Abstract
Recent work has shown that the gender gap in income poverty has widened in post-apartheid South Africa even though overall poverty levels have declined. One of the main criticisms of money-metric studies of gendered poverty differences is that income is only one dimension of poverty and that other measures of welfare may better reflect the relative well-being of women and female-headed households. This article presents a multidimensional approach to measuring the gender poverty gap in post-apartheid South Africa. Using data from the 2008 wave of the South African National Income Dynamic Study, the internationally comparable multidimensional poverty index (the MPI) is used to estimate gender differences in a number of different achievements. The findings suggest that the multidimensional gender poverty gap is similar to the poverty gap measured by the conventional money-metric approach at several national poverty lines. However, the MPI poverty differential between female- and male-headed households is slightly narrower than the income poverty gap between these two household types. In order to explore these findings further, the paper decomposes the components of multidimensional poverty by gender and for both female- and male-headed households. The paper concludes by considering how greater investments in health care delivery and in basic services, particularly in rural areas, may yield progress towards gender equality.
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Notes
Government expenditure on social grants increased in the post-2000 period (starting in 2001–2002) with the proportion of gross domestic product (GDP) spent on social grants rising from about 2 per cent in 1994 to 3.5 per cent in 2005 (Seekings 2007). In real terms, annual government expenditure on non-contributory social grants more than doubled between 1994 and 2006 (from <R20 billion in 1994 to just over R40 billion in 2006- in 2000 prices) (Seekings 2007). The social grant with the single largest number of beneficiaries, the state pension, has a strong gender dimension since women (until 2007) were eligible for the grant at an earlier age and because women tend to live longer than men. Accordingly, roughly three quarters of the grant are awarded to women (Burns et al. 2005). Roughly two-thirds of the income from the poorest income quintile is derived from social grants and most of this income comes from the three child grants (i.e. the Child Support Grant, Care Dependency Grant and the Foster Care Grant) (Leibbrandt et al. 2010). In particular, the Child Support Grant is predominantly awarded (roughly 77 per cent of all CSGs in 2005) to working-age African women (on behalf of children) (Williams 2007).
Labour force data show that roughly half of the growth in female employment between the mid-1990s and 2001 can be attributed to jobs in the informal sector (Casale 2004; Casale and Posel 2002). Moreover, between 1995 and 2001 roughly a quarter of the total female workforce remained in the domestic sector while the percentage that was engaged in informal self-employment increased dramatically from 6 to 20.6 per cent (Casale 2004). The increase in female employment was, therefore, largely concentrated in the informal sector where wages are lower, employment is less secure and benefits are non-existent.
However, there has been some work which has considered other non-income measures of gender inequality— e.g. asset based measures of poverty (see Deere et al. 2012).
In 2010, the MPI replaced the Human Development Index (HDI) as the official way in which the UNDP ranks countries.
The 2003 Demographic and Health Survey is another potential data source but the data are not widely available to researchers and there are a number of well documented concerns with data quality (Finn et al. 2013).
This poverty line is very close to the poverty line of R322 per capita monthly income (in 2000 prices) that has been widely used in the post-apartheid poverty literature (Statistics South Africa 2008).
The MPI headcount is expected to be far lower than poverty headcounts at South Africa’s official poverty lines because it was designed as a measure of ‘acute’ deprivation (Alkire and Santos 2010). As such, the cut-offs assigned for each indicator denote ‘deprivations in very rudimentary services and core human functionings’ (Alkire and Santos 2010, p. 7).
The food poverty line is calculated as the minimum level at which an individual could survive in terms of caloric intake. The equivalent money-metric line of R142 is therefore well below even the lowest possible poverty line that is used in the South African context. However, because the analysis is concerned with gender differences in well-being the equivalent line is used for comparison purposes only and is not intended to denote a particular level of resources at which an individual or household is considered poor.
The sex poverty ratio is measured as the ratio of women’s poverty to men’s poverty such that, when the ratio is >1, women are more likely to be poor than men (see McLanahan et al. 1989).
Individuals are poor if they live in a household which is deprived in more than a third of the weighted indicators identified in Table 1.
The censored headcount is the proportion of the population multidimensionally poor and simultaneously deprived in a particular indicator.
One potential limitation of the MPI measure is that individuals living in households with no children (or no history of children) are identified as non-deprived in school attendance and child mortality (as are households with no women). To some extent, then, the global MPI is sensitive to the composition of households. Santos and Alkire (2011), however, report preliminary evidence suggesting that the bias of MPI measures to households with children and women of reproductive age is not significant.
About 39 per cent of all female-headed households live in rural areas compared with about 30 per cent of male-headed households. Moreover, a third of female-headed households live in traditionally under-serviced former tribal areas but only 20 per cent of male-headed households are located in these areas (own calculations from NIDS 2008).
Meth (2014, p. 151) writes of the MPI in South Africa, ‘… if the dimensions of health, education and standard of living, and the set of indicators by which they can be measured were really guided by the capabilities approach, then either the approach itself, or their use of it is too blame for the fact that they could not even detect a major epidemic, a health and education sector in crisis, and large-scale social unrest because of delivery failure on the part of the South African government’.
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Rogan, M. Gender and Multidimensional Poverty in South Africa: Applying the Global Multidimensional Poverty Index (MPI). Soc Indic Res 126, 987–1006 (2016). https://doi.org/10.1007/s11205-015-0937-2
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DOI: https://doi.org/10.1007/s11205-015-0937-2