The moderating effects of paid family-related
leave on the relationships between national
well-being and women’s entrepreneurial motivations
Student: Yuanming Cao
Student number: S2942712
Supervisor: Dr. Samuele Murtinu
Co-assessor: Dr. Maryse Brand
Study program: MSc Business Administration -
Small Business & Entrepreneurship
Word count*: 12647
Table of Contents
Abstract ... 3
Acknowledgement ... 4
1. Introduction ... 5
2. Theoretical background and hypotheses ... 7
2.1 Women entrepreneurship ... 7
2.1.1 Women’s opportunity entrepreneurship (WOE) ... 8
2.1.2 Women’s necessity entrepreneurship (WNE) ... 8
2.2 National well-being ... 9
2.2.1 Work-life balance ... 9
2.2.2 Educational attainment ... 11
2.2.3 Household income ... 12
2.2.4 Job insecurity ... 14
2.3 Paid family-related leave ... 14
3. Methods ... 17
3.1 Data collection ... 17
3.2 Variables and measurements ... 18
3.2.1 Independent variables ... 18 3.2.2 Dependent variables ... 19 3.2.3 Control variables ... 20 3.2.4 Moderator ... 22 3.3 Data analyses ... 22 4 Results ... 23 4.1 Descriptive statistics ... 23 4.2 Correlation analyses ... 24 4.3 Regression analyses ... 26 4.1 Moderation analyses ... 29
5. Conclusion & discussion ... 33
6. Implications and limitations ... 35
Abstract
Women entrepreneurs make substantial contributions to national entrepreneurship development and socio-economical development, such as job creation, poverty reduction, providing basic necessities for local consumptions. Nevertheless, women are still under-presented in entrepreneurial engagements compared to their men counterparts. Nowadays, many policymakers pay more attention to stimulating women’s entrepreneurial participation in order to bridge this gender gap. In recent years, the number of women entrepreneurs accounts for only half of the men entrepreneurs, which means that more efforts are still required to encourage women entrepreneurship. According to the heterogeneous motivations among women entrepreneurs, it is important to conduct more women-specific research for policymakers to initiate more tailored policies for women entrepreneurs. Additionally, EU policymakers also consider family-related leave as a stimulating factor for women entrepreneurs, so this study further examined how this factor can impact the women entrepreneurs under certain national well-being conditions.
This study was motivated by a research gap in understanding the impacts of family-related leave on women’s heterogeneous entrepreneurial motivations. In order to obtain some generalizable outcomes, this study integrated macro-level data from three OECD databases and the GEM database that involved 34 OECD countries from 2013 to 2016. Four national well-being indicators were selected to represent a part of the national well-being. Quantitative research was performed to examine the potential moderating effects of paid maternity and parental leave on the relationships between four national well-being indicators (i.e. work-life balance, educational attainment, household income, and job insecurity) and two types of women entrepreneurship (i.e. women’s opportunity entrepreneurship and necessity entrepreneurship). Results indicated that work-life balance and job insecurity were significantly correlated with both women’s opportunity entrepreneurship and women’s necessity entrepreneurship. Additionally, a strong association between household income and women’s necessity entrepreneurship was also found. A moderating effect of paid maternity and parental leave has been proven between women’s educational attainment and necessity entrepreneurship.
This study suggests policymakers be more aware of women’s different entrepreneurial motivations so that they can further pave the way for women to undertake more entrepreneurial activities. In particular, policymakers should provide more targeted policies for women’s opportunity entrepreneurship and necessity entrepreneurship respectively. The study also calls for more attention from scholars to those women entrepreneurs who involve in entrepreneurial activities during their motherhood.
Acknowledgement
1. Introduction
Women entrepreneurs have substantial contributions to entrepreneurship development (Ayogu & Agu, 2015) and socio-economic development of a country (Allen et al., 2007; Mayadri, 2014; Fazalbhoy, 2014) from their participation in start-ups and small and medium-sized enterprises in a country (Ayogu & Agu, 2015). Due to the burden of family responsibilities, women entrepreneurs often involve in part-time entrepreneurial activities in lower earning domains, such as wholesale and retail trade, education, and health services (Gurley-Calvez et al., 2009; Ahl & Nelson, 2015). Although these business domains may have lower earning potential than their men counterparts, which do not affect women entrepreneurs’ contributions to the socio-economic development of the country (Allen et al., 2007; Mayadri, 2014; Fazalbhoy, 2014). During their entrepreneurial engagement, they are still able to increase household income for their own family, creating job opportunities to pull other families out of poverty and supply basic goods and services for local consumptions (Malyadri, 2014). Another researcher Quiñones (2016) also drew a similar conclusion to address the role of women entrepreneurs, “women entrepreneurs are key suppliers to economic growth and poverty reduction globally, contributing to household income and growth of their national economics, as producers and consumers” (p. 37). Although women entrepreneurs have significant contributions to global economies, this population is still understudied in entrepreneurship study domain (Brush & Cooper, 2012). Therefore, this study provided more insights on how national well-being correlated with different types of women entrepreneurship.
From the policy perspective, policymakers have been committing to facilitating gender equality in entrepreneurship. Some policies have been launched in recent years to promote gender quality in entrepreneurship. The Organizational Economics and Co-operation and Development (“OECD”) launched Gender Initiative to investigate the existing barriers to gender equality in Education, Employment and Entrepreneurship (the “three E’s) (OECD, 2012a). Similarly, one of the important deliverables of the European Pillar of Social Rights is to strengthen EU policy and legislation regarding women’s work-life balance, family-related leave, labor market participation and self-employment (OECD/EU, 2017). Policymakers have realized a crucial issue that many current policies often neglect the maternity leave coverage for women entrepreneurs (OECD/EU, 2017). They referenced one of the rare policies in Austria where introduced Business Continuation Aides (“Betriebshilfe”) as a means to offer entrepreneurs a qualified replacement in case of their temporary leave, including women’s maternity leave (OECD/EU, 2017). Hence, family-related leave (i.e. paid maternity and parental leave to mothers) can be an important consideration to stimulate women entrepreneurship.
constitute 29% of entrepreneurs (equivalent to 11,6 million population) in Europe, who remarkably represent a majority of one-person enterprises (78%) in health, social-work activities, services, and education domains. Nevertheless, women are still under-represented in entrepreneurial engagements (OECD, 2012a; Moulton & Scott, 2016). Therefore, it is important for policymakers to understand what factors can particularly facilitate or inhibit women entrepreneurship, so they can launch more efficient women-specific policies to further bridge the gender gap. Moreover, previous research rarely discussed the gender gap in the cross-national study. Hence, this study integrated national-level data from different countries to yield some generalizable conclusions.
In addition to policies, the entrepreneurial motivations are important consideration for women entrepreneurs. Regarding the prior research, many scholars did not distinguish the gender differences in entrepreneurial studies. From the scarce women-specific studies, scholars have a stronger preference in comparative study between two distinctive family welfare states (e.g. Schindehutte et al., 2003; Ahl & Nelson, 2015), or individual-level study within a country (e.g. Rønsen, 2014; Kimosop et al. 2016). Few studies focused on women entrepreneurs exclusively (e.g. Brush & Cooper, 2012). Cross-national studies in the area of women entrepreneurship were rarely to be found (e.g. Ribes-Giner et al., 2018). Poggesi et al. (2015) also stressed this issue from 248 published papers, fewer papers have discussed the differences by considering macroeconomic characteristics and institutional environment, for example, regulations, maternity leave coverage. Since women entrepreneurs are a heterogeneous group (Stevenson, 1990; Carter & Allen, 1997), it is important for scholars to analyze their diverse characteristics of women entrepreneurship from different levels, such as macro-, micro- and organizational levels (Picazo, 2012). Therefore, this study was conducted at the macro level because micro-level studies were relatively well studied regarding gender differences in entrepreneurial motivations.
To conclude the contribution of this study, it enriched the understanding of how different well-being conditions interact with women’s entrepreneurial motivations from a national point of view. It also provided a better understanding of the impacts of family-related leave on women entrepreneurial motivations. To achieve that, 34 OECD countries were involved and the data were integrated from the OECD databases and the Global Entrepreneurship Monitor (“GEM”) database in the period of 2013 to 2016. Quantitative research was performed and the results were obtained from descriptive analysis, multiple regression analyses and moderation analyses.
2. Theoretical background and hypotheses
2.1 Women entrepreneurshipWomen entrepreneurship refers to women who are capable of using their knowledge and resources to recognize and develop a business opportunity, actively involves in business management with at least 50 percent ownership for longer than a year (Moore & Buttner, 1997).
From the motivation’s perspective, scholars often distinguish entrepreneurship as opportunity entrepreneurship and necessity entrepreneurship by “pull” and “push” motivations (Turner, 1993; Moore & Buttner, 1997; Maltlay & Storey, 2003; OECD, 2012a; van der Zwan et al., 2016). “Pull” factors are often linked to positive internal motivators for entrepreneurial entry (Walker & Brown, 2014). “Pull" factors motivate people to enter self-employment for better opportunities (Moulton & Scott, 2016). “Push” factors are often linked to negative external motivators for entrepreneurial entry (Walker & Brown, 2014; van der Zwan et al. 2016). Those negative factors push people away from wage employment or retirement into self-employment (Moulton & Scott, 2016). Necessity entrepreneurs are the individuals who lack better alternatives in their career options (Fossen & Buttner, 2013). To summarize the motivation characteristics, women entrepreneurship can also be categorized as women’s opportunity entrepreneurship and women’s necessity entrepreneurship according to their “pull” and “push” motivators, more detailed definitions can be found in Section 2.1.1 and Section 2.1.2.
In terms of women’s entrepreneurial motivations, there are different arguments about categorizing the motivations.
Some scholars have categorized women entrepreneur’s motivations as financial and financial. Women entrepreneurs have a stronger preference on non-financial satisfactions than men entrepreneurs; those non-non-financial satisfactions can come from personal satisfaction, social contributions, goal achievement, and flexibility in balancing their work and family responsibilities (e.g. Brush, 1992; Parasuraman & Simmers, 2001). Women executives were motivated to be entrepreneurs by the desire of career challenge and self-determination (Buttner & Moore, 1997).
Other scholars also categorized the motivations from personal perspectives, organizational perspectives, and family perspectives. Women are motivated to experience an occupational transition from wage employment to entrepreneurship by personal aspirations (e.g. Stroh et al., 1996; Buttner & Moore, 1997), organizational factors (e.g. Moore & Buttner, 1997), and family-relate factors (e.g. Cromie, 1987; Brush, 1992; Marlow, 2002; Maritz & Thongpravati, 2010).
2.1.1 Women’s opportunity entrepreneurship (WOE)
Opportunity entrepreneurs can be defined as individuals who pursue the business opportunity for personal interest (Reynolds et al., 2001). Opportunity entrepreneurs are also defined as individual who pursue business opportunities voluntarily (Fossen & Buttner, 2013). In gender-neutral studies, opportunity entrepreneurship often links to “pull” factors encourage individual’s voluntary desire to seek out greater independence and opportunity (Hughes, 2003). Opportunity entrepreneurship is motivated by “pull” factors (Sriram & Mersha, 2006). Many scholars found some internal factors that “pull” women towards entrepreneurship, including desires of financial success and benefits, independency, self-achievement, desire to achieve a balance between work and family responsibilities (e.g. Turner, 1993; Buttner and Moore, 1997; Walker & Brown, 2004; Sriram & Mersha, 2006; van der Zwan et al., 2016). This study proposed a women-specific definition: women’s opportunity entrepreneurship is described by those women entrepreneurs who voluntarily started their business ventures with positive internal motivators that pull themselves to recognize and develop their business opportunities outside of wage employment.
2.1.2 Women’s necessity entrepreneurship (WNE)
As Sarasvathy (2004) defined, necessity entrepreneurs are individuals who experience job loss, individuals who voluntarily leave their job position because their ideas or inventions have rejected by their boss and individuals who are in weak employment conditions due to their educational or language skills or criminal backgrounds. Moore and Buttner (1997) described women’s necessity entrepreneurship as women entrepreneurs who were more motivated by “push” factors. The push factors can be survival pressures, discouragement of prior job conditions, economic downturn, and unemployment (Moore & Buttner, 1997).
This type of women entrepreneurs is often pushed to enter entrepreneurship because of gender discriminations in corporate organizations (Ozdemir, 2010; Poggesi et al. 2015). Women’s occupational transition can be explained by the pervasive “glass ceiling” phenomenon in wage employment (Moore & Buttner, 1997). That is related to gender discrimination at the workplace. “Glass ceiling” is defined as an occupational barrier, which prevents women from reaching the top ranks of management (Daily et al., 1999; OECD, 2012b). Particularly, “women are disadvantaged when it comes to decision-making responsibilities and senior management positions; by the time they get to the boardroom, there is only one of them for every 10 men” (OECD, 2012b, p. 15). Therefore, women are more likely to quit their jobs in corporate organizations and join entrepreneurial organizations as owners or as employees (U.S. Glass Ceiling Commission, 1995). Those barriers are expected to increase women’s dissatisfactions in wage employment that demotivate them to retain their jobs in corporate organizations. As a result, this disadvantaged work condition is expected to push women to enter entrepreneurship.
be a factor to push women into entrepreneurship (Ozdemir, 2010). Becoming an entrepreneur is considered by women as a means to release some of the stress they experience in wage employment (Buttner & Moore, 1997; Walker, 2000). The overall dissatisfactions in wage employment push women to engage in necessity entrepreneurship.
Lacking income source (Jennings & Brush, 2013) is also a determination for women to become entrepreneurs because of the threats from unemployment and underemployment, economic meltdown (Granger et al. 1995; Moore & Buttner, 1997). When women face pressures from financial shortage and limited career options, they would force themselves to start their own business ventures (Buttner & Moore, 1997).
To conclude the definitions and the corresponding motivations, this study drew a conclusive definition: women’s necessity entrepreneurship refers to those women entrepreneurs who involuntarily started their business ventures with negative external motivators that push them to experience a career transition from wage employment to entrepreneurship.
2.2 National well-being
‘National well-being’ is a broad concept that may vary from country to country. Within the existing literature, the definition of national well-being is not clearly defined. Instead, it is often mentioned by different well-being measures in a country. For example, the OECD researchers use Better Life Index to reflect national well-being which is identified into two essential categories: material living conditions (housing, income, job) and quality of life (community, education, environment, governance, health, life satisfaction, safety, and work-life balance), and each dimension consists of one to four specific indicators (OECD, ND. a). With the absence of a definition, this study proposed a definition: national well-being is the level of satisfaction that citizens are able to have a decent life from desirable material living conditions and quality of life in a country.
Four national well-being indicators were selected from those two categories as a part of the phenomenon for this research topic. Precisely, work-life balance and educational attainment were chosen from the ‘quality of life’ category. Household income and job insecurity were chosen from the ‘material living conditions’ category.
2.2.1 Work-life balance
a high degree of work and nonwork in role engagements with minimal conflicts among their roles. As above, this study defined work-life balance as a status that people manage to allocate their time between work and family with minimal conflicts in their work and family roles.
Work-life balance is considered as a better alternative to reconcile the work and family care among women entrepreneurs (Joona, 2017). Women often play a dominant role in family care (Walker & Brown, 2004). Instead of choosing formal childcare, mothers value their own time with children so that entrepreneurship becomes an attractive career choice to balance their work and family commitments (Rønsen, 2014; Thébaud, 2015a; Thébaud, 2015b; Joona, 2017). Moreover, flextime and flexible work place allow parents to stagger their work hours and get some or all job done at home so that they have more time available for their children (Connelly, 1992a).
Although entrepreneurship is often perceived to be a career option to better balance work and family responsibilities, there are some trade-offs in this career as well (Parasuranman & Simmers, 2001). It is admitted that self-employed women are able to commit more time on family and children than wage-employed women, but self-employed women spend considerably more time on childcare activities rather than their work-related activities (Gurley-Calvez et al., 2009; Gimenez-Nadal et al., 2012). That is why women entrepreneurs are more likely to work part-time in order to set aside more time for family and childcare (Gurley-Calvez et al., 2009). Furthermore, domestic responsibilities have been found a significantly negative impact on women entrepreneurs (Hundley, 2001). Women entrepreneurs often suffer from higher life stress because of less time availability to develop their business (Parasuraman & Simmers, 2001). In short, work-life balance is not always a reasonable motive for women entrepreneurs.
With respect to the decreased time commitment in business, women entrepreneurs have lower income and lower earning potential (Ahl & Nelson, 2015). The gender’s earning gap in entrepreneurship becomes larger than the earning gap in wage employment (Moore, 1983; Lechmann & Schnabel, 2012; Konietzko, 2015).
Given that time constraint in work commitment and earning gap in women entrepreneurship, women may hesitate to follow this career path and they are expected to remain their jobs in wage employment. In this case, this study argued that work-life balance is not a solid motive for neither women’s opportunity entrepreneurs nor necessity entrepreneurs, especially early-stage entrepreneurial activities require even more time commitment regardless of their motivations. So the hypotheses are the following:
H1a: work-life balance is negatively associated with women’s opportunity entrepreneurship.
2.2.2 Educational attainment
Educational attainment is commonly defined as the highest level of education a person has successfully completed (OECD, 2001; U.S. Census Bureau, ND; Statistic Canada, 2016). This study redefined educational attainment as the highest level of education a person completed in a formal educational institution.
In gender-neutral studies, a positive relationship between educational attainment and opportunity entrepreneurship was found, while a negative relationship the relationship between educational attainment and necessity entrepreneurship was found (Xavier-Oliveira et al., 2015). By comparing the educational attainment between these two types of entrepreneurship, educational attainment among opportunity entrepreneurs is higher than necessity entrepreneurs (Mersha et al., 2010; Fossen & Buttner, 2013; Stephan et al., 2015). Generally speaking, necessity entrepreneurs have a relatively low educational background, while opportunity entrepreneurs have at least secondary school education degree (Mersha et al., 2010). Overall, many prior scholars found a significant impact of educational attainment on firm performance (e.g. Brush & Hisrich, 1991; Saffu et al., 2008). Most of the prior researches were rather gender-neutral, so this study included educational attainment to distinguish the interactions with different women’s entrepreneurial motivations.
There are some pros from having higher educational attainment among women entrepreneurs. Women with higher education background have a better capability of recognizing unexploited business opportunities (Langowitz & Minniti, 2007). High-educated women are more likely to achieve a higher quality of entrepreneurial performance, such as business survival, firm growth, and return on investment (van der Sluis et al., 2008; Kimosop et al., 2016). As a result, women with higher educational attainment are more motivated to enter opportunity entrepreneurship by “pull” factors (van der Zwan et al., 2016). Second, education helps to prepare people’s mindset in self-confidence and self-efficacy as well as skills, which can be applied to entrepreneurial activities (Daneels, 2008; European Union, 2012). This positive internal “pull” factor is expected for high-educated women to gain more confidence to enter opportunity entrepreneurship, especially in their expertise area (Daneels, 2008).
As above, higher educated women have more capability to recognize and develop a business opportunity, and they have more self-confidence to get involved in entrepreneurial activities in their expertise areas. Hence, this study assumed a positive interaction between educational attainment and women’s opportunity entrepreneurship.
H2a: educational attainment is positively associated with women’s opportunity entrepreneurship.
On the contrary, educational attainment does not always play a crucial role among women entrepreneurs who started their business out of necessity motives.
(Middleton et al., 2014). Some scholars highlighted the importance of labor market experience and knowledge rather than academic-oriented knowledge in formal education (e.g. Lucas, 1978; Calvo & Wellisz, 1980). Informal learning/education is related to informal activities outside educational institutions (Merriam & Caffarella, 1999), such as social interactions, experiential learning, action learning, observation, job or life experience, self-guided learning (Merriam & Caffarella, 1999; Scott et al., 2001; Sharafizad, 2018). In terms of experiential learning, informal learning contributes to the accumulated absorptive capabilities that grow people’s confidence, knowledge and skills at work (Daneels, 2008). It is also proven that women entrepreneurs have a stronger preference for informal training and personal networks to acquire knowledge and skills for their business venture (Sharafizad, 2018). Considering the greater times constraint and financial constraint among women’ necessity entrepreneurs, Sharafizad (2018) explained that these constraints reduce women’s participation in formal education or training.
Second, the opportunity cost1 to alternative employment is lower among low educated women so they are less risk-averse to occupational transition. The economics term describes opportunity cost as the value of the next-best alternative (Miller, 2011; Colander, 2014). From a gender-neutral study, Keh et al. (2002, p. 139) argued, “entrepreneurs with less education may perceive less risk because the opportunity costs of alternative employment are lower”. Interestingly, the least educated individuals have the highest probability of being self-employed in many European countries (Blanchflower, 2004). Compared to high education individuals, entrepreneurship is less attractive to highly educated people due to lower income level, less secure stream of earnings and the different cultural tradition of working compared to large corporations (Kangasharju & Pekkala, 2002).
Given that different learning process and opportunity cost, the study hypothesized that low educated women have higher tendency to participate in necessity entrepreneurship.
H2b: educational attainment is negatively associated with women’s necessity entrepreneurship.
2.2.3 Household income
Household income is an abbreviation for household net adjusted disposable income. Household income is defined as the money available to a household for spending on goods or services (OECD, ND. c). Household income also refers to the maximum amount that a household can afford to consume without having to reduce its assets or to increase its liabilities; it is measured in US dollars at current PPPs per capita (OECD, 2017). This study chose the definition from OECD (2017) because the measuring unit - Purchasing Power Parities can better reflect in the household consumption across countries because this measure can eliminate the differences in price levels between countries.
Women nowadays are still facing higher hurdles than men in start-up financing (Brush et al., 2014). The possible reasons are lack of entrepreneurial
experience, gender-biased credit scoring, and gender stereotyping in the lending process among women (Alesina et al., 2013; Saparito et al., 2013). Therefore, women reply heavily on personal savings from a household to finance their business venture (OECD, 2016, Singh & Gachui, 2017).
Household income can be seen as a crucial consideration for women to participate in entrepreneurial activities (Stephan et al., 2015). Bruce (1999) has proven a positive correlation between household income and women entrepreneurship. As household income increases, the likelihood of women entrepreneurship increases, especially in low/middle- income countries (Allen et al., 2006; Rodriguez et al., 2009). However, the prior research did not distinguish the differences for women’s entrepreneurial motivations with regards to their household income. In some gender-neutral studies, scholars found a positive interaction between household income and opportunity entrepreneurship (e.g. Xavier-Oliverira et al. 2015; van der Zwan et al., 2016) and a negative interaction between household income and necessity entrepreneurship (Xavier-Oliverira et al. 2015).
Opportunity entrepreneurs have more capital and managerial resources to grow their business ventures from solo or microenterprise to SME (Mersha et al. 2010). Higher household income can better overcome financial constraints when performing entrepreneurial activities (Aidis & Estrin, 2006). Furthermore, household income can be accumulated from the earnings of the spouse. A research found that husband’s earning significantly contributed to a higher household income so that it further facilitated higher probability of women’s entrepreneurship (Bruce, 1999). But the marital status will not be discussed in details. Regarding the abundant household income, women entrepreneurs can benefit from the financial “safety cushion”, such as the contribution of husband’s earning (Carr, 1996; Bugdig, 2006). Thus, this study made a hypothesis as below.
H3a: household income is positively associated with women’s opportunity entrepreneurship.
On the other hand, without sufficient household income to support the business venture, entrepreneurs face more financial consequences that may hinder women’s participation in necessity entrepreneurship. Many scholars stressed that entrepreneurship is a career involving many financial risks, risk of failure and uncertainties (e.g. Richomme-Huet & Vial, 2014), entrepreneurs with lower household income have more severe financial consequences in case of business failure, such as falling into poverty (OECD, 2016). Failure is not only related to the financial failure, but it also can damage personal reputation, credit, and friendships (Shepherd et al., 2009). As mentioned, necessity entrepreneurs often started their business venture out of desired resources (e.g. Moore & Buttner, 1997; Hughes, 2003). Hence, this study assumed that women with lower household income would encounter more financial risks that prevent women from participating in necessity entrepreneurship.
2.2.4 Job insecurity
Job insecurity is described as “the perceived powerlessness to maintain desired continuity in a threatened job situation” (Greenhalgh & Rosenblatt, 1984, p. 438). Job insecurity is an individual’s overall concern of a threat to his or her continued employment and the anticipation of job loss (Sverke & Hellgren, 2002). Job insecurity refers to the overall concern about the continued existence of the job in the future (van der Elst et al., 2010). Job insecurity is the subjectively perceived probability of losing the present job in the future and the fear or worries about job loss (Witte, 2005; van der Elst et al., 2014). It is an important consideration for one’s psychological and physical health (Nella, 2015). This study made a conclusive definition from previous definitions: job insecurity is a perceived concern about the probability of job loss in the future.
First, entrepreneurship can be an outcome of insecure employment from downsizing, restricting, and temporary employment practices that push people away from wage employment (Hughes, 2003). Gender barriers often exist in wage employment, such as “glass ceiling” among women (U.S. Glass Ceiling Commission, 1995; Buttner & Moore, 1997).
Second, many health-related consequences are associated with a high level of job insecurity from the unpredictable and uncontrollable employment (Warr, 1987; Nella, 2015). High level of job insecurity is often linked to a high level of perceived stress (Bardoel et al. 2000). Job insecurity can trigger more detrimental effects on employee’s physical, psychological and social functioning, such as anxiety, depression, marital discord, and musculoskeletal plain (Nella, 2015). Compared to employees, entrepreneurs perceive less job insecurity (Manski & Straub, 1999) and the difference of perception of job insecurity varies little between men and women (Manski & Staub, 1999; Kelan, 2008). To conclude, job insecurity has more detrimental complaints on employees than entrepreneurs. When losing the protection (“safety net”) in continued employment, people would rather pursue an entrepreneurial career as an option to avoid those negative impacts from job insecurity.
However, the mentioned researches were again gender-neutral and the cross-national study about the relationships between job insecurity and women’s entrepreneurial motivations is still quite scarce. Therefore, this women-specific study contributed to investigating the correlations between job insecurity and women entrepreneurs. This study hypothesized that job insecurity can stimulate women to enter entrepreneurship regardless of their motivations.
H4a: job insecurity is positively associated with women’s opportunity entrepreneurship.
H4b: job insecurity is positively associated with women’s necessity entrepreneurship.
2.3 Paid family-related leave
leave, parental leave, leave to care for older children, and leave to take care for sick children (Eurofound, 2004; OECD, 2014). To measure women’s family-related leave, OECD Family database provides data with a combined length of paid maternity and parental leave to mothers (in weeks). Since this study is women-specific, this study therefore described paid family-related leave as the combination of women’s maternity leave and parental leave with eligible financial compensation.
There are several definitions of maternity leave and parental leave. Considering the definitions can vary from country to country, this study mainly extracted the definitions from European countries and the definitions are presented as follows.
Maternity leave is “ leave from work for mothers in the period immediately preceding and following birth” (European Parliament, 2016, p.1). Maternity leave is the prenatal and postnatal break from work taken by mothers of newly born children (Eurofound, 2015). Maternity leave is also defined as pregnancy leave, which is “employment-protected leave of absence for employed women at around the time of childbirth, or adoption in some countries” (OECD, 2017, p. 1). Parental leave refers to employment-protected leave of absence for working parents after the period of women’s maternity leave, which is often supplementary to specific maternity and paternity leave periods (OECD, 2014). Parental leave is a leave scheme after maternity or paternity leave that can be taken by either parent (European Parliament, 2016). European Union (2018) defined parental leave as the period of leave to care for children in their first years of life (p. 1).
Family-related leave2 was a central issue across Europe to improve work-life balance and gender equality (Eurofound, 2004). Although self-employed women in many OECD countries become eligible for such paid leave (OECD, 2017), there are pros and cons for women entrepreneurs. Therefore, it was worthwhile for this study to examine the impacts of family-related leave on women entrepreneurial motivations.
On one hand, family-related leave schemes can be a way for parents to better balance their time spent on work and family care, especially for working mothers. European Commission (2015) proposed broader initiatives to further push current EU policies to improve the protection of mothers in order to better reconciling women’s professional and family life, including maternity and parental leave, work-life balance and the role engagements in family cares.
On the other hand, the “career discontinuity” during the family-related leave leads to greater parental duties for women (Eurofound, 2004), so women entrepreneurs have less time available to business-related activities (Gurley-Calvez et al., 2009; Gimenez-Nadal et al., 2012). Furthermore, a long period of family-related leave can also lead to entrepreneurial inertia that preventing women from carrying out their business ideas. Pinchot (1985) emphasized the importance of putting entrepreneurial ideas into action because ideas often die without action. Although the European Union intended to protect the self-employed women and their spouses by introducing maternity leave, this may be counterproductive since it is very difficult to
combine maternity leave with running a business (Neergaard & Thrane, 2011). Denmark, as a country of heavily favoring employment over entrepreneurship, does not allow one-person proprietor is not allowed to work during the maternity leave, otherwise, the mother will face maternity allowance penalty (Neergaard & Thane, 2011). Women entrepreneurs in Nordic countries often complained about the negative impacts of the long maternity leave because their business development was stagnated during the long maternity leave (Neergaard & Thane, 2011).
According to the prior researches, this study assumed that a longer paid family-related leave leads to more knowledge and network detachments for women entrepreneurs to grow their business. Also, their entrepreneurial motivations can be distracted from the overloaded domestic responsibilities during the long leave. Therefore, this study expected more inhibiting impacts of family-related leave on women’s opportunity entrepreneurship (H5a-H5d).
H5a: Family-related leave can strengthen the negative correlation between work-life balance and women’s opportunity entrepreneurship.
H5b: Family-related leave can weaken the positive correlation between educational attainment and women’s opportunity entrepreneurship.
H5c: Family-related leave can weaken the positive correlation between household income and women’s opportunity entrepreneurship.
H5d: Family-related leave can weaken the positive correlation between job insecurity and women’s opportunity entrepreneurship.
On the contrary, this study assumed that family-related leave might offset the negative correlations and reinforce the positive correlations among women entrepreneurs with necessity motives (H6a-H6d). As discussed, necessity entrepreneurs have less financial resources to support their business ventures. Paid maternity and parental leave can provide them financial compensation to release their financial constraints. For this reason, this study made four hypotheses as below.
H6a: Family-related leave can weaken the negative correlation between work-life balance and women’s necessity entrepreneurship.
H6b: Family-related leave can weaken the negative correlation between educational attainment and women’s necessity entrepreneurship.
H6c: Family-related leave can weaken the negative correlation between household income and women’s necessity entrepreneurship.
Figure 1 conceptual model
3. Methods
3.1 Data collectionIn this study, an empirical research was conducted to observe the moderating effects of family-related leave on the relationships between national well-being and women’s entrepreneurial motivations.
The OECD-Eurostat Entrepreneurship Indicators Programme (EIP) has started to collect internationally comparable data on female entrepreneurship, which facilitates comparisons of the number, characteristics, and performance of women and men enterprises across countries (OECD, 2012a, p. 133). GEM conducted Adult Population Survey (ASP) and they have been collecting national-level data about entrepreneurial behavior and attitude with gender specification. The GEM database offered gender-specific data regarding their characteristics, entrepreneurial motivations and ambitions and attitudes towards entrepreneurship. OECD and GEM provided suitable secondary data for this cross-national study.
Eventually, this study integrated 136 national-level observations3 from OECD Better Life Index, OECD Entrepreneurship, OECD Family Databases from 2013 to 2016, and the GEM Entrepreneurial Behavior and Attitudes database from 2013 to 2015. Geographically, the data involved 34 OECD countries in Europe, North Asia, North America, and South America. However, as of February 2019, GEM’s full datasets were only available until 2015 because GEM explains that the full national-level datasets will be only available to the public three years after data collection. Considering the global policy and economics in 2015 and 2016 were relatively stable, this study assumed negligible changes in women’s TEA ratios in 2016, so this data was duplicated from 2015 to 2016.
3.2 Variables and measurements
3.2.1 Independent variables
Work-life balance. Work-life balance has a considerable amount of definitions in the existing literature. Work-life balance can be described as the ability to balance work and family responsibilities (e.g. Tausig & Fenwick, 2001, Redmond et al., 2006), the success in balancing work and family in terms of role engagement and resource allocation (e.g. Moen et al, 2003), and also a situation of balanced work and nonwork role engagements with minimal conflict among different roles (Sirg & Lee, 2018). GEM (ND.) defined work-life balance as satisfaction with a balance between personal and professional life. Work-life balance does not necessarily refer to 50-50 time allocations for work and life, it is considered as status when people feel fulfillment at work and in their lifestyle (Whitener, 2017).
Work-life balance is measured by the number of hours devoted to leisure and personal care among full-time employed people, including a wide range of indoor and outdoor activities, socializing time with friends and family etc. (OECD, 2017b). When measuring time spent on work and nonwork activities, it is important to have a comparable population. Thus, full-time employed people are a reasonable population for this measure because full-time people apparently devote more time to work than part-time people or unemployed people. OECD (ND. b) also provided another measure for work-life balance, which is the amount of time a person spends at work. OECD’s second work-life balance measure is the opposite of time devoted to leisure and personal activities. It is admitted that none of the two measures takes both work and nonwork activities into consideration. On the other hand, the GEM database does have women-specific data for work-life balance, but unfortunately, the detailed measure cannot be found and the data are only available in 2013. Considering the limited options from the existing databases, the amount of time devoted to leisure and personal activities among full-time employed people is relatively acceptable for this study. As above, this study eventually picked the first measure from the OECD database.
Educational attainment. Educational attainment is one of the education indicators; it is defined as “the highest grade completed within the most advanced level attended in the educational system of the country where the education was
received” (OECD, 2001). It is a glossary that commonly describes educational attainment as the highest education an individual has completed (U.S. Census Bureau, ND.). To include more women-specific data, the study chose the women-specific educational attainment data from the GEM database. Educational attainment is measured by the percentage of women aged 25 to 64 holding at least an upper secondary degree over the population of the same age group (GEM, ND.). The reason is that it is a logical measure to compare the differences within the same age and gender group.
Household income. Household income is an abbreviation for a household net adjusted disposable income (OECD, ND. c). Household income is measured by the amount of money that a household earns every year after taxes and transfers (OECD, ND. c). Household income is measured by the maximum amount that a household can afford to consume without having to reduce its assets or to increase its liabilities; it is measured in US dollars at current PPPs per capita (OECD, 2017). This study chose the latter one to measure household income since purchasing power parities (PPPs) is a proper unit when comparing household income across countries.
Job insecurity4. Job insecurity can be measured by the extent that a person’s perception of job loss (Bustillo & Pedraza, 2010). In 2013, OECD measured job insecurity by the percentage of individuals with a job tenure less than 6 months over the total employment (OECD, 2013). In 2014 and 2015, OECD measured it by the probability of becoming unemployed. For example, OECD researchers calculated job insecurity by the number of people who were unemployed in the current year but were employed in the previous year over the total number of employed in the previous year. The two measures were more related to the tendency of employees having a temporary contract in a country. Since 2016, OECD’s job insecurity measure was related to the expected earning loss from unemployment. It was measured by the percentage of the previous income that was associated with unemployment status (OECD, 2019). This measure was more related to the probability of income loss due to unemployment. These measures have their own limitations from different analytical aspects. Although the measures have been changed without an official explanation from OECD, the percentage of the job insecurity in each country did not have suspicious fluctuations from 2013 to 2016. Thus, this study assumed that the data still could roughly represent the job insecurity in those OECD countries.
3.2.2 Dependent variables
Women entrepreneurship. GEM provided Total Early-stage entrepreneurship Activities (TEA) ratios by gender. TEA represents the percentage of the adult working population (18-64 years old) who are either nascent or new entrepreneurs (Kelly et al., 2017, p. 16). GEM further specified the TEA ratios with response to women’s entrepreneurial motivations. Thus, there are independent datasets to describe two groups of women entrepreneurs, namely women’s TEA with opportunity motives and women’s TEA with necessity motives. This study distinguished these two groups as WOE and WNE. In the OECD database,
entrepreneurship is also categorized by gender. Women entrepreneurship is measured by the percentage of employed women who are own-account workers between 15 to 64 years old (OECD Gender Data Portal, ND.). However, OECD did not specify the heterogeneous motivations for the entrepreneurs. Therefore, this study selected TEA ratios for measuring women’s opportunity and necessity entrepreneurship from the GEM database. The detailed measurements present as below:
Women’s opportunity entrepreneurship. Women’s TEA with opportunity motives were measured by the percentage of women between 18 and 64 years old that participate in entrepreneurship with opportunity motives (GEM database, ND.).
Women’s necessity entrepreneurship. Women’s TEA with necessity motives are measured by the percentage of those involved in early-stage entrepreneurship because they had no better option for work (Bosma & Keylly, 2019, p. 138). The GEM database specified the measures as the percentage women between 18 and 64 years old who engaged in early-stage entrepreneurial activities with necessity motives (GEM database, ND.)
3.2.3 Control variables
European Commission (2018) concluded that female entrepreneurs mostly have challenges about accessing to finance, information, training, network, and reconciling their business and family responsibilities. For controlling the influences of these aspects, access to finance, access to entrepreneurial training, attitude towards entrepreneurial risk and support network. The data of control variables were held consistent as in 2013.
Access to finance. Access to finance is also called startup capital, which refers to the necessary money for starting up a new business for office space, permits, licenses, inventory or any other expense; the access sources can come from bank loan, investors or venture capitalists (EquityNet, ND.). Access to finance is also defined as the incumbent applicant who is exposed to available financial accesses to credit, saving and insurance services (Khaleque, 2018). This study chose the measure of access to finance from the OECD database, indicating by the percentage of women (aged 15-64) who have a positive answer when asking whether they had access to money to start or grow a business, such as personal savings, loans and other sources (OECD, 2016).
mainly rely on personal savings and borrowing money from friends and family (OECD, 2016; Singh & Gachui, 2017).
Without specifying gender differences, an Ethiopian study found that most of the necessity entrepreneurs have very limited financial resource to back up their business, opportunity entrepreneurs often have relatively more financial resources for their business (Mersha et al., 2010). In general, necessity entrepreneurs have considerable negative experience about the availability of financial support and start-up information than opportunity entrepreneurs (van der Zwan et al., 2016). Nevertheless, it is not still clear whether access to finance has a more profound impact on women’s entrepreneurial motivations.
Access to entrepreneurial training. Entrepreneurial training is defined as a distinct training in specific trade skills to help individuals start their own business rather than seeking paid employment (Ismail, 2018). Entrepreneurial training also refers to training that provides skills in starting and operating small businesses (Cornell Law School, ND.). This term is quite self-explanatory. To combine the definitions of entrepreneurial training, access to entrepreneurial training is the availability for individuals to require entrepreneurship-related skills for starting and operating their businesses. In this case, OECD (2016) has a suitable measure for this variable by the percentage of men or women (aged 15-64) declaring that they have access to training on how to start or grow a business, including any formal or informal channels to learn about starting up or growing a business venture.
Compared to men, more women declared having less access to training or funding to create or develop their business venture (OECD, 2015c). Due to different labor market experiences, women can overcome knowledge gaps through training participation (OECD/EU, 2017). Nowadays, more policies encourage women to participate in formal and informal training by providing diverse training courses to overcome the entrepreneurial skill gaps among women entrepreneurs due to different labor market experiences (OECD, 2015c; OECD/EU, 2017). Entrepreneurial training not only provides women required skills and knowledge to create and develop their businesses, but also enables them to access to suitable funding (OECD, 2015c) and support networks in peer communities (Bekh, 2014). Furthermore, attending entrepreneurship training can increase women’s self-confidence and their entrepreneurial awareness (Maden, 2015). The benefits of access to training, finance and support are often interrelated, so it is also important to include them as control variables in this study.
Attitude towards entrepreneurial risk. It is defined as the risk perception when women weigh risks in entrepreneurial activities (OECD, 2016). It is measured by the percentage of women who rather take risk in starting a new business than seeking for paid employment (OECD Gender Data Portal, ND.).
stereotypes also accelerate the risk-averse tendency of women’s self-employment preference in the labor market (Arenius, 2006). Therefore, this variable was also included in this study.
Support network. Women entrepreneur’s support network includes the availability of mentoring, coaching, counseling, peer learning, and information (Bekh, 2014). It is an indicator of perceived social network support in a community (OECD, 2017b). It is measured by “the percentage of people (aged 15 and over) who respond positively based on a question: ‘if you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?’ ”(OECD, 2017b, p. 6).
As mentioned, the support network can also bring some financial access to women entrepreneurs (Bekh, 2014). When women experience lack of support from social networks, they are more likely to be discouraged from entrepreneurial activities (Minniti & Naudé, 2010) and may push women entering low-growth domains (Estrin & Mickiewicz, 2011). Moreover, entrepreneurs in general can leverage the relationship in their support network to form alliances to secure resources and seize more business opportunities (Kor et al., 2007). Therefore, the support network is an important control variable for this study as well.
3.2.4 Moderator
The moderator in this study is paid maternity and parental leave in a country. As discussed, receiving paid compensation during the family-related leave is expected to motivate women entrepreneurship, thus this study chose the measure from the OECD family database (ND.). Women’s family-related leave is measured by the length of the total paid maternity and parental leave available to mothers in weeks (OECD Family Database, ND.).
3.3 Data analyses
Descriptive statistics. By using the SPSS program, the descriptive statistics were summarized to describe the information about all relevant variables. The information provided a generalized estimation for the research population, which involved in women entrepreneurs in OECD countries.
Correlation analyses. Bivariate correlations were computed to test whether the hypotheses were on the expected track, so Pearson correlations provided the existence of the correlations between the independent variables and the dependent variables.
Multiple regression analyses. To further examine the hypotheses, multiple regression analyses were performed separately for women’s opportunity entrepreneurship and women’s necessity entrepreneurship. Precisely, this study examined how work-life balance, educational attainment, household income, and job security were correlated with the two types of women entrepreneurship at which significant level.
and their z-scores were presented. After that, interaction terms were computed by multiplying the centered independent variables and the centered moderator together respectively. The dependent variables of this regression model were the dependent variables (i.e. women’s opportunity entrepreneurship and necessity entrepreneurship). If an interaction term is statistically significant, then corresponding moderation is supported, vice versa.
4 Results
Table 1 illustrates the descriptive statistics of the dependent variable, independent variables, and the control variables. Table 2 shows the correlations between each predicted variables and the outcome variable (i.e. women’s opportunity entrepreneurship, “WOE”). Table 3 shows the correlations between each predictor variables and the outcome variable (i.e. women’s necessity entrepreneurship, “WNE”). Table 4 and Table 5 present the multiple regression results for the two outcome variables respectively. Table 6 and Table 7 present the moderation results for the two outcome variables respectively.
4.1 Descriptive statistics
In 34 OECD countries, the average proportion of the women who participated in opportunity entrepreneurial activities was approximately 5 times more than the women who participated in necessity entrepreneurial activities from 2013 to 2016.
Due to the data availability, only educational attainment was women-specific, work-life balance, household income, and job security were not gender-specific. From the descriptive statistics (Table 1), 52% of women entrepreneurs in OECD countries held at least one upper secondary education degree. In terms of work-life balance, people were able to spend average 14,8 hours per week for leisure and personal care and the differences among the selected countries are relatively small. On average, the (annual) household income was 23872 US dollar per capita. 7,14 % of people perceived risks of job loss in the future.
Looking at the family-related leave, women in OECD countries have an average 55 weeks for paid maternity and parental leave. The longest paid leave was up to 166 weeks and the shortest paid leave was 0 week.
Table 1 Descriptive statistics
Variables N Min. Max. Mean Std.
deviation
Dependent variables
Women's opportunity entrepreneurship a 108 1,13 16,96 5,10 3,17 Women's necessity entrepreneurship a 106 0,00 7,53 1,45 1,37
Independent variables
Work-life balance b 136 11,73 16,36 14,81 0,71
Educational attainment (women) a 105 0,10 0,80 0,52 0,17
Household income b 136 11039 41355 23872,39 6865,02
Job insecurity b 136 0,7 32,0 7,14 4,90
Moderator
Paid maternity and parental leave (women)c
134 0 166 55,22 45,49
Control variables
Access to finance d 128 4,48 49,54 26,77 11,80
Access to entrepreneurial training d 128 12,26 85,11 44,57 18,89 Attitude towards entrepreneurial risk d 128 16,91 70,78 35,97 13,38
Support network d 136 73 98 89,71 5,80
Data sources:
a. GEM Entrepreneurial Behavior and Attitudes database (2013, 2014, 2015) b. OECD Better Life Index database (2013, 2014, 2015, 2016)
c. OECD Family database (2013, 2014, 2015, 2016) d. OECD Entrepreneurship database (2013)
4.2 Correlation analyses
The correlated results guided the study to determine if they were significant predictors to the outcome variables (women’s opportunity entrepreneurship “WOE” and women’s necessity entrepreneurship “WNE”). Although not every independent variable was significantly correlated with the outcome variables, all selected control variables have important contributions for this study.
For women’s opportunity entrepreneurship, several correlations have been found (Table 2), which are presented in the following:
1) Work-life balance was negatively correlated with WOE (p< 0,01);
2) Paid maternity and parental leave was negatively correlated with WOE (p< 0,05);
3) Access to finance was positively correlated with WOE (p< 0,05);
4) Attitude towards entrepreneurial risk was positively correlated with WOE (p< 0,01);
5) Support network was negatively correlated with WOE (p< 0,01).
Regarding women’s necessity entrepreneurship, several correlations also have been found (Table 3):
1) Work-life balance was negatively correlated with WNE (p< 0,05); 2) Household income was negatively correlated with WNE (p< 0,01);
4) Attitude towards entrepreneurial risk was negatively correlated with WNE (p< 0,01);
5) Support network was negatively correlated with WNE (p< 0,01).
Both WOE and WNE have negative correlations with work-life balance, the correlation between WOE and work-life balance was even stronger at the 0,01 significant level. Both types of women entrepreneurs also have negative correlations with their support network. WOE was positively associated with their entrepreneurial risk attitudes when they started their business venture, while WNE has the opposite negative association with risk attitudes. Regarding the unique correlations, WOE was motivated by shorter mother paid leave but abundant finance access and WNE was motivated by less household income and less access to entrepreneurial training.
Table 2 Correlations (WOE)
1 2 3 4 5 6 7 8 9 10 Women’s opportunity entrepreneurship 1 Work-life balance -0,334** 1 Educational attainment (women) -0,003 0,077 1 Household income -0,067 -0,407** 0,240* 1 Job insecurity -0,121 -0,185* 0,001 -0,311** 1
Paid maternity and
parental leave -0,204* 0,124 -0,154 -0,262** -0,040 1 Access to finance 0,202* 0,052 0,418** 0,448** -0,142 -0,153 1 Access to entrepreneurial training -0,004 0,131 0,298** 0,290** -0,047 0,081 0,617** 1 Attitude towards entrepreneurial risk 0,640** -0,298** 0,094 -0,118 0,066 -0,431** -0,079 -0,074 1 Support network -0,353** 0,365** 0,320** 0,418** -0,230** 0,005 0,386** 0,610** -0,285** 1
Table 3 Correlations (WNE) 1 2 3 4 5 6 7 8 9 10 Women's necessity entrepreneurship 1 Work-life balance -0,222* 1 Educational attainment (women) -0,073 0,077 1 Household income -0,339** 0,407** 0,240* 1 Job insecurity -0,056 -0,185* 0,001 -0,311** 1
Paid maternity and
parental leave -0,131 0,124 -0,154 -0,262** -0,040 1 Access to finance -0,182 0,052 0,418** 0,448** -0,142 -0,153 1 Access to entrepreneurial training -0,291 ** 0,131 0,298** 0,290** -0,047 0,081 0,617** 1 Attitude towards entrepreneurial risk 0,666** -0,298** 0,094 -0,118 0,066 -0,431** -0,079 -0,074 1 Support network -0,495** 0,365** 0,320** 0,418** - 0,230** 0,005 0,386** 0,610** -0,285** 1
*. Correlation is significant at the 0,05 level (2-tailed) **. Correlation is significant at the 0,01 level (2-tailed)
4.3 Regression analyses
To identify whether the four well-being indicators have statistically significant effects on women’s entrepreneurial motivations, this study conducted two multiple regression analyses for the two types of women entrepreneurship. The results are presented in Table 4 and Table 5.
Women’s opportunity entrepreneurship (“WOE”). In Model 1 (Table 4), four independent variables and WOE have an adjusted R square of 0,104, this figure implies that the independent variables explained 10,4% of the variances of women’s opportunity entrepreneurship. Significant F changes show that the added control variables significantly improved the prediction. Overall, the model was statistically significant to further examine the interactions between the independent variables and dependent variables.
There were several variables that contributed to the significance of the model. Both work-life balance (b= -1,594, p<0,01) and job insecurity5 (b= -0,126, p< 0,05) have significant negative correlations with women entrepreneur’s opportunity motives. The results imply that an extra hour in women’s time spent on leisure and family care can lead to a decrease by 1,6 times in their opportunity-motivated entrepreneurship; one percent increase in the possibility of job loss can lead to a decrease by 12,6% of women’s opportunity-motivated entrepreneurship. In other words, women were more
motivated to pursue opportunity entrepreneurship when they have a low level of work-life balance and a low level of job insecurity.
The adjusted R square sharply increased in Model 2 (Table 4) from 0,104 to 0,608. That result implies that the independent variables and the control variables explained 60,8% of the variances of women’s opportunity entrepreneurship by considering the control variables. The significant F-change shows that the added control variables significantly improved the prediction for this study. Contrary to the correlation results (Table 3 and Table 4), work-life balance became uncorrelated in the regression analysis after adding the control variables. The negative correlation between job insecurity (b= -0,137, p<0,01) and WOE became stronger after adding control variables. Positive correlations have been found in access to finance (b= 0,105, p>0,01) and attitude towards entrepreneurial risk (b= 0,136, p>0,01). Interestingly, support network (b= -0,252, p>0,01) was negatively correlated with WOE. The results can be interpreted as follows:
1) With one more hour time devoted to leisure and personal care, the possibility of women participated in opportunity entrepreneurship
decreases by 1,6 times;
2) With an increased percentage of job insecurity, women were 13,7% less likely to participate in opportunity entrepreneurship, they would even have a stronger willingness to stay employed in a corporate company; 3) With an increased percentage of access to finance, women were 10,5%
more likely to become opportunity entrepreneurs;
4) With an increased percentage of positive risk-taking attitude towards entrepreneurship, women were 13,6% more likely to become opportunity entrepreneurs as well;
Table 4: Regression models (WOE)
Variables Model 1 Model 2
beta std. error beta std. error
Independent variables
Work-life balance -1,594 (0,469)** -0,033 (0,346)
Educational attainment (women) 0,183 (1,846) -1,875 (1,375)
Household income 0,019 (0,000) -0,045 (0,000)
Job insecurity -0,126 (0,063)* -0,137 (0,043)**
Control variables
Access to finance 0,105 (0,027)**
Access to entrepreneurial training 0,020 (0,016)
Attitude towards entrepreneurial risk 0,136 (0,016)**
Support network -0,252 (0,055)** R square Adjusted R square 0,142 0,104 0,640 0,608 F Change 3,792 30,507 Sig. F Change 0,007** 0,000**
Dependent variable: women's opportunity entrepreneurship *. Significant at the 0,05 level (2-tailed)
**. Significant at the 0,01 level (2-tailed)
Women’s necessity entrepreneurship (“WNE”). Model 1 of Table 5 demonstrates four independent variables and WNE have an adjusted R square of 0,091, this result means that the dependent variable was explained 9,1% by the predictor variables. Overall, the model was statistically significant and the F change’s significance also proved the contributions of the added control variables.
Looking at the variables that contribute to the model. Household income (b= -0,327, p<0,01) has a significant negative correlation with women’s necessity entrepreneurs, while no correlations have been found between the other three independent variables and WNE. After adding the four control variables, Model 2 reveals more existence of correlations. In terms of independent variables, the results show a positive correlation between work-life balance (b= 0,424, p<0,01) and WNE, and two negative correlations between household income (b= -0,267, p<0,01) and WNE, and between job insecurity (b= - 0,056, p<0,01) and WNE.
