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Income inequality is a pervasive problem in South Africa and particularly affects the Eastern Cape, where poverty levels remain high despite several efforts to address this societal issue. The datasets from 1499 households used for this study, were extracted from the 2021 South African General Household Survey. This study thus examined the distributional pattern of households’ income inequality in Eastern Cape, South Africa, through the use of exploratory data analysis and application of regression-based decomposition of inequality. The EDA results revealed that relatively older households are more concentrated than those young people, female-headed households were also predominant in the study area, while the majority of the households are Black South Africans. The magnitude of the monetary variables’ departure from the mean inferred clear evidence of a widened households’ income disparity or inequality in the study area. The decomposition analysis indicated the contributions of various socioeconomic factors to income inequality. The findings from the regression-based decomposition of inequality found household size, access to basic infrastructure services, possession of assets, internet communication facilities, and households’ population group, as the major drivers of the households’ income inequality in the province, while livelihood diversity has a relatively moderate proportionate contribution. On the other hand, factors such as age and gender of the household head, as well as households’ involvements in agriculture have a minor effect on the households’ income inequality. This paper finally concludes that the relative contributions of each factor contributing to inequality were deemed important for designing effective policy-relevant interventions which can be used to promote economic growth and to address this persistent challenge of income inequality in society.

The primary objective of this research paper was to investigate the determinants of poverty in South Africa in the aftermath of the Covid-19 pandemic. To accomplish this, the study employed data from STATS SA collected in 2021, calculating the poverty status of households based on the national poverty line, and generating a binary variable comprising two categories: poor and non-poor. The research employed descriptive, cross-tabulation analysis and binary logistic regression models to ascertain the associations between selected independent variables (gender, age, household size, population group income, and province) and the dependent variable (poverty status). The findings demonstrated that 62% of the households surveyed were living below the upper-bound poverty line, with 49% classified as poor based on the lower-bound poverty line. Furthermore, the regression analysis revealed that gender, age, and income were significant predictors of poverty status, with females, younger household heads, and those with lower incomes being more likely to be poor. The paper concludes with a set of recommendations for the South African government to mitigate poverty, such as investing in education and skills development, promoting job creation, expanding social protection programs, and enhancing access to fundamental services such as water and sanitation.

The understanding of housing insecurity in the context of poverty reduction remains an important area, especially in the conceptualisation emanating from the animal with multiple head metaphor. Poverty is argued to be viewed as an animal with multiple heads in its multidimensional conceptualisation. We contend that a misdiagnosis in the Multidimensional poverty index (MPI) recognises housing insecurity as a leg of living standards (the other two in the MPI are education and health). The argument that housing should be considered as a dimension or a head, according to the Household Housing Insecurity Index (HHII), emanates from the basic needs understanding, where housing stands among food and health, even higher than education. This paper first presents the Household Housing Insecurity Index (HHII) in the context of multidimensional poverty. Then it uses components of the HHII with available data from Statistics South Africa's general household survey to analyse the housing insecurity profile of households in South Africa collected during the covid-19 pandemic. This helps to paint an initial picture of the impact of the pandemic. Initial because there are still lagged effects unfolding beyond the immediate short-run implications. The Regression analysis results show that total household income, the material used for the construction of the house, crowding, gender and population group are significant predictors of household housing insecurity.

Prior to China’s reform, the poverty count surpassed that of the whole African continent. However, within five years, the Chinese could reverse this narrative. Between 1980 and 2020, China lifted a staggering 800 million citizens out of poverty. By February 2021, the Chinese declared that the war on ‘absolute poverty’ had been won when measured using the standard equivalence of $2.30 a day per person. In consonance with the Chinese feat on poverty, the study examines the strategy adopted in defeating poverty and highlights fundamental lessons that rural developmental stakeholders in South Africa may embrace in achieving a similar feat. To achieve this aim, the study adopted a literature review alongside a desktop analysis in deducing critical lessons from the Chinese war on poverty that may apply to rural developmental stakeholders in South Africa. Hence, an extensive assemblage of earlier studies on Chinese poverty alleviation strategies was looked into systematically. The study highlights potential challenges the South African rural developmental stakeholders may face in an attempt to replicate the Chinese stratagem. The study thus concludes with practical steps rural developmental stakeholders in South Africa may adopt to create viable rural spaces devoid of egregious forms of poverty.