1
JOURNAL OF GLOBAL TRADE, ETHICS AND LAW
Volume 3 Issue 1, 2025
ENTREPRENEURIAL DYNAMICS, HUMAN
DEVELOPMENT AND INEQUALITY: EVIDENCE FROM
23 COUNTRIES USING GEM DATA
Emiliano Alzate,1 Oscar Claveria2*
1 Faculty of Economics & Business, University of Barcelona, Barcelona, Spain, emilioalzate@hotmail.com.
2 AQR-IREA, University of Barcelona, Barcelona, Spain, oclaveria@ub.edu.
*Corresponding Author
Abstract. We propose a new approach for the visual inspection of the dynamic
interplay between several determinants of entrepreneurship and other socioeconomic
variables. We focused on the evolution of these variables in 23 countries from 2010
to 2020. First, we ranked the countries according to their growth during the sample
period. Second, we clustered the different states by means of a dimensionality-
reduction technique that enabled synthesising the ordinal information of the rankings
into two dimensions. Finally, countries were projected into a perceptual map
according to their scores in both dimensions. We replicated the analysis both for
2020 and for the growth observed during the decade. In both cases, we observed two
clusters of countries that roughly correspond to European and Latin American
economies. Angola obtained top scores in the two dimensions both in 2020 and
during the decade. Regarding the interactions among variables, for 2020 we observed
that early-stage entrepreneurship shows a negative association with access to
financing and human development. During the decade, we observed a positive link
between early-stage entrepreneurship and market dynamism, which in turn showed
no connection with human development. These findings somehow suggest that the
relative importance of the determinants of entrepreneurship evolved throughout the
decade.
Keywords: Entrepreneurship, National-Level Determinants,
Institutional Environment, Human Development, Inequality, Multivariate Analysis.
JEL: L26, L53, O43, C38
© 2025 Durham & Thunmann, London
Journal of Global Trade, Ethics and Law
ISSN 2977-0025 (Online). Published under Creative Common (CC) BY 4.0 license.
https://doi.org/10.70150/sjyk5e78
Durham &
Thunmann
Emiliano Alzate & Oscar Claveria, 2025
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1. Introduction
Entrepreneurship, understood as the process of starting and running a new business, is
of primary importance to economic growth, especially in the aftermath of economic
crises. The economic impact caused by the pandemic (Belitski et al. 2022; Claveria &
Sorić 2023) highlights the fundamental role of entrepreneurship in overcoming the new
challenges facing the global economy. In this context, measuring and evaluating the levels
of entrepreneurial activity becomes essential to provide policymakers with valuable
insights on how to best foster it to propel economic growth (Amini Sedeh et al. 2022;
Kachuriner & Hrushko 2019).
The only global research source that collects data on entrepreneurship directly from
individual entrepreneurs is the Global Entrepreneurship Monitor (GEM), a joint project
between Babson College and London Business School initiated in 1997 (Reynolds et al.
1999). Since then, GEM carries out annual survey-based research on entrepreneurship
around the world through two surveys: the Adult Population Survey (APS), which
provides information on the characteristics, motivations and ambitions of individuals
starting businesses, as well as social attitudes towards entrepreneurship; and the National
Expert Survey (NES), which looks at the national context in which individuals start
businesses. See Reynolds (2022) and Bosma et al. (2021) for a detailed description of
both surveys.
As opposed to other business surveys, the APS captures the attitudes, behaviours and
expectations of individual adults, providing information on the informal economy,
involving unregistered and unrecorded economic activities and jobs, which can be a
significant part of the national economy beyond the reach of official statistics, especially
in developing countries. Slightly more than 130,000 respondents participated in the APS
in 2020 (GEM 2020). The NES focuses on the entrepreneurial context that influences an
individual decision to start a new business, and subsequent decisions to sustain and grow
that business. For the NES, at least 36 national experts are asked to rate the adequacy, or
otherwise, of a set of predefined Entrepreneurial Framework Conditions (EFCs) that
range from the ease of access to finance to social support for entrepreneurship (Bosma et
al. 2020).
In their seminal work, Reynolds et al. (1999) presented the GEM model, which
analyses the relationship between established and new business activity and economic
growth at the national level. The GEM model assumes that established business activity
at the national level varies with General National Framework Conditions (GNFCs), while
new business activity depends on national levels of entrepreneurial opportunity and
entrepreneurial capacity, which, in turn, vary with EFCs. The model implies that by
controlling for GNFCs governments might ensure superior EFCs and expect higher
national rates of entrepreneurial activity that translate to higher rates of economic growth
(Reynolds et al., 2005).
Consequently, researchers from different fields have examined the factors that may be
influencing entrepreneurship and its relation to a wide range of factors (Abdesselam et al.
2018; Abdullah et al. 2009; Alves et al. 2017; Jafari-Sadeghi et al. 2020; Levie & Autio
Emiliano Alzate & Oscar Claveria, 2025
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2008; Pietrzak et al. 2017; Szerb & Trumbull 2018; Ting et al. 2017; Torres & Augusto
2018). The role of entrepreneurial activity in economic growth—as opposed to other
macroeconomic variables such as consumption or investment—makes it a key variable in
analysing the effect of a complex amalgam of socioeconomic factors on the state of
economies and policymaking around the world (e.g., Dvouletý et al. 2018).
In the present study, GEM data is used to evaluate the dynamic interplay between a set
of drivers of entrepreneurship and entrepreneurial activity in 23 countries between 2010
and 2020. While most GEM-based academic studies draw on data from the APS (Álvarez
et al., 2014), we combine data from both the APS and the NES together with other
socioeconomic variables that measure economic development and inequality. Levie et al.
(2014) stressed the importance of combining GEM data with other cross-national
databases to increase the range of research questions that can be explored, as well as
applying multilevel techniques that take advantage of the cross-country and across-time
clustered properties of the GEM data.
In keeping with this approach, we propose a two-step procedure to analyse the resulting
panel data by means of Categorical Principal Component Analysis (CATPCA), which is
a nonlinear dimensionality-reduction technique that allows analysing qualitative data.
The proposed methodology also makes it possible to work with panel data and, in turn,
avoids the problems derived from cross-sectional causal analysis. See Pérez and Claveria
(2020) for a detailed description of the methodology.
The multivariate procedure used in this study—CATPCA—can be regarded as a
complementary technique to multiple correspondence analysis that can handle nominal,
ordinal and numerical variables simultaneously and can deal with nonlinearities in the
relationships among them. In this study, we use this multivariate procedure to (a)
synthesise the information regarding the evolution of 23 variables in the 23 economies
into two components, and (b) generate perceptual maps with the relative positioning of
the countries and plots that show the interactions between entrepreneurial activity and its
determinants.
In a recent review of the literature, Etemad et al. (2022) have recently noted the
importance to find new solutions to methodological issues. Therefore, in order to
circumvent some of the problems that may arise when dealing with time series from
developing countries, such as the presence of outliers, first, all the information was
transformed into ordinal variables. This was done by ranking the economies according to
the rate of growth of the selected indicators between 2010 and 2020. By assigning a
descending numerical value to each country corresponding to its ranking, we obtained a
set of categorical data. Second, these rankings were then used as input for the analysis,
which is based on CATPCA.
The contribution of the study is twofold. On the one hand, to the best of our knowledge,
this is the first attempt to apply CATPCA to evaluate the dynamics of entrepreneurial
activity at an international level. The study extends the coverage of previous research by
assessing the utility of visualisation techniques in order to shed some light on the complex
interactions amongst human development, inequality, and other variables affecting
entrepreneurial activity. On the other hand, we propose an alternative approach to analyse
Emiliano Alzate & Oscar Claveria, 2025
4
the interplay of key factors behind the dynamics of entrepreneurial activity on the
positioning of economies with respect to the main attributes affecting it. According to our
findings, the relative importance of these determinants of entrepreneurship evolved
throughout the decade, which highlights the importance of including a time dimension in
the analysis of the drivers of entrepreneurial activity.
The study is structured as follows. First, in Section 2 we present the data that were used
and the applied methodology. Section 3 presents the results and, finally in Section 4 we
draw some conclusions and offer suggestions for future research.
2. Data and Methodology
To evaluate the dynamic interplay between a wide range of entrepreneurship
determinants, inequality and economic development, we combined three different sources
of data: GEM data, the Gini index from the World Bank, and the Human Development
Index (HDI) provided by the United Nations. The HDI is a composite indicator of life
expectancy, education, and income per capita (Alzate 2006), whose growth during the
sample period allows us to capture the dynamics of human development from a broader
perspective than the strictly economic one, including the educational dimension (Jafari-
Sadeghi et al., 2020; Sharma and Virani 2023). Table 1 presents and describes the GEM
data used in this study, comprised of variables from both the APS and the NES. We used
the definitions provided by the GEM consortium on their web (GEM Consortium, 2022).
The GEM data set has several features that make it particularly well suited for the
analysis of the drivers of entrepreneurship at the international level, and its contribution
to economic development (Abdesselam et al. 2018; Dvouletý et al. 2018; Estrin et al.
2012; Jafari-Sadeghi et al. 2020). First, GEM is the only globally harmonised data set of
individual-level entrepreneurial behaviours across countries. It is based on representative
samples of the adult working-age population (18–64 years old) and permits the estimation
of prevalence rates of both formal and informal entrepreneurial entries.
Second, GEM data are clustered both across countries and within countries across time,
permitting the analysis of country-level associations. Third, the GEM data offer country-
level cross-sectional time series of up to 15 years for some countries, enabling the study
of within-country change in institutional conditions on the same outcomes. Finally, GEM
uses several screening questions to ensure that it tracks genuine entrepreneurial activity.
For a brief history of GEM, see Levie et al. (2014).
Emiliano Alzate & Oscar Claveria, 2025
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Variables
Definition
TEA
Total early-stage Entrepreneurial Activity (TEA) rate – % of 18-64 population who are
either a nascent entrepreneur or owner-manager of a new business
EBO
Established Business Ownership (EBO) rate – % of adults running a business
perceived
opportunities
% of 18-64 population (individuals involved in any stage of entrepreneurial activity
excluded) who see good opportunities to start a firm where they live
perceived capabilities
% of 18-64 population who believe they have the required skills and knowledge to start
a business
fear of failure
% of 18-64 population who indicate that fear of failure would prevent them from setting
up a business
entrepreneurial
intentions
% of 18-64 population who are latent entrepreneurs and who intend to start a business
within three years
equality ratio TEA
% of female 18-64 population who are either a nascent entrepreneur or owner-manager
of a 'new business', divided by the equivalent percentage for their male counterparts
high job creation
expectation
% of those involved in TEA who expect to create 6 or more jobs in 5 years
services
% of those involved in TEA in the 'Business Services' sector (Information and
Communication, Financial Intermediation and Real Estate, Professional Services or
Administrative Services, as defined by the ISIC 4.0 Business Type Codebook)
financing
The availability of financial resources—equity and debt—for small and medium
enterprises (SMEs) (including grants and subsidies)
policy
Support and Relevance: The extent to which public policies support entrepreneurship -
entrepreneurship as a relevant economic issue
taxes
The extent to which public policies support entrepreneurship – taxes or regulations are
either size-neutral or encourage new and SMEs
programs
The presence and quality of programs directly assisting SMEs at all levels of
government (national, regional, municipal)
education 1
The extent to which training in creating or managing SMEs is incorporated within the
education and training system at primary and secondary levels
education 2
The extent to which training in creating or managing SMEs is incorporated within the
education and training system in higher education
RD transfers
The extent to which national research and development (R&D) will lead to new
commercial opportunities and is available to SMEs
professionalism
Commercial and Legal Infrastructure – The presence of property rights, commercial,
accounting and other legal and assessment services and institutions that support or promote
SMEs
dynamism
The level of change in markets from year to year
openness
The extent to which new firms are free to enter existing markets
infrastructure
Ease of access to physical resources—communication, utilities, transportation, land or
space—at a price that does not discriminate against SMEs
culture
The extent to which social and cultural norms encourage or allow actions leading to new
business methods or activities that can potentially increase personal wealth and income
Table 1: List of variables
Source: Compiled by the authors using the definitions in the GEM web
(https://www.gemconsortium.org/wiki/1154).
Notes: Variables 1 to 8 are expressed as rates. Responses of variables 2 to 5 are computed as the percentage of 18-64
population (individuals involved in any stage of entrepreneurial activity excluded). Responses of variables 9 to 20 are scaled
by means of a Likert-type scale ranging from 0 (very inadequate/insufficient) to 10 (very adequate/sufficient).
Emiliano Alzate & Oscar Claveria, 2025
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These attractive features of the GEM data have inspired a growing body of research in
comparative entrepreneurship that explores associations between country-level attributes
and various aspects of the entrepreneurial processes and seeks to link these to meaningful
outcome variables (Abdesselam et al. 2018; Autio & Acs 2010; Bowen & De Clercq
2008; Ghosh 2022; Jafari-Sadeghi et al. 2020; Levie & Autio 2011; van Stel et al. 2007).
Following Levie et al.’s (2014) suggestions to take advantage of the cross-country and
across-time clustered properties of the GEM data, we propose using a two-step
methodology based on a multivariate dimensionality reduction procedure that allows a
cross-country comparison of the evolution of a wide range of GEM indicators and other
macro variables for 23 European countries in the time period comprised between 2010
and 2020.
Multivariate techniques are able to preserve a high level of information from the
original data set and make no assumptions regarding the direction of causality between
variables. This, coupled with the fact that some of the GEM indicators are bound to
present multicollinearity, make the proposed approach an ideal way to work with and
draw conclusions from a large number of variables. Principal Component Analysis (PCA)
is a widely used method of multivariate dimensionality reduction, however PCA is
limited by its requirement of numerical variables and its assumption of linear
relationships between data, which could pose problems for a study of this nature. For
example, data representing that represent social processes in permanent evolution, such
as entrepreneurial activity, are intertwined and prone to nonlinear linkages between them.
For these reasons, we use CATPCA—also known as nonlinear PCA—to cluster and
position 23 economies from different regions of the world with respect to a set of
socioeconomic indicators, such as development and inequality, the rate of early-stage
entrepreneurial activity and its various potential determinants thereof. This technique can
be regarded as an extension of traditional PCA (Meulman et al., 2002) and allows the
simultaneous treatment of different types of data, including nominal and ordinal data. An
additional advantage of CATPCA is that, due to the nonlinear transformations of the
variables achieved by optimal quantification, it tends to concentrate more variation in the
first few principal components (De Leeuw & Meulman, 1986). This study additionally
aims to highlight the utility of CATPCA for visualising relationships.
In the present study, we ranked the 23 countries in decreasing order according to (i) the
values of each variable in 2020, and (ii) the growth experienced over the period extending
from 2010 to 2020 for each variable. We then assigned a numerical value to each country
corresponding to its position, obtaining a set of categorical data that we used to cluster
the different states. The grouping of all countries is done by means of CATPCA using
IBM SPSS Statistics 27.
Emiliano Alzate & Oscar Claveria, 2025
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3. Results
In this section, we implemented CATPCA to (a) reduce the dimensionality of data and
(b) generate graphs with the relative positioning of the economies and the interactions
between variables. Following Pérez and Claveria’s (2020) two-step procedure, we first
ranked the economies in decreasing order for each variable according to the value
experienced in 2020 as well as to the growth experienced over the period under study,
2010 to 2020. To capture the dynamic interactions between the different factors, we used
the percentage growth rates between 2010 and 2020. In Table 2 we present the summary
statistics of all the variables included in the analysis. We can observe that, on average, all
variables with the exception of ‘services’ and ‘infrastructure’ experienced an increase
during the sample period. That means that only the growth in the share of entrepreneurs
in the business service sector and in the assessment of the ease of access to physical
resources decreased between 2010 and 2020 across all 23 countries. The growth rate of
‘entrepreneurial intentions’ (the percentage of those who intend to start a business within
three years) was, by far, the variable that experienced the highest growth and the highest
dispersion.
Next, in Table 3 and Table 4 we present the countries in decreasing order according to
the growth experienced during the sample period, from 2010 to 2020. The rankings
related to variables 1 through 9 (top panel of Table 2) are presented in Table 3, while
those related to variables 10 through 21 (lower panel of Table 2) are presented in Table
4.
Emiliano Alzate & Oscar Claveria, 2025
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Variables
SD
Min
Max
Rank
TEA
0.413
-0.350
1.309
1.659
EBO
0.396
-0.549
1.236
1.786
opportunities
0.658
-0.680
2.431
3.111
capabilities
0.756
-0.115
3.679
3.794
fear of failure
0.400
-0.575
1.090
1.666
entrepreneurial
intentions
4.861
-0.382
24.000
24.382
equality ratio TEA
0.590
-0.571
2.500
3.071
high job
expectation
0.770
-0.869
2.581
3.449
services
0.331
-0.614
1.042
1.656
financing
0.134
-0.103
0.458
0.561
policy
0.205
-0.084
0.794
0.878
taxes
0.134
-0.211
0.400
0.611
programs
0.146
-0.108
0.496
0.604
education 1
0.141
-0.137
0.441
0.578
education 2
0.135
-0.167
0.407
0.574
RD transfers
0.116
-0.113
0.335
0.448
professionalism
0.107
-0.130
0.301
0.431
dynamism
0.156
-0.159
0.467
0.625
openness
0.129
-0.166
0.401
0.567
infrastructure
0.069
-0.124
0.135
0.259
culture
0.130
-0.074
0.406
0.480
Table 2. Descriptive statistics – Growth rates 2010-2020
Source: Compiled by the authors.
Notes: TEA stands for Total early-stage Entrepreneurial Activity rate, EBO for Established Business Ownership rate.
9
TEA
EBO
Perceived
opportunities
Perceived
capabilities
Fear of failure
Entrepreneurial
intentions
Equality ratio
TEA
High job
expectations
Services
Croatia
Guatemala
Korea
Guatemala
Chile
Arabia
Korea
Guatemala
Iran
Guatemala
Taiwan
Italy
Korea
Uruguay
Croatia
Arabia
Angola
Korea
Korea
Latvia
Croatia
Taiwan
Croatia
Korea
Germany
Brazil
Israel
Uruguay
Croatia
Greece
Italy
Egypt
Egypt
Iran
Colombia
Egypt
Arabia
Korea
Egypt
Croatia
UK
Brazil
Norway
Germany
Spain
Switzerland
Slovenia
Slovenia
Arabia
Slovenia
Germany
Spain
Korea
Croatia
Israel
Arabia
Taiwan
Sweden
Sweden
UK
Slovenia
Greece
Colombia
Egypt
Israel
Latvia
Brazil
Spain
Angola
UK
Norway
Brazil
Latvia
Iran
Germany
Germany
Colombia
Israel
Taiwan
Slovenia
Chile
Greece
Egypt
Arabia
Angola
Arabia
Slovenia
Uruguay
Chile
Norway
Angola
Germany
Brazil
Chile
Brazil
Chile
Angola
Spain
Guatemala
Chile
Angola
Norway
Latvia
Switzerland
Spain
Croatia
Arabia
UK
Colombia
UK
Angola
Slovenia
Angola
Italy
Israel
Switzerland
Latvia
Sweden
Chile
Guatemala
UK
Greece
Switzerland
Latvia
Iran
Germany
Brazil
Greece
Sweden
Spain
Latvia
Uruguay
Colombia
Egypt
Switzerland
Slovenia
Sweden
UK
Norway
Norway
Sweden
Switzerland
Sweden
Sweden
UK
Spain
Uruguay
Greece
Taiwan
Guatemala
Greece
UK
Taiwan
Spain
Switzerland
Spain
Switzerland
Israel
Greece
Guatemala
Latvia
Angola
Germany
Uruguay
Switzerland
Colombia
Germany
Colombia
Brazil
Taiwan
Uruguay
Taiwan
Norway
Israel
Iran
Italy
Latvia
Chile
Uruguay
Italy
Norway
Italy
Chile
Israel
Guatemala
Iran
Sweden
Croatia
Greece
Italy
Brazil
Colombia
Uruguay
Iran
Norway
Egypt
Israel
Slovenia
Iran
Colombia
Iran
Egypt
Korea
Taiwan
Italy
Italy
Arabia
Table 3: Ranking of countries according to their average growth 2010-2020 – Variables 1 through 9
Source: Compiled by the authors.
Note: Countries experiencing a negative average growth during the sample period are marked in bold.
Emiliano Alzate & Oscar Claveria, 2025
10
Financing
Policy
Taxes
Programs
Education 1
Education 2
RD transfers
Professionalism
Market
dynamism
Market
openness
Infrastructure
Slovenia
Greece
Greece
Norway
Egypt
Spain
Spain
Spain
Israel
Latvia
Italy
Greece
Norway
Brazil
Spain
Latvia
Latvia
Guatemala
Taiwan
Greece
Norway
Iran
Iran
Italy
UK
Guatemala
Italy
Egypt
Italy
Slovenia
Norway
Israel
Taiwan
UK
Taiwan
Latvia
Korea
Brazil
Sweden
Iran
Italy
Colombia
Egypt
Latvia
Italy
Latvia
Spain
Egypt
Israel
Slovenia
Greece
Latvia
Korea
Iran
Greece
Korea
Uruguay
Italy
Latvia
Iran
Uruguay
Egypt
Switzerland
Uruguay
Spain
Slovenia
Spain
Iran
Chile
Greece
Sweden
Brazil
Latvia
Sweden
Angola
Italy
Egypt
Sweden
Spain
Israel
Iran
Slovenia
Israel
Norway
Uruguay
Chile
Brazil
Norway
Switzerland
Slovenia
Sweden
Chile
Guatemala
UK
Slovenia
Brazil
Spain
Greece
Chile
Egypt
Guatemala
Uruguay
Uruguay
Norway
Colombia
Korea
Egypt
Arabia
Slovenia
Israel
Guatemala
Israel
Iran
Slovenia
Spain
Norway
Colombia
Germany
Sweden
Arabia
Brazil
Latvia
Brazil
Taiwan
Italy
Uruguay
Greece
Israel
UK
Slovenia
Guatemala
Croatia
Brazil
Korea
Guatemala
Israel
Chile
Guatemala
Chile
Guatemala
UK
Switzerland
Uruguay
Croatia
Angola
Norway
UK
UK
Germany
UK
Chile
Germany
Taiwan
Angola
Uruguay
UK
Croatia
Brazil
Taiwan
Taiwan
Arabia
Greece
Switzerland
UK
Arabia
Norway
Arabia
Slovenia
Taiwan
Colombia
Iran
Uruguay
Iran
Brazil
Angola
Colombia
Germany
Egypt
Egypt
Arabia
Angola
Chile
Sweden
Israel
Croatia
Uruguay
Spain
Israel
Colombia
Switzerland
Angola
Greece
Italy
Angola
Croatia
Latvia
Croatia
Switzerland
Arabia
Switzerland
Arabia
Switzerland
Germany
Switzerland
Germany
Colombia
Egypt
Germany
Guatemala
Colombia
Chile
Germany
Germany
Arabia
Arabia
Taiwan
Norway
Guatemala
Sweden
Germany
Angola
Croatia
Angola
Sweden
Korea
Angola
Brazil
Arabia
Taiwan
Colombia
Sweden
Taiwan
Sweden
Colombia
Colombia
Switzerland
Korea
Switzerland
Angola
Iran
Chile
UK
Chile
Germany
Korea
Croatia
Croatia
Croatia
Croatia
Korea
Italy
Korea
Korea
Table 4: Ranking of countries according to their average growth 2010-2020 – Variables 10 through 21
Source: Compiled by the authors.
Note: Countries experiencing a negative average growth during the sample period are marked in bold.
11
In Table 3 we can observe that Iran, Israel, Italy, and Norway to a lesser extent, tended
to show negative growth rates during the decade, and are therefore ranked last in most
cases. In Table 4, Chile, Colombia, Croatia and Korea were the countries that tended to
be in the lowest positions, showing negative growth rates for most variables. At the
opposite extreme, in the top positions in Table 3, we find Croatia, Guatemala and Korea,
and in Table 4, Greece, Italy, Spain, and to a lesser extent Guatemala.
In the second phase, we assigned a numerical value to each country corresponding to
its position, obtaining a set of categorical data that we used to cluster the different states.
We excluded variable EBO from the CATPCA analysis in order to focus on early-stage
entrepreneurship, and included two nominal variables to control both for income (high,
middle and low income) and region (Africa, Asia and Oceania, Europe and North
America, and Latin America and the Caribbean).
In Table 5, we present a summary of the CATPCA model for 2020. Since the first two
factors accounted for more than 76% of the variance of the variables under analysis, we
retained these two factors. As mentioned before, CATPCA transforms the original set of
correlated variables into a smaller set of uncorrelated variables (Linting et al., 2007),
applying a nonlinear optimal procedure that relates the category quantifications to the
original categories. See Claveria (2016) for an example.
Dimension
Cronbach’s
alpha
Variance
Total
(eigenvalue)
% of variance
1
0.943
7.698
59.212
2
0.590
2.195
16.885
Total
0.974*
9.893
76.097
Table 5: CATPCA Analysis – Summary (Year 2020)
Source: Compiled by the authors.
Notes: *Cronbach’s alpha mean is based on the mean of the eigenvalue.
Next, Table 6 shows the obtained component loadings, which we then used to label the
two dimensions to which we have reduced the dataset. In Fig. 1, we show the relative
weight of each of these components. The factors with the highest loadings in the first
dimension are the rankings related to the level of professionalism, RD transfers and
market openness in 2020. Therefore, the first dimension better captured the aspects
reflecting commercial and legal infrastructure, availability of R&D to SMEs, and the
facility for new firms of entering existing markets; whereas the second dimension
described those more related to the extent to which training in managing SMEs is
incorporated within the education at primary and secondary levels, gender equality and
the rate of total early-stage entrepreneurial activity. Accordingly, we labelled the first
dimension as “legal infrastructure, transfers and openness” and the second as “education,
gender equality and early-stage entrepreneurial activity”.
Emiliano Alzate & Oscar Claveria, 2025
12
Dimension
1
2
professionalism
0.888
0.253
transfers
0.856
0.298
openness
0.843
0.270
capabilities
-0.827
0.220
intentions
-0.804
0.535
hdi
0.800
-0.339
financing
0.780
-0.297
programs
0.737
0.508
income
0.727
-0.379
education_1
0.714
-0.006
taxes
0.701
0.425
TEA
-0.649
0.631
policy
0.622
0.280
infrastructure
0.605
0.396
Gini index
-0.603
0.530
culture
0.532
0.294
opportunities
-0.453
0.277
region
0.431
-0.684
education_2
0.628
0.672
equality
-0.053
0.621
services
0.488
-0.578
expectation
-0.278
0.528
dynamism
-0.067
-0.466
fear
-0.121
0.185
Table 6: Component Loadings (Year 2020)
Source: Compiled by the authors.
Notes: TEA stands for Total early-stage Entrepreneurial Activity rate, and HDI for Human Development Index. See Table 1 for a
detailed explanation of all survey variables.
Emiliano Alzate & Oscar Claveria, 2025
13
Dimension 1
Dimension 2
Figure 1: Variance Accounted for in the First Two Dimensions (Year 2020)
Source: Compiled by the authors.
In order to graphically synthesize the results of the analysis, the two-dimensional
scatterplot in Fig.2 represents the coordinates of the first two retained dimensions for each
country. The top quadrant is completely dominated by the economies of Western and
Southern Europe, which ranked high in variables with high component loadings in the
second dimension (“education, gender equality and early-stage entrepreneurial activity”),
but displayed low positions in the first dimension (“legal infrastructure, transfers and
openness”). In contrast, in the lower quadrant, the economies of Latin America
predominate. The case of Angola deserves special mention, showing the highest score in
the first dimension, followed by Latin American countries. This result suggests that there
seems to be also a positioning linked to the geographical location of the countries, which
Emiliano Alzate & Oscar Claveria, 2025
14
somehow connects with the well-established distinction between ‘opportunity-driven’
and ‘necessity-driven’ entrepreneurial entries (Reynolds et al. 2001).
Figure 2: Object Points Labelled by Country (Year 2020)
Source: Compiled by the authors.
Fig. 3 displays the component loadings (indicators). The coordinates of the endpoint of
each vector are given by the loadings of each variable on the two components. Long
vectors are indicative of a good fit. The variables that are close together in the plot are
positively related, while the variables with vectors that make approximately a 180º angle
with each other are closely and negatively related. Finally, variables that are not related
correspond with vectors making a 90º angle.
Emiliano Alzate & Oscar Claveria, 2025
15
Figure 3: Component Loadings (Year 2020)
Source: Compiled by the authors.
Regarding the interactions among variables, in Fig. 3 we observe that there is a certain
level of association between three groups of variables. On the one hand, between the
early-stage entrepreneurial activity rate, the Gini index, and entrepreneurial intentions
and perceived opportunities (see Pérez-Macías et al., 2022 for a review of the factors that
influence the entrepreneurial intention). On the other hand, between programs,
infrastructure, R&D transfers, taxes, professionalism and openness. And finally, there is
also a positive association between the income level, human development and the
availability of financial resources for SMEs, which they in turn show a negative
relationship with the first group (TEA, Gini index, intentions and opportunities). This
result could be suggesting that the existence of difficulties in accessing financing during
2020 did not seem to be an obstacle to the increase in early-stage entrepreneurship.
Next, we replicated the analysis for the growth rates experienced during the decade
2010-2020. Fig. 4 shows the variance accounted for in each of the first two dimensions.
It can be seen that the ranking related to growth in infrastructure (i.e., the ease of access
to physical resources) is the factor with the highest loading in the first dimension, while
the ranking regarding growth in the level of income is the one with the highest loading in
the second dimension. Accordingly, we labelled the first dimension as “growth in
infrastructure” and the second as “growth in income”.
The two-dimensional scatterplot in Fig. 5 represents the coordinates of the first two
retained dimensions for each country. In the plot, one can observe a slightly positive slope
in the positioning of the economies along both dimensions, which is indicative of a certain
relationship between both dimensions (i.e., growth in infrastructure and income). The
lower quadrant is completely dominated by the European economies, while the top
quadrant is mostly by Latin American countries, which in turn obtained high scores in the
Emiliano Alzate & Oscar Claveria, 2025
16
second dimension. However, in both quadrants, most economies ranked high in the first
dimension, with the exception of Latvia, Slovenia and Israel, which all ranked low in
most variables in Table 3. Guatemala, with the top position in the second dimension, is
also a remarkable case. Angola, in the second place also deserves special mention, since
it also obtained the second position in the first dimension, which somehow hints at an
overall improvement during the decade, similar to Brazil. Again, there seems to be also a
positioning linked to the geographical location of the countries, especially in the case of
European countries, which are clustered together in the lower right cluster, indicating
high ranks in the first dimension but low in the second.
Emiliano Alzate & Oscar Claveria, 2025
17
Dimension 1
Dimension 2
Figure 4: Variance Accounted for in the First Two Dimensions (Growth 2010-2020)
Source: Compiled by the authors.
Emiliano Alzate & Oscar Claveria, 2025
18
Figure 5: Object Points Labelled by Country (Growth 2010-2020)
Source: Compiled by the authors.
Finally, Fig. 6 displays the interactions among variables. On the one hand, we observe
that the growth in TEA was highly associated with the growth in dynamism (i.e., the level
of change in markets from year to year), and negatively linked to the growth
‘education_1’ (i.e., training in SMEs at primary and secondary levels). Similarly, the
growth in human development and in high job creation expectations (i.e., % of those
involved in TEA who expect to create 6 or more jobs in 5 years) showed a link, but they
were negatively associated with the growth in the level of income, and practically showed
no relationship with the rest of variables. Finally, the growth in R&D transfers, programs
and supportive public policies are also connected, and negatively associated with the
growth in fear of failure. Overall, these results are in line with recent empirical research
(e.g., Abdesselam et al. 2018; Dvouletý et al. 2018), and somehow indicate that the
relative importance of the determinants of entrepreneurial activity tends to evolve,
highlighting the importance of incorporating a dynamic and an international dimension
in the analysis of entrepreneurship drivers.
Emiliano Alzate & Oscar Claveria, 2025
19
Figure 6: Component Loadings (Growth 2010-2020)
Source: Compiled by the authors.
4. Conclusion
This study aims to provide researchers with an analytical framework to visualise the
dynamic interplay between determinants of entrepreneurship, development and other
socioeconomic factors, and to position economies with respect to those interactions. The
proposed approach is based on a dimensionality-reduction technique that can handle
ordinal and numerical variables simultaneously and can deal with nonlinearities in the
relationship between them.
With this objective, we first undertook a descriptive analysis of the evolution of a set
of variables from two different surveys conducted annually as part of the GEM project
over the period extending from 2010 to 2020. Then, countries were ranked according to
the observed values in 2020 and the growth experienced over the sample period. We
assigned a descending numerical value to each country corresponding to its ranking to
generate a set of categorical data. By means of categorical principal component analysis,
we synthesised the ordinal information from the rankings into two dimensions and
generated a set of graphs to analyse both the relative positioning of the countries and the
interactions between the different variables. We replicated the analysis both for the year
2020 and for the growth experienced during the sample period.
Emiliano Alzate & Oscar Claveria, 2025
20
First, for 2020, the factors with the highest loadings in the first dimension were those
related to the level of professionalism, the availability of R&D transfers and the facility
for new firms of entering existing markets; whereas the second dimension described those
more related to the extent to which training in managing SMEs is incorporated within the
education at primary and secondary levels, gender equality and the rate of total early-
stage entrepreneurial activity. However, when the analysis is replicated for growth during
the decade, the increase in the facility of access to infrastructure was the most important
factor in the first dimension, and growth in the level of income was the one with the
highest loading in the second dimension.
Regarding the positioning of countries, in both cases, we observed two clusters that
roughly correspond to European and Latin American economies, respectively. Special
mention deserves Angola, which obtained top scores in the two dimensions both in 2020
and during the decade. The resulting perceptual map for the analysis in 2020 differs
notably from the one obtained for growth between 2010 and 2020, where Angola, Egypt,
Iran and Latin American economies were the best positioned in both dimensions when
growth is analysed.
Regarding the interactions among variables, the results obtained also differ markedly
depending on whether the year 2020 is analysed independently or the growth during the
decade. In this sense, while for 2020 it is observed that early-stage entrepreneurship
showed a negative association with the availability of financial resources and with human
development, when replicating the analysis for the growth during the decade, we obtained
a strong link between early-stage entrepreneurship and market dynamism, which in turn
showed no connection with human development. This result suggests that the inverse link
found for a specific year—between entrepreneurship and access to financing and
development—is blurred by introducing a dynamic component in the analysis. This
finding highlights the importance of analysing the dynamic relationship between
entrepreneurship and its determinants.
This study shows the potential of dimensionality-reduction and data-visualisation
techniques to capture the complex set of linkages among entrepreneurship determinants
at the international level, human development and socio-economic factors. Our goal is to
provide researchers with an alternative approach to identifying key attributes in the
positioning of economies. Notwithstanding, this research is not without limitations. First,
we want to note that this is a descriptive study, thus generalizable inferences cannot be
drawn from the results. A question left for further research is the inclusion of additional
variables that could give further insight into other factors operating in explaining
entrepreneurship. An additional aspect left for future research is an extension of the
analysis to other countries as well as the use of other dimensionality-reduction techniques
such as self-organising maps.
Emiliano Alzate & Oscar Claveria, 2025
21
Submission declaration statement
This research is not under consideration elsewhere, and will not be submitted for publication
elsewhere without the agreement of the Managing Editor.
Funding
This research was supported by the project PID2023-146073NB-I00 (Sustainable Territories)
from the Spanish Ministry of Science and Innovation (MCIN) / Agencia Estatal de Investigación
(AEI).
Conflicts of interest/Competing interests
The authors state that there is no conflict of interests. The funders had no role in the design of
the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.
Availability of data and material
The datasets used and/or analysed during the current study are publicly available:
GEM data: https://www.gemconsortium.org/data.
Human Development Index (HDI) provided by the United Nations:
http://hdr.undp.org/en/content/human-development-index-hdi.
Gini Index from the World Bank: http://iresearch.worldbank.org/PovcalNet/index.htm.
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