Good Research Proposal On Statistics (Female Entrepreneurs)

Type of paper: Research Proposal

Topic: Entrepreneurship, Women, Development, Countries, Developing, Business, Developing Country, Emerging Markets

Pages: 5

Words: 1375

Published: 2021/01/11


In the last decades, there has been a lot of discussion about the glass ceiling, or whether there is any difference in career opportunities between men and women. All across the world, women tend to have a smaller chance of success on the labour market, as well as a lower chance of becoming an entrepreneur successfully. A lot of research has been conducted concerning this subject (e.g., Verheul, Van Stel, & Thurik, 2006), but only a small part of this research has focused on the differences within this large pool of female entrepreneurs and nascent female entrepreneurs globally.
The process of starting up and running a business, as well as environmental influences on entrepreneurial activity, seem relatively similar for female and male entrepreneurs (e.g., Ahl, 2002). However, the total amount of female entrepreneurs is significantly smaller than the amount of male entrepreneurs (Koellinger, Minniti, & Schade, 2005). If women are facing the same boundaries like work status, household income and education for becoming an entrepreneur (Minitti & Nardone, 2007), there should not be much of a difference in the size of entrepreneurial activity among sexes. However, some research has demonstrated that for women motivations, opportunities, and resources, as well as constraints in relation to entrepreneurship, differ compared to men (e.g., Carter & Brush, 2004).
It has been shown that female entrepreneurs have a significant positive effect on the innovation, wealth, and creativity in an economy (Baughn, Chua, & Neupert, 2006). Moreover, the number of female entrepreneurs has grown tremendously the last decade (Koellinger, Minniti & Schade, 2005). Research has shown that female entrepreneurship in developing countries in particular has a positive effect on the diversity of the supply of entrepreneurship, the wealth creation and employment (Allen, Langowitz, & Minniti, 2007). Furthermore, this contributes to high rates of economic growth, whereas this diversified supply can contribute to the growing demand for diverse products and services, and more and better entrepreneurship leads to a more sustainable growth (Verheul & Thurik, 2001). Still, Baughn et al. (2006) state that the advantages of female entrepreneurship are not being used in a systematic way, and talent and potential are not entirely recognized. Therefore, more research is needed concerning the variables influencing the female chance of successfully becoming an entrepreneur.
In this paper, the differences in the inclinations of women to become an entrepreneur are investigated, focusing on the differences between developing and developed countries. Moreover, an answer concerning the different rates of female entrepreneurial activity on the macro-level is needed. Using the Global Entrepreneurship Monitor (GEM) database and comparing its data across countries, statistical analysis can show if the effects of certain personal characteristics tend to differ across countries, or if socio-economic characteristics play a role in these inclinations of female entrepreneurs. If the latter case is true, this would mean that entrepreneurship is embedded in the socio-cultural characteristics of a certain country, and policy regulations can influence the probability of women becoming entrepreneurs.
Therefore, the research question is: What are the different effects of reasons to becoming a female entrepreneur between developing and developed countries?

H1: The relative proportion of female entrepreneurs is higher in developing countries.

H2: Women in developing countries are less confident about starting up a business.
H3: The fear of failure for women starting a business is higher in developing countries.
H4: Women in developing countries benefit more from knowing other entrepreneurs.
First, the hypotheses will be discussed in detail supported by the existing literature concerning this subject, and expectations will be revealed. Second, the data used and the methodology will be explained in order to present the validity of the hypotheses and focusing on the model used to test the hypotheses. Afterwards, the results of the probit regression analysis will be discussed, and last, conclusions will be drawn and the limitations of the paper will be discussed.
In this paper, TEA, total early-stage entrepreneurial activity, is used as a measurement instrument, because various research has proven that TEA is a valid measurement instrument and predictor of the entrepreneurial activity in a certain region. The relative ranking of TEA between countries is stable, which indicates that total entrepreneurial activity may be seen as a structural characteristic of an economy and is, therefore, suitable (Van Stel, Carree & Thurik, 2004).

H1: The relative proportion of female entrepreneurs is higher in developing countries.

This research is focused on the differences in opportunities and on the likelihood that women will become an entrepreneur in different countries. To investigate this, two very opposed environments are compared, since there will be a higher probability present for the effect to be more noticeable. Furthermore, research shows that social constraints and discrimination has put women at a disadvantage in their consideration of becoming an entrepreneur, thereby creating a gender gap in the supply of entrepreneurs (Fischer, Reuber, & Dyke, 1993).
The disadvantages women are facing can work in two ways: on the one hand, women in developing countries are less likely to become an entrepreneur - because of the existing social constraints and discrimination, it might be less accepted to become a female entrepreneur. These social constraints can for instance result in more difficulties getting a loan as an aspiring female entrepreneur in a developing country (Hisrich & Ozturk, 1999). Therefore, being a woman in a developing country will reduce the likelihood of becoming an entrepreneur. On the other hand, these socio-economic characteristics can force women to become an entrepreneur, because they have fewer job opportunities on the formal labor market. The last statement is also known as the ‘push factor’ leading to necessity entrepreneurship, and has been described in several papers (e.g., Kirkwood, 2009). Various authors use the gender inequality as a push factor for women in both developed and developing countries (Aidis, Welter, Smallbone, & Isakova, 2007; Baughn et al., 2006). However, there is evidence that this push factor is more of an influence on women in developing countries shown by a smaller ratio of male to female entrepreneurial activities (Wagner, 2006). Literature supports the previous argument as the ratio of male to female entrepreneurs seems to be smaller in less developed countries, which backs the statement that females are more likely to become necessity entrepreneurs in developing countries due to lacking opportunities in the labour market (Reynolds et al., 2004).
Furthermore, the probability of becoming an entrepreneur is higher overall in developing countries, and the correlation rate between male and female entrepreneurship is quite high according to Verheul et al. (2006). Therefore, female entrepreneurship also reflects the total entrepreneurial activity in a certain country (Delmar, 2003). Based on the literature that has been reviewed so far, it can be concluded that there is evidence that women are more likely to become a female entrepreneur in developing countries.
Moreover, it is stated that the differences in entrepreneurial activity among women are larger than the differences between women and men (e.g. Ahl, 2002). The focus should be on in-group differences instead of intergroup differences in gender, which is the ground for the approach of this paper. Many reasons for different rates of total early-stage entrepreneurial activity are brought up in the existing literature about the disparities in total early-stage entrepreneurial activity between countries such as educational background, motivation, social environment and networks (Baughn et al., 2006). It will be analyzed if there is a significant difference in the size of nascent female entrepreneurship, and it is expected that the results will support the hypothesis according to previous research. These differences will be explained further on in the research using variables such as confidence, fear of failure and knowing other entrepreneurs.

H2: Women in developing countries are less confident about starting up a business.

Before going in-depth with the second hypothesis, a definition of what is meant by confidence must be provided. Confidence can be defined as the idea women have about their skills to start a new business and if they think these skills are developed enough to become a successful entrepreneur. More specifically, it is important to know if they have sufficient educational background, experience with small firms or entrepreneurial activities. The existing literature suggests that women are influenced by the aforementioned characteristics when they are considering which career path to follow. Therefore, it can be said that confidence can affect the likelihood of women becoming an entrepreneur.
A large number of women entrepreneurs place themselves in activities and, for example, service jobs, where the required skills are an extension of what they have learned through gender socialization. Thinking inside the gender-aware box, this explains the higher share of women in the service industries, which is typically a female field, and, furthermore, the approach into these areas is easier, although they produce less value (Almeida & Borges, 2009). According to Veira (2008), women begin their careers on paths they are familiar with. Education, training and experience are factors that are highly important when applying for a specific career path. It could be said that women are more influenced by expectations of society when looking for a job. Etzioni (1987) suggests that the likelihood of women becoming entrepreneurs is therefore dependent on the degree of moral approval within a society. The degree of moral approval can be influenced by more attention to entrepreneurship within the educational system, policy implications for entrepreneurs and the social status of female entrepreneurs in a certain region. The latter will be discussed in more details in hypothesis four: the impact of knowing other entrepreneurs. Women need more confidence about their skills than men and are more careful when starting a business. The first step on the entrepreneurial road feels like more of a risk to women in general, because they doubt the viability of their own projects, and thus are less confident.
Being less confident explains why women prepare exhaustively from the beginning, as they need to be reassured that their projects will be successful. As a result, less entrepreneurial activity might emerge in developing countries where the supply training possibilities are inferior.

H3: The fear of failure for women starting a business is higher in developing countries.

An increasing number of scholars agree that opportunity recognition, self-confidence, fear of failure, and knowing other entrepreneurs are, in fact, among the most important drivers of entrepreneurial behavior (Freytag & Thurik, 2007; Arenius & Minniti, 2005; Koellinger, Minniti, & Schade, 2005). According to Minniti and Nardone (2007), perceptual variables influence the decision to start a business. They pay attention to the fact that what is important is not the fear of failure itself but the degree to which it affects the behavior of individuals (Minniti & Nardone, 2007). Some studies show that fear of failure for women may arise from lack of respect in specific countries, which, on the other hand, decreases the propensity of becoming self-employed (Brooksbank, Jones-Evans, Kwong, & Thompson, 2008). The idea that women have low risk tolerance has also been used to explain alleged low growth rates in female-owned companies (Langowitz & Minniti, 2007; Johnson & Powell, 1994).
Overall, the results of the different studies expect that fear of failure will be negatively correlated to the propensity of women starting a business. Koellinger, Minniti and Schade (2011) find out that in their sample of 17 countries, women have higher fear of failure than men in 16 of them. They also find that the differences are consistent with more pronounced degrees of loss aversion often observed among women, but could also reflect less favorable conditions for potential female entrepreneurs (Koellinger, Minniti, & Schade, 2011; Dohmen, Falk, Huffman, Sunde, Schupp, & Wagner, 2010; Wagner, 2004). In another paper, they also find that in Germany fear of failure reduces propensity to start a new business, but once established entrepreneurs report that fear of failure is less likely to stop them from starting a business (Koellinger, Minniti, & Schade, 2005). Arenuis and Minniti (2005) find out that fear of failure has a negative and significant impact on being a nascent entrepreneur, which might be explained by the fact that an increased perception of the probability of failure reduces entrepreneurial incentives by increasing the perceived riskiness of starting a business (Arenius & Minniti, 2005). Last but not least, Langowitz and Minniti (2007) report in their results that entrepreneurial propensity is negatively correlated to age and fear of failure (Langowitz & Minniti, 2007).
So far, the literature suggests that there is a negative correlation between the fear of failure and the likelihood of a person, more likely a woman, to start a business and become an entrepreneur. However, no articles compare the degree of fear of failure between developing and developed countries.

H4: Women in developing countries benefit more from knowing other entrepreneurs.

A broad range of literature has shown how important knowing other entrepreneurs and having networks is to female entrepreneurs, especially in poorer countries (Minniti, 2010; Allen et al., 2007; Aldrich, 1999). According to Koellinger, Minniti and Schade (2011) knowing other entrepreneurs may provide relevant knowledge and social cues and there is a likelihood of it to influence subjective perceptions. The authors state that it is unlikely for the variable knowing other entrepreneurs to trigger directly the start of a new business (Koellinger, Minniti, & Schade, 2011). Knowing other entrepreneurs will immensely help potential entrepreneurs to develop and will increase their confidence by providing advice and support (Aldrich, 1999). It can also serve as a role model effect, which can increase skills and knowledge, and reduce ambiguity relating to the challenges involved (Brooksbank, Jones-Evans, Kwong, & Thompson, 2008; Taylor, Jones, & Boles, 2004). Langowitz, Sharpe, and Godwyn (2006) find that women who are involved in different stages of entrepreneurship appreciate networks and role models (Langowitz, Sharpe, & Godwyn, 2006). Sharpe (2001) supports that view and adds that knowing other entrepreneurs can bring business knowledge, advice and potential source of financial capital to entrepreneurs (Sharpe, 2001). Minniti, Arenius, and Langowitz (2005), however, stress on their finding that a female’s knowledge of another entrepreneur is a strong predictor of her involvement in starting a new business (Minniti, Arenius, & Langowitz, 2005).
In her results, Minniti (2005) finds that there is a strong positive and significant correlation between knowing other entrepreneurs and a person’s involvement with starting a new business (Minniti, 2005). Arenius and Minniti (2005) also find that knowing another entrepreneur is positively and significantly related to being a nascent entrepreneur (Arenius & Minniti, 2005). In her collaboration with Langowitz, Minniti (2007) concludes that knowing other entrepreneurs together with all perceptual variables are the most important factors for both men and women when they decide whether to start a business, after the authors find that there is a positive and significant relationship between the first variable and the likelihood of being involved in starting a business (Langowitz & Minniti, 2007).
Koellinger, Minniti, and Schade (2011) find in one of their papers that individuals in all stages of the entrepreneurial process are more likely to know other entrepreneurs, and they specify in another paper of theirs from 2005 that new business owners and experienced entrepreneurs are more likely to know other individuals who have recently started a business than non-entrepreneurs (Koellinger, Minniti, & Schade, 2011; Koellinger, Minniti, & Schade, 2005).
Moreover, in her paper, Minniti (2010) reports that in low-income countries 45.6 per cent of women who are involved in starting a business know other entrepreneurs compared to 31 per cent in developed countries. The author states that a reason for this might be the higher prevalence rate observed in low-income countries, where relatively more women are pushed into becoming an entrepreneur out of necessity (Minniti, 2010). It is of interest to find out more about the difference between female entrepreneurs knowing other entrepreneurs in developing and developed countries.


The collected data comes from the Global Entrepreneurship Monitor (GEM), which is said to be the largest study of entrepreneurship worldwide. GEM data and numerous reports are used and cited by scholars, researchers, educators and policymakers. The Global Entrepreneurship Monitor project is an annual assessment of the entrepreneurial activity, aspirations and attitudes of individuals across a wide range of countries.
The specific dataset used to check whether there is an actual difference in the inclination to become an entrepreneur for females in developing and developed countries is the GEM 2010 APS Global - Individual-Level Data. APS stands for the Adult Population Survey and it is a comprehensive questionnaire administered to a minimum of 2000 adults in each GEM country, designed to collect detailed information. The individual national surveys are all collected in the same way and at the same time of the year in order to facilitate reliable cross-national and longitudinal comparisons. The APS Global – Individual-Level Dataset contains sixty countries differing from developed countries, such as the US, Norway and Germany, to developing countries, such as Uganda, Bolivia and Pakistan.
The variables included in the regressions will be stated in the next few paragraphs, and the one that will be explained first is the dependent variable that is used in this research. This dependent variable is the rate of Total Early-stage Entrepreneurial Activity for females within a country. The TEA is the percent of working age population that is both about to start an entrepreneurial activity, and that have started one for a maximum period of three and half years. TEA has been used as a measure for entrepreneurial activity in several articles and over several years (Baughn 2006, Ho & Wong 2007). Separate TEA indices were also computed for men and women, and since this research is focused on women, the separate TEA indice for women is used.
Regarding the first hypothesis, the relative proportion of female entrepreneurs is higher in developing countries, a dummy has been included in the regression to check whether there is an actual significant difference in the number of female entrepreneurs in developing and developed countries. The dummy variable takes the value of 1 when a country is a developing country, and the value of 0 when a country is considered to be developed. The deciding factor concerning whether a country is a developing country or a developed country, is the GNI/capita. Economies are divided according to 2012 GNI per capita, calculated using the World Bank Atlas method. The groups are: low income, $1,035 or less; lower middle income, $1,036 - $4,085; upper middle income, $4,086 - $12,615; and high income, $12,616 or more (Country and Lending Groups. (n.d.)). In this research, countries that are not in the high income group are seen as developing countries rather than developed countries. The GNI/capita has been gathered from the data collection from the The World Bank. Because the Global Entrepreneurship Monitor Dataset is for the year 2010, the GNI/capita has also been collected for this year.
Concerning the second hypothesis, which states the following: women in developed countries are more confident about starting a business than women in developing countries, another variable is included besides the dummy variable for a developing country. The additional variable is called the confidence variable, and it tests whether the level of confidence a woman has on becoming an entrepreneur, has a different effect on the probability to start a business in a developing country than it has in a developed country. In the Global Entrepreneurship Monitor Survey, the following question has been asked to all participants: ‘Do you have the knowledge, skill and experience required to start a new business?’ It has been decided to take the answers to this question as a decisive factor for being confident on starting a new business or not. The possible answers to this question were either ‘yes’, or ‘no’, which makes it a binary variable just as the developing dummy variable included in the regression.
The hypothesis investigates whether fear of failure would prevent a woman from starting a business on her own. Thus, the variable FearofFailure is included into the regression. It is added to see if it has a different influence on becoming an entrepreneur in developing countries compared to developed countries. Just as the two variables which are part of the regression for hypothesis one and hypothesis two, this variable is a binary or dummy variable. Moreover, the developing dummy variable, which was included in the second hypothesis, will be part of the regression giving insights on hypothesis number three.
Last but not least, the fourth hypothesis states: Women in developing countries benefit more from knowing other entrepreneurs. The question whether females across all countries if they personally know someone who has started a business in the past two years will be used. Therefore, the variable KnowingOtherEntrepreneurs is included, which is also a binary variable.


The key determinants found to be of influence on starting a business are fear of failure, confidence to start a business and knowing other entrepreneurs. To test whether these determinants have a different effect on females starting a business or not, a probit model is used.
A probit model, or probit regression, is used to model dichotomous or binary outcome variables. Since the dependent variable in this research is binary, because it can only take two possible states, namely being involved in TEA and not being involved in TEA (1 and 0), a probit model is suitable to forecast the outcome. In the probit model, the inverse standard normal distribution of the probability is modeled as a linear combination of predictors. The purpose of the probit regression model is to estimate the probability that the observation with particular characteristics will fall into a specific one of categories. The estimated parameters of the probit regression model are used to “predict” the behavior of an individual between two choices. Therefore, a one-unit change in x leads to a β change in the z-score of y. In case when the estimated probability is higher than 0.5 (p > ½) then y = 1, otherwise y = 0.

The equation for the first hypothesis is scheduled below:

p(Females involved in TEA=aj+ βDevelopingj + εj

With j being the index of the country and ε being the error term.

As is already mentioned in the hypothesis, the expectation is that p(Females involved in TEA)j will be higher when a female is from a developing country, so when the dummy variable for developing is equal to 1.
Concerning the second hypothesis, the effect of the cross-term of Developing * Skill can only be found when the linear term is incorporated as well. This shall also be seen in the last two regressions.
p(Females involved in TEA)j =aj+ βDevelopingj +γSkillj+δDeveloping*Skillj+ εj — With j being the index of the country and ε being the error term.
The expectation is that the effect of the cross-term on p(Females involved in TEA) will be higher when a female comes from a developing country rather than from a developed country. Such is also mentioned in the hypothesis. This would mean that a higher rate of confident females is involved in developing countries than in developed countries.
p(Females involved in TEA)j =aj+ βDevelopingj +γFearOfFailurej+δDeveloping*FearOfFailurej+ εj

With j being the index of the country and ε being the error term.

Regarding the third hypothesis, the crossterm for Developing * Skill has been replaced by the cross-term Developing * FearOfFailure, to account for the difference in effect of FearOfFailure on having a business in developed and developing countries. The expectations are that this effect will be higher in developing countries.
p(Females involved in TEA)j =aj+ βDevelopingj +γKnowingOtherEntrepreneursj+δDeveloping*KnowingOtherEntrepreneursj+ εj

With j being the index of the country and ε being the error term.

For the last hypothesis, the regression has been changed in such matter that the cross-term of Developing*KnowingOtherEntrepreneurs has been incorporated. The expected effect is that there will be a higher estimate for developing countries.


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