CASE Network Studies and Analyses 427 - Entrepreneurship and Economic Growth: An Investigation into the Relationship between Entrepreneurship and Total Factor Productivity Growth in the EU

Materials published here have a working paper character. They can be subject to further
publication. The views and opinions expressed here reflect the author(s) point of view and
not necessarily those of CASE Network.
The paper presents findings obtained in a Master’s Degree research project under Professor
Connell Fanning, Dr John Considine, and Mr John Eakins at University College Cork,
Department of Economics.
This publication has been financed thanks to the assistance of the PEKAO SA.
This publication has been prepared due to the help of Dr Ewa Balcerowicz.
Key words: entrepreneurship, total factor productivity, economic growth, the EU
JEL codes: O11, O30, O47, O52, L26
© CASE – Center for Social and Economic Research, Warsaw, 2011
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CASE Network Studies & Analyses No.427 –Entrepreneurship and Economic Growth: An Investigation…
Table of Contents
Table of Contents................................................................................................................ 2
Abstract ............................................................................................................................... 5
1. Introduction ..................................................................................................................... 6
2. Literature Overview......................................................................................................... 7
2.1 Entrepreneurship and its Role in Economic Theory ............................................... 7
2.2 Entrepreneurship and Growth – Theoretical and Empirical Studies...................... 9
2.3 Hypothesis and Model ..............................................................................................12
3. Data Selection and Analysis..........................................................................................14
3.1 Data Selection and Sources.....................................................................................14
3.2 Description and Summary of the Variables ............................................................15
4. Methodology and Results..............................................................................................22
4.1 Multicollinearity in the Model ...................................................................................23
4.2 Pooled Model - FGLS Regression............................................................................28
4.3. Individual Effects Models ........................................................................................30
5. Conclusions....................................................................................................................35
References..........................................................................................................................37
Appendix.............................................................................................................................41
2

List of Tables and Figures
Table 1. Role of Entrepreneurship in Economic Activity.................................................9
Table 2. Variables in the Model ........................................................................................13
Table 3. Countries included in the study.........................................................................15
Table 4. Variables with description, source, and descriptive statistics .......................16
Figure 1. Total Factor Productivity % Growth .................................................................17
Figure 2. Patent applications to the EPO (per million of inhabitants)............................18
Figure 3. Proportion of self-employed and employers to the total population .............19
Figure 4. Business Birth Rates........................................................................................20
Figure 5. Business Investment as a % share of GDP.....................................................21
Table 5. Bivariate Correlations between the Independent Variables ............................24
Table 6. Correlation of Coefficients in REM ...................................................................25
Table 7. VIF and Tolerance before and after solving for Multicollinearity.....................26
Table 8. Eigenvalue and Condition Number ....................................................................26
Table 9. Bivariate Correlations after solving for Multicollinearity..................................27
Table 10. Wooldridge Test for Autocorrelation ...............................................................28
Table 11. FGLS Regression Results ................................................................................29
Table 12. Hausman Test Results......................................................................................31
Table 13. Random Effects Model Results........................................................................32
Table 14. Breusch-Pagan LM Test for Random Effects ..................................................33
Table 15 Fixed Effects Model Results..............................................................................41
3

Andrzej P. Dabkowski graduated from University College Cork (National University of
Ireland) in 2007 obtaining a degree in Social Sciences. In 2009 he was awarded a Higher
Diploma in Economics and a year later a Master’s Degree in Economic Science. His
academic interests concentrate around the broad topic of entrepreneurship, especially in the
area of operational quantification of entrepreneurial activities and their impact on growth.
Andrzej Dabkowski has been affiliated with CASE since early 2011.
4

5
Abstract
Endogenous growth theory assigns an important role for entrepreneurship in the process of
economic development. This paper sets to formally test the impact of entrepreneurship on
economic growth. Entrepreneurship is represented by a number of proxy variables, whereas
Total Factor Productivity is used as a measure of economic growth. Panel data of 26
European countries repeatedly sampled over a period of 11 years is used to estimate a
Random Effects model. This study finds that entrepreneurship contributes to growth
moderately. It is not, nonetheless, a dominant force shaping changes in TFP growth rates.
Business Birth Rate, Self-employment Rate, Business Investment and Labour Productivity
Growth were all found to be highly significant. The article concludes that more encompassing
measure of entrepreneurship needs to be developed, one that would reflect the complexity of
the notion.

6
1. Introduction
There are numerous studies, both theoretical and empirical, aiming at understanding and
explaining a relationship between entrepreneurship and economic growth. This paper is yet
another attempt to do so, in a coherent and concise manner.
Entrepreneurship is quite an ambiguous concept. It has been studied and explained by
many prominent economists making the notion even more complicated and less clear. This
has led to diminishing interest and certain negligence of it for the better part of 20th century.
Only in the past two decades, it has been reintroduced into the mainstream economic
literature, gaining more and more interest especially among business and industrial
economists. It is hard to identify its precise definition, however when identifying the origins it
can be traced to the early 18th century. An Irish economist - Richard Cantillon, first
developed the concept. In 1730s, he argued that in the world of fixed costs there must be a
role for ‘undertakers’ who can bear the uncertainty of non-fixed returns. He stressed the focal
role of entrepreneurs in growing decentralised markets; with the decrease of monopolies
caused by market growth and rising trade openness, one must observe an emergent number
of suppliers – increased competition - and escalating uncertainty of returns. This, according
to Cantillon, proves that competition must go hand in hand with entrepreneurship, as it
requires constant decision-making and risk-bearing (Cantillon, 1959) – namely
entrepreneurial actions.
Entrepreneurial activity, it can be argued, is an intrinsic part of modern society. It is
universalistic in nature, as it does not pertain to only selected individuals or organizations.
The effects of entrepreneurship can be easily traced on a micro-level, in individuals’
behaviour and actions. It is more challenging, however, to measure its effects on a macro-scale.
This study tries to quantify the effects of entrepreneurship, testing its relevance in
incentivising economic growth.
This paper is divided into five sections. After a brief introduction in Section 1, Section 2
provides a summary of the theoretical framework. It describes previous empirical research
that led to this study and introduces the model as well as the hypothesis. Section 3 focuses
on the analysis of the variables and gives an overview of the data selected and selection
process. Section 4 deals with model estimation, analyses the results obtained through
pooled and random effects model regressions. Section 5 briefly discusses the results,
considers the impact of this study and its contribution. Finally, it identifies areas in which
further academic investigation is necessary.

7
2. Literature Overview
2.1 Entrepreneurship and its Role in Economic Theory
Much has been said and written on entrepreneurship since Cantillon. In 1921, Frank Knight
built upon Cantillon’s idea of risk-bearer and introduced his own ‘entrepreneur’, whose
primary objective was to deal with uncertainty and risk. Risk, in contrary to uncertainty, can
be calculated, whereas the latter cannot (Knight, 1921). Similarly, this feature of
entrepreneur was emphasized in Nathaniel Leff’s overview of the concept (1979). Just a
decade after Knight’s publication, Joseph Schumpeter has introduced a new perspective on
entrepreneurship. In his model (1934), an entrepreneur is seen as an agent, who through the
process of innovation, brings about social change and economic development. Furthermore,
he distinguishes five manifestations of entrepreneurship, “a new good, a new method of
production, a new market, a new source of supply of intermediate goods, and a new
organization” (Schumpeter, 1934, in Karlsson, Friis, & Paulsson, 2005, pp. 88-89).
Baumol (2008) and Dejardin (2000) point out that entrepreneurial activity may not always
lead to increased productivity. Baumol (2008) makes a clear distinction between productive,
unproductive, and destructive entrepreneurship. The last one not necessarily meant in
Schumpeterian manner. Social entrepreneurs may engage in rent seeking behaviour, which
results only in redistribution of profits, and not creation. The two scholars conclude that it is
the structure of the environment and incentives that induce an entrepreneur into the different
activities (Baumol, 1968; Baumol, 2008; Dejardin, 2000). The role for good institutions can
be clearly asserted from this theory (Boettke & Coyne, 2003; Acs & Virgill, 2010).
Izrael Kirzner (1973) shares Schumpeterian acclamation of entrepreneurship as a central
process in growing market economy. He, however, does not see entrepreneur as a ‘creative
destroyer’, constantly breaking away from market equilibrium, but rather the opposite, as an
agent whose primary role is to identify and correct for market disequilibria inherent in
competitive market economy. These two seemingly conflicting actions may not be poles
apart as either one would lead to permanent change in the market environment, hence
achieving similar outcome (Holcombe, 2008).
Wennekers and Thurik (1999, pp. 46-47) have developed a definition that seems to
encompass the characteristics of entrepreneurship mentioned above:

“Entrepreneurship is the manifest ability and willingness of individuals, on their own, in
teams, within and outside existing organizations, to:
· perceive and create new economic opportunities (new products, new production
methods, new organizational schemes and new product-market combinations)
and to
· introduce their ideas in the market, in the face of uncertainty and other obstacles,
by making decisions on location, form and the use of resources and institutions.”
This definition may appear attractive from a theoretical point of view, it is not, however, very
practical, as number of its aspects are impossible to measure. So far, basing on the macro
data available, one could use proxies capturing a single feature and its level as a
measurement of entrepreneurship. Commonly used proxy variables would include business
start-ups or self-employment (Klapper & Quesada Delgado, 2007; Naude, 2008). These,
however, may be criticized for its narrowness and oversimplification, as not every new
business is an example of entrepreneurial behaviour, as well as self-employment could be
caused by the lack of other opportunities rather than simply entrepreneurial aspirations. Acs
& Szerb (2009), Bosma & Levie (2010), and Hancock, Klyver & Bager (2001), suggest using
a Global Entrepreneurial Monitor consortium devised measure of entrepreneurship, like TEA
(Total Early-stage Entrepreneurial Activity). Acs & Szerb (2009), based on GEM framework,
introduce Global Entrepreneurship Index, which captures entrepreneurship in three sub-indexes
that comprise of various indicators and variables. The development of this measure
is still at its early stage, and empirical application and validity is not yet assessed.
Meanwhile, numerous studies confirm the effectiveness of measuring entrepreneurship
through proxies like business start-up rates, labour productivity growth or patent applications
(Bartik, 1989; Carey, 1996; van Praag, 2003; Salgado-Banda, 2005).
Karlsson, Friis, and Paulsson (2005) have summarized that the definitions of
entrepreneurship can be broadly divided into two simple categories: “those that are generally
more encompassing theoretically and the more narrow operational ones” (p. 90). They
continue by suggesting that in research and discussion it may by more convenient to focus
primarily on entrepreneurial activities as it provides attractive feature from operational point
of view – simplicity, despite its limited nature. Especially, when few of the operational
definitions combined, one might acquire a more encompassing tool of entrepreneurship
(Karlsson, Friis, & Paulsson, 2005). This view is shared by Zoltan Acs and Laszlo Szerb in
‘Global Entrepreneurship Index (GEINDEX) (2009).
8

2.2 Entrepreneurship and Growth – Theoretical and Empirical
Studies
Classification of theories employed in this paper has been developed by Wennekers and
Thurik (1999) and further enhanced by Karlsson, Friis, and Paulsson (2005). It not only
presents the most prominent growth theories and links them to entrepreneurship, but also
illustrates various entrepreneurial functions with their role defined in growth process.
Table 1, below, is an excerpt from Wennekers and Thurik (1999). It shows the role of
entrepreneurship, and its importance, in number of different fields of economics.
Table 1. Role of Entrepreneurship in Economic Activity
9
Source: (Wennekers & Thurik, 1999)
In historical view, Schumpeter (1934) and Baumol (1968) share a similar perception on
entrepreneur’s role in economic development. They both see entrepreneur as an agent
bringing about change through constant innovation, in the process of creative destruction,
which introduces instability to ‘static’ markets, creating disequilibria. This attitude goes
against an orthodox view, in which market forces should continuously move towards
achieving equilibrium. Schumpeter stresses, however, that the risk of entrepreneurial
activities lay predominantly on the capitalist and not on the entrepreneur himself.
Neoclassical growth theory, best represented in the Solow model (1957), has little room for
entrepreneurship. In this theory, growth is attributed to accumulation of factors of production,
like capital and labour, which in time should lead to reaching a steady-state of economy.
After this point, innovation change, or technological progress, is the only reason behind

growth. In this model, knowledge, technological progress, is exogenously given, hence there
is no need for entrepreneur. Holcombe (2008), however, argues that technology can be
produced, and it is the application of it that requires entrepreneurs.
In Austrian school of economics, entrepreneurship has a particularly strong role in promoting
growth through perceiving opportunities in the market. Kirzner (1973) describes this function
as the ability to profit from market inefficiencies and deficiencies, leading to improvement in
market structure. Holcombe (2008) explains Austrian’s entrepreneurship in terms of
‘entrepreneurial insights’. He argues that entrepreneurial opportunities in the market are
caused by entrepreneurial insights of other entrepreneurs. Therefore, change that is created
through entrepreneurs’ actions leads to more entrepreneurship, i.e. more change.
What neoclassical growth theory takes as given, exogenous technological progress,
endogenous growth theory, or ‘new growth theory’, tries to explain. In this perspective,
growth can be attributed to investment in knowledge (Romer, 1990). This does not explicitly
assume a role for entrepreneurs, but assigns a simplified function of entrepreneurs to any
profit maximizing individual or organization. Peter Howitt (2006) sees Schumpeterian
entrepreneurship, and creative destruction, as a force responsible for dynamism of
industries, and within the endogenous growth theory framework, a cause behind long-term
economic growth.
From economic history perspective, institutions play a crucial role in determining economic
growth. Following the early 1990s, institutions have been accredited with a central role in
country’s growth process. Starting from Mancur Olson (1996), and followed by historical
experiments of Daron Acemoglu (2003) and Acemoglu, Johnson and Robinson (2001),
economists have realized the importance of ‘good’ institutions in promoting economic growth.
In his 2008 paper, William Baumol, emphasizes the role of institutions for encouragement of
‘productive entrepreneurship’, which can be identified as a primary source of economic
growth, as no other type of entrepreneurship, whether it be ‘unproductive’ or ‘destructive’, is
responsible for creation of additional output. Wennekers and Thurik (1999) and Robert
Lawson (2008) agree with Baumol on principle that the major foundation of long-term
economic growth lies with proper, incentivizing institutions rather than simple growth
accounting.
In industrial economics, Porter’s (1990) diamond model of national competitive advantage
provides an interesting insight into the relationship between economic growth and
entrepreneurship. Wennekers and Thurik (1999) and Wennekers, Uhlaner, and Thurik (2002)
have stipulated that the advantage in a single factor of the model might not be enough to
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ensure economic development but only the overall interaction between the various factors
combined with entrepreneurial activity may ensure prospective growth.
Empirical studies of entrepreneurship and its relationship to economic growth are all
relatively recent. Devising a reliable measure of entrepreneurship has proven to be a difficult
task. It was one of the major reasons responsible for a 40-year stagnation of academic
research in the area of entrepreneurship. Within the last 20 years, however, there have been
numerous studies conducted that would test the nature of the relationship. Most empirical
studies, nevertheless, focus primarily on a single aspect of entrepreneurship, as it is most
difficult form the operational point of view to conduct a research, which could fully
encompass the totality of the concept. Nonetheless, “recent empirical studies suggest that
entrepreneurship – measured as start-up rates, the relative share of SMEs, self-employment
rates, etc. – is instrumental in converting knowledge into products and thereby propelling
growth” (Braunerhjelm, 2010).
Entrepreneurship, therefore, can manifest itself in a number of ways, one of which is
innovation. Salgado-Banda (2005) has measured innovative entrepreneurship using quality
adjusted patent data. He concluded that a positive influence on growth could be asserted for
the 22 OECD countries he has studied. Similar results were found by Lee, Dlorida and Acs
when studying American economy (2004).
Another important feature of entrepreneurship can be described as business ownership.
Thurik (1999) in his 1984 – 1994 cross-sectional study of 23 OECD member countries
provided empirical evidence that increased entrepreneurship, as measured by business
ownership rates, was associated with higher rates of employment growth at the country level.
Also, Carree and Thurik (1999), followed by Audretsch et al (2002), concluded that those
OECD countries that show evidence of higher increases in entrepreneurship, exhibited
through business ownership rates, are the ones that have enjoyed lower unemployment and
greater rates of economic growth.
Most commonly used proxy for measuring entrepreneurship is business start-up rate. Acs
and Armington (2002) have investigated the relative contribution of new start-ups to job
creation. Their findings suggest that new firms may have a far greater role in new job
creation than previously thought. In the study of the U.S. economy, they demonstrated that in
the first half of the 1990s new businesses were responsible for a considerably larger share of
job creation than previously existing companies. Job creation, in turn, can be directly linked
to economic growth. Another study, conducted for the Canadian government, has concluded
that “entrepreneurship is a powerful force driving innovation, productivity, job creation and
economic growth. Countries with a high level of entrepreneurial activity tend to be better off
11

economically” (Fisher & Reuber, 2010). Fisher and Reuben (2010) used a number of
entrepreneurship variables, including business birth rates, death rates and survival rates. All
these variables proved significant and exhibit positive impact on growth rates, with the
exception of business death rates, which is negatively related.
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2.3 Hypothesis and Model
This paper is set to test and estimate the importance of entrepreneurship as a drive of
economic growth. To test this hypothesis, a number of proxy variables is introduced that
describe entrepreneurship. Economic growth is measured by Total Factor Productivity
growth.
In the new growth theory, Total Factor Productivity (TFP) is considered a focal force for
economic growth once the economy achieves a steady-state. In the neo-classical view,
represented by the Solow growth model, it is the residual that emerges after adjusting the
total value added for the impact of labour-capital ratio and the amount of the human capital
per unit of labour (Erken, Donselaar, Thurik, 2008). High (2004) suggested that TFP is the
variable that captures the effect of entrepreneurship on growth. Therefore, to assess the real
influence of entrepreneurship on growth, one should analyze the impact of ‘entrepreneurship
variables’ on TFP growth rates.
The following model has been developed by Erken, Donselaar, and Thurik (2008). It is a
standard fixed-effects linear model.
In the above equation, TFP (for country i and year t) stands for total factor productivity. ‘ln’
denotes the natural logarithm, X expresses independent variable(s) for country i and time t.
DUMi stands for a dummy variable of country i, while DUMt for time t. i,t denotes the error
term for country i at time t.
The model adopted for this study is altered to include eight proxy variables for
entrepreneurship.

CASE Network Studies Analyses No.427 –Entrepreneurship and Economic Growth: An Investigation…
The developed model, however, suffers from severe multicollinearity, as two pairs of
independent variables display serial collinearity. After accounting for multicollinearity, the
model is transformed to:
Description of variables in the specified model is available in the table below.
Table 2. Variables in the Model
TFP % growth Total Factor Productivity percentage growth
Patents Total number of applications to the European
Patent Organisation per million of inhabitants
Self-employ Rate of self-employed and employers to the
total population
Birth Rates Rate of business start-ups over the existing
business
Death Rates Rate of business exits over the existing business
Survival Rate Rate of business survival in two consecutive
periods over existing business
Labour Product. Labour productivity percentage growth
RD Exp. Total business expenditure on RD as a
percentage of GDP
Business Invest. Total business investment as a percentage of
GDP
13

14
3. Data Selection and Analysis
3.1 Data Selection and Sources
The dataset used in the study covers a period from 1997 to 2007. The reason behind this
relatively short interlude is that for most cases, there is no more data available. In addition,
before 1997 some of the variables were not yet developed. These would include ‘traditional’
entrepreneurship variables like enterprise birth rates or survival rates. Furthermore, the data
is not completely balanced. There are some observations missing for individual countries or
years.
This dataset consists of 26 cross-sections that represent 26 European countries. Selection
was based primarily on geographical location of a country, and more precisely, upon its
membership in the European Union. The aim of this research is to evaluate the importance
of entrepreneurship as one of the key drives of economic growth in Europe. Acs and Szerb
(2009) discovered that entrepreneurial activity differs significantly from country to country,
depending on its stage of development. The development steps correspond well to Porter’s
(2002) stages of development, meaning that countries at efficiency-driven stage exhibit
different levels and types of entrepreneurship from the innovation-driven ones. This may lead
to severe heterogeneity in the sample, causing heteroscedasticity. To minimize the problem,
countries selected for the study represent similar development phase; they are mainly at the
innovation-driven stage or rapidly approaching it. Nevertheless, selected countries may be
divided into two groups: Western Europe – at the innovation-driven stage of development,
and Central and Eastern Europe – at the efficiency-driven phase. However, convergence is
strongly present in the sample (European Commission, 2010; De Benedictis and Tajoli,
2003), allowing to estimate an average level of entrepreneurship across the EU, with a use
of appropriate econometric techniques. In this manner, twenty-four EU countries were
chosen and two European Economic Community countries that are not members of the EU –
Switzerland and Norway. Three EU countries were excluded from the study, Poland, Malta
and Greece. No data was collected for these three countries in the period of interest. Table 3
below identifies the countries qualifying for the study, as well as those excluded.

Table 3. Countries included in the study
15
EU member states included in the study
Austria, Belgium, Bulgaria, Cyprus, Czech
Republic, Denmark, Estonia, Finland, France,
Germany, Hungary, Ireland, Italy, Latvia,
Lithuania, Luxembourg, the Netherlands,
Portugal, Romania, Slovakia, Slovenia, Spain,
Sweden, the UK
EU member states not included in the study Greece, Malta, Poland
Non-EU member states (EEC members)
included in the study
Norway, Switzerland
This group consists fully of European countries, of which many are member-states of the
European Union, Organization for the Economic Co-operation and Development,
Organization for Security and Co-operation in Europe. Fourteen countries are part of the
Eurozone - single European currency community. The group may seem quite homogenous,
it does, however, possess heterogenic features, including different demographics, mainly
size, geographic location, islands and continent, or institutional regimes, taxation and legal
framework.
Main contributors to data collection were Eurostat online database and the Conference
Board Total Economy Database. Both sources are open for public and free use, and
specialize in collecting economic data in consistent format, both time-wise and in structure.
For these two reason it was the most convenient to use this data resource for this research.
3.2 Description and Summary of the Variables
Table 4 summarizes the variables used in the estimation of the model, with their respective
descriptive statistics.

Table 4. Variables with description, source, and descriptive statistics
16
Variable
Name
Source Mean Median
St.
Dev
Min Max
TFP%growth
Total
Economy
Database
0.56 0.16 1.78 -7.48 11.64
Patents Eurostat 98.13 64.58
105.2
0
0.19
429.3
4
Self-employment
Eurostat 0.06 0.05 0.02 0.03 0.10
Business
Births
Eurostat 10.45 9.90 3.83 0.00 27.21
Business
Deaths
Eurostat 8.32 8.06 2.78 0.00 18.79
Business
Survival
Eurostat 71.66 72.99 12.62 0.00 90.85
Labour
Product.
Eurostat 2.89 2.30 2.83 -5.80 21.70
RD exp. Eurostat 1.44 1.25 0.87 0.22 4.17
Business
Invest.
Eurostat 19.20 18.80 3.71 9.50 32.70
‘TFP %growth’ accounts for the changes in output not caused by changes in inputs. “TFP
represents the effect of technological change, efficiency improvements, and inability to
measure the contribution of all other inputs. It is estimated as the residual by subtracting the
sum of two-period average compensation share weighted input growth rates from the output
growth rate. Log differences of level are used for growth rates, and hence TFP growth rates
are Törnqvist indexes” (The Conference Board Total Economy Database, 2010). Erken,
Donselaar and Thurik (2008) have suggested using TFP rather than GDP to describe
economic growth, in line with their argument that entrepreneurship is a dominant drive of
TFP growth. Figure 1 shows changes in Total Factor Productivity for the selected countries.

Figure 1. Total Factor Productivity % Growth
Total-factor productivity percentage growth for selected countries introduces two patterns
present. One, countries at their efficiency-driven stage of development exhibit large
variations in Total Factor Productivity, e.g. Slovakia or Romania. Two, for the innovation-driven
economies, TFP growth is quite level over the period, varying from -1 to 4% with a
median value of 2%. It is noticed, moreover, that certain level of convergence can be
observed. Large country level effects are likely to be present, suggesting individual effects
model for the estimation. Over the twelve years recorded, it is Romania that attains the
highest percentage growth, in 2002. Ireland exhibits steady decline in TFP growth.
The ‘patents’ dataset is based on the patent counts received by the EPO (EPO Bibliographic
Database), adjusted by population. Patents are a key measure of RD output, and their
relative numbers reflect the inventive performance of firms, regions and countries. Among
the few available indicators of technology output, patent indicators are probably the most
frequently used. Salgado-Banda (2005) used patent data to measure innovative
entrepreneurship, and found a positive influence on growth. Therefore, it will serve as one of
the proxies of entrepreneurship, together with business RD expenditure trying to capture
innovative entrepreneurship. In Figure 2 (below), one can see the number of applications to
the European Patent Office, for selected states, by country and year, and adjusted for the
demographic differences between those countries.
17

Figure 2. Patent applications to the EPO (per million of inhabitants)
Patent applications variable demonstrates an interesting picture of entrepreneurial countries.
It is very noticeable how the ‘old Europe’ contributes to this count, and how little some of the
new EU member countries. All the 2004/2007 accession countries, i.e. Bulgaria, Cyprus,
Czech Republic, Estonia and Hungary, are in the same group of low applicants.
Nevertheless, there is a trend visible; the numbers are slightly rising throughout the period. It
is observed, however, that France and Belgium seem to be exhibiting very similar pattern,
suggesting that the innovativeness of both countries is almost the same. Figure 3 may
confirm that the twelve new countries are not yet at the innovation-driven stage of
development, exhibited through a low utilization of intellectual performance
‘Self-employment’ covers employers, who can be defined as persons who work in their own
business, professional practice or farm for the purpose of earning a profit, and who employ at
least one other person. As well as self-employed persons, who are defined as persons who
work in their own business, professional practice or farm for the purpose of earning a profit,
and who employ no other persons. Self-employment, sometimes referred to as ‘business
ownership’, is a common suggested proxy for entrepreneurship. Studies employing this
variable include Braunerhjelm (2010) and Thurik (1999). Both researchers found self-employment
to be a good proxy for entrepreneurship.
18

Figure 3. Proportion of self-employed and employers to the total population
‘Business Births’, often referred to as ‘business entries’ or ‘business start-ups’, is expressed
as a ratio of businesses born in a reference period to the total number of businesses. It is the
most common measure of entrepreneurship. Acs and Armington (2002) and Bartik (1989)
have used this indicator to study the US economy obtaining very promising results. This
study introduces two additional variables important in measuring business at its early stage,
‘Business Exits’ and ‘Business Survival Rates’. Both variables have been found significant in
number of articles (Audretsch Keilbach, 2004; Fisher Reuber, 2010). Gartner and Shane
(1995) argue Business Survival Rates to be a valid measurement of entrepreneurial activity,
if properly constructed. Those three variables combined can evaluate the extent of
successful entrepreneurship.
19

Figure 4. Business Birth Rates
Figure 4 above describes the pattern of business birth rates for selected countries. One can
see that the rates have been quite consistent over the years for the Western European
countries, except Portugal, which experienced a significant increase between the years
2002-2004, but continuing at this steady level since. The new EU countries exhibit large
variations in the business birth rates recorded, especially Lithuania, which noted a massive
spike in the entries rate in 2003 and 2004. Mean Business Birth rate is about 10.
Other variables that are included in the study (‘Labour Productivity %growth’, ‘Total Business
expenditure on RD as a percentage of GDP’, and ‘Business investment as a percentage of
GDP’), in this or similar form where introduced by Erken, Donselaar, and Thurik (2008) in
order to capture the innovative and productive1 entrepreneurship (Braunerhjelm, 2010).
1 Productive entrepreneurship, after Lawson (2008), understood as entrepreneurial actions leading to productivity
growth rather than simple redistribution of assets.
20

Figure 5. Business Investment as a % share of GDP
In Figure 5 above one can notice that the level of business investment is quite level over the
period of eleven years studied, and ranges from about 15 to 23 % of GDP. Two most
interesting patterns are exhibited by Spain and Ireland. The first country has continuously
increased its business investment, measured as percentage share of GDP, which slightly
declined in the last year, most likely due to the global recession that started in that year.
Ireland has displayed an S-shaped investment pattern, which first rises modestly then
declines in the period from 2000 to 2002 after which it strongly ascends to reach its peak in
2005. In the period 2005 – 2008 it sharply declines to attain a record low of just above 16 %
share of GDP.
21

22
4. Methodology and Results
As presented in the previous section, the economic model intended to be used in this
research is as follows:
However, after accounting for multicollinearity, the model is transformed to:
This study utilises cross-sectional time-series approach. It uses a panel data of 26 countries,
with periodical observations taken over a period of 12 years, from 1997 to 2008. Gujarati
(2003) argues that with repeated observations of enough cross-sections, panel analysis
permits to study the dynamics of change with relatively short time series. The combination of
time series with cross-sections can enhance the quality and quantity of data in ways that
would be impossible using only one of these two dimensions (Gujarati, 2003, p. 638).
Additional advantage with having panel data is that “it allows us to test and relax the
assumptions that are implicit in cross-sectional analysis” (Maddala, 2001). Cross-sectional
time-series analysis can allow to detect for individual effects within the cross-sectional
dimension, giving panel data “more informative data, more variability, less collinearity among
variables, more degrees of freedom and more efficiency” (Gujarati Porter, 2009).
Before attempting the estimation using a pooled model, one should take into account various
specification problems, especially collinearity among regressors.

23
4.1 Multicollinearity in the Model
The aim of this research is to estimate the importance of entrepreneurship for determination
of economic growth. As outlined in the previous sections, theory suggests Total Factor
Productivity captures entrepreneurship; hence, its impact on growth should be measured in
changes in TFP. There is no single variable that could completely characterise
entrepreneurship; therefore, this research has adopted several proxy variables that are to
represent certain aspects of it. This approach, however, may lead to violation of one of the
classical assumptions in the basic regression model, namely that the explanatory variables
are not to be exactly linearly related.
Maddala (2001) describes multicollinearity as a “problem of high intercorrelations among the
explanatory variables” (p. 268). He explains, “when the explanatory variables are highly
intercorrelated, it becomes difficult to disentangle the separate effects of each of the
explanatory variables on the explained variable” (p. 268). This may lead to very high
standard errors, or low t-values, thus leading to wide confidence intervals for the parameters
in question (cont.). In a situation like this, the analysis of the regression can be unreliable,
meaning that the coefficients may not be estimated precisely (Gujarati Porter, 2009).
Gujarati and Porter (2009) list several consequences of near perfect or high multicollinearity.
First, despite the OLS estimators still being BLUE (Best Linear Unbiased Estimator), it leads
to large variances and covariances, hence making accurate estimation very difficult. Second,
this may contribute to rushed acceptance of “zero null hypothesis”, that the true population
coefficient is zero. T-ratios may exhibit tendency to fall, becoming statistically insignificant.
This effect, however, would not show in the overall R2. Finally, the estimator can be
oversensitive to changes in the data (p. 327).
First attempt to detect for multicollinearity is with a simple bivariate correlations matrix. Table
5, below, presents Bivariate Correlations between the Independent Variables.

Table 5. Bivariate Correlations between the Independent Variables
24
Variable
Patents
Self-employ
Birth
Rates
Death
Rates
Survival
Rates
Labour
Product.
RD
Exp.
Business
Invest.
Patents 1.0000
Self-employ -0.2216 1.0000
Birth Rates -0.4039 -0.1228 1.0000
Death Rates -0.3579 -0.0651 0.7454 1.0000
Survival Rates 0.1657 -0.1959 0.1339 0.1291 1.0000
Labour Product. -0.3658 -0.2081 0.2598 0.2450 0.0497 1.0000
RD Exp. 0.8734 -0.1786 -0.4203 -0.3572 0.1951 -0.3164 1.0000
Business Invest. -0.6296 -0.0845 0.1811 -0.0007 -0.2359 0.2838 -0.5618 1.0000
Examination of the pair-wise correlations detects two high values, one for the correlation
between Patents and RD Expenditure, and two, between Birth Rates and Death Rates.
Gujarati and Porter (2009) suggest a rule of a thumb that if the pair-wise correlation
coefficient between two regressors is in excess of 0.8, multicollinearity could be a serious
problem. In this case, Patents-RD Exp. coefficient is well above the rule, and Birth Rates-
Death Rates is dangerously close.
Correlation of the estimated coefficients can provide additional insight into the
multicollinearity embedded in the variables. Table 6 displays the matrix correlations of
estimated coefficients using Random Effects Model (REM).

Table 6. Correlation of Coefficients in REM
25
Variable
Patents
Self-employ
Birth
Rates
Death
Rates
Survival
Rates
Labour
Product.
RD
Exp.
Business
Invest.
Patents 1.0000
Self-employ 0.1884 1.0000
Birth Rates 0.0341 0.2084 1.0000
Death Rates 0.0513 -0.0891 -0.5607 1.0000
Survival Rates 0.0530 0.1851 -0.0060 -0.0412 1.0000
Labour Product. 0.0110 0.0796 0.1761 -0.1422 -0.0884 1.0000
RD Exp. -0.7725 -0.0522 0.0040 0.0840 -0.1343 0.0790 1.0000
Business Invest. 0.2526 0.0326 -0.2226 0.2598 0.0982 -0.1324 -0.0032 1.0000
Results presented in the table above do not differ significantly from these shown in Table 5.
There is still high correlation between Patens and RD Expenditure, as well as in Birth Rates
and Death Rates.
Examining the tolerances or VIFs may produce more decisive results than the bivariate
correlations of independent variables or their estimated coefficients. Table 7 presents the
variance-inflation factor (VIF) and tolerance before and after accounting for multicollinearity.

Table 7. VIF and Tolerance before and after solving for Multicollinearity
Variable Before After
VIF Tolerance VIF Tolerance
Patents 5.75 0.1739 2.44 0.4100
Self-employ 1.41 0.7089 1.34 0.7438
Birth Rates 2.55 0.3921 1.27 0.7871
Death Rates 2.61 0.3838 - -
Survival Rates 1.17 0.8535 1.12 0.8901
Labour Product. 1.30 0.7692 1.32 0.7566
RD Exp. 4.41 0.2267 - -
Business Invest. 2.24 0.4471 1.86 0.5367
Mean VIF 2.68 1.56
VIFs of 10 or higher (or equivalently, tolerances of 0.10 or less) are considered to indicate
severe multicollinearity. Some researchers suggest, however, a VIF over 2.5 and tolerance
under 0.40 may signify too high collinearity and cause concern. Table above displays VIF
values of 5.75 for Patents, 2.55 for Birth Rates, 2.61 for Death Rates, and 4.41 for RD
Expenditure. The mean VIF is 2.68. Tolerance exhibits similar patterns implying possible
high multicollinearity.
Eigenvalue, condition index and condition number are used to further test for
multicollinearity. Table 8 demonstrates the results of these tests.
Table 8. Eigenvalue and Condition Number
Eigenvalue Condition
26
Index
1 7.4213 1.0000
2 0.9542 2.7888
3 0.3257 4.7733
4 0.1383 7.3244
5 0.0550 11.6159
6 0.0448 12.8719
7 0.0309 15.4984
8 0.0263 16.7890
9 0.0034 46.6148
Condition Number 46.6148

Condition number (CN) of 15 or above suggests uneasy level of multicollinearity present in
the data. CN over 30 is an alarming level signifying serious multicollinearity (Belsley, Kuh,
Welsch, 1980). CN of 46.6148 detected in the test above is well over this threshold,
prompting conclusion that severe multicollinearity is indeed present.
Gujarati and Porter (2009), Gujarati (2003), and Maddala (2001) all suggest similar solutions
to multicollinearity. One, by providing additional data, which is impossible to apply in case of
this research, as more data is simply not available. Two, combining cross-sectional and time
series data, i.e. pooling the data; has shown to ease the problem slightly, however not in
sufficient measure. Finally, dropping one of the collinear variables. Based on the results
obtained, excluding both Death Rates and RD Expenditure should improve the model
ensuing acceptable level collinearity between remaining variables.
VIFs and tolerance indicators in Table 7, as well as bivariate correlations in Table 9, present
significant decrease in multicollinearity. Mean VIF has a value of 1.56, much below the
required 2.5, with tolerance levels varying from 41% to 81%. After deleting two most
troublesome variables – Death Rates and RD Exp. - Condition Number has dropped to
25.4048, which is well in the acceptable region of collinearity.
Table 9. Bivariate Correlations after solving for Multicollinearity
27
Variable Patents
Self-employ.
Birth
Rates
Survival
Rates
Labour
Product.
Business
Invest.
Patents 1.0000
Self-employ.
-0.2587 1.0000
Birth
Rates
-0.3967 -0.1024 1.0000
Survival
Rates
0.2041 -0.1783 0.0023 1.0000
Labour
Product.
-0.3895 -0.1714 0.2693 0.0333 1.0000
Business
Invest.
-0.6252 -0.0395 0.2401 -0.2526 0.3268 1.0000

4.2 Pooled Model - FGLS Regression
First, a Wooldridge Test is performed to test for serial correlation in the panel data model.
Under the test’s assumption, significant test statistic indicates the presence of serial
correlation (Wooldridge, 2002; Drukker, 2003).
H0: no first-order autocorrelation
Table 10 demonstrates the results of the Wooldridge Test.
Table 10. Wooldridge Test for Autocorrelation
F( 1, 14) 0.002
Prob F 0.9690
Application of the test generates an F value of 0.002. Probability of obtaining such a value is
close to 97%. This leads to a conclusion not to reject the null hypothesis, stating the same
that there is no serial correlation in the panel data model.
Pooled model “treats all observations as though they came from the same regression model”
(Koop, 2008). It does not account for heterogeneity, i.e. individuality or uniqueness, of
countries in the dataset. As the set contains 26 cross-sections – countries – that exhibit
considerable differences in the size of observations, the problem of heteroscedasticity is
more than likely to occur (Gujarati Porter, 2009). Heteroscedasticity, difference in
variances of each disturbance term, can lead to inaccurate results of regular OLS estimation,
producing unnecessarily larger confidence intervals. Consequently, the t-tests, or F-tests,
become unreliable, and regression may produce statistically insignificant coefficients
(Gujarati, 2003).
To correct for the heteroscedasticity a Feasible General Least Squares regression is carried.
The results are available in Table 11.
28

Table 11. FGLS Regression Results
Dependent Variable TFP % growth
29
Independent
Variables
Coefficients Std. Err. Z - Stat P - value
Patents .0062888 .0008634 7.28*** 0.000
Self-employ. 26.84751 4.892779 5.49*** 0.000
Birth Rates .0550811 .0252399 2.18** 0.029
Survival
Rates
-.0042126 .0054172 -0.78 0.437
Labour
Product.
.3723613 .0342916 10.86*** 0.000
Business
Invest.
-.048415 .0163605 -2.96*** 0.003
Intercept -2.048344 .6551133 -3.13*** 0.002
Wald chi2 303.93 Prob chi2 0.0000
* Significant at 10%, ** Significant at 5%, *** Significant at 1%
The results obtained from the FGLS regression seem quite satisfactory. Five out of six
variables are statistically significant, leaving Survival Rates as the only independent variable
not significant at any level. Patents, Self-Employment, Labour Productivity, and Business
Investment are significant at 1%, whereas Birth Rates at the level of 5%. The signs on the

coefficients estimated are mostly as expected, with the exception of Business Investment,
which is highly statistically significant, and Survival Rates – insignificant at any level. Both
variables reveal negative signs indicating adverse relationship to Total Factor Productivity
growth. Overall, the regression produces high Wald Chi2 value of 303.93, with probability
approaching zero.
Gujarati (2003) and Gujarati Porter (2009) stipulate, however, there is a major problem
with pooled regression model, as it does not distinguishes between the various cross-sections
and their individual effects. They argue the heterogeneity should be taken into
account when estimating a cross-sectional time-series model; otherwise, the estimated
coefficients may be biased and inconsistent. Individual Effects Models are introduced to help
solve this problem.
30
4.3. Individual Effects Models
“There are two main individual effects models, the Fixed Effects Model (FEM) and the
Random Effects Model (REM)” (Koop, 2008). The FEM “allows for heterogeneity among
subjects by allowing each entity to have its own intercept value. (…) The term ‘fixed effects’
is due to the fact that, although the intercept may differ across subjects, each entity’s
intercept doe not vary over time, that is, it is time-invariant” (Gujarati Porter, 2009, p. 596).
In the REM, the intercepts are treated “as random variables rather than fixed constants”
(Maddala, 2001, p. 575). They are assumed to be mutually independent, as well as
independent of the error terms (cont.).
Statistically, the fixed effects model always gives consistent results, although they may not
be the most efficient. Random effects model, on the other hand, should generate better P-values
as it is a more efficient estimator. To decide which of the individual effects model is
more appropriate for the sample a Hausman Test is carried; the results of which are
available in Table 12.

Table 12. Hausman Test Results
31
Dep. Var.:
TFP %
growth
Coefficients
Independent
Variables
FEM REM Difference S.E.
Patents .010472 .002615 .007857 .009947
Self-employ. 43.91168 28.92261 14.98907 22.27228
Birth Rates .0830283 .0722454 .0107829 .0270996
Survival
Rates
-.0078484 -.0023071 -.0055413 .0207347
Labour
Product.
.328839 .3460685 -.0172295 .015483
Business
Invest.
-.2511747 -.1703243 -.0808504 .0518666
Chi2 3.85 Probchi2 0.6970
The Hausman Test verifies the hypothesis that the Fixed Effects Model estimators do not
differ substantially from the Random Effects Model estimators. A Chi2 value of 3.85 is
obtained, with almost 70% probability of attaining such a chi2 value. This leads to a
conclusion that the null hypothesis should not be rejected; therefore, the REM is a more
appropriate individual effects model for the sample2.
The REM estimation results are found in Table 13.
2
The results of the estimation using Fixed Effects are shown in Table 15, available in the Appendix.

Table 13. Random Effects Model Results
32
Independent
Variables
Coefficients Std. Err. Z - Stat P - value
Patents 0.002615 0.0030333 0.86 0.389
Self-employ. 28.92261 15.37272 1.88* 0.060
Birth Rates 0.0722454 0.0368683 1.96** 0.050
Survival
Rates
-0.0023071 0.0141907 -0.16 0.871
Labour
Product.
0.3460685 0.0418999 8.26*** 0.000
Business
Invest.
-0.1703243 0.0580609 -2.93*** 0.003
Intercept 0.5308452 2.052602 0.26 0.796
Overall R2 0.3529 Prob chi2 0.0000
The results presented in Table 13 show that in general, the regression is found to be
significant, and the hypothesis that all the coefficients of explanatory variables are jointly
equal to zero is rejected; with Wald chi2 value of 77.07 and probability approaching zero.
Four explanatory variables are statistically significant. Two, Labour Productivity and
Business Investment, are significant at 1% level. Birth Rates is significant at 5% level,
whereas Self-employment at 10% level. Both Patents and Survival Rates are insignificant at
any level. Most signs on coefficients are as expected, with the exception of Survival Rates
and Business Investment, which have negative signs. The overall R2, which measures how
much of the variation in the dependant variable is explained by the independent variables
included in the model, has a value of 0.3529. Over 35% of variations in the percentage
growth of Total Factor Productivity is explained by the estimated REM model.
Lee at al. (2004) have found Patents, measured as the total number of patents per 100
thousand of population, to be insignificant at 1%, 5% or even 10% level. After conducting
detail analysis, however, he discovered Patents to have marginal positive effect when taken
only for manufacturing industry, with the level of significance at 10%. The insignificance of
Survival Rates may be due to a slightly different character of the variable. It does not
measure the level of entrepreneurship or entrepreneurial activity, as all other variables do by

proxy, but rather the level of ‘success’ of entrepreneurial actions. C. Mirjam van Praag (2003)
believes survival rate is not the best measure of entrepreneurial success, as it does not
differentiate between ‘forced exits’ and ‘entrepreneurial failures’. Van Praag, as well as
Gartner and Shane (1995), have also found it insignificant in some cases, i.e. when tested in
short time periods.
The model concludes, the greater the ratio of self-employed and employers to the
population, the more TFP grows. Business Birth Rates and Labour Productivity growth have
similar positive effect on TFP % growth, likewise the total number of patent application to the
EPO per million of inhabitants. Marginal negative effect is noted for Business Survival Rates
and Business Investment as a percentage of GDP. Kevin Carey (1996) has discovered
analogous relationship between business investment and TFP growth. He argues that
investment, especially in the form of inventory accumulation, and Total Factor Productivity
growth are negatively related, as inventories are depleted when productivity growth is high.
To determine which model is the most appropriate for the sample, pooled or random effects,
the Breusch and Pagan Lagrange Multiplier Test is conducted to test the hypothesis that
there are no random effects present.
H0: = 0
The results of the test are shown in the Table 14.
Table 14. Breusch-Pagan LM Test for Random Effects
Var SD = sqrt(Var)
33
TFP %
growth
3.815725 1.953388
e .9488458 .9740872
u 1.582822 1.258102
chi2 90.55 Prob chi2 0.0000
Application of the test produces a chi-square value of 90.55. The probability of obtaining a
chi-square value of 90.55 is close to 0%. Therefore, the null hypothesis is rejected, and one
may conclude that random effects are present; hence, the REM is the most appropriate
model for this sample. These results are in line with the Hausman Test, confirming the
validity of Random Effects Model.

Comparing the results of the two models used, it is noticed that the pooled regression
produces more significant coefficients and at the higher level of significance. First, Patents
variable is highly significant in the Pooled Estimation, at the level of 1%, whereas it is not
significant even at the 10% level in the REM. Self-employment and enterprise Birth Rates
are still significant, however at lower levels, while both Labour Productivity and Business
Investment stay significant at the 1% level. Signs on the coefficients follow the same patter in
either of the models.
34

35
5. Conclusions
There has been a significant theoretical contribution on the topic of entrepreneurship and
economic growth. Section 2 of this paper described number of economic schools of thought
assigning different roles to entrepreneurship and its part in stimulating growth. The neo-classical
growth theory, whether it be in the form of growth accounting (Denison, 1985) or
theory of long-run tendencies (Solow, 1970) does not fully explain the economic growth
recorded, leaving a considerable residual of unexplained variations of the growth in the
model. This approach does not leave much space for entrepreneurship, as it describes the
residual to be an effect of exogenous technological progress. Van de Klundert and Smulders
(1992) stress that the technological change, in real terms, stays much unaccounted for, like
Biblical “manna from heaven” (Van de Klundert Smulders, 1992), it is exogenously given.
The new growth theory, however, “puts emphasis on the endogenous role of innovation and
human capital formation in explaining economic growth” (Wennekers Thurik, 1999, p. 36).
This theory gives entrepreneurship a strong, nonetheless implicit, role in growth stimulation,
drawing largely from entrepreneurial innovation and efficiency. Endogenous growth theory
has provided academia with a theoretical framework allowing for empirical investigations into
the complicated relationship of entrepreneurship and economic growth.
The study conducted in this paper is primarily based on the Erken, Donselaar and Thurik
(2008) research. It has found entrepreneurship, expressed in the form of business
ownership, a highly significant variable in explaining economic growth, as represented by
Total Factor Productivity growth. Auxiliary studies have indicated other ‘entrepreneurship’
variables that may explain productivity growth (Salgado-Banda, 2005; Braunerhjelm, 2010;
Thurik, 1999; Acs Armington, 2002; Acs Szerb, 2009; Naude, 2008), most of which were
included in the model, after accounting for multicollinearity. The regression results produced
in this study have, for the most part, confirmed the initial hypothesis.
Entrepreneurship, realised through proxies, explains changes in Total Factor Productivity to
some extent. Overall R2 produced in the Random Effects model reached over the value of
0.35, meaning the model applied can explicate the growth rates of TFP in over 35%. This
result is not enough to stipulate that TFP changes be primarily caused by variations in
entrepreneurship in a country. It can, nevertheless, shed some light on the nature and
intensity of the relationship between entrepreneurship and economic growth.

In the future, a similar study can be conducted with increased number of observations.
Extending the time frame is a natural development, as the measurement of some variables
does not expand before 1997. Observation for 2009 should be available shortly through the
Eurostat. In addition, identifying ‘better’ proxies for various entrepreneurial activities should
become a priority. Number of initiatives has been set to deliver a more encompassing
measure of entrepreneurship, on which the most prominent and promising is the Global
Entrepreneurship Monitor. This consortium’s goal is to study entrepreneurship not only
through entrepreneurial actions (which proved to be comparatively easy to measure), but
also through entrepreneurial attitudes and aspirations (Acs Szerb, 2009; Acs Armington,
2002; Bosma Levie, 2010; Hancock, Klyver, Bager, 2001; Thurik, Wennekers,
Uhlaner, 2002; Acs Z. J., 2007). Unfortunately, the research findings are not readily available
from the consortium, and so is the data.
36

37
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41
Appendix
Table 15 Fixed Effects Model Results
Independent
Variables
Coefficients Std. Err. T - Stat P - value
Patents .010472 .0103992 1.01 0.317
Self-employ. 43.91168 27.06242 1.62 0.108
Birth Rates .0830283 .0457565 1.81* 0.073
Survival
Rates
-.0078484 .0251258 -0.31 0.756
Labour
Product.
.328839 .0446691 7.36*** 0.000
Business
Invest.
-.2511747 .0778538 -3.23*** 0.002
Intercept .8532845 2.768618 0.31 0.759
Overall R
2
0.1678 Prob chi2 0.0000

CASE Network Studies and Analyses 427 - Entrepreneurship and Economic Growth: An Investigation into the Relationship between Entrepreneurship and Total Factor Productivity Growth in the EU

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