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Measure Twice, Cut Once.
Jip Leendertse, Mirella Schrijvers & Erik Stam
Working Paper Series nr: 20-01
Entrepreneurial Ecosystem Metrics
Utrecht University School of Economics (U.S.E.) is part of the faculty of Law, Economics and Governance at Utrecht University. The U.S.E. Research Institute focuses on high quality research in economics and business, with special attention to a multidisciplinary approach. In the working papers series the U.S.E. Research Institute publishes preliminary results of ongoing research for early dissemination, to enhance discussion with the academic community and with society at large.
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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: [email protected]
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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