Master Thesis in Entrepreneurship

Transcription

1 Master Thesis in Entrepreneurship Entrepreneurial activity in developing countries Authors: Ilia Minaev Supervisor: Anna Alexandersson, Lydia Choi Johansson Examiner: Daniel Ericsson Date: Subject: Degree Project Level: Master s Thesis Course code: 4FE16E

2 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. 2

3 Contents 1 Introduction 4 2 Theoretical background Theory of entrepreneurial activity The concept of entrepreneurship Total Early-Stage Entrepreneurial Activity index (TEA) A review of empirical research in entrepreneurship Entrepreneurial activity in developing countries The characteristics of entrepreneurship development in developing countries 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 Individual characteristics Regulatory costs and macroeconomic indicators 26 Conclusions 31 References 32 Online references 34 Appendix 35 3

4 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. 4

5 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 5

6 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 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 6

7 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. 7

8 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 8

9 (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 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. 9

10 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 efficiencydriven 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 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 countries, which are divided into 3 groups depending on the orientation of its economy. There are 3 groups: resource- 10

11 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 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 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 11

12 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 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. 12

13 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 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. 13

14 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, 14

15 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 subgroups: 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 15

16 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 necessitydriven 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 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 16

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 crosscountry 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. 17

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. 18

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 19

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 20

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. 21

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 ( ) 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% - opportunitydriven 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 %, men %). 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. 22

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. 23

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 women Age to 24 years from 25 to 34 years old from 35 to 44 years from 45 to 54 years years Education secondary (complete) and lower initial vocational secondary vocational higher 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 *** *** (-20.57) (-21.38) (-16.08) education Higher (EDU4) base Basic base Secondary (complete) and lower (edu1) Initial vocational (EDU2) Vocational (EDU3) age familiarity with a person who became an -1,013 *** -0,586 *** -0,981 *** (-31.43) (-29.95) (-37.12) -0,515 *** -0,308 *** -0,815 *** (-15.04) (-14.81) (-30.02) -0,805 *** -0,467 *** -0,909 *** (-23.32) (-22.52) (-33.98) *** *** *** (-4.09) (-4.13) (-8.00) *** *** *** (43.77) (45.42) (48.70) 24

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) the availability of adequate knowledge and skills to start a business (1 - I agree, 0 - do not agree) fear of failure may hinder the development of business (1 - I agree, 0 - do not agree) a lot of advertising in the country of a successful business that motivates the respondent to open a business (1 - yes, 0 - no) *** *** *** (-7.35) (-7.23) (-3.38) *** *** *** (36.27) (37.50) (73.88) *** *** *** (-13.85) (-14.72) (-11.05) *** *** *** (8.20) (7.63) (6.43) AIC Number of observations Log likelihood ,916 Chi * 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 25

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 26

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 27

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 (1) (2) (3) TEA_tot TEA_opp TEA_nec days *** *** *** (-8.33) (-6.58) (-6.00) cost * *** * (2.35) (3.52) (2.51) min_capital *** *** *** (-3.50) (-4.24) (-3.81) GDP_growth 0.726* 0.662** (2.58) (2.75) (1.32) laborfreedom (0.85) (0.29) businessfr~m * ** (-2.20) (-3.39) freedomfro~n * (-1.11) (-1.13) (-2.04) UNEMPL (-0.88) _cons 28.44*** 10.34** 12.55*** (4.32) (3.42) (5.67) N t statistics in parentheses * p<0.05, ** p<0.01, *** p<0.001 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 necessitydriven 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 28

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 29

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. 30

31 Table 4 The model with fixed effects by year (1) (2) (3) (4) TEA_tot TEA_opp TEA_nec TEA_tot DTF (-0.40) (-0.03) (-0.49) IEF * (-0.92) (-0.32) (-2.11) (0.34) GDP_growth 1.140** 0.801** 0.349* (2.79) (3.03) (2.12) UNEMPL (-0.51) (-1.02) (0.28) days 0.635** (3.36) min_capital (0.73) _cons 24.81** ** (2.72) (1.96) (3.40) (-0.70) FE year year year country N R-sq adj. R-sq 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 31

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 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. References Acs, J. & Varga, A., The Entrepreneurship, Technological Agglomeration and the change. Papers on entrepreneurship, growth and the public policy. 14 (6), pp Aghion, P., Growth theory endogenous, The MIT press, 9 (2), pp Amor, E. & Levie J., Ten years of the Monitor of Global Entrepreneurship: Accomplishments and Prospects. International, the Journal of Entrepreneurial Venturing. 5 (2), pp Audretsch, D. & Keilbach, M., Entrepreneurship Capital and Economic performance. Regional studies. 38 (8), pp

33 Bosma, V., The Schutjens the Mapping an entrepreneurial activity and an entrepreneurial Attitudes in by European regions For RentAircraft. International. The Journal of the Small Entrepreneurship and the Business. 7 (2), pp Danis, D. & Clercq, D., Are social networks more important for new business activity in emerging than developed economies? Empirical extension of An International, the Business Review, 20 (4), pp Davidsson, P., Study growth of A willingness to small firms Lithuania. In the Journal of business venturing, 4 (3), pp De Luca, G., The SML estimation and the SNP of univariate and bivariate binary-choice models. The Stata the Journal, 82, p Djankov, S., The regulation of Entry. The Quarterly Journal of Economics,117 (1), pp Engelen, F., Cross-Cultural entrepreneurship research. The Journal of International, Entrepreneurship, 7 (3), pp Gelderen, M. & Jansen, P., Autonomy as a Start-Up Motive. Journal of Small Business and Enterprise Development, 13 (1), pp Goncharova, M., Kartashov, B. & Gavrilov, A., Potential for Russian innovation system. Fundamental Research, 5, pp Hessels, J., Gelderen, M. & Thurik, R., Drivers of entrepreneurial aspirations at the country level: the role of start-up motivations and social security. International Entrepreneurship and Management Journal, 4 (4), pp Hizrich, R. & Peters M., Entrepreneurship or how to start own business and succeed, Moscow Progress, p Kanbur, R. & Venables. A., Spatial inequality and development, Oxford University Press, 15, p Kihlstrom, R. & Laffont, J., A General Equilibrium Entrepreneurial Theory of Firm Formation Based on Risk Aversion. Journal of Political Economy, 1979, 87 (4), pp Kiss, A. & Danis, M., Country institutional context, social networks, and new venture internationalization speed. European Journal of Business and Management, 26 (6), pp Lee, S. & Peterson, S., Culture, entrepreneurial orientation, and global competitiveness. The Journal of world business, 35 (4), pp Lingelbach, D., Vina, L. & Asel, P., What is distinctive about growthoriented entrepreneurship in developing countries? Business Center of Global Entrepreneurship, 1, pp Morris, M., The Dilemma of Growth: Understanding Venture Size Choices of Women Entrepreneurs. The Journal of the Small the Business the Management, 44 (2), pp

34 Nielsen, G., Determinants of Cross-National Entrepreneurial Activity. The Journal of Politics and Society, 25 (2), pp Pinillos, M. & Reyes, L., Relationship between individualist collectivist culture and entrepreneurial activity: evidence from Global Entrepreneurship Monitor data. Small Business Economics, 37 (1), pp Porter, M., The Competitive Advantage of Nations, London: Macmillan. 1991, 24 (4), pp Schumpeter, I., Theory of Economic Development Directmedia Publishing, p Specht, P., Munificence and Carrying Capacity of the Environment and Organization Formation. Entrepreneurship: Theory and Practice, 17 (2), pp Stam, E., Why Butterflies Don't Leave: Locational Behavior of Entrepreneurial Firms. Economic Geography, 83 (1), pp Steensma, H., Marino, L. & Weaver, K., Attitudes toward cooperative strategies: A cross-cultural analysis of entrepreneurs. Journal of International Business Studies, 31 (4), pp Thomas, A. & Mueller, S., A Case for Comparative Entrepreneurship: Assessing the Relevance of Culture. Journal of International Business Studies, 31(2), pp Wach, K., Impact of Cultural and Social Norms on Entrepreneurship in the EU: Cross-Country Evidence based on GEM Survey Results. Ejournals, 16 (1), pp, Wennekers, S., Uhlaner, L. & Thurik, R., Entrepreneurship and its Conditions: a Macro Perspective. International Journal of Entrepreneurship Education (IJEE), 1 (1), pp Wong, Y., Financing, regulatory costs and entrepreneurial propensity. Small Business Economics, 28 (3), pp Online References World Bank database [online]. [cit ]. Available from: The Heritage Foundation [online]. [cit ]. Available from: GEM Global Entrepreneurship Monitor [online]. [cit ]. Available from: 34

35 Appendix Figure 1 TEA index changes in developing countries in by the example of 18 countries Source: Stata, own elaboration 35

36 Source: Stata, own elaboration Source: Stata (2016), own elaboration 36

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. 37