Socio-economic inequality in the City of Tshwane, South Africa: a multivariable spatial analysis at the neighbourhood level
- Christian Hamann, Andre Horn
- Date of publication: 31 March 2022
Inequality and segregation are persistent social concerns in developed and developing cities around the world. This is because high levels of inequality can have severe negative effects on the structure, functioning, economic growth and liveability of cities. South Africa remains one of the most unequal countries in the world and research indicates that inequality, notably its spatial dimensions, is a significant challenge at the national, regional and local level. Urban form in the country is significantly shaped by spatial legacies of apartheid, which tie race, class and space together in an intricate and unique way. Despite numerous responses by all levels of government, policy responses to inequality have been unable to make a significant impact towards creating a more equal society. There is an abundance of aspatial studies about inequality that use one variable (usually income) and various statistical descriptors (like Gini coefficient, Lorenz curve and Theil index) to describe inequality trends in South Africa. Studies that focus on the spatial dimensions of poverty and inequality in South African cities are undertaken less frequently. This paper contributes to filling the gap in spatial perspectives by highlighting the spatial dimensions of socio-economic inequality and relating the analysis to various other spatial trends and processes that shape the study area.
The aim of this paper is to provide a multidimensional spatial perspective (using income, employment and highest education levels) on patterns of socio-economic inequality in the City of Tshwane, South Africa. The analysis responds to the extent to which class-based segregation exists in South African cities. Data for median household income, unemployment and highest education levels were used to map socio-economic status at the neighbourhood level, and to understand spatial changes between 2001 and 2011. The approach of the paper is to separately analyse the spatial patterns of median household income, unemployment and education qualifications. The paper argues that these three socio-economic variables constitute key underlying determinants of overall socio-economic inequality in the City of Tshwane, as they fundamentally shape socio-economic opportunity and are highly correlated with each other.
The results show that there is a strong mutually reinforcing relationship between median household income, unemployment levels and tertiary education qualifications which constitute underlying influences to socio-economic inequality and segregation. The spatial patterns of the three socio-economic variables used in the analysis highlight the relationship between the concentration of high annual median household income, low unemployment and the concentration of tertiary education qualifications, and vice versa. It then becomes clear that high and low socio-economic status is spatially polarised. Low socio-economic status is associated to the largest proportion of residents while a minority occupies the top end of the socio-economic spectrum. Between 2001 and 2011, the socio-economic scores for most neighbourhoods (“rich” and “poor”) did not change significantly. Increases in socio-economic scores were mostly seen on the periphery of the city while socio-economic scores near the inner city were more likely to decline during this period.
Understanding the patterns of socio-economic inequality in the City of Tshwane is very important because the municipality includes such a diverse range of urban and peri-urban functions. This analysis can help local government develop spatially targeted, but integrated and coordinated urban policies towards developing a more equal city. The analysis over time, and the broader context of urban development suggest that the historic contestations of race and space cannot be discounted as contributors to present socio-economic inequality. Inclusionary (and mixed) housing developments are essential to give all socio-economic groups access to the opportunities in ‘wealthy’ spaces. It is also important to reduce spatial mismatch between residential areas and employment areas and improve socio-economic mixing within neighbourhoods. Future investigations could also investigate the importance of the quality and quantity of social services (health and education), as well as amenities and public open spaces in affluent areas compared to deprived areas, to further understand the reproduction of past spatial patterns.
“The ‘rich’ and the ‘poor’ in the City of Tshwane are worlds apart (spatially and socially) in terms of their socio-economic status and their lived experience of the city. The emergence of the middle class does serve as a counter-argument for social polarization, but its extent is still very limited and has little influence on the spatial structure of the study area”.
- Neighbourhoods with high socio-economic status are spatially clustered in the south-east of the City of Tshwane, with limited pockets of wealth elsewhere. This is also where a relatively small proportion of the population resides.
- Low socio-economic status neighbourhoods are concentrated in and around legacy townships or dispersed along the diverse but relatively deprived periphery of the city. This is also where the majority of the population resides.
- The spatial structure of the City of Tshwane represents a divide between the ‘richer’ south-east and the ‘poorer’ north-west of the city. These two vastly different socio-economic worlds are juxtaposed in the same administrative boundaries.
- Changes in the socio-economic status of sub-places between 2001 and 2011 confirm that spatial patterns of socioeconomic inequality have not shifted significantly towards a more equal city, thus reinforcing past spatial patterns.
Hamann, C. and Horn, A.C. (2022). Socio-economic inequality in the City of Tshwane, South Africa: a multivariable spatial analysis at the neighbourhood level. GeoJournal. 87. p2001-2018.
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