Poverty remains one of the main socio-economic issues in South Africa and is more prevalent amongst black African females, children aged below 18 years, and rural residents with low levels of education. Many local studies focused on money-metric measures in determining poverty levels but few studies examined factors other than income that are also important to multidimensional non-income welfare. Therefore, this study re-examined the extent of multidimensional poverty in South Africa with the derivation of a Multidimensional Poverty Index (MPI) by considering certain dimensions and indicators that have been ignored in previous studies, such as isolation, vulnerability, voicelessness and powerlessness. Using the General Household Survey (GHS) 2018 data, this study adopted two methods to derive the MPI: Method [A] included the above-mentioned additional dimensions and indicators whereas method [B] only included the indicators from the three commonly considered dimensions (education, health and living standards). Focusing on the results from method [A], the descriptive results indicated deprivations were most profound for unemployed African females living in rural areas in Eastern Cape and Limpopo. These deprivations were the highest in the transport asset, sanitation type, refuse removal frequency, water and receipt of post/mail indicators. Furthermore, the econometric analysis found that unemployed Coloured males residing in rural areas in Eastern Cape, KwaZulu-Natal andGauteng were significantly more likely to be MPI poor.The findings also indicated the overall MPI increased as additional dimensions and indicators are added to the method. The increase mainly emanated from the intensity of poverty, as the headcount values were lower. However, the contributions across personal characteristics, dimensions and indicators varied. Furthermore, the newly added dimension (isolation and vulnerability) suggests relatively high deprivations, as well as the indicator receipt of post or mail, has the second-highest deprivation score. It was also found that the education dimension's contribution to multidimensional poverty significantly decreased as additional dimensions and indicators were added to derive the MPI. Lastly, contrary to previous studies, Gauteng was one of the provinces associated with greater MPI poverty likelihood.
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