In accordance with earlier observations Baron, et.al. (2001) described women as employers viewed less favorably by a financing party. Women do not only receive less money but promising business projects demonstrate results that are lower comparing to the expected ones.

As for the results, Sara and Peter confirmed that gender differences in business financing may actually occur. As a rule, male entrepreneurs use larger sums of money at the start-up stage. As a result of adverse financing, business performance usually turns out to be worse than it could be in terms of sufficient material support in the beginning. Eventually, the survey demonstrated that sources of financial aid are similar for both gender groups. Thus, everyone has an equal chance to determine what type of funding he or she will try to receive.

Speaking about existing business financial support, women rarely come into the institutional organizations (i.e. banks) to apply for getting a loan.

Nevertheless, several surprising results are demonstrated in the study. For instance, men's applications are more frequently declined by institutional organizations. It brings one to the idea that gender discrimination does not exist. However, the statement is controversial since the possible bias takes place. As mentioned before, female entrepreneurs scarcely ever require funds in banks or similar organizations. For this reason, it is implied that women, asking for support in institutional organizations, are more confident and aware of their future business achievements. They have no fear and are properly informed and educated to get through formal procedures on their own. Pointing out the use of guarantees and relationships with lenders, the final conclusion could not be made building on current results.

Subsequent studies may broaden the scope and improve the weak spots by asking more specific questions and narrowing the sample. As entrepreneurship is an arduous notion, the inclusion of quantitative data coupled with qualitative ones is paramount (Sara & Peter, 1998).

Although the research conducted by Naser, et.al. seeks to remedy the issue of female entrepreneurship by collating expectations and subsequent results, the reader should bear in mind that it has several limitations. The current study was unable to analyze social attitudes towards a feminine role in various types of society coupled with their cultural background. Moreover, the number of interviewed women was inadequate making conclusions about the entire women-entrepreneurs population questionable. Lower representation of women and business and the overall tendency of them working for meager payments and facing no appreciation from the employers' side.

Nevertheless, Baron, et.al. (2001) dedicated entire research to establish the differences in recognition of businesswomen and to compare it with men. The group of authors scrutinized previous findings and identified that women, in fact, bear a set of gender stereotypes that are skewed toward the point adverse for female entrepreneurs. Meanwhile, men were characterized as brave, freethinking, and bold. There is no doubt that such qualities favor the male part in business-related functions. Women, then, are regarded as unfitting for a risky job of a business person.

Gender stereotypes influence the general perception of women and men in business in a row with performing routine employee's working activities. Women may be the victims of their own attractiveness. The current study demonstrates that in the public eye entrepreneurial women are less feminine and more attractive. The reason for that is masculine persistence essential for carrying out entrepreneurial duties. At the same time, feminine allure boosts stereotyped thinking. Unlike men as entrepreneurs case, female appearance-related have a negative effect on managerial activities. Such findings suggest a psychological counterpart in the perception of male and female entrepreneurs. The results may be occasionally biased as people rated entrepreneurs basing only on suggested occupation characteristics and business person's photos. In general, public audience attitude evolves depending on a wider range of features ascribed to an individual. Nevertheless, the results of the study serve the ground for further research and developments on gender stereotypes.

The similar results were presented after analyzing 25 in-depth interviews with female Kazakhstani entrepreneurs if 2017 and in Malaysia in 2012. Notwithstanding, it was concluded that women are frequently driven by normative factors: desire to be a role model, to be one's own boss as well as by their family's support. The research conducted in Malaysia used a Likert scale to measure the significance of each motivating factor (Xavier, Ahmad, Nor & Yusof, 2012). Cognitive factors (i.e. personal growth and interest) were highly appreciated by women. The cognitive dimension includes the following motivators: flexible working hours and self-actualization. Working as an employer may considerably change people's lives. Firms may be defined as “psychological child” of their owns because it generates close emotional bonds between entrepreneurs and enterprises. Lewis (2017) scrutinizing four case studies and conducting multiple in-depth interviews revealed that entrepreneurs' organizations are "vehicles for personal expression, development and fulfillment" (p.384). Bond with the firm helped women surpass others' opinion and improve their self-confidence.

Following the trend of questioning the reasons behind the deficient number of female entrepreneurs around the globe, Langowits and Minniti investigate factors influencing the propensity of women by studying the data collected in 17 countries. Authors still recognize the fact that although the rate of female entrepreneurs across the world is growing, it is noticeably lower than that of men. What makes this study stand out from the rest is the inclusion of both sociodemographic factors (i.e. age) and perceptual (psychological) ones. Moreover, the study excludes survival bias by studying people at the stage of business establishment (nascent entrepreneurs). This paper place greater emphasis on influencing factors nature by asking if they are actually a global phenomenon. Additionally, researchers try not only to identify determining factors, influencing women's desire to become entrepreneurs but to compare motivators with male driving factors, assuming they may be similar for both groups of employers.

The fact which is of particular importance is that the study makes several assumptions including the one hypothesizing that the relative significance of each factor is a subject to the type of drivers and its category (opportunity or necessity).

By building a probit-model authors partly confirm the suggested hypothesis: self-confidence in individual skills, having entrepreneurs among acquaintances, acknowledgment of opportunities to establish an enterprise distinguish businessmen and businesswomen from those who made another career choice. Although the analysis includes both objective and subjective measurable, it misses the opportunity to demonstrate the explanation for several variables. For instance, the GEM dataset used for probit-model creation includes a parameter setting if a person knows any other entrepreneurs. It was proven to be a significant but theoretical background and accumulated knowledge may provide various perspectives synonymous with the fact of knowing other entrepreneurs: it may display a person's inclusion in social networks or his presence under the positive influence of a role model.

As mentioned above, self-confidence and awareness may be the reasons for larger male entrepreneurs' inclusion in business society because they tend to be more confident and inclined to see positive outcomes. In general, such features stimulate men to choose an entrepreneurial career option.

Speaking about limitations, it is not possible to investigate how state policies and programs influence both women's and men's decisions to set up a company. In general, the primary goal of any state policy is to foster economic growth within a particular region. Although the final goal of various government policy solutions is alike, a series of measures could target disparate age groups, genders, etc. The introduction of such variables may give a broader view of contextual factors (Langowitz & Minniti, 2007).

Hong T.M. Bui and others in their study “Female Entrepreneurship in Patriarchal Society: Motivation and Challenges” state that women even make a decision to become an employer following global goals as well: for instance, improvement of the economic situation. The study results are limited by the fact that they cannot be generalized. Furthermore, it ignores women who decided not to be involved in entrepreneurship. Needless to say, the work neglected the third party's opinion. It may be improved by discovering male counterparts' and policy-makers' thoughts. Self-confidence as a driver of success was also confirmed in surveys involving women-entrepreneurs in Amsterdam (Robertson et al., 2013). Another study dedicated to women's motivation to become an employer was conducted by K.Terrell and M.Troilo. The authors specifically focused on life and work values. The form constitutes a set of desirable life attributes, the latter concern desirable employment features, appreciated by women. Life values drive women to another labor force; work values define their decision to become either an employer or a salaried worker. This study is valuable in the sense that it avoids sample selection bias by taking into consideration both employed and unemployed women at the first stage of the research which involves the study of data collected across 79 countries over time. It was concluded that women who agreed that “in times of job scarcity, men should have jobs first” (Terrell & Troilo, 2010, p. 278) were 44% less likely to comparing with those who did not have this value. Furthermore, the hypothesis that institutes shape female values and, as a result, their intention to become an entrepreneur is confirmed. The list of values includes of achievement, initiative, and respect. The recommendations for further improvements involve focusing on cultural background; for instance, comparing male/female-entrepreneurs ratio in neighboring countries, Finland and Sweden (4.0 and 1.55 respectively), and it demonstrates that the country's features affect women's values and attitudes towards entrepreneurship.

1.11 “Push” motivating factors

A series of recent studies indicate that women leave their working places and start an entrepreneurial career "pushed" by a negative attitude towards working for an enterprise. Negative factors are the second set of driving factors. The mentioned research conducted by P. McGowan in Northern Ireland involving 14 women-employers concludes that 10 women were partly motivated by adverse factors, however, their efforts were not encouraged. Moreover, women were unable to set flexible working hours or get their maternity leave. Women were likewise left out other promotions and proper services. Additionally, complicated administrative procedures shaped their negative perceptions.

Furthermore, Australian women particularly mentioned the failure of employers to appreciate their achievements as a motivation to leave one's job and establish their own business. The problem was incredibly pressing situations when women were older than their colleagues. The latter regularly got more opportunities for career development. The evidence obtained during the study confirms the idea that, as a rule, women are pushed towards entrepreneurial careers. As presented in Walker and Webster's study, 14 out of 15 women who filled the questionnaire and participated in-depth interviews acknowledged the fact that they had never thought over establishing an enterprise. "Push" factors are central for older women because they have a fear of redundancy (Walker and Webster, 2007). The absence of good work recognition was also mentioned by Baron, et.al. The group of authors emphasizes that such a negative trend is especially perceptible when women hold managerial positions. It also spoils mentoring relationships particularly between two women. All in all, equally trained and experienced men and women are treated differently in the workplace doing men as entrepreneurs a favor.

What is more, women and ethnic entrepreneurs may be forced by family members. The field surveys conducted by Levent et al. emphasize the continuation of other family business was the motivation for 1percentnt of the women-entrepreneurs studied. The percent was considered moderate because 80% of studied women were born in entrepreneurial families. The mentioned study in Malaysia confirms that family-owned business was rarely a motivation for women to opt for small and medium business enterprises (19.6% out of 153 respondents). The authors, however, missed an opportunity to widen a sample and to include those women who leave an entrepreneurial career and came back to work for a company. Nevertheless, entrepreneurial spirit may encourage women's willingness for a business career. Not to mention, the same study confirmed the importance of economic dimension (32% of women indicated pointed out that extra income was important for them). Last but not least, women-entrepreneurs may even become `role models' for other females. The similar results were obtained in Kazakhstan based on 25 in-depth interviews mentioned. Although the former study provided a deliberate list of recommendations (i.e. ones for policy-makers) to improve the current state of female entrepreneurship, it omitted, following the same gap in other studies, the fact that various ethnic groups may show different characteristics in terms of motivating factors and success conditions.

1.12 The implementation for entrepreneurship policy

Policy-makers need to accept entrepreneurship as an enlightened and independent choice. From time to time women's expectations are not met and they face a set of significant barriers. In this case, running her own business becomes the last resort for a woman. Targeting assistance is to be provided. Women of different age groups have various drivers for starting up and continuing business.

Political and economic issues overlap; and, as a result, such a policy as suitable childcare provision can appear. Moreover, it is important to give an opportunity to successful women, who have found the balance between work and private life, to share the experience in entrepreneurship. This practice can help attract more women into business. It will assist them in overcoming some challenges that hinder establishing their own business (McGowan et al., 2012). Ruta Aidis and Julie Weeks in their research paper refer to other authors who stated that one of the most important factors of motivation for beginners was the success of others. It means that women become less skeptical about entrepreneurship and ready to turn their ideas into life (Aidis and Weeks, 2016). Motivation can be decreased by misunderstanding, complicated entry registration process, and taxation restrictions (Bui et al., 2018). Women are psychologically influenced by these factors, perceiving them as threats to make entrepreneurial decisions.

If all these difficulties and constraints are not eliminated or at least reduced, a potential for women entrepreneurship will remain untapped.

The observed articles give an understanding that motivational factors vary. The importance of this particular research is based on the idea to explore to which extend the control variables influence women's desire to set their own business. The widely ignored territorial affiliation will be considered.

2. Statement of the research question

After the World War II entrepreneurship lost its importance entirely and did not receive any until the end of the last century. The focus was switched towards large corporations. Scholars had considered big firms the main incentive of the country's economic expansion. This idea was brought into scientific to the science community by Joseph A. Schumpeter, the author of the “creative destruction” theory. He believed that entrepreneurship could not be a career choice. Entrepreneurs, according to his idea, disrupted existing industries. In fact, they did not contribute to the sector's growth: businesspersons threw businesses of the sector into a loss (Acs & Audretsch, 2010, pp. 1-2).

However, first movements towards studying work-related motivation happened in the middle of the last century. Many things have changed since that time as entrepreneurship research is evolving together with business itself. Initially, psychologists measured motivation for entrepreneurship in static. Later on, deliberate models were invented to study changes in individuals' motivation over time and various circumstances. As was mentioned, the entrepreneurial career choice occurs at any time when an individual decides to establish a new firm upon other actions determining his or her future career: being employed by a company or stay unemployed. This decision is under the impact of weighted utility: if entrepreneurial activities are more promising, they are prioritized (McMullen et al., 2013). One of the main characteristics of an individual is that he bears all risks of owning his or her firm. Entrepreneurship aligns with such descriptive characteristics as uncertain and unpredictable. Thus, entrepreneurial work was earlier seen as unfavorable and far from pleasant.

However, decisions of entrepreneurs could not be defined solely by a simple weighing out process: it is a mixture of economic freedom characteristics, institutional factors, and psychological and personal attributes. An individual is placed in a matrix of the presented attributes. The latter group of factors (personal attributes) has been ignored under the influence of the economic theory state in the 20th century. It was assumed that psychological features were out of place; entrepreneurship was a result of the interaction between a person and the situation he happened to face. As declared by Segal and others (2005), earlier researches mainly focused rather on entrepreneurs' characteristics than on developing a process-based study.

Several views on entrepreneurial motivation exist. Various theories established the basis for drivers' classification. The one used in this paper was highlighted in the paper written by Benjamine Gilad and Philip Levine called “A Behavioral Model of Entrepreneurial Supply”. It unites two conflicting ideas of “push” and “pull” motivation. The authors pointed out how important it was to perceive entrepreneurs individually. Entrepreneurs' aspiration for innovation is the only common trait of business persons, according to Drucker's suggestions.

“Push” theory is supported by those who think that negative attributes leave individuals with no other options. They are not satisfied with the perspectives forecasted at their current workplaces. What is more, the establishment of the enterprise may happen after the fact of one's losing a job. People “pushed” to enter the entrepreneurial labor force frequently do not fit in society and do not feel engaged. Unlike conventional ideas of “push” theory, proponents of the “pull” theory hold the idea that entrepreneurship is chosen for its positive attributes and optimistic future in the end. Nowadays the term “pull” entrepreneurship is synonymous to “opportunity” entrepreneurship because they express similar ideas. At the same time “push” entrepreneurship is similar to “necessity” entrepreneurship. Such differentiation is used in the research presented in this paper. The study aims to remove ambiguity in the theory of opportunity and necessity entrepreneurship by separating institutional, personal, and economic characteristics by TEA type (necessity and opportunity).

The situation has changed: entrepreneurship is a national driving engine in any country. Until now, there has been a considerable expansion in the number of studies dedicated to women's decision to enter the labor force. Although such issues as inequality of genders, pressure experienced by women in the work-place, ability to create and keep work-life balance were studied to some extent, female propensity towards entrepreneurship received little attention. However, female entrepreneurial activities, especially at the early stages, are crucial because they drive the country's economy by all means (Langowitz, Minniti 2007). Entrepreneurship is a cause of job creation and economic growth in every nation. Moreover, it was proved that entrepreneurship moves the poverty level down in developing countries (Mersha, Sriram, & Hailu, 2010). New enterprises do not only bear `real' influence on the economy, which could be measured but bring stimulation of industry competition spring from a high entrepreneurship rate.

Due to the growing awareness of entrepreneurship economical contribution, governments of countries try to create prosperous start-up culture to promote the establishment of new SMEs. Pawкta and Kirillov (2016) testify to the success of such supporting measures as investing climate enhancement to raise capital inflow in research and development effectiveness and volume. Material resources are critical for giving more strength to business incubators and innovative clusters.

In contradiction with the fact that female and male entrepreneurship has been studied in the last twenty years, considerable gaps and omissions in research flow. As a consequence, overall growth of total early-stage entrepreneurial activities is rarely coupled with the growth of female entrepreneurial labor force (especially opportunity total early-stage entrepreneurial activities) or certain ethnic groups entrepreneurship. However, the situation could be changed with the help of targeted measures foreseeing their effect. Their development requires a deep understanding of institutional characteristics and those of opportunity and necessity early-stage entrepreneurial activities (McMullen et al., 2013).

Notwithstanding, entrepreneurship research expansion did not derive it from shortcomings. Ahl (2006) summarized the weak spots and proposed possible directions and further improvements. In fact, misleading results, as a consequence, lead to improper conclusions. One of them is that women are just secondary actors in the economy: their businesses grow at a slower rate and they are in general less successful. An entrepreneur as a male-gendered person has been a center of many papers dedicated to studying entrepreneurship. Schumpeter characterized an entrepreneur as an outstanding and unusual entity. For him an entrepreneur was bold and powerful. Today, opportunity-motivated entrepreneurial activities are more consistent with the ideas of the Austrian economist because OME provide more rapid economic growth and innovations development with higher probability. Thus, opportunity-motivated entrepreneurs are more frequently reported in developed countries. Developing countries (e.g. Thailand and Korea) with lower income are inhabited primary by men and women “pushed” to join entrepreneurial labour force. Moreover, earlier studies attitude towards entrepreneurship is outdated. Since, authors treated entrepreneurship as an economic function, they missed an opportunity to look at it from individual perspective. In addition, Ahl suggests that the omission of structural, historical, and cultural factors is a big mistake. It goes in a row with a deficient amount of theory supporting scholars' argumentation.

Contextual factors around the world, the entrepreneurial experience of women, and opportunity and necessity drivers serve the ground for formulating the following research question: “What factors influence women's choice to embrace opportunity entrepreneurship?” In other words, the study aims to declare the set of factors (institutional and personal characteristics) that influence female motivation. It is widely believed that opportunity entrepreneurship is chosen mainly for men and that it is a more preferable type of entrepreneurship compared to necessity early-stage entrepreneurial activities.

The research question is aimed to produce a universal basic toolkit for stakeholders: those who are interested in promoting female entrepreneurship capable of national economic and social wellbeing improvement. No doubt, the results of this research could not be used separately from other observations and studies. Policy makers, first of all, must identify the current state of the issue and decide, whether female entrepreneurship lacks its presence in labor force of the region. If female entrepreneurship share is lower than it could be, state bodies have to create to set up a strategy formation process. They need to assess the resources and align them with measures needed. The set of measures is individual and is based on barriers and constraints existing in the country. Also, earlier executed measures could be evaluated. Obviously, if preceding operations are gender-blinded, their success is questionable: they should not underpin targeted and renewed policies.

Even though the research question includes only the female part of the society and seeks to establish motives for this half, conclusions include the contrast between motivation for women and that for men. Some measures, in fact, may be prosperous for the entire population if targeted motives of both groups coincide. The same principle is believed to be sound for “pushed” and “pulled” employers. Regulations introduced to diminish negative factors driving the number of NME up have a chance to shift the ratio between OME and NME to “pulled” entrepreneurs' dominance.

The objectives of the following research are to: initially classify possible motivators of female entrepreneurship, select drivers that may influence women's desire to be involved in early-stage entrepreneurship, opportunity early-stage entrepreneurship, necessity early-stage entrepreneurship relying on previous research papers; develop several binary choice models that embody opportunity and necessity entrepreneurship drivers; estimate statistical significance of each factor and identify those that influence the probability of female entrepreneurship to be opportunity driven or necessity driven. The same procedure is to be carried out for the male part of the sample in order to compare motivators classification and the marginal effect of tangible and intangible characteristics included in probit regressions.

Finally, probit models are to be compared using several tools (i.e. ROC curve). Then, the results will be interpreted and presented in the form understandable for the general public. Last but not least, directions for further research and limitations will be highlighted.

As mentioned above, probit regression is the method chosen to answer the research question. It is one of the most frequently used tool in management research papers. Almost every statistical package can estimate a probit model. Moreover, probit model fits the context of the thesis and data sourced from GEM APS.

Speaking about the hypotheses, theoretical background and directions for researches given in the articles scrutinized, two of them will be tested in the paper:

Hypothesis 1: The knowledge and supporting of other entrepreneurs make women choose the entrepreneurship as a carrier option;

Hypothesis 2: The number of household members pushes women in a greater degree towards starting their own business then men.

Hence, the model should include factors characterizing such constructs as knowledge of other entrepreneurs, number of household members, gender and type of entrepreneurial activities. The hypotheses proposed to provide a two-sided view at the research topic and make the results more universal.

3. Methodology

3.1 Early-stage entrepreneurship

The main purpose of the research is to investigate the reasons behind women's decision to choose an entrepreneurial career. As mentioned in the theoretical foundation section, entrepreneurial motivation is a complicated concept. The one-sided approach may be harmful and hamper the rise of the self-employed population all around the world.

Due to increased complexity entrepreneurship may not be treated without breaking up the concept into several stages or areas. Global Entrepreneurship Monitor is a world-leading source of entrepreneurial data trusted by several large international organizations such as The World Bank and The United Nations. The GEM posts several reports relying on the wide interest targeted at the unlikeness of business and its growth and development.

Entrepreneurs as a central focus of the research are categorized by GEM as potential entrepreneurs, nascent entrepreneurs, owner-managers of their firms, owner-managers of an established business, and those who are no longer part of business society. Meanwhile, nascent entrepreneurs are businessmen that are involved in setting up a business.

Female entrepreneurs are the fastest growing group in terms of the number of nascent employers. They considerably influence the economic growth of countries they work in and facilitate innovative technologies development. Their input into the creation of working places may barely be overestimated. Nascent entrepreneurs are people joining the establishment of a new enterprise. A new venture has to be independent and stay aside from existing firms. Wagner (2004) presented a combined definition for nascent entrepreneurs characterizing them as individuals currently trying to create a business, expecting to own its part or the entire enterprise in the future. If a person has been a part of a start-up for longer than one year, he or she is no longer a member of nascent entrepreneurs society. What makes them special is their aspiration for searching the best option and maximizing the utility of their career life. They are free to choose between working as an employee for an enterprise and becoming an independent business person.

The second group is the owner-managers of a new business. According to GEM, owner-managers are members of the population aged 18-64 that own or manage a running business that has paid salaries or wages for more than three months.

Thus, two groups of entrepreneurs: nascent and owner-managers of a new business are acknowledged as parts of early-stage business person society.

Moving forward beyond early-stage entrepreneurship, the next stage is presented by owner-managers of established firms. Established business undermines a mature firm capable of going through a startup stage. Generally, the rate of owner-managers entrepreneurs is lower because the percent of companies that grow from a startup into something bigger is quite moderate.

The GEM presents the other side of that coin: business discontinuance being an early-stage entrepreneurship outcome scenario. Occasionally company's termination is caused by a lack of profitability (noted in 45% of cases worldwide).

To sum up, three groups of running business owners are classified: nascent entrepreneurs, owner-managers of a new business, and those of a business older than 3.5 years. Like their owners, companies ruled by representatives of groups listed above have their own specific features, strengths, and weaknesses. Speaking about firms from a position of the business life cycle, nascent entrepreneurs are working at the concept stage. Feasibility analysis plays a key role in the beginning: nascent businessmen measure how likable the success of their business is. They make fundamental decisions associated with entering the market. Simultaneously, owners are to be aware of potential customers' needs and pains.

If the idea works out, the firm is born. Past nascent entrepreneurs become owner-managers of a recently developed enterprise. The stage is an actual driving process both for a company and for a ruling individual. Individuals shall keep their eyes open and stay sober in order to avoid the fact of being separated from the real world by the company's initial success. Frequently, people leave their job at this stage due to a vague idea of what they genuinely work for.

From that stage employer, who went through intoxicating prosperity of the previous stage, has a transition to the persistence phase. The persistence phase is less rosy and accepts ones calm and wise ones.

That is to say, the scope of this study is limited to studying those who are new to business society - nascent entrepreneurs. With this in mind, the group of early-stage entrepreneurs includes those who are pulled or pushed and men and women who chose an entrepreneurial career for mixed motives.

3.2 Data collection

To trace the reasons behind career choices several modifications of probit regression were built. First of all, it worth mentioning that the source of data was chosen due to its credibility and extensive use in similar studies. The data were sourced from the Global Entrepreneurship Monitor website. The Global Entrepreneurship Monitor (GEM) is accepted globally as the most genuine data source containing information on entrepreneurship activities across the countries. The GEM annually publishes approximately twenty Adult Population Surveys. Three years after data were collected GEM opens them to the public. Data are available for download. GEM is a trusted source of information for such global organizations as the World Economic Forum and the Organization for Economic Co-operation and Development. They do not provide only reports but give an expert opinion on collected data. GEM was established two decades ago with an aim to find out the reason why some countries Aare more `entrepreneurial' than others. GEM publishes data, reports, research papers and characteristics of countries on its website (Kelley, et al., 2017).

The GEM National Teams conduct a population survey every year. More than 2,000 adults, drawn randomly from the adult population in the country, are interviewed with an equal probability of inclusion. The APS survey consists of randomly chosen target's answers in new firms, and baby-business owner-managers, and mature firms. Respondents can be asked whether they tried to start their business and if they had been supported. The random sampling gives an opportunity to perform confident generalization to the entire population of entrepreneurs in the country. The latest data available is ones received in 2015 with selected answers from representatives of 45 countries including the United States, Russia, Egypt, South Africa, Greece, the Netherlands, Belgium, France, Spain, Hungary, Romania, Italy, Norway, Poland, etc. All of these countries are highly diverse. It comes out in every separate factor and in their combinations. As a result, each country gets its portray depicted with the help of factors measured. For instance, GEM pays closer attention to gender differences, innovative technologies, total entrepreneurship activities, industry diversification, family business, and business discontinuance. For instance, Angola is a leading country in terms of early-stage family businesses (14.2%) accompanied by such countries as Guatemala (12.2%), Chile (10.9%), and Thailand (10.7%). Furthermore, the Global Entrepreneurship Monitor suggests that industry profile across the countries is one of the most intriguing things especially in the context of a country's national income level. One of such notable moment is the change over the percentage of wholesale business in the country's entrepreneurial profile. Moving from low-income counties to ones with higher salaries and wages share, previously occupied by workers of retail business, is then replaced by services and technologies. Again, the industry profile provides an overall image of entrepreneurial activities within a particular government. Bearing that in mind, manufacturing makes up one-fourth of total early-stage entrepreneurship projects in the Republic of Korea. At the same time, the same share is occupied by agriculture and construction in Madagascar. In Angola, a leading country regarding family business penetration, 74% of entrepreneurs are engaged in wholesale and retail movement. On contrary, Switzerland, and Austria, acting as European countries with high income, denote dissimilar results: the majority of entrepreneurs are occupied in health and education sectors in Austria and in finance and real estate in Switzerland. Ireland is a leader in ICT entrepreneurial activities (13%) (Bosma & Kelley, 2019).

The data included both men and female, those who have already established a business, as well as people interested in becoming employers, those who worked for an enterprise and did not have particular desire to set up a company in the nearest future. The central indicator presented in GEM reports is TEA index. Total-early Stage Activity is defined as a total percentage of the population (aged 18-64) involved in either nascent entrepreneurship or is owner-managers of their businesses. Besides that, GEM serves figures on humans' intention to establish an entrepreneurial career. Data also publish TEA index for the female and male population.

Historically the Global Entrepreneurship Monitor was built on methodology introduced by the Panel Study of Entrepreneurial Dynamics. PSED was one of the databases provided generalized information on the topic of entrepreneurship since the early 1990s. At the same time, GEM is a follower of the PSED database. Initiated by such famous scholars, working in the sphere of business and entrepreneurship, as Paul Reynolds, Bill Bygrave, Michael Hay, it increases public interest to business activities in countries around the world choosing a quantitative method to measure the difference.

3.3 Current trends in entrepreneurship studies

Prior to the description of the research process, extensive current trends in the field of entrepreneurship and its study are to be portrayed. The reasons for doing so are the need to regulate the direction of the study in accordance with policy concerns or complement present discussions at board meetings. What is more, the understanding of current trends in the field underpin university programs and guide younger scholars in their academic works.

An important point is that scholars can have unrelated approaches with the rest or they can also utilize a unique combination of those at once.

According to Watkins and Reader (2004), the first separate way to start study entrepreneurship is scrutinizing and citing the works of other scholars. The notion which represents the idea of such studying is citation analysis. The authors also mention the substantial drawbacks of this method, the first one is it usually has a time lag. The delay is usually caused by the procedure itself: every scientific paper goes through a long way from writing the first lines of text through its publication in a scientific journal. Thus, it loses the part of its relevance and the work may not be of cutting-edge anymore. Such a process may take up to several years. Citation method is separated into several ones different in their mechanisms. For instance, it could be designed as Document Co-citation Analysis or Author Co-citation Analysis. These methods are trendy in the area of international business today. Bibliometric techniques are used to draw the `intellectual structure' of the branch of knowledge (Garcнa-Lillo, Claver-Cortйs, Marco-Lajara & Ъbeda-Garcнa, 2019). Co-citation measures how frequently two separate scientific works in the field are cited in other researches. Co-citation may also refer to two separate authors working in the same domain of science. Another relatable drawback is high dependence upon building a citation index. Just a few recent studies were, in fact, based on the original citation index in the field of entrepreneurship. Furthermore, the citation index generally prescribes higher weight to journals than to any other sources of scientific works. Thus, new ways to analyze the accumulated volume of academic input were created and used (i.e. cluster analyses). Above all, the latest technologies are genuinely game-changing in the way of extra abilities they serve to social sciences (Watkins, Reader, 2004).

Profound research of the current situation in entrepreneurship research was made in 2012 by Luor, et. al. The work reviews 5,476 articles in 522 Journals listed on Citation Index. As a result, a detailed outlook was published with reference to articles' authors, work topics and journals they are published by. The authors notice a growing trend in the number of studies dedicated to entrepreneurship and its variety.

It was revealed that such countries as the US, Canada, England, Germany, and the Netherlands are the most giving countries measured in a total number of researches striving to enlarge the scope of current research progress in the field.

The new start for entrepreneurship research sprang in the late 1990s when the Internet became accessible for the public. Luor, et. al. (2013) executing content analysis revealed that in 2012 522 scientific journals had articles that included `entrepreneurship' as a keyword. Some of the journals published a few articles but brought noticeable contribution measured by the citation value (e.g. Academy of Management Review). The same may be addressed to scholars contributing to the field. Several of them (e.g. Shane, Baron, Zahra) accumulated a compelling citation frequency (Luor, Lu, Yu & Chang, 2013).

3.4 Methods to measure motivation

Moving forward to the research question put in this paper, the motivation construct is to be discussed. Motivation is a psychological element unable to be seen. Tourй-Tillery and Fishbach, (2014) distinguish four ways motivation may be treated and assessed: cognitive (perception), affective (subjective experience), behavioral (e.g. performance), and psychological responses (based on brain activity) and with the help of reports filled in by individuals. In some cases, it makes sense to measure motivation difference in time. The motivation of one person may significantly change under the influence of inner or outer factors. Such observation leads to the improvement of the research conducted in this paper and described further down below.

The same research paper pays particular attention to distinguishing two types of motivation. They should not be mixed up because the motivation type is a determinant of instruments necessary for measuring an individual's' aspire. In order to minimize the probability of failure, an analyst should be aware of what type of motivation he or she measures: outcome-focused motivation or process-focused one.

Obviously, in the current study, aiming to find the reasons for women being involved in early-stage opportunity entrepreneurship, outcome-based motivation is the basis for the research procedure. In this place, the outcome might be either positive (a woman is involved in opportunity entrepreneurship at an early stage) or negative (a woman is not involved in opportunity entrepreneurship at an early stage). In exploratory purposes, female motivation is compared with the male one. At the same time, results obtained by building a probit regression for total early-stage entrepreneurship are compared with those received after probit regression creation for measuring male motivation for early-stage entrepreneurship (both types: necessity and opportunity), early-stage entrepreneurship by opportunity, and early-stage entrepreneurship by necessity. Similarly, the main probit model is contrasted to secondary regressions measuring the changing in the probability that a woman is involved in early-stage entrepreneurship in general and that a woman is involved in early-stage entrepreneurship by necessity.

Taking the classification, presented in the study by Tourй-Tillery and Fishbach, the action of selecting between two options (being involved in early-stage opportunity entrepreneurship or not being involved in early-stage opportunity entrepreneurship) is of binary nature. Two possible outcomes of people's actions make scholars use a term choice in their study of individuals' behavior. A major weakness of a choice is that it barely identifies how strong individual motivation is. Such a drawback is noticeable whenever two options are equal. However, the strength of a persons' motivation could be determined whenever outcomes are the opposite. In these circumstances, motivation strength in the first case could be compared with motivation in the second one. Obviously, the motivation for one outcome is then stronger than his or her desire to achieve a contrary goal. Researches frequently measure choice motivation by calculating a number of times each outcome was favored.

Studies that focus on the final choice/goal (i.e. choice of binary nature) might use a wide range of instruments, especially within the context of experimental study. An increased number of options is caused by the persistent alignment of actions and an ultimate goal.

Nevertheless, the outcome may be clear, and a person does his best approaching it, there is a place of motivation to the task in the best possible way. Scholars compare the speed and accuracy as constructs defining motivation type. As seeking the result fosters a higher speed of performing smaller tasks, it ordinarily causes a negative effect on the quality of the final product. Vice versa, deliberate labor throughout the way elevate the grade.

Above all, the data received from the GEM website, miss any details on business performance. Hence, it is impossible to assess if a person was in a hurry establishing his or her business or followed a gradual detailed plan. As a result, the research designed could be further extended by adding information about mature firms' performance and company result in every part of its lifecycle.

3.5 Method and model

The research question of this research paper aims to measure how the probability of women choosing entrepreneurship by opportunity depends on external factors and individual characteristics of entrepreneurs. The study at the same time address the difference between each factor's influence significance in two cases: if a person taken into account is male or female. For such purposed GEM data and probit model are the most suitable instruments.

Logit and probit models are equally appreciated by the academic community. Almost 15% of articles placed in the Strategic Management Journal included one of the binary choice models mentioned (Hoetker, 2007). Probit and logit mode keep the choice probability in the interval [0,1] unlike linear regression, which is also frequently used in managerial studies (Hill, Griffiths & Lim, 2011). OLS (Ordinary Least Squares) regression frequently return negative outcomes paired with outcomes that exceed one (Baum, 2006). In general, logit and probit models are equally preferable by econometrists. Two models are, in fact, mostly similar. However, in the logit model conditional probability approaches the values of "0" and "1" at a slower rate compared to the probit model. What is more, there are approximate values of multipliers that let researchers get approximate values of coefficients of the logit model having probit model ones and vice versa. Moreover, the difference between these two methods is that a sample can contain a great number of observations with extreme values (Gujarati & Porter, 2004).

Although the probit model lacks the usual drawbacks of the linear model, Hoetker (2007) claims that one-third of research papers provide an incorrect interpretation of regression coefficients. What is more, a comparison of two or more binary models may end up with meaningless or false results.

Probit model, as the primary way to establish a relationship between dependent and independent variables, is used. The choice of non-linear regression is justified by the binary nature of the dependent variable. The aim of this paper is to provide drivers for women being involved in entrepreneurship of two opposite types. Each variable characterizing the outcome of female behavior takes on only two values: 0 and 1. Dependent variables were derived from GEM Annual Population Survey; each of them says if a person is involved in Total Early-stage Entrepreneurial Activity (TEA), opportunity TEA or necessity TEA. The answers were coded following the condition below, in which y is one of the predicted variables (TEA, opportunity TEA, and necessity TEA):

.

The model used has several modifications obtained to follow three major purposes: compare motivating factors for both genders; identify driving factors for early-stage entrepreneurship, opportunity early-stage entrepreneurship, and necessity early-stage entrepreneurship; compare the fit of various probit models.

Probit regression difference from the logit model, which is also a tool for dichotomous variables estimation, is that probit regression CDF is normally distributed.

Both probit and logit models predict the probability of the dependent valuable to express a positive outcome, rather than to predict its actual value. Predicted values say how likeable individual's decision to choose being involved in TEA (and its different types) rather than not being involved in TEA is.

It must be said, OLS has an additional drawback when a dichotomous variable is predicted: random error terms are not homoscedastic.

Probit regression has an S-shape relationship between x and p. S-curve slope expresses how a unit change in x influences the change of probability p. S-curve distribution illustrates probability retention within the interval [0,1]. The probability density function of a probit model is similar to a density function of a random variable Z with Gaussian (normal) distribution. S-curve is depicted as