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This paper investigates the impact of the COVID-19 pandemic and related policy measures on livelihoods in urban South Africa. Using qualitative research methods, we analyse two rounds of semi-structured phone interviews, conducted between June and September 2020 in the township of Khayelitsha, Cape Town. We contextualise these by presenting a snapshot of the nationwide dynamics using quantitative panel data. Our findings describe how the shock of the COVID-19 pandemic has deepened the economic vulnerability which preceded the crisis. Survivalist livelihood strategies were undermined by the economic disruption to the informal sector, while the co-variate nature of the shock rendered social networks and informal insurance mechanisms ineffective, causing households to liquidate savings, default on insurance payments, and deepen their reliance on government grants. In addition, the impact of the pandemic on schooling may deepen existing inequalities and constrain future upward mobility. [ABSTRACT FROM AUTHOR]

This paper demonstrates a simple methodological sequence for the statistical calculation of a context-sensitive quality of life index especially suitable for use in the Global South, i.e. lower and middle income countries. We draw on the large (n = 24,889), area-sampled, survey dataset of the fifth Quality of Life Survey (QoL V, 2017/18) of South Africa's Gauteng province, conducted by the Gauteng City-Region Observatory. Using overtly exploratory analysis, and a fully reflective conception of indicators, we apply exploratory factor analysis (EFA) in two stages to generate our model, while defending our approach methodologically and empirically and indicating its philosophical foundations. We contrast this approach to previous analyses using the QoL I (2010) data, which defined their dimensions and assign their indicators in advance, drawing on literature and indexes largely from the Global North. We ran series of EFAs on 60 longitudinally available variables from the QoL V data set. This allowed us to determine the optimal number of factors/dimensions, guided by established criteria and the interpretability of the grouped indicators. Each dimension score is calculated arithmetically, using the indicators' factor loadings as weights. Then the dimensions, weighted by their eigenvalues, are aggregated into the composite index, scaled to run from 0 to 100. The resulting seven-dimension, 33-indicator model was validated through confirmatory factor analysis (CFA) on the QoL V data; and its configural invariance, i.e. whether the pattern of indicators amongst the factors holds over time, was checked against the two previous QoL datasets and confirmed. This validates the ability of the approach outlined to generate a stable index. Analysis of the resulting quality of life index by race and municipality reveals trends consistent with the South African context. Overall, the White population group has the highest measured quality of life index, followed in turn by the Indian/Asian, Coloured and Black African population groups. Changes in the quality-of-life ranking of the nine municipalities comprising Gauteng province may be observed over time, using the validated previous models. The value of the exploratory approach in enabling context to influence the index construction is highlighted by the emergence of a distinct dimension of bottom-up political voice, which is relevant for democratic governance. [ABSTRACT FROM AUTHOR]