Marriage as an institution has witnessed large changes in the recent past, and South Africa is no exception to this. An article by Palamuleni (2010) shows a decrease in the percentage of women married in South Africa from 35% in 1996 to 30% in 2001. Various other studies on this contraction of the marital institution were conducted in the South African case (see, e.g., studies by Moore & Govender 2013; Posel & Casale 2009; Posel, Rudwick & Casale 2011; Posel & Casale 2013; Posel & Rudwick 2013). These studies provide much insight into the reason for this decreasing trend in marital status, but very little to no analysis of the impact that this trend will have on other economic outcomes (such as employment trends) is provided. It is here that this article aims to make its contribution, by analysing the impact that marital status can have on employment trends.

Along with this decrease in marriage rates, there has been a trend towards greater female labour force participation and employment has increased across the world, including in the developing world. This can be seen in the studies by Coleman and Pencavel (1993), Mehra and Gammage (1999) and Wamboye, Adekola and Sergi (2015), where it was found that women’s employment is increasing over time. Despite the fact that women had higher employment levels, they were more likely to be retained in less desirable employment that required more working hours for lower wages. These working conditions should also be considered when investigating the employment of women, especially when focusing on marriage rates, as it lowers the incentive for women to enter the labour market if they know that the employment opportunities available to them are not as good as what their husband’s opportunities are. Studies concerning the labour supply of women often involve the inclusion of a marital status or rate variable because of the important role it plays in influencing women’s behaviour and outcomes in the labour market (Hamid 1991; Muller & Posel 2008; Ntuli 2007a, 2007b; Yakubu 2010).

Women tend play a prominent role in a country’s economy, which is evident in the multitude of research, suggesting that greater gender inequality results in lower GDP growth (Agenor & Canuto 2015; Dollar & Gatti 1999; Forbes 2000; Klasen 1999; Klasen & Lamanna 2009; Seguino 2000). This research shows that there is a negative relationship between growth and inequality. So even if one does not deem it to be important from a developmental perspective to consider the principled injustices women face because of gender inequality, it is worth noting that this inequality is to the detriment of the economy as a whole.

Within this global context of changes in marriage trends, the South African gender landscape is unique. It is not just distinctive in terms of other foreign countries, but even on the African continent (Hosegood, McGrath & Moultrie 2009). Because of distinct historical discrimination that was institutionalised in South Africa, there are significant detriments to women. To a large extent, the political and the cultural differences in the marriage institution are what set South Africa apart in studies on marriage. Those effects, along with the costs of paying a bride wealth (ilobolo), by certain groups, and the costs of supporting a family, prove to be great barriers to entry into matrimony (Casale & Posel 2013; Casale, Posel & Rudwick 2011; Mkhizwe 2006; Posel & Rudwick 2013; Posel & Rudwick 2015). Therefore, the decrease in the South African nuptiality has been higher than in most other African countries (Hosegood et al. 2009).

The section starts off discussing past international studies that utilise the theories mentioned above, to gain a better and broader understanding of labour supply characteristics around the world. The second part then focuses on studies that investigate how individual characteristics influence labour supply in South Africa. Women in South Africa are less likely to enter the labour market, and when they do, they are less likely to be employed (Barker 2015). For this reason, the interaction of supply and demand needs to be considered. The final part of this literature review will therefore specifically look at studies that have analysed employment in the context of the individual characteristics of labour supply in South Africa.

In this section, the theoretical underpinnings of the study are discussed. Firstly, theory surrounding labour supply will be uncovered. Here the theoretical determinants of a person’s willingness to participate in the labour force are considered. Secondly, labour demand theory will be discussed to show the other side of the labour coin. The final section looks at the household and specifically at marriage and its influence on the labour force. Here the theoretical link is made between the labour market and the marriage market.

The data used for this article were acquired from the National Income Dynamics Survey (NIDS 2016). National Income Dynamics Survey is a nationally representative individual and household survey that collects data of approximately 28 000 individuals and 7300 households. The same individuals are interviewed for each of the four waves of the survey, all four of which are used as a panel in this study. The four waves cover the years 2008, 2010, 2012 and 2014. Observations with missing variables were dropped, leaving a sample of 12 897 observations.

‘Employed’ includes only those people who are working for a wage. The study was conducted in sections with a panel logistic model that progressed through various stages. In the first logistic model, gender was included as a dummy variable so that the difference between the genders can be seen, in general. In the next model, the genders were split up into two separate regressions to observe the more specific differences in the factors affecting the employment and labour force participation of women and men. In the third model, the effect of having biological children living with the women is included.¹

Based on the theory and data availability, the following overarching model can be specified: pr(EMP=1/X)    =F(β+β0GEN+β1∑j=13MST+β2∑j=14GRP        +β3∑j=13EDS+β4∑j=14EDT+β5∑j=15AGE        +β6∑j=19PROV+β7NEI+β8CHILD) where EMP is the dichotomous dependent variable, indicating employment, where 1 is employed and 0 is not employed; X is a vector of explanatory variables; j is the number of dummies; GEN is the dichotomous variable, indicating gender, where 1 is male and 0 is female; MST is a set of dichotomous variables, indicating marital status with the following categories: married, divorced, widowed, cohabiting and never married; GRP is a set of dichotomous variables, indicating population group, with the following categories: black-, mixed race-, Asian- and white people; EDS is a set of dichotomous variables, indicating education at school level, with the following categories: matric, some schooling and no schooling; EDT is a set of dichotomous variables, indicating education at tertiary level, with the following categories: bachelor’s degree, honours degree, master’s and doctoral degrees and other tertiary education; AGE is a set of dichotomous variables, indicating age, with the following categories: 15–25, 26–35, 36–45, 46–55 and 56–65; PROV is a set of dichotomous variables, indicating province of residence, with the following categories: Gauteng, Western Cape, Eastern Cape, Northern Cape Free State, KwaZulu-Natal, North-West, Mpumalanga and Limpopo; NEI is a dichotomous variable, indicating non-employment income, where 1 is receiving NEI and 0 is not receiving NEI and CHILD is the dichotomous variable, indicating the presence of biological children living in the house, where 1 is that there is a biological child living in the house and 0 is that there is not a biological child living in the house.

The employment dependent variable (EMP) was derived from the following question in the NIDS survey (NIDS 2016):

More specifications of each model will be given in the discussion of each model’s result, below.

The following tables provide some summary statistics of the data being used, hinting towards what could be expected from the models. To ensure that the data remain nationally representative, weightings provided by NIDS (2016) were used. Because all of the variables are dummies, the means can be interpreted as percentages for which the dummy is equal to 1. Table 1 shows the summary statistics for the year 2014. From Table 1, it is evident that 45.5% of the respondents are working for a wage. The majority of respondents have either never married or are currently married: 49.1% and 34.6%, respectively. In 2014, all respondents received some form of NEI, which may present some difficulties when the model is run, attributable to there being not enough difference between the groups analysed.

Table 2 is a cross-tabulation of the employed dummy and the gender dummy. From Table 2, a picture of the situation that women in general face in the labour force can be seen. Of all those who are not employed in the sample, 67.9% were women in 2014. Women also make up 53.2% of the employed population, which is a majority, but seeing as they make up 62.92% of the entire sample, this figure is actually quite low. The chi-squared test shows that there is good model fit between gender and employment, and therefore that there is some linear relationship between the two variables.

Table 3 is a cross-tabulation of employment and marital status. Only the widowed and the never married dummies had a lower portion of employed people than the sample average. Of all of the divorced people in the sample, 46.78% are employed, which is the largest of any of the marital statuses.

The panel tabulation for employment in Table 4 shows 31.94% of all (person-year) observations are employed. It will be seen that 54.72% of all individuals had been employed at some stage between the years 2008 and 2014, for which the surveys were conducted. The total of the Between column shows that 140.8% of the individuals were either employed or unemployed. This means that 40.8% of the sample either made a transition from employed to unemployed, or the other way around. The Within column further confirms that there is considerable time variance for the employed variable.

The employment transition matrix for employment in Table 5 indicates the probability that an individual will transition from, or to, employment in the following year. There is a 26.16% chance that an individual who is currently employed will not have a job in the next year. There is, however, only a 16.74% chance that an individual who does not currently have employment will be employed in the following year.

Now that a rough understanding of the data has been overviewed, the model results will be dealt with. The results are presented below for the employment models (Table 6). Thereafter, analysis of the results will be presented for each model separately.

This study was aimed at providing a deeper understanding of how it is that individual characteristics contribute to employment, especially when it comes to marital status.

The results of the logistic regressions showed that although the probabilities of the two regressions differed, similar patterns were apparent in both. The same characteristics that increased the probability of participating in the labour force also increased the probability of being employed. This is also true for the characteristics that contributed least. There are two reasons that can contribute to this phenomenon. The most obvious of these is that those who are employed are, by definition, also participating in the labour force, and they therefore also significantly contribute to the same results. The other reason for this is that it could be reasonable to assume that people know which characteristics are sought by employers, causing those who satisfy these requirements to be less likely to be discouraged and so continue to look for work.

When men are living with their partner, through either marriage or cohabitation, they were most likely to be employed. The opposite is true for women in that they are least likely to be employed when living with their partner. This should give insight to policy-makers in understanding how differently policies can affect women and men in the labour force. Policies that promote cohabitation and marriage, for example, could improve men’s labour force behaviour but distort women’s participation and employment.

Future research could look into the change of these characteristics over time. Other studies could also do more in-depth analysis of the effects of children in and outside of marriage and the implications on labour force participation. Furthermore, it would be beneficial to undertake more research into the interaction between labour demand and supply, rather than only looking at the one or the other.