A Multidimensional Poverty Index for Gauteng Province, South Africa: Evidence from Quality of Life Survey Data

Abstract

This paper estimates a Multidimensional Poverty Index for Gauteng province of South Africa. The Alkire-Forster method is applied on Quality of Life survey data for 2011 and 2013 which offer an excellent opportunity for estimating poverty at smaller geographical areas. The results suggest that the Multidimensional Poverty Index for Gauteng is low but varies markedly by municipality and by ward, as well as across income groups. Not only are low income households more likely to be multidimensionally poor, they also suffer from higher intensities of poverty. Multidimensional poverty is highest in areas of low economic activity located on the edges of the province. However, pockets of multidimensional poverty do prevail even in better performing municipalities. Government, at all spheres, needs to devise policies that channel investments into lagging areas and avoid approaches that are indifferent to the heterogeneities that exist across localised geographical extents.

Multidimensional poverty in South Africa in 2001–16

“…The floor type and electricity access (only captured in 2011 and 2016 respectively) indicators are excluded from the MPI approach, but are replaced by dwelling type, overcrowding and refuse removal frequency indicators. The respective cut-off points for these indicators are as follows: residing at formal dwellings (same as StatsSA 2014); more than two persons per room (as adopted in Mushongera et al 2017;Omotoso & Koch 2017); less than once a week or no concrete refuse removal system (same as Adams et al 2015). Finally, asset ownership only takes television, landline telephone, cellular telephone, fridge, computer and radio into consideration as they are the only asset variables asked across all four datasets.…”

“…The later studies by Noble et al (2010) as well as Noble & Wright (2013), using the same data, adopted a similar approach to derive the index of multiple deprivation, but the former study focused on the Eastern Cape while the latter study examined the former homeland areas. Noble et al (2006Noble et al ( , 2010, Noble & Wright (2013), Burger et al (2017), Mushongera et al (2017) and StatsSA (2014StatsSA ( , 2017 are rare studies that examined multidimensional poverty by smaller geographical areas. Of these studies, StatsSA (2014) and Burger et al (2017) derived multidimensional poverty trends over time.…”

Multidimensional Poverty Analysis of Urban and Rural Households in South Africa

“…Other studies have also used composite indices approach to develop national and provincial indices of multiple deprivation (Klaseen, 2000;Noble et al, 2006;Noble, Barnes, Wright, and Roberts, 2010). More recently, Finn et al, 2013;Woolard et al, 2013;Stat SA, 2014;Alkire and Santos, 2014;Ntsalaze and Ikhide, 2016;Mushongera et al, 2017;Frame et al, 2016;Rogan 2016;Pasha, 2016;OPHI, 2015;OPHI, 2017) have considered multidimensional poverty vis-a-vis gender dimension, youth dimension and cash grants at the national level using the Alkire and Foster (2011) technique and presenting mostly descriptive inferences. The Alkire and Foster MPI methodology has many advantages, which include its decompostional ability of helping to know how much each indicator and each dimension contributes to overall poverty and its ability to allow poverty comparisons across countries and regions of the world, as well as within-country comparisons between regions, ethnic groups, rural and urban areas, and other key household and community characteristics .…”

“…This indicates that female headed households are significantly more likely to be multidimensionally poor than male counterparts. A spatial analysis of MPI in the Gauteng province of South Africa by Mushongera et al (2017) found out that low income earning households, poor accessibility to infrastructures and unemployment (as a result of low concentration of economic activities in specific locations) increases the likelihood of a household to be multidimensionally poor. Applying generalized additive model (GAM) using regression splines, Ntsalaze and Ikhide (2017) assessed the existence of critical tipping points specifically for age, government grants, education, household size and household debt service-to-income on multidimensional poverty.…”

“…Considering the geographical effect of the Apartheid system of government in South Africa, it is known that the extent of effects differs across provinces and localities. As noted by Mushongera et al (2017), racial and location imbalances persist till to date, despite government efforts in the past 20 years. These imbalances are further seen in the differences in geographical space development, and in prices of goods and services as well.…”