Master Thesis in Entrepreneurship971816/FULLTEXT01.pdf · classification developed by the Global...
Transcript of Master Thesis in Entrepreneurship971816/FULLTEXT01.pdf · classification developed by the Global...
Master Thesis in Entrepreneurship
Entrepreneurial activity in developing countries
Authors: Ilia Minaev
Supervisor: Anna Alexandersson,
Lydia Choi Johansson
Examiner: Daniel Ericsson
Date: 2016-05-31
Subject: Degree Project
Level: Master’s Thesis
Course code: 4FE16E
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Abstract Modern literature has many research in the field of entrepreneurship, but most of them
do not explain the characteristics of entrepreneurial activity in developing countries.
Thus, this research uses regression analysis of panel data for the cross-country
analysis of factors influence the level of entrepreneurial activity in 52 developing
countries. The paper provides empirical information about the individual
characteristics, regulatory standards countries, as well as some macroeconomic
indicators. Individual factors (gender, age), indicators of respondents’ self-evaluation
and assessment of the environment, in which they are located have a significant impact
on entrepreneurial activity in developing economies. In terms of macroeconomic
indicators, it was concluded on the positive effects of GDP growth and the lack of
impact of unemployment on the level of entrepreneurial activity.
Keywords entrepreneurship, entrepreneurial activity, developing countries
Acknowledgement I would like to express my appreciation and gratitude to the tutors Anna Alexandersson
and Lydia Choi Johansson. During the work on thesis Anna Alexandersson and Lydia
Choi Johansson have been with me. Their advices, new ideas and constructive
criticism have had a significant impact on the research approach and they also helped
to achieve research goals.
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Contents
1 Introduction__________________________________________________4
2 Theoretical background_________________________________________5
2.1 Theory of entrepreneurial activity__________________________________5
2.1.1 The concept of entrepreneurship__________________________________6
2.1.2 Total Early-Stage Entrepreneurial Activity index (TEA)______________10
2.1.3 A review of empirical research in entrepreneurship_________________11
2.2 Entrepreneurial activity in developing countries______________________13
2.2.1 The characteristics of entrepreneurship development in developing
countries___________________________________________________13
2.2.2 Factors affecting entrepreneurial activity in developing countries______16
3 Methods, description of data and the tested hypotheses______________18
4 Empirical analysis of entrepreneurial activity in developing countries__22
4.1 Individual characteristics_______________________________________22
4.2 Regulatory costs and macroeconomic indicators_____________________26
Conclusions_______________________________________________________31
References________________________________________________________32
Online references__________________________________________________34
Appendix_________________________________________________________35
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1 Introduction
Entrepreneurship is one of the important economic components for the world.
The role and importance of the entrepreneurial sector in the economies cannot be
overestimated. Entrepreneurship can act as a platform for social and economic
development of the country.
As an evaluation of entrepreneurship, the economic indicator as
“entrepreneurial activity” is commonly used. It is a reflection of the intensity of this
process in a specific economic region. Entrepreneurial activity is an individual
conditional indicator by which it is possible to research the situation on the
entrepreneurship market in the specific conditions (e.g., economic, social,
institutional-legal) for each region.
In terms of the factors that determine susceptibility to successful
entrepreneurship there is a literature describing the process of enterprise
development, which is rich in studies that have focused on psychological and
demographic characteristics of the individual entrepreneurs. Later researchers, such
as, for example, Specht began to move from the research of character traits of the
individuals as the factors influencing entrepreneurial activity, to the costs of creating
own business. Modern researchers focus on the factors that influence the formation
of the organizational structure at a more aggregated or national levels (Specht, 2003).
This paper focus on the research of entrepreneurial activity of the population
and exploring which indicators are the key factors of influencing on the
entrepreneurial initiatives. However, as it is known, all countries differ in many
respects. For example, it is difficult to adequately assess and compare the economic
situation in Europe and Africa. Usually in such cases, many researchers consider the
classification of countries in terms of economic development countries and
distinguish developed and developing states. Cross-country analysis in the existing
paper is the result of the research of the motives of entrepreneurship in developing
countries. This choice can be explained by many reasons. The main motive of the
choice of this type of countries is the lack of entrepreneurial sector trends. In
developing countries, every year the number of people involved in the process of
opening own business, can fluctuate depending on the current the economic situation
in the country at that time, as well as environmental conditions. (Appendix 1). Many
developing countries are in Africa or South America, where the main source of
income for many residents is farming. Thus, it is impossible to track the trend of
growth of entrepreneurial activity index. However, it is possible to consider the
factors that does not change from year to year greatly in order to find out what can
affect the motivation of entrepreneurial activity in addition to unforeseen
circumstances.
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This research focuses on three types of factors, which can be adversely or
positively affect the index of entrepreneurial activity. The first of them is the
individual characteristics, which are considered at the individual level of each
country; the second factor is regulatory costs of creating own business; a third type
of factors is macroeconomic indicators of the country. Last factor include the index
of economic freedom, unemployment, GDP growth, as well as some other factors,
which are included in the index of economic freedom.
The objective of this paper is to identify the factors that influence the level of
entrepreneurial activity in developing countries.
In order to achieve this goal the following tasks are established:
analysis of the existing literature in entrepreneurship and entrepreneurial
activity;
determining the characteristics of entrepreneurship in developing countries;
identification of factors affecting the level of entrepreneurial activity in
developing countries;
selecting of methodology and data for the analysis of the factors influencing
entrepreneurial activity in developing countries;
statistical and econometric implementation and interpretation of analytical
results.
Current research consists of the following parts. The first of which is devoted
to the review of the existing literature in entrepreneurship in general as well as a
literature review revealing the characteristics of entrepreneurial activity in
developing countries. Next chapter includes methodology, description of data and
presenting of the tested hypotheses. Last chapter is an essential part of this research
and it is devoted to the empirical analysis of the factors influencing entrepreneurial
activity in developing economies. This chapter presents the results of the regression
analysis and its interpretation.
2 Theoretical background
The theoretical background is consist of two interrelated parts. In the
beginning, existing theoretical and empirical literature in entrepreneurship are
presented, paying special attention to the influenced factors and indicators of
entrepreneurial activity. Second part is more oriented on the features of
entrepreneurship development in developing countries.
2.1 Theory of entrepreneurial activity
The region's economy in the constantly changing modern market system is not
so much a geographical area of accumulation and allocation of economic assets as
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the social diffusion system that concentrates the spiritual, political and economic
interests of different agents. One of the mechanisms of sublimation and
implementation of different groups of economic interests is an entrepreneurial
activity, which is a special tool for increasing the intensity of the economic
development of both the region and the country as a whole.
Currently, modern economic science has many research conducted to study the
interaction of entrepreneurship, in particular entrepreneurial activity of people, and
various economic indicators. Economists of many countries want to find answers to
the questions: “how to motivate people to start their own business?” What are the
key factors in choosing entrepreneurship as the main type of income of people? In
order to conduct this type of research it is necessary to examine in detail the entire
process of becoming an entrepreneur. Thus, the whole cycle of becoming an
entrepreneur will be considered, each stage of development of the entrepreneur from
starting a business until its closure.
In the current research, we focus on the study of entrepreneurial activity of the
population. It is supposed to find out what economic indicators are the key factors
of influence on the entrepreneurial initiative of people. However, as it is known, all
countries differ in many respects. For example, there is no way to adequately assess
and compare the economic situation in Europe and Africa. In such cases, it is
appropriate to use of the classification of countries in terms of economic
development (developed and developing); however, considered in this paper, the
concept of entrepreneurial activity is closely linked with the labor market. Thus, it
is important to learn the specifics of formation of motivation of the population of
each country in the concrete regional conditions. For the research it will be used the
classification developed by the Global Entrepreneurship Monitor (GEM) on
economic types: resource-oriented economy, efficiency-oriented economy and
innovation-oriented economy. However, despite the efficiency of GEM
classification, this research is based on a standard classification of countries in terms
of economic development, thus the main direction of research is the cross-country
analysis of developing countries.
2.1.1 The concept of entrepreneurship
Nowadays there are many definitions of “entrepreneurship” and
“entrepreneurial activity”. The contents of these two concepts has changed over
time, with the development of scientific-technical progress and society as a whole.
For example, an American scientist, Professor R. Hizrich (2002) talks about
entrepreneurship as “the process of creating something new that has value”,
respectively, of the entrepreneur as “a person who spends time and energy, takes on
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the burden of psychological, financial and social risk, in return for money and the
desired result”. According to Goncharova, Kartashov and Gavrilova (2009),
entrepreneurship is presented as activity of people, carried out at their own risk with
a view to profit. It is possible to consider the process of entrepreneurship on the other
hand. For example, Acs (2004) wrote that entrepreneurship should be considered as
“the realization of the special abilities of the individual, which is expressed in a
rational combination of factors of production based on the innovative approach of
risk.” It is worth noting that in all cases highlights risky nature of the above activities.
Entrepreneurship plays a principal role in the development of any country.
Joseph Schumpeter (1934), an Austrian scientist, stated that the entrepreneur is “the
economic entity whose function is just the implementation of new combinations.”
In the competitive environment, the entrepreneurs can be considered as the
main actors, as their competition leads to a reduction of costs, reduction of not only
economic losses, but also the value of goods and services. It also leads to many
modernization processes through the introduction of advanced technologies. For a
long time the European Society considered entrepreneurship as a secondary activity,
unworthy for people with high social status.
Entrepreneurship has an impact both on the social and on the economic systems
of the country. The solution of many socio-economic problems of unemployment
and low income (possibility of forming a middle class among the economically
active population) is the result of the implementation of the functions of
entrepreneurship in general. It also gives the possibility of forming a new production
of different functional orientation, which in turn leads to the creation of a favorable
business and investment environment of the regional or national economic system.
As an assessment of entrepreneurial activity, it is common to use the economic
indicator as “entrepreneurial activity”. It is a reflection of the intensity of this process
in a specific economic region. Entrepreneurial activity is a separate conditional
indicator by which it is possible to study the situation in entrepreneurship in the
specific conditions for each region (economic, social, institutional-legal etc.)
Entrepreneurial activity is a concept that defines a dynamic process of
entrepreneurial development. Therefore, in this case it is important to consider all
the phases of becoming an entrepreneur, which will be covered in more detail in the
following paragraphs of this paper.
The opinion of the population in relation to the opening of new business
characterizes the general mood to the entrepreneurship in general and to
entrepreneurs in particular. Thus, it can generate a favorable social and
psychological climate for the opening and development of new companies in the
country and stimulate the involvement of large investments, creating infrastructure
and business-community.
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During the research of factors that are relevant to entrepreneurship, it should be
noted that there are both individual characteristics and the national characteristics of
the region or country. As it was noticed earlier in this paper, there is the resource
“Global Entrepreneurship Monitor”, in which listed the following indicators:
1. Individual:
• assessment of the favorability of the environment for starting a business in the
next 6 months in the area where the respondent lives;
• the existence of an individual entrepreneurial skills, depending on own
assessment of people their knowledge, skills and experience, sufficient to start
their own business;
• fear of “collapse” of business, which is a negative factor for the development
of their own business;
• the presence of friends of entrepreneurs who started their own business within
last 2 years.
These factors are used in econometric analysis of this research, and added some
other control variables such as gender and age.
2. National characteristics:
• the system of values that has formulated in the society, which includes
indicators such as a value of entrepreneurship to career development, the
prestige of entrepreneurship in society and the pursuit of high standards of
living;
• public opinion on the creating of the own businesses, which in most cases
formed by media involvement in shaping the image of a successful
entrepreneur.
Considering these factors as a whole, it was concluded that the evaluation of
external opportunities has a positive effect on the level of entrepreneurial activity.
However, it is worth noting that more attention is paid not to the actual state of the
environment, but how people accept a new perspective of business creation into
account. Many factors influence on the public perception of the new perspectives of
entrepreneurship development. These factors include general economic conditions
of the region or country, development of entrepreneurial culture, historical
experience and education. Thus, the level of entrepreneurial activity is a reflection
on the interaction of perceptions of individual external opportunities for the business
and its own opportunities and abilities to entrepreneurship. Only when in public
perceptions external opportunities are complemented by necessary competences, the
economy and society receive social stratum, which is a potential for replenishing the
ranks of entrepreneurs.
However, in addition to above-mentioned factors, it can be identified other key
channels of influence on the entrepreneurial aspirations of the people at the country
level. For example, according to the research of Hessels, van Gelderen and Thurik
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(2008), authors consider 3 indicators as an indicator of entrepreneurial activity of
people: employment growth, increase innovation and increase in exports.
Researchers in their articles describe the analysis of the impact on the performance
of the following factors listed above:
1. The need to open own business. This case is about those people who are forced
to open their businesses for several reasons: lack of jobs, structural unemployment.
Thus, their “survival” depends on the organization and development of their
business. However, most often necessity-driven entrepreneurship is common in
weak developing regions, which leads to limited access of the population to the
human capital, financial capital, technology and other resources that can suppress
their potential for innovation, job growth and the creation of benefits for
competition, which subsequently leads to reduction in exports. Such potential
entrepreneurs are interested in business development, but the reasons listed above
may impair their expectations. According to Hessels, van Gelderen and Thurik
(2008), it have not been revealed significant coefficients for necessity-driven
entrepreneurship.
2. Increase in income. This factor relates to opportunity-driven entrepreneurship
according to GEM classification. Opening of the new company, motivated by
increase in income has a positive effect on the ambitions associated with the growth
of employment and innovation. Indeed, Cassar showed proof of this hypothesis,
reviewing the relationship between financial motives and the resulting variables.
Regression analysis showed that at the 0.001 significance level, growth of
preferences, risk and return of opening of the new companies can be explained in
terms of factor of increasing profitability. Hessels, van Gelderen and Thurik (2008),
using regression analysis stated that indicator of innovation development does not
depend on the motive of increasing wealth, but there is a positive connection to the
10% level of significance between the desire to increase income and the employment
in organizations with average employment growth.
3. Motive of independence of employees from employer. Regarding the
independence and autonomy of the employee, the main motive for the individual
business is a freedom associated with the needs of the individual. Thus, people can
change their lifestyle; control their aims, methods of doing business, and planning
time. In this term, most likely, it will be opening of small firms by the potential
entrepreneurs. Hessels, van Gelderen and Thurik (2008) did not find any relationship
between independence and the growth of innovation or between independence and
employment growth in their research. This result confirms the findings of Kolvereid
paper in “Growth aspirations among Norwegian entrepreneurs” and Morris (2006).
However, Cassar (2007) found a negative relationship, conducting a similar research
between above-mentioned indicators.
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In the current paper, the data of the Global Entrepreneurship Monitor (GEM)
are used, because it is necessary to specify the factors influencing entrepreneurial
activity. GEM model has its particularity, because this project is studying three
groups of countries: resource-oriented economy, efficiency-oriented economy and
innovation - oriented economy. Accordingly, during the review of entrepreneurship
in different countries it is necessary to consider characteristics of its development,
the changing nature of entrepreneurship and contribution of entrepreneurship to the
development. For countries with resource-oriented economy, such basic indicators
drive economic development as the development of institutions, infrastructure,
macroeconomic stability, health and primary education. In efficiency-driven
economies, the government should focus on ensuring the smooth operation of
mechanisms, such as the proper functioning of the market, higher education systems,
product and labor markets, and technological efficiency. Even if these conditions are
not directly related to the entrepreneurship in terms of Schumpeterian (1934)
“creative destruction”, these are indirectly related to the development of markets.
Thus, it will also attract new potential entrepreneurs and give them more
opportunities for entrepreneurship.
According to the GEM project, two basic types of entrepreneurs are presented:
opportunity-driven and necessity-driven entrepreneurs. Opportunity-driven
entrepreneurs, or voluntary entrepreneurs, those who try to seize opportunities and
benefit from business activities. Necessity-driven entrepreneurs or forced
entrepreneurs are characterized by attempts to open their own business because they
have no other income opportunities. In 2013, the proportion of necessity-driven
entrepreneurs was 18.3% in innovation-driven countries, 28.8% in the efficiency-
driven countries and 30.3% in resource-oriented countries. Within the group, there
are significant differences. For example, in the group of economically developed
countries the spread of the maximum and minimum values is about 9 times.
2.1.2 Total Early-Stage Entrepreneurial Activity index (TEA)
The GEM project has lots of data characterizing the entrepreneurship market in
the countries-participants of the project. Using GEM data as the primary database
has led to use the generalized index of entrepreneurial activity (Total Early-Stage
Entrepreneurial Activity, TEA) as the main variable that describes the
entrepreneurial activity. It characterizes the level of entrepreneurial activity in the
early stages. This index indicates the percentage of the population aged 18 to 64
years who are nascent entrepreneurs and owners of newly established enterprises.
However, this is not a simple sum of the two parameters. If we consider the GEM
research, we can analyze the data for 2011. 54 countries, which are divided into 3
groups depending on the orientation of its economy. There are 3 groups: resource-
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oriented economies, efficiency-oriented and innovation-oriented economies. On
average, 16 efficiency-oriented countries participating in GEM in 2010 and 2011
significantly increased the TEA index almost 25%. Argentina, Chile and China have
among those countries whose level of TEA in 2010 was already at a high level, and
then in 2011 again experienced significant growth.
2.1.3 A review of empirical research in entrepreneurship
Theoretical research in entrepreneurship are developing rapidly. Many
researchers in the field of management and economics investigate the problems of
entrepreneurship. They reveal the specifics of entrepreneurial activity, a large
number of paper devoted to the evaluation of entrepreneurial opportunities and
factors that characterize the motivation of entrepreneurs.
This research is based on a set of already published investigations of authors
from around the world. Each of these researches is an integral part of the data
analysis, but it is worth mentioning some of which served as an impulse and a
framework for this kind of research.
First, it is worth noting one of those articles that cited by many authors, which
is article by Richard E. Kihlstrom and Jean-Jacques Laffont was published back in
1979. The mentioned article is one of the earliest investigation devoted to
entrepreneurship, namely the tendency of individuals to open their own company.
The authors constructed a theory of competitive equilibrium in the face of
uncertainty, using already existing at the time the model Knight Entrepreneurship.
The authors notice that people have their own work, which they can then make
available as workforce in a competitive labor market, or use it as an entrepreneurial
activity. All entrepreneurs have the same access to technologies and receive all the
profits of their companies. The dynamic process of creating companies and exit of
enterprises from the economy is stable. The resulting balance is only effective when
all owners risk neutral. The ineffectiveness of the number of firms and the
distribution of labor in enterprises leads to a risk allocation inefficiencies caused by
institutional constraints.
This paper investigates the factors of influence on entrepreneurial activity in
developing countries that is why it is required to bring some of the research of the
authors, focused on cross-country analysis.
Thomas and Mueller (2000) in their research carried out an analysis to find the
relationship between culture and 4 core of individual characteristics, oriented on
entrepreneurial activity, on the example of 8 countries (USA, Canada, Ireland,
Belgium, China, Singapore, Slovenia and Croatia). The main characteristics of the
used parameters such as human creativity, a sense of self-control (previous studies
have shown that compared to non-entrepreneurs, entrepreneurs have a greater sense
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of self), the propensity for risk (entrepreneurs tend to have a higher risk tolerance)
and activity of the individual. In the latter case, it is meant, as a person is willing to
devote himself to the work. Typically, entrepreneurs in this respect more active.
Using multivariate logistic regression, the authors were able to make the main
conclusions: in “individualistic” countries, the great importance for the
entrepreneurial activity has a sense of self-control, while in the countries in the high
level of satisfaction and confidence the great impact on the motivation of people to
become entrepreneurs is propensity to risk.
The research of Steensma, Marino and Weaver (2000) presented the analysis
of entrepreneurship and its various factors.
The focus the authors made on a study of the desire of individuals to unite in
order to make a profit. The paper considers the situation using not all firms but only
small and medium-sized in 7 countries (Australia, Finland, Greece, Indonesia,
Norway, Mexico, Sweden). Steensma, Marino and Weaver (2000) used hierarchical
regression analysis, with which they provided the following conclusions: there is a
negative relationship between the human tendency toward individual work and
decision-making cooperative, but a positive relationship of mutual cooperation of
the human tendency to self-determination has been detected.
In 2015, Krzysztof Wach published a paper, using the Global Entrepreneurship
Monitor data. The main purpose of his work is to explore the impact of social and
cultural norms towards entrepreneurship in the European Union based on data from
the last report GEM 2013. Entrepreneurial activity has been studied in 23 countries
of the European Union. The author tested three hypotheses:
1. Level of entrepreneurial activity is higher in countries with innovation-oriented
economy than with the efficiency-oriented countries. For this purpose, it was
used t-statistic and the median test.
2. People are more willing to use entrepreneurial opportunities, which leads to an
increase in entrepreneurial activity in countries with a developed
entrepreneurial environment (using the Pearson linear correlation).
3. Countries with a high level of entrepreneurial culture have low level of
necessity-driven entrepreneurship; since these two variables are negatively
correlated with each other (comparison coefficients rank correlation Spearman
and Pearson linear coefficients).
The author presented following conclusions: there is no any difference in
entrepreneurial culture between the innovation-oriented and efficiency-oriented EU
economies, Wach (2015) confirmed hypothesis 2 and stated that than the higher the
index of entrepreneurial culture of the country (GEM), the higher the index of new
opportunities to start a business. The third hypothesis about necessity-driven
entrepreneurship in countries with well-developed entrepreneurial sector is also
confirmed.
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Hessels, van Gelderen, Thurik (2008) also presented a cross-country analysis
in their research. The authors answer the question whether the reasons are to start
their own business and the level of social security of the country to explain the
prevalence of entrepreneurial aspirations. In order to research the entrepreneurial
aspirations and motivations the authors used Global Entrepreneurship Monitor data
(GEM) in 2005 for 29 countries (Argentina, Australia, Austria, Belgium, Brazil,
Canada, Chile, Denmark, Finland, France, Germany, Greece, Hungary, Iceland,
Ireland, Italy, Japan, Mexico, Netherlands, New Zealand, Norway, Slovenia, South
Africa, Spain, Sweden, Thailand, United Kingdom, United States, Venezuela). In
terms of indicators of entrepreneurial aspirations Jolanda Hessels, Marco van
Gelderen, Roy Thurik used the data that characterize the innovativeness of the
country, expectations of job growth and export orientation. The results of these
economists shown that the level of social security has a negative impact on citizens'
entrepreneurial intentions. The results also suggested that entrepreneurial aspirations
in terms of employment and export growth positively correlated with an increase in
motivation to accumulate wealth.
A review of the existing literature in entrepreneurship has shown that today
there is many investigations devoted to the research of entrepreneurial activity.
However, most of them explains the choice of a particular set of countries that
adopted for the research is thus not possible to identify the factors that only affect
developing countries or only developed. In order to solve the existing lack of
information about the developing countries, this paper is devoted to the empirical
analysis of the factors influencing entrepreneurial activity in developing countries.
2.2 Entrepreneurial activity in developing countries
This chapter focuses on the consideration of entrepreneurial activity in the
developing economies, emphasizing the specifics of entrepreneurship development
in this group of countries, as well as highlighting the factors influencing
entrepreneurial activity in these regions.
2.2.1 The characteristics of entrepreneurship development in
developing countries
Uneven regional development is a feature of most countries. Recent studies on
the development of regions, showed an increase of regional inequality within many
developing countries. These results can be explained by the theory of endogenous
growth and new economic geography: the different levels of investment in human
and physical capital in different conditions agglomerations lead to the urbanization
of the economy, which in the following is the cause of regional inequalities.
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Employers play an important role in the perception of investment opportunities
in different regions and production, acting as a coordinator of material resources. In
addition, businesses are essential subjects as channels and mechanisms for the
displacement associated with agglomeration. Thus, the entrepreneurial capital, as
measured by the level of entrepreneurship, is an essential factor in many economic
indicators at the regional level. In his research, Wennekers, Uhlaner, & Thurik
(2002) noted the impact of entrepreneurship on the individual level, at the level of
companies and at the level of society, affecting the private person wealth,
profitability and company growth. Also, such an author as Stam (2006) in his article
points out that regional differences in levels of development of start-ups are an
important source of uneven regional development. The authors mentioned above
suggest a dependence between economic development and the entrepreneurship.
There are three reasons that can be explained by the choice of researching
developing countries. The first and main reason is a low level of entrepreneurial
activity in developing countries than in others. Thus, the research on developing
country-level factors may become the answer to the question of the development of
the regions. The second reason is new jobs. Many studies support the hypothesis
about the impact of the development of entrepreneurship in the creation of new jobs
(Hessels, van Gelderen & Thurik, 2008). Consequently, the identification of key
factors influencing the entrepreneurial activity of people can help reduce
unemployment in the developing countries. The final reason states that developing
countries are less subject to historical change, thus they were not able to use
innovation changes and entrepreneurship can be a good start for the development of
the developing countries in terms of new technologies. Thus, the main causes were
identified, confirming the importance of researching entrepreneurial activity in
developing countries.
So, it is required to consider what is meant by entrepreneurship in developing
countries.
The group of developing countries are countries with low levels of economic
development. According to the International Monetary Fund, 121 countries out of
182 are developing economies. Developing countries are characterized by features
such as: a large population and vast territory. In general, about 28% of world GDP
account on the developing countries.
Developing countries combine several features:
• the presence of a mixed economy with various forms of ownership, ranging
from the traditional economy to the public sector;
• relatively low overall level of development of the productive forces: the gap
between developed and developing countries is 1:20;
• dependent position in the world economy due to the fact that the economic
development of the colonies for centuries was not determined by their needs,
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and today their development is highly dependent on the inflow of foreign
capital;
• prevailing agro-raw orientation in economic development ;
• the low level of the produced GDP, including per capita (about 4 thousand.
USD. per year), poverty of many people.
All countries with developing economies can be divided into smaller sub-
groups: the newly industrialized countries, the countries-exporters of oil and the
least developed countries. The first group of countries united countries, which in
recent decades demonstrate strong economic growth per capita GDP (some countries
of Asia, Latin America, and most countries in the Persian Gulf). A special category
of developing countries is oil-exporting countries. The main participants in this
subgroup of countries are the 12 members of the Organization of Petroleum
Exporting Countries (OPEC), although some countries are oil exporters such as
Mexico, Brunei, etc. are not included in OPEC. In the countries of this sub-group,
there is a marked differentiation in per capita GDP (from less than 1 thousand. USD.
In Nigeria to more than 24 thousand. Dollars. in Kuwait, if we consider the
purchasing power parity), but despite this, the huge oil reserves were the basis for
the cause of development of these kind of developing countries and will contribute
to its growth in the future. There is also a group of countries which, for various
reasons (lack of minerals and landlocked, the unstable political situation in the
country, often unfavorable climate) were the least developed countries group. 32 out
of 47 of these countries are now in the territory of sub-Saharan Africa, 10 - in Asia,
4 - in Oceania and 1 - in Latin America. Their main problem is not even in the
backwardness and poverty, and in the absence of significant economic resources,
which could help them to overcome the difficulties associated with the development
of the regions.
In the period between 1945 and 1980, nearly 100 colonies in Africa and Asia
have tried to become independent and have begun the process of strategy
development. However, many of these countries have not been able to achieve any
economic development or a significant increase in GDP per capita.
Among the main directions of development of the state prevailed two forms of
industrial policy. The first one included the process of industrialization, which
mainly consisted of imports of foreign products for the domestic market. However,
in the 1980s the economic crisis was the reason for the transition to a new state
concept of development - export promotion. However, none of these industrial
policies showed no significant economic improvements with the exception of some
East Asian countries.
After of failed attempts at economic development, using import and export,
developing countries have begun to focus on their entrepreneurial environment and
the formation of the economic space, which can lead to the development of private
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entrepreneurship as a local (e.g., local entrepreneurs) and foreign (e.g., direct foreign
investments). Indeed, the policies related to the promotion of entrepreneurship has
led to positive results: the recent growth in the number of small and medium-sized
companies has become a source of development of the countries with developing
economies.
The classification given earlier in this chapter shows a significant advantage
over the third world resource-oriented countries (all the countries in this category
are developing) and efficiency-oriented countries in the GEM classification. The
first characterized by the fact that in countries firms compete on price, use the basic
factors of production, primarily unskilled labor and natural resources. The
distinctive feature of the second group of countries are the efficient production to
increase productivity. Unlike resource-oriented countries, the competition here is
achieved because of higher education, market efficiency and the ability to benefit
from existing technologies.
GEM data show that during the economic development the level of necessity-
driven entrepreneurship decreased, but the degree of opportunity-driven
entrepreneurship and voluntary entrepreneurship grows. On the contrary, the need
for entrepreneurship is prevalent in less developing countries.
2.2.2 Factors affecting entrepreneurial activity in developing
countries
Several groups can be distinguished among the factors influencing
entrepreneurial activity which are the individual characteristics of each individual
country's; macroeconomic indicators and indicators describing the process of
entrepreneurial development, which include the number of procedures required to
start their own business, a minimum capital, etc.
Research will be carried out in two stages according to the groups of factors
mentioned earlier. In order to identify indicators, largely affecting the
entrepreneurship in the country it was conducted a literature review for theoretical
justification. Further, in the post-econometric analysis it will be confirmed or refuted
used hypotheses.
In 2012 it was released an article “International entrepreneurship research in
emerging economies: A critical review and research agenda”, authored by Kiss,
Danis and Cavusgil. In this paper, the authors analyzed already published
investigations, devoted to the study of entrepreneurship in developing economies.
The authors took into account the findings of 26 out of the 88 studies, all of them
were based on the results of various kinds of regression. This research was provided
by a comparison of results of previous studies on a geographical basis. The authors
noted that, despite the difference in the location of the countries that have been
17
studied, in most investigations the focus of the research is the phenomenon of
networking. Comparing the developed and developing countries, in the second case
the owners increasingly rely on networks as a means of overcoming the difficulties
associated with the development of their business (Lee & Peterson, 2001). In their
paper, the authors also highlighted common in each of the studied articles is focusing
on personal entrepreneurial characteristics of the individual, which may somehow
or push the person to opening own business, or vice versa to limit its business
activities. The main personal characteristics studied in the articles are activity,
experience, leadership skills and a desire to become an entrepreneur. The authors
believe they can become potential entrepreneurs mechanisms to overcome external
negative factors in the establishment and management of firms in developing
countries. The results showed that the individual characteristics of a person have a
greater impact on the business, rather than the form of the company and the industry,
to which it relates.
Considering the impact of resources, opportunities and conditions of
development of the industry, it can be stated that they have an indirect connection
with the process throughout the business world, though they are important factors in
the discovery and management of new businesses. This is the conclusion the authors
do the same for developing countries.
A relatively small number of research provide conclusions resulting cross-
country and cross-cultural analysis of the entrepreneurship. However, some results
of the analysis of the impact of various socio-cultural factors in the development of
the certain sectors have been presented in research of Engelen, Heinemann, Brettel
(2009).
As the macroeconomic indicators can be considered normative-legal
environment, which is generally considered to be an important determinant of
economic performance of the country. Strict regulation of product and labor markets
is one of the most frequently cited reasons for the slow growth and high
unemployment. Deregulation is strongly recommended for countries such as Italy,
France and Germany, as well as for developing countries to improve their
economies. The regulatory environment can affect the growth and employment
through many channels. In the context of this paper, it will be considered the impact
of the environment on the rate at which new businesses are creating. According to
Schumpeter, the emergence of new businesses plays an important role in the process
of creative destruction, which contributes to the development of innovation,
employment and growth. Despite the growing number of studies on the impact of
regulation of product and labor markets to the GDP growth, investment and
employment with economic data, there is a lack of knowledge about the interaction
of the regulatory environment, and individual decisions of people to participate in a
new business activity.
18
In economic theory, the views of the impact of regulatory compliance on
businesses are differ. For example, in the theory of public choice, such regulation is
socially inefficient. In addition, it can be for two reasons: either because the staff of
the industrial sphere are able to lobby the opinion of officials with the adoption of
laws or because politicians use their position to extract own benefit. Thus, legal
regulation in itself is a burden not only to new, but also for existing companies. Rules
relating to entry of new firms is recognized as a barrier to market entry. Porter
suggested that government regulation might impose barriers to the emergence of
new market players. Regulatory and procedural requirements entail business costs
(e.g., financial costs, time costs), which are borne by the participants. Excessively
high cost may deter potential entrepreneurs and to force them to move into the
informal economy, which hampers their ability to grow and contribute to economic
growth due to lack of adequate access to social, legal and entrepreneurial
infrastructure. However, there is also the opposite view. The theory of general
interest states that there is a regulation in order to eliminate market failures. In this
case, measures that are more stringent contribute to better social outcomes.
However, in the study of entrepreneurial activity of people it is considered the first
point of view. It is supposed that stringent legal regulation of the process of opening
own business can lead to a decrease in entrepreneurial activity.
3 Methods, description of data and the tested hypotheses
In current study, following research methods have been implemented: literature
review and regression analysis. Literature review describes existing literature in
entrepreneurship and presents valuable views in the research of entrepreneurial
activity in developing countries, paying particular attention to the specifics,
indicators and factors of entrepreneurship in these above-mentioned economies. In
the empirical analysis, special attention was devoted to the data collection. Further,
based on the knowledge of the previous researchers, the regression analysis based
on panel data is realized in next part. This kind of econometric approach is used to
explain the influence of the specific determinants on entrepreneurial activity during
the time. In order to distinguish individual factors that influence the entrepreneurial
activity of people, binary choice model have been used. Three kinds of models for
binary variable, which are logit, probit, SNP were constructed to study the influence
of individual characteristics, each of the models was built separately with the fixed
effects by country and separately with fixed effects by year. For each of these models
it was calculated marginal effects in order to detect statistically significant values,
as well as comparing and selecting the most appropriate model for the interpretation
of results. Additional details and the key assumptions of used methods are explained
in the process of implementing it in the following text.
19
For a long time economists who investigated the field of entrepreneurship
experienced big difficulties due to the lack of sufficient reliable data. However,
nowadays there is a large number of databases, which offer great opportunities for
economists in entrepreneurial analysis. One such database is the Global
Entrepreneurship Monitor (GEM). The main indicators used in the empirical
analysis of current paper were taken from GEM. Thus, the dependent variable Total
Early-Stage Entrepreneurial Activity index (TEA), which is the main indicator of
entrepreneurial activity and it is widely used by many economists, was also taken
from the GEM.
For the empirical analysis of entrepreneurial activity in developing countries
panel data from 2009 to 2013 have been used. As mentioned earlier, the main
indicators used in the practical part is the result of the international project
GEM. However, every year a different number of countries are involved in the GEM
project and the list changes every time, in connection with which an unbalanced
model will be built in this paper. It is worth noting that only developing countries
from all the list of countries participating in the GEM project have been chosen.
Thus, the number of observations is significantly reduced and changed from year to
year: in 2009 there were 32 countries, in 2010 there were 29 countries, in 2011 there
were 24 countries, in 2012 there were 36 countries in 2013 there were 38 countries.
A complete list of countries for which data were used in the empirical analysis, can
be seen in Appendix (please see Figure 1 and list following it).
In the current study, it was carried out a two-level empirical analysis:
identifying the individual characteristics that affect the growth of entrepreneurial
activity, as well as in-country research of the factors, which in most cases do not
change in a short period of time and remain unchanged over time. Such factors may
include, for example, cultural and national characteristics of the country. Thus, the
data collected from different databases for more comprehensive coverage studies
and the levels of several factors.
A description of all the variables used for the empirical analysis, begin with the
individual factors that influence the level of entrepreneurship in the country. As
previously indicated, the GEM project enables to use the adult population survey
results, the participants of the Global Entrepreneurship Monitor as explanatory
variables. For example, in this paper as individual characteristics are used the impact
of media communications on the decision about starting own business; the feeling
of fear of failure future business; availability of opportunities for the creating and
development of business; familiarity with a person who became an entrepreneur for
the past 2 years and the personal opinion of the individual that there had sufficient
capacity for becoming an entrepreneur. Some of the variables listed above have
been used in Bosma (2009) article but for European countries. Thus, after this
research it is possible to evaluate and compare the results for developing countries
20
and European countries. These variables were selected after reviewing the literature
in order to compare the results obtained in the end:
media - the percentage of the population aged 18 to 64 years, who agree with the
statement that the presence in the press of business success stories can push them to
the creating of their business;
skills - the percentage of the population aged 18 to 64 years, who believe that their
knowledge and skills sufficient for a successful opening and further business;
fear - the percentage of the population aged 18 to 64 years, with good opportunities
for starting a business, but they think that the feeling of fear of failure of their
business can be an obstacle to their business;
opport - the percentage of the population aged 18 to 64 years, who believe there is a
good environment for running own business in their region;
knowent - the percentage of the population aged 18 to 64 years, who are familiar
with the person who became an entrepreneur for the past 2 years.
Thus, characteristic features in developing countries, as well as a review of available
literature allow formulating the following expected results (hypotheses):
1. Impact of advertising on the successful business has a significant positive
impact on entrepreneurial intentions.
2. The respondent’s opinion that his knowledge and ability enough to open his or
her own business, leads to an increase in entrepreneurial activity.
3. The highest value of fear leads to a decrease in entrepreneurial activity index.
4. Having a good business opportunities in the area in which the respondent lives
leads to an increase in entrepreneurial activity.
5. If the respondent is familiar with the person who became an entrepreneur in the
past two years, his or her propensity for entrepreneurial activity increases.
Researching the legal regulation of entrepreneurship, particular attention
should be paid to procedures required to open a business in a particular country. In
this case it is required to pay attention to the cost (not only money but also time),
which carries a potential entrepreneur during the opening of his or her business.
Therefore, the data obtained from the database “World Bank Doing Business” are
used. World Bank data base contains information about the four measures regulating
the costs of opening new enterprises: the number of procedures ( Procedures ),
needed to pass in order to register a business; the total number of days to reach the
same goal ( Days ); cash costs at the opening of business ( Cost ) and the minimum
capital required for business registration ( Min_capital ). Measures of monetary
21
value standardized as a percentage of per capita income for the purpose of
comparison between countries. The result of the research of these four figures
became the composite index (overall_DTF), calculated as a weighted average of
these four indicators. However, it should also be noted that all individual values were
standardized in the preparation of the index.
Next database which indexes are considered in the current paper is “The
Heritage Foundation”, Index of Economic Freedom. The main index, which was
used in this study, the index formed based on ten indicators, some of which are also
used in econometric models of this research. All figures below are estimated on a
scale from 0 to 100.
freedom from corruption - characterizes the degree of corruption in the
country, based on the CPI index (Corruption Perceptions Index), which ranges from
0 to 10, but this basis for comparison of the index multiplied by 10. Thus, the
database described by the value 0 equates to a very high degree of corruption in the
country.
business freedom – is a measure of the effectiveness of state regulation of
business. This quantitative assessment is derived from the dimensions of an array of
complexity of creating, maintaining and closing a business. The value of this
variable is in the range from 0 to 100, where 100 indicates the most “free” business
environment. This estimate is calculated based on the ten factors; all of them have
the same total weight in the index. The data for ten indicators index have been taken
from the World Bank database. In this paper, also used World Bank data (cost, days,
min_capital, procedures), so there may be multicollinearity models.
labor freedom - is a quantitative measure that includes various aspects of the
legal and regulatory framework of the state of the labor market, including the rules
relating to the minimum wage, laws preventing the dismissal, severance pay
requirements, and measurable regulatory restrictions on employment and hours
worked.
The paper also uses GDP per capita growth rate of the population, which is
calculated as a percentage of the previous year. The unemployment rate was also
taken from the World Bank database. [38]
As in the case with the individual characteristics, for the macroeconomic indicators
and indicators of regulatory costs, following hypotheses have been put forward:
1. For all types of entrepreneurship the complexity of doing business index
(Overall _ the DTF) has a negative significant value.
2. Variables of Cost, Days, Procedures, Min_Capital have greatest value for
opportunity-driven entrepreneurs.
3. GDP growth is a significant factor for all entrepreneurial types.
4. Indicator of Labor freedom has a significant influence for all types of
entrepreneurs.
22
5. The index of “business freedom” significantly negative effect on the
necessity-driven entrepreneurship.
4 Empirical analysis of entrepreneurial activity in developing
countries
In order to explore the entrepreneurial activity firstly it is needed demonstrate
at how the entrepreneurial activity has been changing in the past five years (2009 -
2013) in different developing countries (please see Appendix, Figure 1).
As can be seen from the graphs, the entrepreneurial activity index fluctuates
during the research period, which confirms our premise of the absence of positive or
negative trends in entrepreneurial activity index. Thus, this paper can show
interesting results, which in the future may become the basis for further research in
the field of entrepreneurship.
In this paper, the use of regression models based on panel data is demonstrated.
As the dependent variables, Total Early-Stage Entrepreneurial Activity index (TEA)
is used. The regression models are estimated in the software Stata.
4.1 Individual characteristics
Firstly, it is demonstrated the regression model with individual characteristics
that affect the index of entrepreneurial activity. The regression models are estimated
in the software Stata. In this case, data from a survey of the adult population of the
countries-participants of the GEM is used, where the results of the survey each year
and each country are categorical variables and each respondent is asked to carry
themselves with a certain category of persons. It is worth noting that the indicators
used in this study are not averaged across the country, thus it increases the validity
of the obtained coefficients.
Table 1 shows the values of the control variables, namely, their distribution in
the total sample of respondents who are nascent entrepreneurs. In the period from
2009 to 2013 in developing countries entrepreneurial intentions often manifested
among persons aged 18 to 34 years (49,39% - total index, 50.62% - opportunity-
driven entrepreneurs, 47.50% - necessity-driven entrepreneurs). Among people who
have entrepreneurial intentions, men slightly ahead of women, their share among
nascent entrepreneurs is higher. However, in the category of necessity-driven
entrepreneurs the situation is different: the proportion of women exceeds that of men
(women - 51.44%, men - 48.56%).
Table 1 demonstrates that with increasing age the entrepreneurial intentions of
people are reduced. Younger people are more likely to take the initiative in creating
their own business.
23
As for the variable as education, it is worth noting that the entrepreneurial
intention is higher in people with professional education. People with higher
education to a lesser extent become entrepreneurs. In most cases, this may be
because they have fewer problems with employment, as they have higher education.
Comparing the types of entrepreneurs, we can note the following fact: the share of
nascent entrepreneurs with secondary education is higher among necessity-driven
entrepreneurs (39.3%) than among opportunity-driven entrepreneurs (31.84%). This
result suggests that necessity-driven entrepreneurship prevails among the
population, who have received an initial basic schooling.
In order to identify individual factors that influence the entrepreneurial activity
of people, it is used binary choice models. Among them logistic model is used most
often, as well as the probit model. These models are characterized by a relatively
symmetrical distribution of the alternatives of dependent variable. It is also
important to consider the performance of assumptions about the nature of the
residues distribution using parametric models of binary choice. Failure to do so may
result in insolvency assessments. However, there is another method of binary choice
(semiparametric estimation) which does not have severe restrictions on the nature of
the distribution of residues. In this research, there are all three methods, but the basic
method is the SNP (seminonparametric method), as it allows checking the
robustness of the model results due to the possibility of using a flexible functional
form for the approximation of the unknown distribution of residues.
In order to identify the factors influencing entrepreneurial activity in
developing countries, it is used Total Early-Stage Entrepreneurial Activity index
TEA as the dependent variable. In models with the individual characteristics this
index represents a binary variable (1 - the respondent is involved in the business
process in the early stages, 0 - not involved in the business process in the early
stages). Three kinds of models for binary variables: logit, probit, SNP (please see
Appendix, Table 14) were constructed to research the influence of individual
characteristics, each of the models was built separately with the fixed effects by
country and separately with fixed effects by year. For each of these models it was
calculated marginal effects in order to detect statistically significant values, as well
as comparing and selecting the most appropriate model for the interpretation of
results.
24
Table 1 Characteristics of individuals in the sample: control variables
Characteristic
Distribution of nascent entrepreneurs,% among those who intend
to set up a business (% of total sample)
total opportunity-driven necessity-driven
Gender
men 53.69 54.57 48.56
women 46.31 45.43 51.44
Age
to 24 years 19.31 19.55 19.08
from 25 to 34 years old 30.8 31.17 28.42
from 35 to 44 years 23.79 23.73 22.87
from 45 to 54 years 17.41 15.86 17.46
55 years 8.69 9.69 12.17
Education
secondary (complete)
and lower 36.27 31.84 39.3
initial vocational 30.3 29.62 25.21
secondary vocational 16.77 19.69 22.59
higher 16.66 18.85 12.9
Source: GEM (2016), own elaboration
Table 2 Marginal effects for binary choice models with fixed effects by country
(logit, probit, SNP)
Variable Model
Logit probit SNP
control variables
Paul (1 - male, 0 -
woman) -0,229 *** -0,136 *** -0.0937 ***
(-20.57) (-21.38) (-16.08)
education
Higher (EDU4) base Basic base
Secondary
(complete) and lower
(eDu1)
-1,013 ***
(-31.43)
-0,586 ***
(-29.95)
-0,981 ***
(-37.12)
Initial vocational
(EDU2)
-0,515 ***
(-15.04)
-0,308 ***
(-14.81)
-0,815 ***
(-30.02)
Vocational
(EDU3)
-0,805 ***
(-23.32)
-0,467 ***
(-22.52)
-0,909 ***
(-33.98)
age
-0.000381
*** -0.000214 *** -0.000140 ***
(-4.09) (-4.13) (-8.00)
familiarity with a
person who became an
0.258 *** 0.152 *** 0.247 ***
(43.77) (45.42) (48.70)
25
entrepreneur in the past
2 years (1 - a sign, 0 - do
not know)
there are good
opportunities in the
respondent's country for
the development of
successful business (1
agree 0 - do not agree)
-0.0239 *** -0.0133 *** -0.00595 ***
(-7.35) (-7.23) (-3.38)
the availability of
adequate knowledge
and skills to start a
business (1 - I agree, 0 -
do not agree)
0.175 *** 0.106 *** 0.343 ***
(36.27) (37.50) (73.88)
fear of failure may
hinder the development
of business (1 - I agree,
0 - do not agree)
-0.0774 *** -0.0457 *** -0.0337 ***
(-13.85) (-14.72) (-11.05)
a lot of advertising in
the country of a
successful business that
motivates the
respondent to open a
business (1 - yes, 0 - no)
0.0343 *** 0.0184 *** 0.0156 ***
(8.20) (7.63) (6.43)
AIC 199060.3 198774.2 200095.8
Number of observations 222337 222337 222337
Log likelihood -99985,916
Chi2 5978.348
* P <0.05, ** p <0.01, *** p <0.001
Source: Stata, own elaboration
Table 2 interprets the results of the estimation model of the binary variable by
three methods. It is worth noting that in these models the variables gender, age and
education are basic, and in parentheses are robust standard errors. The results show
that all the coefficients of the control variables are significant in all the models. Thus,
we can conclude that gender and age, and education influence the choice of the
respondent to open own business. As it earlier mentioned for this type of model best
fits the data, built by seminonparametric method (SNP), and it was assumed normal
distribution of residuals and built logit and probit model (please see Table 2). The
results for all variables, including a dummy, are presented in Appendix (please see
Table 14). After building these models, they are compared using AIC test. As the
estimated model is selected SNP specification - model.
Further, it is considered the individual characteristics that affect entrepreneurial
activity. All variables are statistically significant. For example, other things being
equal, if the variable knowent is 1 (respondent familiar with the person who became
26
an entrepreneur in the last 2 years), the probability of being involved in
entrepreneurial activity increases by 24.7%. When suskill = 1 (respondent's
knowledge and skills enough to start a business), the likelihood of becoming an
entrepreneur is increased by 34.3%. It is also worth noting the importance of the
coefficient of the indicator, characterized by the presence of good opportunities in
the country of the respondent for the development of a successful business, but this
coefficient is negative, indicating that there is the opposite effect of this factor, thus
it is rejected the original assumption.
The results of the model with fixed effects by year (please see Appendix, Table
12) show the results with the same importance as the model with fixed effects for
the country. However, it was found that the significance of SNP-model was
disappeared. The other specifications of the importance has not changed.
After analyzing the results, it is possible to conclude that all the variables have
a significant influence on the level of entrepreneurial activity in developing
countries. Considering the measure of the respondent's familiarity with a person who
became an entrepreneur for the past 2 years, it is worth noting that it is also observed
a significant positive result in the research of Bosma and Shutjens (2009). However,
the authors observed European regions in their research. Thus, the degree of
economic development has no effect on this indicator. In the same research of Bosma
and Shutjens (2009), it is stated about a sense of fear, where there has not been
marked by significant results for this indicator, that is not true of current paper. In
the analysis conducted in this paper, it was found a significant negative effect that is
why in this case it can be noticed the influence of different factors in different groups
of countries.
4.2 Regulatory costs and macroeconomic indicators
In this part of the paper, it is presented econometric analysis to identify the
impact of regulatory costs and performance of macroeconomic indicators in the
index of entrepreneurial activity. The dependent variables are index TEA (share of
the population aged 18 to 64 years involved in the business process in the early
stages) and TEA index for necessity-driven entrepreneurs (proportion of the
population aged 18 to 64 years involved in the business process forced in the early
stages) and opportunity-driven entrepreneurs (the proportion of the population aged
18 to 64 years involved in the business process in the early stages because of the
presence of good opportunities).
Appendix (Table 5) shows the model OLS estimation results for the dependent
variable TEA_tot. As it shown on the table, for each dependent variable it was
constructed by 7 models to select the most appropriate model to estimate
coefficients. For each of these models it is demonstrated the value of determination
27
coefficient and mean square error. The results showed that the coefficient of
determination in all models is not big, that was the reason for a new evaluation of
the modified model. In order to build the most appropriate model to the original
model (Model1) new variables that were significant in other specifications were
added. Thus, it is presented a new model, the results of which can be adequately
evaluated as the coefficient of determination is significantly increased, and the
standard error decreased vice versa. It was found that instead of an index that
characterizes the ease of starting a business (overall_DTF), it is more correct to use
its internal indexes separately. As indicated in Table 3, a greater value for the
entrepreneurial activity index has a measure of the monetary cost of opening own
business, but it has a positive sign, indicating that if the latter was increased by one
percentage point overall entrepreneurial activity index increased by 9.5%.
Considering figure TEA_opp and seven built OLS models (please see
Appendix, Table 6), the coefficients for each of the models are also small, which
suggests the possibility of changing the model specification. The new model has
been built by correcting a set of variables in the model for the variable characterizing
the percentage of opportunity-driven entrepreneurs, in which the standard error is
much diminished, but R^2 increased. Thus, the best model specification for the
variable TEA_opp has been selected.
Further, move on to the research of necessity-driven entrepreneurs. As it was
stated earlier in this research, necessity-driven entrepreneurship prevails in the
developing economies, thus the research of factors affecting it becomes important.
Appendix (Table 7) shows originally built models that are similar to the basic models
for the previous two dependent variables. The coefficients of determination and
mean square error are in Appendix (Table 8). Consider a modification of the model,
which shows the highest coefficient of determination (Model4). After changing the
set of variables in the model, it is used a new modified model for the correct
evaluation of the regression coefficients. In this model, all factors are significant,
besides of the growth of GDP.
After conducting an econometric analysis of entrepreneurial activity for the
three types of indexes, reflecting the percentage of people involved in the business
in the early stages, it was emphasized three basic models, based on which factors
affecting entrepreneurial activity will be assessed.
Table 3 shows the three regression models, which have been selected as the
most suitable to produce results close to truth.
For all models (1-3), tests were performed to detect multicollinearity and
heteroscedasticity. From correlated covariates difficult to assess the unique
contribution of each of them, which leads to an increased standard errors of
estimated coefficients, which in turn is the cause of some of the insignificance of the
results, although it is possible from an economic point of view, have to show a
28
significant result. However, this result may also be the consequence of
heteroscedasticity. In order to eliminate specification errors, Breusch-Pagan test was
conducted in the case of detection of heteroscedasticity and correlation matrix is
constructed to detect multicollinearity.
Table 3 OLS - models for estimating the regression coefficients
Source: Stata, own elaboration
Tests conducted for all models showed a negative result, indicating that have
been selected the correct specifications (please see Appendix, Table 10). Thus, it is
possible to draw conclusions.
Three factors are significant for all types of entrepreneurs: the variables inside
the DTF index, which are cost, min_capital, days. The biggest impact has the index
the cost of starting a business (cost), but it is a positive sign. With the growth of
interest expenses of GDP by one percentage point TEA_tot growing at 9.59%, a little
less for TEA_opp which has an increase of 7.63%, while the share of necessity-
driven entrepreneurs is growing at 4.84%. The number of days that must be spent to
register as an entrepreneur also has a significant impact, but negative. The highest
* p<0.05, ** p<0.01, *** p<0.001
t statistics in parentheses
N 69 64 69
(4.32) (3.42) (5.67)
_cons 28.44*** 10.34** 12.55***
(-0.88)
UNEMPL -0.0959
(-1.11) (-1.13) (-2.04)
freedomfro~n -0.0531 -0.0439 -0.0336*
(-2.20) (-3.39)
businessfr~m -0.226* -0.104**
(0.85) (0.29)
laborfreedom 0.0507 0.0125
(2.58) (2.75) (1.32)
GDP_growth 0.726* 0.662** 0.128
(-3.50) (-4.24) (-3.81)
min_capital -0.0756*** -0.0505*** -0.0367***
(2.35) (3.52) (2.51)
cost 0.0959* 0.0763*** 0.0484*
(-8.33) (-6.58) (-6.00)
days -0.0432*** -0.0217*** -0.0188***
TEA_tot TEA_opp TEA_nec
(1) (2) (3)
29
rate is observed in front of this indicator in the model for opportunity-driven
entrepreneurs. The proportion of such kind of entrepreneurs falls to 2.17% with an
increase in number of days per unit. Important and significant influence also has the
minimum required capital ratio: the proportion of all types of entrepreneurs
decreases with an increase in the minimum capital. Comparing necessity-driven
entrepreneurs and opportunity-driven entrepreneurs, for the latter, this variable has
the greatest value, the percentage of opportunity-driven entrepreneurs reduced by
5.05%. As for necessity-driven entrepreneurs the coefficient was significant
characterizing corrupt country. This indicator has a negative effect, but it was stated
that most of its value indicates the lowest level of corruption in the country. Thus,
low levels of corruption leads to a decrease in the proportion of necessity-driven
entrepreneurs to 3.36%.
The model with fixed effects by year
In this case, it was demonstrated the models with fixed effects for years.
Consider the impact of regulatory costs on business activity. For this these model
were built with fixed effects for years. As explanatory variables are variables listed
in paragraph 3. Variations factors are used at different stages of the analysis and in
different models.
Models 1-3 (please see Appendix, Table 10) shows the results of estimating the
model with fixed effects data for the variables that characterize the regulatory costs
of opening their own business. The data demonstrate significant results for both
types of entrepreneurship. As it has been suggested in paragraph 3, the indicators
mostly negative impact on entrepreneurial activity as a whole and separately on
necessity-driven entrepreneurs and opportunity-driven entrepreneurs. For the latter,
unlike the rest of the dependent variables there is significant factor characterizing
the effect of the number of procedures required for the formation of an entrepreneur,
but it is positive. Thus, if there is increasing the number of procedures per unit, share
of the necessity-driven entrepreneurs increases by 30.4%. However, considering the
index of the number of days spent on the process of starting a business, it is presented
the opposite result, for all considered the dependent variable, percentage of people
involved in the business process, decreases with increasing number of days. For
example, the total entrepreneurial activity index fall by 3.4%. In the case of indices
separately for each type of entrepreneurship, the coefficient of the explanatory
variable of the following, indicating that at least a significant impact. The negative
sign has also been identified for the index of the minimum capital required to start a
business. For all types of entrepreneurs, increase of above-mentioned index leads to
a decrease in entrepreneurial activity index. A higher value for this ratio has
opportunity-driven entrepreneurs, than necessity-driven entrepreneurs. In this case,
the necessity-driven entrepreneurs, in which there are no other way out of difficult
situations in life, less thinking of the minimum capital, as they are forced to act on
30
this because of the lack of jobs. It is worth noting that, comparing necessity-driven
entrepreneurs and opportunity-driven entrepreneurs, the greatest impact of these
mentioned indicators still have the last of them. Opportunity-driven entrepreneurs
have a choice to remain in the running status, which they have to date, or take the
risk and invest their capital in a deal that could “fail”.
Consider the results for models 4-6, which have included some variables
describing the macroeconomic indicators of the country. As for the overall index of
entrepreneurial activity, as well as for other types of indexes for entrepreneurs
individually, significant positive impact has only GDP growth. In this case, it may
be due to economic stability in the region. In countries with rapid GDP per capita,
population feels safer with respect to the development of entrepreneurship. If the
individual realizes that the economy is growing, the probability of stability and its
potential business is also growing. Thus, as noted earlier, besides the coefficient in
front of these indicators is higher for opportunity-driven entrepreneurs, than
necessity-driven entrepreneurs. For the opportunity-driven entrepreneurs with an
increase in the value of GDP per capita growth by 1-percentage point leads to a
positive shift in the proportion of entrepreneurs is on 85% while for necessity-driven
entrepreneurs the figure is on 39.2%.
However, there is a lack of models built in covariates for such number of
observations. Consequently, these models have the need to be modified to use a
different specification. The base model was derived model 4. Table 4 shows the
results of the modified model. In this case, the importance of factors has hardly
changed: only for necessity-driven entrepreneurs, the significance received
coefficient characterizing the index of economic freedom, as well as showing how
difficult/ easy to open a business in the country concerned. However, this figure
based on the results of evaluation model with fixed effects by year has a negative
sign.
The model with fixed effects by country
Consider models built based on the fixed effects by country. Appendix (Table
12) presents the results of the main basic models for the overall index of
entrepreneurial activity (TEA_tot). The table above shows that the two variables
were excluded from the model due to collinearity. Thus, the original model have
been modified (please see Appendix, Table 12), after which they were compared to
the results of AIC test (please see Appendix, Table 13).
The most preferred model was the second model. It shows that there was only
a significant factor, characterized by the influence of the number of days required to
start their own business. With an increase of this indicator by one percent of the
people involved in entrepreneurial activity in the early stages, increased by 63.5%.
Such a result is contrary to our hypothesis.
31
Table 4 The model with fixed effects by year
---------------------------------------------------------------------------
(1) (2) (3) (4)
TEA_tot TEA_opp TEA_nec TEA_tot
----------------------------------------------------------------------------
DTF -0.0430 -0.00233 -0.0214
(-0.40) (-0.03) (-0.49)
IEF -0.133 -0.0296 -0.124* 0.212
(-0.92) (-0.32) (-2.11) (0.34)
GDP_growth 1.140** 0.801** 0.349*
(2.79) (3.03) (2.12)
UNEMPL -0.108 -0.139 0.0242
(-0.51) (-1.02) (0.28)
days 0.635**
(3.36)
min_capital 0.851
(0.73)
_cons 24.81** 11.58 12.53** -30.76
(2.72) (1.96) (3.40) (-0.70)
----------------------------------------------------------------------------
FE year year year country
N 64 64 64 72
R-sq 0.173 0.193 0.184 0.314
adj. R-sq 0.086 0.108 0.099 -0.433
----------------------------------------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001
Source: Stata, own elaboration
All observations were grouped according to the code of the country that led to
the formation of 35 groups. In order to assess the adequacy of the model, Wald test
was conducted. Its significance test rejects the null hypothesis of equality of
coefficients between the groups. Consequently, we can consider the model in Table
4 for evaluation.
Conclusions
The entrepreneurial sector plays an important role in the economy of each
country, so its research can lead to meaningful results and cause the development of
the region. Nowadays, many researchers around the world have written their studies
dedicated to the identification of factors contributing to the increase in the level of
entrepreneurial activity in the countries. Most of articles on entrepreneurial activity,
carried out cross-country analysis, but only some authors justify their chosen set of
countries for analysis. Thus, there is now a problem of a lack of empirical studies on
specific types of countries to assess the impact of factors.
This research includes research on entrepreneurship in developing countries. In
order to achieve this goal, the analysis of the existing literature was conducted, on
the basis of which it have been put forward suggestions about the impact of factors
on the level of entrepreneurial activity, and the necessary methodology and data
32
were determined. The conclusions reached at the end of the analysis were compared
with previous results available in the research of other authors.
Analysis of the current literature on the subject has allowed identifying the main
dependent variable, which characterizes the level of entrepreneurial activity in the
country, as well as explaining its variables. This research used the Global
Entrepreneurship Monitor data for 52 developing economies in order to assess the
effects of individual characteristics, as well as indicators of regulatory costs and
certain macroeconomic indicators. These have a panel structure for the period from
2009 to 2013.
The results of this paper show that, taking into account individual effects, all
control variables, which are gender and age (except education), indicators of
respondents’ self-evaluation and assessment of the environment, in which they are
located have a significant impact on the level of entrepreneurial activity in
developing countries (the choice of the respondent to open own business). Education
level models with fixed temporal effects was not statistically significant. The results
of our paper on the effect of individual characteristics are similar to the results of
previous research of many authors. Looking at the macroeconomic indicators and
indicators characterizing the regulatory cost analysis conducted in this paper,
showed variable insignificance of unemployment in all specifications. The same
results came Nielsen (2014). However, there is a difference with their work. The
authors considered as one of the variables of GDP and showed a significant negative
result. In our study, the opposite result with the developing countries has been
revealed. Therefore, we can talk about the different factors influence the level of
entrepreneurial activity in different groups of countries.
The research of this problem can be extended by dividing the developing
countries on the groups in terms of different continents, which will define the
characteristics of entrepreneurship development for each region.
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35
Appendix
Figure 1 TEA index changes in developing countries in 2009 – 2013 by the
example of 18 countries
Source: Stata, own elaboration
36
Source: Stata, own elaboration
Source: Stata (2016), own elaboration
37
Source:Stata, own elaboration
The list of countries used for the research of entrepreneurial activity of the
population in developing countries
Algeria, Argentina, Brazil, Chile, Colombia, Dominican Republic, Ecuador,
Guatemala, Iran, Israel, Jamaica, Jordan, Lebanon, Malaysia, Morocco, Panama,
Peru, Saudi Arabia, South Africa, Syria, Tonga, Tunisia, Uganda, Uruguay ,
Venezuela, West Bank & Gaza Strip, Yemen, Egypt, Mexico, Turkey, Pakistan,
Ghana, Angola, Zambia, Portugal, Costa Rica, Bolivia, Azores, Vanuatu, Trinidad
& Tobago, Taiwan, Bangladesh, Barbados, Nigeria, Singapore, Thailand, Botswana,
El Salvador, Ethiopia, Malawi, Namibia, Palestine, India, Indonesia, Libya,
Luxembourg, Philippines, Puerto Rico, Suriname, Vietnam.
38
Table 5 Basic OLS - models TEA_tot
Source: Stata, own elaboration
Table 6 Basic OLS - models TEA_opp
Source: Stata, own elaboration
* p<0.05, ** p<0.01, *** p<0.001
t statistics in parentheses
N 71 71 64 64 66 144 84
(5.03) (1.28) (3.26) (3.05) (5.00) (7.83) (5.38)
_cons 13.06*** 15.67 30.59** 27.51** 34.15*** 30.62*** 12.79***
(-2.00) (-2.16) (-4.10)
businessfr~m -0.266* -0.268* -0.246***
(-1.34) (-1.78) (-3.81)
freedomfro~n -0.0775 -0.100 -0.143***
(1.18) (1.59) (3.08)
laborfreedom 0.0796 0.0966 0.137**
(-1.11) (0.29) (-0.28)
UNEMPL -0.176 0.0526 -0.0489
(2.25) (2.71) (3.43)
GDP_growth 0.897* 0.971** 1.101***
(-2.46) (0.14) (-0.01)
overall_DTF -0.409* 0.0254 -0.00182
(-0.23) (0.99)
IEF -0.0339 0.165
(-3.67) (-3.53)
min_capital -0.0747*** -0.0751***
(3.83) (3.65)
cost 0.102*** 0.0999***
(-7.43) (-7.26)
days -0.0338*** -0.0338***
(1.41) (0.90)
procedures 0.368 0.322
TEA_tot TEA_tot TEA_tot TEA_tot TEA_tot TEA_tot TEA_tot
(1) (2) (3) (4) (5) (6) (7)
* p<0.05, ** p<0.01, *** p<0.001
t statistics in parentheses
N 71 71 64 64 66 143 84
(6.20) (1.06) (2.72) (2.39) (3.89) (5.81) (6.19)
_cons 10.79*** 8.492 15.72** 14.02* 19.10*** 21.02*** 9.417***
(-1.23) (-1.53) (-3.54)
businessfr~m -0.111 -0.137 -0.173***
(-1.09) (-1.50) (-2.28)
freedomfro~n -0.0538 -0.0719 -0.0760*
(1.18) (1.84) (2.47)
laborfreedom 0.0514 0.0715 0.0933*
(-1.69) (-0.55) (-0.73)
UNEMPL -0.171 -0.0618 -0.0803
(2.51) (2.87) (3.37)
GDP_growth 0.653* 0.692** 0.718**
(-1.91) (0.27) (0.14)
overall_DTF -0.202 0.0326 0.0167
(0.30) (1.07)
IEF 0.0298 0.123
(-3.36) (-3.38)
min_capital -0.0435** -0.0432**
(4.25) (4.35)
cost 0.0653*** 0.0670***
(-7.01) (-7.14)
days -0.0201*** -0.0201***
(0.37) (0.46)
procedures 0.0630 0.103
TEA_opp TEA_opp TEA_opp TEA_opp TEA_opp TEA_opp TEA_opp
(1) (2) (3) (4) (5) (6) (7)
39
Table 7 Basic OLS - models TEA_nec
Source: Stata, own elaboration
Table 8 Determination coefficients and standard errors for the basic models for
the dependent variables TEA_tot, TEA_opp, TEA_nec
TEA_tot TEA_opp TEA_nec
R2 RMSE R2 RMSE R2 RMSE
Model1 0.26 7.95 0.2 5.55 0.3 3.07
Model2 0.26 8 0.2 5.58 0.33 3.04
Model3 0.25 8.16 0.23 5.46 0.27 3.26
Model4 0.3 8 0.25 5.48 0.4 3.02
Model5 0.24 8.27 0.15 5.8 0.35 3.05
Model6 0.24 8.35 0.15 7.17 0.33 3.07
Model7 0.12 8.72 0.12 5.86 0.08 3.49
Source: Stata, own elaboration
* p<0.05, ** p<0.01, *** p<0.001
t statistics in parentheses
N 71 71 64 64 66 143 84
(1.70) (1.55) (3.74) (3.18) (5.04) (8.49) (2.71)
_cons 1.789 8.052 14.65*** 11.63** 13.59*** 12.23*** 2.678**
(-3.15) (-2.88) (-4.64)
businessfr~m -0.159** -0.132** -0.111***
(-2.28) (-2.47) (-5.12)
freedomfro~n -0.0510* -0.0537* -0.0713***
(0.96) (0.84) (2.42)
laborfreedom 0.0283 0.0232 0.0490*
(-0.07) (1.67) (0.66)
UNEMPL -0.00402 0.123 0.0442
(1.71) (2.06) (3.18)
GDP_growth 0.253 0.263* 0.403**
(-2.20) (0.56) (0.30)
overall_DTF -0.172* 0.0367 0.0165
(-1.39) (0.03)
IEF -0.0814 0.00217
(-3.44) (-3.14)
min_capital -0.0302** -0.0310**
(2.81) (2.22)
cost 0.0371** 0.0323*
(-5.32) (-5.06)
days -0.0138*** -0.0138***
(2.57) (1.09)
procedures 0.300* 0.190
TEA_nec TEA_nec TEA_nec TEA_nec TEA_nec TEA_nec TEA_nec
(1) (2) (3) (4) (5) (6) (7)
40
Table 9 Tests to detect multicollinearity and heteroscedasticity in the estimated
models
Pairwise correlation matrix for TEA_tot
Source: Stata, own elaboration
Test Breusch -Pagana for heteroscedasticity for TEA_tot
Source: Stata, own elaboration
Test Breusch -Pagana for heteroscedasticity for TEA_opp
Source: Stata, own elaboration
Pairwise correlation matrix for TEA_opp
Source: Stata, own elaboration
Test Breusch -Pagana for heteroscedasticity for TEA_nec
Source: Stata, own elaboration
Pairwise correlation matrix for TEA_nec
Source: Stata, own elaboration
freedomfro~n -0.1144* -0.3612* -0.1179* -0.1220* 0.3641* 0.4829* 1.0000
businessfr~m -0.3434* -0.5997* -0.0726* -0.2840* 0.2446* 1.0000
laborfreedom 0.1374* -0.0788* -0.1720* 0.0026 1.0000
GDP_growth 0.0306* 0.1983* 0.0613* 1.0000
min_capital -0.0292* 0.4662* 1.0000
cost 0.3206* 1.0000
days 1.0000
days cost min_ca~l GDP_gr~h laborf~m busine~m freedo~n
Prob > chi2 = 0.0201
chi2(1) = 5.40
Variables: fitted values of TEA_tot
Ho: Constant variance
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Prob > chi2 = 0.1034
chi2(1) = 2.65
Variables: fitted values of TEA_opp
Ho: Constant variance
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
freedomfro~n -0.3612* -0.1179* -0.1144* -0.1220* 0.1298* 0.3641* 1.0000
laborfreedom -0.0788* -0.1720* 0.1374* 0.0026 -0.1292* 1.0000
UNEMPL -0.1688* -0.0360* -0.0615* -0.3157* 1.0000
GDP_growth 0.1983* 0.0613* 0.0306* 1.0000
days 0.3206* -0.0292* 1.0000
min_capital 0.4662* 1.0000
cost 1.0000
cost min_ca~l days GDP_gr~h UNEMPL laborf~m freedo~n
Prob > chi2 = 0.0001
chi2(1) = 15.15
Variables: fitted values of TEA_nec
Ho: Constant variance
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
min_capital 0.0613* -0.1179* -0.0726* -0.0292* 0.4662* 1.0000
cost 0.1983* -0.3612* -0.5997* 0.3206* 1.0000
days 0.0306* -0.1144* -0.3434* 1.0000
businessfr~m -0.2840* 0.4829* 1.0000
freedomfro~n -0.1220* 1.0000
GDP_growth 1.0000
GDP_gr~h freedo~n busine~m days cost min_ca~l
41
Table 10 The basic model with fixed effects
Source: Stata, own elaboration
Table 11 The basic model with fixed effects for the country TEA_tot
Source: Stata, own elaboration
.
* p<0.05, ** p<0.01, *** p<0.001
t statistics in parentheses
N 71 71 71 52 52 52
(4.65) (5.58) (1.64) (3.60) (2.95) (4.04)
_cons 13.01*** 10.83*** 1.770 39.88*** 21.22** 18.31***
(-1.85) (-1.70) (-1.64)
subsidies -9.510 -5.667 -3.441
(-1.09) (-0.91) (-1.43)
govern_progr -2.796 -1.521 -1.502
(-0.05) (-0.50) (0.59)
UNEMPL -0.0120 -0.0812 0.0608
(3.01) (3.15) (2.31)
GDP_growth 1.252** 0.850** 0.392*
(0.68) (1.14) (-0.16)
IEF 0.121 0.132 -0.0113
(-0.28) (-0.19) (-0.50)
DTF -0.0323 -0.0136 -0.0233
(-2.93) (-2.43) (-3.09)
min_capital -0.0738** -0.0425* -0.0301**
(3.95) (3.58) (3.73)
cost 0.0996*** 0.0626*** 0.0364***
(-2.60) (-2.26) (-2.72)
days -0.0340* -0.0205* -0.0138**
(1.26) (0.32) (2.60)
procedures 0.381 0.0679 0.304*
TEA_tot TEA_opp TEA_nec TEA_tot TEA_opp TEA_nec
(1) (2) (3) (4) (5) (6)
N 71 71 64 64 66 144 84
(0.30) (-0.29) (-0.67) (-0.22) (-0.09) (-0.08) (1.60)
_cons 7.589 -12.90 -29.12 -6.599 -2.820 -1.176 10.71
(0.27) (0.46) (1.24)
businessfr~m 0.0698 0.118 0.168
(-0.05) (0.26) (1.52)
freedomfro~n -0.0188 0.0967 0.307
(0.61) (0.41) (-0.72)
laborfreedom 0.193 0.129 -0.0929
(1.19) (1.01) (0.96)
UNEMPL 1.041 1.005 0.847
(-0.48) (-0.21) (0.11)
GDP_growth -0.181 -0.0750 0.0367
(.) (.) (.)
overall_DTF 0 0 0
(0.55) (0.92)
IEF 0.345 0.612
(0.50) (0.35)
min_capital 0.575 0.417
(-1.34) (-1.40)
cost -0.719 -0.765
(4.14) (3.68)
days 0.694*** 0.759***
(.) (.)
procedures 0 0
TEA_tot TEA_tot TEA_tot TEA_tot TEA_tot TEA_tot TEA_tot
(1) (2) (3) (4) (5) (6) (7)
42
Table 12 Models with fixed effects for countries to assess the influence of factors
on TEA_tot
Source: Stata, own elaboration
Table 13 Test results AIC for models with fixed effects for countries to assess
the influence of factors on TEA_tot
-------------------------------------------------- --------------------------
Model | Obs ll (null) ll (model) df AIC BIC
+ ------------- ------------------------------------ ---------------------------
(1) | 88 -225.0546 -218.3006 5 446.6013 458.9879
(2) | 72 -182.2274 -168.6618 4 345.3237 354.4303
(3) | 72 -182.2274 -168.7087 4 345.4175 354.5241
Source: Stata, own elaboration
Table 14 The results of estimation models of binary variables for individual
characteristics (1-3 - fixed effects for countries 4-6 - fixed effects by year) ------------------------------------------------------------------------------------------------------------
(1) (2) (3) (4) (5) (6)
TEAyy TEAyy TEAyy TEAyy TEAyy TEAyy
------------------------------------------------------------------------------------------------------------
TEAyy
EDU1 -1.013*** -0.586*** -0.981*** -0.0657 -0.0299 8.215
(-31.43) (-29.95) (-37.12) (-1.60) (-1.28) (0.08)
EDU2 -0.515*** -0.308*** -0.815*** -0.00780 0.00121 8.226
(-15.04) (-14.81) (-30.02) (-0.18) (0.05) (0.08)
EDU3 -0.805*** -0.467*** -0.909*** -0.0431 -0.0198 8.200
(-23.32) (-22.52) (-33.98) (-1.01) (-0.81) (0.08)
country_1 1.742*** 0.919*** 0.421***
* p<0.05, ** p<0.01, *** p<0.001
t statistics in parentheses
N 88 72 72
(-0.20) (-0.70) (-0.92)
_cons -4.585 -30.76 -20.84
(0.73) (0.77)
min_capital 0.851 0.890
(0.34)
IEF 0.212
(0.94)
freedomfro~n 0.328
(0.22)
laborfreedom 0.0415
(0.06) (0.26)
businessfr~m 0.0139 0.0637
(1.92) (3.36) (3.82)
days 0.120 0.635** 0.596***
TEA_tot TEA_tot TEA_tot
(1) (2) (3)
43
(25.55) (26.11) (14.41)
country_2 3.603*** 2.096*** 4.307***
(55.77) (64.59) (157.53)
country_3 2.173*** 1.190*** 0.716***
(33.41) (36.11) (24.99)
country_4 0.626*** 0.316*** 0.141***
(8.71) (8.87) (4.95)
country_5 1.185*** 0.612*** 0.376***
(17.18) (17.57) (13.30)
country_6 1.380*** 0.718*** 0.449***
(21.03) (21.93) (16.83)
country_7 1.381*** 0.720*** 0.451***
(21.75) (22.89) (17.63)
country_8 1.416*** 0.740*** 0.477***
(22.68) (24.04) (19.13)
country_9 0.182* 0.0852* 0.000498
(2.26) (2.16) (0.02)
country_10 0.814*** 0.409*** 0.312***
(8.67) (8.54) (8.29)
country_11 1.395*** 0.731*** 0.512***
(19.61) (20.15) (17.22)
country_12 1.308*** 0.685*** 0.492***
(20.14) (21.20) (18.60)
country_13 0.604*** 0.299*** 0.157***
(7.66) (7.59) (5.01)
country_15 0.867*** 0.436*** 0.241***
(10.90) (10.81) (7.49)
country_16 0.789*** 0.400*** 0.223***
(11.29) (11.49) (7.93)
country_17 1.455*** 0.767*** 0.460***
(13.91) (13.51) (9.03)
44
country_18 0.691*** 0.342*** 0.147***
(9.45) (9.34) (5.01)
country_19 -0.449*** -0.211*** -0.192***
(-4.68) (-4.64) (-5.22)
country_20 2.272*** 1.247*** 0.994***
(31.88) (33.40) (22.46)
country_21 2.079*** 1.135*** 0.784***
(28.00) (28.75) (18.46)
country_22 1.957*** 1.057*** 0.777***
(24.49) (24.80) (18.49)
country_24 1.107*** 0.558*** 0.245***
(13.29) (12.93) (7.09)
country_25 2.165*** 1.182*** 0.849***
(31.08) (32.70) (22.52)
country_26 2.175*** 1.195*** 0.896***
(31.37) (33.19) (23.25)
country_27 1.257*** 0.646*** 0.319***
(14.35) (14.01) (8.42)
country_29 1.838*** 0.988*** 0.677***
(23.11) (23.16) (17.03)
country_30 -0.412*** -0.186** -0.157***
(-3.31) (-3.20) (-3.32)
country_31 1.629*** 0.863*** 0.549***
(20.55) (20.64) (14.91)
country_32 1.236*** 0.636*** 0.380***
(13.97) (13.70) (10.04)
country_33 1.564*** 0.819*** 0.480***
(20.03) (19.97) (14.00)
country_34 2.262*** 1.237*** 0.884***
(19.87) (18.91) (11.25)
45
country_36 2.327*** 1.282*** 0.945***
(31.89) (33.17) (23.11)
country_37 1.753*** 0.936*** 0.626***
(25.13) (26.17) (20.26)
country_38 0.958*** 0.486*** 0.266***
(12.79) (12.82) (8.70)
country_39 -0.620*** -0.280*** -1.795***
(-3.32) (-3.34) (-16.39)
country_40 1.307*** 0.682*** 0.400***
(13.09) (12.80) (8.96)
country_41 2.913*** 1.640*** 1.358***
(32.69) (32.79) (14.76)
country_42 1.940*** 1.054*** 0.749***
(20.12) (19.53) (11.99)
country_43 1.380*** 0.734*** 0.519***
(8.56) (8.27) (6.40)
country_44 0.835*** 0.420*** 0.199***
(10.83) (10.71) (6.32)
country_45 1.981*** 1.072*** 0.745***
(29.01) (30.60) (23.05)
country_46 0.275** 0.133** 0.108***
(3.23) (3.18) (3.31)
country_47 1.650*** 0.880*** 0.559***
(17.58) (17.10) (11.36)
country_48 1.386*** 0.728*** 0.403***
(13.08) (12.67) (8.22)
country_49 0.762*** 0.381*** 0.182***
(7.19) (6.97) (4.16)
country_50 0.542*** 0.258*** 0.0457
(6.21) (5.86) (1.33)
country_51 3.444*** 1.928*** 1.528***
46
(34.16) (34.22) (23.71)
country_52 0.855*** 0.422*** 0.181***
(11.32) (11.07) (5.96)
gender -0.229*** -0.136*** -0.0937*** -0.235*** -0.132*** -0.0849***
(-20.57) (-21.38) (-16.08) (-20.69) (-20.65) (-13.21)
age -0.000381*** -0.000214*** -0.000140** -0.000296*** -0.000151** -0.0000744
(-4.09) (-4.13) (-2.94) (-3.36) (-3.19) (-1.51)
knowent 0.258*** 0.152*** 0.247*** 0.191*** 0.118*** 0.247***
(43.77) (45.42) (48.70) (33.95) (35.19) (47.10)
opport -0.0239*** -0.0133*** -0.00595*** -0.0195*** -0.0109*** -0.00638**
(-7.35) (-7.23) (-3.38) (-6.04) (-5.94) (-3.22)
suskill 0.175*** 0.106*** 0.343*** 0.169*** 0.104*** 0.379***
(36.27) (37.50) (73.88) (35.47) (36.68) (92.88)
fearfail -0.0774*** -0.0457*** -0.0337*** -0.183*** -0.0960*** -0.183***
(-13.85) (-14.72) (-11.05) (-28.09) (-29.00) (-30.58)
nbmedia 0.0343*** 0.0184*** 0.0156*** -0.00258 -0.00128 0.00281
(8.20) (7.63) (6.43) (-0.61) (-0.53) (1.07)
year_1 0.548*** 0.320*** -8.519
(31.89) (32.02) (-0.08)
year_2 -0.0626*** -0.0315*** -8.864
(-4.32) (-3.90) (-0.08)
year_3 0.145*** 0.0822*** -8.743
(8.52) (8.52) (-0.08)
_cons -1.782*** -0.987*** -1.283*** -0.807***
(-26.18) (-28.05) (-28.58) (-31.46)
------------------------------------------------------------------------------------------------------------
g_1
_cons 9.929*** 12.90***
(8.44) (6.17)
------------------------------------------------------------------------------------------------------------
g_2
_cons 3.936*** 4.353***
(7.80) (6.16)
47
------------------------------------------------------------------------------------------------------------
g_3
_cons -2.838*** -4.013***
(-8.80) (-6.38)
Fixed
effects + + + + + +
------------------------------------------------------------------------------------------------------------
N 222337 222337 222337 211730 211730 222337
------------------------------------------------------------------------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001