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Abstract

The study uses data from the 2017 South African National Income and Dynamics Survey (NIDS) to investigate the determinants of female labour force participation in South Africa. The majority of women form part of the female labour force (50.21%). These estimates are similar in urban areas (55.77%) but lower among women in rural areas (40.29%). Age and education emerge as dominant predictors of female labour force participation. A large household size decreased the odds of labour force participation. The results suggest that more needs to be done in terms of encouraging the full economic participation of women.

Keywords

Women labour force participation South Africa employment urban–rural

Introduction

The rise in female labour force participation is one of the most remarkable economic transformations of the 21st century as previously women were unlikely to enter the formal labour market (Ntuli, 2007; Ortiz-Ospina et al., 2020; Psacharopoulos and Tzannatos, 1989; Wyrwich, 2019). Women’s participation in the formal economy has been positively linked to economic development in numerous ways (Kongolo, 2009; McLanahan and Carlson, 2001). An increase in female labour force participation has driven economic transformation and increased women’s economic and financial independence (Doepke and Tertilt, 2019; Vapnek, 2009). When women are able to equally participate in existing markets, they gain control over productive resources as well as the ability to engage in meaningful economic decision-making (O’Neil et al., 2014). The empowerment of women in this regard has contributed towards gender equality (Mosomi, 2019). In addition, increasing the participation of women in the labour market is a step towards attaining the Sustainable Development Goals (SDGs) and enhancing the development of the African continent. Female labour force participation and economic development have a far more complex relationship than is often portrayed in academic literature. The complex nature of female labour force participation makes it necessary to enhance development at various levels, but especially within economic and educational spheres. In developed countries, economic growth is one of the main drivers of female labour force participation. However, in developing countries, female labour force participation is a coping mechanism in response to economic shocks (Verick, 2018). Therefore, female labour force participation is especially important in the African context as a driver for effective economic growth (Idowu and Owoeye, 2019). In addition, the increasing participation of women in the labour force has been driving employment trends and decreasing gender gaps (Ackermann and Velelo, 2015; Anyanwu and Augustine, 2013).

Globally, economic growth has been threatened by the novel coronavirus, COVID-19. This virus has spread rapidly across the globe, especially among the most vulnerable populations. The virus has severely affected people from various contexts and has led to the shutdown of all non-essential services in many countries, including South Africa, resorting to national lockdowns (Nott, 2020). During the pandemic, women had to battle with vulnerability of their employment as well as balance domestic and employment responsibilities, where providing additional childcare contributed to the triple burden of challenges. Even though there were several policies implemented to reduce vulnerability, the negative impact of the COVID-19 pandemic has had an unprecedented impact on women (Naidoo and Nithiseelan, 2022). However, the restrictions on economic activities across the country has had widespread negative implications for millions (Nott, 2020). Many lost their means to earn a livelihood and this worsened the experience of poverty and hunger as many were left without the resources to provide for their families.

In South Africa, unemployment is a serious concern with the official unemployment rate for the fourth quarter of 2019 estimated at 29.1% (Statistics South Africa, 2020). This was significantly higher and increased from the preceding year, with estimates even higher for women. During the October-December 2018 period, estimates indicate that unemployment among women was 29.5%, increasing to 31.3% a year later (Statistics South Africa, 2020). In South Africa, the current unemployment crisis is likely to worsen existing inequalities and create new ones. In the context of this study, the implications of job loss, unemployment and poverty threaten to raise inequalities across society and adversely affect many. This will also threaten the strides achieved in terms of employment and gender equality. The novelty of this study lies in the timely approach of producing estimates pertaining to female labour force participation to outline existing determinants and work towards improving the economic participation of women across society.

There is an increasing number of women engaged in economic activities; however, there is still a need for greater efforts to enable more women to participate in the labour force. According to Verick (2018), the global labour force participation of women in working age groups (15 years and older) has declined from 51.3% in 1998 to 48.5% in 2018. Research further suggests a projected stagnancy in the rates of unemployment among women from 2018 to 2021 (Ortiz-Ospina et al., 2020). These statistics outline a number of persistent challenges that confront women in the labour force. The gender wage gap remains wide as women continue to be paid less than men (Blau and Kahn, 2017; Mosomi, 2019). Women also continue to bear the disproportionate responsibility of unpaid care and domestic work, which is one of the main hindrances to their full economic participation and, as such has been negatively correlated with female labour force participation (Kongolo, 2009). Other factors that affect their full labour force participation include education, race, place of residence (urban vs rural) and marital status (McLanahan and Carlson, 2001; Posel and Rogan, 2009).

Emphasis on female labour force participation between urban and rural communities within developing nations reveals distinct disparities. For example, some developing countries, such as Bangladesh, have experienced increases in female labour force participation, across the whole country, from 23.9% in 1990 to 36% in 2010, whereas during the same period, the labour force participation of rural-based women stagnated or even declined (Rahman and Islam, 2013). Although similar studies have been conducted in countries such as China, India and Bangladesh, limited studies were identified in the sub-Saharan African context (Barrett et al., 1991; Chatterjee et al., 2015; Che and Sundjo, 2018). In addition, the majority of studies on female labour force participation focus on women in urban areas and, in fewer instances, on rural women (Afridi et al., 2018; Jain, 2016; Kaur and Nagaich, 2019). In some cases where the female labour force is independently investigated in rural–urban contexts, studies focus on specific aspects and determinants such as household responsibilities, childcare, and domestic violence (Jain, 2016; Naidu, 2016; Paul, 2016). Further, South Africa presents a unique case because while some experiences may be comparable to other countries globally, for the past three decades women in South Africa historically had to struggle for freedom from oppression, for community rights and, importantly, for gender equality.

Among women, some groups were challenged with more difficult circumstances in comparison with others, such as those living in rural areas. Thus, the urban–rural divide of female labour force participation is an important area of interest. This study is timely as research suggests that although there has been a global decline in female labour force participation in South Africa, the inverse is observed. Data from the Post-Apartheid Labour Market Series (PALMS) shows that the proportion of unemployed women increased from 10% to 30% between 1993 and 2015 for those younger than 25 (Mosomi, 2019). This presents a window of opportunity for enhancing economic participation and gender equality across South Africa. Generally, urban areas offer substantial employment opportunities in comparison with rural areas. According to the Development Policy Research Unit (2018), there are great disparities in the labour market conditions between urban and non-urban areas within South Africa. Estimates indicate that the labour force participation rate is substantially higher in urban areas than rural areas. However, the non-urban labour force participation rate continually decreases (Development Policy Research Unit, 2018). Even though employment rates are higher in urban areas, the rapid influx of people into the city is a nation-wide concern. Migration and population growth are highest in metropolitan areas, and even though poverty is more pronounced in rural areas, there are concerns about the urbanization of poverty (Arndt et al., 2018). In addition, increasing urbanization is not necessarily a sustainable trajectory because urban areas are expected to face the challenge of increasing employment demands (Mlambo, 2018).

South Africa has a diverse and heterogeneous population with varying levels of development. Accessibility of opportunities across the country varies by geographic location (urban–rural divide). This assumption forms the basis of this study, which places specific emphasis on the determinants of labour force participation among women according to their geographical location. Dayıoğlu and Kirdar (2010) indicated that although the overall female labour force participation rate is low, it is higher and more stable in rural areas. Therefore, this suggests the need to prioritize and include the urban–rural divide to produce empirical analysis which can inform further research towards effective policy implementation and a more holistic approach towards labour force participation across the country.

Ensuring a holistic approach to improving labour force participation among women is paramount due to the adverse impact of the coronavirus pandemic; it is expected that the economy could contract by 10% and over one million people could be left unemployed (Omarjee, 2020). Female labour force participation may have an influence on many important factors such as gender equality, economic transformation and women’s lives. It is an important topic of study that continually needs to be examined in order to advance national and global development agenda and ensure that existing policy and initiatives are strengthened and reinforced. Their labour force participation is empowering as women have greater control over income and resources, have more independent decision-making opportunities and they are included in family decision-making, such as fertility outcomes (Duflo, 2012). This research analyses the labour force participation of South African women using data from the nationally representative National Income Dynamics Study (NIDS), wave 5. The aim is to explore determinants of female labour force participation in rural and urban contexts. The focus is on investigating the relationship between female labour force participation and socio-economic determinants such as educational attainment, marital status, household size and income level comparatively between urban and rural contexts. This research is situated in the African context, specifically South Africa, where there are distinct historical, cultural and social norms which have continually affected the life cycle of women. For decades, South Africa has suffered the effects of an unjust system which widened the inequality gap between men and women and different groups of people. Women in South Africa were faced with a number of challenges which hindered them from becoming economically independent which has had a profound impact on their decision-making power. This in turn has affected other areas of their lives such as their educational attainment and childbearing patterns. Gender norms, restrictive cultural practices, discriminatory laws and segmented labour markets were a hindrance to many women, particularly African women, who wanted to participate in the labour market (Ackermann and Velelo, 2015; Idowu and Owoeye, 2019). However, since the shift to a democratically elected government, the labour market has become more accessible to women, yet there is still a distinct gender and wage divide that exists in the labour market (Bosch and Barit, 2020). In order to continue to encourage women to participate in the labour market and contribute towards economic transformation contemporary evidence needs to be continually generated and examined to ensure that policy and initiatives are implemented timely.

Methodology

The study uses a quantitative research approach to examine female labour force participation and various socio-economic factors and predictors. Existing studies have previously examined working women in South Africa from an in-depth lens and focused on their life histories rather than broader statistical data (Bozzoli and Nkotsoe, 1991; Cock, 1989; Lee, 2009). A nationally representative analysis is best suited to provide evidence on the most prominent factors which affect almost all women. Thus, a broader analysis would serve as an effective tool in providing a national snapshot of a sample of representative women. While a statistical approach may not be able to delve deeper into the findings which a qualitative component offers, this analysis has the potential to influence large-scale policy and initiatives which would benefit a wide range of women. Thus, in this study, through the application of the complex survey design the results are generalizable.

Data set

NIDS is a panel study that has been following 28,000 South Africans since 2008; however, this study employs a cross-sectional analysis of the most recent data. The data collected in this study includes demographic, health, income, wealth, employment indicators of the household and individual well-being. This is a face-to-face longitudinal investigation that is conducted every 2 years to track changes in South African households and individuals. The 2017 NIDS sample consists of 12,000 households and over 45,000 individuals. NIDS has three criteria for defining a household: (1) ‘Household members must have lived “under this roof” or within the same compound/ homestead at least 15 days in the last 12 months’; and (2) ‘When they are together they share food from a common source’; and (3) ‘They contribute to or share in a common resource pool’ (Southern Africa Labour and Development Research Unit (SALDRU), 2009). The household is therefore not defined by the regularity of the physical presence of members but by their belonging to the household in terms of living arrangements and resource contribution or consumption (Hall and Mokomane, 2018).

Variables and measures

This study hypothesizes that the effect of women’s participation in the labour force is non-linear and is dependent on various independent variables at the individual and household level.

Labour force participation is universally defined as the total number of people or individuals who are currently employed or in search of employment. People who are not looking for a job such as full-time students, homemakers, those younger than 15 and those older than 65 will not form part of the labour force (Hosney, 2016; Sackey, 2005; Yakubu, 2010). According to Statistics South Africa (2002a, 2002b), the labour market in South Africa refers to the working-age population aged 15–65 years. It comprises three broad categories of individuals – the employed, the unemployed and the not economically active population. The labour force or economically active population comprises all individuals of working age (15–65 years in South Africa) who are either employed or unemployed (Statistics South Africa, 2002a, 2002b). Therefore, for this study, the dependent variable, which is female labour force participation (FLFP), is based on the market definition, as well as the guidelines set out by Statistics South Africa, consisting of women who are either engaged in economic activity for the purposes of market exchange or those who are seeking work and those who are economically inactive (Assaad and Krafft, 2013; Statistics South Africa, 2002a, 2002b).

More specifically so, drawing from the 2017 NIDS wave 5 questionnaire and data, employment status is coded using the definition of the International Labour Organization (ILO) to assign respondents to one of the following categories: employed, unemployed (strict definition), unemployed and discouraged (broad definition) and not economically active. According to NIDS, the respondent was determined to be employed if they were economically active and reported having any form of employment at the time of the interview, including a primary job, secondary job, self-employment, paid casual work, or personal agricultural work, or if they assist others in business activities. Unemployment is differentiated into broad and narrow unemployment according to the standard definitions, by distinguishing those who are actively searching for work and those not actively searching (Brophy et al., 2018). The variable was grouped into four categories in the NIDS wave 5 data: (0) Not economically active, (1) Unemployed and discouraged, (2) Unemployed – strict, (3) Employed. The variable FLFP was regrouped in STATA and created as a binary outcome with categories 0 and 1 = 0 and were composed of the not economically active sample and categories 2 and 3 = 1 which comprised of the economically active study sample. Those who are in the inactive category refer to individuals who are younger than 15 and older than 65 and include those who are studying full-time, homemakers, the retired and individuals who are not willing to accept work (Brown et al., 2012). Those that were in the economically active category were further restricted to ages 15–65 years old. In addition, in this context, broadly, unemployed refers to all those individuals who are willing to accept work and are actively seeking employment.

There are also various independent variables of interest that were analysed at the individual and household level. The individual-level variables such as age, education, marital status and population group or race were recoded into categorical variables. The household-level variables, such as household size and household income, were recoded into categorical variables and thereafter presented as continuous variables for the different models of the regression. In the context of this study, the total household income captures and includes women’s individual income. Household income is an important factor, as an increase in this has been correlated to better family outcomes, especially for children (Cooper and Stewart, 2021). Individual-level characteristics such as age and number of children that a woman resides with were also presented as continuous variables to take into consideration interaction effects. A description of the variables are presented in Table 1.

Table 1.

Variable description.

Dependent variable
Female labour force participation	Binary; 0 = not economically active women; 1 = economically active women aged 15–65
Independent variables
Individual
Age	Continuous and categorical (categorized broadly by 10-year age groups); 15–24 = 1; 25–34 = 2; 35–44 = 3; 45–54 = 4; 55–65 = 5
Education	Categorical; None (no schooling) = 1; Primary (completed primary level or less) = 2; Secondary (completed secondary level or less) = 3; Tertiary (completed a diploma/degree) = 4
Marital status	Binary; Married = 1; Unmarried = 2
Population group	Categorical; African = 1; Coloured = 2; Indian = 3; White = 4
Number of children living	Continuous and categorical; 1 child = 1; 2–4 children = 2; 5 or more children = 3
Household
Household size	Continuous and categorical; 1–3 members = 1; 4–6 members = 2; 7–9 members = 3; 10 or more = 4
Household income	Continuous and categorical; Less than R3500.00 = 1; R3500.00–R11000.00 = 2; More than R11000.00 = 3*South African Rands

Data analysis

This study relied on secondary data analysis of the NIDS wave 5 dataset to measure the degree to which certain factors are associated with FLFP in South Africa. Data analysis was conducted using STATA version 15.0. Logistic regression analysis was employed for the analysis. The regression analysis looks at the chances that an event will occur and is presented as odds ratios. This model was appropriate because a logistic regression is considered to be the most suitable for a binary dependent variable, that is, one that takes one of only two values representing the presence or absence of an attribute of interest. The equation for a logistic model is a linear function of the predictors and the model can handle any number of numerical and/or categorical variables. The equation is as follows

${Log}_{10} = \log (p / 1 - p) = B_{0} + B_{1} X_{1} + B_{x} X_{x}$

where p = the probability of the event that Y = 1; B₀ = the y-intercept. It is the log odds of the event that; Y = 1 when the predictors x₁ = x₂ = x₀; B₁ = odds that Y = 1 increases by a factor of 10¹; B_x = odds that Y = 1 increases by a factor of 10^x.

Results

Table 2 presents an overview of labour force participation in the South African population. The estimates are presented as weighted percentages to ensure that the results are reflective of the general South African population. The analysis indicates that the majority of the South African population is economically active (57.87%). However, there are more males who are economically active (66.27%) than females (50.48%).

Table 2.

Labour force participation in South Africa.

Labour force participation	Male(%)	Female(%)	Total(%)
Not economically active***	33.73	49.52	42.13
Economically active***	66.27	50.48	57.87
Total	100.00	100.00	100.00

Chi² test significant at ***p < 0.001; **p < 0.01; *p < 0.05.

Table 3 displays the type of work sector of the economically active population segregated by gender. Overall, the results indicate that the majority of positions were occupied by men. More men (51.56%) occupied managerial positions in comparison with women (48.33%). However, it was interesting to note that more women (61.93%) occupied positions termed as ‘professionals’ in comparison with men (38.07%). It was also noted that men largely dominated the skilled agricultural, forestry and farming sectors (70.29%) as well as the plant and machine operating sector (91.20%). Women were mostly concentrated in the clerical profession (69.08%) and elementary occupation (60.41%).

Table 3.

Type of occupation segregated by gender.

Type of occupation	Male***	Female***	Total
Armed forces	60.13	39.87	100.00
Managers	51.56	48.33	100.00
Professionals	38.07	61.93	100.00
Technicians and associate profession	59.80	40.20	100.00
Clerical support workers	30.92	69.08	100.00
Service and sales workers	47.59	52.41	100.00
Skilled agricultural, forestry and farming	70.29	29.71	100.00
Crafts and related trades workers	83.45	16.55	100.00
Plant and machine operators and associates	91.20	8.80	100.00
Elementary occupation	39.59	60.41	100.00

Chi² test significant at ***p < 0.001; **p < 0.01; *p < 0.05.

Table 4 presents the weighted percentages of the study sample characteristics. The study sample is focused on women aged 15–65 years who are economically active, or unemployed but are of working age, and considered to be part of the labour force. Table 4 presents the study sample characteristics of all women aged 15–65 and then further segregated by geographical divide (urban–rural).

Table 4.

Study sample characteristics of economic women aged 15–65, segregated by geographical divide.

Characteristic	Sub-category	All women(%)	Urban women(%)	Rural women(%)
Individual characteristics
Age categories	15–24	25.73***	30.53***	18.48
	25–34	69.84***	73.35***	62.21
	35–44	72.61***	77.27***	63.87
	45–54	62.54***	67.82***	52.48
	55–65	33.53***	39.02***	23.94
Education	None	16.43***	25.18***	12.96
	Primary	31.81***	34.99***	28.36
	Secondary	48.40***	52.04***	41.49
	Tertiary	80.01***	79.87***	80.53
Marital status	Married	54.89***	59.55***	45.26
Marital status	Unmarried	47.94***	53.83***	38.17
Population group	African	50.42	58.32***	39.86
	Coloured	50.91	49.92***	63.28
	Indian	39.85	41.39***	29.75
	White	49.82	49.75***	54.20
No. of children living with mother	1	52.35**	57.60	44.05
	2–4	58.56**	63.84	49.75
	5 or more	47.73**	60.58	37.48
Household characteristics
Household size	1–3	58.41***	61.64***	49.51
	4–6	46.39***	51.18***	37.40
	7–9	42.63***	48.27***	36.63
	10 or more	44.10***	60.11***	34.63
Total household income	Less than R3,500.00	42.79***	48.87***	34.85
	R3,500.00–R11,000.00	54.05***	58.63***	45.45
	More than R11,000.00	64.54***	65.13***	61.50
Total (%)		50.21	55.77	40.29
Total (N)		13,993	7332	6661

Chi² test significant at ***p < 0.001; **p < 0.01; *p < 0.05.

The results from the overall sample of women aged 15–65 years (N = 13,993) suggest that approximately 50% of all women are part of the female labour force. Further analysis of the descriptive statistics for the urban sample of women (N = 7332) suggests that approximately 55% of women formed part of the female labour force. This estimate was lower for the rural sample as only 40% of women (N = 6661) formed part of the labour force.

The majority of the women that were part of the labour force in the overall sample were aged between 35 and 44 years (72.61%). This was similar for the urban (77.27%) and rural (63.87%) sample of women. The analysis of educational attainment reveals that approximately 80% of all women, including those segregated by the urban–rural divide, had a tertiary-level education. Within the sample of all women, it was noted that the majority of women were either African or Coloured (approximately 50%). However, this was significantly different for the urban sample of women as it was noted that a large majority, approximately 58%, of African women were part of the female labour force. This was different for the rural sample, as it was estimated that only 40% of African women were part of the female labour force. Analysis of the number of children that reside with their mothers were segregated into broad categories. It was evident from the analysis that the majority of women who were part of the labour force had between 2 and 4 children living with them (58.56%). Varying observations were noted for the urban (63.84%) and rural (49.75%) sample of women.

The analysis also extended into household-level characteristics. The majority of all women (58.41%) that were part of the labour force resided within a smaller household which consisted of one to three members. The estimates from the urban (61.64%) and rural (49.51%) sample of women suggest a similar pattern. Household income was further grouped into broad categories. The majority of women resided within households with an income of more than R11,000.00 per month. The estimates were similar for those from the urban (65.13%) and rural (61.50%) sample.

Table 5 presents the correlates of FLFP in South Africa, which is further analysed by geographic location. From Table 5, it is observed that age is correlated to employment of women. The statistics for all models suggest that a woman has an increased likelihood of forming part of the female labour force as she ages. However, this likelihood changes as the women become significantly older. Within the total sample of women, those aged between 25–34 and 35–44 years have six and eight times, respectively, higher odds of being part of the female labour force. However, this declines significantly at ages 45–54 and 55–65 years. These observations are also highly significant for the urban and rural sample of women who are aged 25–34 as they have five to six times the odds of being part of the female labour force in comparison with the reference category (15–24 years old). In all models, women with a tertiary-level education had higher odds of being part of the female labour force compared to women with no education. When examining marital status it was evident that unmarried women were more likely to form part of the female labour force in comparison with married women. Unmarried women from the total sample were 41% more likely to be part of the labour force. These results were similar for unmarried women who reside in urban areas. Urban, unmarried, women were also 41% more likely to be part of the labour force in comparison with married women. When examining population group, it was evident that Coloured women have significantly higher odds of forming part of the female labour force in comparison with African women. Coloured women, from the total sample were 55% more likely to be part of the female labour force in comparison with African women. Similarly, when – examining the urban sample, Coloured women were 19% more likely to be part of the female labour force in comparison with African women. However, Coloured women from rural areas were three times more likely to form part of the female labour force in comparison with African women. There was a decreased likelihood of Indian women forming part of the female labour force. The same was observed for White women; however, this was not statistically significant.

Table 5.

Determinants of female labour force participation among women.

Variable	Sub-category	All womenmodel 1	Urban womenmodel 2	Rural womenmodel 3
Variable	Sub-category	OR [95% CI]	OR [95% CI]	OR [95% CI]
Individual characteristics
Age	15–24	1.00	1.00	1.00
	25–34	6.11*** [5.46–6.85]	5.94*** [5.09–6.93]	6.39*** [5.39–7.57]
	35–44	8.25*** [7.24–9.41]	8.36*** [6.96–10.03]	8.59*** [7.08–10.42]
	45–54	6.18*** [5.39–7.10]	6.25*** [5.19–7.54]	6.39*** [5.18–7.89]
	55–65	1.89*** [1.63–2.20]	1.62*** [1.33–1.97]	2.43*** [1.91–3.08]
Education	None	1.00	1.00	1.00
	Primary	1.27* [1.03–1.57]	1.24[0.84–1.84]	1.25[0.97–1.61]
	Secondary	1.95*** [1.59–2.38]	1.93*** [1.32–2.81]	1.88*** [1.46–2.41]
	Tertiary	5.49*** [4.34–6.94]	4.86*** [3.24–7.29]	6.39*** [4.61–8.85]
Marital status	Married	1.00	1.00	1.00
Marital status	Unmarried	1.42*** [1.30–1.56]	1.41*** [1.24–1.60]	1.46*** [1.27–1.68]
Population group	African	1.00	1.00	1.00
	Coloured	1.55*** [1.37–1.74]	1.19** [1.04–1.36]	3.23*** [2.34–4.45]
	Indian	0.54*** [0.39–0.75]	0.48*** [0.33–0.69]	0.52[0.23–1.21]
	White	0.96[0.76–1.22]	0.87[0.67–1.11]	1.08[0.33–3.44]
Household characteristics
Household size	1–3	1.00	1.00	1.00
	4–6	0.67*** [0.60–0.74]	0.68*** [0.60–0.77]	0.71*** [0.61–0.83]
	7–9	0.61*** [0.54–0.69]	0.59*** [0.49–0.69]	0.73*** [0.61–0.87]
	10 or more	0.52*** [0.45–0.60]	0.64*** [0.50–0.81]	0.58*** [0.47–0.71]
Household income	Less than R3500.00	1.00	1.00	1.00
	R3500.00–R11000.00	1.45*** [1.32–1.58]	1.37*** [1.21–1.55]	1.41*** [1.23–1.61]
	More than R11000.00	1.68*** [1.45–1.94]	1.61*** [1.35–1.92]	1.56*** [1.19–2.02]
N		12,579	6721	5858
Log-likelihood		−7233.5232	−3868.1147	−3317.7639
constant		0.000	0.000	0.000

OR: odds ratio; CI: confidence interval.

Significant at ***p < 0.001; **p < 0.01; *p < 0.05.

When examining household-level characteristics, it was evident that a larger household reduced the odds of women participating in the labour force. In the total sample of all women (model 1), women who resided in household with 10 or more members were 48% less likely to participate in the labour force. This was similar for the sample of rural women, as it was estimated that they were 42% less likely to participate in the labour force if they resided within a household of 10 or more people. The estimates for the urban sample of women indicate that women were 36% less likely to participate in the labour force if they resided in households which consisted of 10 or more people in comparison with those women who lived in households with one to three members. It was interesting to note that an increase in total household income increased the likelihood that women would participate in the labour force. Those women who resided in households where the total income exceeded R11,000.00 per month were 68% (model 1), 61% (model 2) and 56% (model 3) more likely to participate in the labour force in comparison with those that resided within households with an income of R3500.00 or less per month.

Table 6 presents the correlates of FLFP in South Africa, which is further analysed by geographical location. For the estimates provided in Table 6, variables such as age, household size and income were presented as continuous-level variables to assess the interactive effects that they have on the outcome of FLFP. In addition, for the various models presented below, the number of children that the women resided with was included as a continuous-level variable in the analysis. The results from each of the regression models suggest that as age increases, the likelihood of participating in the labour force decreases. Educational attainment significantly increased the odds of participation in the labour force. Women with a tertiary-level education had much greater odds of participating in the labour force than women with no education. When examining marital status, it was evident that unmarried women were also more likely to participate in the labour force than married women. A further examination of race indicated that Coloured women had higher odds of participating in the labour force in comparison with African women. In model 6, Coloured women had two times the odds of participating in the labour force in comparison with Indian and White women. Across all models, women were more likely to participate in the labour force if the number of children that resided with them increased. Household size significantly decreased the odds of women participating in the labour force across all models. Interestingly, there was no association observed between FLFP and total household income. These results are displayed in Table 6.

Table 6.

Determinants of female labour force participation among women.

Variable	Sub-category	All womenmodel 4	Urban womenmodel 5	Rural womenmodel 6
Variable	Sub-category	OR [95% CI]	OR [95% CI]	OR [95% CI]
Individual characteristics
Age		0.97*** [0.97–0.98]	0.97*** [0.96–0.97]	0.98*** [0.97–0.99]
Education	None	1.00	1.00	1.00
	Primary	1.85*** [1.48–2.31]	1.86** [1.21–2.88]	1.84*** [1.41–2.40]
	Secondary	2.97*** [2.38–3.70]	3.00*** [1.96–4.59]	2.89*** [2.19–3.81]
	Tertiary	8.92*** [6.86–11.60]	7.92*** [4.98–12.6]	10.33*** [7.22–14.78]
Marital status	Married	1.00	1.00	1.00
Marital status	Unmarried	1.13** [1.02–1.26]	1.13[0.98–1.30]	1.19* [1.02–1.38]
Population group	African	1.00	1.00	1.00
	Coloured	1.40*** [1.23–1.60]	1.08[0.92–1.26]	2.80*** [1.94–4.05]
	Indian	0.40*** [0.28–0.59]	0.37*** [0.24–0.56]	0.34* [0.12–0.94]
	White	0.64** [0.46–0.88]	0.63*** [0.45–0.89]	0.30[0.07–1.25]
No. of children living with		1.24*** [1.18–1.30]	1.26*** [1.17–1.36]	1.22*** [1.15–1.30]
Household characteristics
Household size		0.91*** [0.90–0.93]	0.91*** [0.89–0.94]	0.93*** [1.15–1.30]
Household income		1.00*** [1.00–1.00]	1.00*** [1.00–1.00]	1.00*** [1.00–1.00]
N		8507	4307	4200
Log-likelihood		−5265.0847	−2626.2416	−2601.0346
constant		0.144	0.455	0.001

OR: odds ratio; CI: confidence interval.

Significant at ***p < 0.001; **p < 0.01; *p < 0.05.

Discussion

The overall aim of this study was to investigate the determinants of labour force participation among women in South Africa with a specific emphasis on urban–rural differentials. For decades structural, social and historical factors influenced women’s labour force participation. In South Africa during the apartheid era, there were widespread and persistent effects of racism, classism and sexism, which hindered many women, particularly African women, from securing an income. Historically, access to the labour market for African women was largely shaped by the gendered nature of the migrant labour system, intense discrimination and restrictive legal measures that hindered their entry into the employment sector (Bhorat and Leibbrandt, 2001; Lalthapersad, 2003; Ntuli, 2007). These historical factors continue to influence patterns of labour force participation. While there have been great strides to include women in the labour force, most high-ranking employment positions are dominated by men (Statistics South Africa, 2020). In this study only half of the total female population were considered as economically active. This indicates that more needs to be done in terms of encouraging the full economic participation of women from various socio-economic contexts since almost half of all women in South Africa were considered to be economically inactive or not part of the labour force. This is concerning as South Africa, like many other countries in Africa, has a relatively youthful population as well as reformed policy and legislature which is directed at offering women equal opportunities in the formal employment sector. Currently, women have greater opportunity to further their education and are able to participate in decisions regarding family planning which was largely restricted in the past due to existing cultural and social norms (Osuafor et al., 2018). Traditionally, women were expected to take care of the home, and men were expected to provide an income. Thus, childcare was often a deterrent for women with young children who want to secure employment and this hindered their opportunity to participate in the labour force (Maharaj and Dunn, 2022).

As a result of the persistent barriers to securing employment for many women, unemployment continues to rise and young women are disproportionately affected. According to Statistics South Africa (2019), unemployment in the first quarter of 2019 increased by 0.5%, and the current unemployment rate was 27.6% (Statistics South Africa, 2019). The majority of unemployed people in South Africa are women between the ages of 15–34 years (Statistics South Africa, 2019). This is concerning as the majority of the female labour force across the entire study sample, and further segregated by geographical divide, consists of women between the ages of 25–34 and 35–44 years old. The descriptive findings of this study further suggest there are disparities between urban and rural areas such that when assessed independently, the labour force participation of urban women shows positive trends of relatively higher estimates of labour participation. These positive trends in the urban areas are masked by the very high rates of those that are not economically active among rural women when calculating the overall average. Past studies have shown that rural women in South Africa often affect the unemployment count (Posel and Casale, 2001). This is because although not actively engaged in the formal labour markets, these women often engage in alternative economic activities such as subsistence farming which are typically excluded from official statistics (Posel and Casale, 2001). In some countries such as India the labour force participation of rural women has even been observed to decline as they opt to engage in wage work and self-employment (Dubey et al., 2017). This finding reinforces the need to distinguish between rural and urban contexts when discussing the labour force participation of women in South Africa.

The findings revealed that there were significantly lower levels of labour force participation among younger women aged 15–24 years; however, the likelihood of being part of the labour force increased with age. This observation can be attributed to the increase in the number of women staying longer in school. Women in younger age groups are more likely to still be enrolled in the secondary education system therefore this result is consistent with past research which showed that although the FLFP increased in post-apartheid South Africa, the ages at which women entered the workforce increased due to them staying in school for longer (Mosomi, 2019). This highlights the significant role of education in the economic participation of women. Moreover, the results obtained in this study emphasizes the positive influence of education on employment opportunities among women. The majority of women with tertiary-level education had significantly higher odds of participating in the labour force in comparison with those with little or no education levels. This observation was further consistent in the urban and rural contexts. In the past, the rise in FLFP has been associated with the closing of gender gaps in schools and universities (Heath and Jayachandran, 2016; Nagac and Nuhu, 2016; Verick, 2018).

Patterns of marriage have changed drastically across South Africa which has seen an observable decline, a pattern similar to other countries (Garenne, 2016; Hosegood et al., 2009). Marriage is no longer the norm therefore it would be expected that more unmarried women would be part of a large share of the female labour force; however, the estimates did not indicate such. In this study, more married women formed a large part of the labour force in comparison with unmarried women. Less than 50% of women in the rural area contribute to the female labour force in this study, indicating the need for significant change in this context. Past research conducted in developed Western countries showed increases in FLFP from the latter half of the 20th century among married women, a situation that has been described as one of the most dramatic socio-economic changes in the West (McLanahan and Carlson, 2001). The urban trend of higher labour force participation among married women is consistent with trends in developed countries because such areas are characterized by greater access to economic opportunities and this encourages participation in formal labour markets (McLanahan and Carlson, 2001; Verick, 2018). The limited economic participation of married women in Africa’s rural areas stems from prevailing socio-cultural norms of men as providers and also from the migrant labour system, where men were employed in urban areas and women attended to household-related affairs (Delius, 2017; Posel, 2001). This does not nullify previous conclusions made with regard to the relationship between marital status and women’s economic participation but rather, it points to the existence of additional influencing factors that impact women’s labour force participation regardless of their marital status.

This study also set out to investigate the relationship between FLFP and household size. The logistic regression results suggest that women who live in households with many members are less likely to participate in the labour force. These women have been reported to have more children and elderly dependents which means that as the number of household members increases there is a greater need for women to carry the burden of unpaid household work and responsibilities which constrains their ability to become gainfully employed (Dungumaro, 2008; Floro and Komatsu, 2011). Some South African studies have attributed the changes in household structure to increasing labour force participation, and found that larger households have higher unemployment rates than smaller households (Pirouz, 2004). As the household size increases, the impact on economic participation is the same for both urban and rural women. This could mean that an increase in household size demands women to take primary responsibility in activities such as meal planning, preparation and shopping (Flagg et al., 2014; Lyonette and Crompton, 2015).

The findings from the study suggest that women who reside within a household that receives a higher income are more likely to participate in the labour force. This finding suggests that the contribution of women’s earnings to overall household income is not insignificant, as supported by other studies (Dungumaro, 2008; Ortiz-Ospina et al., 2020). Some studies have suggested that women are beginning to earn more because they have begun penetrating previously male dominated professions and share the burden of responsibility with their partners (Lyonette and Crompton, 2015). This finding supports the argument that limited labour force participation by women inhibits economic growth, as their incomes have the potential to benefit other household members (Dungumaro, 2008; Verick, 2018). In terms of racial background, the results show that Coloured and African women have higher odds of participating in the labour force in comparison with White and Indian women. This finding could be linked to the socio-economic disparities that exists between the various sub-populations in South Africa. For example, as shown in this study, married women are less likely to be employed, yet past research has shown that White women are more likely to be married than African and Coloured women (Posel and Casale, 2009). This present finding highlights the socio-economic disparities between women and how this affects FLFP in different ways.

Given that the study is current and aims to produce timely estimates to encourage FLFP across the South African population, the adverse effects of the novel coronavirus (COVID-19) must be considered. The implications of COVID-19 on the economy are expected to be long-term and devastating. While the virus does not discriminate on the basis of race, sex and geographic location, it is expected to disproportionately affect the poor and vulnerable (Sekyere et al., 2020). This is particularly concerning for a country such as South Africa that has high levels of poverty. In South Africa, the lockdown restricted economic activities and this has exacerbated poverty and unemployment. Many have limited means of earning an income and countless others have lost their jobs. The implications are especially challenging for those that were self-employed and the informal sector, which consists mostly of small and medium scale enterprizes (Sekyere et al., 2020). However, all sectors of the economy were affected. The South African economy was already experiencing a recession prior to the lockdown. In order to ensure a swift economic response, this study recommends that a series of recovery scenarios should largely include women to encourage FLFP and enhance equality. A holistic approach will positively contribute towards economic growth and development and further enhance equality across society by ensuring that no one is left behind; in the context of this study, women, especially those from rural areas.

Drawing from the findings of this study it is recommended that nation-wide effort towards supporting employment opportunities, as well as labour force participation, in the rural areas will aid towards national poverty reduction strategies. Unemployment, especially among the youth and women, is one of South Africa’s most challenging issues. Labour force participation is not only an economic problem as it has an impact on other areas of life. For instance, it is integral to how individuals and families contribute to and take part in society. Thus, programmes to enhance employment should remain a crucial priority. At the national level publicly funded employment programmes should continue to be an integral component of poverty alleviation and inequality reduction strategies, especially for women. In addition, scrutinizing the urban–rural divide will allow policy makers to equip women with the resources needed in order to secure employment in the future (Che and Sundjo, 2018). For example, education opportunities should be enhanced within rural areas to potentially increase the employability of women. In Nigeria, it was found that female education is necessary to ensure the effective participation of women in the labour market (Olowa and Adeoti, 2014). The study by Olowa and Adeoti (2014) further highlighted that most educated females in rural Nigeria have a high probability of securing employment. It is worth noting that poorer households are usually found in rural areas, and relying on a single income is not sufficient therefore the emphasis on FLFP (Che and Sundjo, 2018; Ningaye and Njong, 2015; Statistics South Africa, 2017). However, many women choose not to migrate to urban areas as they have less education and may have very limited opportunities to gain employment. Access to education is advantageous in securing jobs in the future (Chen and Wu, 2007), therefore, this presents the need to prioritize and include the urban–rural divide to produce empirical analysis which can inform further research towards effective policy implementation. At the community level, specific initiatives and programmes which equip women with educational tools and practical skills to earn an income should be implemented and steered by local leaders in an effort to not only combat unemployment but also poverty and gender inequality.

There are a number of limitations of this study. First, the analysis for this study is performed using panel data. However, only the most recent wave of the panel; wave 5, 2017; is used. Panel data usually contains unobserved heterogeneity and omitted time-varying variables, and therefore control function methods can be used to account for both problems. The analysis also does not control for age of living children which could impact FLFP, especially if these are biological mothers who are primary caregivers of young children. Another primary limitation of this study is that the researchers do not control for endogeneity. The researchers did not perform a panel analysis on the data but rather a cross-sectional analysis therefore did not control for endogeneity exclusively. The researchers acknowledge that population heterogeneity is an important factor to consider. Due to the pervasiveness of heterogeneity in the South African context, it is impossible to draw causal inferences at the individual level. Therefore, the causal inferences are drawn at the group level, i.e. all women, and then segregated by the geographical divide.

This study adds to the existing body of knowledge on labour force participation among women, specifically in the South African context. The contribution of this study is unique as it analyses recent data, making the study timely. However, given the statistical nature of the study, undertaking a qualitative study would provide detailed perceptions and experiences that we are unable to ascertain in this current study. Despite this limitation, this study provided empirical analysis segregated by geographical location. This is an important contribution, as there are limited studies that explore the geographical divide pertaining to FLFP.

Conclusion

This study has assessed the determinants of labour force participation among South African women by geographical location, that is, rural and urban contexts, using individual and household characteristics. There is a need for future research to exhaustively investigate South African trends in labour force participation using individual characteristics in various contexts, as South Africa is not a homogeneous society. The findings of this study have highlighted the plight of women who are less educated, married, and reside in rural areas, making them less economically active as they engage in activities that generate little or no income and are thus often overlooked in labour force data. Increasing FLFP creates unprecedented opportunities for economic growth. On a broader spectrum, directing emphasis towards context-specific research on FLFP is imperative for achieving greater gender equality and employment equity as well as making strides towards the achievement of the 2030 Agenda for Sustainable Development.

Footnotes

Authors’ contributions

P.M. formulated and conceptualized the study. S.D. analysed and interpreted the data. All authors were involved in the drafting of the manuscript,revisiting it critically for intellectual content and contributed significantly to the final version of the article. All authors read and approved the final manuscript.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This work is based on the research supported by the South African Research Chairs Initiative of the Department of Science and Innovation and National Research Foundation of South Africa (grant number 64816).

ORCID iD

Shanaaz Dunn

Availability of data and materials

The datasets analysed during the current study are available from the NIDS,DataFirst,website ( http://www.nids.uct.ac.za/nids-data/data-access ). The data for the study were derived from the following resources available in the public domain: (

Author biography

Shanaaz Dunn,PhD,is the Data Manager at the Developmental,Capable and Ethical State at the Human Sciences Research Council. She has been involved in multi-disciplinary research which includes family,fatherhood,sexual and reproductive health among young adults,migrants in the informal economy and older people. Her research interests are in the arena of family demography,family dynamics and young people and adolescents.

Pranitha Maharaj,PhD,is a Professor in the School of Built Environment and Development Studies at the University of KwaZulu-Natal,Durban. Much of her research has focused on issues affecting the family including early parenting,health and care.

References

Ackermann

Velelo

(2015) The position of women in the South African labour force: an overview. PULA: Botswana Journal of African Studies 27(1): 153–168.

Afridi

Dinkelman

Mahajan

(2018) Declining female labour force participation in rural India: the supply side. Working paper, TATA Centre for Development at Uchicago, Chicago, IL, 8 March.

Anyanwu

Augustine

(2013) Gender equality in employment in Africa: empirical analysis and policy implications. African Development Review 25(4): 400–420.

Arndt

Davies

Thurlow

(2018) Urbanization, Structural Transformation, and Rural-Urban Linkages in South Africa. Washington DC: International Food Policy Research Institute.

Assaad

Krafft

(2013) The Egypt labor market panel survey: introducing the 2012 round. IZA Journal of Labor and Development 2(1): 8.

Barrett

Bridges

Semyonov

, et al. (1991) Female labor force participation in urban and rural China 1. Rural Sociology 56(1): 1–21.

Bhorat

Leibbrandt

(2001) Modeling vulnerability and low earnings in the South Africa labour market. In: Bloat

Leibbrandt

Van der Berg

, et al. (eds) Fighting Poverty: Labour Markets and Inequality in South Africa. Cape Town, South Africa: University of Cape Town Press, pp.1–26.

Blau

Kahn

(2017) The gender wage gap: extent, trends, and explanations. Journal of Economic Literature 55(3): 89–865.

Bosch

Barit

(2020) Gender pay transparency mechanisms: future directions for South Africa. South African Journal of Science 116(3–4): 1–6.

10.

Bozzoli

Nkotsoe

(1991) Women of Phokeng: Consciousness Life Strategy and Migrancy in South Africa, 1900–1983. Portsmouth, NH: Heinemann Educational Books.

11.

Brophy

Branson

Daniels

, et al. (2018) National Income Dynamics Study Panel User Manual. Version 1. Cape Town, South Africa: Southern Africa Labour and Development Research Unit.

12.

Brown

Daniels

De Villiers

, et al. (eds) (2012) National Income Dynamics Study Wave 2 User Manual. Cape Town, South Africa: Southern Africa Labour and Development Research Unit.

13.

Chatterjee

Murgai

Rama

(2015) Job Opportunities Along the Rural-Urban Gradation and Female Labor Force Participation in India. Washington, DC: The World Bank.

14.

Che

Sundjo

(2018) Determinants of female labour force participation in Cameroon. International Journal of Applied Economics, Finance and Accounting 3(2): 88–103.

15.

Chen

(2007) The relationship between education and employment: a theoretical analysis and empirical test. Frontiers of Economics in China 2(2): 187–211.

16.

Cock

(1989) Maid and Madams: Domestic Workers Under Apartheid. London: Women’s Press.

17.

Cooper

Stewart

(2021) Does household income affect children’s outcomes? A systematic review of the evidence. Child Indicators Research 14(3): 981–1005.

18.

Dayıoğlu

Kırdar

(2010) Determinants of and Trends in Labor Force Participation of Women in Turkey. Washington, DC: The World Bank.

19.

Delius

(2017) The History of Migrant Labor in South Africa (1800–2014). Oxford: Oxford Research Encyclopedia of African History.

20.

Development Policy Research Unit (2018) Monitoring the Performance of the South African Labour Market: An Overview of the South African Labour Market for the Year Ending 2017 Quarter 2. Cape Town, South Africa: Employment Promotion Program, University of Cape Town.

21.

Doepke

Tertilt

(2019) Does female empowerment promote economic development? Journal of Economic Growth 24: 309–343.

22.

Dubey

Olsen

Sen

(2017) The decline in the labour force participation of rural women in India: Taking a long-run view. The Indian Journal of Labour Economics 60(4): 589–612.

23.

Duflo

(2012) Women empowerment and economic development. Journal of Economic Literature 50(4): 1051–1079.

24.

Dungumaro

(2008) Gender differentials in household structure and socioeconomic characteristics in South Africa. Journal of Comparative Family Studies 39(4): 429–451.

25.

Flagg

Sen

Kilgore

, et al. (2014) The influence of gender, age, education and household size on meal preparation and food shopping responsibilities. Public Health Nutrition 17(9): 2061–2070.

26.

Floro

Komatsu

(2011) Gender and work in South Africa: what can time-use data reveal? Feminist Economics 17(4): 33–66.

27.

Garenne

(2016) A century of nuptiality decline in South Africa: a longitudinal analysis of census data. African Population Studies 30(2): 2403–2414.

28.

Hall

Mokomane

(2018) The shape of children’s families and households: a demographic overview. In: Hall

Richter

Mokomane

, et al. (eds) Children, Families and the State: Collaboration and Contestation. Cape Town, South Africa: University of Cape Town, pp.32–45.

29.

Heath

Jayachandran

(2016) The Causes and Consequences of Increased Female Education and Labor Force Participation in Developing Countries (No.w22766). Cambridge, MA: National Bureau of Economic Research.

30.

Hosegood

McGrath

Moultrie

(2009) Dispensing with marriage: marital and partnership trends in rural KwaZulu-Natal, South Africa 2000–2006. Demographic Research 20: 279–312.

31.

Hosney

(2016) Factors Influencing Female Labor Force Participation in Egypt and Germany: A Comparative Study (SOE Papers on Multidisciplinary Panel Data Research No. 826). Berlin: Deutsches Institut für Wirtschaftsforschung (DIW).

32.

Idowu

Owoeye

(2019) Female labour force participation in African countries: an empirical analysis. Indian Journal of Human Development 13(3): 278–293.

33.

Jain

(2016) Public pre-schooling and maternal labour force participation in rural India. Oxford Development Studies 44(2): 246–263.

34.

Kaur

Nagaich

(2019) Female labour force participation: a comparative analysis between high and less developed districts of Himachal Pradesh. Available at: https://ssrn.com/abstract=3456457 (accessed 7 June 2020).

35.

Kongolo

(2009) Factors limiting women’s involvement in development: lesson from Ithuseng, South Africa. African Research Review 3(4): 13–30.

36.

Lalthapersad

(2003) Historical analysis of African women workers in South Africa during the period 1900 to 2000. South African Journal of Economic and Management Sciences 6(2): 262–273.

37.

Lee

(2009) African Women and Apartheid: Migration and Settlement in Urban South Africa. London: IB Tauris (Tauris Academic Studies).

38.

Lyonette

Crompton

(2015) Sharing the load? Partners’ relative earnings and the division of domestic labour. Work, Employment and Society 29(1): 23–40.

39.

McLanahan

Carlson

(2001) Poverty and gender in affluent nations. In: Smelser

Baltes

(eds) International Encyclopedia of the Social and Behavioral Sciences. Amsterdam: Elsevier, pp.11894–11900.

40.

Maharaj

Dunn

(2022) ‘It’s not easy being a mom especially when you are unemployed’: navigating childcare, a necessary social service in South Africa. Journal of Social Service Research 48(4): 472–484.

41.

Mlambo

(2018) An overview of rural-urban migration in South Africa: its causes and implications. Archives of Business Research 6(4): 63–70.

42.

Mosomi

(2019) An empirical analysis of trends in female labour force participation and the gender wage gap in South Africa. Agenda 33(4): 29–43.

43.

Nagac

Nuhu

(2016) The role of education in female labor force participation in Nigeria. Journal of Economics and Finance 7(1): 56–62.

44.

Naidoo

Nithiseelan

(2022) Vulnerability of South African women workers in the COVID-19 pandemic. Frontiers in Public Health 10(964073).

45.

Naidu

(2016) Domestic labour and female labour force participation. Education 6(76): 7–2.

46.

Nott

(2020) The COVID-19 response for vulnerable people in places affected by conflict and humanitarian crises. The Lancet 395(10236): 1532–1533.

47.

Ningaye

Njong

(2015) Determinants and spatial distribution of multidimensional poverty in cameroon. International Journal of Social Science Studies 3(1): 91–103.

48.

Ntuli

(2007) Determinants of South African Women’s Labour Force Participation, 1995–2004 (Discussion Paper No.3119). Cape Town, South Africa: University of Cape Town and the Institute for the Study of Labor.

49.

Olowa

Adeoti

(2014) Effect of education status of women on market participation in rural Nigeria. American Journal of Economics 4(1): 72–81.

50.

Omarjee

(2020) Coronavirus| SA business alliance expects 1 million job losses, economy to contract by 10%. Available at: https://www.fin24.com/Economy/South-Africa/coronavirus-sa-business-alliance-expects-1-million-job-losses-economy-to-contract-by-10-20200414 (accessed 12 December 2020).

51.

O’Neil

Domingo

Valters

(2014) Progress on women’s empowerment: from technical fixes to political action. Development Progress Working Paper, 6. Available at: https://odi.org/en/publications/progress-on-womens-empowerment-from-technical-fixes-to-political-action/

52.

Ortiz-Ospina

Tzvetkova

Roser

(2020) Women’s employment: What is determining whether women participate in the labor market? How is it changing? Our World in Data, 24 March 2018. Available at: https://ourworldindata.org/female-labor-supply (accessed 12 December 2020).

53.

Osuafor

Maputle

Ayiga

(2018) Factors related to married or cohabiting women’s decision to use modern contraceptive methods in Mahikeng, South Africa. African Journal of Primary Health Care and Family Medicine 10(1): 1–7.

54.

Paul

(2016) Women’s labour force participation and domestic violence: evidence from India. Journal of South Asian Development 11(2): 224–250.

55.

Pirouz

(2004) Have labour market outcomes affected household structure in South Africa? A preliminary descriptive analysis of households. In: Conference on African development and poverty reduction, vol. 15, Cape Town, South Africa, 13 October.

56.

Posel

Casale

(2001) Gender aggregates: women subsistence farmers affect the unemployment count. Agenda 16(49): 82–88.

57.

Posel

Casale

(2009) Sex ratios and racial differences in marriage rates in South Africa. Economic Research South Africa (ERSA) Working paper no. 153. Available at: https://econrsa.org/wp-content/uploads/2022/06/wp153.pdf

58.

Posel

Rogan

(2009) Women, income and poverty: gendered access to resources in post-apartheid South Africa. Agenda 23(81): 25–34.

59.

Posel

(2001) Who are the heads of household, what do they do, and is the concept of headship useful? An analysis of headship in South Africa. Development Southern Africa 18(5): 651–670.

60.

Psacharopoulos

Tzannatos

(1989) Female labor force participation: an international perspective. The World Bank Research Observer 4(2): 187–201.

61.

Rahman

Islam

(2013) Female Labour Force Participation in Bangladesh: Trends, Drivers and Barriers. Geneva: International Labour Organization, DWT for South Asia and Country Office for India.

62.

Sackey

(2005) Female Labour Force Participation in Ghana: The Effects of Education. Nairobi, KE: African Economic Research Consortium.

63.

Sekyere

Bohler-Muller

Hongoro

, et al. (2020) The Impact of COVID-19 in South Africa. African Program Occassional Paper. Washington, DC: The Wilson Center.

64.

Southern Africa Labourand Development Research Unit (2009) National Income Dynamics Study Wave 1: User Document. Cape Town, South Africa: SALDRU, University of Cape Town.

65.

Statistics South Africa (2002a) Labour Force Survey–Statistical Release P0210. Pretoria, South Africa: Statistics South Africa.

66.

Statistics South Africa (2002b) The South African Labour Market. Selected Time-Based Social and International Comparisons. Pretoria, South Africa: Statistics South Africa.

67.

Statistics South Africa (2017) Living Conditions of Households in South Africa: An Analysis of Household Expenditure and Income Data Using the LCS 2014/2015. Pretoria, South Africa: Statistics South Africa.

68.

Statistics South Africa (2019) Quarterly Labour Force Survey Quarter 1: 2019. Pretoria, South Africa: Statistics South Africa.

69.

Statistics South Africa (2020) Quarterly Labour Force Survey Quarter 4: 2019. Pretoria, South Africa: Statistics South Africa. Available at: http://www.statssa.gov.za/publications/P0211/P02114thQuarter2019.pdf (accessed 12 December 2020).

70.

Vapnek

(2009) Breadwinners: Working Women and Economic Independence, 1865–1920. Vol. 159. University of Illinois Press, IL: University of Illinois Press.

71.

Verick

(2018) Female Labor Force Participation and Development. Bonn: IZA World of Labor.

72.

Wyrwich

(2019) Historical and current spatial differences in female labour force participation: evidence from Germany. Papers in Regional Science 98(1): 211–239.

73.

Yakubu

(2010) Factors influencing female labor force participation in South Africa in 2008. The African Statistical Journal 11: 85–104.