Measure Twice, Cut Once.

Jip Leendertse, Mirella Schrijvers & Erik Stam

Working Paper Series nr: 20-01

Entrepreneurial Ecosystem Metrics

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U.S.E. Research Institute Working Paper Series 20-01 ISSN: 2666-8238

Measure Twice, Cut Once. Entrepreneurial Ecosystem Metrics

Jip Leendertse1, Mirella T. Schrijvers2, Erik Stam2

1: Innovation Studies, Copernicus Institute of Sustainable Development Utrecht University

2: Utrecht University School of Economics Utrecht University

Update May 2020

Abstract An entrepreneurial ecosystem comprises a set of interdependent actors and factors that are governed in such a way that they enable productive entrepreneurship within a particular territory. While the entrepreneurial ecosystem approach is useful to think about regional economies, it currently lacks full-fledged metrics to enable policy. In this paper, we bridge this gap by quantifying and qualifying regional economies using the entrepreneurial ecosystem approach. We operationalize ten elements of entrepreneurial ecosystems for 274 regions in the 28 countries of the European Union. The ecosystem elements show strong and positive correlations between them, confirming the systemic nature of entrepreneurial economies, and the need for a complex systems perspective. Our results show that formal institutions and physical infrastructure take a central position in the interdependence web, providing a first indication of these elements as fundamental conditions of entrepreneurial ecosystems. We then use the elements to calculate an index that measures the quality of entrepreneurial ecosystems. This index is robust and performs well in regressions to predict entrepreneurial output, which we measure using novel data on productive entrepreneurship. Keywords: entrepreneurial ecosystem; regional dynamics; entrepreneurship; economic development; economic policy; entrepreneurship policy

JEL classification: D2, E02, L26, M13, O43, P00, R1, R58

Comments welcomed to: e.stam@uu.nl

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1. Introduction

An entrepreneurial ecosystem comprises a set of interdependent actors and factors that are

governed in such a way that they enable productive entrepreneurship within a particular territory

(Stam, 2015; Stam and Spigel, 2018). The entrepreneurial ecosystem approach has become popular

in economic policy because of the recent shift from managerial economies to entrepreneurial

economies (Thurik et al., 2013). In these entrepreneurial economies entrepreneurship is a key

driver of economic change (Schumpeter, 1911). For scientific research on the entrepreneurial

economy to be relevant for economic policy, it needs to provide a sound understanding of how the

economy works and provide an actionable framework that guides policymaking. The

entrepreneurial ecosystem approach has the promise of providing such an actionable framework.

It offers a lens to empirically trace the systemness of entrepreneurial economies and the degree to

which economic systems produce entrepreneurship, as an emergent property of the system (Brown

and Mason, 2014; Isenberg, 2010; Stam, 2015). It is especially useful to synthesize and integrate

a large variety and quantity of data to measure the (changing) nature, outputs and outcomes of

(regional) economies (Stam, 2015).

Economic policy often fails to achieve its objectives. One cause of this failure is a lack of diagnosis

and monitoring in the policy cycle. We develop entrepreneurial ecosystem metrics to better

measure entrepreneurial economies. These metrics enable adequate diagnosis of entrepreneurial

economies and monitoring of economic change affected by policy and other dynamics. This paper

takes heed of the old carpenter’s adage “measure twice, cut once”, reducing policy failures with

better measurement tools. Even though the academic literature on entrepreneurial ecosystems has

been flourishing recently, it does not yet provide an actionable framework for economic policy.

In this paper we address this gap in the literature by developing and extending the entrepreneurial

ecosystem framework as a tool to analyse and improve entrepreneurial economies. The objective

of this paper is to quantify and qualify regional economies with an entrepreneurial ecosystem

approach. We focus on the regional level instead of the national level (Ács et al., 2014; Radosevic

and Yoruk, 2013), because entrepreneurship is largely a regional event (Feldman, 2001), and there

is substantial variation in entrepreneurship between regions within countries. Quantification

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involves measuring its key elements with a wide range of data sources (Credit et al., 2018).

Qualification involves developing a methodology that provides insight into the extent to which the

elements are interdependent, into the overall quality of the entrepreneurial economy, and relate

this to entrepreneurial outputs. We have three main research questions. First, how can we compose

a harmonized data set with which the quality of key elements of entrepreneurial economies can be

measured? We develop a universal set of constructs for each element of entrepreneurial economies

and compose a harmonized data set to measure these constructs in the context of 274 regions in

the 28 countries of the European Union. The European Union provides an excellent laboratory for

analysing entrepreneurial economies because it contains a large number of regions that exhibit

striking variation in socio-economic conditions, entrepreneurial activity, and economic growth.

Second, to what extent and how are the elements of entrepreneurial economies interdependent?

Can entrepreneurial economies be seen as complex systems, with strong interdependencies

between the elements? Interdependence is a key aspect of complex systems. We show with

multiple statistical methods to what extent and how the elements of entrepreneurial economies are

interdependent. Third, how can we determine the quality of entrepreneurial economies? We will

answer this question by composing an entrepreneurial ecosystem index and analysing its relation

to entrepreneurial outputs. Entrepreneurial output, realizing large scale innovations, is an indicator

of the emergent property of the entrepreneurial economy as a complex system. We use multiple

data sources and metrics to determine entrepreneurial outputs at the regional level. We do this with

novel methods including web scraping and geocoding to determine the entrepreneurial outputs in

the form of the number of (Crunchbase listed) innovative new firms and unicorns - young private

firms with a valuation of more than $1 billion - in a region.

The outline of our paper is as follows. First, we discuss the key measures and mechanisms that

explain entrepreneurship and economic development, and provide a new, complex systems

methodology of understanding regional economies. Second, we discuss the measures that are

needed to approximate the key elements of entrepreneurial economies and that allow us to quantify

these elements and to qualify entrepreneurial economies, and relate this to entrepreneurial outputs.

Third, we use these measures to quantify and qualify entrepreneurial economies in Europe. The

final section concludes, reflects on the findings, policy implications, and sets out an agenda for

further research.

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2. Entrepreneurship and economic development

The empirical literature on entrepreneurship and (regional) economic development can be divided

in the entrepreneurship growth literature, focusing on the aggregate economic effects of

entrepreneurship, and the geography of entrepreneurship literature, focusing on the causes of the

spatial heterogeneity of entrepreneurship. In the next two sections we summarize the insights from

these two literatures.

2.1 Entrepreneurship and economic growth

The role of entrepreneurship in economic development has been studied for a long time, going

back to Schumpeter (1934), Leibenstein (1968) and Baumol (1990). The entrepreneurship growth

literature is mainly concerned with the question how and to what extent entrepreneurship affects

economic growth. Even though the entrepreneurship and economic growth literatures do not

provide full consensus on the positive effects of entrepreneurship, there seems to be more evidence

in favour of than against positive (causal) effects of entrepreneurship on economic growth

(Audretsch et al., 2006; Bosma et al., 2018; Carree and Thurik, 2010; Fritsch, 2013). Key causal

mechanisms being the creation and diffusion of innovations, and competition created by

entrepreneurs (Bosma et al., 2018). The direction and strength of the effect of entrepreneurship on

economic growth depends on the type of context and type of entrepreneurship: ambitious,

opportunity and growth oriented types of entrepreneurship are more likely to lead to economic

growth than self-employed, necessity based entrepreneurship (Bosma et al., 2018, 2011; Fritsch,

2013; Stam et al., 2011; Stam and Van Stel, 2011). In addition, entrepreneurship is most productive

in conditions of inclusive and growth enhancing institutions (Bosma et al., 2018; Sobel, 2008).

Entrepreneurship does not occur in a vacuum, but is very much a local event (Feldman, 2001).

There is also substantial regional variation in the prevalence of entrepreneurship, with underlying

causes being very much spatially bound.

2.2 The geography of entrepreneurship

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The geography of entrepreneurship literature has provided numerous insights into the role of

different factors enhancing the prevalence of entrepreneurship in regions (Bosma et al., 2011; Stam,

2010; Stam and Spigel, 2018; Sternberg, 2009). We summarize the empirical geography of

entrepreneurship literature with ten elements affecting the prevalence of entrepreneurship (cf.

Stam and Van de Ven, 2020). The first element, institutions, provides the fundamental

preconditions for economic action (Granovetter, 1992) and for resources to be used productively

(Acemoglu et al., 2005). Institutions are not only a precondition for economic action to take place,

they also affect the way entrepreneurship is pursued and the welfare consequences of

entrepreneurship (Baumol, 1990). Informal institutions also have strong effects on the prevalence

of entrepreneurship, one example being entrepreneurship culture, reflecting the degree to which

entrepreneurship is valued in society (Fritsch and Wyrwich, 2014). Networks of entrepreneurs

provide an information flow, enabling an effective distribution of knowledge, labour and capital

(Malecki, 1997). Leadership provides direction for the entrepreneurial ecosystem. This leadership

is critical in building and maintaining a healthy ecosystem (Feldman, 2014). This involves a set of

‘visible’ entrepreneurial leaders who are committed to the region (Feldman and Zoller, 2012). The

high levels of commitment and public spirit of regional leaders might be a reflection of underlying

norms dominant in a region (Olberding, 2002). A highly developed physical infrastructure

(including both traditional transportation infrastructure and digital infrastructure) is a key element

of the context to enable economic interaction and entrepreneurship in particular (Audretsch et al.,

2015). Access to finance—preferably provided by investors with entrepreneurial knowledge—is

crucial for investments in uncertain entrepreneurial projects with a long-term horizon (see e.g.

Kerr and Nanda, 2009). Perhaps the most important condition for entrepreneurship is the presence

of a diverse and skilled group of workers (‘talent’: see e.g. Acs and Armington, 2004; Glaeser et

al., 2010; Lee et al., 2004; Qian et al., 2013). An important source of opportunities for

entrepreneurship can be found in knowledge, from both public and private organizations (see e.g.

Audretsch and Lehmann, 2005). The supply of support services by a variety of intermediaries can

substantially lower entry barriers for new entrepreneurial projects, and reduce the time to market

of innovations (see e.g. Clayton et al., 2018; Howells, 2006; Zhang and Li, 2010). Finally, the

presence of financial means in the population to purchase goods and services—preferably locally,

but possibly also on a further distance—is essential for entrepreneurship to occur at all. The

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presence of demand thus is an important element of the entrepreneurial ecosystem. Income and

purchasing power in a region is both a cause and an effect of entrepreneurship in a region

(Berkowitz and DeJong, 2005), hinting at the role of feedback effects in the evolution of

entrepreneurial ecosystems.

2.3 An entrepreneurial ecosystem framework

The empirical literatures on the geography of entrepreneurship and economic growth reveal

several factors to be of relevance in explaining the spatial heterogeneity in entrepreneurship. This

suggests that there is a limited set of factors, or elements that affects the prevalence of

entrepreneurship in a region. We integrate the insights from the empirical literatures on the

geography of entrepreneurship and economic growth into one figure, reflecting an entrepreneurial

ecosystem framework with ten elements (see Fig. 1). This framework with ten elements provides

a compromise between other frameworks with five (Vedula and Kim, 2019), six (Isenberg and

Onyemah, 2016), seven (Radosevic and Yoruk, 2013) and 14 elements (Ács et al., 2014). We build

on these frameworks and develop them further by separating inputs and outputs of the system,

providing an academically grounded set of elements, and using empirical indicators more closely

reflecting productive entrepreneurship (Baumol, 1990; Schumpeter, 1934).

Fig. 1. Elements and outputs of the entrepreneurial ecosystem (adapted from Stam and Van de

Ven, 2020).

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2.4 Understanding entrepreneurial economies as complex systems

To understand the long-term development of (regional) economies and the role of entrepreneurship,

the approaches of entrepreneurship growth and geography of entrepreneurship need to be

combined. Entrepreneurship plays a double role: it is the output variable in the geography of

entrepreneurship approach, and it is the input variable in the entrepreneurship growth approach.

To complicate matters even more, entrepreneurship and economic growth also affect the inputs of

the geography of entrepreneurship approach, for example with serial entrepreneurs becoming

venture capitalists and creating networks; and with economic growth leading to growth in demand,

investments in knowledge, and congestion effects in the physical environment. One solution to

these conceptual complications is to build on complex systems approaches (Arthur, 2013; Hidalgo

and Hausmann, 2009; Ostrom, 2010; Simon, 1962) to develop and use a complex systems

perspective on the evolution of entrepreneurial economies (Roundy et al., 2018; Stam and Van de

Ven, 2020). A complex systems perspective is able to integrate the geography of entrepreneurship

(environmental conditions of entrepreneurship) and the entrepreneurship and economic growth

literature (the conditions and effects of productive entrepreneurship). We build on the integrative

model of entrepreneurial ecosystems by Stam and Van de Ven (2020), which includes institutional

arrangement and resource endowment elements (see Fig. 1). They focus on three key mechanisms:

interdependence and coevolution of elements, upward causation of the ecosystem on

entrepreneurship, and downward causation of entrepreneurial outputs on the quality of the

ecosystem (Stam and Van de Ven, 2020).

Entrepreneurial eco(logical) systems do not exist as such, in contrast to economies that do exist,

and can be more or less complex, systemic, and entrepreneurial. Systemic in the sense of elements

interacting with each other, and complex in the sense of creating distinct properties that arise from

these interactions (including nonlinearity, emergence, adaptation, and feedback loops). In this

paper we focus on emergence, and conceptualise economies as complex systems from which

transformative innovations can emerge. Transformative innovations are the product of interacting

agents, enabled by interdependent components of the system in which they act (Arthur, 2013).

Complex systems have distinct properties that arise from interdependencies, such as nonlinearity,

emergence, tipping-points, spontaneous order, adaptation, and feedback loops. A complex systems

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perspective on the evolution of entrepreneurial economies can provide new answers and new

questions to the literature on entrepreneurship and economic development. We take a complexity

perspective to better understand the dynamics of economic systems and the interdependencies

between the elements of a system. Our complexity approach provides the tools for tracing

nonlinear dynamics. Small changes in the conditions of an ecosystem can have big effects: just

like the introduction of a wolf can change a whole natural ecosystem, the introduction of a new

law or a new actor can change a whole economic system. Also, when a threshold (tipping point)

is reached in producing scale-ups in a particular territory, this might trigger a virtuous cycle of

successful exits that provide a fertile breeding ground for next generations of scale-ups, as these

successfully exited entrepreneurs may become venture capitalists, role models, and network

builders in their home-region (Garnsey and Heffernan, 2005; Mason and Harrison, 2006). Such

analyses provide novel insights into the recursive causal connections between entrepreneurship

and elements in the economic system such as venture capital (Lerner, 2012) and culture (Minniti,

2005).

3. Measuring entrepreneurial ecosystems

The ecosystem framework discussed above identifies ten key elements of an entrepreneurial

ecosystem. In this section we operationalize these elements into measurable variables at the

appropriate geographical level. First, we discuss the boundaries of an ecosystem to determine the

relevant level of analysis. Then we shortly illustrate the main data sources and describe the

operational measure of each ecosystem element (for an overview see Table 1).

3.1 Level of analysis

The outputs and outcomes of entrepreneurial ecosystems are the result of a complex set of actors

and factors that occur in a temporal and varying regional setting. As Feldman and Lowe (2015, p.

1785) rightly state there is often a disconnect “between the theoretical definition of a region as

integrated contiguous space and the political and census geography for which data are readily

available.” In addition, since ecosystems are continuously evolving and are not limited to a specific

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sector, it is hard to precisely determine their boundaries (Stam and Van de Ven, 2020). The primary

demarcation criterium should be the spatial reach of the causal mechanisms involved. This does

not lead to one straightforward unit or spatial level of analysis. First, given the multiplicity of

causal mechanisms involved in nurturing entrepreneurship, there will be different spatial reaches:

for talent it may be the daily urban system (within a 50 mile radius), while for credits it may be

the local bank, and for venture capital a two hour drive radius (which may overlap with the regional

level in large countries, but may be beyond the national level for small countries). Second, there

is a spatial nestedness of contexts: formal institutions at the municipal, regional, national and

supranational level may be important context conditions. These first two considerations make it

difficult to delineate the spatial boundary of entrepreneurial ecosystems, from a causal mechanism

point of view. From a practitioners’, the stakeholders of entrepreneurial ecosystems, point of view

the relevant boundaries will again be different depending on their role in the ecosystem. For civil

servants it will be a particular jurisdiction, while for entrepreneurs it may be a multiplicity of

layered (regional, national) or connected ecosystems (different city-regions). To determine the

spatial level of analysis (although almost always imperfect) we therefore search for a common

spatial denominator in combination with data availability (to allow for comparisons). It should be

kept in mind that even though we choose a spatial unit to represent the entrepreneurial ecosystem,

entrepreneurial ecosystems are not closed containers, but open systems.

In the European context, the most relevant spatial level of analysis is likely to be between the

municipal and national level, since the spatial reaches of the different elements are most likely to

overlap with regional boundaries (e.g. the 50 mile radius for talent). The regional level in Europe

is best defined through the NUTS 2 classification, which identifies 281 geographical regions over

the 28 member states. The boundaries of these areas are based on existing administrative

boundaries and population thresholds. The population of a NUTS 2 unit is roughly between

800,000 and 3 million people (European Commission, 2018). By defining entrepreneurial

ecosystems at the NUTS 2 level we use the same region size as the recent study by Stam and Van

de Ven (2020). Our study looks at a larger set of observations than Stam and Van de Ven (2020)

since we include all countries in the European Union instead of only one. This also results in a

substantial larger variety in our data. Studying regions instead of countries allows us to look at

entrepreneurial at a more detailed and appropriate scale. A disadvantage of looking at regions

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instead of countries is that data on a regional level is scarcer than national data. However, the

European Union performs several large data collection exercises on regional level to inform

regional policy, this results in the availability of a fairly large amount of regional data. Furthermore,

we use a number of novel methodologies to create new metrics at the NUTS 2 level. Finally, we

use several national measures to account for the aforementioned spatial nestedness of for example

institutions. This combination of data on different geographical levels is discussed in detail for

each element below and summarized in Table A1 in the appendix.

3.2 Data sources

To measure entrepreneurial ecosystem elements we combine three datasets from studies executed

by the European Commission and complement this with data from other sources as well as new

data we collected using innovative data analytics. The three main datasets are all available for

NUTS 2 units in the European Union. The Regional Competitiveness Index (RCI) is a large study

performed every three years (Annoni and Dijkstra, 2019). It uses multiple data sets to construct

indicators for areas such as infrastructure, human capital and innovation, and subsequently

computes an index that is said to reflect the competitiveness of a region. A related dataset that is

more explicitly focused on entrepreneurship is the Regional Ecosystem Scoreboard (RES) (Léon

et al., 2016). In addition to combining several statistical sources, this study also performs its own

survey among cluster organizations and regional development agencies. The data used in the RES

is mainly from the 2010-2015 period and we obtained this through web scraping. Finally, we also

take data from the Regional Innovation Scoreboard (RIS) which is a smaller dataset solely focused

on the innovation performance of regions (Hollanders et al., 2019). This is the regional version of

the European Innovation Scoreboard (EIS) and measures for instance R&D expenditure,

innovation by SMEs and human capital. These main data sources are combined with several

statistics from Eurostat. For some indicators data is only available at the NUTS 1 level in certain

countries. In those cases we follow the approach of the original datasets and impute the NUTS 1

for the NUTS 2 regions (Annoni and Dijkstra, 2019; Hollanders et al., 2019; Léon et al., 2016).

Table 1 provides an overview of the data source for each measure while a more detailed version,

including all original data sources, can be found in Table A1 in the appendix.

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Table 1.

Operationalisation of the indicators of entrepreneurial ecosystem elements and output.

Elements Description Empirical indicators Data source

Formal

institutions

The rules of the

game in society

Two composite indicators measuring the

overall quality of government (consisting

of scores for corruption, accountability,

and impartiality) and the regulatory

framework for entrepreneurship (number

of days to start a business, difficulties

encountered when starting a business, the

barriers to entrepreneurship and the ease

of doing business)

Quality of

Government

Survey and the

Regional

Ecosystem

Scoreboard

Entrepreneurship

culture

The degree to

which

entrepreneurship is

valued in a region

A composite measure capturing the

regional entrepreneurial culture,

consisting of entrepreneurial motivation,

cultural and social norms, importance to

be innovative and trust in others

Regional

Ecosystem

Scoreboard

Networks The connectedness

of businesses for

new value creation

Percentage of SMEs that engage in

innovative collaborations as a percentage

of all SMEs in the business population

Regional

Innovation

Scoreboard

Physical

Infrastructure

Transportation

infrastructure and

digital

infrastructure

Four components in which the

transportation infrastructure is measured

as the accessibility by road, accessibility

by railway and number of passenger

flights and digital infrastructure is

measured by the percentage of

households with access to internet

Regional

Competitiveness

Index

Finance The availability of

venture capital and

bank loans to firms

Two components: availability of venture

capital, availability of bank loans for

capital investments

Regional

Ecosystem

Scoreboard

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Leadership The presence of

actors taking a

leadership role in

the ecosystem

The number of coordinators on H2020

innovation projects per 1000 inhabitants

CORDIS

(Community

Research and

Development

Information

Service)

Talent The prevalence of

individuals with

high levels of

human capital,

both in terms of

formal education

and skills

Eight components: tertiary education,

vocational training, lifelong learning,

innovative skills training,

entrepreneurship education, technical

skills, creative skills, e-skills

Regional

Ecosystem

Scoreboard

New Knowledge Investments in new

knowledge

Intramural R&D expenditure as

percentage of Gross Regional Product

Eurostat

Demand Potential market

demand

Three components: disposable income per

capita, potential market size expressed in

GRP, potential market size in population.

All relative to EU average.

Regional

Competitiveness

Index

Intermediate

services

The supply and

accessibility of

intermediate

business services

Two components: the percentage of

employment in knowledge-intensive

market services and the percentage of

incubators/accelerators per 1000

inhabitants

Eurostat and

Crunchbase

Output Entrepreneurial

output

The number of Crunchbase firms founded

in the past 5 year per 1000 inhabitants

Crunchbase

Unicorn output The absolute number of unicorns in the

region

CB Insights

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3.3 Element construction

Seven of the ten elements are constructed through multiple indicators. For these elements we

calculate the element score by first standardizing the individual measures (mean as 0 and standard

deviation of 1). This ensures that the different measures each have a proportionate influence on

the composite indicator. We then take the average of the standardized measures. In section 3.14

we go into more detail on how the different elements are used to calculate an index for the

entrepreneurial ecosystem.

To measure four of our variables, leadership, the number of incubators, and both output measures

we use the location of individual organizations to determine our regional variables. We here

explain the methodology of geocoding and region allocation which we use in all four occasions.

First, we use the nominatim package in R to geocode the given locations using OpenStreetMap

(OpenStreetMap, 2019; Rudis, 2019). This is an online map which allows users to pass a list of

locations into the software and obtain their coordinates. For the few regions without a match in

this procedure we manually search and add its coordinates. With this procedure we obtained the

coordinates for all organization. Subsequently, we used Eurostat shapefiles to determine in which

NUTS 2 region these coordinates are located. The shapefiles contain an exact overview of the

NUTS 2 boundaries (Eurostat, 2019). We then use the rgdal package in R to assign the coordinates

to the corresponding NUTS 2 region (Bivand et al., 2019; Eurostat, 2019). Through this we are

able to assign all except for about 0.1% of the organizations to a region. We filled in the remaining

geocodes through the browser tool of Open Street Map. After this we were able to assign all

organizations for each of the four variables to a region. For each of the four variables we then

count the number of organizations/firms in each NUTS 2 region and divide this by the population

of the region to obtain our final measure.

3.4 Formal institutions

Well-functioning institutions are essential for any entrepreneurship to take place at all (Granovetter,

1992). Even when fundamental conditions of the institutional framework, e.g. property rights, are

in place, the quality of these institutions will affect entrepreneurship (Baumol, 1996; Boudreaux

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and Nikolaev, 2019; Webb et al., 2019). To operationalize this element, we use a generic and an

entrepreneurship specific indicator. These indicators cover two different aspects of the institutional

environment, namely the quality of government and the regulatory framework for businesses. To

operationalize the general quality of government we use the Quality of Government study (QOG),

which is the largest sub national governance study that has been performed (Charron et al., 2019a).

The quality of government indicator consists of three components: corruption, accountability and

impartiality. These are each measured by a large citizen survey in each European region and

complemented by the World Governance Indicators on a national level. The survey questions

measure both experiences and perceptions of citizens and ask for example about the quality of

public education in their region or corruption in the police force in their area (Charron et al.,

2019b).1 It is important to note that all questions specifically refer to the region of the respondent.

One potential threat is that some of the NUTS areas do not precisely coincide with local

administrative units and in some countries local administrative units may not be very powerful.

However, NUTS 2 regions are devised to overlap with administrative regions as much as possible

and even in more centralized countries previous studies found substantial regional variation. This

measure thus accounts for the nestedness of the regional variation in quality of governance within

national institutions.

To measure the entrepreneurship specific regulatory framework we use the composite indicator

‘Regulatory framework for starting a business’ from the RES (Léon et al., 2016). This consists of

the following four measures: number of days to start a business, difficulties encountered when

starting a business, the barriers to entrepreneurship, and the ease of doing business index (for a

more detailed overview of all the indicators see Table A1 in the appendix). Using a combination

of general and entrepreneurship specific institutions is a significant improvement over the

operationalization of formal institutions as implemented by Stam and Van de Ven (2020).

3.5 Entrepreneurship culture

1 Whenever possible the word for region in the survey was replaced by the relevant administrative region in the

country e.g. Bundesland in Germany.

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The next element of entrepreneurial ecosystems, culture, represents an informal institution.

Specifically, how much entrepreneurship is valued and stimulated in a society (Fritsch and

Wyrwich, 2014). The cultural context can have a substantial effect on entrepreneurship by

influencing the aspirations of entrepreneurs and whether people are likely to become an

entrepreneur at all (Hayton et al., 2002). To measure entrepreneurship culture we use the RES

indicator for entrepreneurial culture, which consists of five components: entrepreneurial

motivation, cultural and social norms, business and entrepreneurship education, importance to be

innovative and creative, and trust in others. However, we exclude the entrepreneurship education

measure as we deem this measure to be more fitting for the talent element. These components were

measured by two large surveys: the Global Entrepreneurship Monitor (Bosma and Kelley, 2019)

and the European Social Survey (Norwegian Center for Research Data, 2014).2

3.6 Networks

When actors in a region are well connected in networks this allows information, labour and

knowledge to flow to firms which can use it most effectively (Malecki, 1997). Networks are

essential for entrants as it helps new firms to build social capital, which firms can leverage to get

access to resources, information and knowledge (Eveleens et al., 2017; van Rijnsoever, 2020). The

connections between firms can be measured through their cooperation projects. Our focus on

entrepreneurship entails that we specifically want to measure cooperation on innovative projects.

Therefore, we measure networks as the number of Small and Medium Enterprises (SMEs) with

innovation cooperation activities as percentage of all SMEs in a specific region. The focus on

innovation projects means this measure captures the kind of productive collaboration that is likely

to contribute to entrepreneurial output. In addition, the size of SMEs (enterprises with between 10

and 250 employees) matches with our focus on entrepreneurial growth since it does not include

micro firms (less than 10 employees) which are less relevant for our output measure. Larger firms

2 Stam and Van de Ven (2020) use the number of new firms per 1000 inhabitants as an alternative measure of culture.

We initially aimed to combine our current indicator with this data. However, there is (not yet) a harmonized dataset

on this variable for all European NUTS 2 regions and we thus had to use a combination of OECD, Eurostat, and

national statistics offices to construct this variable (see Table A1). These data sources were not consistent in their

definitions and data demarcations. Hence, we deemed the validity of this alternative measure to be questionable and

we excluded this measure from our analyses. We did perform a robustness test in which we combined the birth rate

of new firms with our current culture measure. The results of our analyses remained largely identical.

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are also excluded from this measure, mainly because almost all large firms participate in some

cooperation activities so this does not provide relevant information. Stam and Van de Ven (2020)

use a similar measure in their study. We use the data from the RIS, complemented with the

European Innovation Scoreboard for countries with only one NUTS 2 region. The RIS and EIS

base their data on the Community Innovation Survey (Arundel and Smith, 2013).

3.7 Physical infrastructure

Physical infrastructure is essential for economic interaction between actors and thus essential for

entrepreneurship as well (Audretsch et al., 2015). In this highly digital world not only physical

infrastructure enables this interaction but also digital infrastructure. Digital infrastructure provides

the opportunity to meet other actors, even if they are not in close physical proximity. Therefore, it

is important to include this when creating an empirical measure of infrastructure. For our indicator

we follow the approach of the RCI which uses the accessibility by road, accessibility by railway

and number of passenger flights to measure the physical (transportation) infrastructure of a region

(for details see Table A1). To this we add a measure for the digital infrastructure of a region, which

is the percentage of households with access to internet and also available in the RCI (Annoni and

Dijkstra, 2019).

3.8 Finance

An important condition for starting a new firm and growing an existing firm is access to capital

(see e.g. Kerr and Nanda, 2009; Samila and Sorenson, 2010). We measure the availability of capital

with two indicators from the Regional Ecosystem Scoreboard (RES): the availability of venture

capital and the availability of bank loans for capital investments (Léon et al., 2016). The data is

taken from the RES survey which measures the perceived availability of capital on a 5-point scale.

Venture capital is defined as equity not noted on the stock market including replacements and

buyouts. It would arguably be better to have the actual amount of venture capital in a region.

17

However, although this data is available on a national level from the EIS, there is not sufficient

regional data on the actual availability of venture capital.3

3.9 Leadership

Leadership in an entrepreneurial ecosystem is necessary to provide the actors in the ecosystem a

certain direction or vision to work towards and can make the ecosystem function more effectively

(Normann, 2013). Leadership can be provided by individual leaders but also by collaborative

efforts that try to guide the system in a certain direction. Since leadership is an intangible concept

it is quite hard to measure and remains understudied (Sotarauta et al., 2017). In our study we

operationalize leadership as the number of project coordinators of Horizon2020 innovation

projects in a region4. We thus follow the approach of Stam and Van de Ven (2020) who use the

number of innovation project leaders as their operationalization for leadership. To construct this

variable we use the CORDIS database which, after removing duplicates, contains data on 23,693

innovation projects that are subsidized as part of the Horizon 2020 program of the European Union

(CORDIS, 2019; European Commission, 2019). We then use the geocoding approach outlined in

section 3.3 to create our leadership indicator, the number of innovation leaders per 1000

inhabitants.

3.10 Talent

Human capital (or talent) encompasses the skills, knowledge and experience possessed by

individuals (Stam and Van de Ven, 2020). Human capital is a critical input for entrepreneurship

and has been shown to be linked to new firm formation (see e.g. Acs and Armington, 2004; Glaeser

et al., 2010). It is clearly quite a broad concept which asks for several empirical measures to

properly cover the different facets. We break human capital down into two different components,

general human capital and entrepreneurship-specific human capital (Becker, 1964; Rauch and

3 To test the robustness of our measure we performed a correlation between the actual venture capital data for the

UK and DE with the RES data which results in a correlation of only 0.40. 4 Horizon 2020 is the research and innovation program funded by the European Commission. It encompasses

private-public partnerships working on innovation projects with the aim to stimulate economic growth in the

European Union (European Commission, 2019).

18

Rijsdijk, 2013). General human capital is not directly related to a certain job (Rauch and Rijsdijk,

2013). To compute the human capital indicator we use data from the RES which offers an extensive

measure of education and skills (Léon et al., 2016). The general indicator includes the percentage

of population having completed tertiary education, the percentage share of companies providing

vocational training and the percentage of population aged 25-64 that participates in education or

training (lifelong learning).

Entrepreneurship specific human capital is directly related to start-up activities (Brüderl et al.,

1992; Rauch & Rijsdijk, 2013). We include five measures, measuring entrepreneurship and

business education, innovative skills training given at companies, creative skills, technical skills,

and e-skills. The inclusion of digital skills is highly relevant since digital literacy is almost essential

for working in any type of enterprise in the current digital society. In addition, a lot of innovation

nowadays involves some digital aspect. The talent measure we use is a significant improvement

over earlier papers which almost solely focused on formal education.

3.11 Knowledge

The creation of new knowledge by either private or public organizations provides new business

opportunities (Kim et al., 2012; Qian et al., 2013). It is therefore an important source of

entrepreneurship. We measure this element with intra-mural R&D expenditure as a share of the

total Gross Regional Product (GRP). This measure includes R&D spending in both public and

private sectors. The data for this variable is available in both the Regional Competitiveness Index

(Annoni and Dijkstra, 2019) and Regional Innovation Scoreboard (Hollanders et al., 2019). We

chose to use the data from the RCI as this is available at the NUTS 2 level for a larger number of

regions.

3.12 Demand

The purchasing power and potential demand for goods and services is important for entrepreneurs,

since it will only be interesting to market new products if the population has the financial means

to buy them. Several studies have shown that market growth increases firm entry (Eckhardt and

19

Shane, 2003). Even though most firms nowadays serve larger markets than just those in their own

region, it will be important for start-ups to have a potential regional market which they can easily

access (Cortright, 2002; Reynolds et al., 1994; Schutjens and Stam, 2003). We measure the

demand using data from the RCI which combines three measures to create an indicator for market

size (Annoni and Dijkstra, 2019). The measures are disposable income per capita, potential market

size expressed in GRP and potential market size expressed in population.

3.13 Intermediate services

Intermediate services or producer services can help producers to start a new enterprise and market

an innovation. This support can substantially lower entry barriers of new entrepreneurial projects

and speed up the introduction of innovations (Howells, 2006; Zhang and Li, 2010). For this

element we compose an indicator based on two measures. Similar to the talent and formal

institutions element we combine a general and a specific measure. We operationalize the general

measure as employment in knowledge intensive market services, which represents the general

availability of intermediate services. The required data is available in Eurostat.

For the specific measure we look at incubators and accelerators as intermediate service providers.

These are organizations specifically aimed at helping people with innovative ideas start their own

companies. Incubators and accelerators normally provide various services such as access to

networks of entrepreneurs and training in business skills (Cohen et al., 2019; Eveleens et al., 2017;

van Weele et al., 2017). In addition, incubators are an important form of intermediate services

since they also act as developers of entrepreneurial ecosystems (Van Rijnsoever, 2020). To identify

the population of incubators we scraped a total of 950 incubators and accelerators from the

Crunchbase website. We then use the geocoding approach outlined in section 3.3 to determine the

number of incubators per 1,000 inhabitants.

Several studies have shown that incubators and accelerators can significantly contribute to the

success of start-ups (see for recent reviews Ayatse et al. (2017) and Eveleens et al. (2017)). Since

these organizations are put in place to support entrepreneurs and can improve the performance of

new firms, it is important to include them in the analysis. Note that we measure the prevalence of

20

intermediate services in general, and incubators and accelerators in particular, but not the quality

of these services per se.

3.14 Entrepreneurial Ecosystem Index

The ten elements of the entrepreneurial ecosystem which we have operationalized in the previous

sections are then used to calculate an index. This is done using the same method as applied in Stam

and Van de Ven (2020). We have first standardized the composite indicators which we have

calculated. This ensures that all elements get similar weights in the creation of the index. So the

calculation of the index is based on the assumption that all ten elements are of equal importance

in the ecosystem. Future research could investigate whether the index can be improved by giving

certain elements more weight than others. Subsequently, to enable normalizing the standardized

values we first take the inverse natural log of the standardized values, this is necessary because

normalizing requires division by the mean which is 0 after standardization. We then normalize the

element values by setting the European average of each element to 1 and by letting all other

regional values deviate from this. If an element in a region performs less than average this results

in a value between 0 and 1, above average performing regions have a value above 1. This allows

us to compute an index value based on the ten elements and compare the quality of different

entrepreneurial ecosystems. We calculate the index values in three ways. First, in an additive way

where (E1 + E2 +…En). Regions with an average value on each element will thus score an index

value of 10. Second, to better account for the systemic nature of the entrepreneurial ecosystem we

also calculate the index in a multiplicative manner (E1*E2*…En). The disadvantage of the

normalization around 1 in both these indices is that values above 1 have a stronger effect on the

index than below average values (which are between 0 and 1). We therefore take the natural

logarithm to let the values oscillate symmetrically around 0, this logarithmic way (log(E1) +

log(E2) +….log(En)) is our third index value.

3.15 Output

The output of the entrepreneurial ecosystem is productive entrepreneurship (see Fig. 1). This kind

of entrepreneurship contributes to net output of the economy and consequently leads to aggregate

21

value creation, which is the outcome of the system (Baumol, 1990). Previous research has shown

that productive entrepreneurship indeed has a strong effect on economic growth and job creation

(Criscuolo et al., 2014; Haltiwanger et al., 2013; Stam et al., 2011; Wong et al., 2005). Productive

entrepreneurship is a subset of total entrepreneurship and thus requires another measure than, for

example, the total number of new firms.

In this study we take the number of new enterprises, founded less than 5 years ago, that are

registered in Crunchbase as our measure for entrepreneurial output (Crunchbase, 2019; Dalle et

al., 2017). Crunchbase predominantly captures venture capital oriented/innovative entrepreneurial

firms and largely ignores companies without a growth ambition and is thus a good source for data

on productive entrepreneurship (Dalle et al., 2017). We choose this specific timeframe to ensure

that we select firms who experience their growth phase during the same time period (2015-2019)

as most of our indicators are measured (see Table A1). This time period also helps to limit our

sample towards entrepreneurial firms as Crunchbase does not exclusively contain entrepreneurial

firms. The data on Crunchbase mostly comes from two channels, a community of contributors and

a large investor network. In addition, the data is validated with other data sources using AI and

machine-learning algorithms. Nevertheless, Crunchbase is known to have a bias towards firms

receiving capital investments. We downloaded the data from Crunchbase. A limitation of the

Crunchbase dataset is that it is uncertain if the coverage of start-ups is equal among the different

countries. Overall, we find that around 0.2% of all new European firms are registered in

Crunchbase. 5 This varies between 0.003% and 1.5% and follows a (zero-inflated) normal

distribution.6 We further acknowledge that not all start-up entrepreneurs are innovative (cf. Autio

et al., 2014), and are also aware that our measure of entrepreneurial output does not capture all

innovative activity in the economy. Nevertheless, Crunchbase is currently the most comprehensive

dataset available to measure high growth entrepreneurship as an entrepreneurial output (Dalle et

al., 2017). Crunchbase is increasingly used for academic research (Dalle et al., 2017; Nylund and

Cohen, 2017). We also explored using the ORBIS data of Bureau Van Dijk as an alternative

5 The data sources for the number of new firms in each country is outlined in table A1. 6 However, one specific region (UKI3 – Inner London West) has an extreme value of 11,3%. This extreme value is

also reflected in our Crunchbase output measure and to achieve a normal distribution for the regression analyses we

performed a Tukey transformation (λ = 0.175) on this variable. In the next section we discuss the remaining

transformations in our data preparations.

22

(Bureau van Dijk, 2020; Dalle et al., 2017). However, we elected not to do this for two reasons.

The serial correlation between the different years in the database was very low. The data also

contained disproportionally large differences between countries which were hard to render and

would thus impede cross country regional comparisons.

In addition to the Crunchbase output measure we use a measure for extreme entrepreneurial output

in the form of unicorns, which are entrepreneurial ventures valued above $1 billion. We used the

2018 CB Insights unicorn dataset and identified a total of 24 unicorns in Europe (CBInsights,

2018). We then used the geocoding procedure to allocate these unicorns to a total of 17 NUTS 2

regions. As such, unicorns are a very rare and selective form of high growth entrepreneurship that

is only present in a small number of regions. Besides unicorns being a very rare event, the value

of unicorns as a measure of productive entrepreneurship has also been a topic of discussion, which

is why we only use this as an additional output measure (see for example Aldrich and Ruef, 2018;

Economist, 2019).

3.16 Extreme values

Since the European Union covers a large and diverse set of regions, the data show a lot of variety.

In particular, for the measures of knowledge, intermediate services, leadership and output there

are a few regions that have very high values (up to 17 times the standard deviation). Even though

this variation is plausible, these outliers do disproportionally influence the correlation results and

regression results. Most importantly, for the regions that score extremely high on one particular

indicator, the index for the quality of the Entrepreneurial Ecosystem is disproportionally

influenced by one indicator. This does not reflect the systemic nature of entrepreneurial

ecosystems as argued in the existing academic literature (Spigel, 2017; Stam, 2015). Therefore,

we performed two transformations on the data to provide better interpretable results. First, before

the standardization of the composite indicators we cap the maximum value at four standard

deviations of the mean (for more information on the standardization procedure see section 3.14 on

index calculation). 7 In practice this means that we change the values for UKI3 (Inner London-

7 We also implemented a cap at 3 standard deviations, this required capping a total of nine regional values but did

not significantly change our further findings.

23

West) of the Crunchbase output, leadership, and intermediate services measures, for DE91

(Braunschweig) of knowledge (as a result of the high R&D intensity), and for DK01 (Hovedstaden)

of leadership. Without these transformations the high deviation of these values skew the outcomes

of the normalization process in such a way that only a few regions achieve above average scores.

Second, we set the maximum score for any single element to 5 in order to prevent a

disproportionate influence of strong performing ecosystem elements on the overall index. We

perform a number of robustness checks on the construction of our index which we discuss in

section 4.5.

24

4. Quantifying and qualifying entrepreneurial ecosystems in Europe

4.1 Descriptive statistics

The descriptive statistics of the empirical measures for the ten ecosystem elements, entrepreneurial

outputs, and the index scores are shown in Table 2. In total our data covers 274 NUTS 2 regions

divided over the 28 EU member states.

Table 2

Descriptive statistics

N Mean

Standard

Deviation Minimum Maximum

Crunchbase

output 274 0.852 0.995 0.0091 5.000 (36.627)

Unicorn output 274 0.088 0.436 0.000 5.000

Formal

institutions 274 1.000 0.778 0.071 3.333

Culture 274 0.977 0.922 0.013 5.000 (10.229)

Networks 273 0.984 1.142 0.117 5.000 (6.070)

Physical

Infrastructure 273 0.907 1.065 0.058 5.000 (8.411)

Finance 274 1.000 0.770 0.067 5.000 (5.061)

Leadership 274 0.594 0.991 0.154 5.000 (49.816)

Talent 274 0.955 1.002 0.029 5.000 (10.902)

Knowledge 274 0.721 1.031 0.108 5.000 (33.48)

Demand 274 1.000 0.939 0.032 4.667

Intermediate

services 274 0.591 0.891 0.060 5.000 (99.945)

EE index additive 273 8.713 5.517 0.990 31.021

EE index mult 273 52.598 572.851 0.000 9295.560

EE index log 273 -6.340 6.436 -24.475 9.138

25

Notes: The uncorrected maximum value of each element is presented between brackets. We do not have

data for all elements for Aland, a small island region of Finland, so the total number of regions for which

we calculate the index is 273.

We standardize all variables relative to the EU average to account for the different scales of

measures. The mean is calculated based on the corrected maximum values and can therefore be

below one. We also see a large variation around the mean for several variables, from regions with

less than 2 percent of the EU average to regions who have over 160 times the average value. These

findings are nevertheless in line with our expectations since we study regions across different

countries and levels of development. Looking at the three index values that we calculated using

the methods of Stam and Van de Ven (2020), we find that the difference between the smallest and

largest value for the multiplicate index is a factor 1016. This difference is disproportionately large

in comparison with the actual variation in the data as a result of the multiplicative way of

calculating the index. Hence, we deem the external validity of the multiplicative index to be

insufficient and instead use the additive and the logarithmic indices in our further analyses. We

primarily present the additive index due to the intuitiveness of its interpretation.

4.2. Entrepreneurial Ecosystem Index

To provide a closer insight in the strongest and weakest regions in Europe according to the

Entrepreneurial Ecosystem Index, we display the scores for the ten highest (Fig. 2) and lowest

ranking (Fig. 3) regions. The highest scoring regions are, as expected, mainly Western European

and densely populated while the lowest scoring regions are mainly Bulgarian and Greek rural

regions. To look at the different Entrepreneurial Ecosystems in more detail, Fig. 4 shows the map

of Europe with all NUTS 2 regions colored based on the value of the Entrepreneurial Ecosystem

Index. The highest index values can be found in European capital regions, such as Berlin, Paris,

Vienna and London. Many regions in Eastern Europe show very low index values as do some of

the more rural areas in Spain. The map supports the claim that there is a substantial difference

between urban and rural areas. Most of the high-scoring regions include large cities. In addition,

we see the Northern European countries scoring very high.

26

Fig. 2

NUTS 2 regions with the highest Entrepreneurial Ecosystem Index scores.

Fig. 3

NUTS 2 regions with the lowest Entrepreneurial Ecosystem Index scores.

27

Fig 4.

Map of NUTS 2 regions showing Entrepreneurial Ecosystem Index (273 regions are divided

among groups of equal size).

28

4.3. Interdependence between entrepreneurial ecosystem elements

Table 3 shows the correlations between the different elements of the entrepreneurial ecosystem,

the outputs and the index. In general, we see high, positive and significant correlations between

almost all of the elements of the ecosystem. 8 Only for finance there is one small negative

correlation with networks (and an insignificant negative correlation with intermediate services).

The strong positive correlations illustrate the interdependencies in the entrepreneurial ecosystem.

This corresponds to the results shown in Stam and Van de Ven (2020) and confirms the systemic

nature of entrepreneurial ecosystems. Considering the entrepreneurial output measures, we see

positive and significant correlations with almost all elements, and with the entrepreneurial

ecosystem indices we constructed. This supports the proposition regarding upwards causation,

stating that the ecosystem elements influence the occurrence of productive entrepreneurship.

Table 3.

Correlation matrix (correlation coefficient is indicated by colour and the significance level by size,

only correlations that are significant at 5% level are shown)

8 For an overview of the numeric correlation coefficients with p-values see Table A2.

29

The interdependencies between the ten elements are shown in the form of a network plot in Fig. 5.

Physical infrastructure, formal institutions and also demand take the most central position in the

interdependence web. This central role is supported by the finding that when looking at

interdependencies with a correlation above 0.5 where physical infrastructure and formal

institutions each have five interdependencies (Fig. 6a), and again confirmed by the

interdependency web with correlations above 0.6 (Fig. 6b). This provides an indication for a

potential role of these elements as fundamental conditions of the entrepreneurial ecosystem.

Fig. 5.

Interdependence webs of entrepreneurial ecosystem elements with blue lines showing positive and

red lines negative correlations. The edge weight is defined based on the correlation strength.

30

Fig. 6a. and 6b.

Interdependence webs of entrepreneurial ecosystem elements with correlations above 0.5 (left) and

0.6 (right)

To further explore the interdependencies and to test the reliability of using the sum scores method

to calculate the index, we performed principal component analyses (PCA) on the 10 individual

elements. The results are presented in Table 4, the first component explains 36.1% and has loadings

of 0.23 or higher for all components except finance (0.08). The three elements with the highest

loadings are physical infrastructure (0.43), formal institutions (0.40), and demand (0.40). This

result confirms our findings from the interdependency graphs which show a strongly connected

set of elements with a central role for these elements. The second component, which explains an

additional 14.6% of the variation, has loadings of 0.24 or higher for all components except

leadership (0.14), intermediate services (0.09), and formal institutions (0.06). Similarly, for the

third component six elements have loadings above 0.30, while physical infrastructure (0.15), talent

(0.20), knowledge (0.14) and demand (0.19) have lower loadings. The results of the PCA thus

confirm the strong interdependencies between the entrepreneurial ecosystem elements and thereby

validates our approach of building an index.

31

Table 4.

Principal components analysis

PC1 PC2 PC3

Proportion of

Variance 0.361 0.146 0.117

Standard Deviation 1.900 1.206 1.083

Cumulative Variance 0.361 0.507 0.624

Formal institutions -0.396 0.063 -0.496

Culture -0.324 0.244 -0.371

Networks -0.245 -0.331 -0.309

Physical

infrastructure -0.434 -0.310 0.148

Finance -0.082 0.436 0.379

Leadership -0.286 0.139 0.311

Talent -0.235 0.473 -0.197

Knowledge -0.248 0.402 0.138

Demand -0.396 -0.356 0.189

Intermediate -0.357 -0.094 0.406

As a second alternative approach to classify regional economies we perform a cluster analysis on

the ten ecosystem elements. We use the k-means clustering method which minimizes the total

intra-cluster variation (sum of squared errors) using Euclidean distance measures for an a priori

fixed number of clusters (Tan et al., 2018). The K-means clustering technique is the most popular

clustering technique and was originally proposed by MacQueen (1967). The number of clusters is

a parameter that has to be set by the user. After considering the total intra-cluster variation, the

average silhouette of clusters, the gap statistic, and the interpretability of the outcomes we selected

the approach with three clusters. The results show a large first cluster which includes the low-

performers and forms the largest group, including for example Athens, Budapest and Sicily. The

second cluster forms a middle group and includes Manchester, Cologne and North Brabant

(including Eindhoven). Finally, the third cluster is the smallest group with high performing regions

including Berlin, London, and Brussels. The average index values for these three clusters are, as

32

shown in Table 5, in line with our expectations. A particularly interesting finding is that the regions

in the third cluster have clearly higher outputs than the middle and laggard group, both in terms of

Crunchbase and unicorn output.

Table 5.

Summary statistics of index and output by cluster

4.4. Entrepreneurial Ecosystem Index and entrepreneurial output

After discussing the creation and reliability of the Entrepreneurial Ecosystem Index we now use

regression analysis to study if regions with better ecosystems indeed have higher entrepreneurial

outputs. The results of the regressions with the indices as independent variable and the Crunchbase

output as dependent variable are shown in Table 6.9 The results of the regression analyses with the

unicorn output as dependent variable are consistent with the findings reported in Table 6. However,

we chose not to report these results because of the limited number of regions with unicorn

observations (17 out of 274). In the linear regressions of the additive and log index in column 1

9 As an additional robustness test, we also performed the regression analyses using the principal components (see

section 4.3). The results of these regressions showed nearly identical results, serving as evidence for the robustness

of our results.

33

and 3 of Table 6 both show a positive and significant coefficient (p<0.001). The R2 is higher for

the regression with the additive index at 0.323.

Table 6.

Regression results of the additive and logarithmic index on the Crunchbase output variable

including non-linear effects

Crunchbase output (1) (2) (3) (4)

EE index additive 0.103*** -0.023

(0.013) (0.033)

EE index additive

squared 0.005***

(0.002)

EE index log 0.077*** 0.172*** (0.010) (0.024)

EE index log

squared 0.007***

(0.002)

Constant -0.042 0.480*** 1.343*** 1.392*** (0.121)) (0.137)) (0.120) (0.116)

Observations 273 273 273 273

R2 0.323 0.375 0.249 0.367

Adjusted R2 0.320 0.370 0.247 0.362

F Statistic 129.258*** (df = 1;

271)

80.895*** (df =

2; 270)

90.082*** (df = 1;

271)

78.127*** (df =

2; 270)

Notes: Clustered standard errors at country level in parentheses. * p<0.05 ** p<0.01 ***

p<0.001

The graph in Fig. 7 shows that the relation between the index and entrepreneurial output does not

appear to be linear, since an increase in performance on the index goes together with a

disproportionately large increase in the number of high growth entrepreneurial ventures. To

capture this nonlinearity in the relation between the quality of an entrepreneurial ecosystem and

its entrepreneurial outputs, we added quadratic effects to the model. These effects are significant

(p<0.001) and show that the relation between the index and the entrepreneurial output is indeed

nonlinear.

34

However, the convex relation between the index and output means that adding quadratic effects

forces an inverse U-shaped quadratic curve on the observations. This is an unintended side effect

of using quadratic effects in a linear regression and is in our case evident from the negative (but

insignificant) sign of the linear coefficient for the additive index. Using the two lines test of

Simonsohn (2018) we find that there is in fact no inverse U shape relation between the index and

output. An increase in the index value never has a negative effect on entrepreneurial output.

Fig. 7 Scatter plot with line showing the fitted values of the piecewise linear regression

Therefore, to better capture the nonlinear relationship between the index and output we instead

perform a piecewise linear regression. This allows breakpoints in the line that is fitted to the data.

The results are presented in Figure 7 and Table 7. The breakpoint that optimizes model fit for the

additive index is located at an index score of 19.10 At this point the slope quite sharply increases

from 0.07 to 0.35. For both the first and the second line we find a positive and significant relation

between the index and entrepreneurial output (p<0.001). The cut-off in the regression further

shows that at the high end of the index there is a small group of regions with very high performance

regarding entrepreneurial output. This corresponds with our findings in the cluster analysis

10 We get a very similar result when we allow for a structural break in the line. The main method shown assumes a

continuous relation and uses the R package ‘segmented’ (Muggeo, 2008).

35

presented above. The results of the piecewise linear regression with the log index are qualitatively

similar.

Table 7.

Piecewise linear regression Crunchbase output

(1) (2)

EE index additive 0.070***

(0.010)

Difference slope EE

index additive

0.282***

(0.083)

EE index log 0.043***

(0.011)

Difference slope EE

index log 0.593***

(0.076)

Constant 0.183* 1.013*** (0.108) (0.140)

Observations 273 273

R2 0.3933 0.4392

Adjusted R2 0.3865 0.433

F Statistic 58.125***(df=3;269) 70.228***(df=3;269)

Notes: Clustered standard errors at country level in parentheses. * p<0.05 **

p<0.01 *** p<0.001

The scatter plot (Fig. 7) shows several regions that do not fit well with the plotted line. When we

look closer we see that there are some regions with very high entrepreneurial output and low index

values. The regions in the upper left corner are for example Malta, Luxembourg and Cyprus, all

known for very favourable tax regulations, which in previous studies have been shown to increase

the amount of high growth entrepreneurship (Guzman and Stern, 2015). On the other hand, regions

that have high index values but low entrepreneurial output are for example several outer London

regions.11 These are all regions with good conditions for entrepreneurship but located very close

to more ‘vibrant’ entrepreneurial areas which are deemed more attractive locations (e.g. Inner

11 For some regions this also has to do with the fact that the data for some indicators is measured at the NUTS-1

level, as is described in table A1.

36

London). So while not many high growth firms are active in these regions, they have a high index

score as they benefit from the successful ecosystems nearby.

Since we compare regions in different countries, it is important to check whether the index does

not just capture between country differences but also has explanatory power within countries. We

therefore run a multilevel analysis with country-specific intercepts and our Entrepreneurial

Ecosystem Index. The results of the multilevel analysis are presented in Table 8. The index

variables still show a significant and positive relation with the entrepreneurial output (p<0.001).

Adding country specific intercepts improves the model as evidenced by an increased R2 as well as

the likelihood ratio tests. The random effects in the bottom of the table show the regional variation

(σ2) and the variation between countries (τ00). The strong coefficient estimates for the index show

that even when we compare regions within countries the regions with a higher index value have a

significantly higher entrepreneurial output. The high regional variation in entrepreneurial output

and index values supports our choice to focus on the regional level when studying entrepreneurial

ecosystem performance.

Table 8.

Multilevel analysis

Crunchbase output

(1) (2)

EE index additive 0.147 ***

(0.010)

EE index log

0.151 ***

(0.011)

Constant -0.278 *

(0.138)

2.048 ***

(0.167)

Random Effects

σ2 0.38 0.38

37

τ00 0.23 country 0.44 country

ICC 0.38 0.54

N 23 country 23 country

Observations 268 268

Marginal R2 0.522 0.534

Conditional R2 0.702 0.784

Notes: This regression excludes countries that exist of only a single NUTS 2 region, which are

Luxembourg, Malta, Estonia, Cyprus and Latvia. Standard errors in parentheses. * p<0.05; **

p<0.01; *** p<0.001

4.5. Robustness of index construction

In addition, we performed four robustness checks on the sensitivity of our index to different

calculation methods and to extreme values. First, we do not execute any of the modifications

outlined in section 3.16. This robustness test actually results in an R2 of 0.99 (Table A3). However,

the results are now strongly influenced by the extreme values measured in several regions that we

discussed in the methodology section. Therefore, we performed a second robustness test which

follows the approach outlined in the method section but instead removes those regions with a value

more than four standard deviation from the mean. This concerned Inner London-West (as a result

of a high number of incubators, leadership, and Crunchbase start-ups per population),

Braunschweig (as a result of the high R&D intensity) in Germany, and Hovedstaden (as a result

of leadership) in Denmark (Table A4). Since we prefer not to discard observations of which the

data is reliably measured, we also performed the regression with all observations after

transforming the data. We transformed the data using the Tukey transformation for all the variables

with a huge range of variation (standard deviations above 4), instead of only the output variable as

we did in the main analysis (Tukey, 1957) (Table A5). The result of this transformation is a

distribution of data which is close to a normal distribution, thus reducing the standard deviations

from the variables with extreme values. Fourth, we used a categorical approach to create each of

38

the index elements and the output by using quantiles to give each element a score from 1-10. The

index then has a minimum value of 10 and maximum value of 100 (Table A6). The findings of all

four robustness tests are consistent with those presented in the main analysis, indicating the

robustness of our chosen approach of calculating our index.

Finally, we find that many of the top performing regions are regions in which a capital city is

located (Fig. 3). To test whether the explanatory power of our index holds after controlling for the

influence of capital cities on the output variable we run the regressions with a capital city indicator

added, which is a dummy variable indicating whether a region contains a capital city (no = 0, yes

= 1). The results are displayed in Table A7 and indeed show that capital regions perform

significantly better than non-capital regions (p<0.001). Nevertheless, the effect of the

Entrepreneurial Ecosystem Index remains significant (p<0.001) and only shows a small decrease

in coefficients.

39

5. Discussion and conclusions

This paper discussed and applied new metrics and methodologies to quantify and qualify

entrepreneurial economies, applying this to European regions. The objective of this paper was to

quantify and qualify regional economies with an entrepreneurial ecosystem approach.

Quantification involves measuring its ten key elements with a wide range of data sources.

Qualification involved developing a network methodology that provides insight into the extent to

which the elements are interdependent, the construction of an Entrepreneurial Ecosystem Index to

capture the overall quality of entrepreneurial economies, and relating this to entrepreneurial

outputs.

We have answered three main research questions. First, how can we compose a harmonized data

set with which the quality of key elements of entrepreneurial economies can be measured? We

built on prior entrepreneurial ecosystem research that developed a universal set of constructs for

each element of entrepreneurial economies, and composed a harmonized data set to measure these

constructs in the context of 274 regions in the 28 countries of the European Union. We sourced a

wide variety of data and constructed a rich dataset. However, not all elements could be measured

in a fully satisfactory way. Often, more adequate data is available, but not at the same regional

level or for all regions. An example is the data we used for the finance element: we prefer to have

a composite indicator that includes objective data on the supply of different types of finance,

including bank loans for SMEs, debt and equity crowdfunding, and regular equity funding. This is

not yet available for all European regions. Another example is the data we used for the networks

element. Even though the data provided on the engagement of SMEs in innovative collaborations

is very informative, additional network data on collaborative networks and influencer networks,

for example based on Twitter or LinkedIn data, could enrich the diagnosis of entrepreneurial

ecosystems (Eveleens, 2019). This kind of network data would also allow for more refined

measures of network diversity and density. For some elements there is no straightforward data

available and new variables had to be constructed. This is the case for leadership, for which others

(Stam and Van de Ven 2020) have constructed country specific indicators, and we have created a

pan-European indicator. However, even though this indicator provides information of the

prevalence of (public-private) leadership behaviour in regions, improvements can be made to

40

measure leadership that is relevant for the quality of entrepreneurial economies, for example with

the prevalence of public-private leadership in regional partnerships (see Olberding, 2002). Overall,

there is a significant trade-off between getting richer context-specific data (often only available in

a relatively small number of regions) and getting widely available, harmonized data, which enables

comparisons between regions.

Second, to what extent and how are the elements of entrepreneurial economies interdependent?

We performed correlation analyses, principal component analyses, and developed a network

methodology to visualize the interdependencies between elements. These analyses revealed that

entrepreneurial economies are systems with elements that are highly interdependent, and are not a

collection of isolated factors and actors. Our analyses also showed that in particular formal

institutions and physical infrastructure provide foundational conditions for entrepreneurial

economic systems.

Third, how can we determine the quality of entrepreneurial economies? We answered this question

by composing an entrepreneurial ecosystem index and analysing its relation to entrepreneurial

outputs. We used multiple data sources and metrics to determine entrepreneurial outputs at the

regional level. We also used novel methods including web scraping and geocoding to determine

the entrepreneurial outputs in the form of the number of high-growth firms in a region. We have

shown that it is possible to measure the quality of entrepreneurial economies, in a way that has

external validity: showing a ranking of European regions and range of variation that is credible.

Our analyses reveal the wide-ranging quality of entrepreneurial ecosystems in Europe, showing a

large group of substantially lagging regions, while a smaller group of leading regions is clearly

ahead of the European average. We also tested the internal validity using the fact that high quality

entrepreneurial economic systems are more likely to produce emergent properties, which we

measured with indicators of productive entrepreneurship. The prevalence of innovative new firms

is strongly positively and statistically significantly related to quality of entrepreneurial ecosystems,

as captured with differently constructed entrepreneurial ecosystem indices. This upward causation

confirms earlier findings of Stam and Van de Ven (2020) and Vedula and Kim (2019). This internal

validity should be tested more carefully, in particular with other (more direct) tests of causality,

with longer time lags between changes in the quality of entrepreneurial ecosystems and the

41

resulting entrepreneurial outputs, and with some quasi-natural experiments in which a set of

similar regions is confronted with substantially different changes in one or a few elements. Other

methods, including qualitative comparative analysis, could also play an important role in

improving our understanding of the workings of ecosystem.

Did we fulfil the policy promise of the entrepreneurial ecosystem approach? We developed

entrepreneurial ecosystem metrics to better measure entrepreneurial economies. These metrics

enable adequate diagnosis of (regional) entrepreneurial economies and also enables monitoring

economic change after policy interventions and other dynamics have changed the system. This

paper thus takes heed of the old carpenter’s adage “measure twice, cut once”, reducing policy

failures with better measurement tools.

There are nevertheless many opportunities for improvement of these metrics. Two directions

deserve substantial attention in follow-up research. First, we need to move from a comparative

static analysis to a dynamic analysis, and for this we need longitudinal datasets. This would make

it possible to better trace processes within entrepreneurial ecosystems (Spigel and Harrison, 2018),

and allow us to measure the distinct properties of complex systems that arise from

interdependencies, such as nonlinearity, emergence, tipping-points, spontaneous order, adaptation,

and feedback loops. Second, even though the European Union provides a wide variety of regions

to develop and test our entrepreneurial ecosystem metrics, these metrics need to be developed and

tested in other contexts as well, in large sets of regions in the US, Asia, Africa, and Latin America.

Statistical regions are not always overlapping with either the relevant jurisdictions or the spatial

reach of the causal mechanisms involved (for example as related to culture and the provision of

finance). Developing tailor made spatial units and taking into account the nestedness of elements

(cities, in regions, in countries) and neighbourhood effects is also a task for future research.

Scientific progress and societal impact are often achieved with better tools. In this paper we

developed entrepreneurial ecosystem metrics, with which entrepreneurial economies can be

quantified and qualified. These metrics enables researchers and practitioners to gain insight in, and

a better understanding of, these economies. Using measurement tools to capture and comprehend

the current state of the economy is a necessary condition for effective policy.

42

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51

Appendix

Table A1.

Description of indicator data sources

Element Indicators Measurement and description Source Geographical level Year

Formal

institutions

Corruption z-score based on survey answers Quality of Government

Index

Country-level IE and

LT, NUTS 1: BE,

DE, EL, SE, UK,

others NUTS 2

2017

Formal

institutions

Quality and

accountability

z-score based on survey answers Quality of Government

Index

Country-level IE and

LT, NUTS 1: BE,

DE, EL, SE, UK,

others NUTS 2

2017

Formal

institutions

Impartiality z-score based on survey answers Quality of Government

Index

Country-level IE and

LT, NUTS 1: BE,

DE, EL, SE, UK,

others NUTS 2

2017

Formal

institutions

Number of days for

starting a business

Absolute values World Bank Country 2015

Formal

institutions

Difficulties

encountered when

starting a business

Growth rate between 2009-2012 Flash Eurobarometer Country 2012

52

Formal

institutions

Barriers to

entrepreneurship

Composite indicator (complexity of

regulatory procedures, administrative

burden, protection of incumbents)

OECD Country 2013

Formal

institutions

Ease of doing

business index

Index based on several dimensions: starting

a business, dealing with permits,

registering property, credit access,

protecting investors, taxes, trade, contract

enforcement and closing a business

World Bank Doing

Business Report

Country 2011

Entrepreneurship

culture

Entrepreneurial

motivation

Percentage of early stage entrepreneurs

motivated by a desire to improve their

income or a desire for independence

Global

Entrepreneurship

Monitor

Country 2014

Entrepreneurship

culture

Cultural and social

norms

Rating: 1=highly insufficient, 5=highly

sufficient

Global

Entrepreneurship

Monitor

Country 2014

Entrepreneurship

culture

Innovative and

creative

Percentage of respondents that agree to: it

is important to think of new ideas and be

creative

European Social Survey NUTS 2 2014

Entrepreneurship

culture

Trust Survey question on scale 0-1: Most people

can be trusted

European Social Survey NUTS 2 2014

Entrepreneurship

culture

robustness

Birth of new firms Number of new firms per 1,000 inhabitants Eurostat, OECD and

national statistics offices

Country-level EL,

CY, MT, LU, NUTS

1: DE, UK, others

NUTS 2

2010-

2016

53

Networks Innovative SMEs

collaborating with

others

Percentage of innovative SMEs in SME

business population collaborating with

others

RIS & EIS (for countries

which are a NUTS 2

region) (also available

in RCI)

NUTS 1 for BE, UK,

FR and AT, others

NUTS 2

2016

Physical

Infrastructure

Accessibility via

road

Population accessible within 1h30 by road,

as share of the population in a

neighbourhood of 120 km radius

DG Regio NUTS 2 2016

Physical

Infrastructure

Accessibility via rail Population accessible within 1h30 by rail

(using optimal connections), as share of the

population in a neighbourhood of 120 km

radius

DG Regio NUTS 2 2014

Physical

Infrastructure

Number of

passenger flights

Daily number of passenger flights

accessible in 90 min drive

Eurostat /

Eurogeographics /

National Statistical

Institutes

NUTS 2 2016

Physical

Infrastructure

Household access to

internet

Percentage of households with access to

internet

Eurostat NUTS 2 2018

Finance Availability of

venture capital

Survey question: In the last 2 years, access

to venture capital in my region has been. [1

extremely difficult to 5 extremely easy]

Survey RES NUTS 2 2015

Finance Availability of bank

loans

Survey question: In the last 2 years, access

to bank loans for capital investments for

members of my cluster has been. [1

extremely difficult to 5 extremely easy]

Survey RES NUTS 2 2015

54

Leadership The presence of

actors taking a

leadership role in the

ecosystem

The number of coordinators on H2020

innovation projects per 1,000 inhabitants

CORDIS (Community

Research and

Development

Information Service)

NUTS 2 2014-

2019

Talent Tertiary education Percentage of total population that

completed tertiary education

Eurostat NUTS 1 for BE,

GER, UK , others

NUTS 2

2013

Talent Vocational training Percentage of companies that provide

initial vocational training

Eurostat Country 2010

Talent Lifelong learning Percentage of population aged 25-64

participating in education and training

Eurostat NUTS 1 for BE,

GER, UK , others

NUTS 2

2013

Talent Innovative skills

training

Availability of innovation training and

innovative skills coaching programs in last

two years

RES NUTS 2 2015

Talent Business and

entrepreneurship

education

Rating: 1=highly insufficient, 5=highly

sufficient

Global

Entrepreneurship

Monitor

Country 2014

Talent Technical skills Percentage of private enterprises with

employees with technical skills

RES Country 2010

Talent Creative skills Percentage of private enterprises with

employees with creative skills

RES Country 2010

Talent E-skills Percentage of individuals in active

population with high levels of e-skills

Eurostat Country 2014

55

New knowledge R&D expenditure Intramural R&D expenditure as percentage

of Gross Regional Product

Eurostat NUTS 2 2015

Demand Disposable income

per capita

Net adjusted disposable household income

in PPCS per capita (index EU

average=100)

Eurostat NUTS 2 2014

Demand Potential market

size in GRP

Index GRP PPS (EU population-weighted

average=100)

Eurostat NUTS 2 2016

Demand Potential market

size in population

Index population (EU average=100) Eurostat NUTS 2 2018

Intermediate

services

Incubators Percentage of incubators in total business

population

Own data NUTS 2 2019

Intermediate

services

Knowledge

intensive services

Percentage employment in knowledge-

intensive market services

Eurostat NUTS 2 2018

Productive

entrepreneurship

Innovative new

firms

Number of new firms registered in

Crunchbase in the last five years per 1,000

inhabitants

Crunchbase NUTS 2 2019

Productive

entrepreneurship

High-value new

firms (unicorns)

Absolute number of entrepreneurial firms

valued above $1 billion

CB Insights NUTS 2 2019

56

Table A2.

Correlation table

Formal

institutions Culture Networks

Physical

infrastructure Finance Leadership Talent Knowledge Demand Intermediate

EE index

add

EE index

log

Crunchbase

output

Formal

institutions

Culture 0.65****

Networks 0.67**** 0.30****

Physical

infrastructur

e

0.69**** 0.43**** 0.52****

Finance 0.13* 0.19** -0.05 0.19**

Leadership 0.36**** 0.17** 0.39**** 0.46**** 0.03

Talent 0.64**** 0.36**** 0.59**** 0.49**** 0.36**** 0.34****

Knowledge 0.50**** 0.31**** 0.40**** 0.56**** 0.35**** 0.58**** 0.57****

Demand 0.55**** 0.30**** 0.44**** 0.84**** 0.20*** 0.35**** 0.37**** 0.57****

Intermediate 0.38**** 0.24**** 0.45**** 0.61**** -0.01 0.62**** 0.32**** 0.42**** 0.47****

EE index add 0.82**** 0.58**** 0.71**** 0.82**** 0.32**** 0.56**** 0.72**** 0.71**** 0.73**** 0.62****

EE index log 0.80**** 0.58**** 0.68**** 0.85**** 0.34**** 0.60**** 0.73**** 0.76**** 0.75**** 0.64**** 0.98****

Crunchbase

output 0.47**** 0.27**** 0.46**** 0.54**** 0.07 0.74**** 0.47**** 0.46**** 0.35**** 0.73**** 0.62**** 0.65****

Unicorns 0.18** 0.14* 0.12 0.22*** 0.04 0.28**** 0.11 0.22*** 0.21*** 0.28**** 0.28**** 0.27**** 0.27****

Note: *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001

57

Table A3.

Regression with no transformation of extreme values

Crunchbase output

(1) (2)

EE index add 0.421***

(0.083)

EE index log 0.724

(0.159)

Constant -3.208*** 13.273

(0.799) (10.578)

Observations 273 273

R2 0.854 0.076

Adjusted R2 0.854 0.080

F Statistic 1,587*** (df = 1; 271) 23.6*** (df = 1; 271)

Notes: Clustered standard errors at country level in parentheses.

*p<0.05; **p<0.01; ***p<0.001

Table A4.

Regression excluding observations with extreme values

Crunchbase output

(1) (2)

EE index add 0.058***

(0.017)

EE index log 0.036**

(0.011)

Constant -0.166 0.568 ***

(0.117) (0.115)

Observations 269 269

R2 0.147 0.078

Adjusted R2 0.144 0.074

58

F Statistic 46.19*** (df = 1; 268) 22.53*** (df = 1; 268)

Notes: Clustered standard errors at country level in parentheses.

*p<0.05; **p<0.01; ***p<0.001

Table A5.

Regression including Tukey transformation to

variables with extreme values

Crunchbase output

(1) (2)

EE index add 0.057***

(0.004)

EE index log 0.039***

(0.005)

Constant -0.105 0.633***

(0.060) (0.052)

Observations 273 273

R2 0.316 0.206

Adjusted R2 0.313 0.203

F Statistic 125 *** (df = 1; 271) 70.4*** (df = 1; 271)

Notes: Clustered standard errors at country level in parentheses.

*p<0.05; **p<0.01; ***p<0.001

59

Table A6.

Regression with categorical calculation of the index

Crunchbase output

(1)

Categorical Index 0.097***

(0.009)

Constant 0.188

(0.520)

Observations 273

R2 0.429

Adjusted R2 0.427

F Statistic 203.3*** (df = 1; 271)

Notes: Clustered standard errors at country level in parentheses.

*p<0.05; **p<0.01; ***p<0.001

Table A7.

Regression with dummies for capital cities

Crunchbase output

(1) (2)

EE index add 0.079***

(0.009)

EE index log 0.058***

(0.009)

Capital city 0.915** 1.086***

(0.323) (0.315)

Constant 0.042 1.080***

(0.106) (0.105)

Observations 273 273

R2 0.403 0.371

Adjusted R2 0.399 0.366

F Statistic 91.08*** (df = 2; 270) 79.56 *** (df = 2; 270)

Notes: Clustered standard errors at country level in parentheses.

*p<0.05; **p<0.01; ***p<0.001

60

Data appendix

NUTS2 code

Crunchbase output

Formal institutions

Culture Networks Physical

infrastructure Finance Leadership Talent Knowledge Demand Intermediate

EE index

additive

EE index

log

AT12 0.46 1.13 0.26 1.44 1.31 0.54 0.33 5.00 1.48 1.81 0.35 13.64 -0.78

AT13 3.05 1.21 0.41 1.44 1.31 0.32 5.00 5.00 1.48 1.81 1.64 19.62 3.49

AT11 0.32 1.17 0.13 1.44 0.62 0.42 0.18 2.33 0.25 1.29 0.27 8.09 -6.20

AT21 0.43 1.09 0.31 2.09 0.24 0.67 0.44 2.87 1.81 0.68 0.28 10.47 -3.02

AT22 0.87 1.15 0.33 2.09 0.35 0.64 1.06 2.78 5.00 0.81 0.43 14.65 -0.07

AT31 0.85 1.12 0.58 1.55 0.50 0.73 0.28 3.03 1.86 1.09 0.29 11.01 -1.72

AT32 0.37 1.23 0.84 1.55 0.42 0.31 0.23 1.85 0.40 0.85 0.30 7.99 -4.68

AT33 0.62 1.33 0.49 1.55 0.31 1.10 0.41 2.75 1.74 0.86 0.35 10.89 -1.59

AT34 0.27 1.36 0.20 1.55 0.71 0.62 0.16 2.63 0.52 1.12 0.23 9.11 -4.53

BE10 2.96 0.52 0.26 3.17 1.58 3.03 5.00 1.73 1.90 2.33 3.69 23.19 5.66

BE24 1.12 0.92 0.20 3.17 1.58 0.84 5.00 2.27 1.90 2.33 0.90 19.10 3.56

BE31 1.70 0.62 0.21 3.17 1.58 1.24 2.54 1.99 1.90 2.33 1.38 16.94 3.21

BE21 1.39 0.92 0.20 5.00 1.91 0.84 0.42 2.27 1.84 2.33 0.58 16.32 1.28

BE22 0.84 0.92 0.20 5.00 1.05 0.84 0.21 2.27 0.35 1.94 0.36 13.16 -2.31

BE23 1.18 0.92 0.20 5.00 1.11 0.84 1.21 2.27 1.02 2.28 0.36 15.22 0.70

BE25 0.43 0.92 0.20 5.00 0.85 0.84 0.16 2.27 0.28 1.65 0.27 12.46 -3.45

BE32 0.34 0.62 0.21 2.47 0.79 1.24 0.20 1.99 0.45 1.45 0.22 9.63 -4.02

BE33 0.62 0.62 0.21 2.47 1.35 1.24 0.28 1.99 0.69 1.29 0.41 10.55 -2.23

BE34 0.33 0.62 0.21 2.47 0.53 1.24 0.16 1.99 0.21 0.78 0.41 8.63 -5.35

BE35 0.19 0.62 0.21 2.47 0.64 1.24 0.22 1.99 0.32 1.12 0.22 9.05 -4.72

BG31 0.01 0.10 0.05 0.16 0.06 0.07 0.16 0.03 0.17 0.13 0.08 0.99 -24.48

BG32 0.11 0.19 0.08 0.16 0.08 0.07 0.15 0.03 0.16 0.17 0.14 1.24 -22.11

BG33 0.37 0.16 0.08 0.16 0.12 0.33 0.17 0.08 0.14 0.14 0.22 1.58 -19.31

BG34 0.21 0.10 0.11 0.16 0.12 0.07 0.16 0.03 0.14 0.12 0.16 1.17 -22.33

BG41 1.72 0.12 0.08 0.17 0.21 1.39 0.22 0.13 0.41 0.32 0.95 4.00 -13.33

BG42 0.23 0.14 0.10 0.17 0.18 1.19 0.16 0.07 0.16 0.15 0.12 2.45 -17.87

CY00 2.90 0.20 0.28 0.78 0.46 0.07 1.81 0.25 0.16 0.25 1.12 5.38 -10.50

CZ01 2.84 0.39 0.81 0.38 0.67 0.78 0.41 0.55 1.08 0.95 2.10 8.11 -3.52

CZ02 0.16 0.30 0.38 0.38 0.67 0.25 0.19 0.21 1.08 0.95 0.35 4.74 -9.22

CZ03 0.23 0.36 0.74 0.30 0.26 0.74 0.17 0.27 0.43 0.40 0.14 3.82 -10.93

CZ04 0.09 0.25 0.22 0.31 0.28 0.46 0.15 0.20 0.14 0.54 0.15 2.70 -14.09

CZ05 0.19 0.38 3.08 0.62 0.23 1.30 0.16 0.36 0.34 0.50 0.16 7.14 -7.92

CZ06 0.62 0.42 0.57 0.30 0.33 1.26 0.24 0.81 1.36 0.49 0.29 6.07 -6.79

CZ07 0.15 0.41 0.58 0.49 0.24 0.73 0.16 0.30 0.33 0.49 0.14 3.88 -10.73

CZ08 0.28 0.36 0.41 0.40 0.33 1.16 0.16 0.33 0.30 0.61 0.15 4.21 -10.31

DE30 5.00 1.13 0.86 0.60 2.94 3.75 0.71 0.64 1.44 1.65 4.63 18.37 3.49

DE40 0.47 1.21 1.18 0.60 2.94 1.31 0.25 0.56 1.44 1.65 0.22 11.37 -1.41

DE11 0.62 1.45 1.42 0.39 1.00 1.42 0.22 0.65 5.00 2.60 0.28 14.44 -0.48

DE12 0.81 1.45 1.42 0.35 1.99 1.42 0.61 0.65 5.00 2.57 0.20 15.65 0.72

DE13 0.34 1.45 1.42 0.33 0.84 1.42 0.27 0.65 1.16 1.88 0.15 9.57 -3.07

DE14 0.30 1.45 1.42 0.43 0.55 1.42 0.27 0.65 5.00 2.04 0.15 13.37 -1.71

61

NUTS2 code

Crunchbase output

Formal institutions

Culture Networks Physical

infrastructure Finance Leadership Talent Knowledge Demand Intermediate

EE index

additive

EE index

log

DE21 2.14 1.67 1.40 0.36 2.00 1.28 2.90 0.61 5.00 2.55 1.03 18.79 3.91

DE22 0.23 1.67 1.40 0.16 0.76 1.28 0.17 0.61 0.32 1.27 0.14 7.78 -6.12

DE23 0.28 1.67 1.40 0.23 0.81 1.28 0.22 0.61 0.64 1.17 0.16 8.18 -4.72

DE24 0.31 1.67 1.40 0.31 0.64 1.28 0.22 0.61 0.60 1.32 0.11 8.16 -4.96

DE25 0.41 1.67 1.40 0.31 1.34 1.28 0.31 0.61 3.30 1.72 0.30 12.23 -0.93

DE26 0.32 1.67 1.40 0.38 0.90 1.28 0.22 0.61 0.70 1.62 0.21 8.99 -3.41

DE27 0.46 1.67 1.40 0.48 1.11 1.28 0.17 0.61 0.43 1.79 0.18 9.11 -3.79

DE50 0.61 1.46 0.63 0.41 0.92 1.06 0.65 0.57 1.31 1.40 0.38 8.80 -2.35

DE60 3.05 1.59 0.34 0.29 2.13 1.59 0.44 0.61 0.79 2.65 2.30 12.74 -0.36

DE71 0.98 1.44 1.58 0.35 2.65 1.48 0.26 0.66 1.83 2.68 0.99 13.92 0.96

DE72 0.21 1.44 1.58 0.40 1.28 1.48 0.19 0.66 1.06 1.72 0.19 10.01 -2.55

DE73 0.30 1.44 1.58 0.25 0.68 1.48 0.17 0.66 0.43 1.19 0.19 8.07 -5.10

DE80 0.28 1.52 0.60 0.37 0.49 0.82 0.22 0.47 0.57 0.52 0.16 5.73 -7.35

DE91 0.24 1.59 1.01 0.29 0.70 2.10 0.34 0.56 5.00 1.31 0.21 13.11 -1.73

DE92 0.54 1.59 1.01 0.52 1.01 2.10 0.22 0.56 0.93 1.55 0.20 9.71 -2.74

DE93 0.19 1.59 1.01 0.37 0.81 2.10 0.17 0.56 0.24 1.52 0.22 8.59 -4.89

DE94 0.25 1.59 1.01 0.34 0.71 2.10 0.18 0.56 0.24 1.23 0.17 8.14 -5.49

DEA1 0.53 1.23 1.44 0.29 2.33 1.10 0.19 0.65 0.51 3.31 0.38 11.43 -2.23

DEA2 0.90 1.23 1.44 0.50 2.13 1.10 0.45 0.65 1.34 2.87 0.53 12.24 0.21

DEA3 0.39 1.23 1.44 0.56 1.50 1.10 0.19 0.65 0.28 2.30 0.19 9.43 -3.70

DEA4 0.35 1.23 1.44 0.39 0.93 1.10 0.19 0.65 0.56 1.86 0.18 8.51 -4.15

DEA5 0.43 1.23 1.44 0.54 1.77 1.10 0.20 0.65 0.46 2.35 0.19 9.92 -2.98

DEB1 0.38 1.49 1.22 0.34 1.52 0.58 0.16 0.64 0.19 2.08 0.18 8.40 -5.54

DEB2 0.18 1.49 1.22 0.52 0.58 0.58 0.18 0.64 1.51 1.40 0.15 8.27 -4.44

DEB3 0.42 1.49 1.22 0.36 1.85 0.58 0.27 0.64 2.15 2.23 0.25 11.04 -1.93

DEC0 0.43 1.42 0.57 0.56 0.85 1.31 0.22 0.56 0.42 1.43 0.17 7.51 -5.05

DED2 0.43 1.27 1.69 0.63 0.60 0.73 0.41 0.65 3.96 0.98 0.30 11.22 -1.69

DED4 0.25 1.27 1.69 1.33 0.56 0.73 0.18 0.65 0.52 1.10 0.13 8.17 -4.59

DED5 0.93 1.27 1.69 0.93 1.17 0.73 0.31 0.65 0.66 1.09 0.48 8.98 -2.13

DEE0 0.41 1.12 1.25 0.66 0.92 0.75 0.19 0.55 0.36 0.86 0.15 6.80 -5.82

DEF0 0.28 1.47 0.28 0.44 0.94 1.31 0.22 0.61 0.39 1.39 0.30 7.37 -5.31

DEG0 0.34 1.36 1.10 0.44 0.58 3.21 0.24 0.56 0.64 0.92 0.14 9.18 -4.28

DK01 5.00 2.83 4.73 0.63 4.50 1.57 5.00 4.19 5.00 0.89 1.68 31.02 9.14

DK02 0.49 2.67 4.57 0.59 1.15 1.41 0.23 3.27 0.29 0.58 0.30 15.07 -0.81

DK03 1.15 2.87 4.30 0.74 0.56 0.78 0.33 3.33 0.60 0.41 0.24 14.15 -1.36

DK04 1.72 3.33 3.12 0.58 0.58 1.20 1.50 3.82 1.10 0.42 0.36 16.02 1.39

DK05 1.07 2.85 3.41 0.49 0.55 2.71 0.60 3.07 0.40 0.29 0.23 14.60 -1.07

EE00 5.00 1.60 1.31 0.66 0.41 1.16 1.07 4.08 0.40 0.10 0.45 11.25 -2.91

EL30 0.78 0.12 0.35 0.94 0.64 0.12 0.28 0.12 0.29 0.92 1.63 5.42 -9.98

EL41 0.32 0.10 0.22 0.64 0.19 0.07 1.23 0.06 0.20 0.03 0.34 3.09 -17.35

EL42 0.35 0.10 0.33 0.33 0.17 0.19 0.16 0.11 0.12 0.09 0.30 1.91 -17.65

EL43 0.24 0.10 0.67 1.58 0.13 0.19 0.23 0.11 0.42 0.11 0.18 3.71 -14.43

62

NUTS2 code

Crunchbase output

Formal institutions

Culture Networks Physical

infrastructure Finance Leadership Talent Knowledge Demand Intermediate

EE index

additive

EE index

log

EL51 0.12 0.09 0.38 0.56 0.12 0.07 3.60 0.06 0.19 0.12 0.21 5.40 -15.67

EL52 0.33 0.09 0.27 0.63 0.18 0.28 0.96 0.12 0.22 0.27 0.26 3.29 -13.50

EL53 0.01 0.09 0.27 1.13 0.11 0.28 0.17 0.12 0.15 0.15 0.10 2.57 -17.07

EL54 0.30 0.09 0.41 0.26 0.10 0.07 0.22 0.06 0.31 0.11 0.20 1.82 -19.00

EL61 0.22 0.11 0.50 0.57 0.11 0.07 0.19 0.06 0.19 0.17 0.21 2.18 -17.79

EL62 0.31 0.11 0.14 0.42 0.17 0.07 0.17 0.06 0.16 0.09 0.19 1.57 -20.07

EL63 0.20 0.11 0.16 0.57 0.10 0.19 0.26 0.12 0.34 0.14 0.10 2.10 -17.33

EL64 0.22 0.11 0.23 0.41 0.12 0.19 0.16 0.11 0.17 0.31 0.22 2.04 -16.76

EL65 0.12 0.11 0.42 0.32 0.15 0.19 0.15 0.11 0.15 0.17 0.19 1.96 -17.21

ES11 0.55 0.25 0.95 0.40 0.35 0.64 0.24 0.30 0.23 0.35 0.31 4.02 -10.18

ES12 0.63 0.36 1.48 0.36 0.41 0.42 0.45 0.20 0.20 0.35 0.29 4.51 -9.73

ES13 0.29 0.40 0.28 0.24 0.44 0.52 0.29 0.24 0.22 0.37 0.24 3.24 -11.68

ES21 0.86 0.45 0.21 0.70 0.58 0.73 3.54 0.68 0.59 1.05 0.39 8.91 -4.14

ES22 0.72 0.41 0.10 0.35 0.42 0.89 1.50 0.31 0.45 0.63 0.29 5.36 -8.49

ES23 0.75 0.36 0.09 0.29 0.32 0.68 1.00 0.48 0.23 0.37 0.12 3.95 -11.42

ES24 0.48 0.33 0.53 0.25 1.07 0.42 0.76 0.21 0.23 0.28 0.20 4.29 -10.09

ES30 2.13 0.28 0.71 0.26 3.10 1.59 1.65 0.60 0.49 2.02 1.32 12.02 -1.10

ES41 0.41 0.27 0.96 0.24 0.44 0.62 0.27 0.50 0.25 0.28 0.17 4.00 -10.49

ES42 0.19 0.27 1.16 0.25 0.74 0.63 0.17 0.30 0.17 0.31 0.14 4.12 -11.26

ES43 0.26 0.32 0.45 0.24 0.27 0.64 0.21 0.52 0.19 0.13 0.14 3.10 -13.03

ES51 2.02 0.26 0.72 0.26 1.28 0.68 1.92 0.27 0.41 0.86 0.72 7.37 -5.22

ES52 0.88 0.25 0.45 0.24 0.60 0.66 0.39 0.49 0.26 0.53 0.29 4.16 -9.42

ES53 0.67 0.24 0.15 0.13 0.62 0.38 0.19 0.26 0.14 0.31 0.29 2.71 -14.19

ES61 0.47 0.22 0.69 0.23 0.38 0.71 0.25 0.70 0.26 0.31 0.21 3.95 -10.53

ES62 0.41 0.30 0.20 0.21 0.55 0.78 0.29 0.46 0.22 0.39 0.25 3.66 -11.07

ES70 0.43 0.22 1.18 0.17 0.49 1.93 0.21 0.53 0.16 0.21 0.24 5.34 -10.19

FI19 1.21 1.91 1.24 0.94 0.40 0.75 0.43 2.94 1.42 0.17 0.28 10.50 -2.85

FI1B 5.00 2.08 2.18 0.88 1.53 2.58 5.00 1.57 2.77 0.74 4.28 23.62 7.00

FI1C 1.19 1.99 1.66 1.23 0.63 2.80 0.30 3.26 0.64 0.27 0.49 13.28 -0.48

FI1D 1.09 2.02 2.53 0.78 0.30 1.79 0.49 3.03 1.04 0.05 0.29 12.33 -2.99

FI20 0.74 3.22 2.11 NA NA 0.07 0.15 0.56 0.13 0.07 5.00 NA NA

FR10 2.92 0.87 0.63 0.77 5.00 3.45 2.06 5.00 1.48 3.52 2.40 25.19 6.85

FRB0 0.37 0.84 1.08 0.47 0.71 1.84 0.18 1.93 0.46 0.73 0.13 8.36 -4.77

FRC1 0.38 0.78 1.80 0.47 0.40 1.63 0.18 1.78 0.25 0.49 0.14 7.93 -6.00

FRC2 0.32 0.74 0.60 0.47 0.26 0.75 0.19 1.68 1.25 0.63 0.19 6.78 -6.20

FRD1 0.27 0.82 1.21 0.47 0.33 1.09 0.18 1.54 0.33 0.51 0.19 6.68 -6.48

FRD2 0.31 0.85 1.43 0.47 0.84 1.63 0.16 1.91 0.38 1.00 0.23 8.90 -3.86

FRE1 0.48 0.78 0.63 0.53 0.97 1.13 0.18 1.87 0.23 1.01 0.21 7.54 -5.36

FRE2 0.26 0.83 0.47 0.53 1.21 2.17 0.18 1.51 0.37 1.23 0.39 8.89 -3.66

FRF1 0.46 0.82 0.18 0.48 0.71 0.75 0.27 1.99 0.49 1.18 0.17 7.03 -6.23

FRF2 0.33 0.80 0.39 0.48 0.95 1.63 0.16 1.52 0.20 0.46 0.16 6.76 -7.06

FRF3 0.38 0.76 0.64 0.48 0.47 0.75 0.16 1.53 0.32 0.75 0.13 5.97 -7.42

63

NUTS2 code

Crunchbase output

Formal institutions

Culture Networks Physical

infrastructure Finance Leadership Talent Knowledge Demand Intermediate

EE index

additive

EE index

log

FRG0 0.48 0.98 0.38 0.63 0.50 2.16 0.18 1.98 0.31 0.67 0.28 8.08 -5.22

FRH0 0.46 1.01 0.80 0.63 0.36 2.16 0.21 2.11 0.64 0.57 0.21 8.71 -4.29

FRI1 0.61 0.97 0.51 0.68 0.52 0.33 0.22 1.48 0.44 0.52 0.26 5.94 -6.77

FRI2 0.28 0.93 0.44 0.68 0.17 0.64 0.19 1.80 0.25 0.37 0.16 5.63 -8.78

FRI3 0.29 0.80 0.82 0.68 0.35 2.16 0.16 1.72 0.24 0.50 0.17 7.60 -6.27

FRJ1 0.55 0.72 1.71 0.57 0.53 1.45 0.18 1.97 0.93 0.50 0.26 8.82 -3.73

FRJ2 0.60 0.84 0.83 0.57 0.45 1.26 0.29 2.60 5.00 0.51 0.71 13.06 -1.18

FRK1 0.46 0.85 0.43 0.76 0.34 1.45 0.18 3.06 0.80 0.49 0.10 8.46 -5.79

FRK2 0.70 0.91 0.89 0.76 0.59 1.47 0.26 2.90 1.32 0.94 0.29 10.32 -1.96

FRL0 0.72 0.75 0.82 0.45 0.74 1.93 0.21 2.08 1.01 0.74 0.42 9.16 -2.92

FRM0 0.52 0.70 1.24 0.45 0.24 0.07 0.15 0.58 0.13 0.14 0.55 4.25 -12.08

HR03 0.49 0.14 0.13 0.22 0.22 0.68 0.17 0.21 0.14 0.12 0.41 2.44 -15.73

HR04 0.49 0.13 0.13 0.29 0.22 0.68 0.18 0.22 0.27 0.24 0.24 2.60 -14.60

HU11 1.97 0.23 0.20 0.32 0.68 1.36 0.56 0.61 0.57 0.72 2.11 7.35 -5.55

HU12 0.28 0.23 0.20 0.32 0.68 1.36 0.17 0.61 0.57 0.72 0.22 5.07 -8.99

HU21 0.38 0.30 0.14 0.22 0.34 1.34 0.19 0.48 0.24 0.46 0.11 3.83 -12.23

HU22 0.26 0.29 0.28 0.26 0.31 0.86 0.18 0.46 0.17 0.38 0.10 3.28 -12.69

HU23 0.25 0.29 0.39 0.21 0.15 0.55 0.15 0.40 0.15 0.22 0.10 2.62 -14.72

HU31 0.22 0.28 0.25 0.25 0.16 1.94 0.17 0.47 0.16 0.32 0.10 4.10 -13.02

HU32 0.37 0.25 0.34 0.18 0.14 1.11 0.17 0.46 0.28 0.25 0.09 3.28 -13.62

HU33 0.31 0.33 0.32 0.25 0.17 0.61 0.18 0.42 0.46 0.25 0.10 3.08 -12.98

IE04 1.46 0.97 2.42 0.63 0.18 1.94 1.14 1.33 0.57 0.20 0.22 9.60 -3.91

IE05 1.43 0.93 1.62 0.69 0.29 1.94 0.78 1.40 0.28 0.37 0.42 8.72 -3.59

IE06 5.00 0.93 1.62 0.68 0.87 1.94 3.06 1.40 0.29 0.66 1.63 13.07 0.84

ITC1 0.51 0.28 0.11 0.39 0.79 0.70 0.31 0.40 0.74 1.23 0.45 5.41 -7.96

ITC2 0.01 0.37 0.19 0.20 0.24 0.07 0.21 0.18 0.19 0.76 0.30 2.71 -14.82

ITC3 0.39 0.28 0.07 0.22 0.66 0.44 0.67 0.43 0.38 0.85 0.85 4.84 -9.29

ITC4 0.91 0.41 0.39 0.27 0.75 1.04 0.40 1.10 0.32 2.04 0.95 7.67 -4.70

ITF1 0.30 0.19 0.19 0.28 0.27 0.48 0.19 0.40 0.24 0.57 0.43 3.24 -12.07

ITF2 0.24 0.29 0.23 0.27 0.13 0.07 0.17 0.15 0.19 0.50 0.30 2.29 -16.01

ITF3 0.23 0.20 0.19 0.17 0.38 0.94 0.19 0.36 0.32 0.68 0.47 3.91 -11.01

ITF4 0.23 0.24 0.91 0.29 0.30 0.25 0.18 0.34 0.25 0.42 0.35 3.51 -11.50

ITF5 0.39 0.22 1.70 0.16 0.18 0.48 0.18 0.43 0.18 0.33 0.27 4.13 -11.95

ITF6 0.20 0.17 0.23 0.26 0.21 0.48 0.18 0.36 0.19 0.27 0.30 2.65 -13.78

ITG1 0.16 0.24 0.19 0.21 0.27 1.50 0.16 0.31 0.25 0.37 0.31 3.81 -12.10

ITG2 0.44 0.28 0.42 0.59 0.30 1.50 0.18 0.37 0.21 0.23 0.31 4.40 -10.36

ITH1 0.35 0.44 1.08 0.28 0.17 0.68 0.37 0.26 0.20 0.76 0.16 4.40 -10.21

ITH2 1.18 0.44 0.44 0.45 0.21 0.34 3.87 0.43 0.53 1.01 0.33 8.04 -6.33

ITH3 0.41 0.42 0.81 0.24 0.51 0.60 0.31 0.26 0.28 1.23 0.41 5.06 -8.18

ITH4 0.52 0.41 0.17 0.30 0.37 0.98 0.34 0.27 0.42 0.81 0.30 4.39 -9.51

ITH5 0.47 0.42 0.43 0.22 0.52 0.80 0.48 0.43 0.53 1.39 0.34 5.56 -7.06

ITI1 0.41 0.34 0.46 0.26 0.39 2.56 0.36 0.27 0.34 0.86 0.46 6.29 -7.56

64

NUTS2 code

Crunchbase output

Formal institutions

Culture Networks Physical

infrastructure Finance Leadership Talent Knowledge Demand Intermediate

EE index

additive

EE index

log

ITI2 0.36 0.24 0.15 0.27 0.25 3.59 0.27 0.28 0.25 0.68 0.39 6.36 -10.08

ITI3 0.29 0.26 0.19 0.28 0.40 3.59 0.21 0.27 0.22 0.68 0.34 6.45 -9.74

ITI4 0.67 0.24 0.53 0.42 0.87 5.00 0.69 0.27 0.44 1.15 0.61 10.21 -4.33

LT01 3.66 0.48 0.37 1.70 0.28 0.50 0.44 0.84 0.26 0.28 2.04 7.19 -6.07

LT02 0.41 0.48 0.37 0.87 0.19 0.50 0.18 0.84 0.26 0.19 0.16 4.05 -10.90

LU00 4.18 0.55 0.09 0.50 0.69 0.07 0.93 0.35 0.33 1.88 1.10 6.49 -8.29

LV00 1.34 0.34 1.39 0.15 0.23 0.07 0.23 0.36 0.18 0.11 0.33 3.37 -14.40

MT00 4.28 0.14 0.01 0.20 0.41 0.07 0.48 0.15 0.21 0.24 1.22 3.13 -16.93

NL23 1.22 2.91 1.43 0.99 2.73 0.07 0.19 0.33 0.48 1.79 1.29 12.21 -2.92

NL32 5.00 2.59 3.82 0.99 2.73 0.58 2.39 0.92 0.48 1.79 5.00 21.28 4.98

NL11 1.21 2.94 5.00 1.40 0.86 0.33 3.43 1.95 0.69 0.72 0.80 18.12 2.74

NL12 0.55 2.94 2.98 0.88 1.11 0.07 0.19 0.31 0.22 0.72 0.58 10.02 -5.74

NL13 0.40 2.94 1.58 1.44 0.79 0.07 0.17 0.31 0.21 0.88 0.74 9.14 -5.97

NL21 1.17 2.91 2.71 1.03 1.54 0.51 0.53 0.64 0.56 1.15 0.53 12.10 -0.32

NL22 0.75 2.91 5.00 1.10 2.75 0.42 0.99 0.65 0.74 1.67 0.84 17.07 2.53

NL31 2.27 2.59 3.22 1.36 3.47 0.07 5.00 0.35 0.79 2.28 1.84 20.96 2.73

NL33 1.78 2.59 3.50 1.15 2.92 1.23 2.05 0.84 0.75 2.00 2.49 19.51 5.47

NL34 0.38 2.59 3.96 1.20 1.02 0.07 0.16 0.31 0.17 1.54 0.55 11.57 -5.08

NL41 1.18 2.76 2.61 1.07 3.02 1.06 0.53 0.76 1.27 1.90 1.20 16.17 3.35

NL42 0.70 2.76 1.83 1.04 2.02 0.07 0.58 1.08 0.56 1.79 0.60 12.34 -1.30

PL21 0.69 0.27 0.23 0.17 0.22 1.77 0.18 0.34 0.40 0.59 0.17 4.34 -11.51

PL22 0.20 0.26 1.00 0.20 0.29 1.77 0.16 0.33 0.18 0.87 0.19 5.25 -10.09

PL41 0.41 0.26 0.27 0.16 0.25 1.28 0.17 0.33 0.20 0.44 0.14 3.52 -12.85

PL42 0.32 0.27 0.95 0.17 0.29 1.82 0.17 0.27 0.14 0.26 0.19 4.53 -11.83

PL43 0.10 0.27 0.35 0.15 0.23 1.52 0.15 0.29 0.12 0.31 0.14 3.53 -13.70

PL51 0.56 0.26 1.04 0.17 0.23 0.94 0.18 0.38 0.22 0.50 0.20 4.13 -11.07

PL52 0.14 0.28 1.05 0.19 0.21 0.46 0.16 0.25 0.14 0.47 0.15 3.37 -13.03

PL61 0.17 0.28 0.38 0.18 0.25 0.20 0.16 0.25 0.15 0.36 0.15 2.37 -14.96

PL62 0.21 0.28 0.27 0.17 0.22 0.91 0.16 0.24 0.14 0.21 0.10 2.69 -15.09

PL63 0.52 0.31 0.46 0.16 0.39 4.22 0.17 0.35 0.28 0.34 0.31 6.99 -9.60

PL71 0.22 0.24 0.39 0.15 0.28 1.22 0.16 0.28 0.19 0.51 0.20 3.62 -12.39

PL72 0.15 0.25 1.43 0.18 0.14 1.75 0.17 0.30 0.18 0.38 0.09 4.87 -12.24

PL81 0.19 0.24 0.33 0.18 0.17 1.75 0.17 0.30 0.27 0.27 0.11 3.79 -13.26

PL82 0.21 0.24 1.10 0.20 0.18 1.75 0.16 0.32 0.33 0.29 0.12 4.70 -11.51

PL84 0.22 0.26 0.16 0.18 0.20 1.75 0.16 0.28 0.20 0.19 0.10 3.48 -14.60

PL91 1.86 0.25 0.79 0.18 0.62 1.22 0.29 0.40 0.50 0.98 1.79 7.03 -5.85

PL92 0.16 0.25 0.79 0.18 0.28 1.22 0.15 0.40 0.50 0.59 0.13 4.50 -10.45

PT11 0.53 0.76 0.34 0.26 0.32 0.27 0.31 0.67 0.35 0.40 0.17 3.86 -10.43

PT15 0.66 0.67 0.06 0.14 0.38 0.29 0.23 0.74 0.14 0.21 0.20 3.05 -14.39

PT16 1.23 0.82 0.20 0.42 0.26 0.26 0.35 0.63 0.31 0.32 0.14 3.72 -11.12

PT17 1.97 0.83 0.48 0.34 1.27 0.21 0.65 1.09 0.41 1.04 1.67 7.98 -4.01

PT18 0.41 0.90 0.17 0.30 0.17 0.47 0.28 1.04 0.16 0.24 0.12 3.85 -12.21

65

NUTS2 code

Crunchbase output

Formal institutions

Culture Networks Physical

infrastructure Finance Leadership Talent Knowledge Demand Intermediate

EE index

additive

EE index

log

PT20 0.28 0.79 0.22 0.16 0.39 0.07 0.15 0.57 0.14 0.04 0.21 2.74 -16.34

PT30 0.69 0.86 0.22 0.21 0.36 0.07 0.24 0.38 0.14 0.13 0.15 2.77 -15.19

RO11 0.61 0.08 0.42 0.14 0.15 0.79 0.17 0.20 0.15 0.21 0.13 2.44 -16.41

RO12 0.29 0.09 0.18 0.14 0.10 0.79 0.16 0.20 0.14 0.22 0.11 2.12 -17.78

RO21 0.28 0.09 0.91 0.12 0.10 0.46 0.16 0.21 0.15 0.17 0.06 2.43 -17.64

RO22 0.12 0.07 0.45 0.15 0.10 1.06 0.16 0.16 0.11 0.19 0.16 2.62 -16.91

RO31 0.13 0.11 0.68 0.14 0.15 0.45 0.16 0.21 0.14 0.34 0.18 2.56 -15.41

RO32 1.23 0.09 0.70 0.16 0.54 0.45 0.22 0.25 0.23 1.88 1.45 5.97 -9.38

RO41 0.12 0.09 0.85 0.12 0.12 0.07 0.16 0.18 0.12 0.18 0.08 1.96 -19.54

RO42 0.34 0.10 0.75 0.13 0.16 0.63 0.16 0.21 0.15 0.21 0.12 2.63 -15.87

SE11 5.00 1.50 3.26 0.46 1.63 1.50 1.74 5.00 3.35 1.24 5.00 24.67 6.90

SE12 0.83 1.50 0.99 0.80 0.62 1.18 0.77 3.06 3.78 0.41 0.97 14.08 1.12

SE21 0.48 1.52 2.09 0.72 0.36 1.73 0.19 2.33 0.41 0.20 0.41 9.96 -3.83

SE22 1.72 1.52 2.87 0.38 1.05 1.65 0.90 5.00 2.05 0.62 1.88 17.91 3.42

SE23 1.16 1.52 3.40 0.39 0.60 1.68 0.72 3.35 3.44 0.43 1.54 17.07 2.41

SE31 0.42 1.39 2.33 1.31 0.32 1.59 0.20 2.08 0.34 0.12 0.44 10.12 -4.12

SE32 0.84 1.39 1.77 0.74 0.32 0.23 0.15 1.61 0.20 0.05 0.36 6.82 -9.08

SE33 0.85 1.39 0.43 0.68 0.28 0.62 0.54 1.89 1.22 0.03 0.30 7.39 -7.06

SI03 0.39 0.50 0.36 0.52 0.29 0.38 0.22 0.46 0.45 0.46 0.15 3.80 -10.29

SI04 1.52 0.50 0.30 0.71 0.47 2.35 1.64 0.61 1.17 0.51 0.42 8.68 -3.53

SK01 2.36 0.41 1.05 0.51 0.71 1.35 0.51 0.79 0.55 1.23 0.85 7.97 -3.02

SK02 0.16 0.40 0.35 0.23 0.19 1.35 0.17 0.56 0.23 0.51 0.13 4.13 -11.30

SK03 0.16 0.49 0.55 0.35 0.11 1.35 0.17 0.54 0.29 0.39 0.12 4.34 -11.07

SK04 0.24 0.47 0.49 0.33 0.12 1.35 0.16 0.53 0.20 0.30 0.12 4.08 -11.79

UKH2 1.16 1.83 1.98 2.25 5.00 1.30 0.22 0.72 0.34 4.67 0.78 19.10 2.36

UKH3 0.57 1.83 1.98 2.25 5.00 1.30 0.19 0.72 0.34 4.67 0.67 18.96 2.07

UKI3 5.00 1.76 1.96 2.25 5.00 0.40 5.00 0.66 0.34 4.67 5.00 27.05 6.01

UKI4 0.33 1.76 1.96 2.25 5.00 0.40 0.63 0.66 0.34 4.67 5.00 22.68 3.94

UKI5 0.30 1.76 1.96 2.25 5.00 0.40 0.18 0.66 0.34 4.67 1.72 18.95 1.61

UKI6 0.49 1.76 1.96 2.25 5.00 0.40 0.17 0.66 0.34 4.67 4.01 21.23 2.41

UKI7 0.73 1.76 1.96 2.25 5.00 0.40 0.20 0.66 0.34 4.67 2.98 20.22 2.26

UKC1 0.66 1.89 1.10 3.54 0.57 0.25 0.31 0.55 0.25 0.82 0.25 9.52 -4.72

UKC2 1.24 1.89 1.10 3.54 0.63 0.25 0.41 0.55 0.32 0.61 0.39 9.68 -3.95

UKD1 0.77 1.55 1.41 1.89 0.38 1.50 0.15 0.72 0.31 0.59 0.29 8.79 -4.28

UKD3 1.83 1.55 1.41 1.89 1.39 1.50 0.38 0.72 0.24 1.88 0.88 11.84 -0.06

UKD4 0.71 1.55 1.41 1.89 0.99 1.50 0.24 0.72 0.23 1.48 0.23 10.24 -2.47

UKD6 1.38 1.55 1.41 1.89 1.32 1.50 0.21 0.72 4.21 2.26 0.88 15.94 2.32

UKD7 0.76 1.55 1.41 1.89 1.56 1.50 0.32 0.72 0.43 1.62 0.31 11.31 -0.71

UKE1 0.53 1.69 0.80 4.72 0.61 0.58 0.18 0.63 0.21 0.86 0.17 10.46 -4.83

UKE2 1.18 1.69 0.80 4.72 0.69 0.58 0.54 0.63 0.45 1.28 0.46 11.85 -1.47

UKE3 0.81 1.69 0.80 4.72 1.14 0.58 0.56 0.63 0.32 1.57 0.27 12.29 -1.59

UKE4 0.94 1.69 0.80 4.72 1.20 0.58 0.45 0.63 0.26 1.65 0.42 12.41 -1.47

66

NUTS2 code

Crunchbase output

Formal institutions

Culture Networks Physical

infrastructure Finance Leadership Talent Knowledge Demand Intermediate

EE index

additive

EE index

log

UKF1 0.77 1.67 0.58 3.79 0.96 0.14 0.39 0.54 1.26 1.65 0.34 11.32 -2.61

UKF2 0.93 1.67 0.58 3.79 1.44 0.14 0.24 0.54 0.30 1.63 0.49 10.82 -3.79

UKF3 0.73 1.67 0.58 3.79 0.39 0.14 0.21 0.54 0.14 0.96 0.24 8.66 -7.21

UKG1 0.87 1.89 0.66 2.92 1.44 1.50 0.20 0.68 2.04 1.70 0.74 13.78 1.01

UKG2 0.70 1.89 0.66 2.92 1.15 1.50 0.18 0.68 0.18 1.52 0.27 10.95 -2.86

UKG3 1.09 1.89 0.66 2.92 2.31 1.50 1.72 0.68 0.57 1.52 0.69 14.47 2.19

UKH1 1.96 1.83 1.98 1.96 0.68 1.30 5.00 0.72 5.00 1.07 0.52 20.06 4.15

UKJ1 2.45 1.79 0.72 3.64 3.40 0.84 3.67 0.68 2.82 3.08 1.48 22.14 6.08

UKJ2 1.49 1.79 0.72 3.64 4.44 0.84 0.31 0.68 0.43 3.45 1.29 17.59 1.94

UKJ3 1.12 1.79 0.72 3.64 2.25 0.84 0.34 0.68 0.73 1.92 0.73 13.65 0.76

UKJ4 0.85 1.79 0.72 3.64 4.73 0.84 0.22 0.68 0.32 2.30 0.55 15.80 0.12

UKK1 1.85 1.88 1.41 1.44 1.15 0.67 0.80 0.67 0.69 1.51 0.68 10.90 0.11

UKK2 1.01 1.88 1.41 1.44 0.55 0.67 0.23 0.67 0.22 1.09 0.42 8.58 -3.83

UKK3 0.85 1.88 1.41 1.44 0.54 0.67 0.15 0.67 0.14 0.37 0.37 7.65 -5.86

UKK4 0.93 1.88 1.41 1.44 0.58 0.67 0.89 0.67 0.28 0.62 0.35 8.79 -2.92

UKL1 0.50 1.80 1.45 2.49 0.54 0.58 0.22 0.66 0.21 0.51 0.23 8.70 -4.89

UKL2 1.55 1.80 1.45 2.49 0.77 0.58 0.40 0.66 0.30 0.86 0.53 9.84 -2.26

UKM5 1.27 1.74 0.96 3.27 0.47 0.53 0.32 1.38 0.37 0.49 1.04 10.56 -2.20

UKM6 0.63 1.74 0.96 3.27 0.26 0.53 0.21 1.38 0.16 0.14 0.10 8.76 -7.58

UKM7 1.76 1.74 0.96 3.27 1.11 0.53 2.09 1.38 0.71 0.77 0.49 13.05 0.91

UKM8 1.33 1.74 0.96 3.27 3.70 0.53 0.77 1.38 0.28 1.01 0.50 14.14 0.48

UKM9 0.37 1.74 0.96 3.27 1.14 0.53 0.16 1.38 0.32 0.59 0.17 10.26 -3.74

UKN0 0.89 1.40 0.59 2.91 0.68 0.38 0.27 0.56 0.45 0.47 0.42 8.12 -4.81