Multidimensional poverty in South Africa in 2001–16

ABSTRACT

This study uses the Census 2001 and 2011 as well as Community Survey 2007 and 2016 data to derive a multidimensional poverty index in South Africa for each year, before assessing the changes in non-money-metric, multidimensional poverty over time. Both the incidence and intensity of multidimensional poverty decreased continuously, and these declines were more rapid than that of money-metric poverty. The decrease in multidimensional poverty between 2001 and 2016 was most rapid for female Africans residing in rural areas in Eastern Cape and KwaZulu–Natal provinces. Multidimensional poverty was most serious in numerous district councils in these two provinces, despite the fact that poverty decline was also most rapid in these district councils. The results of the multidimensional poverty index decomposition indicated that Africans contributed more than 95% to multidimensional poverty, while unemployment, years of schooling and disability were the three indicators contributing most to poverty.

KEYWORDS:

• Multidimensional poverty
• multidimensional poverty index
• South Africa

JEL:

• J30
• J32

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 These programmes include the Reconstruction and Development Program, Growth, Employment and Redistribution, Accelerated and Shared Growth Initiative of South Africa and the more recent New Growth Path and National Development Plan.

2 For more detailed discussion of subjective poverty measures, refer to Govendor et al. (2006) and Jansen et al. (2015).

3 Refer to Yu (2016) for a more detailed discussion.

4 For detailed explanation of this approach, refer to Hirschowitz (2000:76–79).

5 For more information on the Bristol method, refer to Gordon et al. (2003).

6 Van der Walt & Haarhoff (2004) provide a thorough explanation of this composite index approach.

7 This is also the main drawback of the other statistical approaches mentioned in Section 2.

8 In the event where the contribution of poverty by a particular sub-group greatly exceeds its population share, it implies a very unequal distribution of poverty, for example, where females account for only 40% of the total population but contribute 90% to the multidimensional poverty of the country.

9 This means that someone is only included as part of the poor in an indicator if both of these two conditions are met: x_i< z_i and c_i ≥ 1/3.

10 Noble et al. (2006, 2010) and Noble & Wright (2013) also used Grade 7 as the threshold.

11 Disability was also included in recent local (Frame et al., 2016; Omotoso & Koch 2017) and international (e.g. Suppa, 2015; Hanandita & Tampubolon, 2016; Martinez Jr & Perales, 2017) studies.

12 Table A10 shows the MPI results by municipality. Since the geographical demarcation of municipalities has changed drastically during the 15-year period, this study rather focuses on MPI poverty by DC.

13 For detailed explanation of this approach, see Raghunathan et al. (2001), Lacerda et al. (2008) and Yu (2009).