Gender differences in the usage of resources in the entrepreneurial opportunity identification process in Slovenia and Croatia

Abstract

While entrepreneurship research is usually focused on early-stage or established entrepreneurship, in this article we are taking a step back to earlier stages in the entrepreneurial process and focus on the opportunity identification and resources that might support it. The article provides a clearer insight into the extent of resources possessed by adults in Slovenia and Croatia, into gender differences in the opportunity identification process and the usage of resources within this process. Our research shows that women possess on average lower levels of resources, which explain their lower opportunity identification prevalence. Results show that the increase in resources, especially in the area of human capital consisting of skills, knowledge and experiences for entrepreneurship, has a significant and positive effect on opportunity identification among women.

Keywords:

• opportunity identification
• gender
• resource utilisation

JEL classification:

• L26
• J24

1. Introduction

An examination of entrepreneurial behaviour around the globe shows that in most countries a significant gender gap exists for both early-stage entrepreneurial participation and established business ownership. This is confirmed by empirical evidence that can be found in the Global Entrepreneurship Monitor (GEM) (Allen et al., 2007; Bosma et al., 2012) and in other studies (Der Zwan et al., 2012). GEM is a collaborative study dedicated to the measurement of global entrepreneurial activities and their contribution to economic prosperity (Reynolds et al., 2005) that combines research efforts for over 80 countries around the world since 1997. GEM data usually serves in the analysis of the early-stage entrepreneurship and factors which influence it (Arenius & Minniti, 2005). However, in this article we are taking a step back to even earlier stages in the entrepreneurial process and focus on opportunity identification and resources that might support it.

For our study of gender differences in opportunity identification we selected two neighbouring countries, Slovenia and Croatia, which shared a common half-century long socialist and communist background until they gained their independence in 1991. Despite the similarities in their historical, political and economic systems, their development as independent countries and their current international positions show substantial differences. Slovenia has been a member of the European Union since 2004, while Croatia struggled with the accession negotiations, which were eventually closed in 2011. The indicators of their different positions, according to the Global Competitiveness Report, are shown in Table 1. These indicators show that Slovenia was more successful in the process of transition to a market economy and became an innovation-driven economy, while Croatia has just recently joined the group of countries transitioning from an efficiency-driven economy to an innovation-driven economy. Despite Slovenia’s 2004 accession to the European Union, the transition processes in both Slovenia and Croatia are still not complete (Tominc & Rebernik, 2004), with the recent recession further slowing them down.

Table 1. Global Competitiveness Report rankings for Slovenia and Croatia.

Global Competitiveness Report rankings^1	Slovenia	Croatia
Global Competitiveness Index	57	76
Basic requirements	39	52
Efficiency enhancers	51	72
Innovation and sophistication factors	45	82

Source:

¹ World Economic Forum (2011), The Global Competitiveness Report 2011–2012.

With the fall of the Communist regime in the early 1990s, structural inequalities between men and women became evident (Tominc, 2002). While the percentages of unemployment are higher in Croatia for both men and women, as shown in Table 2, reflecting the difference in the countries’ economic activity, it is interesting to observe that the gap between men and women is broader in Croatia compared to Slovenia. This is in accordance with the claim (Leinert-Novosel, 2000) that in Croatia, after the gain of independence, women had been continually discouraged to improve their social status through career advancement due to the politics which highlighted the role of the Church and supported the view that a woman’s place was primarily in the home.

Table 2. Gender differences in employment in Slovenia and Croatia.

Labour force characteristics	Slovenia	Croatia
Percentage of female labour force unemployed 2006–2009^1	5.8	10.2
Percentage of male labour force unemployed 2006–2009^1	5.9	8.0
Women in labour force, ratio to men^2	0.88	0.83

Sources:

¹ World Bank 2011, World Development Indicators (most recent available data).

² World Economic Forum (2011), The Global Competitiveness Report 2011–2012.

The main purpose of this article is broadening the scope of research beyond the obvious and often mentioned fact that the entrepreneurial activity is lower among women than among men. From the perspective of entrepreneurial research, we are trying to contribute to the evident efforts of both countries to support women in entrepreneurship (Singer et al., 2009). We depart from the opportunity identification concept and investigate the interactive gender and predictor variables effect on the opportunity identification prevalence of individuals.

2. Literature review and hypotheses

The starting point for every business venture is opportunity identification that represents the most distinctive and fundamental entrepreneurial behaviour (Eckhardt & Shane, 2003). How are entrepreneurs different from other people in terms of their ability to identify an opportunity and combine resources in a new way has been an interesting topic of research for many decades now. In this article we contribute to this research by analysing resources that form the human and social capital of individuals.

2.1. Opportunity identification and resources

Alvarez and Busenitz (2001) linked the resource-based theory, often applied in strategic management research, to the entrepreneurship theory arguing that entrepreneurs have individual-specific resources that facilitate the identification of new opportunities and the assembling of resources for the venture. Easier access to resources increases an individual’s ability to detect and act upon discovered opportunities (Davidson & Honig, 2003). The focus of this article is on the opportunity identification itself and the resources that might influence the probability of opportunity identification, regardless of the existence of opportunity exploitation intentions.

To begin with, we are interested in the prevalence of perceiving good business opportunities. The ability to perceive a good business opportunity is a rather vague term because there is no means of confirming whether the observed opportunity was really a good one. However, this is not a major limitation since start-up companies also differ among themselves according to their success potential, yet they are all included in the measurements of the national entrepreneurial activity.

One of these measurements is the Total Early-Stage Entrepreneurial Activity (TEA) index, which represents the percentage of population aged 18–64 that consists of either nascent entrepreneurs or owner-managers of new businesses (that are less than 42 months old). Women are in general less entrepreneurially active than men in the majority of European countries men and this holds true also for Croatia and Slovenia. In 2010, there were on average 7.15% and 6.35% of adult men and 3.91% and 2.86% of adult women in Croatia and Slovenia respectively included in the early-stage entrepreneurship.

Opportunity identification is an inevitable step in the entrepreneurial process. Therefore opportunity identification is not expected to differ between the countries. However, analysis of the data had indicated that the overall level of opportunity recognition is significantly different in both countries. In Croatia, 23% of all respondents perceived good opportunities to start a business, compared to 27% in Slovenia (p < 0.05). As explained further in the article, the control ‘country’ variable was introduced into the regression model to test the difference of the likelihood of opportunity recognition among adults in Croatia and Slovenia.

In each country the gender differences in the opportunity recognition are also significant. In Croatia, 27% of all males perceived good opportunities to start a business, compared to 20% of females (p < 0.01). In Slovenia 31% of all males perceived good opportunities to start a business, compared to 22% of females (p < 0.01).

2.1.1. The role of human capital in opportunity identification

Human capital theory argues that knowledge provides individuals with increases in their cognitive abilities, leading to more productive and efficient potential activity (Davidsson & Honig, 2003). One of these cognitive abilities is also opportunity identification, so those individuals with more or higher quality human capital should be better at perceiving opportunities.

Generally, education seems to provide the knowledge base and to improve analytical and problem-solving skills (Aidis & Mickiewicz, 2004). The limitations regarding data availability enable us to analyse the role of human capital in opportunity identification through two proxy variables that describe formal education and individuals’ self-confidence into knowledge, skills and experience, required to start a business, as follows.

The data about formal education (Table 3) show that the overall education of the population is higher in Slovenia than in Croatia. Also, the percentage of women who obtain basic education or less is higher than men’s percentage in both countries, while the percentage of women who obtain upper secondary education is lower in both countries compared to men. The smallest percentages of the two populations have higher education, with Slovenian women outnumbering men in that percentage.

Table 3. Population aged 15 and over, by level of education (in percentages).

	Croatia^1		Slovenia^2
	Men	Women	Men	Women
Basic education or less	32.2	48.3	20.5	29.1
Upper secondary education	55	40.5	63.4	50.1
Higher education	12.8	11.2	16.1	20.1

Sources:

¹ Croatian Bureau of Statistics (2011): Women and Men in Croatia 2011, the data is based on the 2001 census.

² Statistical Office of the Republic of Slovenia (2010): Statistical Yearbook 2010, the data is based on the Labour Force Survey 2009.

In the literature education is often used as a proxy for human capital and a source of innovative actions. Davidsson and Honig (2003) found that years of education also had a positive influence on the likelihood of identifying new opportunities. Therefore the hypothesis:

• Hypothesis 1: Adults with a higher level of formal education are significantly more likely to recognize an opportunity than those with lower levels of education.

Launching a successful venture requires the potential entrepreneur to have the necessary start-up skills, knowledge and experiences (Acs & Szerb, 2011). We may also argue that those individuals who believe that they have knowledge, skills and experiences to start a business are more likely to recognise an opportunity than people with a lower level of self-confidence in their knowledge, skills and experience required to start a business. In Croatia, 53% of all respondents are confident in having skills, knowledge and experiences needed for entrepreneurship, compared to 56% in Slovenia (p < 0.05). We pose the following hypotheses to illuminate the impact of knowledge, skills and experience, on opportunity identification:

• Hypothesis 2: Adults with a higher level of knowledge, skill and experience required to start a business are significantly more likely to recognize an opportunity than people with a lower level of knowledge, skill and experience required to start a business.

2.1.2. Entrepreneurs’ social capital

Social capital is defined as the tangible and intangible (identity, reputation, respect for some achievement) resources that support the achievement of the goals of individuals through social structures (Portes, 1999). Clausen (2006) found that those individuals with access to entrepreneurship capital in social networks are significantly more likely to be engaged in entrepreneurial behaviour. Researchers also highlighted the importance of role models by finding that people in contacts with a role model had higher propensity to step into self-employment (Wagner & Sternberg, 2004).

The limitations regarding availability and content of the GEM data base enable us to analyse the role of social capital in opportunity identification through a proxy variable that describes the basic networking possibility of a potential entrepreneur by knowing those who lately became entrepreneurs. A described variable has already been used as a proxy for networking at the individual level (Acs & Szerb, 2011). Slovenian adults, both male and female are significantly more often acquainted with an entrepreneur than adults in Croatia (35% of adults in Croatia and 49% of adults in Slovenia; p < 0.001).

(Light & Rosenstein, 1995) found that networks were rich sources of information about the opportunities that entrepreneurs pursue, such as information on relative profitability of different industries or the success of industrial innovation. That is why the following hypothesis examines the impact of knowing someone that had started a new business in previous two years on the business opportunities attentiveness:

• Hypothesis 3: Adults who know someone personally who had started a business in the last two years are significantly more likely to recognise an opportunity than people who do not.

2.2. Gender perspective on resources for opportunity identification

2.2.1. Human capital

As previously mentioned, empirical studies confirm that there is a significantly lower number of women involved in starting a business compared to the number of men. GEM reports often emphasise the importance of understanding that real progress cannot be made without an investment in both men and women in a given country. Allen et al. (2007) emphasise that women are more likely to share their gains in education, health, and resources with members of their families and their communities at large and states more explicitly that, in economic terms, an investment in women’s entrepreneurship is an important way for countries to exponentially increase the impact of new venture creation.

In order to facilitate the participation of women in entrepreneurship it is important to understand the gender differences in the entrepreneurial process. Many researchers contributed to this area. Bates (1995) showed that there is a difference between factors that predict the self-employment entry of woman and those that predict the self-employment entry of men. However, he acknowledges the possibility that these differences reflect the differing industry concentrations among men and women. Because women are more likely to enter self-employment in skilled services fields, the human capital variables are the strongest predictors of female entrants. Following hypothesis tests whether gender and formal education interact in determining the probability of individual’s opportunity identification.

• Hypothesis 4: Gender and formal education of adults interact in determining the opportunity identification.

DeTienne and Chandler (2007) concluded that women and men utilised different types of human capital to identify opportunities. They found that the number of previous jobs positively predicted the number of opportunities identified for both men and women, but the proportion of variance explained by this specific human capital in opportunity identification variable amounted to 20% and 5% for women and men respectively. In both countries the gender differences regarding skills, knowledge and experiences needed to start a business are significant. In Croatia, 63% of all males believe that they have the necessary skills to start a business, compared to 44% of females (p < 0.001). Gender differences are significant also in Slovenia, where on average 65% of all males, and 47% of females (p < 0.001) believe they have the necessary skills, knowledge and experience to start a business.

In order to investigate whether gender and knowledge, skill, and experience required to start a business have an interactive effect on the opportunity identification probability of an individual, the following hypothesis is posed:

• Hypothesis 5: Gender and skills, knowledge and experiences required to start a business interact in determining the opportunity identification.

2.2.2. Social capital

Tominc and Rebernik (2004) found that knowing someone that had started a new business in the previous two years was associated with the perception of good business opportunity. Also, men exploited this circumstance in their entrepreneurship activities more often than women. In Croatia, male adults show significantly higher entrepreneurial network behaviour than women– on average 40% of men and 30% of women (p < 0.001). In Slovenia the situation is similar: 55% of men and 42% of women (p < 0.001) know someone personally who had started a business in the last two years. In order to identify the interaction of gender in this context, the following hypothesis is posed:

• Hypothesis 6: Gender and knowing someone personally who had recently started a business interact in determining the opportunity identification.

3. Data, variables and methods

The data-set used in this research is based on the adult population survey for the year 2010 in Slovenia and Croatia with a representative sample of adults, N = 2.000 for Croatia and N = 3.020 for Slovenia.

The dependent variable used in testing the hypotheses is ‘opportunity identification’: Respondents were asked if they believed that, in the six months following the survey, good business opportunities would exist in the area where they lived. The variable is dichotomous nominal with YES/NO answers. Predictor variables are:

1. Education: Respondents were assigned to two categories in terms of their educational level: secondary or less and post-secondary degree. In the regression analysis (as presented by table 5) we entered the variables as dichotomous.

2. Self-confidence in skills, knowledge and experience for entrepreneurship: Respondents were asked whether they believed they had the knowledge, skill, and experience required to start a business. The variable is dichotomous nominal with YES/NO answers.

3. Knowing entrepreneurs: Respondents were asked whether they knew someone personally who had started a business in the 24 months preceding the survey. The variable is dichotomous nominal with YES/NO answers.

We included two control variables in the analysis that would enable us to test the impact of predictor variables beyond the impact of these controls: gender and country that are both dichotomous (0 – male, 1 – female; 0 – Croatia, 1 – Slovenia).

We formally tested our hypotheses using the logistic regression, that estimates the probability of an event happening, which in our case was the recognition of opportunities or not, by adults. Maximum likelihood estimations were used to estimate the coefficients of logistic regression function, which denote changes in the log odds of the independent variable. The goodness of fit of the model was assessed by the Model χ2-test, the rate of correct classifications and the Nagelkerke R2. In order to test the significance of the regression coefficient we used the Wald test. The SPSS 19.0 was used for the analysis.

In order to test our hypotheses we ran three logistic regression models. Model I includes only control variables and Model II includes both, control and predictor variables. In order to test whether the addition of the predictor variables led to a significant improvement of the model, we examined the Block χ2-test. In Model III we test hypotheses on gender differences. Two explanatory variables interact in determining a response variable when the partial effect of one depends on the value of the other (Fox, 1997); interaction refers to a manner in which explanatory variables combine to affect a response variable, not to the relationship between the explanatory variables themselves. We assume that gender and predictor variables interact in determining the opportunity recognition. Interactions can be incorporated by coding interaction regressors, taking products of dichotomous control variables with quantitative explanatory variables. In our case explanatory variables are also dichotomous, therefore the following interaction regressors are also dichotomous, with value 1 indicating women who possess particular characteristics that were hypothesised to have an impact on opportunity identification and value 0 for others:

• Gender*Education,

• Gender*Self-confidence in knowledge, skill, and experience required to start a business,

• Gender*Knowing entrepreneurs.

Again, in order to test whether the addition of the interaction regressors led to a significant improvement of the model, we examined the Block χ2-test. To test the interaction the test of statistical significance of regression coefficients of interaction regressors was performed (as already mentioned, we used the Wald test in the logistic regression).

4. Findings

Model I and Model II, described above, are presented in Table 4. We report the maximum likelihood estimate (β) and its significance, Wald statistics and the odds ratio for each of predictor variables. It can be seen that Model II, which includes control and predictor variables, is significant (Model χ² = 169.238, p < 0.001) and Block χ² indicates that inclusion of predictor variables significantly improved the Model II compared to Model I (Block χ² = 131.636, p < 0.001).

Table 4. Results of logistic regression, Model I and Model II.

Variables	Model I			Model II
	β	Wald	Exp(β)	β	Wald	Exp(β)
Gender (0 – male, 1 – female)	−0.465***	36.731	0.628	−0.292***	12.495	0.747
Country (0 – Croatia, 1 – Slovenia)	0.177**	4.877	1.193	0.158*	3.029	1.172
Formal education (0 – secondary or less, 1 – post secondary)				0.242*	7.560	1.172
Skills (0 – no, 1 – yes)				0.544***	38.349	1.723
Knowing entrepreneurs (0 – no, 1 – yes)				0.583***	48.859	1.791
Model
Constant	−0.971***	176.793	0.379	−1.743***	242.875	0.175
N	3.708			3.369
Model χ^2	47.727**			169.238***
Block χ^2				131.636***
% of correct predictions	74.4%			74.4%
R^2 (Nagelkerke)	0.017			0.071

Source:

Authors’ calculations.

*** significant at 0.01 level;

** significant at 0.05 level;

* significant at 0.10 level.

Results of the regression suggest that the relationship between opportunity identification and education is significant (p < 0.05). Individuals with post-secondary education are more likely to perceive opportunities as compared to those with lower levels of educations (β = 0.242, p < 0.05). It means that we found support for Hypothesis 1 (‘Adults with a higher level of formal education are significantly more likely to recognise an opportunity than those with lower levels of education.’).

Hypothesis 2 states that adults with a higher level of skills, knowledge and experience required to start a business are significantly more likely to recognise an opportunity than people with a lower level of knowledge, skill and experience required to start a business. Our research results support this hypothesis (β = 0.544, p < 0.001). Those who are confident in their own skills, knowledge and experience for entrepreneurship are more likely to perceive business opportunities than those who are not.

From the Table 4 it can be seen that we find the support for Hypothesis 3 (‘Adults who know someone personally who had started a business in the last two years are significantly more likely to recognise an opportunity than people who do not.’). Knowing other entrepreneurs is positively and significantly related to identification of opportunities (β = 0.583, p < 0.001).

In terms of control variables, we found the gender effect as well as country effect. It means, that results confirm that adults in Slovenia are more likely to identify opportunities than those in Croatia (β = 0.158, p < 0.1). It also means that women are, on average, less likely to identify opportunities than men (β = −0.292, p < 0.001). The odds ratio indicates that the probability of identifying opportunities for women is on average only less than seven tenths of that of men (Exp(β) = 0.747).

Model III enables us to test Hypotheses 4, 5 and 6, about interaction of gender and each of the three predictor variables in determining a dependent variable – identification of opportunities. Model III is presented in Table 5. It can be seen that Model III, which includes control and predictor variables, as well as interaction regressors, is significant (Model χ² = 179.874, p < 0.001). Block χ² indicates that inclusion of interaction regressors significantly improved the model (Block χ² = 10.636, p < 0.05).

Table 5. Results of logistic regression, Model III.

	Model III
	β	Wald	Exp(β)
Gender (0 – male, 1 – female)	−0.373***	5.507	0.689
Country (0 – Croatia, 1 – Slovenia)	0.156*	2.918	1.169
Formal education (0 – secondary or less, 1 – post secondary)	0.351***	9.366	1.420
Skills (0 – no, 1 – yes)	0.306***	6.445	1.359
Knowing entrepreneurs (0 – no, 1 – yes)	0.707***	39.783	2.029
Gender*Formal Education (1 – women and post-secondary ed.)	−0.250	2.186	0.779
Gender*Skills (1 – women and skills – yes)	0.495**	7.970	1.641
Gender*Knowing ent. (1 – women and knowing ent. – yes)	−0.266	2.523	0.767
Model
Constant	−1.687***	167.950	0.185
N	3.369
Model χ^2	179.874***
Block χ^2	10.636**
% of correct predictions	74.4%
R^2 (Nagelkerke)	0.076

Source:

Authors’ calculations.

*** significant at 0.01 level;

** significant at 0.05 level;

* significant at 0.10 level.

Our research results suggest that the effect of education on opportunity identification does not vary by gender – the regression coefficient at the regressor Gender*Formal Education is not significant (p > 0.1). Although the level of formal education (β = 0.351, p < 0.1), as well as gender itself (β = −0.373, p < 0.011), have an impact on the opportunity identification for both men and women, the interactive impact of higher level (post-secondary) of education by women on the opportunity identification is not significant. Therefore the research does not support Hypothesis 4 (‘Gender and formal education of adults interact in determining the opportunity identification’).

Similarly we found no support for Hypothesis 6 (‘Gender and knowing someone personally who had lately started a business interact in determining the opportunity identification.’), although the research showed that having the knowledge, skill, and experience required to start a business itself is significant variable for explaining the variance in opportunity identification variable (β = 0.306, p < 0.01).

Hypothesis 5 stated that gender and the skills, knowledge and experience required to start a business interact in determining the opportunity identification. We found support for this hypothesis, since the logistic regression show that being a woman and having the knowledge, skill, and experience required to start a business, significantly increases the prevalence of opportunity identification (β = 0.495, p < 0.05).

5. Concluding remarks

The analysis of differences regarding opportunity identification showed that adults in Croatia identify business opportunities significantly less often than adults in Slovenia. The inter-country analysis examining gender differences confirmed that there is also a significant gender difference both among the Slovenian adults and Croatian adults regarding their ability to perceive good business opportunities, with men identifying opportunities more often than women. This difference in the opportunity identification prevalence could be explained by the difference in the level of resources that men and women have at their disposal and/or by the gender difference in the impact intensity of different resources on opportunity identification.

The overall education of the population is higher in Slovenia than in Croatia. In spite of the high levels of human capital acquired through formal education, Slovenian women identify business opportunities less frequently than Slovenian men. Women in Croatia are identifying business opportunities on average even less likely than Slovenian women (and significantly less in comparison with Croatian men).

The increase in education increases the opportunity identification prevalence. For people with post-secondary education, this education increases their ability to identify opportunities, which holds for both men and women, but the interactive impact of higher level (post-secondary) of education by women on the opportunity identification is not significant.

Slovenian adults, both male and female, more often believe that they have the knowledge, skill and experience required to start a business compared to Croatian adults. In both countries there is a significant gender difference, with men reporting these qualities more often. This is in accordance with the logistic regression model, which showed that having the knowledge, skill, and experience required to start a business is a significant variable for explaining the variance in the opportunity identification variable.

Interestingly, interaction effect of gender and the possession of knowledge, skill and experience on opportunity identification is significant suggesting that being a woman and possessing these qualities has a positive effect on the opportunity identification. Thus, one reason for lower opportunity identification among women is the lower proportion of women who believe that they possess the knowledge, skill and experience required to start a business, and we identified the stronger impact of these qualities on the opportunity identification among women, compared to men.

Slovenian adults, both male and female, are significantly more often acquainted with an entrepreneur, which is quite interesting when we consider that the TEA index is higher in Croatia being an efficiency driven economy than in Slovenia being an innovation-driven economy. The logistic regression model shows that people who know someone personally who started a business within two previous years are significantly more likely to recognise an opportunity than people who do not. But the results show that knowing a person who recently started a business affects opportunity identification and gender do not interact in determining the dependent variable.

The conclusion is that women in Slovenia and Croatia possess on average lower levels of resources, which explain their lower opportunity identification prevalence, but this is not a complete explanation. Our results show that the increase in resources, especially in that part of the human capital consisting of skills, knowledge and experiences for entrepreneurship, has a significant and positive effect on opportunity identification among women. This is in line with the findings that women entrepreneurs perceive their lack of management experience and business skills as a major constraint (Sibylle, 2004).

For the policy creators, our results suggest that the policies focused on the entrepreneurial education and networking of potential women entrepreneurs are useful, but of a limited scope. The future research should illuminate the impact of other factors, such as creation of women-friendly business environments and elimination of gender stereotypes, which would both relieve a woman’s role in the society and possibly make women more confident in their own skills and knowledge for entrepreneurship. Although this problem is usually described as a woman’s dilemma between overworked employees/wives/mothers and unemployed housewives, today we are witnessing another dilemma: the one between overworked employees/wives/mothers and career women who give up their family life. Policy creators should strive to eliminate those dilemmas, which would have a positive impact on women’s engagement in the business world in general, and entrepreneurship in specific.

In future research it would be important to examine what exactly represents an opportunity for the GEM respondents when they are asked whether they believe that, in the six months following the survey, good business opportunities will exist in the area where they live. Beside this the measurement of ‘amount’ of available business opportunities in the economy is also an important issue. This is important so we could develop a better understanding of potential entrepreneurs, both men and women. The results so far indicate that women in Slovenia and Croatia are not used to think in terms of opportunities and how they could benefit from those opportunities. However, maybe these women are aware of the situations that could present entrepreneurial opportunities, but they do not see them as opportunities and do not report them due to self-criticism or greater risk aversion. Both explanations are possible and require further research, which would aid the fine-tuning of public policies in support of women entrepreneurs.