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Gender and Entrepreneurship
Is the decision for becoming an entrepreneur different among gender?
Julian Eskes 10641653
BSc in Economics & Business University of Amsterdam Supervisor: Andras Kiss 17/02/2017
Abstract:
This thesis examines the decision to become an entrepreneur based on perceptual variables. More specific, whether there a difference between males and females in the way fear of failure, knowing another entrepreneur, perceived opportunities and perceived capabilities influences the decision to become self-employed. This thesis will be an extending on the thesis of Blom (2016) about gender and the decision to become an entrepreneur in the Netherlands. However, I will use different data and will provide cross country analysis. For the purpose of this thesis the Global Entrepreneurship Monitor (GEM) adult population survey of 2012 worldwide is used. The results suggest that there is a difference for fear of failure, perceived opportunities and perceived capabilities.
1 Statement of originality
This document is written by Julian Eskes who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no other than those mentioned in the text and its references have been used in creating it. The faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
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INTRODUCTION
As stated by the OECD, one of the most important resources for entrepreneurship are women,1 while only nearly twice as little women as men, are involved in starting a business (Langowitz and Minniti, 2007; Minniti and Arenius, 2005). This phenomenon makes it interesting to research the cause of this gender gap between men and females in entrepreneurship throughout different countries.
The rate at which women are starting new businesses has increased significantly according to recent studies (Langowitz and Minniti, 2007). However, there is still a large gender gap in entrepreneurship in most countries (Global Entrepreneurship Monitor, 2015). Several studies have shown that the number of females involved in early stage
entrepreneurial activity is significant and systematically lower than that of males (Delmar and Davidson, 2000; Langowitz and Minniti, 2007).
According to Ramos-Rodríguez, Medina-Garrido and Ruiz-Navarro (2012), the interest in entrepreneurship has grown since the beginning of this century and a part of the growth is due the growth in female entrepreneurship studies. However, according to Greene et al. (2007) it is important that more research will focus on female
entrepreneurship, because it is widely understudied. To be more specific, it is important to determine which factors influence the decision to become an entrepreneur and to check whether these factors are different among gender. This leads to the main question that is been focused in this thesis:
“Is the decision to become an entrepreneur different among gender? While looking at perceptual variables cross countries.”
Just like Blom (2016) did a research on the gender gap in the Netherlands. This paper will extend her method, by not only looking at one country specific, but looking at cross countries. By doing this, the results can be generalized throughout different countries instead of only one. Besides the generalization, results will also provide a across country
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http://www.oecd-ilibrary.org/docserver/download/3012011ec004.pdf?expires=1487273210&id=id&accname=guest&checksum =F0174AD2D6696A533552CC890F3549FF
3 analyses with the most recent data available and can give a universal overview of the
influences of the perceptual variables. The variables that are used in this paper, will therefore be the same as used by Blom (2016). Langowitz and Minniti (2007) will be the base of my research when looking at different countries as a whole rather than individually.
The decision to become an entrepreneur is difficult and people take many things into account. Several studies have shown that the decision making process is far more complex and different for females (Langowitz and Minniti, 2007). This is due to a variety of reasons, including psychological and demographic differences between the genders and the social consideration towards entrepreneurship as a typically masculine choice (Langowitz and Minniti, 2007; Bird and Brush, 2002). Langowitz and Minniti (2007) have proven that demographic variables like age, income, work status and education are important for the decision-making process, and therefore these are added as control variables. However, this paper will focus on the effect of fear of failure, knowing other entrepreneurs, perceived
capabilities and perceived opportunities in early-stage entrepreneurial activity in order to
examine the mechanism within the decision to become an entrepreneur. By studying the effect of those perceptual variables in the decision-making process to become an
entrepreneur across countries, this paper will contribute to the lack of literature on female entrepreneurship worldwide.
The expectation for this thesis is that the decision-making process for becoming an entrepreneur differs between males and females due to several reasons. First of all, I would expect that females are less likely to take the risk because they are not willing to put family life at risk. Second, the gendered nature of the entrepreneurial environment may raise additional barriers because the career choice to start an own business is socially
discouraged for women. Lastly, I would expect that females underestimate their capabilities of starting a business.
For the purpose of this paper a cross country analysis has been made for the year 2012. The data is provided by the Global Entrepreneurship Monitor and the data for 2012 consists of 70+ countries. The data consists of a representative sample of at least 2000 individuals per country. Our dependent variables consider whether individuals are involved in total early-stage entrepreneurial activity. Besides, the reason why they are involved will be checked. Our independent variables consist of four perceptual variables. In this thesis I
4 test to see if these perceptual variables can offer an explanation in the gender differences across countries. The GEM database is very well suited for the purpose of this paper, because this is one of the first initiatives that conducted individual level surveys about entrepreneurship and they asked identical questions in each country researched.
The results show that not all perceptual variables have a significant effect on the decision to become an entrepreneur. It is found that the level of self-confidence in having the sufficient skills, opportunity perception and fear of failure is more significant for females than for males when each decides to become an entrepreneur. However, a difference has not been found in the decision to become an entrepreneur based on knowing other
entrepreneurs. Overall, there is a difference between males and females when deciding
whether to become self-employed across countries. This only holds for sufficient skills, seeing good opportunities and fear of failure.
THEORETHICAL FRAMEWORK
In order to answer the research question as mentioned above, first will be looked at the presence of gender gap throughout different countries. Followed by the general influences of the perceptual variables. After that the gender differences in those perceptual variables will be discussed and the hypotheses will be formulated. Finally, the literature behind the control variables will be elaborated.
Starting with the presence of the gender gap, difference among gender across countries will be investigated. In order to do this, it is important to discuss if there are differences across regions between males and females in entrepreneurship. Therefore, I will use seven different regions that are also used by the Global Entrepreneurship Monitor. The regions are: Latin America & Caribbean, Middle East & North Africa, Sub Saharan Africa, Asia Pacific & South Asia, European Union, Non-European Union and the United states.
According to Global Entrepreneurship Monitor report (Xavier et al., 2012) the biggest difference is seen in Middle East & North Africa where men are 2.8 times more likely to become self-employed compared to women. Xavier et al. (2012) also showed that in all regions the percentage of male entrepreneurs in total entrepreneurial activity is higher compared to females (see figure 1). The gap differs between countries, but according to several scholars that investigated female entrepreneurship across different countries, there
5 are significant more males involved in entrepreneurship compared to females in all
countries(Langowitz and Minniti, 2007; Koellinger et al., 2013). Koellinger et al. (2013) even state that on average there are 1.9 times more males involved in total early
entrepreneurship in their sample of 17 countries. According to the literature a gender gap exist in all countries and therefore it is reasonable to test the differences in perceptual variables across all countries.
Figure 1 TEA by gender for Geographic regions 2
First of all the general influences of the perceptual variables will be discussed. These are rendered by: (1) fear of failure, (2) knowing another entrepreneur, (3) perceived
opportunities, and (4) perceived capabilities.
Starting off with (1) fear of failure. In the GEM survey, individuals where asked if fear of failure would prevent them to start their own business. On average 42% of working age adults see good opportunities to start an own business, but one third of them will be constrained from starting an own business due to the fear of failure (Kelley, Singer and Herrington, 2016). Fear of failure is associated with risk; therefore it is a component of risk aversion (Arenius and Minniti, 2005). Yet it differs because it requires an internal measuring device for counting outcomes as successes or failures. The outcomes are judged to a level of success; therefore it can be seen as the loss aversion component of risk (Morgan and Sisak, 2015). Most individuals are risk averse and fear of failure is a key element of the risk related
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6 to start an own business (Arenius and Minniti, 2005). Therefore, fear of failure has a
negative impact on the decision to become an entrepreneur, where higher fear of failure discourages starting an own business (Morgan and Sisak, 2015).
Secondly, the perceptual variable (2) knowing another entrepreneur will be
reviewed. In the GEM survey individuals where asked if they know someone who started an own business in the past 2 years. According to Weber and Milliman (1997) knowing another entrepreneur is significantly and positively related to being an entrepreneur. This might be explained by the fact that networks and role models reduce uncertainty and can provide information to starting entrepreneurs. This belief is supported by Ellsberg (1961) and
Tversky & Kahneman (1962) who conclude that most individuals are not only risk averse, but also uncertainty averse. The uncertainty, according to Minniti (2005), is reduced by the possibilities to observe other entrepreneurs. Because, other entrepreneurs can provide information and social clues in uncertain environments, where starting an own business is characterized as an uncertain situation (Koellinger, Minniti and Schade, 2005). Additional, social networks can provide information, resources and ideas for new businesses (Larson and Starr, 1993). This is also supported by social science studies, where knowing other
entrepreneurs can be viewed as role models. Role models are important because of their
ability to enhance an individual’s sense of self-efficacy (Begley and Boyd, 1987; Baron, 2000). So, a larger business network increases the likelihood of starting an own business (Thébaud, 2010).
Next (3) perceived opportunities will be discussed. Arenius and Minniti (2005) stated that individuals who perceive opportunities are 2.3 times more likely to become
self-employed than those who do not perceive opportunities. According to Koellinger et al. (2005) opportunity perception is consistently correlated and several scholars agree that opportunity recognition represents the most distinctive and fundamental variable in the self-employed decision (Shane & Venkataraman, 2000). Entrepreneurs are more aware of opportunities and more likely to identify and exploit profitable opportunities (Kirzner, 1979).
Lastly, a literature review for (4) perceived capabilities will be provided. In the GEM Survey, the participants were asked if they believe to have the knowledge, skill and
experience required to start an own business. According to Arenius and Minniti (2005), individuals who believe to have the required skills are 6.4 times more likely to become an
7 entrepreneur. Several studies have shown that confidence in skills, knowledge and
experience have a positive influence on the decision to become an entrepreneur (Arenius and Minniti, 2005; Koellinger et al., 2007). According to Koellinger et al. (2007), confidence in one’s own abilities seems to be stronger among individuals in the early stages of
entrepreneurial activity. They also found that countries with higher rates of entrepreneurial confidence have higher start-up activity.
In the next section the gender differences in perceptual variables will be discussed and the hypotheses will be provided. For further more detailed theory about the perceptual variables I refer to thesis of Blom (2016) and the paper of Langowitz and Minniti (2007).
Gender differences and hypotheses development
In this part a clear literature review for the gender differences in the perceptual variables in the decision-making process will be provided. After the discussion of each perceptual variable and the variable gender a hypotheses is formulated which will be tested in the model part. I will start with the variable gender, secondly the differences in fear of failure, thirdly the differences in knowing another entrepreneur, next perceived opportunities and lastly the perceived capabilities will be discussed.
Starting as an entrepreneur is generally more accepted for men compared to women (Diaz- García & Jiménez-Moreno, 2010). Moreover, research has shown that males create more firms compared to females (Arenius and Minniti, 2005). According to Bosma and Levie (2009), there is a growth in female entrepreneurship, but unfortunately there are still two times more male entrepreneurs. This leads to the following hypotheses:
H1 (females): Across all countries in our sample, females are less likely to start as an entrepreneur compared to males.
Males and females react different to fear of failure in the area of entrepreneurship and this is due to several reasons. Attitudes towards risks are different between men and women in terms of entrepreneurial behavior and 41% of the females, in the total sample GEM sample, would be prevented from starting as an entrepreneur compared to 34% of males (Kelley et al., 2014). Men and women react differently to emotions, which results in differences in risk taking. Men are more confident than women and view risky situations as challenges, which increases risk tolerance (Croson and Gneezy, 2009).This is supported by Camelo-Ordaz, Diánez-González and Ruiz-Navarro (2016). Their findings reveal that fear of failure
8 constraints the entrepreneurial intentions for females more than it does for men.
Secondly, the social consideration towards entrepreneurship as a typically masculine choice influences the fear of failure for women. This gendered nature of the entrepreneurial environment may raise additional barriers because the career choice to start an own
business is socially discouraged for women and this may raise some additional barriers in terms of fear of failure when thinking about starting as entrepreneur (Shinnar et al., 2012; Camelo-Ordaz et al., 2016).
According to the literature, fear of failure is more negatively associated for the decision-making process for females than for men and therefore the following hypotheses will be tested:
H2 (fear of failure): Across all countries in our sample, the decision to become an entrepreneur is more associated by fear of failure for females than for males.
Knowing another entrepreneur has, according to the literature, a positive influence on the
decision to become an entrepreneur, but is the influence different among gender? As discussed the positive influence of knowing another entrepreneur is due the fact that social networks, role models and network opportunities reduce uncertainty. Greene (2000) has found that social capital has a positive influence on the female decision-making process in becoming an entrepreneur. Social network, role models and network opportunities reduce ambiguity and risk aversion. This especially holds for females, for whom social networks and role models are positively related to become self-employed (Langowitz and Minniti, 2007). Therefore the following hypotheses will be tested:
H3 (knowent): Across all countries in our sample, the decision to become an entrepreneur is more associated by knowing another entrepreneur for females than for males.
Koellinger, Minniti and Schade (2013) did not find a significant difference between
opportunity perception and the decision in becoming an entrepreneur. Nevertheless, when considering opportunity costs, several scholars suggest that women face higher opportunity costs when considering self-employment, primarily because the role they play in the family (Koellinger et al., 2013). Complementary, the findings of Minniti and Nardone (2007) show that males are more likely to become self-employed when males and females face equal opportunities. Additional, Shinnar et al. (2012) states that the recognition of opportunities differ because women tend to start businesses in overcrowded and competitive female
9 typed industries (retail, cosmetics and foodservices) and this makes it more difficult to perceive opportunities. Since the ability to see opportunities is associated with the number of available opportunities in their environment (Ramos-Rodríguez et al., 2012)
H4 (opport): Across all countries in our sample, the decision to become an entrepreneur is more associated by perceived opportunities for females than for males.
According to Koellinger et al. (2013), the absence of confidence among females in their own entrepreneurial skills is a main reason for the gender gap. They suggest that this can be explained in 3 different ways. Men and women perceive their own skills differently or how they perceive opportunities differs, or they objectively have different skills in different circumstances. Several studies have shown that women are less confident in their entrepreneurial skills, knowledge and experience (Koellinger et al., 2013; Minniti and Nardone, 2007). Moreover, males are two times more likely to think that they have the ability to become an entrepreneur compared to their female counterparts (Thébaud, 2010). Thus, if women believe in their own entrepreneurial skills and abilities, and if they believe that their abilities will lead them to success, they will more likely become self-employed (Minniti and Nardone, 2007). The following hypothesis is formulated to test the difference:
H5: Across all countries in our sample, the decision to become an entrepreneur is more associated by perceived capabilities for females than for males
Literature on control variables
For the purpose of this thesis several control variables are added. In this section the control variables: work status, age, household income and education will briefly be discussed. The number of children and self-employed parents are also important for the decision-making process, but unfortunately those variables cannot be used as control variables because there is no data available (Edwards and Field-Hendrey, 2002;).
First of all, age will be discussed. Individuals between 25 and 34 years are most likely to be involved in entrepreneurship. The activity in entrepreneurship declines subsequently after this age group(Reynolds et al., 2003). According to the Global Entrepreneurship Monitor report of 2016 the highest participation rates in entrepreneurship are not only between 25-34, but also between 35-44 and this holds for both men and women.
10 availability of income weakens financial constraints (Smallbone and Welter, 2001).
Individuals that have greater family wealth, face less liquidity constraints, and are more likely to become an entrepreneur (Arenius and Minniti, 2005). The importance of financial resources and constraints are widely discussed in several papers, and entrepreneurial decisions are shown to be positively related to household income (Arenius and Minniti, 2005).
Work status is commonly associated with the likelihood of being self-employed. Working individuals are more likely to become entrepreneur compared to individuals in other occupational groups (Arenius and Minniti, 2005). According to Arenius and Minniti (2005) this is consistent with their observations that many individuals become self-employed while still holding on to a wage job.
Lastly education will be discussed. There is no clear evidence that there is a
relationship between education and entrepreneurial intentions, except for richer countries where post graduates have a positive effect on high tech start-up rates (Blanchflower, 2004).
METHODOLOGY
The source of the data is based on several other studies and papers. Langowitz and Minniti (2007) used the database from the Global Entrepreneurship Monitor. In their research, across countries, they used the data from 2001 for 17 countries. Arenius and Minniti (2005) used a sample of 28 countries from the GEM database and studied the effect of perceptual variables on nascent entrepreneurship. Blom (2016) did make use of the same database and examined the effect of perceptual variables on the decision to become an entrepreneur. Other data bases are available, but the GEM database has the most extensive data. Therefore, the GEM database will be used in this paper.
The Global Entrepreneurship Monitor is the world’s foremost study of
entrepreneurship3. The GEM database consists of data acquired over a span of 17 years, where 200.000 participants are interviewed annually in 100+ countries, with a minimum of 2000 surveys per country. More than 500 specialist in entrepreneurship, 300 academic &research institutions and 200 funding institutions are involved in this institution. The GEM database is also used by important international organizations like the United Nations.
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11 The GEM database consists of different databases, but for this thesis the Adult Population Survey (APS) will be used. This survey is based on specific traits of individuals and their attitude towards entrepreneurship. In this survey there are questions about the
perceptual variables and control variables used in this thesis. This thesis is especially interested in the universality of the influences of those perceptual variables and this is possible with the large number of observations in this sample.
This paper will make use of the APS survey of 2012 and consist out of 70 countries. A few papers that examine gender differences in the decision to become self-employed, base their analysis on all countries for a specific year. Unfortunately, it is hard to control specific influences of macroeconomic factors, therefore most researchers control for country
specific effects by adding a country dummies to their regression (Arenius and Minniti, 2005). For this reason country dummies will be added in the model in order to correct for country specific influences. The total sample collected in 2012 consists of 193,391 individuals and 24,193 total early-stage entrepreneurs for whom the complete dataset is available for this thesis.
This paper will contribute to literature because due to the size of the data, it is possible to establish whether such factors are universal across countries. Therefore, we can give a universal overview of the differences among gender across countries, when deciding to become self-employed.
For the purpose of this thesis a probit estimation model is created that is based on several other studies. Blom (2016) used the database of the Netherlands to analyze if there is a gender difference, based on perceptual variables, in the decision to become an
entrepreneur in the Netherlands. Blom (2016) did make use of the same variables, in her model, and to test if there are gender differences she included gender interaction terms. Langowitz and Minniti (2007) investigated what variables influence the entrepreneurial propensity of woman using a large sample of individuals in 17 countries. But they did investigate the gender influences per country and this thesis will provide a universal
overview. Arenius and Minniti (2005) used a cross country dataset to analyze the influence of perceptual, demographic and economic variables in the decision-making process of an individual to become an entrepreneur. They concluded that individuals particularly rely on perceptual variables when deciding to become an entrepreneur. The main differences: this
12 research will provide a across country analyses with the most recent data available and can give a universal overview of the different influences of the perceptual variables among gender.
The dependent variable will be a measure of entrepreneurship, the GEM dataset provides the total early-stage entrepreneurial activity (TEA) and this will be the measure of entrepreneurship in this model. According to the Global Entrepreneurship Monitor (GEM) the classification of TEA is as follows: “An individual is either a nascent entrepreneur or an owner/manager of a new business”4 (GEM). An individual that is currently a nascent entrepreneur is an individual that is “actively involved in setting up a business that he/she will own or co-own, this business has not paid salaries, wages, or any other payments to the owners for more than three months”(GEM). An individual that is currently an
owner/manager is an individual that is “currently owning and managing a running business that has paid salaries, wages, or any other payments to the owners for more than three months, but not more than 42 months” (GEM).
In this model four independent variables will be included: Fear of failure, sufficient skills, opportunities and knowing. These will be used to test if there is a subjective
perception effect on the decision-making process in becoming an entrepreneur and to determine if this process is different among gender. To capture the gender differences, interaction variables with gender are included in the model (Blom, 2016). The first variable will be fear of failure (fearfail); this is a measure of fear and risk aversion as discussed in section 2.1. The measure is based on the GEM survey question: “Would fear of failure prevent you from starting a business”? The second variable is sufficient skills (suskill), this is a measure for skills with regard to the individual’s belief in having sufficient skills,
knowledge and experience to start an own business. The measure is based on the GEM survey question: “Do you have the knowledge, skill and experience required to start a new business?”’ The third variable is ‘knowent’. This variable is based on the principle if an individual personally knows someone who started an own business in the past two years. So, the question in de GEM survey was: “do you know someone personally who started a business in the past 2 years?” The last dependent variable is opportunities (opport): The people in the GEM survey were asked if there will be good opportunities for starting a
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13 business in the area where they live in the next six months.
To make the model more valid several control variables are added. The control variables are: age, work status, education, experience and household income. These control variables are also used by Blom (2016) and Langowitz & Minniti (2007). The first control variable is age, the respondents are asked to give their age and the range that is fitted to their age. There are 5 difference ranges in this model: 18-24, 25-34, 35-44, 45-64, 55-64 and 65+. The second control variable is work status: In the survey, individuals had to choose between 3 categories regarding their main occupation. The third control variable is education, and people were asked to give their highest level of education. The fourth control variable is experience and is based on the survey question ‘’if an individual had sold, shut down, quit or discontinued a business in the 12 months preceding the survey’’. The variable experience is included in the model as a dummy variable where ‘1’ is answered if the person has experience and ‘0’ if the person has no experience. Lastly, the respondents had to choose between different incomes categories. For the complete survey and
definitions I refer to the Global Entrepreneurship Monitor survey of 2012.5
The model used in this paper is inspired by Arenius and Minniti (2005), Langowitz and Minniti (2007) and especially by Blom (2016). The model:
RESULTS
In this chapter the main results of the research will be discussed. First the general influences of the perceptual variables will be discussed. Secondly, the results of the gender interactions terms will be summarized. Thirdly, the hypotheses will be discussed and the main
differences with the findings of Blom (2016). Lastly, for the interested reader, the results of the control variables will be discussed. The marginal effects of the regression are provided in table 2 and they are assorted in the same way Blom (2016) did. There are 3 different models
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14 provided to give a clear overview of the differences among gender. In the first model, only males are included, in the second, only females and in the third model both genders are included.
In model 1 and 2, all the marginal effects of the perceptual variables are substantial. As was expected, the perceptual variable of knowing another entrepreneur, perceived
opportunities and perceived capabilities are significantly positively related to the decision to
become entrepreneur, which holds for both males and females. Fear of failure has a significant negative impact on the decision to become an entrepreneur, which holds for both men and women. The marginal coefficients for opportunity perception, knowing
another entrepreneur and sufficient skills are larger for males compared to their female
counterparts. This indicates that males are more associated with opportunity perception,
knowing another entrepreneur and sufficient skill in the decision to become entrepreneur
compared to their female counterparts. The marginal coefficients for fear of failure are smaller for males compared to females. This indicates that males are less influenced by fear of failure compared to their female counterparts. The existing literature supports this argument (Croson and Gneezy, 2009).
To test if the gender differences of the models 1 and 2 are significant, a gender interaction effect is included in model 3 and the same was done by Blom (2016). Model 3 is the probit regression on total early-stage entrepreneurial activity for both males and
females. For this regression, the marginal coefficients are provided in column 3. It holds that the perceptual variables, the same as in model 1 and 2, are all substantial and positively or negatively related to entrepreneurship according to the literature. Before the hypotheses are discussed, the findings of the gender interactions terms will be summarized.
The gender interaction terms, with the perceptual variables, that are included in model 3 are not all significant. The gender*opportunity and gender*sufficient skills are statistically significant at 5% and gender*fear of failure is significant at 10%. For the interaction term gender*knowing another entrepreneur no significant difference is found. The results prove that females are more likely to become self-employed if they perceive good opportunities. This also holds when they believe to have the required skills and knowledge. It is less likely if they face fear. The hypothesis that was formulated in section 2 will now be discussed based in the results.
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H1 (females): across all countries in our sample, females are less likely to start as an entrepreneur compared to males.
The results of model 3 prove that there is a gender difference, as the variable gender is negative and statistical significantly at a 10% level. For females it is (-0.018) less likely to be involved in total early entrepreneurship.
H2 (fear of failure): across all countries in our sample, the decision to become an entrepreneur is more associated by fear of failure for females than for males.
The results show a 10% statistical significant difference in the interaction variable
gender*fear of failure. Besides the significance, the sign for the coefficient is negative, so for females the decision to become entrepreneur is more associated with fear of failure
compared to their male counterparts. The results found for fear of failure are in line with the literature. Moreover, females are 0,7% less likely to become an entrepreneur compared to males if they face fear of failure.
H3 (knowent): across all countries in our sample, the decision to become an entrepreneur is more associated by knowing another entrepreneur for females than for males.
The results show no significant result for the gender interaction variable with knowing another entrepreneur in model 3. Based on the results in table 2 there is no proof that the effect of knowing another entrepreneur on the decision for becoming an entrepreneur is more positively correlated for females. Although the existing literature states that knowing another entrepreneur has a positive influence on the decision to becoming an entrepreneur. This specially accounts for females, for whom social networks and role models are positively related to becoming self-employed (Koellinger, Minniti and Schade, 2005; Langowitz, Sharpe and Godwyn, 2006).
H4 (opport): across all countries in our sample, the decision to become an entrepreneur is more associated by perceived opportunities for females than for males.
The interaction variable between gender and perceived opportunities is statistically significant at 5% and is positive. This proves that females that believe to see good
opportunities are more likely to become an entrepreneur compared to males. This indicates that, across all countries in our sample, females are more associated with perceiving
16 counterparts. Moreover, women that perceive good opportunities are 0.9% more likely to become an entrepreneur.
H5: across all countries in our sample, the decision to become an entrepreneur is more associated by perceived capabilities for females than for males.
The interaction variable between gender and sufficient skills is statistically significant at 5% and positive. This proves, according to the existing literature, that females who believe in their own skills are more likely to become an entrepreneur compared to males (Minniti and Nardone, 2007). Therefore, the belief of having the skills and knowledge is more positively correlated to the decision to becoming an entrepreneur for women compared to men. Moreover, females are 0,97% more likely to become self-employed compared to men if they believe in their own skills.
The differences in the findings with the paper op Blom (2016) will now be discussed. The only coefficient that was significant in the thesis of Blom (2016) was knowing another entrepreneur*gender and this was the only coefficient in my research that wasn’t
statistically significant. The sign of this coefficient was negative in her research and that is in contrast with the literature. She did not found any significant difference for the other gender interaction variables and the signs where even the opposite compared to my
research. This can be due to the fact that my research is across countries and her research in done only for the Netherlands, but further research should examine this.
The main result of the control variables will be discussed. Starting with the control variable household income. There is no significant difference found between males or females with higher levels of household income are more likely to be involved in
entrepreneurship. According to the literature, the results prove (Reynolds, Bygrave and Hay, 2003) that the likelihood of becoming an entrepreneur increases until the age of 24-34 and decreases thereafter. The results also prove that females in the age of 25-34 are significant more, and in the age of 45+ significant less likely, to become an entrepreneur. For males there can only be concluded that becoming an entrepreneur is declining after the age of 35. According to the literature, education has no significant effect on the decision to become an entrepreneur (Blanchflower, 2004). The results contrarily show that higher educated males across all countries in our sample, are more likely to become an entrepreneur. For females this result is not found. The signs for the coefficients of female education are even negative.
17 This suggests that females in our sample are less likely to become an entrepreneur if they attended secondary, post-secondary education or if they graduated, compared to females that attended some secondary education. Individuals who are not working, retired or students are significant less likely to be involved in entrepreneurship, and this counts for both genders. Lastly, both men and women are significantly more likely to become an entrepreneur, if they have previous experience.
19 Descriptive statistics
For the purpose of this thesis, the APS survey data 2012 from the Global Entrepreneurship Monitor is used. The total sample consists of 198,391 observations, where of 24,193 are involved in total early-stage entrepreneurial activity. This being said, more than 12% of the total observation is involved in entrepreneurship. In figure 2 the main findings are provided in order to give an overview of the data. In the total sample, there are more female (gender =1) respondents (51% is female), where in the TEA sample there are more males. Note that there are more males than females involved in our TEA sample, which can preliminarily be reviewed as an indication for a gender gap in entrepreneurship. In the total sample
individuals see less opportunities on average to start an own business compared to the individuals that are involved in TEA. This could be due to the fact that entrepreneurs are more optimistic and see more opportunities. On average respondents who are involved in TEA have a larger network of entrepreneurs than in the total sample. Because, if you are involved in a business, you will be in contact with other entrepreneurs: Think of suppliers, clients, rivals and partners. Individuals involved in TEA perceive less fear and on average believe that they have more sufficient knowledge and skills to start an own business
Figure 2: Perceptual variables, total sample compared to involved in TEA
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Total sample Involved in TEA
20 Figure 3: Control variables, total sample compared to involved in TEA
There are in total 5 control variables in the regression and the descriptive statistics of those variables are provided in figure 3. Firstly education will be reviewed. The results for
education are quite similar, the most respondents finished secondary education and 7% graduated. As observed, there are more individuals working full- or part-time involved in TEA than in the total sample, whereas the not working/retired/student group is smaller. Age is divided into different categories in order to provide an overview of different age classes. As observed, there are more young individuals (<44 years) and less old individuals (>45 years) involved in total early entrepreneurship compared to the total sample. In the total sample, household income is more divided over the sample group compared to the
distribution in the sample of TEA. In the TEA sample, there are more individuals that have an upper state household income compared to the total sample. Lastly, individuals that are involved in TEA do have more experience compared to the total sample.
Correlations
In table 1 the correlation coefficients are assorted across all variables used in the model for the total sample of males and females. Where (***) denotes that the correlation
coefficients are significant at a 1% significance level. The coefficients gender, fear of failure, education, work status and age are negatively correlated with total early-stage
entrepreneurial activity. This is in line with the literature because according to the theory if
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Total sample Sample TEA
21 people face more fear of failure, they will be less likely to start a business. If individuals become older and are not working (r=-0.19), it is less likely that they are involved in a TEA. According to the table, females are less likely to become entrepreneurs. Education is surprisingly negatively correlated with TEA(r=-0.01), the expectation is that individuals with higher levels of education will be more involved in TEA.
The coefficients opportunity, knowing other entrepreneurs, sufficient skills,
experience and household income are positively correlated with TEA. The likelihood of being involved in TEA increases with experience (r= 0.11), higher household income (r=0.06), if individuals knows other entrepreneurs (r=0.22) and if they believe to have sufficient skills (r=0.26). The overall correlation between the dependent variables is moderate. The highest correlation is between sufficient skills and knowing another entrepreneur (r=0.26), but this is still moderate. Observed in the correlation matrix there are no highly correlated variables and therefore we can exclude the problem of multi-collinearity.
22
DISCUSSION
The purpose of this thesis was to provide an answer to the following question: is the decision for becoming an entrepreneur different among gender. To elaborate this further, this thesis examines the differences among gender in perceptual variables (fear of failure,
perceived opportunities, perceived capabilities and knowing another entrepreneur) on the
decision to become self-employed cross countries. To answer this question the Global Entrepreneurship Monitor database of 2012 is used.
The first limitation of this research can be found in the methodology. In the model country dummies are added in order to control for country specific influences.
Unfortunately, country dummies will only capture a part of the country specific influences (Arenius and Minniti, 2005; Langowitz and Minniti, 2007). Further investigation can extend this research by adding more years to the data and add a country*year dummy in the regression.
Secondly, the decision to become an entrepreneur is difficult and is depending on many different factors (Langowitz and Minniti, 2007). Therefore, it is difficult to make a statement about the gender differences in this decision. In this research only 5 control variables and 4 perceptual variables are added. So, there are a lot of other variables which have a certain influence on this decision. Further research can extend this investigation by adding more variables to the model like the number of children and self-employed parents. As according to the literature those variables have, especially for females, an effect on the decision to become entrepreneur (Edwards and Field-Hendrey, 2002; Hout and Rosen, 2000). But unfortunately those data is not available at the moment.
Lastly, in this research I try to provide a universal overview across countries by assuming that there are no country specific influences and that there is a gender gap in all regions in the GEM sample. Further research can study the differences among development regions in order to see if there are gender differences in and within these regions.
23
CONCLUSION
All in all there seems to be a universal difference between gender in the decision to become an entrepreneur worldwide, based on perceptual variables, but our results did not find significant differences for all the gender interaction variables. However, the results prove that in all the models the perceptual variables are significantly correlated with the decision to become an entrepreneur for both men and women. According to the literature the perceptual variables knowing, perceived opportunities and perceived capabilities are positive, and fear of failure is negatively related to the decision (Langowitz and Minniti (2007). In all the 3 models, the results for the perceptual variables suggests that fear of failure is negative, and knowing another entrepreneur, perceived opportunities and
perceived capabilities are positively correlated to the decision to become an entrepreneur. The gender differences in the perceptual variables are found in the way fear of failure, perceived opportunities and perceived capabilities influences the decision to
become an entrepreneur. In the results there is no significant gender difference in knowing another entrepreneur. The result shows that females are more likely to become an
entrepreneur compared to males if they perceive good opportunities and believe to have sufficient skills. And the entrepreneurial decision-making process is more correlated with fear of failure for females compared to males.
Minniti and Nardone (2007) show that males are more likely to become self-employed when males and females face equal opportunities. Several studies have shown that women are less confident in their entrepreneurial skills, knowledge and experience (Koellinger et al., 2013; Minniti and Nardone, 2007). And the findings of Camelo-Ordaz et al., (2016) reveal that fear of failure constraints the entrepreneurial intentions for females more than it does for men. The literature stated that females are more associated with knowing
another entrepreneur in the decision to become an entrepreneur (Langowitz and Minniti,
2007). Unfortunately, the results didn’t show a difference in the gender*knowing another
entrepreneur.
So, is the decision to become an entrepreneur different among gender? While
looking at perceptual variables cross countries.”. According to the results, there is a
gender difference in the way fear of failure, perceived capabilities and perceived opportunities influences the entrepreneurial decision-making process.
24
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