The Shape of South African Society and its Challenges

South Africa's negotiated settlement and its transition to democracy reads like a modern fairy tale. A brief review of South Africa's social indicators serves to temper some of optimism about the country's future. The indicators reflect the society's quality of life which has been shaped by its turbulent history. Political “caste formation”, changing political alliances, the reforms intended to forestall the demise of apartheid, and the race for global competitiveness have left indelible marks on the society's social indicators. A comparison of living conditions in South Africa with those of roughly comparable economies indicates that the country lags behind in securing overall and widespread socio-economic upgrading of the population at large. A review of a cross-section of South African indicators and their trends over time shows that South Africa is still a very deeply divided society with a very large backlog in socio-economic development. There is evidence of breakdown in the society's social cohesion. Popular expectations of future quality of life indicate that the euphoria following on the first democratic elections has been replaced by a sense of realism among all sectors of the population. It is concluded that quality of life as reflected in South Africa's social indicators may get worse before it improves. The challenge will be to avoid new forms of economic “apartheid” which would depress the quality of life of marginal sectors of the population at the expense of the economically privileged.     

Satisfied and dissatisfied South Africans: results from the General Household Survey in international comparison

Who are the satisfied South Africans 10 years into democracy? How do material factors contribute to their life satisfaction? These are the questions addressed in this paper. Earlier South African research has consistently found a close positive relationship between life satisfaction and material standards of living in the apartheid and post-apartheid era. Recently, a new source of information has become available to shed further light on the association between material and subjective well-being. In 2002, Statistics South Africa, the country’s official source of statistical information, agreed to ask South Africans participating in the General Household Survey whether they were satisfied or dissatisfied with life. The 2002 General Household Survey (n26’000) used a measure developed for the Euromodule that allows for international comparison. The wide-ranging information contained in South Africa’s official?household survey offers a unique opportunity to explore what makes for satisfied and dissatisfied South Africans in relation to their material living standards. Results indicate that the improved living standards afforded to many black South Africans under democracy are associated with increases in life satisfaction. Furthermore, habituation does not appear to have diluted the positive relationship between living standards and well-being. However, political factors continue to play an important role in shaping subjective well-being. In conclusion, it is argued that material gains might also have restored the pride and dignity denied to black South Africans in the past.     

Confounding adjustment methods for multi-level treatment comparisons under lack of positivity and unknown model specification

Imbalances in covariates between treatment groups are frequent in observational studies and can lead to biased comparisons. Various adjustment methods can be employed to correct these biases in the context of multi-level treatments (> 2). Analytical challenges, such as positivity violations and incorrect model specification due to unknown functional relationships between covariates and treatment or outcome, may affect their ability to yield unbiased results. Such challenges were expected in a comparison of fire-suppression interventions for preventing fire growth. We identified the overlap weights, augmented overlap weights, bias-corrected matching and targeted maximum likelihood as methods with the best potential to address those challenges. A simple variance estimator for the overlap weight estimators that can naturally be combined with machine learning is proposed. In a simulation study, we investigated the performance of these methods as well as those of simpler alternatives. Adjustment methods that included an outcome modeling component performed better than those that focused on the treatment mechanism in our simulations. Additionally, machine learning implementation was observed to efficiently compensate for the unknown model specification for the former methods, but not the latter. Based on these results, we compared the effectiveness of fire-suppression interventions using the augmented overlap weight estimator.