Determinants of female entrepreneurship in developing countries

The case of India

ERASMUS UNIVERSITY ROTTERDAMErasmus School of EconomicsMaster Thesis

Student: Sharisma RamdhaniStudent number: 336275srSupervisor: Dr. S.J.A. HesselsCo- reader: Dr. P.W. van der ZwanRotterdam,

ABSTRACTPurpose – Several scholars have found important factors which might influence (female)

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entrepreneurship in developed countries. The aim of this thesis is to find out if these factors are also important for female entrepreneurship in India. Design/methodology/Approach – The research is performed based on the GEM dataset for India for the years 2006, 2007 and 2008. The analysis is performed using the Logit Model and the Multinomial Logit Model.Findings – The results show that having a network of entrepreneurs is positively related to necessity entrepreneurship among Indian females. Research limitations/implications – During the development of this research, several limitations came to the fore. The sample size is small, for example, and the data is cross-sectional and based on one developing country only, namely India. Second, the sample is based on the period 2006, 2007 and 2008: This is a period before the financial crisis. It might very well be that the number of entrepreneurs and factors related to self-employment have changed since the financial crisis. The sample is not divided in rural and urban regions. The relations between the determinants and self-employment might differ per region. Also, several independent variables are based on the perception of the respondents. For example, respondents were asked if they believe they have enough skills and knowledge to set-up a business. Respondents with similar skills and knowledge, however, might not be confident about his or her abilities. Therefore, respondents with similar skills and knowledge might answer this question differently. There is still room for further research on female entrepreneurship in developing countries. A suggestion is to include more developing countries in the sample and categorize them based on similar characteristics. Examples of categories are; according to continent and level of freedom. A suggestion could also be to divide the sample in rural and urban regions, since the relations between the determinants and self-employment might differ per region. A large longitudinal sample (after 2008) which includes other developing countries might show results that are more representative for developing countries. A longitudinal sample would provide us the possibility to find causality between the determinants and female entrepreneurship. Another suggestion would be to add other factors that might be relevant for female entrepreneurship in developing countries mainly because this might improve the understanding of female entrepreneurship in developing countries. Examples of such variables are (gender) discrimination, having a self-employed husband or parents, the role of the government, number of children etcetera.Value – This study provides information on determinants that influence Indian females to become an entrepreneur. This information can help policymakers develop methods and projects to encourage female entrepreneurship in developing countries.

Key words: Female entrepreneurship, India, necessity entrepreneurship, opportunity entrepreneurship, developing countries, network of entrepreneurs.

TABLE OF CONTENTS

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1. INTRODUCTION------------------------------------------------------------------------------------5

2. LITERATURE REVIEW---------------------------------------------------------------------------8

2.1. Risk and entrepreneurship---------------------------------------------------------------------------8

2.2. Entrepreneurial skills and knowledge------------------------------------------------------------12

2.3. Knowing other entrepreneurs---------------------------------------------------------------------13

2.4. Status-------------------------------------------------------------------------------------------------15

2.5. Necessity - and opportunity entrepreneurship--------------------------------------------------17

3. DATA AND METHOD-----------------------------------------------------------------------------20

3.1. Data--------------------------------------------------------------------------------------------------203.1.1.Variables--------------------------------------------------------------------------------------------------------------213.1.2. Control variables----------------------------------------------------------------------------------------------------22

3.2. Method-----------------------------------------------------------------------------------------------24

4. RESULTS---------------------------------------------------------------------------------------------25

4.1. Model 1----------------------------------------------------------------------------------------------25

4.2. Model 2 and 3---------------------------------------------------------------------------------------26

5. CONCLUSION--------------------------------------------------------------------------------------29

6. LIMITATIONS AND FUTURE RESEARCH DIRECTIONS-----------------------------32

7. REFERENCES---------------------------------------------------------------------------------------35

1. Introduction

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Entrepreneurs contribute to the economic and social development of a society (Lafuente & Vaillant, 2008). Entrepreneurs create jobs, are innovators, spot and exploit new business opportunities (Cowling & Bygrave, 2002). Entrepreneurship contributes to the stimulation of economic growth and reduces poverty in a country (Chowdhury (2007). More than 400 million people in developing countries are business owners or managers of new companies (Lingelbach et al., 2005). Half of them are Chinese or Indian, compared to 18 million in the United States (Reynolds et al., 2005). Small businesses in Asia are responsible for 50-80% of the workforce of their country (Narain, 2003). Yet, the number of studies on entrepreneurship in developing countries is low, compared to the studies on entrepreneurship in developed countries (Lingelbach et al., 2005).

The number of female entrepreneurs has increased in developed and developing countries (Brush, 2006, Koellinger et al., 2011). Yet, self-employed females are still a minority when compared to males (Langowitz & Minniti, 2007). Studying determinants of female entrepreneurship in developing countries might provide us information on why fewer women are self-employed compared to men. This information can be helpful for creating policies to stimulate female entrepreneurship in developing countries. Stimulation of entrepreneurship in developing countries is welfare-enhancing (Naudé, 2010). Entrepreneurship drives innovation and technical change and therefore generates economic growth (Schumpeter, 1939, Llussa et al., 2010). Stimulation of entrepreneurship can lead to job creation for entrepreneurs. In addition, entrepreneurs can provide jobs and income for unemployed individuals (Desai, 2009). Entrepreneurship allows people to escape from absolute and relative poverty (Naudé, 2010).

Scholars are unable to agree on the definition of entrepreneurship (Bygrave & Hofer, 1991). Schumpeter (1939) described the entrepreneur as an innovator, Peterson (1988) claims that the task of the entrepreneur is to identify and explore business opportunities (Cunningham & Lischeron, 1991). Gartner (1990) defines an entrepreneur as a person who creates a new venture; This is probably the broadest definition of entrepreneurship. Gartner (1990), unlike Schumpeter (1939) and Peterson (1988), focuses on what the entrepreneur does and not on who he or she is. The creation of a new venture is a complex process and a contextual event. Entrepreneurship is related to the following four factors; individuals (involved in starting new venture), processes (action undertaken to start a new venture), organization (kind of firm) and environment (surrounding and influencing the new venture) (Gartner, 1990). A large number of scholars have employed the definition of Gartner (1990). The definition of Gartner (1990) fits in this study, because we are going to research determinants that might influence females in developing countries to become self-employed. Therefore, the following definition will be used:“The entrepreneur is part of a complex process of new venture creation” (Gartner, 1990, p. 57)

A large number of studies on entrepreneurship are published by authors from developed countries and many focus on developed countries (Naudé, 2010, Venkataraman, 1997). A

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great deal is being written and said about the determinants of (female) entrepreneurship, in which several determinants repeatedly came forward. In this paragraph we are going to review the factors that are found important for (female) entrepreneurship. Koellinger et al. (2006) studied female entrepreneurship in developed countries. This study suggests a variety of factors related to (female) entrepreneurship. Female entrepreneurs in developed countries were more likely to be risk-seeking, compared to female employees. Schooling and having entrepreneurial experiences and skills were positively related to self-employment. Furthermore, individuals with a network of entrepreneurs were more likely to become self-employed. Risk tolerance is frequently identified as a fundamental driving force of entrepreneurship (Grilo & Irogoyen, 2006). There are several risks associated with entrepreneurship. Entrepreneurs have to bear more risks than employees (Grilo & Irogoyen, 2006). Take for example, the income of the entrepreneur. The income of entrepreneurs is more variable and less certain than the income of employees. This can be explained by the fact that the income of business owners depends on the performance of the company (De Wit, 1993, Oosterbeek et al., 2010). Half of the new ventures fail in the first five years of the company’s existence (Begley & Tan, 2001, Caliendo et al., 2006). Failure can affect the entrepreneur financially and emotionally. The entrepreneur can lose his or her investments, in case of failure (Knight, 1921). Failure can lead to an emotional break down and can have a negative impact on the future standard of living of the entrepreneur (Brockhaus, 1980). It is important for aspiring entrepreneurs to understand the risks associated with being an entrepreneur. Those who are willing to bear the risks of entrepreneurship are more likely to become self-employed (Djankov et al., 2006, Grilo & Irigoyen, 2006). Education increases the ability to develop business skills and perceive business opportunities (Ucbasaran et al., 2008, Sternberg & Wennekers, 2005, Arenius & Minniti, 2005). Highly educated individuals are more efficient and faster in finding solutions in complex situations, compared to low educated individuals. Furthermore, highly educated entrepreneurs are less likely to fail compared to low educated entrepreneurs (Bates, 1995, Coleman, 2007). Similar results are found for work-, managerial-and previous start-up experiences. Experiences can increase the ability to perceive opportunities and can be important during the start-up phase of a company (Davidsson & Honig, 2003, Rehman & Elahi, 2012). Having a network of entrepreneurs is also considered important during the start-up phase of a company (Davidsson & Honig, 2003). Other entrepreneurs can provide certain information which might cost time and money, if the entrepreneur tries to find this information independently (Dubini & Aldrich, 1991). Again other entrepreneurs can provide the required skills, different perspectives, relevant knowledge and emotional support (Hoang & Antoncic, 2003, Greve, 1995, Allen, 2000, Koellinger et al., 2011, Kwong et al., 2012, Uzzi, 1998, Langowitz & Minniti, 2007). Women can face gender discrimination and might have a lower income compared to men whilst still performing the same job (Khaushik et al., 2006). They might decide to become self-employed, with the expectation that they will receive a higher level of social status and respect (Kaur & Bawa, 1999, Lee-Gosselin and Grise, 1990).

It is still unclear to what extent these factors are relevant as determinants of female entrepreneurship in developing countries. The social position of women in developed countries differs from those in developing countries. Religion might have a large influence on

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the social position of women in developing countries. Take for example, the Hindu ‘caste system’ in India. The majority of the population in India is Hindu. The ‘caste system’ implied within Hinduism is an extremely hierarchical social system. Even though the ‘caste system’ is illegal, it still plays an important role in the society (Handy et al., 2007). Women from the lower caste are restricted in their freedom. They have the full responsibility for the household and childcare (Minniti & Arenius, 2003, Langowitz & Minniti, 2007). They are less likely to be employed or self-employed, because of their responsibilities and limited freedom (De Vita et al., 2013). These factors might not influence female entrepreneurship in developed countries, but can be important for women in developing countries.

For a better understanding of the determinants of female entrepreneurship in developing countries, it is important to make a distinction between necessity - and opportunity entrepreneurship. Necessity entrepreneurs become self-employed, because they have no other job opportunities (Reynolds et al., 2003). There are more necessity entrepreneurs in developing countries than in developed countries. Women in developing countries are often poor and have bad or no job opportunities (Langowitz & Minniti, 2007). These women are more likely to become a necessity entrepreneur (Reynolds et al., 2003, Minniti & Arenius, 2003). Opportunity entrepreneurs become self-employed, because they perceive business opportunities (Reynolds, 2004). In this study we are going to research if the most frequently identified determinants of entrepreneurship in developing countries are also relevant for female entrepreneurship in developing countries. We will also study to what extent the determinants influence opportunity- and necessity entrepreneurship in developing countries.

The research question of this study is:

Are fear of failing as an entrepreneur, schooling, work-and managerial experiences, having a network of entrepreneurs and social status determinants of female entrepreneurship in developing countries?

The study is based on one developing country; India. There are several reasons why India is a suitable country to study. India is in the forefront of the developing world as well in South Asia, in terms of economic growth and scientific productivity (Ghosh & Das, 2004). The average growth in the period 1992-2003 was 5, 9% (Sen et al., 2004). India is one of the developing countries with high entrepreneurial activity in low-and high technological sectors (Bhagavatula et al., 2008, Van Stel et al., 2005). To be able to answer the research question the Global Entrepreneurship Monitor (GEM) is used for the years 2006, 2007 and 2008.

The setup of this thesis is as follows: The first chapter contains a literature study on the determinants of female entrepreneurship in India. In this section the hypotheses will be developed. In the second chapter the data and methodology of the statistical research will be described and explained. The results will be discussed in the fourth chapter and the conclusion in chapter five. Limitations and future research directions will be discussed in chapter six.

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2. Literature review

2.1. Risk and entrepreneurshipRisk-taking is one of the earliest identified personal characteristics of the entrepreneur. Taking and coping with risks is one of the main tasks of the entrepreneur Cantillon (1755). Marshall (1975) claimed that young risk takers are more likely to become self-employed compared to the rest of the population. Say (1803) and Knight (1921) added that it is important for aspiring entrepreneurs to understand the risks associated with entrepreneurship. In this section of the literature review we are going to study the relationship between fear of failing as an entrepreneur and female entrepreneurship in India.

Entrepreneurial risk Van Gelderen et al. (2000) categorize risks and uncertainties in three dimensions: industry level, firm level and personal level. Risks and uncertainties at an industry level are external and cannot be controlled by the entrepreneur. Examples are change within the industry, actions of competitors, change in consumer preferences and developments in technology (Zahra, 2005, Van Gelderen et al., 2000). At the firm level, there is the uncertainty of the entrepreneur about whether or not his or her firm will succeed. The entrepreneur attempts to control resources, resulting in a smaller amount of resource uncertainty. Unfaithful customers, unreliable suppliers, lack of finance and opportunistic employees are examples of resource uncertainty. Resource uncertainty remains when the business owner has dealt with these resources (Van Gelderen et al., 2000). Risks at personal level are related to the confidence of the entrepreneur. Entrepreneurs who are confident about their abilities are more likely to be self-employed (Van Gelderen et al., 2000). A business owner has more responsibilities, a higher workload and is confronted with difficult and risky dilemmas, compared to employees (Ekelund et al., 2005, Xu & Reuf, 2000, Segal et al., 2005, Rauch & Frese, 2007). Risks at industry and firm level occur in established businesses and cannot be fully controlled by the entrepreneur. This study focuses on the relationship between fear of failing as a business owner and self-employment among Indian females. Therefore, this study focuses on risks and uncertainties at a personal level.

Entrepreneurs face several risks that may affect them financially and emotionally. The income of the entrepreneur depends on the company’s performance (Knight, 1921, Say (1803), Cantillon, 1755, Muller 1999, De Wit, 1993, Oosterbeek et al., 2009). The chances of failing is high, considering that half of new businesses fail in the first five years of the company’s existence (Begley & Tan, 2001, Caliendo et al., 2006). The entrepreneur can lose his or her investments in case of failure (Knight, 1921). The worst case scenario thinkable for business owners is to lose their savings, house and other personal assets (Allah & Nakhaie, 2011). Failure can lead to an emotional breakdown and can negatively impact the future standard of living of the entrepreneur (Brockhaus, 1980). Furthermore, it can affect the reputation and status of the entrepreneur in a negative way (Begley & Tan, 2001).

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It is essential for aspiring entrepreneurs to understand the risks related to entrepreneurship (Brockhaus, 1980). He claims that the decision to become self-employed is related to risk attitude. Risk-seeking individuals are more likely to become self-employed compared to risk neutral and risk adverse individuals (Kihlstrom & Laffont, 1979, Tuunanen & Hyrsky, 1999, Van Praag & Cramer 1999, Douglas & Sheperd 2002, Marshall, 1890).

Several studies have attempted to determine the relationship between entrepreneurship and fear of failing as a business owner. Most of the studies are performed at country level (Djankov et al, 2006, Grilo & Irogoyen, 2006, Arenius & Minniti, 2005) or at individual level (Langowitz & Minniti, 2007, Elston & Audretsch 2010). The results of these studies are mixed. The different results of the studies will be discussed in the following paragraphs. This study focuses on female entrepreneurship at an individual level. The results of the studies performed at individual level might help us understand the relationship between fear of failing as an entrepreneur and self-employment.

Several scholars have found a positive relationship between entrepreneurship and risk attitude. Djankov et al. (2006) studied entrepreneurship in China. The analysis was based on a random sample of 414 Chinese entrepreneurs from six different cities. They found a positive relationship between being risk-seeking and self-employment. A study by Grilo and Irogoyen (2006) based on survey data of the USA and 15 European countries also found a positive relationship between risk attitude and self-employment. Cramer et al. (2003) performed an empirical study on the relationship between risk aversion and entrepreneurship. The dataset was based on 5,800 Dutch schoolchildren aged 12, from the province Noord-Brabant. They were first interviewed in 1952. The ones, who could be traced, were re-interviewed in 1983 and 1993. Respondents who were interviewed in 1993, were asked the maximum amount of money they would pay for the entrants in a hypothetical lottery. There were 10 tickets available and the winning price was 1000 guilders. The average price for non-entrepreneurs was 23.5 guilders. The average price that entrepreneurs were willing to pay was 34.5 guilders. Entrepreneurs were willing to take a higher risk, because they were willing to pay a higher price for the entrants in the lottery. A study by Caliendo et al. (2006) found a positive relation between necessity entrepreneurship and fear of failing as an entrepreneur. However, they only found significant results for entrepreneurs who were previously employed. They did not find significant results for unemployed individuals. Other factors might drive unemployed individuals to become self-employed. Begley (1995) states that individuals with previous business experience have a higher risk propensity and are more likely to start a new business. The relationship between previous experiences and self-employment will be discussed in chapter 2.2.3.

Several scholars have found a negative relationship between entrepreneurship and fear of failing as a business owner. Arenius and Minniti (2005) did research on 28 countries based on the GEM dataset. The dataset was collected in the spring of 2002. They found a negative relationship between fear of failing as a business owner and necessity entrepreneurship. They suggest that individuals are less likely to become self-employed if they are aware of the risks associated with entrepreneurship (Arenius & Minniti, 2005).

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Some studies have not found any relationship between risk attitude and entrepreneurship. Wang and Wong (2004) and Vaillant and Lafuente (2007) did not find a relationship between risk attitude and entrepreneurship. Ram et al. (2013) did research on entrepreneurial behavior of females in Manipur, India. Manipur has the biggest market in Asia and is dominated by women. The market is known as the Ima Market. Around 3,500 females were active at the market. The dataset consisted of 150 females. Most of the products offered at the market were locally produced. The majority of the female entrepreneurs (66, 60%) were middle aged. The age of the middle group varies from 32 to 62 years. Seventy percent of the female entrepreneurs were married and 58.60% of them belonged to the middle social class. The results show that nearly half of the female entrepreneurs (48, 60%) were medium risk bearing, 34.60% were risk-averse and 12, 70% were risk-loving. Risk attitude did not play a significant role in their decision to become self-employed. Education, family size and social economic status had a positive influence on entrepreneurial behavior of women active at the Ima market. Highly educated women were more likely to take business decisions compared to low educated women. Female entrepreneurs with a large family and with a medium or low social status were more likely to become self-employed, because of the need to provide for the family.

It is clear that the results of the studies are inconsistent. Risk perception is influenced by internal and external factors (Schiller & Crewson, 1997, Ray, 1994). Tuunanen and Hyrsky (1997) suggest that risk attitude is correlated with knowledge and experience. Individuals who are experienced in a certain business field have the capability to identify and control risks, because they are aware of the risks. However, the same individuals can be risk adverse in another business field, because they are not experienced in that field (Minniti & Nardone, 2007).

Another factor that influences risk attitude is the way individuals perceive information and stories about entrepreneurship. It is possible that people tend to value successful business stories higher than stories about failed businesses. They are not aware or not willing to understand the risks related to entrepreneurship. As a result, the entrepreneur might underestimate the risks related to entrepreneurship and might unknowingly take higher risks than he or she would be willing to take (Xu & Reuf, 2004). Elston and Audretchs (2010) concluded that financial capital is related to risk attitude. Entrepreneurs with high startup capital are less risk averse compared to individuals with low startup capital. Blanchflower and Oswald (1998) also found a positive relationship between financial capital and risk attitude. The entrepreneur has enough resources to compensate for the financial losses, and is willing to take higher risks.

Female entrepreneurship and riskWomen might face several factors that could influence their risk perception towards entrepreneurship. Women are often responsible for the household and childcare. As a result, putting the family’s resources in danger by starting an own business could make women more apprehensive to do so. Failing means losing the resources and having no financial capital to provide for the family (Langowitz & Minniti, 2007). Another factor is the perception society

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has of women. There are stereotypical thoughts of females not having the capabilities and characteristics to become self-employed. Society might react negatively towards women who fail as a business owner (Driga et al., 2008). These stereotypical thoughts can lead women to become risk averse towards entrepreneurship.

Several studies have found a positive relationship between risk attitude and self-employment. Indian females take initiatives to create innovative entrepreneurial ideas and are not afraid to take risks (Lal, 2012). Handy et al., (2007) studied the behavior of women entrepreneurs in India in the profit and non - profit sectors. Female entrepreneurs from both sectors saw themselves as risk takers. Kundu and Rani (2004) found a positive relationship between risk attitude and entrepreneurship. This study was based on a group of 105 young Indian female trainees from the India Air Force Academy. De Vita et al. (2013) claim that females from developing countries have a higher risk propensity towards entrepreneurship compared to females from developed countries. This could be explained by the fact that, in many cases, these women becoming entrepreneurs is the only feasible way to have an income.

Risk attitude in developing countries There are barriers that might demotivate individuals from developing countries to become self-employed. One of the barriers is that the laws surrounding bankruptcy and businesses in most of the developing countries are strict, compared to the rest of the world. The consequences of failure can be disastrous. Entrepreneurs who fail will be put on a blacklist, making it harder for them to become an entrepreneur in the future (Lee et al., 2005). In developing countries, aspiring entrepreneurs have to deal with many complex rules and are obligated to supply many documents to their governments (Lee & Peterson, 2001). These factors can discourage individuals in developing countries to become self-employed. Subsequently in these countries it may be even truer that, those who have no fear of failing as an entrepreneur are more likely to enter self-employment compared to those who are afraid of failing as a business owner.

Not all studies found a positive relationship between fear of failing as an entrepreneur and self-employment. Studies that found a negative or no relationship between risk attitude and self-employment were based on a specific area in India or were not focused on female entrepreneurship in developing countries (Bhagavatula et al, 2010, Vaillant & Lafuente, 2007, Wang & Wong, 2004, Arenius and Minniti, 2005). There is evidence that Indian women have a high risk propensity towards entrepreneurship (Handy et al., 2007, Kundu & Rai, 2007). Women in developing countries might face extra barriers. They’re environment might not be open to the belief that women have the capabilities to become self-employed. Also, the rules surrounding bankruptcy are stricter in developing countries (Lee et al., 2005, Lee & Peterson, 2001, Driga et al., 2008). Therefore, we expect a positive relationship between fear of failing as an entrepreneur and female entrepreneurship in India. The following hypothesis will be tested:

Hypothesis 1: Indian women who have no fear of failing as a business owner are more likely to become an entrepreneur.

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2.2. Entrepreneurial skills and knowledgeHuman capital is the collective skills, knowledge, experiences and other intangible assets of individuals (Coleman, 2007). Human capital increases the knowledge and capabilities to solve problems (Becker, 2009). Dakhli & De Clercq (2004) have made a distinction between different types of human capital. Firm-specific human capital consists of knowledge and skills that are valuable within a specific company. Industry-specific human capital is one’s knowledge and skills derived from experience specific to an industry. Individual-specific human capital includes managerial and entrepreneurial experiences and is applicable to a broad range of firms and industries.

The focus in this study is on industry-specific and individual-specific human capital. We want to study how education, experiences and skills are related to female entrepreneurship in India. Firm-specific knowledge is only valuable within a specific company and is not transferable. This type of human capital has a limited effect on the decision to become self-employed (Dakhli & De Clerq, 2004) and is therefore irrelevant to this study.

Education Formal education is a powerful tool that can help people to develop business skills and business opportunities (Ucbasaran et al., 2006, Sternberg & Wennekers, 2005, Arenius and Minniti, 2005). Education can improve one’s communication skills, stimulate critical thinking and can help to develop other skills that could be important for entrepreneurs (Kim et al., 2006). Furthermore, education can also improve one’s ability to perceive business opportunities (Davidsson and Honig, 2003, Bates, 1995, Gimeno, et al., 1997, Robinson and Sexton, 1994). Highly educated entrepreneurs are less likely to fail compared to low educated entrepreneurs (Bates, 1995, Coleman, 2007). They can easily adapt in difficult situations and are more efficient and faster in finding solutions in complex situations.

Several studies have found a negative or no relationship between being educated and entrepreneurship. Grilo and Irigoyen (2006) did research on entrepreneurship in the EU. They found no relationship between schooling and entrepreneurship. Wang and Wong (2004) did research on the entrepreneurial propensity of Singaporean honor students. Students with business knowledge were aware of the risks of entrepreneurship and were less motivated to become self-employed, compared to students without business knowledge. Similar results were found by Reynolds et al. (2003), Blanchflower, (2001) and Djankov, (2006). Highly educated individuals might prefer to work for a firm, because they have better outside options. They might earn a higher income under better working conditions, as an employee, than as an entrepreneur (Van der Sluis et al., 2008).

Education and female entrepreneurship Several empirical studies have found a positive relationship between schooling and female entrepreneurship. Handy et al. (2007) did research on the behavior of female entrepreneurs in India, in the profit and non-profit sectors. The sample consisted of 40 Indian female business owners. Half of them had a post-graduate level of education. Highly educated Indian females had fewer difficulties in negotiating the administrative requirements necessary for starting up

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a business compared to low educated Indian females. A study by Coleman (2007) based on US firm owners found a positive relationship between being educated and self-employment. Das (1999) did research on female entrepreneurship in the Indian states Tamil Nadu and Kerala. The sample consisted of 35 Indian female entrepreneurs. Half of the interviewed women had a university degree. Those with a degree were more likely to become a successful entrepreneur.

The results on the relationship between schooling and entrepreneurship are not consistent. However, several studies have found a positive relationship between being highly educated and female entrepreneurship in India (Handy et al., 2007, Das, 1999). Therefore, we propose the following hypothesis:

Hypothesis 2: Highly educated Indian women are more likely to become an entrepreneur.

Entrepreneurial skillsWork-, managerial- and previous start-up experiences can help individuals to perceive business opportunities (Davidsson & Honig, 2003, Rehman & Elahi, 2012). Managerial experiences can be important during the start-up phase where a lot of coordination is needed for starting a business. Individuals with previous entrepreneurial experiences might have more confidence in their abilities, because they know the pitfalls and risks related to the start-up phase of a business (Kim et al., 2006). Individuals with previous managerial skills have fewer difficulties in managing a company (De Vita et al., 2013).

Several theoretical and empirical studies have found a positive relationship between experiences and entrepreneurship. Becker (1964) claims that labor, management and entrepreneurial experience are positively correlated with entrepreneurship. Coleman (2007) and Heilman and Chen (2003) found a positive relationship between work-experience and entrepreneurship. Masud et al. (1999) studied female entrepreneurship in Malaysia. Davidsson & Honig (2003) found that work and pervious start-up experiences are important for entrepreneurship, however having previous start-up experiences were found particularly important during the start-up phase of a company.

Several studies claim that knowledge, skills and work-, managerial- and previous start-up experiences are positively related to female entrepreneurship. Therefore the following hypothesis will be tested:

Hypothesis 3: Indian women who possess entrepreneurial knowledge, skills and experiences are more likely to become an entrepreneur.

2.3. Knowing other entrepreneursSocial capital can be defined as an asset embedded in relationships of individuals, communities, networks or societies (Liao & Welsch, 2002, Davidson & Honig, 2003). Social networks can provide access to human capital, financial capital and other types of capital (Renzulli et al., 2000, Greve & Salaff, 2003). Studying entrepreneurship through social networks can be very helpful, because entrepreneurship is a social activity (Greve, 1995). Social capital can be divided into three categories; organizational level, societal level and

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individual level (Davidsson & Honig, 2003). Organizational social capital is the relationship of the members of an organization, with the goal to engage in a collective action. Societal capital has been researched on a macro level with the goal to examine the impact it has on the society. Individual level of social capital consists of the set of non-formal relationships. Examples of such relationships are: college friends, old colleagues, spouse, relatives etcetera. Individual networks can help to perceive business opportunities, provide valuable industrial specific information and contribute to a person’s entrepreneurial goals (Carter et al., 2003, Greve & Salaff, 2003). The current study will focus on individual social capital, because the study will be performed at an individual level. We are going to research how one’s social network influences the decision to become self-employed.

Entrepreneurial networkCapital and skills are two important key factors during the start-up phase of a business (Kim et al., 2006). Not all individuals possess the required skills to become a successful entrepreneur (Pyysainen et al., 2006). An aspiring entrepreneur might have innovative business ideas, but might not be sure whether the ideas are both realistic and feasible. This can lead the individual to misjudge his or her ideas, for example when one overestimates the capital costs and makes an incorrect estimation of the market demand (Kwong et al., 2012). The entrepreneur talks the most with others during the start-up phase of a company (Greve & Salaff, 2003). Entrepreneurial colleagues can play a crucial role in this phase (Davidsson & Honig, 2003). They can provide different perspectives, relevant knowledge and offer emotional support (Hoang & Antoncic, 2003, Greve, 1995, Allen, 2000, Koellinger et al., 2011, Kwong et al., 2012, Allen, 2000, Uzzi, 1998, Langowitz & Minniti, 2007). The entrepreneur can save money and time by appealing to his or her own network instead of searching for information independently (Dubini & Aldrich, 1991).

Entrepreneurs can help broaden the network of their entrepreneurial colleagues, which can positively influence access to more information (Greve & Salaff, 2003). Furthermore, entrepreneurs can act as a broker introducing two business owners to each other. This can lead to collaborations which can increase the sales profit for both entrepreneurs (Greve, 1995). These benefits can help make the entrepreneurs’ ideas more realistic and manageable (Greve, 1995, Greve & Salaff, 2003) and increase the chances of entrepreneurial success (Langowitz & Minniti, 2007).

Minniti and Arenius (2005) did research on entrepreneurship in 28 countries. The research was based on the GEM dataset. Having a network of entrepreneurs was found positively related to necessity entrepreneurship. They suggest that having a role model - for example a business owner - and being a part of a network can reduce ambiguity. Autio & Acs (2007) did research based on 500,000 interviews over a period of 6 years. The sample was retrieved from the GEM dataset. The results show that having a network of entrepreneurs increases the chances that an individual becomes self-employed and is associated with business growth. Allen (2000) did research on social networks and self-employment based on adults from Wisconsin, USA. They found that having a network of entrepreneurs and self-employed family members is positively correlated with opportunity entrepreneurship. The quality of

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knowledge provided by the entrepreneur was found more important than the size of the network.

Females and their entrepreneurial network Langowitz and Minniti (2007) studied the entrepreneurial propensity of women. The study was based on 17 countries. They found a positive relationship between having a network of and the entrepreneurial propensity of women. They claim that having a network of entrepreneurs is one of the most important variables when considering becoming self-employed. Koellinger et al. (2011) did a follow up study on the gender differences in entrepreneurial propensity. In line with the results of Langowitz and Minniti (2007), they found that having a network of entrepreneurs increases the chances that a woman becomes self-employed. Allen (2000) and Langowitz et al. (2006) found that female entrepreneurs motivate other females to become self-employed.

On the other hand, evidence has been found highlighting that women have a small social network, because they lack in communication skills and do not participated in network events. (Koellinger & Minniti 2006, Davidsson 1995, Rutashobya et al., 2009). This could explain why fewer women are self-employed compared to men (Renzulli et al., 2000). The female’s network mainly consists of (self- employed) family members. Self-employed family members can provide information and resources (for example, financial capital). Family members are more likely to provide a loan than a bank. The support of family members can be very important for exploring business opportunities (Davidsson & Honig, 2003, Greve & Salaff, 2003, Renzulli et al., 2000).

Based on the discussion above we believe that having a network of entrepreneurs increases the probability that an Indian woman becomes self-employed. Therefore we are going to test the following hypothesis:

Hypothesis 4: Indian women with a network of entrepreneurs are more likely to become an entrepreneur.

2.4. Status An important characteristic of people is the need to compare themselves with others in terms of status, financial wealth and success (Parker & Praag, 2009). Weber (1978) was one of the first scholars who studied in the economic field and introduced ‘group status’. The way individuals value the status of the group they belong to is referred to as ‘group status’. Group status is related to two theories; the social identity theory (Tajfel & Turner, 1986) and the reference group theory (Ahmed, 2007). The social identity theory proposes that the level of status of the group the individual belongs to is positively related to his or her self-confidence. The reference group theory claims that individuals have the desire to belong to groups with a high level of status (Parker & Praag, 2009). Individuals, who find status very important, have the desire to be recognized (Jayawarna et al., 2011). In the current study we are going to study the status of the ‘group’; female entrepreneurs. We want to examine whether and to what extent social status is related to female entrepreneurship in India.

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Entrepreneurship and statusEntrepreneurship is considered to be a difficult occupation. Some entrepreneurs become very successful and others fail - because not everyone has what it takes to become successful (Lee, 1997). People reward individuals with a difficult profession with a high level of status (Lee, 1997, Parker & Praag, 2009). Entrepreneurs take risks, have an instable income and invest a huge amount of money in their company. According to Wagner and Ziltener (2008) and Parker and Praag (2009) individuals become self-employed, because they believe that their current social position will improve. They still carry on with the business, even when the profits are low, because they have the desire to be independent and want to hold their status and reputation (Jayawarna et al., 2011).

Tiryakian (1958) conducted research on occupations in a developing country, namely the Philippines. Respondents were asked to rank thirty occupations. Examples of the occupations listed were priest, engineer, small retailers, manager of business, policeman, physician and farmers. Respondents ranked ‘physician’ at number one. ‘Priest’ was ranked at number 6 and ‘manager of business’ at number 7. It is interesting to see that ‘retail business owners’ was ranked at position 16. An explanation could be that most of the entrepreneurs in developing countries have a low social background. Semi-skilled and low-skilled occupations are related to low social status (Tiryakian,1958).

Cromie (1987) did research on the motives of self-employment. The results of his study show that autonomy, achievement and job satisfaction are the main reasons why individuals become self-employed. Status was found the least important reason to become self-employed. In some cultures it is not the norm to be honest about having the desire to gain social status. In other words, entrepreneurs in certain society may become self-employed in order to gain status, but are not honest about it, because they are afraid they will be judged by the society (Getz & Peterson, 2005).

Female entrepreneurship and statusWomen relate entrepreneurship to status (Hays, 2013, Manolova et al., 2003). Women might associate starting an own business with receiving higher level of status and respect, because entrepreneurship is deemed a typical male job (Leach & Sitaram, 2002). Women face gender discrimination, have lower wages and are not taken seriously by society (Khaushik et al., 2006). As a result, they believe that they will receive a level of higher social status as an entrepreneur than as an employee (Lee, 1997, Khaushik et al., 2006).

A study by Orhan (2001) found that French women find social status and prestige less important than their counterpart. They explain this result by claiming that female entrepreneurs find it more important to contribute to the economic development than social status.

There is mixed evidence about the relationship between social status and entrepreneurship. However, several scholars claim that entrepreneurship is considered to be a difficult profession and not every entrepreneur becomes successful (Lee, 1997, Parker & Praag, 2009).

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Individuals with a difficult profession receive a high level of social status form the society (Lee, 1997). Moreover, there is also evidence that Indian women relate self-employment with gaining higher social status (Khaushik et al., 2006). Therefore we are going to test the following hypothesis:

Hypothesis 5: Indian women who believe that entrepreneurs in their country gain high respect and status are more likely to become an entrepreneur.

2.5. Necessity - and opportunity entrepreneurshipIn order to gain a better understanding of the determinants of self-employment, it is important to understand the differences between necessity entrepreneurship and opportunity entrepreneurship. The terms necessity - and opportunity entrepreneurship were first introduced by the Global Entrepreneurship Monitor. Necessity entrepreneurs start their own business because they have no better job opportunities (Cowling & Bygrave, 2002, Reynolds et al., 2003). Opportunity entrepreneurs become self-employed, because they notice business opportunities (Koster & Rai, 2008, Reynolds et al., 2003).

Push and pull factorsSeveral scholars have made a distinction between positive factors that ‘pull’ and negative factors that ‘push’ individuals towards entrepreneurship (Verheul et al., 2010). Individuals can be pushed to become an entrepreneur because they have no other options (necessity entrepreneurship) or they can be pulled towards entrepreneurship because they see business opportunities (opportunity entrepreneurship) (Bhola et al., 2006).

Oxenfeldt (1943) argued that unemployed individuals or individuals with low prospects for wage-employment may become self-employed to earn a living. Another factor that could push individuals to become self-employed is job dissatisfaction (Verheul et al., 2009). Having a low wage and limited freedom to commercialize ideas might trigger individuals to resign and become self-employed (Kirkwood, 2009, Verheul et al., 2009). Family related factors might also push individuals into entrepreneurship (Kirkwood, 2009). The challenge of combining waged and domestic labor might motivate individuals to become self-employed. Being a part-time entrepreneur might provide the individuals the chance to balance family responsibilities and work (Jamali, 2008).

Factors that could pull individuals towards entrepreneurship are independence, self-fulfillment, entrepreneurial drive, desire for wealth, social status and power (Verheul et al., 2009). Someone who voluntarily leaves his or her job to start a business, because he or she perceives opportunities, is an opportunity entrepreneur. The opportunity entrepreneur has often had several jobs where he or she has developed competencies that could be essential for starting up a business (Block & Wagner, 2010). Opportunity entrepreneurs are generally older and more experienced than necessity entrepreneurs (Bhola et al., 2006).

This study expects that the developed hypotheses are positively related to opportunity entrepreneurship. Opportunity entrepreneurs become self-employed because they perceive business opportunities (Koster & Rai, 2008, Reynolds et al., 2003). Highly educated and

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experienced individuals have a higher ability to perceive business opportunities compared to low educated and inexperienced individuals (Bates, 1995, Coleman, 2007, Davidsson & Honig, 2003, Rehman & Elahi, 2012). A network of entrepreneurs can be an important source of knowledge and resources which can be essential in exploring business opportunities (Koellinger et al., 2011, Davidsson & Honig, 2003, Greve & Salaff, 2003, Renzulli et al., 2000).

People with a difficult occupation receive high social status. Entrepreneurship is considered to be a difficult occupation. The entrepreneur faces many risks. Their income depends on the performance of the company (Knight, 1921, Say,1803 , Cantillon, 1755, Muller 1999, De Wit, 1993, Oosterbeek et al., 2009, Cramer et al., 2000). Koster and Rai (2008) claim that opportunity entrepreneurs are more likely to invent new things and be active in high technology sectors. The environment of high technology industries is uncertain because of the quick change of technologies (Kim et al., 2006). Opportunity entrepreneurs are challenged to be up to date on the technology developments. In order to be up to date the entrepreneur has to invest in courses and training related to the industry he or she is involved in. They might have to invest in expensive devices or machines. The costs of training and devices can become very high. The opportunity entrepreneur can lose his investments in if the company fails (Knight, 1921). Opportunity entrepreneurs might receive a high status from the society because they face several risks, invent new things and contribute to the economic development of a country. Furthermore, opportunity based businesses are more likely to grow and create new jobs compared to necessity entrepreneurs (Acs, 2006).

Push factors of female necessity entrepreneurship in developing countriesThe majority of entrepreneurs in developed countries are opportunity-based, while necessity -motivated entrepreneurs compromise up to half of those involved in entrepreneurship in developing countries (Bosma, 2002). Cowling and Bygrave (2002) did research on entrepreneurship and unemployment based on 37 countries. The sample was based on the GEM dataset and consists of the years 2001 and 2002. The results show that India is one of the countries with the highest percentage rate of necessity entrepreneurs. Norway, France, Denmark and Germany have a rate of less than 1%. The global mean in 2002 was 1.95%. In 2001 India had the highest rate of necessity entrepreneurs (7, 69%) while the global mean was set at 2, 53%.

There are several factors that could push women in developing countries towards necessity entrepreneurship. In some developing countries women become self-employed because of job dissatisfaction or face gender discrimination at the job market (Baughn et al., 2006). In most of the developing countries firms do not offer day care, part-time jobs etcetera (Minniti & Arenius, 2003). Furthermore, the government in developing countries offers limited support for individuals who have lost their job (Koster & Rai, 2008, Cowling & Bygrave, 2002). These factors can push unemployed females to become a necessity entrepreneur (Minniti & Nardone, 2007). Women from developing countries often become a part-time entrepreneur, because it gives them the ability to combine earning money and running the household (Jamali, 2008). The business of a female part-time entrepreneur is most likely to grow slow

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(Desai, 2009, Koster & Rai, 2008), because they have to divide their time between the household and their business (Minniti & Arenius, 2003).

3. Data and Method

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3.1. DataThe data for the statistic analysis is taken from the Global Entrepreneurship Monitor (GEM). The goal of the GEM project is to measure the entrepreneurial activity worldwide. For this research, we use data from India for the years 2006, 2007 and 2008. It is a stratified sample where random people have been interviewed by telephone or through door to door interviews. The sample consists of 5,693 interviewed individuals. The majority of the respondents are between the ages of 18 and 40 years and the average age is 37 years. The age distribution is similar to the age distribution of India found by Infobanc1. This company is specialized in collecting and analyzing business information of India. Their main focus is on small and medium enterprises. They found that around 50% of the population in 2001 was between 15 and 44 years.

The data shows that 648 of the respondents are involved in early stage entrepreneurship. The data also makes a distinction between necessity and opportunity entrepreneurship. Table1 shows that the majority of female entrepreneurs in India are involved in opportunity entrepreneurship.

Opportunity Necessity Total Male 333 100 433

77% 33% 100%Female 118 38 156

76% 24% 100%Total 451 138 589

77% 23% 100%

Table 1: Distribution of gender of the respondents involved in total early - stage entrepreneurship.Sample size: 589, there are 59 missing values. Source: GEM dataset India, 2006, 2007 and 2008.

1 www.infobanc.com

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3.1.1.Variables

Dependent variableThe dependent variable in this analysis is total early-stage entrepreneurial activity (TEA). The term TEA is a combination of two phases of entrepreneurship. The first phase relates to nascent entrepreneurs who are active in setting up a business. The second phase refers to the period of 42 months or less after the start-up phase (GEM manual, version 2012-9). There are three models developed to test the hypotheses. In the first model TEA is a dummy variable. Value 0 stands for women who are not involved in TEA. Value 1 stands for women who are involved in TEA. With this model we will test the 5 hypotheses. This model will show if there is a relationship between the independent variables and female entrepreneurship in India.

In the second and third model TEA has 3 values. Value 0 stands for women who are not involved in TEA. If the variable has value 1, the woman is involved in opportunity entrepreneurship. At last, value 2 stands for women who are a necessity entrepreneur. The second model is a restricted model without the control variables. The third model contains of the independent and control variables. With model 2 and 3 we are able to see if and to what extent the independent variables influence female opportunity- and necessity entrepreneurship in India. The total number of observed TEA is 648. 176 of them are female and 472 male.

Independent variables

Fear of failureRespondents were asked if fear of failure would prevent them from starting a business. Fear of failure is a dummy variable that can either have value 0 or 1. Value 0 means that the fear of failure would not prevent the woman to become an entrepreneur. If the value is 1, fear of failure would prevent the woman to become self-employed. In this case, the individual has a risk averse attitude towards self-employment. With this variable we are able to test hypothesis 1.

EducationThe variable education is a dummy variable. Value 0 stands for women who are low- educated (some secondary). Value 1 means that the woman is high educated. These women have a post secondary and/or university degree. This variable will gives us information whether and to what extent education is related to female entrepreneurship in India.

Entrepreneurial skillsOne of the survey questions was if the woman thinks she has the required knowledge, skills and experience to set up a business. This is a dummy variable which can have value 0 or 1. Value 0 stands for women who believe that they do not have the required knowledge, skills and experiences to set up a business. Value 1 means that the woman thinks she possesses the required knowledge, skills and experiences to start-up a business. With this dependent variable we will be able to test hypothesis 3.

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Knowing other entrepreneursRespondents were asked if they knew someone personally who had started a business in the past 2 years. The variable is a dummy which has value 0 or 1. If the value is 0, the woman does not know someone who started a business in the last 2 years. Value 1 means that the entrepreneur knows someone who has started a business in the last 2 years. With this variable we are able to find out if and to what extent the variable knowing other entrepreneurs is related to the decision of Indian women to become self-employed.

StatusThe dummy variable status measures if those who become successful business owners receive a high level of status and respect from the society. This variable has value 0 or 1. Value 0, indicates that the woman believes that successful businesses do not receive a high level of status and respect. Value 1 means that the woman believes that successful new businesses receive a high level of status and respect. With this variable we will test hypothesis 5.

3.1.2. Control variables

AgeParker (2009) suggests that there are several reasons why individuals become entrepreneurs at an older age. The reasons seem to be that they are more experienced and have a broader network. Before they become an entrepreneur, they have worked as an employee, have developed skills, gained experience and have a large network (Heilman and Chen, 2003). Parker (2009) claims that entrepreneurs are more likely to become an entrepreneur after they have reached a certain age. The average age lies between 35 and 45. After this age, they will refrain from becoming an entrepreneur because they have family responsibilities and are less risk-seeking. Earlier we have seen that a majority of the respondents in the dataset are between 18 and 40 years. They are younger than the average age of entrepreneurs Parker (2009) (35-45 years). It is expected that the majority of females in India are necessity entrepreneurs, because necessity entrepreneurs in general are younger than opportunity entrepreneurs (Blanchflower, 2004). Based on these statements, age will be used as a control variable to check if the results of the model change after adding the variable age. Furthermore, we will able to see the effect of age on the necessity-and opportunity entrepreneurship. The age range is between 18 and 64 years and is a continuous variable.

Shut down a business in the last 12 monthsRespondents were asked if they had abandoned, discontinued or quit a business in the last 12 months. This variable will provide an objective and exogenous indicator whether someone has entrepreneurial skills and experiences. This variable can indirectly influence the likelihood to become self-employed (Koellinger & Minniti, 2006). To test any changes in the results, the variable ‘shut down a business in the last 12 months’ will be used as a control variable. This variable is a dummy variable, with value 0 standing for a woman that has not shut down a business in the last 12 months and value 1 meaning the opposite.

Household Income The financial wealth of the individual can influence the decision to become self-employed,

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according to Arenius and Minniti (2005). The relationship between self-employed and household income is U-shaped. Individuals with a low income start a business in order to receive higher returns or because they have difficulties finding a job which forces them to start-up a business. Individuals with a high household income have lower financial barriers and are more likely to be self-employed. According Koellinger and Minniti (2006), ‘household income’ indirectly influences the perceptual variables of becoming an entrepreneur. Examples of such perceptual variables are: entrepreneurial skills, fear of failure and knowing other entrepreneurs (Koellinger & Minniti, 2006). We will be able to see if the control variable household income will change the results of the model. The interviewed respondents were asked to provide information about their annual household income. Household income is a dummy variable with value 0 and 1. Value 0 stands for women with a low or middle household income level. Value 1 means that the woman has a high household income.

Opportunity perceptionOpportunity recognition is considered to be an important characteristic of the entrepreneur (Langowitz & Minitti, 2007). Entrepreneurial skills and education increase the ability to recognize business opportunities (Langowitz & Minnitti, 2007). Being risk-seeking and having confidence increases the likelihood of entrepreneurship (Langowitz & Minniti, 2007). Taking these arguments into consideration it is helpful to use this variable as control variable in the models. The interviewed females were asked whether they thought that good opportunities for starting a business could be found in the area where they lived in the following 6 months. The variable opportunity perception is a dummy variable; value 1 means that the respondent sees business opportunities, and value 0 indicates that the respondent does not see any business opportunities in the following 6 months.

Mean 1 2 3 4 5 6 7 8 91 Total early-stage entrepreneurial act.

2 Fear of failure 0.4 -0.06

3 Education 0.427 0.06 -0.02** **

4 Entrepreneurial skills 0.671 0.18 -0.11 0.07** **

5 Knowing other entrepreneurs 0.68 0.14 -0.005 0.07 0.34** ** **

6 Status 0.8 0.09 0.011 0.05 0.14 0.02** ** **

7 Age 37.18 -0.07 0.041 -0.2 -0.047 -0.04 -0.06** **

8 Shut down a business in last 12 months 0.16 0.03 0.1 -0.0008 0.14 0.1 0.009 0.06** ** ** ** ** **

9 Household Income 11387.14 0.104 -0.07 0.3 0.07 0.09 -0.05 0.02 0.02** ** ** ** ** **

10 Opportunity perception 0.61 0.13 -0.06 0.15 0.23 0.22 0.05 -0.05 -0.02 0.08** ** ** ** ** ** **

Table 2: Correlation Matrix of all the variables. ** Significant at the level 0.10, two-tailed.* Significant at the level 0.05, two-tailed.Source: GEM dataset India, 2006, 2007, 2008

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Before carrying out the analysis, it is important to check the correlation between the variables. The value of the correlations lies between -0.0008 and 0.23, which means that there is no multicollinearity. The analysis can be performed safely.

3.2. MethodIn this research, two models will be employed in order to test the hypotheses. The dependent variable in the first model is TEA and is a dummy variable. Value 0 stands for females who are not involved in TEA and value 1 stands for females who are involved in TEA. The statistical analysis will be performed with the Logit method because TEA is a dummy variable. Using the Logit method transforms the dummy variable into a continuous variable. The value of the outcomes lies between 0 and 1; therefore, the results will be easier to interpret. This model will give an insight on how the independent and control variables influence the decision of Indian females to become self-employed.

In the second and third model TEA is a categorical variable. The variable has 3 values. Value 0 stands for females who are not involved in TEA. Value 1 are females involved in necessity entrepreneurship and value 2 stands for opportunity-based female entrepreneurs. A multinomial Logit regression will be performed. This model is suitable for a regression analysis with a dependent variable with more than 2 outcomes. The marginal effects of the independent variables will be included in both the models. The marginal effect shows the difference in the predicted outcomes for cases in one category relative to the reference group.

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4. Results

In the following paragraphs, the results of the performed analysis will be discussed. The results of each model will be described separately.

4.1. Model 1The independent variable ‘knowing other entrepreneurs’ is the only significant variable on a 5% significance level, in the restricted model. For every unit of change of the variable ‘knowing other entrepreneurs ‘, the log odds of someone becoming an entrepreneur increases with 1.385. The marginal effect of ‘knowing other entrepreneurs’ is 12.8, which means that the probability of becoming an early-stage entrepreneur having ‘knowing other entrepreneurs’ increases by 12.8 percentage points, ceteris paribus. The coefficient of ‘education’ is insignificant, however the marginal effect is significantly negative on a significance level of 10%. The marginal effect of ‘education’ is -5.9. The probability of becoming an early-stage entrepreneur having ‘education’ decreases the probability by -5.9 percentage points, ceteris paribus.

Table 3 shows that ‘knowing other entrepreneurs’ is the only significant variable in the unrestricted model, on a 5% significance level. For every unit of change of variable ‘knowing other entrepreneurs ‘, the log odds of someone becoming an entrepreneur increases by 1.195. The marginal effect of this variable is 14.9 percentage points. The probability of becoming an early-stage entrepreneur having ‘knowing other entrepreneurs’ increases the probability with 14.9 percentage points, ceteris paribus. The control variable ‘shut down a business in the last 12 months’ is insignificant, but the marginal is significant on a significance level of 10%. The marginal effect is -11.4, which means that the probability of becoming an early-stage entrepreneur having ‘shut down a business in the last 12 months’ decreases the probability by 11.4 percentage points, ceteris paribus.

This model supports hypothesis 3. The model shows that ‘knowing other entrepreneurs’ is positively related to early-stage among Indian women. Similar results are found by Langowitz and Minniti (2007) and Koellinger et al. (2011). Both studies were also based on the GEM dataset.

In the restricted model the chi square is 54.49 with a p-value of 0.000. After adding the control variables, the chi square decreases to 14.26 with a p-value of 0.114. We are not able to interpret the chi square of the unrestricted model, because it is insignificant. In the restricted model the number of observations is 347 and in the unrestricted model 180. Both models have missing values. The log likelihood increases from -139.76 to -85.65. This implies that the unrestricted model fits the data better.

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Model 1Total Early stage Entr. Activity

Restricted model Unrestricted model Coef. P-value Margins P-value Coef. P-value Margins P-value

Constant -3.369 0.000 -2.594 0.007Fear of failure -0.330 0.305 -0.037 0.293 0.293 0.484 0.046 0.498Education -0.550 0.105 -0.059 0.086 -0.511 0.255 -0.074 0.236Entrepreneurial skills 0.606 0.169 0.062 0.119 0.604 0.313 0.080 0.247Knowing other entrepreneurs 1.385 0.005 0.128 0.000 1.195 0.041 0.149 0.009Status 0.424 0.342 0.044 0.291 0.388 0.487 0.054 0.450

Control variablesAge -0.315 0.328 -0.472 0.326Shut down a business last 12 months -0.944 0.152 -0.114 0.065Household Income -0.079 0.898 -0.012 0.896Opportunity perception 0.091 0.841 0.0134 0.839

Table 3: Regression analysis of the restricted and unrestricted model of Model 1. Results of the Logit analysis and the marginal effects.Sample size: 156 females Source: GEM dataset India, 2006, 2007 and 2008

4.2. Model 2 and 3A multinomial Logit regression is performed for model 2. The base outcome in this model is TEA=0. So, we will compare the results of the model with females who are not involved in early-stage entrepreneurship.

Opportunity entrepreneurship Variables ‘entrepreneurial skills’ and ‘knowing other entrepreneur’ are found significant in the restricted model. Variable ‘entrepreneurial skills’ is significant on a 10% significance level and ‘knowing other entrepreneurs’ is significant on a 10% significance level. If variable ‘entrepreneurial skills’ increases by one unit, the multinomial log odds of someone becoming an opportunity based entrepreneur relative to the base outcome, will increase by 1.177 holding the other variables constant. The probability of someone becoming an opportunity based entrepreneur, given the variable ‘entrepreneurial skills’ increases the probability by 7.7 percentage points, ceteris paribus. One unit increase of the variable ‘knowing other entrepreneurs, will increase the multinomial log odds of someone becoming an opportunity based entrepreneur relative to the base outcome by 1.114, holding other variables constant. The marginal effect of ‘knowing other entrepreneurs’ is 7.3 percentage points, ceteris paribus.

The variables in the unrestricted model are insignificant. We do see that the marginal effect of ‘entrepreneur skills’ is significant on a 5% significance level. The marginal effect is 12.2 percentage points, ceteris paribus. The marginal effect of variable ‘shut down a business in the last 12 months’ is significant on a 5% significance level and is -11.0 percentage points, ceteris paribus. However, because the variable ‘knowing other entrepreneur’ is not significant in the unrestricted model, we state that there is no relationship between having a network of entrepreneurs and opportunity entrepreneurship.

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Necessity entrepreneurshipThe variables ‘education’ and ‘knowing other entrepreneurs’ in the restricted model are significant on a 10% significance level. If the variable ‘education’ increases by one unit, the multinomial log odds of someone becoming a necessity based entrepreneur relative to the base outcome will decrease by 1.571 holding other variables constant. The probability that a female will become a necessity based entrepreneur, given variable ‘education’ decreases the probability by 4.3 percentage points, ceteris paribus. If the variable ‘knowing other entrepreneurs’ increases by one unit, the multinomial log odds of someone becoming a necessity based entrepreneur relative to the base outcome will increase by 2.048 holding the other variables constant. The probability of becoming a necessity based entrepreneur, given the variable ‘knowing other entrepreneurs’ increases the probability by 4.5 percentage points, ceteris paribus.

Knowing other entrepreneurs is the only significant variable in the unrestricted model. The variable is significant on a 5% significance level. If variable ‘knowing other entrepreneurs’ increases by one unit, the multinomial log odds of someone becoming an necessity based entrepreneur relative to the base outcome will increase by 20.74 holding the other variables constant. The marginal effect is 7.5 percentage points, ceteris paribus.

The chi square in the restricted model is 207.86 and has a p-value of 0.000. The chi square in the unrestricted model is slightly lower; 175.31. The p-value is 0.000 and is significant on a 5% significance level. The number of observation decreases from 669 to 518. The log likelihood increases from -359.897 to -249.038. This implies that the unrestricted model fits the data better.

Model 2Total early-stage entr. activity Restricted modelRestricted model Opportunity Neccesity

Coef. P-value Margins P-value Coef. P-value Margins P-valueConstant -4.024 0.000 -4.541 0.000Fear of failure -0.488 0.207 -0.038 0.189 0.025 0.961 0.001 0.973Education -0.207 0.585 -0.012 0.672 -1.571 0.041 -0.043 0.022Entrepreneurial skills 1.177 0.061 0.077 0.012 -0.237 0.701 -0.011 0.627Knowing other entrepreneurs 1.144 0.038 0.073 0.013 2.048 0.051 0.045 0.008Status 0.345 0.505 0.024 0.490 0.616 0.435 0.015 0.383

Table 4: The regression of the restricted model of Model 2. The analysis is performed with the multinomial method. The margin for each coefficient is added. TEA = 0 (Females who are not involved in early-stage entrepreneurship) is the base outcome. Sample size: 156 femalesSource: GEM dataset of India, 2006, 2007, 2008.

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Model 3Total early-stage entr. Activity Unrestricted modelUnrestricted model Opportunity Neccesity

Coef. P-value Margins P-value Coef. P-value Margins P-valueConstant -3.698 0.005 -21.022 *Fear of failure 0.239 0.630 0.025 0.643 0.312 0.645 0.0001 0.709Education -0.293 0.567 -0.029 0.559 -1.373 0.121 -0.001 0.118Entrepreneurial skills 1.696 0.110 0.122 0.009 -0.820 0.317 -0.001 0.324Knowing other entrepreneurs 0.569 0.354 0.043 0.391 20.74 0.000 0.075 0.006Status -0.014 0.982 -0.001 0.982 1.373 0.228 0.0004 0.237

Control variablesAge -0.0128 0.727 -0.013 0.728 -0.847 0.138 -0.0003 0.069Shut down a business last 12 months -1.597 0.131 -0.110 0.016 -0.349 0.688 -0.0001 0.773Household Income 0.040 0.952 0.004 0.952 -0.413 0.737 -0.0002 0.690Opportunity perception 0.473 0.418 0.046 0.387 -0.624 0.341 -0.0004 0.342

Table 5: The regression of the unrestricted model of Model 2. The analysis is performed with the multinomial method. The margin for each coefficient is added. TEA =0 (females who are not involved in early-stage entrepreneurship) is the base outcome. Sample size: 156 females* no value Source: GEM dataset of India, 2006, 2007, 2008.

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5. Conclusion

In the introduction we have proposed the following research question:

Are the fear of failing as an entrepreneur, schooling, work - and managerial experiences, having a network of entrepreneurs and social status determinants of female entrepreneurship in developing countries?

We have developed two models. The first model provides information on how the proposed factors influence the decision to become an entrepreneur, among Indian women. Model 2 and 3 gives information on how the determinants influence female opportunity - and necessity entrepreneurship in India.

The research has showed that not all the factors identified as determinants of female entrepreneurship in developing countries are relevant for female entrepreneurship in India. Having a network of entrepreneurs is found to be positive for necessity female entrepreneurs. Gender discrimination at the job market and job dissatisfaction can push women in developing countries towards necessity entrepreneurship (Baughn et al., 2006). Necessity entrepreneurs are often inexperienced and low educated (Bates, 1995, Coleman, 2007, Davidsson & Honig, 2003, Rehman & Elahi, 2012). They are more likely to have limited knowledge about entrepreneurship and the business field they are active in, compared to educated and experienced entrepreneurs. Having a network of opportunity entrepreneurs might be less important for opportunity female entrepreneurs, because they are educated and often have had several jobs where they have developed competencies that could be essential for starting up a business (Block & Wagner, 2010). Having a network of entrepreneurs could be very essential for female necessity entrepreneurs. Other entrepreneurs might provide necessity entrepreneurs information about entrepreneurship and the business field they are active in, while this information might already be known by educated and experienced (opportunity) entrepreneurs.

Fear of failing as an entrepreneur does not have an effect on the decision to become self-employed, among Indian women. Similar results are found by Wang and Wong (2004), Vaillant and Lafeunte (2007) and Ram et al. (2013). Yet, there are studies that conclude that there is a positive relationship between the fear of failing as an entrepreneur and entrepreneurship (Djankov et al., 2006, Hederson & Roberson, 2000, Cramer et al., 2002, Caliendo et al., 2006 and Begley, 1995). The differences in results could be explained by the differences of the samples. The studies which found no significant results were based on Asian countries or rural regions. The studies which found a positive relationship were based on samples from developed countries, except for the study by Djankov et al., (2006). Their sample was based on Chinese entrepreneurs and non-entrepreneurs. According to De Vita et al. (2013), females from developing countries are less likely to be risk averse towards entrepreneurship compared to females from developed countries. This could be explained by

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the fact that females in developing countries have to overcome more barriers compared to females in developed countries in order to become self-employed. An example of a barrier is the strict rules around bankruptcy and failure (Lee et al., 2005). The fact that this study shows no relationship might be explained by the fact that the extra barriers that these females face are not included in this study. Including such barriers might change the results.

Furthermore, the results of this study show that there is no relationship between being educated and opportunity- necessity entrepreneurship. A similar result was found in a study by Grilo and Irigoyen (2006). They did research on entrepreneurship in the EU. They found no relationship between schooling and entrepreneurship. According to Langowitz & Minniti (2007), the majority of female entrepreneurs in developing countries is often poor-, and has bad or no job opportunities. One of the reasons why these women cannot find a job could be that these women are less well-educated (Bhagavatula et al., 2010, and Jamali, 2008). These women are more likely to become a necessity entrepreneur and are more likely to be active in low technology sectors (Verheul et al., 2009, Bhagavatula et al., 2010, Jamali, 2008). The insignificant results between being educated and entrepreneurship could be explained by the fact that technical skills, job trainings and specific industry experiences are more important than formal education, in low technology sectors (Kim et al., 2006, Bhagavatula et al., 2010). Women with a high degree have good job perspectives. They can work for a large firm in a stable environment (Van der Sluis et al., 2008). It is possible that highly educated women are less likely to consider becoming self-employed, because of the good job perspectives in larger firms. Therefore, it is possible that education does not play a large role in the decision to become self-employed.

Having entrepreneurial experiences, skills and knowledge are not found to be significantly related to the self-employment. Earlier studies by Becker (1964), Coleman (2007 and Davidsson and Honig (1995) did find a positive relationship between entrepreneurial skills and entrepreneurship. The different results might be explained by the way experiences, skills and knowledge was measured in these studies. Davidsson and Honig (2003), for example, measured work and managerial experiences in years. In this study respondents were asked whether or not they believed they had enough experience, knowledge and skills to become self-employed, naturally this is quite a subjective question. Individuals with similar experiences might answer this question differently. Furthermore, it is possible that different entrepreneurial skills are not equally important. It could be the case that managerial skills are more important than administrative knowledge. Having relevant managerial skills can increase the growth and efficiency of the business. Administrative knowledge can be learned through education or could be hand over to third parties. The importance of certain skills might differ per industry. Sales experiences, for example, could be beneficial for the advertising business but less beneficial for in the retail business. In future research it would be helpful to categorize the type of entrepreneurial skills and test how important they are for female entrepreneurship. This can change the results.

Finally, this study finds no significant relationship between social status and early-stage entrepreneurship. A study by Tiryakian (1958) also found no relationship between social

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status and entrepreneurship. Other studies by Kaur & Bawa, (1999), Yetim, (2008), Lee-Gosselin and Grise, (1990), Chepurenko, (2011), Lee, (1997) and Khaushik et al., (2006) found a positive relationship between social status and entrepreneurship. This could beexplained by the fact that entrepreneurs in developing countries are often poor and active in low technology sectors (Jamali, 2008). As a result, society in developing countries relates entrepreneurship with a low level of social status. Semi-skilled and low-skilled occupations are related to a low social status (Tiryakian, 1958). It is possible that female entrepreneurs do not have the goal to earn a higher social status. There could be different triggers for why women become self-employed. For a group of (poor) women social status might be the main reason to become self-employed, because in this way they are able to earn more money and gain more respect from their environment (Khaushik et al., 2006). Other women become entrepreneurs because they want to contribute to the economic development or help the weaker people in society. And another group of women might become self-employed out of necessity because they do not have other options to provide for their family (Koster & Rai, 2008).

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6. Limitations and future research directions During the development of this research, several limitations came to the fore. One of the limitations is the small sample size. The sample only contains 156 females involved in early-stage entrepreneurial activity. A small sample size can lead to a large standard deviation. The standard deviation of a sample indicates how true the results of the survey are. The larger the standard deviation, the less accurate the results will be. A small sample can also lead to variability, which might lead to bias. The data were collected through telephone and door to door interviews. It is possible that a number of females are not included in the sample, because they were not reachable by phone or not at home.

Cross-sectional data is actually a snapshot of a single moment in time. The importance of the determinants might change overtime. The number of opportunity and necessity entrepreneurs can also change overtime. These possible changes are difficult to measure with cross-sectional data. Furthermore, cross-sectional data does not provide the possibility to establish causality between the determinants and entrepreneurship.

Another limitation of this study is that a number of independent variables are based on the perception of the individual or are not possible to verify. The respondents were asked if they thought they had the required skills and experiences to set up a business. A female with certain entrepreneurial skills might be convinced that she has enough skills to set up a business. Someone with similar skills, however, at the same time might not be confident about her abilities and will answer this question with ‘no’. The respondents were also asked if they believed that a successful entrepreneur receives a high level of social status and respect from their society. The answer of the respondents is again subjective. Individuals who look up to successful entrepreneurs are more likely to answer this question with ‘yes’ to this question. Others, who are not interested in social status or relate high social status with other occupations, will most likely answer this question with ‘no’. Women were also asked if they saw good opportunities for starting a business in the area they lived in the following six months. Someone who is interested in self-employment and has studied the business opportunities will most likely answer ‘yes’. Someone who is not interested or does not have the capabilities to see business opportunities will probably answer this question with ‘no’, while there may actually be enough business opportunities in the area she lives. At last, respondents were asked to provide information about their annual household income. This is quite a private question. Respondents could refuse to answer this question or give a wrong answer about their income. The information provided is not verifiable.

The fourth limitation of this research is that the study was not done at a regional level. The determinants of female entrepreneurship in urban and rural areas might differ from each other. It is more likely that high technology industries are present in the urban areas, and low technology industries in rural areas. Formal education plays an important role in the decision

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to become self-employed for females living in urban areas, because of the high technology sectors present in those areas (Kim et al., 2006). On the other hand, formal education might not be an important factor for those living in rural areas, because of the low technology sectors present in those areas. An example of such a sector is the handloom sector. Designing and weaving skills play a large role in the decision to become self-employed in the handloom sector. The importance of formal education is quite small (Bhagavatula et al., 2010). There is more poverty and less job opportunities present in rural areas. It is possible that due to employment individuals from the rural areas become self-employed out of necessity.

The time period is another limitation is this research. This study is performed based on a sample for the years 2006, 2007 and 2008, meaning that the economic crisis had not fully occurred yet. The results could very well be different when examining a period after the economic crisis. The number of female entrepreneurs, for example, may have decreased during the credit crisis, e.g. because women might prefer to work for a firm that offers stability and constant income. Another effect of the credit crisis is that a large number of people have lost their job. This effect could have led to a higher number of necessity entrepreneurs, due to unemployment.

The sixth limitation of this study is that the research was based on one developing country only. India might not represent all developing countries. It is difficult to define a developing country. India is a developing country but the number of college graduates is the largest in the world, while at the same time the majority of the population lives under the poverty line (Nayar, 2012). On the other hand, a country such as Syria is not stable and is facing war. The country is less developed than India. Therefore, the results found in this research do not necessarily have to be true for Syria, to take just one example.

There is still enough room for future research on female entrepreneurship in developing countries. One idea is to include more developing countries in a future study. It would be a suggestion to categorize the types of countries based on similar characteristics. For example, continent, level of freedom, or whether women have the right to vote. Comparing the results would give us an idea about the effect of the determinants of female entrepreneurship for each category.

Another suggestion would be to do a longitudinal study on female entrepreneurship in developing countries. Collecting information about female entrepreneurship over a longer period of time will give us the opportunity to see if the importance and effect of the determinants change. It will also give us the possibility to see if the ratio of opportunity and necessity female entrepreneurs changes overtime.

Furthermore, including other variables in future studies might improve our understanding of female entrepreneurship in developing countries. Examples of such variables are, (gender) discrimination, having self-employed husbands or parents, the role of the government, number of children etcetera. Gender discrimination might occur more often in developing countries, for example due to religion. Take for example, the Hindu ‘caste system’ in India. Even though it is illegal, people still take the ‘caste system’ seriously. The future of a woman

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is determined by the caste she is born in. It influences her occupation, economic well-being and the freedom of movement (Handy et al., 2007). Women from the lower caste often have no freedom of movement. This can be a huge barrier if they want to become self-employed. A self-employed husband and (or) self-employed parents might stimulate and motivate women to become self-employed. They can provide knowledge and financial capital (Davidsson & Honig, 2003, Greve & Salaff, 2003, Renzulli et al., 2000). The government can play an important role in the decision to become self-employed. The number of female entrepreneurs in a country can be high, if the government uses proper tools to stimulate female entrepreneurship. An example of such a tool is offering women workshops and seminars on setting up a business. The number of children can affect the decision to become self-employed. In most poor countries people do not know a lot about birth control (Goldberg et al., 1989). As a result, the family size is larger in developing countries than in developed countries. Women in developing countries are often responsible for childcare. Women with a large family are limited in their freedom because they have to take care of the children. These factors might be very important for women in developing countries to become self-employed and less important for women in developed countries.

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