Preliminary about combined early phase entrepreneurship index

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					      The Global Entrepreneurship and Development Index
                           (GEDI)

                                          Zoltán J. Ács
                                     School of Public Policy
                                    George Mason University
                                    Fairfax, VA, 22030, USA
                                     E-mail: zacs@gmu.edu

                                          László Szerb
                                       University of Pécs
                               Faculty of Business and Economics
                               Pécs, Rákóczi 80, H-7622, Hungary
                                    E-mail: szerb@ktk.pte.hu

                                          March. 2011.

Abstract: This paper constructs a Global Entrepreneurship and Development Index (GEDI) that
captures the contextual feature of entrepreneurship across countries. We find the relationship
between entrepreneurship and economic development to be mildly S-shaped not U-shaped or L-
shaped. We find that the stages of development are more varied at the innovation driven stage than
at either the factor driven stage or the efficiency driven stage. Implications for public policy
suggest that institutions need to be strengthened before entrepreneurial resource can be fully
deployed.

Key Words: Entrepreneurship, Development, Stages of Growth, Globalization, Innovation,
Institutions.
JEL: L26, O1, O3
Acknowledgements: László Szerb would like to thank the OTKA Research Foundation (NK
69283) for financial support and Zoltan J. Acs thanks Imperial College Business School for
supporting his study leave and George Mason Universtiy for research support. Elaine Allen, Niels
Bosma, Erkko Autio, Paul Reynolds, Sander Wennekers, Jolanda Hessels, Alicia Coduras, Jose
Ernesto Amoros, Attila Varga, David B. Audretsch, Saul Estrin, David Storey, Thomas
Mickiewicz, Samee Desai, Hezekiah Agwara, Roy Thurik, Mick Hancock, Pekka Stenholm, Leora
Klapper, Peter Smith and Lois Stevenson provided valuable comments on earlier drafts. Alex Acs
and Jonathan Levie edited the entire manuscript while providing valuable comments. We also
thank seminar participants at the University of Pecs, Max Planck Institute of Economics, London
School of Economics. George Mason University, USAID, and Imperial College Business School
for valuable comments. We would also like to thank the national teams for permission to use the
data. All errors and omissions are our responsibility.
                                                  2

1. Introduction

Joseph Alois Schumpeter pointed out over one hundred years ago that entrepreneurship is crucial
for understanding economic development (Acs and Virgill 2009). Today, despite the global
downturn, entrepreneurs are enjoying a renaissance the world over according to a recent survey in
the Economist magazine (2009). The dynamics of the process can be vastly different depending on
the institutional context and level of development within an economy. Therefore, if one is
interested in studying entrepreneurship within or across countries, the broad nexus between
entrepreneurship, institutions and economic development is a critical area of inquiry.

Of course the interdependence of economic development and socio-political change is generally
recognized by social scientists (Adelman and Morris 1965). This environment is marked by
interdependencies between economic development and institutions, which affect other
characteristics such as quality of governance, access to capital and other resources, and the
perceptions of entrepreneurs. Institutions are critical determinants of economic behavior and
economic transactions in general, and they can impose direct and indirect effects on both the
supply and demand of entrepreneurs. Entrepreneurship depends on the mutual interplay of the
individual level and institutional variables (Busenitz and Spencer 2000).

Over the past two decades the role played by institutions in economic development has become
increasingly clear to economists and policymakers alike (Acemoglu, Johnson and Robinson 2001).
At least three large research projects at the World Bank, The Heritage Foundation and the World
Economic Forum are actively involved in measuring the quality of institutions across countries and
over time. While the measurement of institutions has been an ongoing activity for decades, the
measurement of entrepreneurial activity is a relatively new subject that represents a gap in our
understanding of why countries are rich and poor. As institutions are strengthened, and the
incentive structure changes, more and more entrepreneurial activity is shifted towards productive
entrepreneurship strengthening economic development (Acemoglu and Johnson 2005). This
entrepreneurial activity explodes through the innovation-driven stage and culminates in a high
level of innovation with entrepreneurship leveling out as institutions are fully developed
(Fukuyama 1989).

Recently several international research projects have been underway that have as their explicit
mission the measurement of the business formation process across countries. The Global
Entrepreneurship Monitor (GEM) project is a large research project that is interested in
understanding economic development albeit from a slightly different perspective. The business
formation process is an important aspect of how technology and institutions interact to produce
innovations and deliver new goods and services to society. However, how successful different
countries are at this process is not easily discernable from either the GEM project or from several
other projects that try to measure the business formation process.

The purpose of this paper is to contribute to our understanding of economic development by
constructing a global entrepreneurship and development index (GEDI) that captures the essence of
the contextual features of entrepreneurship and fills a gap in the measure of development. We
develop a Global Entrepreneurship Index that offers a measure of the quality and quantity of the
business formation process in 71 of the most important countries in the world. The GEDI captures
the contextual feature of entrepreneurship by focusing on entrepreneurial attitudes, entrepreneurial
activity and entrepreneurial aspirations. These data and their contribution to the business formation
process are supported by three decades of research into entrepreneurship across a host of countries.
                                                  3

Building on previous measures of entrepreneurship, we define the basic requirements for
construction of an entrepreneurship and development index. First, the index should be sufficiently
complex to capture the multidimensional feature of entrepreneurship. Second, besides the quantity,
or level-related measures, there should be indicators referring to quality-related differences. Third,
the index should incorporate individual level as well as institutional variables. Fourth, there should
be detailed information about the applied data set and the sources of the variables. The 14
individual pillars of entrepreneurship used in the construction of our index are calculated by
involving more than 963 000 individuals from the 71 countries. The pillars themselves are
constructed through an interaction of individual level and institutional variables. All of the
institutional variables are from the Global Competitiveness Index, others are from the Doing
business, Index of economic Freedom or from multinational organizations such as the UNIDO or
OECD. While we tried to find a single institutional variable for each of the individual variables,
sometimes it proved to be not executable. Therefore some of these institutional variables are
themselves complex ―indexes‖. Comparing to the previous versions of our index we avoided the
duplication or the multiplication of the same institutional factors in different part of the index.

In the following section we examine the stages of economic growth that lead to innovation. In
section thee we define entrepreneurship and in section four the institutional variables and the
weighting method are explained. The following two sections examine the penalty for bottleneck,
the logic of the index and the data and the variables. Section seven presents the results. A cluster
analysis is performed in section eight. The final section concludes.

We find that the relationship between entrepreneurship and economic development appears to be
mildly S-shaped. Our findings suggest moving away from simple measures of entrepreneurship
across countries illustrating a U-shaped or L-shaped relationship to more complex measures, which
are positively related to economic development. The interaction between institutions and
entrepreneurs varies with the stages of economic development. Institutional change is more
important at lower levels of development and entrepreneurial activity becomes more important at
higher levels of development. The model has important implications for development policy.


2. The Stages of Economic Development and Entrepreneurship

In his classic text W.W. Rostow (Rostow 1960) suggested that countries go through five stages of
economic growth: (1) the traditional society (2) the preconditions for take-off (3) the take-off (4)
the drive to maturity and (5) the age of high mass-consumption. While these stages are a simplified
way of looking at the development of modern economies, they identify critical events. While
Rostow focused on the age of high mass-consumption, Porter following recent developments in the
economics of innovation. Michael Porter (Porter et al 2002) has provided a modern rendition of
this approach by identifying three stages of development: (1) a factor-driven stage, (2) an
efficiency-driven stage and (3) an innovation-driven stage.

The factor-driven stage is marked by high rates of agricultural self-employment. Countries in this
stage compete through low-cost efficiencies in the production of commodities or low value-added
products. Sole proprietorships—i.e. the self-employed—probably account for most small
manufacturing firms and service firms. Almost all economies experience this stage of economic
development. These countries neither create knowledge for innovation nor use knowledge for
exporting. To move into the second stage, the efficiency-driven stage, countries must increase their
production efficiency and educate the workforce to be able to adapt in the subsequent technological
                                                 4

development phase: the preconditions for take-off plays a crucial role. The drive to efficiency
describes the first transition that is predominantly institutional in nature.

To compete in the efficiency-driven stage, countries must have efficient productive practices in
large markets, which allow companies to exploit economies of scale. Industries in this stage are
manufacturers that provide basic goods and services. The efficiency-driven stage is marked by
decreasing rates of self-employment. In the efficiency-driven economy capital and labor play a
crucial role in productivity and the focus is on technology, in the decision making process. For
over a century there has been a trend in economic activity—exhibited in virtually every developing
country—toward larger firms.

In 1957 Robert Solow at MIT modified Douglas‘s earlier findings on the contribution of capital
and labor by a kind of exponential growth factor suggested by Schumpeter early on in the Century.
As the Nobel Laureate Paul Samuelson (2009, 76) recently pointed out, ―This ‗residual‘ Solow
proclaimed, demonstrated that much of post-Newtonian enhanced real income had to be attributed
to innovational change (rather than, as Douglas believed, being due to ‗deepening‘ of the
capital/labor K/L ratio).‖

The transition to the innovation driven stage is characterized by increased activity by individual
agents. In the innovation-driven stage knowledge provides the key input. In this stage the focus
shifts from firms to agents in possession of new knowledge (Acs et al 2009). The agent decides to
start a new firm based on expected net returns from a new product. The innovation-driven stage is
biased towards high value added industries in which entrepreneurial activity is important.

Institutions dominate the first two stages of development. In fact, innovation accounts for only
about 5 percent of economic activity in factor-driven economies and rises to 10 percent in the
efficiency driven stage. However, in the innovation-driven stage when opportunities have been
exhausted in factors and efficiency, innovation accounts for 30 percent of economic activity. We
see an S-shaped relationship between entrepreneurship and economic development because in the
first transition stage entrepreneurship plays a minimal role in productive entrepreneurship. It
increases in the efficiency driven stage. However, as we move from the efficiency driven stage to
the innovation driven stage (the knowledge driven stage) entrepreneurship plays a more important
role increasing at an increasing rate and then leveling off as economies become fully developed.

Historically, an elite entrepreneurial class appears to have played a leading role in economic
development. Entrepreneurship is considered to be an important mechanism for economic
development through employment, innovation and welfare. However, entrepreneurship does not
appear like manna from heaven as a country moves to the innovation driven stage. It has a role to
play in all stages of development and is a continuous process over many years. Figure 1 shows the
relationship between entrepreneurship and economic development. Economists have come to
recognize the ―input-competing‖ and ―gap-filling‖ capacities of entrepreneurial activity in
development (Leibenstein 1968).
                                                         5

Figure 1: Entrepreneurship and the corresponding stages of developed.




The intersection of the S-curve on the vertical axis suggests that entrepreneurship is also a
resource, and that all societies have some amount of economic activity, but that activity is
distributed between productive, unproductive and destructive entrepreneurship (Baumol 1990). As
institutions are strengthened more and more entrepreneurial activity is shifted towards productive
entrepreneurship strengthening economic development (Acemoglu and Johnson 2005).

3. Defining entrepreneurship

All index building should start with a definition. While a generally accepted definition of
entrepreneurship is lacking there is agreement that the concept comprises numerous dimensions.1
The most common features of the various definitions involve unique traits, risk taking, opportunity
recognition, motivation and exploitation, and innovation. Besides these characteristics, others focus
on the output or impact contributed to entrepreneurship, such as value creation, spillover effects or
high growth (Autio 2005, 2007, Praag and Versloot 2007). Instead of providing exact definition
Shane and Venkataraman provides a conceptual framework describing the distinctive domain of
entrepreneurship research (Shane and Venkataraman 2000).

While recent entrepreneurship theories imply a multidimensional definition most empirical
investigations rely on simple, one-dimensional approaches. Self employment, business ownership
rate or new venture creation, the Total Early-stage Entrepreneurship Activity Index (TEA) are from
the same vein; they refer to the level and/or the dynamics of entrepreneurship and identify the

1
  For example, Gartner (1990) describes eight themes of entrepreneurship out of 90 attributes, while Davidsson (2004)
lists seven characteristics, Wennekers and Thurik (1999) outline thirteen and Godin et al (2008) identify six.
                                                            6

percentage of the working-age population that is engaged or willing to engage in ―entrepreneurial‖
activity.2 A major shortcoming with interpreting these measures as an entrepreneurship index is
that they do not capture quality differences across entrepreneurial activity, such as opportunity
recognition, skills, creativity, or innovation and high growth. Therefore, such an index would give
policymakers guidance on the quantity of ―entrepreneurship‖ rather than its quality. Moreover,
these measures do not take into account differences in environmental factors; the efficiency and
level of the institutional setup could have a major influence on the quality of entrepreneurship.

Besides viewing entrepreneurship as a process it is equally important investigating the contextual
nature of entrepreneurship. The widely interpreted social and cultural factors have a strong effect
on business launch (Aldrich and Fiol 1994, Hofstede et al 2004). The influence of general
institutional factors such as property rights, the size of government and regulatory barriers on
entrepreneurial entry is also a function of economic development. The recently amended GEM
model provides a long list of entrepreneurship contextual features such as education, infrastructure,
government support, R&D transfer, and venture capital that shape new entry as well as the quality
of start-up (Bosma et al 2008, Bosma et al 2009).

We believe that any entrepreneurship index should (1) be a complex creature3, (2) involve quality
differences and (3) include individual as well as institutional/environmental variables, yet
recognize that entrepreneurship is distinct from small businesses, self-employment, craftsmanship,
usual businesses, or not associated as a phenomenon with buyouts, change of ownership or
management succession. Our index takes into account the degrees of contribution from
entrepreneurship, in that some businesses have a larger impact on markets, create more new jobs
and grow faster and larger than others (Davidsson 2004). Finally, like other indexes, we consider
the availability of the data.

Taking into account all of these possibilities and limitations, we define entrepreneurship as a
dynamic interaction of entrepreneurial attitudes, entrepreneurial activity, and entrepreneurial
aspiration that vary across stages of economic development. This approach is consistent with the
revised version of the GEM conceptual model (Bosma et al 2009). According to this definition we
propose four steps to build the index: (1) selection of variables and weights, (2) indicators, (3) sub-
indexes and finally (4) the super-index. All three sub-indexes contain several indicators or, in other
name, pillars; therefore they can be interpreted as quasi-independent building blocks of this
entrepreneurship index. In this section we describe the sub-indexes, indicators, and weights. In a
later section, we describe the variables. The three sub-indexes of activity, aspiration and attitudes
constitute the entrepreneurship super-index, which we call the Global Entrepreneurship
Development Index.

While the activity and aspiration sub-indexes (outlined below) captures actual entrepreneurship
activity and aspiration that relates to nascent and startup business activities, the entrepreneurial
attitude (ATT) sub-index aims to identify entrepreneurial attitudes associated with the
entrepreneurship-related behavior of a country‘s population. The opportunity perception potential

2
  About self employment see Acs et al (1994), Blanchflower et al (2001), Grilo and Thurik (2008), about business
ownership rate see Carree et al (2002), Cooper and Dunkelberg (1986), about new venture creation see Gartner (1985),
Reynolds et al (1994), about the Total Early-stage Entrepreneurship Activity Index (TEA) see Acs et al (2005), Bosma
et al (2008).
3
  Others may think that this statement is generally not true and a complex phenomenon can be described by a simple
indicator or an index that contains only a few variables. Our three level index building logic allows the application of a
simple entrepreneurship measure by analyzing one of the three sub-indexes (see later).
                                                       7

is essential to recognize and explore novel business opportunities. In addition, it is necessary to
have proper startup skills and personal networks to be able to exploit these opportunities. Fear of
failure to start a business can have a negative effect on entrepreneurial attitudes even when
opportunity recognition as well as startup skills exist. Entrepreneurial attitudes are believed to be
influenced by the crucial institutional factors of market size, education, the riskiness of a country in
general, the usage rate of the internet in population, and culture that enter to the indicator as
interaction variables (Reynolds 2007, Schramm 2008, Uhlaner and Thurik 2007).

The entrepreneurial activity (ACT) sub-index is principally concerned with measuring high growth
potential start-up activity. This high growth potential is approached by quality measures, including
opportunity start-up motives, belonging to a technology intensive sector, the level of education as
well as the uniqueness of the offered product/service. The institutional variables used include the
ease of doing business, the availability of the latest technology, the level of human development,
and the freedom of business operation.

The entrepreneurial aspiration (ASP) sub-index refers to the distinctive, qualitative, strategy
related nature of entrepreneurial activity.4 Entrepreneurial businesses are different from the
regularly managed business. In this respect it is particularly important to be able to identify the
most relevant institutional and other quality-related interaction variables. The newness of the
product and of technology, internationalization, high growth ambitions and finance variables are
included in this sub-index. The institutional variables measure the R&D potential, the
sophistication of business and of innovation, the level of globalization, and the availability of
venture capital.

One could also ask about the selection of the particular variables for the sub-index construction.
Attitude index variables are from those survey questions that are answered by the total population,
and activity and strategy variables are limited to those who are nascent or baby business owners
and managers. A potential problem can be the high growth and the finance measures; they could be
in the activity sub-index too, but it is more realistic to view finance and growth together with
innovation and internationalization. However, the involvement of these two variables in the
activity sub-index causes a minor change in the overall rank of the countries.

4. The institutional variables and the weighting method

As mentioned previously, an entrepreneurship index should incorporate individual level as well as
environmental, institutional variables. Another crucial point about building an index is the
application of proper weights. To avoid the accusation of using arbitrary methodology, most
indexes do not use weighting. Without weights, the calculation is relatively easy, and non-
professionals can also interpret it in a straightforward fashion. The Doing business index and the
Index of Economic Freedom follow this approach. However, weighting is very useful when the
different components of the index have different influences. A previous version of the Global
Competitiveness Index (GCI) assigned different weights to the indicators based on the stages of
development of the country. Nevertheless, this approach had several shortcomings, including the
arbitrary choice of the weights and the negation of the potential country differences. The latest
version of the GCI uses a sophisticated methodology and econometric techniques to merge
together the indicators and determine the appropriate weights. The new weighting method avoids
the arbitrary selection problem, but does not handle country differences.


4
    For a review of the literature see Acs   (2006).
                                                          8


In order to solve the problem of country level weighting a different technique should be developed.
Another reason for developing a new method has to do with the need to work with the potentially
different interpretations of entrepreneurship across countries. Moreover, we should incorporate the
institutional variables into the index. Since most of these environmental data are not in the GEM
survey we have to rely on other, outside sources (Acs and Varga 2005). This practice is not unique;
all previously mentioned indexes use data from other sources. For example, the Index of Economic
Freedom uses the Doing business data to derive the Business Index sub-index, and the Corruption
Perception Index to identify business corruption.

The novelty of our approach is that we consider the institutional variables as interaction variables,
not as independent indicators. The interaction variable approach is used in regression analysis,
where two independent variables are multiplied by each other to demonstrate their conditional
effect on the dependent variable (Acs and Varga 2005). Here, institutional variables enter into the
index as a part of a particular indicator. A key task is to find the appropriate institutional variable
for a particular entrepreneurship variable. We believe that this methodology can clarify
interpretation of the questions in the GEM survey.

Another potential perception of the institutional variables is to view them as weighting variables. A
major advantage of this proposed approach is the capability to assign the proper weight to a
particular variable on a variable basis; therefore country differences can be incorporated in the
index. Moreover, the arbitrary selection of the weight can also be eliminated.

An alternative solution of the incorporation of the environmental variables could have been to
involve these variables as independent factors. We have tested several versions of the model
involving this latter alternative. While the overall rank of the countries are not really sensitive to a
few variable changes and rearrangements in the system, the use of the pure measures enlarges the
effect of the individual level as opposed to institutions and thus could provide a potentially false
policy implication. Similarly, if institutional variables are entered independently then they become
more dominant factors. While individual level measures favor lower developed countries, quality
and institutional factors favor developed countries. Therefore, the applied interaction method
seems to provide a good balance to these opposing development effects.

A further potential criticism of our method—as with any other indexes—might be the apparently
arbitrary selection of institutional variables and neglecting other important factors. In all cases, we
aimed to collect and to test alternative environmental factors before selecting the present ones.
Choice was constrained by limited data availability in many countries. This was the reason for
omitting the World Bank new business registration data set, for example. The selection criteria for
a particular institutional/environmental variable were

(1) the potential to logically link to the particular entrepreneurship variable.

(2) the clear interpretation and explanatory power of the selected variable. For example, we have
had interpretation problems with the taxation variables. 5

5
 A former version of our index (Acs and Szerb 2009) was criticized because of not incorporating the taxation effect (A
European Paradise p. 25). While it is true that high taxation can be harmful for entrepreneurship, ceteris paribus, it
should not be forgotten that high taxation countries can provide better public services and environment favorable to
business start-ups. While Scandinavian countries have high taxation they lead also the ranks in government
effectiveness and regulatory quality reported by the World Bank Aggregate Governance Indicator data set
(http://info.worldbank.org/governance/wgi/index.asp)
                                                        9


(3) the avoidance of the appearance of the same factor more than once in the different institutional
variables. The previous version of the index was accused to incorporate multiple environmental
factors. This was particularly problematic in such cases when variables were complex by itself, i.e.
consisting of many variables (e.g. Doing Business Index, Index of Economic Freedom). In this
version we aimed to use simple institutional variables. In six cases (Business Climate Rate,
Corruption Perception Index, Business Freedom, Innovation Index, Market Sophistication Index,
Economic Globalization) the application of the complex measure proved to be more useful as
compared to the single variable. After carefully checking the components of the potential complex
institutional variables we eliminated all the duplication. Moreover, instead of using the whole
complex index we intended to apply only a sub-index more relevant to entrepreneurship. Business
Climate Rate is a part of Country Risk Rate, Business Freedom is a component of the Index of
Economic Freedom and Economic globalization is a subset of the Globalization Index.

5. The penalty for bottleneck

We have defined entrepreneurship as the dynamic interaction of entrepreneurial activity, aspiration
and attitudes across different levels of development. One issue this definition raises is how to bring
dynamism into the model. Configuration theory provides a useful way of thinking about this issue
(Miller 1986, 1996). Configurations are defined as ―…represent(ing) a number of specific and
separate attributes which are meaningful collectively rather than individually… configurations are
finite in number and represent a unique, tightly integrated, and therefore relatively long-lived set of
dynamics‖ (Dess et al 1993, pp. 775-776).

Two closely related theories, the theory of the weakest link (TWL) and the theory of constraints
(TOC) provide us another way to view the interrelation of the elements. These theories argue that
the performance of the system depends on the element that has the lowest value in the structure.
According to the TOC, improvement can only be achieved by removing the weakest link that
constraints the performance of the whole system (Goldratt 1994). In another way, the TWL claims
that there is not a perfect but only a partial substitution that exists amongst the elements of the
system (Yohe 2006, Yohe and Tol 2001). While both principles are mainly applied in production
and operation management, there are a few applications in the humanities.6 According to the
popular Six sigma management theory the improvement of the production process can be achieved
by removing the causes of mistakes (weakest link) and decreasing the variation in the system
(Nave 2002, Stamatis 2004). The notion of constrains is also present in the institutional literature
implying that economic development or growth depends on improving the binding institutional
barriers (North 1990, Rodrik 2008).

The weakest link postulate in entrepreneurship is also present. According to Lazear entrepreneurs
perform many tasks and therefore must be generalists, ―jacks-of-all-trades‖ (Lazear 2004). The
performance of a venture depends on the weakest link of the skills of the entrepreneur. Therefore
the development of the business can be achieved by improving the worst performing skill of the
entrepreneur as opposed to an employee who is rather focusing on specialization. We claim that the
generalist perspective can be applied not only the entrepreneurial traits but in other aspects of the
business and entrepreneurship.


6
  In a public choice paper Harrison and Hirshleifer (1989) present a model where the individual social composition
function is constructed by taking into account the weakest link. The financial system can also be described by the
weakest link postulate (Rajan and Bird 2001).
                                                    10

A practical application of the TWA and TOC theory is the Penalty for Bottleneck (PFB)
methodology. Bottleneck is defined as the worst performing weakest link, or binding constraint in
the system. With respect to entrepreneurship, by ―bottleneck‖ we mean a shortage or the lowest
level of a particular entrepreneurial indicator as compared to other indicators of the sub-index. This
notion of bottleneck is important for policy purposes. Our model suggests that attitudes, activity
and aspiration interact; if they are out of balance, entrepreneurship is inhibited. The sub-indices are
composed of four or five components, defined as indicators that should be adjusted in a way that
takes this notion of balance into account. After normalizing the scores of all the indicators, the
value of each indicator of a sub-index in a country is penalized by linking it to the score of the
indicator with the weakest performance in that country. This simulates the notion of a bottleneck; if
the weakest indicator were improved, the particular sub-index and ultimately the whole
GEDINDEX would show a significant improvement. Moreover, the penalty should be higher if
differences are higher. Looking from either the configuration or the weakest link perspective it
implies that stable and efficient sub-index configurations are those that are balanced (have about
the same level) in all indicators.

Technically, equation (1) describes the PFB methodology:

xi,j = min yi(j) + ln(1 + yi,j – min yi(j))                                    (1)

where xi,j is the modified, after penalty value of the entrepreneurship feature j of country i
      yi,j is the normalized value of the original entrepreneurship feature j of country i
      i = 1, 2,……m, (the number of countries)
      j= 1, 2,……n (the number of entrepreneurial features)

The bottleneck is achieved for each indicator by adding one plus the natural logarithm of the
difference between that indicator‘s country value and the value for the weakest indicator for that
country. Thus improving the score of the weakest indicator will have a greater effect on the index
than improving the score of stronger indicators. For example, assume the normalized score of a
particular indicator in a country is 0.60, and the lowest value of the indicators of a certain sub-
index is 0.40. The difference is 0.20. The natural logarithm of 1.2 is equal to 0.18. Therefore the
final adjusted value of the indicator is 0.40 + 0.18 = 0.58 instead of 0.60. The largest potential
difference between two indicators can be 1, when a particular country has the highest value in one
indicator and the lowest values in another. In this case the natural logarithm of 2 = 0.693, so the
maximum penalty is 1-0.693 = 0.307.

We suggest that this dynamic index construction is particularly useful for enhancing
entrepreneurship in a particular country. Although one could argue that entrepreneurship is a
horizontal policy concept with relevance across a number of traditional policy domains (e.g., trade
policy, regulatory policy, fiscal policy), the application of the dynamic index construction would
enable the effectiveness of different policy steps toward entrepreneurship to be measured This
method could rearrange the rank order of the countries in a particular feature. The level of the
rearrangement depends on the relative position of a country in terms of bottlenecks compared to
the others. If every country has similar differences in terms of the features, then the rank order does
not change too much; if one country is very unbalanced compared to the others then a lower rank
for that particular country can be expected. The policy message is that weak performances in a
particular feature, i.e. a bottleneck, should be handled first because it has the most negative effect
on all the other features.
                                                 11

There are two potential drawbacks of the PFB method. One is the arbitrary selection of the
magnitude of the penalty. There is no research that can underline how big the penalty should be.
This is the reason why we applied a conservative estimation of the penalty. Comparing the
correlations between the GDP per capita and the GEDINDEX index calculated as the simple
average of the indicators (r = 0,893) and the PFB methodology (r= 0,888) provides about the same
correlation coefficient, with no statistically significant differences. The other problem is that we
cannot exclude fully the potential that a particularly good feature can have a positive effect on the
weaker performing features. While this could also happen, most of the entrepreneurship policy
experts hold that policy should focus on improving the weakest link in the system. Altogether, we
claim that the PFB methodology is theoretically better than the arithmetic average calculation.
However, the PFB adjusted GEDINDEX is not necessary an optimal solution since the magnitude
of the penalty is unknown.

6. Data and variable description

In this part of the paper we put together the pieces and present how to build the index. We start
with the variables that are directly coming from the original data sources for each country involved
in the analysis. The variables can be individual (personal or business) level or institutional
(environmental). All individual level variables are from the GEM Adult Population Survey. The
institutional variables are obtained from various sources, reported below. We calculate all pillars or
indicators from the variables using the interaction variable method, i.e. multiplying the individual
variable with the proper institutional variable. The indicators are the basic building blocks of the
sub-index, Entrepreneurial Attitudes, Entrepreneurial Activity and Entrepreneurial Aspirations.
The PFB method serves to calculate the three sub-indexes from the indicators. Finally, the super-
index, the Global Entrepreneurship Development Index, is simply the average of the three sub-
indexes. The structure of the methodology can be seen in Table 1.

Table 1 The structure of the Global Entrepreneurship Development Index




We apply the following methodology in all three sub-indexes and the GEDINDEX:

   1. Indicator values (truncated if relevant) are normalized to 0 as the lowest and 1 as the
      highest value. Other normalization methods such as a mean of 0 and variance of 1 cannot
      be applied because we need all variables in the same range for the PFB technique. This
                                                12

       approach has the disadvantage of increasing the differences even if real deviations are
       minimal. However, other indexes such as global competitiveness index, the index of
       economic freedom, KOF globalization index, corruption perception index etc. rely on this
       technique even if the range of the index varies from 1-7, 1-10, or 0-100. Others (such as
       Doing business) apply the ranking order to the index building, but it is an ordinal rather
       than a cardinal scale. Since we want to measure quality differences amongst countries, the
       ordinal scale is not appropriate for us.

   2. The PFB is applied as outlined in section 5 to get the indicator adjusted PFB values.

   3. The value of a sub-index for any country is the arithmetic average of its PFB-adjusted
      indicators for that sub-index. The maximum value of the sub-indexes is 1 and the potential
      minimum is 0, which both reflect the relative position of a country in a particular indicator.

   4. The global entrepreneurship index is the simple arithmetic average of the three sub-indexes.

Sixteen out of the 31 variables are from the master data sets of the GEM annual adult population
surveys. Data for 64 of the 71 countries are from the 2002-2008 years; eight countries, Algeria,
Guatemala, Jordan, Panama, Saudi Arabia, Syria, and Tunisia are from the 2009 survey. The lack
of proper institutional variables limited to exclude other nations where the 2009 GEM survey was
conducted. (Yemen, Lebanon, West Bank and Gaza Strip). Actual individual variables were
calculated from the 2002-2008 pooled data set except for the new countries where the single 2009
year values were applied. The size of the sample in different years as well as the participated
countries can be seen in the following Table 2.

Table 2: The size of the sample and the list of participating countries

        Country          2002 2003    2004    2005    2006    2007    2008 2009     Total
Algeria                      0    0       0       0       0       0       0 2000     2000
Argentina                 1999 2004    2003    2008    2007    2018    2031    0    14070
Australia                 3378 2212    1991    2465    2518       0       0    0    12564
Austria                      0    0       0    2197       0    2002       0    0     4199
Belgium                   4057 2184    3879    4047    2001    2028    1997    0    20193
Bolivia                      0    0       0       0       0       0    2000    0     2000
Bosnia and Herzegovina       0    0       0       0       0       0    2028    0     2028
Brazil                    2000 2000    4000    2000    2000    2000    2000    0    16000
Canada                    2007 2028    2451    6418    2038       0       0    0    14942
Chile                     2016 1992       0    1997    2007    4008    4515    0    16535
China                     2054 1607       0    2109    2399    2666       0    0    10835
Colombia                     0    0       0       0    2001    2102    2001    0     6104
Croatia                   2001 2000    2016    2000    2000    2000    1996    0    14013
Czech Republic               0    0       0       0    2001       0       0    0     2001
Denmark                   2009 2008    2009    2010   10000    2001    2012    0    22049
Dominican Republic           0    0       0       0       0    2081    2019    0     4100
Ecuador                      0    0    2010       0       0       0    2142    0     4152
Egypt                        0    0       0       0       0       0    2636    0     2636
Finland                   2005 2005    2000    2010    2005    2005    2011    0    14041
France                    2029 2018    1953    2005    1909    2005    2018    0    13937
Germany                  15041 7534    7523    6577    4049       0    4751    0    45475
Greece                       0 2000    2008    2000    2000    2000    2000    0    12008
Guatemala                    0    0       0       0       0       0       0 2163     2163
Hong Kong                 2000 2000    2004       0       0    2058       0    0     8062
                                                13

Hungary                  2000     0   2878   2878   2500   1500   2001     0 13757
Iceland                  2000 2011    2002   2002   2001   2002   2002     0 14020
India                    3047     0      0      0   1999   1662   2032     0   8740
Indonesia                   0     0      0      0   2000      0      0     0   2000
Iran                        0     0      0      0      0      0   3124     0   3124
Ireland                  2000 2000    1978   2000   2008   2007   2001     0 13994
Israel                   2004     0   1933      0      0   2019   2030     0   7986
Italy                    2002 2003    2945   2001   1999   2000   3000     0 15950
Jamaica                     0     0      0   2180   3669      0   2407     0   8256
Japan                    1999 2000    1917   2000   2000   1860   2001     0 13777
Jordan                      0     0      0      0      0      0      0 2006    2006
Kazakhstan                  0     0      0      0      0   2000      0     0   2000
Korea                    2015     0      0      0      0      0   2000     0   4015
Latvia                      0     0      0   1964   1958   2000   2011     0   7933
Macedonia                   0     0      0      0      0      0   2000     0   2000
Malaysia                    0     0      0      0   2005      0      0     0   2005
Mexico                   1002     0      0   2011   2015      0   2605     0   7633
Morocco                     0     0      0      0      0      0      0 2001    2001
Netherlands              3510 3505    3507   3582   3535   3539   3508     0 24686
New Zealand              2000 2009    1933   1003      0      0      0     0   6945
Norway                   2036 2040    2883   2015   1999   2037   2049     0 15059
Panama                      0     0      0      0      0      0      0 2000    2000
Peru                        0     0   2007      0   1997   2000   2052     0   8056
Philippines                 0     0      0      0   2000      0      0     0   2000
Poland                   2000     0   2001      0      0      0      0     0   4001
Portugal                    0     0   1000      0      0   2023      0     0   3023
Puerto Rico                 0     0      0      0      0   1998      0     0   1998
Romania                     0     0      0      0      0   2046   2206     0   4252
Russia                   2190     0      0      0   1894   1939   1660     0   7683
Saudi Arabia                0     0      0      0      0      0      0 1881    1881
Serbia                      0     0      0      0      0   2200   2297     0   4497
Singapore                2005 2008    3852   4004   4011      0      0     0 15880
Slovenia                 2030 2012    2003   3016   3008   3020   3019     0 18108
South Africa             6993 3262    3252   3268   3248      0   3270     0 23293
Spain                    2000 2000 16980 19384 28306 27880 30879           0 127429
Sweden                   2000 2025 26700     2002   2003   2001      0     0 36731
Switzerland              2001 2003       0   5456      0   2148      0     0 11608
Syria                       0     0      0      0      0      0      0 2002    2002
Thailand                 1043     0      0   2000   2000   2000      0     0   7043
Tunisia                     0     0      0      0      0      0      0 1994    1994
Turkey                      0     0      0      0   2417   2400   2400     0   7217
Uganda                      0 1035    2005      0      0      0      0     0   3040
United Arab Emirates        0     0      0      0   2001   2180      0     0   4181
United Kingdom          16002 22010 24006 11203 43033 42713       8000     0 166967
United States            7059 9197 14914     2021   2080   2166   5249     0 42686
Uruguay                     0     0      0      0   1997   2000   2027     0   6024
Venezuela                   0 2000       0   2000      0   1794      0     0   5794
Total                  113534 96712 156543 117833 170618 156108 135987 16047 963382

The full list and description of the applied GEM individual variables can be seen in the Table 3:
                                                      14

Table 3.: The description of the individual variables used in the GEDINDEX
 Individual                                      Description
  variable
OPPORTUNITY      The percentage of the 18-64 aged population recognizing good conditions to start business
                 next 6 months in area he/she lives,
SKILL            The percentage of the 18-64 aged population claiming to posses the required
                 knowledge/skills to start business
NONFAIRFAIL      The percentage of the 18-64 aged population stating that the fear of failure would not
                 prevent starting a business
KNOWENT          The percentage of the 18-64 aged population knowing someone who started a business in the
                 past 2 years
NBGOODAV         The percentage of the 18-64 aged population saying that people consider starting business as
                 good carrier choice
NBSTATAV         The percentage of the 18-64 aged population thinking that people attach high status to
                 successful entrepreneurs
CARSTAT          The status and respect of entrepreneurs calculated as the average of NBGOODAV and
                 NBSTATAV
TEAOPPORT        Percentage of the TEA businesses initiated because of opportunity start-up motive
TECHSECT         Percentage of the TEA businesses that are active in technology sectors (high or medium)
HIGHEDUC         Percentage of the TEA businesses owner/managers having participated over secondary
                 education
COMPET           Percentage of the TEA businesses started in those markets where not many businesses offer
                 the same product
NEWP             Percentage of the TEA businesses offering products that are new to at least some of the
                 customers
NEWT             Percentage of the TEA businesses using new technology that is less than 5 years old average
                 (including 1 year)
GAZELLE          Percentage of the TEA businesses having high job expectation average (over 10 more
                 employees and 50% in 5 years)
EXPORT           Percentage of the TEA businesses where at least some customers are outside country (over
                 1%)
INFINVMEAN       The mean amount of 3 year informal investment
BUSANG           The percentage of the 18-64 aged population who provided funds for new business in past 3
                 years excluding stocks & funds, average
INFINV           The amount of informal investment calculated as INFINVMEAN* BUSANG


Some of the variables (OPPORTUNITY, SKILL, KNOWENT, CARSTAT, and BUSANG) are
based on the answers of the total 18-64 aged population while all the remaining variables are based
on the data Total Early-Phase Entrepreneurship (TEA) businesses. TEA businesses are those young
businesses that age less than 42 months old age and nascent businesses that are in the start-up
process and/or has not paid salaries for longer than three months.

Since GEM lacks the necessary institutional weighting variables, we substitute the index with other
widely used relevant data from Transparency International (Corruption Perception Index),
UNESCO (Tertiary education enrollment, GERD), World Economic Forum (Domestic market
size, Business sophistication, Innovation, Technology absorption capability, Staff training, Market
dominance, Venture capital availability), International Telecommunication Union (Internet usage)
Heritage Foundation and World Bank (Economic freedom), United Nations (Urbanization index),
KOF Swiss Economic Institute (Economic globalization) and Coface (Business climate risk). The
full description of the institutional variables, their sources and the year of the survey can be found
in the Table 4.
Table 4. The description and source of the institutional variables used in the GEDINDEX

Institutional                                             Description                                                    Source                   Data availability
variable                                                                                                                 of data
MARKETDOM          Domestic market size that is the sum of gross domestic product plus value of imports of         World Economic         The Global Competitiveness Report
                   goods and services, minus value of exports of goods and services, normalized on a 1–7           Forum                  2008-2009, p. 470
                   (best) scale data are from the World Economic Forum Competitiveness Index 2007-2008                                    The Global Competitiveness Report
                   except 2009 countries that are from 2008-2009                                                                          2009-2010 p. 450

URBANIZATION       Urbanization that is the percentage of the population living in urban areas, data are from      United Nations         http://esa.un.org/unup/index.asp?pan
                   the Population Division of the United Nations, 2005, 2009 countries are from 2010                                      el=1
MARKETAGGLOM       The size of the market: A combined measure of the domestic market size and the
                   urbanization that later measures the potential agglomeration effect. Calculated as              Own calculation        -
                   MARKETDOM*URBANIZATION
EDUCPOSTSEC        Gross enrolment ratio in tertiary education, 2008 or latest available data                      UNESCO                 http://stats.uis.unesco.org/unesco/Tab
                                                                                                                                          leViewer/tableView.aspx?ReportId=
                                                                                                                                          167
BUSINESS RISK      The business climate rate ―assesses the overall business environment quality in a               Coface
                   country… It reflects whether corporate financial information is available and reliable,                                http://www.trading-safely.com/
                   whether the legal system provides fair and efficient creditor protection, and whether a
                   country's institutional framework is favorable to intercompany transactions‖
                   (http://www.trading-safely.com/). It is a part of the Country Risk Rate. The alphabetical
                   rating is turned to a seven point Likert scale from 1 (―D‖ rating) to 7 (A1 rating). Data are
                   from 2008 except 2009 countries that are from 2009.
INTERNETUSAGE      The number Internet users in a particular country per 100 inhabitants, 2008, except 2009        International          http://www.itu.int/ITU-
                   countries that are from 2009                                                                    Telecommunication      D/ict/statistics/
                                                                                                                   Union
CORRUPTION         The Corruption Perceptions Index (CPI) measures the perceived level of public-sector            Transparency           http://www.transparency.org/policy_r
                   corruption in a country. ―The CPI is a "survey of surveys", based on 13 different expert        International          esearch/surveys_indices/cpi/2009
                   and business surveys.‖
                   (http://www.transparency.org/policy_research/surveys_indices/cpi/2009 ) Overall
                   performance is measured on a ten point Likert scale. Data are from 2008 except 2009
                   countries that are from 2009.
FREEDOM            ―Business freedom is a quantitative measure of the ability to start, operate, and close a
                   business that represents the overall burden of regulation, as well as the efficiency of         Heritage Foundation/   http://www.heritage.org/Index/
                   government in the regulatory process. The business freedom score for each country is a          World Bank
                   number between 0 and 100, with 100 equaling the freest business environment. The score
                   is based on 10 factors, all weighted equally, using data from the World Bank‘s Doing
                                                                             16

                Business study‖. (http://www.heritage.org/Index/pdf/Index09_Methodology.pdf)
TECHABSORP      Firm level technology absorption capability: ―Companies in your country are (1 = not able     World Economic       The Global Competitiveness Report
                to absorb new technology, 7 = aggressive in absorbing new technology)‖ )‖ Iran is             Forum,               2008-2009, p. 461
                estimated as Syria. Data are from 2007-2008 except 2009 countries that are from 2008-                              The Global Competitiveness Report
                2009                                                                                                               2009-2010 p. 441
STAFFTRAIN      The extent of staff training: ―To what extent do companies in your country invest in          World Economic       The Global Competitiveness Report
                training and employee development? (1 = hardly at all; 7 = to a great extent)‖ Iran is        Forum,               2008-2009, p. 419
                estimated as Syria. Data are from 2007-2008 except 2009 countries that are from 2008-                              The Global Competitiveness Report
                2009                                                                                                               2009-2010 p. 401
MARKDOM         Extent of market dominance: ―Corporate activity in your country is (1 = dominated by a        World Economic       The Global Competitiveness Report
                few business groups, 7 = spread among many firms)‖ Iran is estimated as Syria. Data are       Forum,               2008-2009, p. 423
                from 2007-2008 except 2009 countries that are from 2008-2009                                                       The Global Competitiveness Report
                                                                                                                                   2009-2010 p. 405
GERD            Gross domestic expenditure on Research & Development (GERD) as a percentage of                UNESCO               http://stats.uis.unesco.org/unesco/Tab
                GDP, year 2007 or latest available data Puerto Rico, Dominican Republic, and United                                leViewer/tableView.aspx?ReportId=
                Arab Emirates are estimated                                                                                        1782
INNOV           Innovation index points from GCI: a complex measure of innovation including investment        World Economic       The Global Competitiveness Report
                in research and development (R&D) by the private sector, the presence of high-quality         Forum                2008-2009, p. 18
                scientific research institutions, the collaboration in research between universities and                           The Global Competitiveness Report
                industry, and the protection of intellectual property.                                                             2009-2010 p. 20
BUSS STRATEGY   Refers to the ability of companies to pursue distinctive strategies, which involves           World Economic       The Global Competitiveness Report
                differentiated positioning and innovative means of production and service delivery. Iran is   Forum                2008-2009, p. 18
                estimated as Syria. Data are from 2007-2008 except 2009 countries that are from 2008-                              The Global Competitiveness Report
                2009                                                                                                               2009-2010 p. 20
GLOB            A part of the Globalization Index measuring the economic dimension of globalization. The      KOF Swiss            Dreher, Axel (2006): Does
                variable involves the actual flows of trade, Foreign Direct Investment, portfolio             Economic Institute   Globalization Affect Growth?
                investment and income payments to foreign nationals as well as restrictions of hidden                              Evidence from a new Index of
                import barriers, mean tariff rate, taxes on international trade and capital account                                Globalization, Applied Economics
                restrictions. (http://globalization.kof.ethz.ch/static/pdf/variables_2009.pdf)                                     38, 10: 1091-1110.
VENTCAP         A measure of the venture capital availability on a 7-point Likert scale generating from a     World Economic       The Global Competitiveness Report
                statement: Entrepreneurs with innovative but risky projects can generally find venture        Forum                2008-2009, p. 453
                capital in your country (1 = not true, 7 = true)‖ Iran is estimated as Syria. Data are from                        The Global Competitiveness Report
                2007-2008 except 2009 countries that are from 2008-2009                                                            2009-2010 p. 433
                                                   17


In some country cases there are a few variables missing. Since we did not want to lose any
countries from the sample, we estimated the missing data by expert estimation technique as
follows: The GERD measure lacked data for Puerto Rico, Dominican Republic and the United
Arab Emirates. In these cases, other government sources and data from nearby, similar
countries served to give estimation. KOF globalization index data for Puerto Rico,
Kazakhstan, Hong Kong, and Serbia are estimated similar to GERD, applying nearby country
data points. Business freedom data is missing only for Puerto Rico that is set below the US
data. Moreover, the 2009 data point is applied in the case of Serbia. The World Economic
Forum data are missing for Iran that is estimated the same as Syria. All the other data are
available for all countries therefore we do believe that these rough estimations do not
influence our results noticeably.7

A frequent problem of any data set is the existence of outliers. Since extreme values could
influence the results in an unsatisfactory way, a proper handling is necessary. To address this
potential problem we used the truncation method. The level of truncation was on the indicator
level, except Iceland‘s INFINV variable that was a so huge outlier that the adjustment took
place at the individual variable level. The maximum value is set to decrease the difference
between the first and the second country and the second and third country in a particular
entrepreneurial feature indicator to 5%. This method preserves the rank order of the countries
in a particular entrepreneurial feature but decreases the relative differences between the
leading country and the other nations. The following countries and indicators are capped:
Saudi Arabia (OPPORTUNITY PERCEPTION), New Zealand (STARTUP SKILLS), Iceland
(NETWORKING), Canada and Denmark (TECHNOLOGY SECTOR ), Puerto Rico and
Denmark (QUALITY OF HUMAN RESOURCE), Korea and Israel (NEW PRODUCT),
Sweden (NEW TECH), Puerto Rico and Saudi Arabia (HIGH GROWTH), Singapore
(INTERNATIONALIZATION).

7. The Global Entrepreneurship and Development Index 2010 Rankings
We report the ranks of the 71 countries in terms of the GEDINDEX and the three sub-indexes.
The applicability and the validity of the GEDINDEX are examined in comparison to other
important, widely used indexes. Later, the pillar values of the three sub-indexes are also
presented.

We present the GEDINDEX in terms of country development measured by the purchasing power
parity GDP per capita. The GEDINDEX overall rank of the countries is shown in Table 5.
Nordic and Anglo-Saxon countries in the innovation driven stage of development are in the
front ranks. Two Scandinavian countries Denmark and Sweden lead the rank. New Zealand,
an outlier with about $26,000 GDP is in fifth place due to its excellent performance in
attitudes that counterbalance its relatively weak performance in aspiration. Four of the five
Nordic countries, Denmark, Sweden, Iceland, and Norway, are in the top ten and Finland is
13th still with a good performance.

The US is in third place following Canada because of its weaknesses in attitude measures.8
Australia, Ireland and Switzerland are all good performers, but possess weaknesses in at least

7
  In order to check potential biasness the index was calculated without these countries but the GEDINDEX
values and the rank order of the involved countries were basically unchanged.
8
 This may be a temporary aberration. Opportunity perception, for example, was much higher in the US in the
early 2000‘s, according to GEM executive reports for those years.
                                                18

one of the sub-indexes. The most populous EU countries are in the middle part of the ranking
list; UK is 14th, Germany is 16th, France is 18th, Italy is 27th followed by Spain in 28th place. A
likely explanation of the relatively weak economic performance of the EU countries over the
last decade (as well as Japan 29th place) is low levels of entrepreneurship. Low GDP-level
factor-driven countries, such as Jamaica, Bosnia-Herzegovina, Venezuela, Brazil, Philippines,
Iran, Bolivia, Ecuador, and Uganda are on the bottom of entrepreneurship ranking, as
expected. Two former socialist countries Hungary and Russia, however, should have higher
level of entrepreneurship as implied by the trend-line.
                                                 19

Table 5. The Global Entrepreneurship Index Rank of the Countries

Rank    Country                 GDP* ACTINDEX Rank Country                            GDP*      ACTINDEX
    1   Denmark                   35890         0.763    37 Poland                      14095         0.286
    2   Canada                    34926         0.737    38 Croatia                     15599         0.284
    3   United States             44474         0.717    39 Peru                         7558         0.284
    4   Sweden                    36358         0.685    40 China                        5087         0.281
    5   New Zealand               26773         0.679    41 Colombia                     8336         0.279
    6   Ireland                   44402         0.631    42 South Africa                 9565         0.277
    7   Switzerland               40183         0.630    43 Turkey                      12747         0.272
    8   Norway                    49014         0.623    44 Mexico                      14135         0.270
    9   Iceland                   35490         0.617    45 Dominican Republic           7709         0.261
   10   Netherlands               38083         0.616    46 Indonesia                    3459         0.256
   11   Australia                 34073         0.598    47 Hungary                     18639         0.253
   12   Belgium                   34584         0.576    48 Romania                     13217         0.246
   13   Finland                   33869         0.564    49 Macedonia                    9632         0.242
   14   United Kingdom            34726         0.561    50 Egypt                        5383         0.237
   15   Singapore                 39508         0.558    51 Morocco                      4248         0.235
   16   Germany                   34512         0.544    52 Jordan                       5092         0.234
   17   Puerto Rico               20223         0.541    53 Panama                      11947         0.227
   18   France                    33412         0.498    54 India                        2656         0.227
   19   Slovenia                  24913         0.489    55 Brazil                       9376         0.225
   20   Korea                     25481         0.488    56 Venezuela                   11333         0.224
   21   Israel                    25868         0.472    57 Thailand                     7974         0.221
   22   Austria                   36836         0.454    58 Russia                      14121         0.218
   23   Hong Kong                 39089         0.446    59 Tunisia                      7758         0.218
   24   United Arab Emirates      39900         0.417    60 Jamaica                      6848         0.207
   25   Czech Republic            22110         0.415    61 Algeria                      7887         0.189
   26   Chile                     13609         0.414    62 Serbia                      10853         0.183
   27   Italy                     30248         0.407    63 Kazakhstan                  10477         0.179
   28   Spain                     31241         0.401    64 Bosnia and Herzegovina       8077         0.177
   29   Japan                     33288         0.397    65 Ecuador                      7597         0.166
   30   Saudi Arabia              23428         0.381    66 Bolivia                      4242         0.163
   31   Malaysia                  12681         0.364    67 Syria                        4476         0.163
   32   Latvia                    15574         0.361    68 Guatemala                    4661         0.149
   33   Portugal                  22595         0.350    69 Iran                        10625         0.145
   34   Greece                    28024         0.318    70 Philippines                  3186         0.125
   35   Uruguay                   10844         0.304    71 Uganda                        918         0.100
   36   Argentina                 12769         0.300
    *   Per capita GDP average in PPP 2006-2007 World Bank, PL, NZ, JO, UG UAE are from 2004, 2005 average,
        EG, KR,IR, BA, MK, BO, EC are from 2007-2008 average

It is also worthwhile to examine the connection between GEDINDEX and other major widely
applied indexes. In Table 6 we report the correlation coefficients between GEDINDEX, the global
competitiveness index, the doing business index, the economic freedom index, the corruption
index, and GDP per capita.
                                                           20

Table 6. The correlation coefficients between GEDINDEX and other major indexes

                                                                    1      2       3       4             5          6
                                                                          **      **      **             **         **
 1 Global Entrepreneurship Index                                1.00 ,704    ,805    ,880           ,919      ,886
 2 Index of Economic Freedom                                          1.00 ,761** ,699**            ,770**    ,658**
 3 Doing Business Rank (normalized)                                           1.00 ,840**           ,819**    ,764**
 4 Global Competitiveness Index                                                       1.00          ,883**    ,843**
 5 Corruption Perception Index                                                                       1.00     ,865**
 6 Per capita GDP in PPP                                                                                       1.00
All coefficients are significant at a level better than 0.001

In all cases the indexes show significant and high correlations with one another and with the GDP
PPP per capita values. While measures of competitiveness, red tape, economic freedom, and
corruption are available, a vital aspect of wealth creation and development, entrepreneurship, has
been missing from the picture. It seems that our global entrepreneurship index fits into the picture
and may be capable of providing valuable insight to understand entrepreneurship and its
components and their role in economic development.

Figure 2: The comparison of the 3As in, efficiency, and innovation driven countries




                                              1. OPPORT UNIT Y PERCEPT ION
                                                          (AT T )
                                                      1.000
                            14. RISK CAPIT AL (ASP)                  2. ST ART UP SKILLS (AT T )
                                                      0.800
              13. INT ERNAT lONALIZAT ION
                                                                             3. NONFEAR OF FAILURE (AT T )
                          (ASP)                       0.600

                                                      0.400
               12. HIGH GROWT H (ASP)                 0.200                          4. NET WORKING (AT T )

                                                      0.000

            11. NEW T ECHOLOGY (ASP)                                                 5. CULT URAL SUPPORT (AT T )



                   10. NEW PRODUCT (ASP)                                        6. OPPORT UNIT Y ST ART UP (ACT )

                           9. COMPET IT ION (ACT )                       7. T ECH SECT OR (ACT )
                                           8. QUALIT Y OF HUMAN RESOURCE
                                                         (ACT )




                              Factor driven          Efficiency driven         Innovation driven



A more detailed picture emerges when we examine the average values of the indicators for
countries grouped according to the three stages of development. According to Figure 2, the
entrepreneurial performance of the innovation driven countries is significantly different from
                                               21

the efficiency-driven countries in all indicators but one. The exception is the opportunity
perception potential (OPPORTUNITY PERCEPTION).

Factor-driven and efficiency-driven countries are more similar regarding entrepreneurship
indicators, but the differences are the highest in the cases of the attitude indicators of
NONFEAR OF FAILURE and CULTURAL SUPPORT. It implies that attitude development
is vital for those countries that transit from the factor-driven stage to the efficiency-driven
stage.

Aspiration related INTERNATIONALIZATION is also a key to forward to the efficiency
driven stage. While a further complex development of all of the indicators is necessary to be
able to successfully transit from the efficiency to the innovation driven stage, activity and
aspiration indicators play the key role. The largest differences between the stage 2 and 3
countries are in NEW PRODUCT, NONFEAR OF FAILURE, INTERNATIONALIZATION,
and RISK CAPITAL. The three groups of the countries are the most similar to each other,
besides the previously mentioned OPPORTUNITY PERCEPTION, in HIGH GROWTH.
This may imply that there are other sources of HIGH GROWTH than entrepreneurship in the
lower phases of economic development.

8. Cluster analysis and country grouping

In this section we investigate those country groups that posses similar entrepreneurial
features. Cluster analysis provides the proper tool for doing it. The normalized scores of the
fourteen pillars of entrepreneurship were used to group the similar feature of countries. We
also calculated the ATT, the ACT, the ASP sub-indexes, the GEDI super-index, and the per
capita GDP mean values for the five groups. Similar calculation is made for the individual
and the institutional variable means for each of the five groups. The results of the cluster
analysis of five groups can be seen in Table 7.

The distribution of the number of countries seems to be very good for over the five clusters: It
is between ten and twenty. Most of the countries in the first cluster are factor driven
economies with minimal international connections and relatively low level of human
resources. The second cluster is mostly efficiency driven economies trying to increase
entrepreneurship from a relatively lower level of development. The innovation driven
economies breaks into three separate clusters. The most entrepreneurial country group, the
Leaders, is followed by a cluster that focuses on followers (Innovation Followers) while
another group of innovation driven countries seems to have some relative advantages in
challenging the leaders (Innovation Challengers).
Examining the ranks of the five clusters in terms of the three sub-indexes and the
GEDINDEX, it is clear that the GEDINDEX and all the three sub-index increase from
through the stages of development. The average development of the countries measured by
the per capita GDP is in close correlation with the GEDINDEX that is not a surprise from the
previous results: Factor driven economies are in the bottom, followed by the efficiency
driven, innovation challengers, innovation followers and finally innovation Leaders are the
most developed ones.

Investigating the deviations amongst the five groups in terms of the pillars, more alterations
can be noticed providing an inside view about the role of entrepreneurship in economic
development. This is particularly true for the ATT and ASP pillars. ACT related indicators
increase monotonically in terms of entrepreneurship development.
                                                  22

Table 7.: The five groups of countries based on cluster analysis
Pillars/                  Factor Driven    Efficiency    Innovation     Innovation   Innovation   Mean
Cluster names                             Transformers   Challengers    Followers     Leaders
OPPORTUNITY
PERCEPTION                     0,555         0,193          0,505         0,254        0,629      0,427
STARTUP SKILLS                 0,450         0,291          0,445         0,408        0,685      0,446
NONFEAR OF FAILURE             0,243         0,402          0,618         0,759        0,834      0,517
NETWORKING                     0,169         0,189          0,379         0,459        0,696      0,341
CULTURAL SUPPORT               0,245         0,269          0,544         0,536        0,823      0,440
OPPORTUNITY
STARTUP                        0,210         0,230          0,505         0,554        0,799      0,413
TECH SECTOR                    0,236         0,237          0,345         0,673        0,770      0,411
QUALITY OF HUMAN
RESOURCE                       0,242         0,336          0,480         0,525        0,609      0,406
COMPETITION                    0,236         0,285          0,480         0,526        0,801      0,427
NEW PRODUCT                    0,063         0,104          0,184         0,647        0,519      0,256
NEW TECHOLOGY                  0,194         0,316          0,309         0,557        0,606      0,368
HIGH GROWTH                    0,239         0,219          0,543         0,426        0,383      0,329
INTERNATlONAL-
IZATION                        0,196         0,400          0,575         0,748        0,636      0,459
RISK CAPITAL                   0,057         0,165          0,309         0,332        0,626      0,263
Entrepreneurial Attitudes
Index score (ATT)              0,300         0,245          0,462         0,439        0,706      0,403
Entrepreneurial Activity
Index score (ACT)              0,218         0,253          0,426         0,539        0,719      0,393
Entrepreneurial
Aspirations Index score
(ASP)                          0,136         0,213          0,337         0,489        0,519      0,304
Global Entrepreneurship
Index score (GEDI)             0,218         0,237          0,408         0,489        0,648      0,367
Institutional variable
averages                       0,276         0,340          0,534         0,698        0,783      0,481
Individual variable
averages                       0,374          0,401           0,506        0,486        0,571      0,451
Per capita GDP PPP ($US)       8792           9739           24858         31051        37558      19697
Number of countries              20             18              10           10           13         71
Countries                 Algeria         Bosnia and     Chile          Austria      Australia
                          Argentina       Herzegovina    Hong Kong      Belgium      Canada
                          Bolivia         China          Italy          Czech        Denmark
                          Brazil          Croatia        Latvia         Republic     Finland
                          Colombia        Egypt          Malaysia       France       Iceland
                          Dominican       Greece         Portugal       Germany,     Ireland
                          Republic        Hungary        Puerto Rico    Israel       Netherlands
                          Ecuador         India          Saudi Arabia   Japan        New Zealand
                          Guatemala       Indonesia      Spain          Korea        Norway
                          Iran            Jamaica        United Arab    Singapore    Sweden
                          Jordan          Macedonia      Emirates       Slovenia     Switzerland
                          Kazakhstan      Morocco                                    United Kingdom
                          Mexico          Poland                                     United States
                          Panama          Romania
                          Peru            Russia
                          Philippines     South Africa
                          Serbia          Thailand
                          Syria           Tunisia
                          Turkey          Uganda
                          Uruguay
                          Venezuela
                                                23


Examining the attitude sub-index pillars, OPPORTUNITY PERCEPTION and STARTUP
SKILLS are the highest in the case of the Leaders. However, Factor Driven are second
followed by Innovation Challengers. Comparing resource based and efficiency based, it seems
that institutional improvement, increased INTERNATIONALIZATION, and technological
progress is not followed by opportunity and skill development. Mainly lacking individual
characteristics of opportunity recognition and managerial startup skills and not institutional
deficiencies cause the low level of attitudes in the cases of efficiency driven and Innovation
followers. This finding underlines the general beliefs that changing institutions is relatively
easier than changing individual characteristics. Hence, efficiency driven and innovation
followers need to improve OPPORTUNITY PERCEPTION and STARTUP SKILLS while
not to decrease other indicator values.

Though ASP is increasing in terms of economic development, the aspiration related pillar
values do not necessary follow this trend. NEW PRODUCT is the highest in the innovation
follower cluster as is INTERNATIONALIZATION. Innovation Challengers lead in HIGH
GROWTH, followed by Innovation Followers, and the Innovation Leaders are only in third
place implying that gazelle type of fast growing start-ups is the weakest pillar in leading
entrepreneurial countries. To a lesser extent, this is also true in the Institutional developers.

Moreover, HIGH GROWTH is the lowest in the efficiency cluster. At the same time,
Innovation Challengers have a low level of NEW TECHNOLOGY, lower than that of the
efficiency driven economies! Innovation Challengers are good in industries that require
relatively old technology and product innovation as opposed to innovation followers. Further
examination is necessary to clarify why HIGH GROWTH is lower in the more innovative
Institutional developers cluster. Leaders are ahead of the Followers and Challengers because
of the excellent formal and informal venture finance (RISK CAPITAL), technology
application and adaptation (NEW TECH) and a more balanced performance (least deviations)
over the five ASP pillars.

Above we have analyzed the role of individual and institutional variables in terms of
economic development. Here, we can provide further details. While the institutional and
individual variable averages are very close to each other, it seems to be balanced only in the
Innovation Challengers cluster. In the cases of the Innovation followers and Efficiency
Transformers individual variable averages are higher than institutional ones. However
efficiency driven countries improved more in institutions than in individual characteristics.
Therefore, the key factor in the case of lower level of development is to enhance institutions.

In the later phase of development institutional improvement is ahead of individual
development. This is particularly true in the Innovation follower cluster the difference
between institutional and individual means is almost ten times as compared to the Individual
developers. While the difference between the individual and institutional variables is the same
in the leaders as in the Institutional developers the relative difference is the same. To reach a
higher level of development and capture the leading entrepreneurial nations, Institutional
developers should improve in individual characteristics while Individual developers should
enhance their institutions.

If we look at the regional location of the different cluster countries, an interesting pattern can
be noticed. The Innovation Leaders are the same countries that lead the GEDINDEX rank:
thirteen out of the first fourteen countries constitute the Leader group, only the 12th Belgium
                                              24

got into another group. These are the Anglo-Saxon, and Nordic countries together with the
Netherlands and Switzerland. Most Latin American countries can be found in the factor
driven cluster. Only Jamaica and Chile are in the efficiency driven cluster. Eastern European
and Balkan countries, except Serbia, the Czech Republic, Slovenia and Latvia, are in the
efficiency transformers cluster together with five out of the six African countries. The
exception is Algeria.

It seems the Asian countries are the most diverse nations in terms of entrepreneurship: They
can be found in four groups. Poorer Asian countries are in the resource driven clusters (5) and
the factor driven (4) clusters. The most populous Asian nations, China, India and Indonesia
also belong to the efficiency driven cluster. The most entrepreneurial Asian nations Hong
Kong, Saudi Arabia and the United Arab Emirates are in the innovation challenger cluster.
Other rich Asian countries, Israel, Japan, Korea and Singapore can be found in the Innovation
followers cluster. None are in the Innovation Leaders cluster.


9. Summary
In this paper we have described the index building methodology and the data set. The GEDI is
a complex index reflecting the multidimensional nature of entrepreneurship. The GEDI
consists of three sub-indexes, fourteen pillars and thirty-one variables. While some
researchers insist on simple entrepreneurship indicators none of the previously applied
measures were able to explain the role of entrepreneurship in economic development.
There are several novelties in the Global Entrepreneurship and Development Index design.
First, the construction of the pillars combines together the individual and the institutional
variables similar to the interaction variable methodology. In this case institutional-
environmental variables can also be interpreted as country-level weights of the individual
variables. Second, we created the first dynamic index that meets with the requirements of
configuration theory. This approach is particularly useful in addressing the bottleneck
problem of the low development of one or a few factors in entrepreneurship indicators and
sub-indexes. According to the ―penalizing for bottleneck method,‖ entrepreneurship policy
can most efficiently remove barriers to entrepreneurship development by focusing on the
bottleneck that is the ―weakest link‖ amongst the indicators.

Our index building logic differs from other widely applied indexes in two respects: It
incorporates individual as well as institutional variables and takes into account the weakest
link in the system. The institutional variables can also be viewed as country specific
weighting factors. Moreover, institutional variables can balance out the potential
inconsistency of the GEM data collection. The weakest link refers to the decreased
performance effect of the bottleneck. Practically it means that the higher pillar values are
adjusted to the weakest performing pillar value of the particular sub-index. While the exact
measure of the penalty is unknown meaning that the solution is not necessarily optimal it still
provides a better solution as compared to calculating the simple arithmetic averages.
Consequently the newly developed PFB can be applied in such cases where an imperfect
substitutability exists amongst the variables and the efficiency of the system depends on the
weakest performing variable. The method is particularly useful for policy making.
                                              25



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Appendix A:

Table A-1: GEDINDEX, the three sub-index values and rank by countries

                 GEDINDEX   GEDI   ATTINDEX       ATT    ACTINDEX   ACT    ASPINDEX   ASP
Country                     Rank                  Rank              Rank              rank
Algeria            0.189     61      0.227         59      0.182     63      0.159     52
Argentina          0.300     36      0.375         36      0.308     41      0.218     41
Australia          0.598     11      0.804         2       0.564     16      0.425     19
Austria            0.454     22      0.553         13      0.470     22      0.339     30
Belgium            0.576     12      0.512         18      0.693     10      0.524     13
Bolivia            0.163     67      0.217         61      0.199     58      0.072     69
Bosnia and
Herzegovina        0.177     64      0.209         63      0.107     69      0.215    42
Brazil             0.225     54      0.333         42      0.188     60      0.155    53
Canada             0.737     2       0.773         3       0.886     2       0.552    9
Chile              0.414     26      0.517         16      0.330     37      0.394    23
China              0.281     40      0.261         54      0.210     53      0.373    26
Colombia           0.279     41      0.383         34      0.280     45      0.174    49
Croatia            0.284     38      0.323         44      0.217     52      0.313    32
Czech Republic     0.415     25      0.386         33      0.335     36      0.525    12
Denmark            0.763     1       0.754         5       0.966     1       0.569    6
Dominican
Republic           0.261     45      0.390         32      0.259     50      0.134    58
Ecuador            0.166     66      0.207         65      0.162     65      0.128    62
Egypt              0.237     50      0.232         58      0.303     43      0.175    48
Finland            0.564     13      0.685         9       0.623     14      0.385    24
France             0.498     18      0.448         23      0.556     19      0.491    15
Germany            0.544     16      0.447         24      0.621     15      0.564    7
Greece             0.318     34      0.367         37      0.325     39      0.263    36
Guatemala          0.149     69      0.197         67      0.201     57      0.050    71
Hong Kong          0.446     23      0.441         27      0.371     29      0.527    11
Hungary            0.253     47      0.302         49      0.266     49      0.190    44
Iceland            0.617     9       0.653         10      0.558     18      0.641    2
India              0.227     53      0.215         62      0.233     51      0.233    40
Indonesia          0.256     46      0.172         68      0.460     24      0.137    57
Iran               0.169     65      0.239         57      0.184     62      0.085    68
Ireland            0.631     6       0.523         14      0.832     4       0.538    10
Israel             0.472     21      0.365         38      0.473     21      0.580    4
Italy              0.407     27      0.495         19      0.363     30      0.362    27
Jamaica            0.207     60      0.302         48      0.205     56      0.115    64
Japan              0.397     29      0.305         47      0.468     23      0.417    22
Jordan             0.234     51      0.354         39      0.164     64      0.182    45
Kazakhstan         0.179     63      0.249         55      0.188     61      0.100    67
Korea              0.488     20      0.480         21      0.505     20      0.479    17
Latvia             0.361     32      0.397         31      0.432     27      0.254    37
Macedonia          0.242     49      0.245         56      0.210     54      0.272    34
Malaysia           0.364     31      0.485         20      0.447     26      0.160    51
Mexico             0.270     44      0.332         43      0.344     32      0.134    59
Morocco            0.217     59      0.342         41      0.139     67      0.170    50
Netherlands        0.616     10      0.703         7       0.665     12      0.479    16
New Zealand        0.679     5       0.859         1       0.685     11      0.493    14
Norway             0.623     8       0.700         8       0.743     5       0.425    20
Panama             0.227     52      0.295         50      0.270     48      0.114    65
                                     30

Peru            0.284   39   0.430        28   0.277   47   0.143   56
Philippines     0.125   70   0.271        52   0.047   71   0.058   70
Poland          0.286   37   0.310        45   0.205   55   0.344   29
Portugal        0.350   33   0.446        26   0.315   40   0.289   33
Puerto Rico     0.541   17   0.456        22   0.834   3    0.333   31
Romania         0.246   48   0.267        53   0.291   44   0.181   47
Russia          0.218   57   0.139        70   0.304   42   0.211   43
Saudi Arabia    0.381   30   0.418        29   0.374   28   0.351   28
Serbia          0.183   62   0.294        51   0.131   68   0.124   63
Singapore       0.558   15   0.378        35   0.711   9    0.583   3
Slovenia        0.489   19   0.520        15   0.562   17   0.385   25
South Africa    0.277   42   0.222        60   0.344   33   0.264   35
Spain           0.401   28   0.515        17   0.454   25   0.235   38
Sweden          0.685   4    0.766        4    0.714   7    0.574   5
Switzerland     0.630   7    0.597        12   0.730   6    0.561   8
Syria           0.163   68   0.150        69   0.155   66   0.182   46
Thailand        0.221   56   0.206        66   0.328   38   0.128   61
Tunisia         0.218   58   0.208        64   0.343   34   0.102   66
Turkey          0.272   43   0.306        46   0.278   46   0.233   39
Uganda          0.100   71   0.082        71   0.067   70   0.152   55
United Arab
Emirates        0.417   24   0.446        25   0.339   35   0.467   18
United
Kingdom         0.561   14   0.603        11   0.662   13   0.418   21
United States   0.717   3    0.752        6    0.713   8    0.686   1
Uruguay         0.304   35   0.403        30   0.354   31   0.154   54
Venezuela       0.224   55   0.349        40   0.193   59   0.128   60
                                             31

Table -2: Entrepreneurial Attitudes Index and pillar values ranked by country

                 ATTINDEX   OPPORTUNITY STARTUP NONFEAR NETWORKING CULTURAL
Country                      PERCEPTION  SKILLS OF FAILURE          SUPPORT
Algeria            0.227        0.529     0.254    0.155   0.136      0.131
Argentina          0.375        0.882     0.821    0.167   0.162      0.175
Australia          0.804        0.842     0.810    0.841   0.771      0.762
Austria            0.553        0.420     0.512    0.749   0.559      0.595
Belgium            0.512        0.391     0.483    0.929   0.344      0.567
Bolivia            0.217        0.392     0.650    0.043   0.035      0.162
Bosnia and
Herzegovina        0.209         0.186        0.365        0.071          0.241   0.252
Brazil             0.333         0.819        0.213        0.408          0.143   0.282
Canada             0.773         0.756        0.690        0.953          0.643   0.897
Chile              0.517         0.536        0.560        0.712          0.274   0.718
China              0.261         0.277        0.100        0.728          0.109   0.255
Colombia           0.383         0.852        0.396        0.483          0.116   0.370
Croatia            0.323         0.172        0.429        0.428          0.408   0.260
Czech Republic     0.386         0.302        0.369        0.716          0.286   0.332
Denmark            0.754         0.919        0.583        0.807          0.697   0.873
Dominican
Republic           0.390         0.462        0.551        0.357          0.316   0.302
Ecuador            0.207         0.361        0.401        0.171          0.096   0.085
Egypt              0.232         0.223        0.444        0.349          0.056   0.208
Finland            0.685         0.479        0.715        0.847          0.664   0.877
France             0.448         0.259        0.339        0.676          0.494   0.618
Germany            0.447         0.259        0.343        0.633          0.434   0.720
Greece             0.367         0.213        0.938        0.460          0.131   0.386
Guatemala          0.197         0.376        0.228        0.131          0.119   0.164
Hong Kong          0.441         0.567        0.119        0.796          0.379   0.750
Hungary            0.302         0.063        0.475        0.659          0.236   0.309
Iceland            0.653         0.414        0.641        0.543          1.000   0.916
India              0.215         0.277        0.082        0.590          0.025   0.271
Indonesia          0.172         0.361        0.156        0.307          0.056   0.053
Iran               0.239         0.400        0.325        0.075          0.383   0.124
Ireland            0.523         0.340        0.605        0.690          0.350   0.788
Israel             0.365         0.369        0.470        0.367          0.219   0.474
Italy              0.495         0.439        0.516        0.667          0.443   0.435
Jamaica            0.302         0.225        0.259        0.209          0.600   0.280
Japan              0.305         0.055        0.106        0.984          0.392   0.342
Jordan             0.354         0.416        0.513        0.149          0.286   0.569
Kazakhstan         0.249         0.488        0.466        0.223          0.077   0.130
Korea              0.480         0.145        0.516        0.828          0.848   0.501
Latvia             0.397         0.243        0.492        0.395          0.543   0.400
Macedonia          0.245         0.267        0.382        0.179          0.141   0.300
Malaysia           0.485         0.531        0.294        0.563          0.689   0.479
Mexico             0.332         0.628        0.228        0.670          0.154   0.167
Morocco            0.342         0.417        0.152        0.528          0.395   0.344
Netherlands        0.703         0.653        0.441        0.966          0.728   1.000
New Zealand        0.859         0.657        1.000        0.921          0.950   0.906
Norway             0.700         0.549        0.666        1.000          0.627   0.768
Panama             0.295         0.391        0.650        0.113          0.241   0.236
Peru               0.430         0.747        0.544        0.356          0.300   0.310
Philippines        0.271         0.649        0.430        0.288          0.037   0.180
                                32

Poland          0.310   0.145   0.410   0.599   0.241   0.279
Portugal        0.446   0.224   0.657   0.691   0.283   0.584
Puerto Rico     0.456   0.437   0.499   0.796   0.226   0.530
Romania         0.267   0.170   0.219   0.447   0.311   0.233
Russia          0.139   0.197   0.171   0.300   0.098   0.000
Saudi Arabia    0.418   1.000   0.467   0.235   0.268   0.348
Serbia          0.294   0.312   0.569   0.206   0.214   0.225
Singapore       0.378   0.185   0.208   0.802   0.251   0.675
Slovenia        0.520   0.157   0.737   0.905   0.764   0.538
South Africa    0.222   0.186   0.058   0.674   0.043   0.335
Spain           0.515   0.539   0.633   0.541   0.360   0.584
Sweden          0.766   0.621   0.739   0.843   0.948   0.760
Switzerland     0.597   0.419   0.448   0.853   0.628   0.787
Syria           0.150   0.439   0.152   0.000   0.111   0.150
Thailand        0.206   0.040   0.348   0.390   0.072   0.299
Tunisia         0.208   0.050   0.242   0.113   0.231   0.522
Turkey          0.306   0.507   0.323   0.334   0.114   0.386
Uganda          0.082   0.000   0.000   0.207   0.000   0.251
United Arab
Emirates        0.446   0.530   0.213   0.779   0.322   0.616
United
Kingdom         0.603   0.762   0.615   0.714   0.364   0.761
United States   0.752   0.760   0.950   0.866   0.673   0.603
Uruguay         0.403   0.500   0.539   0.324   0.179   0.669
Venezuela       0.349   0.950   0.740   0.201   0.169   0.087
                                             33

Table A-3: Entrepreneurial Activity Index and pillar values ranked by country

                 ACTINDEX OPPORTUNITY TECHNOLOGY   QUALITY OF   COMPETITION
Country                     STARTUP     SECTOR   HUMAN RESOURCE
Algeria            0.182      0.402       0.015       0.169        0.231
Argentina          0.308      0.190       0.382       0.329        0.369
Australia          0.564      0.747       0.846       0.194        0.909
Austria            0.470      0.610       0.462       0.208        0.831
Belgium            0.693      0.819       0.613       0.747        0.620
Bolivia            0.199      0.229       0.204       0.209        0.160
Bosnia and
Herzegovina        0.107         0.063            0.094            0.103        0.176
Brazil             0.188         0.023            0.261            0.222        0.329
Canada             0.886         0.814            1.000            0.900        0.848
Chile              0.330         0.303            0.445            0.132        0.564
China              0.210         0.000            0.361            0.578        0.079
Colombia           0.280         0.227            0.259            0.454        0.209
Croatia            0.217         0.103            0.326            0.157        0.328
Czech Republic     0.335         0.272            0.578            0.174        0.409
Denmark            0.966         1.000            0.950            1.000        0.919
Dominican
Republic           0.259         0.252            0.298            0.212        0.282
Ecuador            0.162         0.202            0.275            0.142        0.062
Egypt              0.303         0.331            0.427            0.451        0.113
Finland            0.623         0.761            0.605            0.555        0.592
France             0.556         0.549            0.429            0.625        0.671
Germany            0.621         0.577            0.852            0.409        0.795
Greece             0.325         0.346            0.288            0.360        0.309
Guatemala          0.201         0.201            0.217            0.000        0.527
Hong Kong          0.371         0.512            0.317            0.407        0.281
Hungary            0.266         0.361            0.296            0.323        0.134
Iceland            0.558         0.824            0.672            0.406        0.428
India              0.233         0.111            0.187            0.264        0.425
Indonesia          0.460         0.237            0.491            0.698        0.568
Iran               0.184         0.128            0.262            0.223        0.134
Ireland            0.832         0.767            0.900            0.757        0.928
Israel             0.473         0.335            0.893            0.576        0.268
Italy              0.363         0.460            0.363            0.271        0.383
Jamaica            0.205         0.336            0.113            0.058        0.393
Japan              0.468         0.604            0.681            0.402        0.291
Jordan             0.164         0.213            0.033            0.207        0.253
Kazakhstan         0.188         0.195            0.097            0.531        0.036
Korea              0.505         0.345            0.678            0.804        0.329
Latvia             0.432         0.496            0.377            0.688        0.265
Macedonia          0.210         0.091            0.239            0.302        0.249
Malaysia           0.447         0.562            0.379            0.370        0.501
Mexico             0.344         0.514            0.271            0.399        0.238
Morocco            0.139         0.455            0.000            0.010        0.186
Netherlands        0.665         0.744            0.763            0.431        0.886
New Zealand        0.685         0.910            0.796            0.478        0.691
Norway             0.743         0.783            0.829            0.655        0.727
Panama             0.270         0.458            0.158            0.238        0.274
Peru               0.277         0.284            0.240            0.279        0.311
Philippines        0.047         0.024            0.085            0.087        0.000
                                34

Poland          0.205   0.130        0.256   0.239   0.211
Portugal        0.315   0.649        0.103   0.303   0.360
Puerto Rico     0.834   0.683        0.815   0.950   0.960
Romania         0.291   0.275        0.135   0.694   0.186
Russia          0.304   0.178        0.373   0.572   0.174
Saudi Arabia    0.374   0.640        0.040   0.496   0.676
Serbia          0.131   0.042        0.190   0.130   0.186
Singapore       0.711   0.872        0.855   0.846   0.460
Slovenia        0.562   0.559        0.690   0.455   0.582
South Africa    0.344   0.246        0.311   0.213   0.702
Spain           0.454   0.569        0.418   0.379   0.471
Sweden          0.714   0.885        0.819   0.485   0.820
Switzerland     0.730   0.663        0.840   0.644   0.802
Syria           0.155   0.151        0.028   0.173   0.322
Thailand        0.328   0.357        0.126   0.690   0.292
Tunisia         0.343   0.467        0.227   0.291   0.432
Turkey          0.278   0.207        0.400   0.385   0.165
Uganda          0.067   0.049        0.011   0.044   0.180
United Arab
Emirates        0.339   0.172        0.196   0.805   0.339
United
Kingdom         0.662   0.731        0.537   0.577   0.867
United States   0.713   0.759        0.459   0.839   1.000
Uruguay         0.354   0.187        0.497   0.275   0.558
Venezuela       0.193   0.071        0.555   0.173   0.068
                                            35

Table A-4: Entrepreneurial Aspirations Index and ASP pillar values ranked by country.

                 ASPINDEX      NEW       NEW      HIGH   INTERNATlON-  RISK
Country                      PRODUCT     TECH    GROWTH    ALIZATION  CAPITAL
Algeria             0.159      0.012     0.335     0.168      0.187    0.174
Argentina           0.218      0.151     0.380     0.327      0.303    0.044
Australia           0.425      0.355     0.671     0.247      0.475    0.505
Austria             0.339      0.606     0.054     0.315      0.701    0.340
Belgium             0.524      0.428     0.768     0.275      0.860    0.549
Bolivia             0.072      0.048     0.025     0.107      0.144    0.047
Bosnia and
Herzegovina         0.215       0.002    0.092     0.205        0.511         0.466
Brazil              0.155       0.078    0.487     0.164        0.161         0.001
Canada              0.552       0.519    0.552     0.502        0.843         0.427
Chile               0.394       0.260    0.447     0.591        0.593         0.218
China               0.373       0.371    0.375     0.449        0.400         0.294
Colombia            0.174       0.039    0.145     0.486        0.256         0.049
Croatia             0.313       0.119    0.362     0.372        0.701         0.189
Czech Republic      0.525       0.469    0.393     0.576        1.000         0.360
Denmark             0.569       0.746    0.385     0.465        0.527         0.879
Dominican
Republic            0.134       0.028    0.040     0.282        0.328         0.057
Ecuador             0.128       0.014    0.330     0.119        0.229         0.013
Egypt               0.175       0.022    0.201     0.197        0.265         0.272
Finland             0.385       0.864    0.423     0.262        0.464         0.158
France              0.491       0.571    0.524     0.255        0.762         0.552
Germany             0.564       0.563    0.818     0.472        0.811         0.350
Greece              0.263       0.103    0.369     0.126        0.422         0.403
Guatemala           0.050       0.000    0.261     0.000        0.000         0.020
Hong Kong           0.527       0.232    0.595     0.645        0.932         0.568
Hungary             0.190       0.118    0.292     0.172        0.499         0.011
Iceland             0.641       0.700    0.485     0.445        0.804         0.952
India               0.233       0.105    0.637     0.107        0.367         0.091
Indonesia           0.137       0.002    0.310     0.084        0.149         0.210
Iran                0.085       0.074    0.001     0.286        0.012         0.095
Ireland             0.538       0.298    0.476     0.425        0.775         0.988
Israel              0.580       0.950    0.929     0.509        0.797         0.222
Italy               0.362       0.276    0.348     0.348        0.621         0.273
Jamaica             0.115       0.002    0.149     0.060        0.404         0.036
Japan               0.417       0.900    0.514     0.512        0.342         0.141
Jordan              0.182       0.076    0.420     0.203        0.216         0.066
Kazakhstan          0.100       0.010    0.027     0.260        0.228         0.023
Korea               0.479       1.000    0.713     0.367        0.553         0.170
Latvia              0.254       0.163    0.050     0.499        0.621         0.144
Macedonia           0.272       0.028    0.189     0.279        0.475         0.644
Malaysia            0.160       0.165    0.101     0.075        0.396         0.109
Mexico              0.134       0.144    0.185     0.075        0.297         0.021
Morocco             0.170       0.055    0.320     0.131        0.480         0.002
Netherlands         0.479       0.323    0.526     0.277        0.627         0.833
New Zealand         0.493       0.179    0.805     0.342        0.857         0.691
Norway              0.425       0.324    0.581     0.291        0.650         0.369
Panama              0.114       0.031    0.158     0.206        0.161         0.044
Peru                0.143       0.053    0.193     0.274        0.231         0.024
Philippines         0.058       0.013    0.161     0.051        0.079         0.000
                                    36

Poland           0.344   0.115   0.838   0.228   0.731   0.131
Portugal         0.289   0.156   0.280   0.249   0.660   0.206
Puerto Rico      0.333   0.151   0.172   0.993   0.552   0.119
Romania          0.181   0.084   0.000   0.320   0.692   0.018
Russia           0.211   0.203   0.181   0.598   0.248   0.006
Saudi Arabia     0.351   0.048   0.602   1.000   0.342   0.210
Serbia           0.124   0.029   0.112   0.244   0.148   0.121
Singapore        0.583   0.530   0.584   0.571   0.950   0.417
Slovenia         0.385   0.457   0.273   0.408   0.706   0.214
South Africa     0.264   0.318   0.246   0.256   0.545   0.084
Spain            0.235   0.307   0.178   0.126   0.373   0.239
Sweden           0.574   0.754   1.000   0.358   0.460   0.525
Switzerland      0.561   0.714   0.549   0.344   0.652   0.717
Syria            0.182   0.040   0.210   0.412   0.207   0.127
Thailand         0.128   0.065   0.253   0.138   0.156   0.055
Tunisia          0.102   0.136   0.159   0.120   0.047   0.059
Turkey           0.233   0.302   0.032   0.560   0.360   0.094
Uganda           0.152   0.021   0.713   0.093   0.109   0.003
United Arab
Emirates         0.467   0.085   0.317   0.900   0.661   1.000
United Kingdom   0.418   0.376   0.472   0.463   0.487   0.323
United States    0.686   0.590   0.950   0.557   0.653   0.765
Uruguay          0.154   0.085   0.072   0.303   0.248   0.099
Venezuela        0.128   0.024   0.300   0.247   0.121   0.014

				
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