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