The informal economy forms a key component of strategies to alleviate poverty and to support the creation of sustainable livelihoods. By its very nature, informal economic activity goes unrecorded and is therefore difficult to measure, but some estimates value the informal economy at around 28% of South Africa’s GDP. The majority of municipalities fail in providing local economic development (LED)–friendly and inclusive informal economy policies and by-laws (Siqwana-Ndulo 2013b) to stimulate the development of informal entrepreneurship.

Entrepreneurship is the pursuit of an opportunity irrespective of the resources (Amine & Staub 2009:183). An entrepreneur is defined as somebody who is actively involved in starting a business or is the owner of a business (Harding et al. 2005). The informal sector was historically defined as being in opposition to the formal sector, and Chen (2012) stated that it would disappear once developing countries achieved sufficient economic growth. There is a wide range of entrepreneurial activities included in the informal economy, such as street vendors, taxi drivers, rubbish collectors, food markets and home-based care workers, making it almost as diverse as the formal economy. Informal businesses are unregistered, unregulated and untaxed businesses that are often run from street pavements or homes or according to other informal arrangements (Horn 2011). Wilkinson (2013) notes that:

The relative share of the informal economy by province indicates a correlation with the overall unemployment rate, supporting the idea that for many people the informal economy is the alternative to unemployment. It is argued that without the informal economy, the unemployment rate would rise from the current 25% to 47.5% (Stats SA 2014).

The emergence of a growing community of women entrepreneurs has been described as the most significant economic and social development in the world. A key phenomenon in most rural villages in developing economies is the emergence of female heads of households. The economic landscape of South African rural villages displays similarities such as that the men migrate to urban areas to earn a living while the women stay behind to raise their children. In parts of Kenya and Tanzania, over 50% of households are headed by women because of the migration of men to urban industrial sectors. In most parts of the world, including developing nations, women are a powerful force in their communities. When women earn an income and control what they do with the money, their children are more likely to finish school, their families eat better and stay healthy, their communities thrive and entire nations are in a better economic position.

Literature from India and Sri Lanka (De Mel, McKenzie & Woodruff 2013), Somalia (Ali & Ali 2013), Ghana (Akpalu, Alnaa & Aglobitse 2012), Nigeria (Otoo et al. 2012) and South Africa (Siqwana-Ndulo 2013) point to the fact that during the last two decades women are increasingly becoming the primary economic providers of their families going out to markets to do business.

There is a significant body of literature in social work on the importance of helping women to increase their influence over their lives and their environments. However, central to poor women exercising control over their lives should be the improvement of their economic condition (Raheim & Bolden 1995). Women business owners cite a number of reasons for becoming entrepreneurs. An analysis of the main reasons includes an increase in income, attractions of entrepreneurship, self-determination and autonomy, family concerns such as balancing career and family and the lack of career advancement. It was found that semi-literate women in developing countries start more businesses than men because of social inequalities they face and their access to other job opportunities (Williams & Gurtoo 2012).

The desire to make a social contribution seems to be a strong motivating factor for women entrepreneurs. Research suggests that this caring attitude manifests in women’s leadership style and that goals other than economic growth guide women’s businesses. There is a strong belief amongst women that a successful business should give back to the community, thus enforcing the social contribution factor (Williams & Gurtoo 2012).

Starting a business is difficult under the best circumstances and is made even more difficult in Sub-Saharan Africa by complex environmental factors (Amine & Staub 2009). Factors that have been identified by various researchers as barriers to women-owned informal enterprises are discussed below.

Inadequate access to credit by the poor has been identified as one of the factors that contribute to poverty (Hobson 2011). Women informal entrepreneurs find it extremely difficult and often impossible to access credit through the formal credit system (Otoo et al. 2012). Formal lending institutions are averse to financing smaller loans because of the high administration and information costs and high risk with little collateral (Otoo et al. 2012). Women in rural communities are especially vulnerable to the regulatory discrimination of financial institutions (Amine & Staub 2009).

O’Neill and Viljoen (2001) described the following specific problems faced by women entrepreneurs who tried to obtain finance: no credit record, most assets registered in husband’s name, inability to qualify for loans because of stringent criteria applied by banks and a lack of business and management skills.

An increase in women’s access to microfinance could potentially contribute to increasing efficiency in output and consequently reduce poverty and empower women (Akpalu et al. 2012). Informal women entrepreneurs do not need large amounts of money to get started. Amine and Staub (2009) found that microloans to women of less than $100 have brought about profound growth in their enterprises.

One of the crucial barriers to women succeeding in business is the lack of support systems around them. Women are socially defined as the caretakers of the family: they look after their children, care for their husbands and family units and are more likely to be socially punished for not fulfilling this role (DTI 2005). Women suffer from a lack of support from families. The community finds it difficult to accept the female entrepreneur and men are prejudiced towards the woman entrepreneur (O’Neill & Viljoen 2001). Social support from family and friends who provide positive role models as well as from parents who promote entrepreneurial aspirations during childhood contributes to create positive environmental conditions favouring women’s entrepreneurship (Amine & Staub 2009).

Chiloane and Mayhew (2010) and Stander (2011) found that in South Africa there was a need to restructure local business service centres in the local communities in such a way that information could be more widely disseminated and accessed. Information on business networks should have been distributed in the same way as information about social services.

Hobson (2011) highlighted a lack of entrepreneurial spirit and knowledge in the field of business, a lack of acceptance in the community and male prejudices as well as a lack of management skills as barriers to entering informal business enterprises.

Women’s roles and responsibilities in Sub-Saharan Africa are made more difficult and time-consuming because of problems caused by a lack of basic infrastructure and services at the local level, such as clean water, electricity, reliable transportation and modern communication (Amine & Staub 2009). A lack of transport, inadequate access to market stalls and toilet facilities and the high incidence of HIV- and/or AIDS-related illnesses further complicate their situation (Hobson 2011).

Women’s entrepreneurial activities are increasingly promoted as a means of creating growth and development. They are not a homogenous group, and their motivations for entry as well as performance of their business vary significantly (Franck 2012).

Most rural women in the informal sector in South Africa had been raised in strict traditional homes, had little education, married relatively young and had little or no urban experience (Mitchell 2004). While generation of income, whether for profit or sustaining family livelihood, is a driving force behind women’s informal entrepreneurship, there is also growing recognition that it is not only motivated by poverty. Informal enterprises may be motivated by a variety of ‘voluntary’ and ‘opportunity’-driven factors (Franck 2012). The factors that motivate women to become entrepreneurs can also be described as ‘necessity’ or push factors or ‘opportunity’ or pull factors, or a combination of both (Ali & Ali 2013).

Push factors are factors that encourage women to start business enterprises driven by financial need because of the family situation (Ali & Ali 2013). Push factors are a product of desperate conditions; entrepreneurship is the only option available to earning a living, rather than a positive choice. According to Ali and Ali (2013) push factors are factors such as insufficient family income, dissatisfaction with a salaried job, difficulty in finding work and a need for flexible work schedules because of family conditions. Opportunity or pull factors are the factors associated with perceptions of entrepreneurship as an enticing avenue for a better, more fulfilling life and are associated with independence, fulfilment, entrepreneurial drive and desire for wealth, social status and power (Ali & Ali 2013).

Women entrepreneurs tend to be motivated by the need to provide security for their families and their family circumstances and not profit maximisation (Mitchell 2004). Women are motivated by their need for independence, economically and otherwise. Furthermore, entrepreneurship allows women to combine caring for their family with bringing in the money needed for day-to-day survival.

Constraints common to all informal enterprises tend to be particularly severe for female informal enterprises. There are many factors that affect the development of women entrepreneurship in Sub-Saharan Africa (De Vita, Mari & Poggesi 2014). The first problem identified by De Vita et al. (2014) is closely related to family responsibilities: high fertility rates of five to six births per woman. This has a considerable effect on personal consequences for women who try to start their own enterprises (Amine & Staub 2009; De Vita et al. 2014).

The second problem, which is related to the first, is the low social recognition accorded to women entrepreneurs. Sub-Saharan Africa men believe that independent and successful women do not deserve to be considered chaste and worthy of respect (De Vita et al. 2014). Customary patriarchal norms and rules are still in place. Sub-Saharan women seem to be struggling to find acceptance in the market and respect from their customers (De Vita et al. 2014). This finding contradicts the results of O’Neill and Viljoen (2001) that South African women are now more accepted as equals, that government policies in South Africa favour women and that women are entering the market at a faster rate than before. Mazonde and Carmichael (2016) interviewed 43 female entrepreneurs in Zimbabwe and the majority of the participants reported that their husbands looked down on their entrepreneurial activities. In an effort to keep their family intact, these women were willing to bow to patriarchy.

The third problem identified by De Vita et al. (2014) is related to inadequate education and training of business women. The impact of education on performance is more pronounced in women-owned businesses. A lack of market information, managerial skills and technical competence limits the participation of women in value-adding business and the size of the enterprises that are often small and where most employees are unskilled (De Vita et al. 2014).

Education and training are key elements in successful enterprise creation (Omwenga, Mukulu & Kanali 2013). The authors, in their study of women entrepreneurs in Nairobi, Kenya, found that many more women than men lack the requisite level of education and training, including business and technical skills and entrepreneurship training. They also found that women were often disadvantaged in terms of access to training. Women were often unaware of training opportunities and less likely to be able to afford the cost of training (Omwenga et al. 2013).

Many of the opportunity or pull factors resonate with concepts from the field of positive psychology (Wissing et al. 2015). A focus on ‘what went right’ as opposed to ‘what went wrong’ could provide a more optimistic description regarding the status of informal women entrepreneurship in the North-West Province in South Africa.

The 2015 Female Entrepreneurship Index (Terjesen & Loyd 2015) analyses 77 countries, which is an increase from 30 in 2014. The index utilises an established theoretical framework to measure entrepreneurial environment ecosystems and individual aspirations and score nations from 0 to 100. The 2015 index revealed that despite global progress, 61% of the nations (47 of 77) scored below 50 points – an indication that these countries must pursue significant changes in order to reduce barriers for female entrepreneurs. According to the study, South Africa ranked in the 36th place with a score of 44.5 of the 77 countries. From a Sub-Saharan perspective, South Africa ranked the highest and Malawi the lowest, where globally, Malawi scored second lowest overall in the 76th place with an index of 15.5., almost equal to Pakistan (77th place) in the last position with a score of 15.2. Table 1 presents the Sub-Saharan Female Entrepreneurship Index scores for African countries.

Scholars in the developing world have begun to acknowledge the significant role of the informal economy in employment provision and its function as a buffer between employment and unemployment. South Africa exhibits a broad range of maturity of LED activity. It seems that despite the growth in policy significance of LED, there is still a need to study and capture data on LED experiences in South Africa.

Ethics approval was obtained from the School of Business Leadership, UNISA Ethics Committee and the participants’ right to privacy and anonymity was respected. Participants were also informed that they could withdraw at any stage should they want to terminate the interviews.

The demographic information of the respondents is summarised in Table 2. In terms of age, the majority of the respondents (65%) were older than 40 years. It is clear that the younger generation is not attracted to the informal sector because only 7.5% were younger than 29 years. Almost half of the respondents (45%) were single and only 21% had completed matric. Less than 2% of the respondents had a post-matric qualification or a degree.

This affirmed that the majority of informal woman entrepreneurs are not well educated and therefore not marketable to the formal sector. They have basic literacy and numeracy skills and need to enhance them to manage a profitable business. The majority of respondents (81%) used personal savings to start their business. None of the respondents had access to any microloans or finance from banks. The majority of the respondents (60%) had been in the informal business for more than 6 years. Only 8% of respondents had been in business for less than a year and 16% of the women reported a period of 1–3 years.

The data were captured manually during the face-to-face interviews, coded into MS Excel for data cleaning and editing and imported into SPSS version 22 for analysis.

According to Pallant (2011), principal component analysis (PCA) is the most common method of exploratory analysis. The aim of a PCA is to arrive at a relatively small number of factors and transform the original variables into smaller sets of linear combinations, with all of the variance in the variables being used (Pallant 2011). The research aimed to identify relevant underlying hypothetical constructs emerging from the data that could add to the interpretation of the data, describe the relationships between possible constructs and present motivational factors that affect women entrepreneurs to start their own informal businesses.

The first step in PCA is to assess the suitability of the data for a factor analysis, followed by a factor extraction and finally a factor rotation and interpretation (Pallant 2011). An exploratory analysis of the 15 items was performed on the data to determine which items could be reduced to underlying dimensions. PCA was concerned only with establishing which linear components existed within the data and how a particular variable might contribute to that component (Field 2009). The authors inspected the correlation matrix for evidence of coefficients greater 0.3, indicating sufficient strength of intercorrelations amongst the items to continue with the analysis (Tabachnick & Fidell 2013), which was indeed the case.

Consequently, a factor extraction was performed to determine the smallest number of factors or components that can be used to best represent the interrelationships amongst the components. Although the intercorrelations amongst the items indicated sufficient strength, they were not particularly high and the authors decided on an orthogonal rotation, and specifically a Varimax rotation. This rotation attempts to maximise the dispersion of loadings within factors (in this case motivational factors of women to start informal businesses). Furthermore, Varimax intends to load a smaller number of variables highly onto each factor resulting in more interpretable clusters of factors (Tabachnick & Fidell 2013).

Two additional measures to help assess the factorability of the data were calculated. The Kaiser–Meyer–Olkin measure of sampling adequacy was calculated, and according to Pallant (2011) a value of 0.6 is adequate for a good factor analysis. A value of 0.58 was calculated, which the authors accepted as an adequate value to proceed with the analysis. The Bartlett’s test of sphericity should be significant (p < 0.05) as Tabachnick and Fidell (2013) stated and was calculated at a significant measure of p < 0.001.

Table 3 illustrates the validity and reliability of the constructs measured. Cronbach’s alpha coefficients were calculated to determine the validity and reliability of the constructs measured in the questionnaire. Tabachnick and Fidell (2013) stated that a Cronbach’s alpha score determines the internal consistency of a scale and is expressed as a value between 0 and 1. Adequate measuring values of Cronbach’s alpha range from 0.70 to 0.95 (Tabachnick & Fidell 2013). In agreement, Pallant (2011) noted that alpha values of 0.70 and higher are considered suitable. However, in exploratory research within social sciences, a cut-off point may be reduced (Pallant 2011) and accordingly, the authors accepted 0.5 to describe the motivational factors for women entrepreneurs in Mahikeng to start their own businesses.

Ethical clearance was granted by UNISA SBL before the research commenced.