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Content uploaded by Ondrej Dvoulety
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All content in this area was uploaded by Ondrej Dvoulety on Mar 15, 2018
Content may be subject to copyright.
1
How to Analyse Determinants of Entrepreneurship and Self-employment at the
Country Level? A Methodological Contribution
Please Cite as:
Dvouletý, O. (2018). How to Analyse Determinants of Entrepreneurship and Self-employment at the
Country Level? A Methodological Contribution, Journal of Business Venturing Insights (in print)
Abstract
The aim of the article was to empirically support a hypothesis, that no matter what measure of
entrepreneurship or self-employment we choose at the country level, the determinants indicate the
same direction of impact. Methodologically, four measures of entrepreneurial and self-employment
activity were utilized as dependent variables in regression models. Entrepreneurial activity in the
article was operationalized by Eurostat and OECD self-employment rates, and by Global
Entrepreneurship Monitor rates of established business ownership rate and total early-stage
entrepreneurial activity (TEA). Based on the obtained results, the determinants of entrepreneurship
and self-employment influence all four presented measures in the same direction.
Keywords - Measuring Entrepreneurship; Comparability; Entrepreneurial Activity; Self-
employment Rate; Established Business Ownership Rate; Total Early-stage Entrepreneurial Activity;
Regression Analysis
JEL codes - M2, M1, L260
2
1. Introduction
Increasing data availability allows us to conduct empirical studies in the field of entrepreneurship
more frequently. As Koellinger and Thurik (2012) together with Davidsson and Wiklund (2007) note,
there is a large number of published studies with a focus on different levels of analysis, such as micro
(firms or companies), meso (regions or industries) and macro (countries or cross-countries). Each of
these above-mentioned analyses require, besides a theoretical background, a proper empirical and
methodological strategy. Collected empirical evidence, allows us to seek the most suitable solutions,
when it comes to the selection of data sources, variables and scientific methods. Such a debate might
improve the quality of future studies in the fields of entrepreneurship and self-employment (e. g.
Apergis and Payne, 2016; Stenholm et al., 2013; Iversen et al., 2007; Congregado, 2007 or Coviello
and Jones, 2004).
Presented study aims to extend the empirical knowledge on the measurement of entrepreneurship at
the country level and its determinants. The motivation for conducting this study lies in a large number
of recently published studies focused on the cross-country determinants of entrepreneurial activity
and self-employment (e. g. Nikolaev et al., 2018; Rusu and Roman, 2017; Roman et al., 2017;
Dempster and Isaacs, 2017; Dvouletý, 2017a; Nicolae et al., 2017; Canever and Menezes, 2017; Hall
et al., 2016; Hoogendoorn et al., 2016; Carbonara et al., 2016, Calá et al., 2015 or Valdez and
Richardson, 2013) which are often based on different measures. First, the question is whether the
various studies, based on different operationalisations of entrepreneurial activity and self-
employment indicate the same impact of the cross-country determinants or not. If the studies, aiming
to explore drivers of entrepreneurship and self-employment, deliver contradictory conclusions on the
impact of economic and institutional variables, then it is very difficult to form any policy
recommendations, i. e. aiming to change the business environment (Szerb et al., 2013 or Parker,
2009). Second, from an empirical experience (e. g. Baptista and Thurik, 2007 or Grilo and Thurik,
3
2004) it is well known, that determinants of entrepreneurship and self-employment might change
over the time and across regions. Therefore if we want to compare the different measures of
entrepreneurship and self-employment methodologically, then we need to work with the same group
of countries and follow it for the exact same time period. This kind of empirical exercises, aiming for
a harmonization are still very rare in entrepreneurship research, despite the fact that these studies are
very important for the whole community.
The debate on the measurement of entrepreneurship and self-employment at the country level is not
novel (see e. g. Henrekson and Sanandaji, 2014; Acs et al., 2014; Marcotte, 2013; Rogoff, 2012; Acs
et al., 2008, Iversen et al., 2007 or Congregado, 2007), however, this article aims to push this
discussion further on, by an empirical assessment of the differences across various indicators on an
example of a harmonized sample. Particularly, the article exploits a dataset of eleven countries over
the period 2001-2015. Methodologically, four measures of entrepreneurial and self-employment
activity are utilized as dependent variables, and for each of the dependent variables, a comparative
regression model is estimated with a set of country-level determinants. Entrepreneurial activity in the
article is operationalized by Eurostat (2017) self-employment rate, OECD (2017) self-employment
rate, and by Global Entrepreneurship Monitor (2017) rates of established business ownership rate and
total early-stage entrepreneurial activity (TEA). The main aim of the article is to empirically support
a hypothesis, that no matter what measure of entrepreneurship or self-employment we choose at the
country level, the determinants indicate the same direction of impact, because the country-level
determinants affect the most of entrepreneurs and self-employed individuals in the economy.
The structure of the article is conventional. The following part is dedicated to the discussion on the
measuring country level of entrepreneurship and self-employment. Section three introduces the
collected dataset and variables, and it presents the empirical strategy and obtained econometric
estimates. The final section concludes the article and it suggests avenues for future research.
4
2. Measuring Entrepreneurship and Self-employment Rates at the Country Level
According to Marcotte (2013), Acs et al. (2008), Iversen et al. (2007) and Congregado (2007), the
measurement of entrepreneurial and self-employment activity at the country and cross-country levels
is still an under-represented area of research, despite the need to have reliable data for conducting
empirical studies. Empirical scholars operationalize entrepreneurship/self-employment differently.
According to Stenholm et al. (2013) there two approaches how to measure country level of
entrepreneurial activity. The first one relies on self-reports of randomly selected individuals (surveys)
and the second one is based on the records obtained from national business registries. Iversen et al.
(2007) have tried to compare the historical perception of entrepreneur with the particular measures
of entrepreneurship and self-employment in the economy. A very comprehensive overview of
existing measures was recently written by Marcotte (2013).
One common approach is to express entrepreneurial and self-employment activity as a ratio of the
population of registered businesses/number of self-employed (e. g. Koellinger and Thurik, 2012 or
Dvouletý and Mareš, 2016a; 2016b). Frequently is also used the variable, representing the rate of
newly established/registered enterprises (e. g. Dempster and Isaacs, 2017; Dvouletý, 2017b; Nicolae
et al., 2017; Carbonara et al., 2016 or Fritsch et al., 2015). Nevertheless, Congregado (2007) together
with Van Stel (2005) argue, that methodology of national statistical offices differ, and therefore it is
better to use adjusted harmonized data for instance from Eurostat or OECD databases. Inspired by
this idea, Van Stel (2005), with his colleagues created EIM Compendia database, where they adjusted
and harmonized American and European data obtained from OECD. Unfortunately, this dataset is
limited by available years and countries (e. g. Hoogendoorn et al., 2016). Other scholars (e. g. Lado-
Sestayo et al., 2017; Ferreira et al., 2017; Acs et al., 2008, Reynolds et al., 2005 or Sternberg and
Wennekers, 2005) work with the data obtained from the Global Entrepreneurship Monitor surveys,
5
particularly with the rates of established business ownership rate, total early-stage entrepreneurial
activity (TEA), high-growth activity or TEA innovation activity. Additionally, Kaufman index of
entrepreneurial activity for the US should be mentioned (Hafer, 2013).
Another approach, how to solve, this measurement issue, is to work with more complex indices
aiming to capture the whole entrepreneurial ecosystem, such as Global Entrepreneurship Index,
former Global Entrepreneurship and Development Index
1
(Acs and Szerb, 2009; Acs et al., 2014).
At the same time, we need to mention the fact, that there are indicators measuring “general level of
entrepreneurship and self-employment” (overall rates) and those, aiming to monitor just the “specific
rates” (e. g. high-growth enterprises, necessity/opportunity driven entrepreneurship). However, from
the economic and institutionalist’s perspective, the macroeconomic environment influence the most
of the entities present in the economy (e. g. Davidsson and Wiklund, 2007, Van Metre and Hall, 2011
or Chauchan and Das, 2017).
However, the variety of utilized indicators does not reflect their comparability in empirical practice.
Generally, a little is known about the differences in various measures of activity and correlations
between them. Marcotte (2013) was one of the first scholars who employed bivariate correlation
analysis and compared different measures of entrepreneurial activity. She has found highly positive
and significant correlations between registered business activity (obtained from World Bank) and
data from Global Entrepreneurship Monitor. Her observation was later supported by Henrekson and
Sanandaji (2014). Nevertheless, Marcotte (2013) admits, that robustness of her findings is limited by
the sample size and she encourages other scholars to validate her results when more observations are
available. Presented studies were limited by period till 2010. Positive correlations between different
1
Please note that the data from Global Entrepreneurship Index are available for period of years 2006-2018, for details
see Acs et al. (2017) and for the most recent data see Global Entrepreneurship Index (2018) on the following link:
https://thegedi.org/2018-global-entrepreneurship-index-2/.
6
“stock levels” of entrepreneurship and self-employment have been also observed in studies by
Iversen et al. (2007) and Stenholm et al. (2013). Nevertheless, despite these positive correlations, the
country-levels of entrepreneurship and self-employment (and their relative rankings) differ across
used measures and indicators. In other words, it does not mean that these indicators capture the same
pool/number of entrepreneurs – they just approximate the level of activity from different
methodological and theoretical angles.
3. Empirical Comparisons
The objective of the study is to empirically support a hypothesis, that no matter what measure of
entrepreneurship or self-employment we choose at the country level, the determinants indicate the
same direction of impact, because the country-level determinants affect the most of entrepreneurs and
self-employed individuals in the economy no matter what measure we use. We may argue to what
extent, but most of them are influenced in a similar way by the legislative and institutional framework
(North, 1990; Bruton et al., 2010), economic (demand-side) determinants and (supply-side)
population characteristics (Grilo and Thurik, 2004).
The empirical approach begins by an estimation of bivariate correlations between four measures of
entrepreneurial/self-employment activity. The second empirical exercise is based on the employment
of multivariate regression analysis, aiming to demonstrate differences in determinants of
entrepreneurship and self-employment across indicators.
3.1 Correlations between Indicators
The starting point is the estimation of correlations between four measures of entrepreneurial/self-
employment activity, operationalized by Eurostat (2017) self-employment rate, OECD (2017) self-
7
employment rate, and by the Global Entrepreneurship Monitor (2017) rates of established business
ownership rate (EBOR) and total early-stage entrepreneurial activity (TEA). The selection of these
four measures was motivated mainly by its usage in the previous research and by the data availability.
The aim was to compare indicators that represent the stock of the population rather than new entry
rates (Iversen et al., 2007). In addition, data from the national statistical offices could not be used for
this purpose, because of the differences in the methodology of the national statistical offices.
Therefore first selection of available measures offered occupational data from OECD and Eurostat –
that were also often used in empirical studies (e. g. Carree et al., 2002; Noorderhaven et al., 2003).
The second source of available measures was the Global Entrepreneurship Monitor survey, where the
scholars most often work with two rates, Established Business Ownership Rate (EBOR) and Total
Early-Stage Entrepreneurial Activity (TEA). EBOR (used for instance by Sternberg and
Wennekers, 2005 and Dvouletý, 2017a) seems to be the closest counterpart to the rates from OECD
and Eurostat, because it also represents to some extend occupational choice of being an entrepreneur,
especially in terms of receiving payments/profits from the activity. On the contrary, TEA is quite
different from the three above-mentioned indicators and it differs mainly in case of payments. Nascent
entrepreneurs still do not need to receive regular income/payments from their activity. Surprisingly,
this indicator belongs to those most frequently used in the previous studies (e. g. Ferreira et al., 2017;
Roman et al., 2017; Calá et al., 2015) and therefore it should be taken into account in this empirical
exercise as well.
Collected variables cover years 2001-2015 for eleven European countries, namely for Belgium,
Denmark, France, Germany, Greece, Hungary, Ireland, Netherlands, Slovenia, Spain and Sweden.
The countries and measures were selected based on the availability of the data. For the comparability
of the results, it was important to collect a sample of countries, having complete data for the all four
measures and at the same time, having data for the whole time period (see Table 1). Therefore only
8
eleven countries and four indices could have been included in the empirical analysis. Development
of all four average rates over time is depicted in Figure 1 below.
Table 1: Number of Observations for the Dependent Variables across Countries
Country/Variable
Self-Employment
Rate Eurostat2
Self-Employment
Rate OECD3
Established Business
Ownership Rate4
Total early-stage
Entrepreneurial
Activity (TEA)5
Belgium
15
15
15
15
Denmark
15
15
13
13
France
15
15
14
14
Germany
15
15
14
14
Greece
15
15
13
13
Hungary
15
15
14
14
Ireland
15
15
14
14
Netherlands
15
15
15
15
Slovenia
14
15
14
14
Spain
15
15
15
15
Sweden
15
15
13
13
Total (N)
163
165
154
154
Source: STATA 14, own calculations
2
Self-Employment Rate from Eurostat (2017) represents a percentage of economically active population “who work in
their own business, farm or professional practice. A self-employed person is considered to be working if she/he meets
one of the following criteria: works for the purpose of earning profit, spends time on the operation of a business or is in
the process of setting up his/her business.”
3
Self-employment Rate from OECD (2017) represents “a percentage of 18-64 population who are currently an owner-
manager of an established business, i.e., owning and managing a running business that has paid salaries, wages, or any
other payments to the owners for more than 42 months.”
4
Established Business Ownership Rate from Global Entrepreneurship Monitor (2017) represents “of 18-64 population
who are currently an owner-manager of an established business, i.e., owning and managing a running business that has
paid salaries, wages, or any other payments to the owners for more than 42 months.”
5
Total early-stage Entrepreneurial Activity (TEA) from Global Entrepreneurship Monitor (2017) represents “a percentage
of 18-64 population who are either a nascent entrepreneur or owner-manager of a new business.”
9
Figure 1: Average Entrepreneurial and Self-employment Activity over years 2001-2015
4
6
8
10
12
14
16
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15
Self-employment Rate Eurostat
Self-Employment Rate OECD
Established Business Ownership Rate
Total early-stage Entrepreneurial Activity (TEA)
Years (2001-2015)
Entrepreneurial Activity (%)
Source: Eviews 9, own calculations
The next step is the estimation of correlations between measures of entrepreneurial activity, because
it is very important to see, whether the correlation coefficients are tightly linked with each other, i. e.
to observe similarity among indicators. Results can be found in Table 2. All correlation coefficients
were found to be statistically significant (p < 0.05) and positive. The highest coefficient is between
the self-employment rates obtained from Eurostat (2017) and OECD (2017). On the contrary, the
lowest coefficient was observed for total early-stage entrepreneurial activity (TEA). This is quite to
be expected, since the three above mentioned indicators capture the active population, compared with
TEA which represents only early-stage activity. Additionally, both GEM (2017) indicators, are also
10
highly correlated as they come from the same survey. These positive correlations are in the line with
the findings of Marcotte (2013) and Iversen et al. (2007).
However, the previous exercise also confirms the differences between the actual values and
development of all indicators over time (see again Figure 1). One may for example notice that TEA
shows completely different development, compared to the other three indicators. This might be
caused by the fact that TEA measures only a specific part of the whole entrepreneurial activity. On
the other hand, quite close development over time indicate EBOR and Self-employment Rate from
Eurostat. Nevertheless, given the theoretical assumption and the high-level of positive correlations
we might still assume similarity in the impact of country-level determinants.
Table 2: Correlations between Measures of Entrepreneurial Activity (*p < 0.05)
Variable
Self-
Employment
Rate Eurostat
Self-
Employment
Rate OECD
Established
Business
Ownership Rate
Total early-stage
Entrepreneurial Activity
(TEA)
Self-Employment Rate
Eurostat
1.0000
Self-Employment Rate
OECD
0.9783*
1.0000
Established Business
Ownership Rate
0.7361*
0.7533*
1.0000
Total early-stage
Entrepreneurial Activity
(TEA)
0.3620*
0.3004*
0.4957*
1.0000
Source: STATA 14, own calculations
3.2 Independent Variables and Summary Statistics
The following part is dedicated to a multivariate analysis. Building on the initial observation from the
previous section, the additional drivers/determinants of entrepreneurship and self-employment need
to be introduced in order to estimate multivariate regression models.
11
Roman et al. (2017) and Dvouletý (2017a) have recently reviewed findings of scholars dealing with
the cross-country determinants of entrepreneurship. Determinants of entrepreneurship may be divided
into several categories that take into account the legislative and institutional framework (North, 1990;
Bruton et al., 2010), economic (demand-side) determinants and (supply-side) population
characteristics (Grilo and Thurik, 2004) and entrepreneurship, R&D and innovation policies
(Dvouletý, 2017a). Following this categorization, extent of the study and the data availability, we
have tried to include in the analysis most of the previously mentioned categories of variables. We
have ended-up with the factors controlling for the role of economic development, foreign direct
investments and business environment.
Economic development is represented by a non-linear development of unemployment rate (e. g.
Dvouletý, 2017c or Fritsch et al., 2015) and the variable was obtained from the World Bank database
(2017). Additional variable accounts for the role of foreign direct investments (FDI), net inflows (%
of GDP), which was used for instance in the recent study by Abdesselam et al. (2017) or Danakol et
al. (2017). The business environment is furthermore operationalized by the two variables, Doing
Business statistics variable measuring a number of start-up procedures to establish an enterprise
(World Bank, 2017), and by the Economic Freedom Index, which is published by Heritage
Foundation (2017). Both variables were used in the previous empirical studies (e. g. Saunoris and
Sajny, 2017; Dempster and Isaacs, 2017, Carbonara et al., 2016, Aparicio et al. 2016; Van Metre and
Hall, 2011 or Bruothová and Hurný, 2016). For the rest of the determinants we had to be control by
the set of country-dummy variables. Table 3 presents summary statistics below.
12
Table 3: Summary Statistics
Variable / Statistics
Mean
Min
Max
N
Self-Employment Rate Eurostat
11.50
6.67
26.92
163
Self-Employment Rate OECD
15.38
8.65
39.61
165
Established Business Ownership Rate
6.04
1.27
19.61
154
Total early-stage Entrepreneurial Activity (TEA)
5.81
1.63
11.37
154
Unemployment Rate
8.64
2.12
27.47
165
FDI Inflows
7.45
16.07
87.44
164
Start-up Procedures
6.31
3.00
15.00
143
Economic Freedom Index
69.01
54.00
82.60
165
Source: STATA 14, own calculations
3.3 Determinants of Entrepreneurship and Self-employment
The next part of the article is dedicated to the testing of a hypothesis, that no matter what measure of
entrepreneurship or self-employment we choose at the country level, the determinants indicate the
same indicate the same direction of impact. For each of the dependent variables, a comparative
regression model is estimated with a set of country-level determinants. Models were estimated in
software STATA 14 with the robust standard errors to overcome potential threats of
heteroscedasticity and autocorrelation. The level of collinearity among the estimated parameters was
tested with the help of correlation matrices and Variance Inflation Factors test and one can conclude,
that no multicollinearity is present in estimates (Wooldridge, 2010). Stability and robustness of results
are increased by a set of dummy variables for each of the country and by bootstrapping estimation
(Royston and Sauerbrei, 2009). Final models are based on 10,000 replications and they can be found
in Table 4 below. Despite the fact that not all variables were found to be statistically significant, the
signs of coefficients in all four estimated models are in a harmony and they empirically support the
stated hypothesis. Based on the presented findings, it looks like the determinants of entrepreneurship
and self-employment influence all four presented measures similarly and thus increases the
robustness of presented empirical findings. The purpose of the study is not to dive into the
interpretation of obtained coefficients, which are generally in the line of existing research on the
determinants of entrepreneurship and self-employment.
13
Nevertheless, it is worth mentioning that non-linear and changing influence of unemployment rate
was observed for example in the recent studies written by Dvouletý (2017c) and Fritsch et al. (2015).
The obtained finding indicates, that especially when there is an increase in unemployment, some
individuals join entrepreneurship/self-employment out of necessity. However, this non-linearity
suggests that if there is a substantial increase in unemployment (e. g. an economic recession), then,
the initial positive effect turns into a negative (as the recession/economic crisis has effect on the most
entrepreneurs and self-employed in the economy).
The role of inward foreign direct investments (FDIs) has been questioned by several scholars, among
others by Barbosa and Eiriz (2009), De Backer and Sleuwaegen (2003), Danakal et al. (2017) or by
Abdesselam et al. (2017). Authors discuss the crowding out the effect of inward FDIs on domestic
level of entrepreneurship that can be also seen in obtained econometric estimates. The role of business
environment (represented by the economic freedom index) and start-up bureaucracy (represented by
number of start-up procedures to establish an enterprise) is rooted in the theory of institutions,
suggesting that bad environment and formal institutions might discourage individuals to establish a
business (e. g. Dvouletý, 2017a; Dempster and Isaacs, 2017, Carbonara et al., 2016 or Aparicio et al.
2016). Both of these assumptions were supported also by the obtained empirical evidence indicating
that lower economic freedom and more bureaucratic procedures were during the analysed period
associated with lower levels of entrepreneurship and self-employment.
14
Table 4: Determinants of Entrepreneurship and Self-employment across Different Measures
of Entrepreneurial Activity for Years 2001-2015
Model Number
(1)
(2)
(3)
(4)
Independent Variables / Dependent
Variable
Self-
Employment
Rate
Eurostat
Self-
Employment
Rate OECD
Established
Business
Ownership
Rate
Total early-
stage
Entrepreneurial
Activity (TEA)
Unemployment Rate
0.0234
(0.0699)
0.207**
(0.0807)
0.192*
(0.116)
0.190*
(0.113)
Unemployment Rate Squared
-0.00703***
(0.00236)
-0.00741***
(0.00283)
-0.00386
(0.00415)
-0.00906**
(0.00362)
FDI Inflows
-0.00513
(0.00560)
-0.000507
(0.00731)
-0.00862
(0.0121)
-0.00609
(0.0119)
Start-up Procedures
-0.0116***
(0.0466)
-0.147**
(0.0584)
-0.0205
(0.0760)
-0.259***
(0.0744)
Economic Freedom Index
0.0794**
(0.0392)
0.0555
(0.0487)
0.208***
(0.0615)
0.120*
(0.0694)
Constant
6.072**
(2.927)
10.23**
(3.652)
-11.94**
(4.527)
-4.175
(5.111)
Country Dummies
Yes
Yes
Yes
Yes
Observations
142
143
135
135
R2
0.982
0.988
0.771
0.510
Adjusted R2
0.980
0.987
0.743
0.448
AIC
293.0
365.2
522.7
474.0
BIC
340.3
412.6
569.2
520.5
Estimated Robust SE Regressions with Country Dummies based on 10,000 Replications; Standard errors in
parentheses; * p < 0.1, ** p < 0.05, *** p <0.01
Source: STATA 14, own calculations
Concluding Remarks
The main aim of the article was to empirically support a hypothesis, that no matter what measure of
entrepreneurship or self-employment we choose at the country level, the determinants indicate the
same direction of influence. The study was motivated by an increasing number of recently published
studies focused on the cross-country determinants of entrepreneurial activity and self-employment,
as well as in the variety of utilized dependent variables used in these empirical studies. The paper
contributes to debate on the measurement of entrepreneurship and self-employment at the country
level (see e. g. Henrekson and Sanandaji, 2014; Marcotte, 2013; Acs et al., 2008, Iversen et al., 2007
or Congregado, 2007), by an empirical assessment of the differences across various indicators on an
example of a harmonized sample. Particularly, the article exploited a dataset of eleven countries over
15
the period 2001-2015. The countries and measures included in the analysis were selected based on
the availability of the data. Methodologically, four measures of entrepreneurial and self-employment
activity were utilized as dependent variables. Entrepreneurial activity was operationalized by Eurostat
self-employment rate, OECD self-employment rate, and by Global Entrepreneurship Monitor rates
of established business ownership rate and total early-stage entrepreneurial activity (TEA).
In the first step of the empirical analysis, correlations between measures of entrepreneurial/self-
employment activity were inspected. In the line with the findings of Marcotte (2013), bivariate
correlations between four measures of entrepreneurial/self-employment activity were found to be
statistically significant and positive. Building on these promising comparable findings, multivariate
regression models, aiming to demonstrate differences in determinants of entrepreneurship and self-
employment across indicators were estimated. For each of the dependent variables, a comparative
regression model was estimated with a set of country-level determinants. Based on the obtained
results, it looks like the determinants of entrepreneurship and self-employment influence all four
presented measures similarly and thus increases the robustness of presented empirical findings.
Such an observation is very important for the entrepreneurship researchers and policy-makers because
it suggests that macroeconomic institutional and economic environment influences the most of the
entities (both established and early-stage enterprises) present in the economy in a similar direction.
From a research perspective, if the various measures of entrepreneurial and self-employment activity
are to be affected by the determinants similarly, then the harmonization and comparability of the
previously published studies on the determinants of entrepreneurship and self-employment might be
substantially increased. Moreover, if we will be able to confirm the harmonization of the different
measures, we will be able to even more reliably able compare the previously published studies based
on different indicators. Given the obtained estimates, researchers should empirically check the
various measures of entrepreneurial and self-employment activity, in order to increase robustness and
16
reliability of their empirical studies. However, this study is limited by the number of analysed
European countries and its determinants. It is very important to have a look at the comparison outside
of Europe, to see whether these findings will hold in Africa, America or Asia, and especially in the
regions, where the environment is more diverse and dynamic.
In addition to the presented findings, the authors aiming to map the overall activity in the country,
they should not stick only to TEA indicator (that was nowadays used most frequently in the previously
published studies), that accounts only for the level of nascent entrepreneurship. Scholars should also
work also with indicators taking into account the occupational definition of entrepreneurship and self-
employment (e. g. self-employment rates from Eurostat/OECD or EBOR). Forthcoming studies
should build on the presented findings, when more data will be available and they should also inspect
the differences in determinants of entrepreneurship and self-employment with additional measures,
especially with the usage other developed indices (e. g. Global Entrepreneurship Index or Kaufman
index of entrepreneurial activity). Unfortunately, till this moment, the Global Entrepreneurship Index
still does not provide the sufficient number of available years for a dynamic analysis and the Kaufman
index of entrepreneurial activity maps only the United States. Especially, the Global Entrepreneurship
Index brings an important question for the whole research community by asking – should we stick to
the factor-based/stock-based/rate-based definition of entrepreneurship and self-employment, or
should we understand entrepreneurship more as a whole ecosystem that captures all phases and nature
of business formation, expansion and growth in the particular region?
References
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University of Caen and CNRS.
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