.160***

Instrumental variable F test 288.400***

Hausman (endogeneity) 3.356

Pesaran CD test -1.545 -1.555

Breusch-Godfrey test 644.300*** 644.300***

Breusch-Pagan test 670.530*** 670.910***

Note: rob = robust standard errors *p<0.1**p<0.05***p<0.01

37

4.2. Results Entrepreneurial success (full sample)

Here the models are also estimated in a sequential manner (OLS, LSDV, 2SLSDV), results are shown in

Table 4-2. The independent variable of concern is also lagged for two years here, so the results are again

based on a balanced panel with: n = 380, t = 5, n = 1900. In the standard OLS all the variables (except

university presence) are significantly correlated with the dependent variable. Both the significant F test

(p<0.01) and the significant Hausman specification test (p<0.01) favor the LSDV fixed effects model over

the standard OLS model (Hausman, 1978). The significant Breusch-Godfrey test (p<0.01) on serial

correlation and the significant Breusch-Pagan test (p<0.01) on heteroscedasticity indicate the need for

robust standard errors as was the case previously. Controlling with the ‘Arellano’ method shows that only

two out of the seven variables are significantly correlated with the dependent variable. The variable

household income (β = 0.854 and p<0.05) only significantly correlates without robust standard errors. Both

FttO availability (2 year lag) (β = 0.015 and p<0.05) and university presence (β = -15.194 and p<0.01) are

significantly correlated with the dependent variable fast growing firms. This result is in support of

hypothesis 2.

The insignificance of the Hausman specification test between the LSDV and 2SLSDV coefficients indicates

no concerns of endogeneity between the variables (Hausman, 1978), however as stated before this is only

valid if the instrument is also valid. The significant Breusch-Godfrey test on serial correlation (p<0.01) and

significant Breusch-Pagan test (p<0.01) on heteroscedasticity indicate the necessity of robust standard

errors. Controlling with robust standard errors based on the ‘Arellano’ method shows that only university

presence (β = -15.214 and p<0.01) is correlated with the dependent variable. The result of this model is

therefore not in support of hypothesis 2, because the variable FttO availability (2 year lag) is not significant

in this model.

38

Table 4-2: Results LSDV model and 2SLSDV model with full sample and dv = fast growing firms

Dependent variable: Fast growing firms (p. 1000 establishments)

Pooled OLS LSDV model 2SLSDV model LSDV model (rob) 2SLSDV model (rob)

FttO % (2 year lag) 0.013*** 0.015** 0.012 0.015** 0.012

(0.004) (0.006) (0.007) (0.007) (0.008)

Employment (p. 1000 inh) 0.022*** -0.002 -0.002 -0.002 -0.002

(0.001) (0.006) (0.006) (0.013) (0.013)

Establishments (p. 1000 inh) -0.081*** -0.010 -0.010 -0.010 -0.010

(0.005) (0.024) (0.024) (0.039) (0.039)

Pop mutation (p. 1000 inh) 0.025* -0.002 -0.002 -0.002 -0.002

(0.014) (0.014) (0.014) (0.034) (0.034)

Household Income median -0.212*** 0.854** 0.865** 0.854 0.865

(0.056) (0.435) (0.435) (0.651) (0.652)

Unemployment % -0.988*** -0.053 -0.062 -0.053 -0.062

(0.100) (0.276) (0.277) (0.302) (0.305)

Uni_dum1 0.202 -15.194*** -15.214*** -15.194*** -15.214***

(0.503) (3.689) (3.689) (5.294) (5.292)

Constant 19.686*** 2.904 2.757 2.904 2.757

(1.924) (8.968) (8.972) (13.847) (13.876)

Observations 1,900 1,900 1,900

Adjusted R2 0.437 0.690 0.690

F Statistic 211.575*** (df = 7;1892) 11.861*** (df = 389; 1510) 4,610.011***

F test (for two-way effects) 5.039***

Hausman (fixed vs random) 68.640***

Instrumental variable F test 2888.400***

Hausman (endogeneity) 0.356

Pesaran CD test -1.064 -1.070

Breusch-Godfrey test 684.690*** 684.690***

Breusch-Pagan test 1483.700*** 1484.500***

Note: rob = robust standard errors *p<0.1**p<0.05***p<0.01

39

4.3. Results subsamples

As a reminder, urbanity level 1 refers to regions that are very strongly urban, urbanity level 2 to region

that are strongly urban, urbanity level 3 to regions that are moderately urban, urbanity level 4 to regions

that are slightly urban, and finally urbanity level 5 refers to regions that are not urban. The independent

variable of concern is lagged for two years, so the panels are balanced with n depending on the size of the

subsample and t = 5. Preliminary analysis of the Hausman specification test for endogeneity between the

variables showed no significant signs of an endogeneity problem between the subsample variables and

the dependent variables, therefore only the LSDV model is estimated on the subsamples for both

dependent variables (Hausman, 1978). Preliminary analysis did show significant results of the Breusch-

Godfrey test for serial correlation and the Breusch-Pagan test for heteroscedasticity, so the models are

also estimated with robust standard errors based on the ‘Arellano’ method. The results on the dependent

variable new firm entry are shown in table 4-3 and the results on the dependent variable fast growing

firms are shown in table 4-4.

The model with the subsample of regions that are very strongly urban (urbanity = 1) shows significance of

the variable employment with the dependent variable new entry firms (β = 0.097 and p<0.01) and with

the dependent variable fast growing firms (β = 0.063 and p<0.01). The variable population mutation is

also slightly significant, but only on the dependent variable new entry firms (β = 0.302 and p<0.1).

For the subsample of regions that are strongly urban (urbanity = 2), the model shows significance of the

variable university presence on the dependent variable new entry firms (β = 48.554 and p<0.01). There is

also slight significance of the variable FttO availability (β = 0.021 and p<0.1) and significance of the variable

unemployment (β = -0.725 and p<0.01) on the other dependent variable fast growing firms.

The model with the subsample of regions that are moderately urban (urbanity = 3) shows slight

significance of the variable FttO availability (2 year lag) on the dependent variable new entry firms (β =

0.065 and p<0.1) and on the dependent variable fast growing firms (β = 0.018 and p<0.1). There is also

significance of the variable unemployment on the dependent variable new entry firms (β = -15.214 and

p<0.05). Furthermore, the variables household income (β = 1.959 and p<0.1) and university presence (β =

-23.631 and p<0.01) show significance on the dependent variable fast growing firms.

The results in the subsample with regions that are slightly urban (urbanity = 4) show significance of the

variable population mutation on the dependent variable new firm entry (β = 0.095 and p<0.05) and slight

significance of the variable household income (β = 1.593 and p<0.1) on the dependent variable fast

growing firms.

Finally, the last subsample with regions that are not urban (urbanity = 5) only show slight significance of

the variable FttO availability (β = 0.094 and p<0.1) on the dependent variable new firm entry.

40

Table 4-3: Results LSDV models with subsamples and dv = new entry firms

Dependent variable: New entry firms (p. 1000 establishments)

LSDV LSDV (rob) LSDV LSDV (rob) LSDV LSDV (rob) LSDV LSDV (rob) LSDV LSDV (rob)

Urbanity = 1 Urbanity = 2 Urbanity = 3 Urbanity = 4 Urbanity = 5

FttO % (2 year lag) -0.138 -0.138 0.019 0.019 0.065** 0.065* 0.035 0.035 0.094 0.094*

(0.114) (0.108) (0.028) (0.029) (0.032) (0.035) (0.034) (0.034) (0.066) (0.048)

Employment 0.097 0.097*** 0.013 0.013 -0.028 -0.028 0.004 0.004 0.050 0.050

(0.065) (0.035) (0.031) (0.038) (0.029) (0.026) (0.031) (0.032) (0.045) (0.079)

Establishments -0.097 -0.097 0.220* 0.220 0.174 0.174 -0.109 -0.109 0.074 0.074

(0.267) (0.258) (0.127) (0.176) (0.138) (0.184) (0.144) (0.221) (0.142) (0.143)

Pop mutation 0.302* 0.302* 0.148 0.148 0.035 0.035 0.095 0.095** 0.125 0.125

(0.156) (0.153) (0.100) (0.132) (0.087) (0.085) (0.073) (0.046) (0.086) (0.117)

Household Income 4.048 4.048 -2.784 -2.784 0.785 0.785 5.573** 5.573 0.431 0.431

(4.455) (3.990) (2.369) (3.587) (2.685) (2.704) (2.828) (3.400) (2.294) (2.443)

Unemployment % -1.805 -1.805 -0.096 -0.096 3.624** 3.624** -1.022 -1.022 1.411 1.411

(1.717) (1.374) (1.183) (1.274) (1.694) (1.561) (1.834) (1.432) (2.061) (1.928)

University_dum1 23.503 23.503 48.554*** 48.554*** -5.753 -5.753

(43.167) (33.260) (14.286) (17.800) (20.310) (20.386)

Constant -17.692 -17.692 176.727*** 176.727** 50.318 50.318 -31.373 -31.373 24.666 24.666

(114.180) (99.676) (54.272) (80.417) (53.266) (50.260) (66.618) (86.621) (60.268) (59.493)

Observations 95 370 390 675 370

Adjusted R2 0.800 0.690 0.412 0.359 0.399

F Statistic 14.400*** (df = 28; 66) 10.877*** (df = 83; 286) 4.135*** (df = 87; 302) 3.624*** (df = 144; 530) 3.954*** (df = 83; 286)

Pesaran CD -1.366 -1.506 -1.267 -1.185 -1.524

Breusch-Godfrey 39.402*** 151.710*** 141.260*** 238.520*** 138.170***

Breusch-Pagan 31.996 125.550*** 122.950*** 241.890*** 196.180***

Note: rob = robust standard errors *p<0.1**p<0.05***p<0.01

41

Table 4-4: Results LSDV models with subsamples and dv = fast growing firms

Dependent variable: Fast growing firms (p. 1000 establishments)

LSDV LSDV (rob) LSDV LSDV (rob) LSDV LSDV (rob) LSDV LSDV (rob) LSDV LSDV (rob)

Urbanity = 1 Urbanity = 2 Urbanity = 3 Urbanity = 4 Urbanity = 5

FttO % (2 year lag) 0.005 0.005 0.021** 0.021* 0.018* 0.018* 0.009 0.009 0.036 0.036

(0.032) (0.047) (0.009) (0.012) (0.010) (0.011) (0.010) (0.013) (0.035) (0.036)

Employment 0.063*** 0.063*** 0.014 0.014 -0.012 -0.012 -0.003 -0.003 -0.027 -0.027

(0.018) (0.018) (0.010) (0.011) (0.009) (0.012) (0.009) (0.017) (0.024) (0.064)

Establishments -0.138* -0.138 -0.056 -0.056 0.032 0.032 0.053 0.053 -0.059 -0.059

(0.074) (0.102) (0.040) (0.047) (0.044) (0.075) (0.042) (0.103) (0.075) (0.071)

Pop mutation -0.033 -0.033 0.022 0.022 -0.004 -0.004 0.011 0.011 -0.032 -0.032

(0.043) (0.035) (0.031) (0.039) (0.028) (0.032) (0.021) (0.032) (0.045) (0.108)

Household Income 1.799 1.799 0.221 0.221 1.959** 1.959* 1.593* 1.593* -0.865 -0.865

(1.235) (1.398) (0.740) (0.672) (0.863) (1.072) (0.833) (0.821) (1.211) (1.829)

Unemployment % -0.152 -0.152 -0.725* -0.725*** 0.524 0.524 -0.523 -0.523 0.228 0.228

(0.476) (0.282) (0.370) (0.275) (0.544) (0.468) (0.540) (0.619) (1.088) (1.198)

University_dum1 0.445 0.445 3.798 3.798 -23.631*** -23.631***

(11.961) (14.001) (4.462) (4.377) (6.526) (8.866)

Constant -48.332 -48.332 11.933 11.933 -21.635 -21.635 -26.194 -26.194 40.505 40.505

(31.639) (35.606) (16.951) (16.336) (17.116) (22.622) (19.627) (19.954) (31.815) (51.900)

Observations 95 370 390 675 370

Adjusted R2 0.786 0.776 0.764 0.585 0.476

F Statistic 13.308*** (df = 28; 66) 16.406*** (df = 83; 286) 15.456*** (df = 87; 302) 7.608*** (df = 144; 530) 5.035*** (df = 83; 286)

Pesaran CD -1.553 -1.162 -1.514 -1.448 -1.118

Breusch-Godfrey 39.554*** 121.300*** 144.230*** 229.950*** 152.420

Breusch-Pagan 43.109** 142.770*** 142.930*** 465.770*** 302.400***

Note: rob = robust standard errors *p<0.1**p<0.05***p<0.01

42

5. Discussion

5.1. Most important findings

The literature review in chapter 2 showed that there exists a general consensus in the literature based on

anecdotal and empirical evidence suggesting that broadband internet positively impacts

entrepreneurship in regions. However, there have been significant improvements of broadband internet

in terms of capacity speed in the last decades. At the same time, there is a lack of studies investigating

the impact of these improvements, especially outside of the United States. It is however important to

understand the effects of these improvements on the economy. From a scientific perspective, but

especially from a policy perspective. Policymakers should be able to base their decisions regarding the

broadband infrastructure on sound empirical evidence pointing towards the effects. To help close the gap

and provide empirical evidence, this research was therefore designed to investigate the effect of very high

capacity broadband on entrepreneurial activity and entrepreneurial success in regions of the Netherlands.

The results support the expectancy of the first hypothesis, indicating that the availability of very high

capacity broadband for businesses is an important positive regional determinant of entrepreneurial

activity. The LSDV model showed a significant effect suggesting that a 10% increase in very high capacity

broadband availability for businesses results two years later in 0.45 new independent firm entries per

1.000 business establishments. Very high capacity broadband availability enables new levels of advanced

use of the internet through activities such as big data analytics, cloud services, and intensive ICT

applications (Phippen & Lacohée, 2016; Ross & Blumenstein, 2015). This can facilitate more

entrepreneurial activity in regions in two ways. First, the advanced use within businesses can result in new

entrepreneurial opportunities originating in the region that can be exploited by potential entrepreneurs

that become aware of these new possibilities. Second, the new levels of advanced use also offer many

benefits for SME’s, like decreased costs of firm activities and increased levels of performance based on

advanced use (Phippen & Lacohée, 2016). When entrepreneurs are aware of the increased importance of

ICT for businesses, then it can influence their location decisions in favor of areas with very high capacity

broadband over locations without very high capacity broadband.

The 2SLSDV model did not show a significant effect. However, the Hausman specification test did not

show significance, indicating no sign of endogeneity between the variables. If the instrument is valid, then

this favors the LSDV model over the 2SLSDV model. This is in line with Mccoy and others (2016), that

already argued that the total number of establishments suffers more from endogeneity than the start-up

rate. The result also relied on a two year lag of broadband availability, further validating that the

relationship did not suffer from reverse causality. When there does not exist endogeneity then the LSDV

model is expected to be a better predictor than the 2SLSDV model, because it has higher levels of

statistical power (Semademi et al., 2014). This result is in line with other research that found a positive

effect of very high capacity broadband availability, but they looked at broadband availability for

households instead of businesses and the total number of business establishments in counties in the US

as proxy of entrepreneurial activity instead of new firm entry (Lapointe, 2015).

The results also support the expectancy of the second hypothesis, indicating that the availability of very

high capacity broadband for businesses is an important positive regional determinant of entrepreneurial

success. The LSDV model showed a significant effect suggesting that a 10% increase in very high capacity

43

broadband availability for businesses results two years later in 0.15 fast growing businesses per 1.000

business establishments. Very high capacity broadband enables new levels of advanced use of the

internet. This can facilitate entrepreneurial success in the region, because it has the potential to help

entrepreneurs survive and grow. Access to very high capacity broadband provides SME’s with a set of

powerful tools to work faster, more efficiently and also differently.

Again, the results of the 2SLSDV model did not show significance. However, the same arguments in favor

of the LSDV model apply here. The Hausman specification test on endogeneity was not significant and

broadband availability is measured with a two year lag. To our knowledge there has not been prior

research linking very high capacity broadband availability with entrepreneurial success.

This research also takes the heterogeneous effect of broadband availability into account, by looking at

subsets of regions with different urbanity levels. The results show that there does indeed exist

heterogeneity across different regions when it comes to the effect of very high capacity broadband

availability for businesses on entrepreneurial activity. Only regions that are moderately urban and regions

that are not urban showed slight significance (p<0.1), indicating that a 10% increase in very high capacity

broadband availability for businesses in those regions results two year later in 0.65 new independent firm

entries per 1.000 business establishments and 0.94 new independent firm entries per 1.000 business

establishments respectively. This is in line with other research that found that rural regions benefit from

broadband and that those regions closer to urban regions benefited more than very remote urban regions,

indicating that regions with a moderate urbanity level benefit the most from broadband infrastructure

(Kim & Orazem, 2016). For entrepreneurial success the results show slight significance (p<0.1) for strongly

urban and moderately urban regions, indicating that a 10% increase results two years later in 0.21 fast

growing firms per 1.000 business establishments and 0.18 fast growing firms per 1.000 business

establishments respectively.

Although not part of the scope of this study, it is worth mentioning that compared to a standard OLS many

of the control variables expected to be important regional determinants of entrepreneurial activity and

entrepreneurial success were not significant in the LSDV models with municipality and year fixed effects.

This research also encountered an unanticipated finding. The variable university presence showed a

statistically significant negative effect of university presence on the number of fast growing firms per

1.000 establishments, while the literature suggests that there should be a positive effect. However, the

subsamples show that this effect is only found in moderately urban regions, while the strongly urban and

very strongly urban regions show a positive effect, although not significant. The slightly urban and no

urban regions did not have a single university present. This variable is most likely stable over time, so the

effect of the presence of a university is captured by the municipality fixed effect. It is therefore possible

that the effect of very few newly established universities in a specific area is captured with this variable.

This could be an explanation for the unanticipated finding.

The findings of this thesis are an important step towards closing the gap that exists in the literature on

the importance of broadband capacity speed. To our knowledge there does not exist another study that

looks at the impact of very high capacity broadband availability on entrepreneurial activity and

entrepreneurial success with the proxies of entrepreneurship that were used. The other two benefits are

that this study looks directly at broadband availability for businesses instead of the usual household

availability and that this study looks at areas outside of the United States, where most research on

broadband availability is conducted.

44

This research therefore uniquely contributes to both the broadband internet literature and literature on

regional determinants of entrepreneurship, by suggesting that very high capacity broadband availability

for businesses is an important regional determinant of both entrepreneurial activity and entrepreneurial

success. The presence of very high capacity broadband in a region may be able to influence an

entrepreneurs decision to start a new venture there, because advanced use of the internet has the

potential to bring new entrepreneurial opportunities to the region that can be exploited by potential

entrepreneurs and advanced use offers many benefits to SME’s and can therefore influence the location

decisions of an entrepreneur in favor of areas with very high capacity broadband. The presence of very

high capacity broadband might also help new firms and SME’s survive and grow, because new levels of

advanced use of the internet offers them a set of powerful tools to work faster, more efficiently and

differently. However, the effects are not very large, this could be explained due to the fact that many firm

activities are already satisfied by participation use and do not require advanced use. It could also be

explained by considering delayed adoption and the development of complementary digital awareness and

digital literacy skills. Mack & Faggion (2013) studied the productivity benefits of broadband and found

that time is an important dimension for productivity impacts to be realized by new technologies. Humans

need to adjust to new routines and protocols. This could also be the case for entrepreneurial benefits of

very high capacity broadband. Potential entrepreneurs might need time to adjust. Mack and others (2017)

already suggested that especially inexperienced entrepreneurs often lack the digital awareness necessary

to recognize opportunities and effectively make use of the internet, let alone make use of new very high

capacity internet. To really benefit potential entrepreneurs need to adopt very high capacity internet and

develop digital awareness and literacy skills. A final reason could lie in the adjustment of the business

markets to very high capacity broadband. On the broadband market fiber connections mostly offer speeds

up to 1 Gbps (OECD, 2015). Devices are often not (yet) capable of handling such high speeds and therefore

could potentially limit entrepreneurial activity and entrepreneurial success, although the infrastructure is

there. This research also suggests that there exists heterogeneity to the impact of very high capacity

broadband availability across space. Areas with different urbanity levels showed differences in effects and

significance. This heterogeneity across space is also shown in previous research on broadband availability

with lower capacity speeds (Kolko, 2012; Mack et al., 2011).

This is important information, especially for policy makers, because it provides them with the empirical

evidence on which they can base their policy decisions regarding broadband. This research in the

Netherlands points out that the targets of the European Union towards a gigabit society can make sense

for an economic perspective, because of the suggested positive relationship with entrepreneurial activity

and entrepreneurial success. It further points out that is important to consider the urbanity level of the

areas that they plan to provide with very high capacity broadband, because the effects can differ

accordingly. However, it is also important for policy makers to keep in mind that improving the availability

of very high capacity broadband in regions does in itself not offer economic gains to the region. To be able

to benefit the new broadband needs to be adopted and to really benefit it also requires the potential

entrepreneur or small business owner to have sufficient complementary digital awareness and digital

literacy skills. Studies have evaluated establishment adoption of ICT and found that firm size plays a key

part between the presence of In-house IT support (Forman & Goldfarb, 2005; Gibbs & Tanner, 1997).

Many small businesses are unaware of the advantages of utilizing ICT compared to the bigger companies

and are therefore less likely to adopt (Center for an Urban Future, 2004). This also seems to be the case

for entrepreneurs, research suggests that especially inexperienced entrepreneurs lack the digital

45

awareness and digital literacy skills that are necessary to effectively makes use of the internet (Mack et

al., 2017).

5.2. Limitations

It is important to mention, that the results of this study should be taken lightly, because they definitely

do not prove causality. Without causality, suggesting policy recommendations remains difficult.

Establishing causality requires the construction of a valid counterfactual. In our case this means what

would have happened to a municipality if the very high capacity broadband increase was not deployed.

This outcome is however fundamentally unobservable, therefore we tried to rebuild it by comparing

municipalities with higher and lower very high capacity broadband availability over a longer period of

years. However, there are obviously differences between those municipalities and thus differences

between the ‘control group’ and the ‘treatment group’. This research is then not proving causality, but

does create a strong case for the likelihood of causality. Stronger then prior research that only relied on

cross sectional rather than time series data or research that did not properly try to reconstruct the

counterfactual. The use of a two year lagged policy variable also guards against the possibility of reverse

causality, where it is not the availability that results in entrepreneurship, but the other way around. This

concern of this type of endogeneity is mention often in the broadband literature (Gaasbeck, 2008; Kolko,

2012; Minges, 2016), the use of a lagged variable in combination with dependent variables that are less

prone to suffer hereof, largely seem to deal with this issue. However, it is still possible that there exist

other variables that are not included in this study that cause both to decrease or increase simultaneously.

The use of time and unit fixed effects can only deal with this issue to some extent.

Another important factor mentioned often in the broadband availability literature is that next to

heterogeneity across space, there exists heterogeneity across industries (Kolko, 2012; Mack et al., 2011).

Prior research suggest that the impact of broadband availability differs significantly across industries, with

much more importance in the high tech and knowledge intensive industries. It is likely that this is also the

case, and possibly even more prominent with very high capacity broadband, because the advantages of

very fast internet can differ considerably between industries. Unfortunately due to data limitations it was

not possible to take this heterogeneity into account in this study, resulting in an important limitation.

Another limitation of this study lies in the proposed need for spatial econometrics mentioned in prior

research (Bhat et al., 2014; Mack, 2014; Mack et al., 2011). Mack and others (2011) suggest that it is

important to take spatial autocorrelation into account when measuring the local impact of broadband

availability. In this study we did not find a possibility of controlling for spatial autocorrelation with models

of spatial lag and spatial error, because the policy variable is lagged for two years. Spatial econometric

models only worked without lagged variables. There is the possibility that this might have led to some

bias on the results, although we did control for serial correlation and cross sectional dependence possibly

mitigating some of the bias.

The rollout is considered a good proxy of broadband availability, but it does not take into account the

advertised bandwidth and realized speeds. These are known to vary considerably (Kolko, 2012). Even

though this is difficult to control for, it does result in a limitation of this study.

The control variable university presence does not seem to be the best proxy of regional knowledge

creation in this research, possibly because of the small amount of universities present in the Netherlands.

46

It is possible that this is different in samples with more universities, but in this study it might have been

better to consider another proxy.

The final limitation lies in the generalizability of the results, or to what extent can the results be replicated

in regions and areas of other countries. This study only looked at regions in the Netherlands and the results

might be different in regions of other countries.

5.3. Future research

More research should focus on closing the broadband evolution gap. Next to entrepreneurship there are

many more economic growth outcomes to consider when it comes to the impact of very high capacity

broadband. For example, employment and productivity. More research is also necessary on the impact of

entrepreneurship in regions, because this is one of the first studies to do so and this study has its

limitations. Although prior research has made progress, proving causality remains an issue in the

broadband internet literature. It is therefore important that future research keeps looking for the best

way to deal with endogeneity, also when it comes to the impact of entrepreneurship. This research can

only show correlation and make a suggestion towards causality. This is already relevant for both literature

and policy, but ultimately far from ideal.

It is also important for future research to take heterogeneity across industries into account. This possibly

provides an even better understanding of the impact of very high capacity broadband to entrepreneurial

activity and entrepreneurial success. It is probable that certain industries benefit much more from very

high capacity broadband than other industries.

Spatial econometrics can provide a better way of modeling the impact of broadband availability,

something this research was unable to do. Future research can make use of a spatial lag and a spatial error

model to makes sure that the results are not biased by spatial autocorrelation.

Research on the relationship between very high capacity broadband and entrepreneurship should also be

repeated in different geographical areas. This might show different results and can give insight into the

generalizability of this study. What works in the Netherlands does not necessarily work in other countries

or geographical areas, because every geographical area has its own characteristics that might influence

results.

In this research we only took fixed broadband into account, while in the future it might also be interesting

to look at the impact of mobile broadband for entrepreneurship and businesses. The capacity speed of

mobile internet has not been sufficient for many businesses in the past, but with the advancements

towards 5G mobile internet this may seriously change. If the capacity speed is sufficient to be a good

substitute over fixed broadband then mobile internet certainly has one big advantage over fixed

broadband for policymakers. There is no digging involved, so especially in remote areas and difficult

terrain this could prove to be a much better option in the future.

Finally, future research should also look at adoption instead of availability and if possible take advertised

and realized broadband speeds into account. Availability is thought of as a good proxy, but it is not without

its own limitations. It is therefore also important to look at adoption, because this might show different

results.

47

6. Conclusion

To answer the research question that started this research:

‘To what extent is the availability of very high capacity broadband for businesses an important regional

determinant of Entrepreneurship in the Netherlands?

It was necessary to first get a clear picture of the rollout of very high capacity broadband for businesses

in the Netherlands. The results showed that between 2011, where the rollout of fiber broadband roughly

started to take off, up to 2017 where it roughly started to slow down again, that the rollout in the

Netherlands was very heterogeneous across municipalities. Kolko (2010) already showed that this was

also the case for broadband with lower capacity speeds and predicted that if would be more prominent

with higher broadband capacity speeds. This led to a good opportunity to measure if these differences

actually had an important impact on entrepreneurship in different regions measured as the amount of

entrepreneurial activity and entrepreneurial success. This is an important step towards filling up the gap

in the literature that exists on the impact of broadband with higher capacity speeds.

Does very high capacity broadband for businesses then matter for entrepreneurship? The results of this

study suggest that it does. With a two year lagged broadband availability policy variable we found a

positive significant effect on the number of new firms and the number of fast growing firms in a region.

With a 10% increase 0.45 new independent firm entries per 1.000 business establishments and 0.15 fast

growing businesses per 1.000 business establishments. The results also show that the effect is

heterogeneous across space with different impacts on municipalities with different urbanity levels.

This suggests that the availability of very high capacity broadband internet is an important regional

determinant of entrepreneurship. This can be explained by the following mechanism. The presence of

very high capacity broadband enables new levels of advanced use of the internet. This can facilitate

entrepreneurial activity in two ways. First, it can offer new entrepreneurial opportunities in the region

that can be exploited by potential entrepreneurs. Second, the firm benefits of new levels of advanced use

can influence an entrepreneurs decision to locate in areas with very high capacity broadband over areas

without very high capacity broadband. The presence of very high capacity broadband can also facilitate

entrepreneurial success in the region, because it can help entrepreneurs survive and grow. Access

provides SME’s with a set of powerful tools that can be utilized to work faster, more efficiently and

differently. The effects are however modest and also seem to depend on the urbanity level of the region.

This can help policymakers make more informed decisions on rollout strategies and decide whether the

benefits outperform the costs. However, it is also important for them to keep in mind that entrepreneurs

also need to adopt the very high capacity broadband and that is also necessary for the potential

entrepreneur or small business owner to have sufficient complementary digital awareness and digital

literacy skills, something that they often lack according to research (Mack et al., 2017).

This study does have its limitations, it does not prove causality, but makes a strong case for the possibility

of causation and it was not able to look at the differences across industries. Future research is needed,

nevertheless this study provides a good first step in untangling the impact that very high capacity

broadband can have on the economy. So far, it looks like it makes a difference on entrepreneurship in

regions.

48

7. Bibliography

Acs, Z. J., & Armington, C. (2003). Endogenous Growth and Entrepreneurial Activity in Cities. Washington, DC: Center for Economic Studies, Bureau of the Census, 53(9), 1689–1699. https://doi.org/10.1017/CBO9781107415324.004

Acs, Z. J., Audretsch, D. B., Braunerhjelm, P., & Carlsson, B. (2012). Growth and entrepreneurship. Small Business Economics, 39, 289–300. https://doi.org/10.1007/s11187-010-9307-2

Acs, Z. J., Parsons, W., & Tracy, S. (2008). High-Impact Firms : Gazelles Revisited. Washington, 21 DC: Center for Economic Studies, Bureau of the Census, (September 2010).

Armington, C., & Acs, Z. J. (2002). Firm Formation The Determinants of Regional Variation in New Firm Formation. Regional Studies, 36(1), 33–45. https://doi.org/10.1080/00343400120099843

Audretsch, D. B., Grilo, I., & Thurik, A. R. (2007). Explaining entrepreneurship and the role of policy : a framework. In The handbook of research on entrepreneurship policy. Cheltenham: Edward Elgar.

Autio, E., Kenney, M., Mustar, P., Siegel, D., & Wright, M. (2014). Entrepreneurial innovation: The importance of context. Research Policy, 43(7), 1097–1108. https://doi.org/10.1016/j.respol.2014.01.015

Benkler, Y. (2009). Next Generation Connectivity: A review of broadband Internet transitions and policy from around the world. The Berkman Center for Internet & Society at Harvard University.

Bhat, C. R., Paleti, R., & Singh, P. (2014). A Spatial Multivariate Count Model for Firm Location Decisions. Journal of Regional Science, 54(3), 462–502. https://doi.org/10.1111/jors.12101

Bosma, N. S. (2009). The Geography of Entrepreneurial Activity and Regional Economic Development. New York.

Bourreau, M., Feasey, R., & Hoernig, S. (2017). Demand-Side Policies to Accelerate the Transition to Ultrafast Broadband. Brussels.

Brennenraedts, R., Vankan, A., te Velde, R., Veldkamp, B. M. J., & Kaashoek, B. (2014). De impact van ICT op de Nederlandse economie. Dialogic, 100. Retrieved from https://www.dialogic.nl/documents/2014.062-1430.pdf

Bresnahan, T., & Trajtenberg, M. (1995). General puprose technologies as engines of growth? Journal of Econometrics.

Breusch, T. S., & Pagan, A. R. (1979). A Simple Test for Heteroscedasticity and Random Coefficient Variation. Econometrica, 47(5), 1287–1294.

Brynjolfsson, E., & McAfee, A. (2012). Race against the machine: How the digital revolution is acelerating innovation, driving productivity and irreversibly trnasforming employment and the econo. MIT Sloan School of Management. https://doi.org/10.1016/S2213-8587(14)70016-6

Burgelman, R. A. (1983). A Process Model of Internal Corporate Venturing in the Diversified Major Firm. Administrative Science Quarterly, 28(2), 223–244.

Cantillon, R. (1755). Essai sur la nature du commerce en général.

Caves, R. E. (1988). Industrial New Organization and the of Findings on Mobility Firms. Journal of

49

Economic Literature, 36(4), 1947–1982. Retrieved from http://www.jstor.org/stable/2565044

Dahlqvist, J., & Wiklund, J. (2011). Measuring the market newness of new ventures. Journal of Business Venturing. https://doi.org/10.1016/j.jbusvent.2010.12.001

Deloitte. (2017). Fibre infrastructure as the future-proof choice and its impact on innovation, (January), 1–44. Retrieved from https://www.stokab.se/Documents/Nyheter bilagor/InnovBroadbEn.pdf

European Commission. (2016). Connectivity for a Competitive Digital Single Market - Towards a European Gigabit Society.

European Commission. (2018). Broadband Europe _ Digital Single Market. Retrieved October 12, 2018, from https://ec.europa.eu/digital-single-market/en/broadband-europe

Forman, C., & Goldfarb, A. (2005). ICT Diffusion to Businesses. Handbooks in Information Systems.

Forman, C., Goldfarb, A., & Greenstein, S. (2005). Geographic location and the diffusion of Internet technology. Electronic Commerce Research and Applications, 4, 1–13. https://doi.org/10.1016/j.elerap.2004.10.005

Fritsch, M., & Schmude, J. (2006). Entrepreneurship in the region. New York: Springer Science.

Gaasbeck, K. A. Van. (2008). A rising tide : Measuring the economic effects of broadband use across California. The Social Science Journal, 45, 691–699. https://doi.org/10.1016/j.soscij.2008.09.017

Gartner, W. B., & Shane, S. A. (1995). MEASURING ENTREPRENEURSHIP OVER TIME *. Journal of Business Venturing, 9026(March 1994), 283–301.

Gibbs, D., & Tanner, K. (1997). Information and communication technologies and local economic development policies: The British case. Regional Studies, 31(8), 765–774. https://doi.org/10.1080/713693400

Godfrey, L. G. (1978). Testing Against General Autoregressive and Moving Average Error Models when the Regressors Include Lagged Dependent Variables, 46(6), 1293–1301.

Haller, S. A., & Lyons, S. (2019). Effects of broadband availability on total factor productivity in service sector firms: Evidence from Ireland. Telecommunications Policy, 43(1), 11–22. https://doi.org/10.1016/j.telpol.2018.09.005

Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46(6), 1251–1271.

Heger, D., Veith, T., & Rinawi, M. (2012). The Effect of Broadband Infrastructure on Entrepreneurial Activities: The Case of Germany. Ssrn, (11). https://doi.org/10.2139/ssrn.1992957

Henderson, J., Low, S. A., & Weiler, S. (2007). The Drivers of Regional Entrepreneurship in Rural and Metro Areas. Entrepreneurship and Local Economic Development, (January), 1–29.

Kim, Y., & Orazem, P. F. (2016). Broadband internet and new firm location decisions in rural areas. American Journal of Agricultural Economics, 99(1), 285–302. https://doi.org/10.1093/ajae/aaw082

Kolko, J. (1999). The Death of Cities ? The Death of Distance ? Evidence from the Geography of Commercial Internet Usage.

Kolko, J. (2010). Does Broadband Boost Local Economic Development? Public Policy Institute of California, (January). Retrieved from http://www.nativelink.net/Broadband_Link.pdf

50

Kolko, J. (2012). Broadband and local growth. Journal of Urban Economics, 71(1), 100–113. https://doi.org/10.1016/j.jue.2011.07.004

Kotnik, P., & Stritar, R. (2015). ICT as the Facilitator of Entrepreneurial Activity: An Empirical Investigation. Amfiteatru Economic, 17(38), 277–290.

Lapointe, P. (2015). Does Speed Matter? The Employment Impact of Increasing Access to Fiber Internet. Journal of the Washington Academy of Sciences, 101(1), 9–33.

Low, S. A. (2009). DEFINING AND MEASURING ENTREPRENEURSHIP FOR REGIONAL RESEARCH: A NEW APPROACH. University of Illinois.

Lumpkin, G. T., & Dess, G. (1996). Clarifying the entrepreneurial orientation construct and linking it to performance. The Academy of Management Review, 21(1), 135–172.

Mack, E. A. (2014). Businesses and the need for speed: The impact of broadband speed on business presence. Telematics and Informatics, 31(4), 617–627. https://doi.org/10.1016/j.tele.2013.12.001

Mack, E. A., Anselin, L., & Grubesic, T. H. (2011). The importance of broadband provision to knowledge intensive firm location. Regional Science Policy & Practice, 3(1), 17–35. https://doi.org/10.1111/j.1757-7802.2011.01026.x

Mack, E. A., Marie-Pierre, L., & Redican, K. (2017). Entrepreneurs’ use of internet and social media applications. Telecommunications Policy, 41(2), 120–139. https://doi.org/10.1016/j.telpol.2016.12.001

Mack, E., & Faggian, A. (2013). Productivity and Broadband: The Human Factor. International Regional Science Review (Vol. 36). https://doi.org/10.1177/0160017612471191

Mccoy, D., Lyons, S., Morgenroth, E., Palcic, D., & Allen, L. (2016). The impact of local infrastructure on new business establishments. MPRA, Munich Personal RePEc Archive, (69074). Retrieved from https://mpra.ub.uni-muenchen.de/69074/%0D

Mccoy, D., Lyons, S., Morgenroth, E., Palcic, D., & Allen, L. (2017). The impact of broadband and other infrastructure on the location of new business establishments (No. 282). London.

Minges, M. (2016). Exploring the Relationship Between Broadband and Economic Growth. World Development Report, 1, 21. Retrieved from http://pubdocs.worldbank.org/en/391452529895999/WDR16-BP-Exploring-the-Relationship-between-Broadband-and-Economic-Growth-Minges.pdf%0Ahttp://documents.worldbank.org/curated/en/2016/02/25851781/world-development-report-2016-digital-dividends-exploring-rel

Negroponte, N., Harrington, R., Mckay, S. R., & Christian, W. (1997). Being digital. Computers in Physics, 11, 261. https://doi.org/10.1063/1.4822554

Pesaran, M. H. (2004). General Diagnostic Tests for Cross Section Dependence in Panels (No. 1229).

Phippen, A., & Lacohée, H. (2016). The impact of fibre connectivity on SMEs: Benefits and business opportunities. The Impact of Fibre Connectivity on SMEs: Benefits and Business Opportunities. https://doi.org/10.1007/978-3-319-47554-7

Prieger, J. E. (2013). The Broadband Digital Divide and the Benefits of Mobile Broadband for Minorities. Telecommunications Policy, 37(6–7), 483–502. https://doi.org/DOI 10.1016/j.telpol.2012.11.003

51

Prieger, J. E., Lu, H., & Zhang, H. (2017). The Importance of Transportation , Broadband , and Intellectual Infrastructure for Entrepreneurship (No. 68). Retrieved from http://digitalcommons.pepperdine.edu/sppworkingpapers/68

Reynolds, P. D., Miller, B., & Maki, W. R. (1995). Explaining Regional Variation in Business Births and Deaths : U. S. 1976-88. Small Business Economics, 7, 389–407.

Reynolds, P., Hunt, S., Servais, I., Lopez-garcia, P., & Chin, N. (2005). Global Entrepreneurship Monitor : Data Collection Design and Implementation. Small Business Economics, 24, 205–231. https://doi.org/10.1007/s11187-005-1980-1

Richardson, R., & Gillespie, A. (1996). Advanced communications and employment creation in rural and peripheral regions: A case study of the Highlands and Islands of Scotland. Annals of Regional Science, 30(1), 91–110. https://doi.org/10.1007/BF01580539

Ross, P. K., & Blumenstein, M. (2015). Cloud computing as a facilitator of SME entrepreneurship. Technology Analysis and Strategic Management, 27(1), 87–101. https://doi.org/10.1080/09537325.2014.951621

Semademi, M., Certo, S. T., & Withers, M. C. (2014). The Perils of Endogeneity and Instrumental Variables in Strategy Research: Understanding through Simulations. Strategic Management Journal, 35, 1070–1079. https://doi.org/10.1002/smj

Shane, S., & Venkatraman, S. (2000). The Promise of Entrepreneurship as a Field of Research. The Academy of Management Review, 25(1), 217–226.

Spurge, V., & Roberts, C. (2005). Broadband technology. Journal of Property Investment & Finance, 23(6), 516–524. https://doi.org/10.1108/14635780510626556

Steinfield, C. (2013). Situated Electronic Commerce : Toward A View as Complement Rather than Substitute for Offline Commerce. Urban Geography, 25(4), 353–371. https://doi.org/10.2747/0272-3638.25.4.353

Stenholm, P., Acs, Z. J., & Wuebker, R. (2013). Exploring country-level institutional arrangements on the rate and type of entrepreneurial activity. Journal of Business Venturing, 28(1), 176–193. https://doi.org/10.1016/j.jbusvent.2011.11.002

Sternberg, R. (2009). Regional Dimensions of Entrepreneurship. Foundations and Trends® in Entrepreneurship, 5(4), 211–340. https://doi.org/10.1561/0300000024

Sternberg, R., & Tamásy, C. (1999). Munich as Germany’s No. 1 high technology region: Empirical evidence, theoretical explanations and the role of small firm/large firm relationships. Regional Studies, 33(4), 367–377. https://doi.org/10.1080/713693560

Sternberg, R., & Wennekers, S. (2005). Determinants and Effects of New Business Creation Using Global Entrepreneurship Monitor Data. Small Business Economics, 24(3), 193–203. https://doi.org/10.1007/s11187-005-1974-z

Thornton, P. H. (1999). The Sociology of Entrepreneurship. Annual Review Sociology, 25, 19–46. https://doi.org/10.1093/oxfordhb/9780199736362.013.0007

Thurik, A. R., Stam, E., & Audretsch, D. B. (2013). Technovation The rise of the entrepreneurial economy and the future of dynamic capitalism. Technovation, 33(8–9), 302–310.

52

https://doi.org/10.1016/j.technovation.2013.07.003

What works centre for local economic growth. (2015). Evidence Review 6 Broadband.

Whitacre, B., Gallardo, R., & Strover, S. (2013). Rural Broadband Availability and Adoption : Evidence, Policy challlenges, and Options. National Agricultural & Rural Development Policy Center.

Whitacre, B., Gallardo, R., & Strover, S. (2014). Broadbands contribution to economic growth in rural areas: Moving towards a causal relationship. Telecommunications Policy, 38(11), 1011–1023. https://doi.org/10.1016/j.telpol.2014.05.005

Xiong, Z. (2013). Socio-economic Impact of Fiber to the Home in Sweden. KTH Royal Institute of Technology.

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8. Appendix

8.1. VIF test for multicollinearity

VIF is smaller than 5 for every variable, so there are no concerns of multicollinearity

Table 8-1: VIF test to check for multicollinearity of the variables

FttO % (2 year lag) Employment Establishments Pop mutation Household Income Unemployment % University_dum1

1.092 1.271 1.339 1.235 2.240 2.101 1.207

8.2. First stage of the 2SLSDV model

Table 8-2: First stage of the 2SLSDV model with percentage Fiber to all fixed objects as instrumental variable

Dependent variable:

FttO % (2 year lag) Entry (p. 1000 est.) Fast growing (p. 1000 est.)

First stage 2SLSDV model 2SLSDV (robust) 2SLSDV model 2SLSDV (robust)

FtA % (2 year lag) 0.590***

(0.011)

FttO % (2 year lag) 0.023 0.023 0.012 0.012

(0.021) (0.022) (0.007) (0.008)

Employment -0.017 -0.001 -0.001 -0.002 -0.002

(0.014) (0.016) (0.019) (0.006) (0.013)

Establishments 0.062 0.146** 0.146 -0.010 -0.010

(0.058) (0.066) (0.092) (0.024) (0.039)

Popmutation -0.002 0.104** 0.104*** -0.002 -0.002

(0.036) (0.041) (0.038) (0.014) (0.034)

Household Income -1.549 0.918 0.918 0.865** 0.865

(1.084) (1.227) (1.495) (0.435) (0.652)

Unemployment % -2.212*** -0.179 -0.179 -0.062 -0.062

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(0.685) (0.780) (0.677) (0.277) (0.305)

University_dum1 12.472 -12.493 -12.493 -15.214*** -15.214***

(9.181) (10.403) (11.900) (3.689) (5.292)

Constant 50.539** 69.101*** 69.101** 2.757 2.757

(22.375) (25.300) (30.853) (8.972) (13.876)

Observations 1,900 1,900 1,900

R2 0.911 0.787 0.753

Adjusted R2 0.888 0.732 0.690

F Statistic 39.785*** (df = 389;

1510) 5,569.524*** 4,610.011***

Note: *p<0.1**p<0.05***p<0.01