Economic mobility in South Africa: evidence from household survey data

High levels of inequality, poverty and unemployment are some of the most substantial challenges facing post-apartheid South Africa. Most of the research addressing these questions has used micro datasets to compare snapshots of welfare over time. Although these studies are both interesting and useful, they have been unable to extend their analysis into a nationally-representative dynamic setting, due to the lack of available data. The paucity of large longitudinal datasets has also limited the number of studies of economic mobility, which allows researchers to track the welfare measures of the same individuals over time. This means that while we know a great deal about how South Africans are doing at a particular point in time, we know far less about how they are faring dynamically. Understanding how and why economic mobility happens in South Africa is therefore a question that demands attention. From both a distributive justice as well as a policy point of view, the distinction that arises when we drop the assumption of anonymity and move from a cross-sectional measure of welfare to a dynamic one is important. This is because many of the conclusions about longer-run welfare are dependent on the level of economic mobility present in society. This study contributes to the body of work on welfare in South Africa by addressing three different aspects of economic mobility. The first of these is about how a particular kind of measurement error in household surveys is best detected, and what effect its presence has on the understanding of labour market mobility. The second is about how best to model money-metric poverty dynamics in South Africa in order to better understand who escapes poverty and who enters poverty over time. The third is about how the persistence of intergenerational earnings should be calculated in a society with high unemployment, and what the role of education is in shaping these mobility dynamics

The Policies for Reducing Income Inequality and Poverty in South Africa

Trends in inequality, poverty, and redistribution in post-apartheid South Africa have received intense attention especially in terms of measuring inequality and poverty levels and the proximate causes of these levels. We review this literature and find a set of established trends. Inequality levels have increased but the face of inequality has changed with present-day inequality displaying a lessened racial make-up than under apartheid. In contrast, poverty has decreased but is still bears the strong racial makers of apartheid. The labour market continues to drive inequality. A related literature has concentrated on fiscal redistribution in South Africa after the transition, arguing that social policies are well targeted towards the poor with social grants being central in lifting people out of poverty. At the same time, these policies have not succeeded in reversing inequality trends and in providing equal opportunities for all South Africans. To bulk of paper probes this further. We use fiscal incidence analysis to show that redistribution increased slightly since 1993, that this redistribution is higher than in Latin America but far below European levels. Second, looking at spending for all social services we find a mixed picture. There has been an increase in spending since the end of apartheid on social policy and for a number of social policy items in the progressivity of this spending. At the same time, spending has not increased as a percentage of GDP and has become less progressive for social grants. Finally, we examine education policy in more detail. We find that the importance of tertiary education, as a predictor of income has increased considerably whereas individuals with low or incomplete secondary education were worse off in 2008, compared to 1993. Second, we find that state spending on education has increased since the early 1990s. The spending gap between rich and poor provinces has become much narrower but spending equality has not been reached. The academic achievements of students display high inequality, compared to international standards and there is also evidence that the capabilities of students have decreased, rather than increased, suggesting that increased spending has not translated into an increase in the quality of education provision.

The significant decline in poverty in its many dimensions since 1993

The measurement of poverty should include dimensions of well-being that cannot be measured in monetary terms. Data on health, education and standards of living can be used to calculate a so-called Multidimensional Poverty Index (MPI). Results suggest that both the prevalence and the intensity of multidimensional poverty fell significantly from 1993 to 2010. The decline in multidimensional poverty is much greater than the decline in poverty as measured in terms of income and/or expenditure. Better social services and infrastructure have played a large role

Rare Copy Number Deletions Predict Individual Variation in Intelligence

Phenotypic variation in human intellectual functioning shows substantial heritability, as demonstrated by a long history of behavior genetic studies. Many recent molecular genetic studies have attempted to uncover specific genetic variations responsible for this heritability, but identified effects capture little variance and have proven difficult to replicate. The present study, motivated an interest in “mutation load” emerging from evolutionary perspectives, examined the importance of the number of rare (or infrequent) copy number variations (CNVs), and the total number of base pairs included in such deletions, for psychometric intelligence. Genetic data was collected using the Illumina 1MDuoBeadChip Array from a sample of 202 adult individuals with alcohol dependence, and a subset of these (N = 77) had been administered the Wechsler Abbreviated Scale of Intelligence (WASI). After removing CNV outliers, the impact of rare genetic deletions on psychometric intelligence was investigated in 74 individuals. The total length of the rare deletions significantly and negatively predicted intelligence (r = −.30, p = .01). As prior studies have indicated greater heritability in individuals with relatively higher parental socioeconomic status (SES), we also examined the impact of ethnicity (Anglo/White vs. Other), as a proxy measure of SES; these groups did not differ on any genetic variable. This categorical variable significantly moderated the effect of length of deletions on intelligence, with larger effects being noted in the Anglo/White group. Overall, these results suggest that rare deletions (between 5% and 1% population frequency or less) adversely affect intellectual functioning, and that pleotropic effects might partly account for the association of intelligence with health and mental health status. Significant limitations of this research, including issues of generalizability and CNV measurement, are discussed

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Toxic effects of phenothiazines on the eye

Publications about the retinotoxic action of phenothiazine derivatives led the author to undertake an ophthalmological investigation in two psychiatric hospitals in The Netherlands. The pharmacological actions of phenothiazine preparations are listed and a survey of the phenothiazine derivatives which are at present in use is given. Some retinotoxic substances are discussed and a survey is given of the literature on the ocular complications of phenothiazine therapy. The eyes of 561 patients were examined. of whom 541 are included in this study. 343 of these patients(63.4 %) were found to have retinopathy. The correlation between the retinopathy and the total dose of phenothiazine preparations taken. and between the retinopathy and the duration of treatment. was highly significant. The correlation between the retinopathy and the average daily dose taken was significant. The retinopathy was associated with a reduced standing potential of the eye. as determined by electro-oculography. It was possibly responsible for diminished visual acuity in some cases, and for an abnormally large proportion of protans in the group of patients with colour defects. It was not possible to ascribe a more severe retinotoxic action to one or more specific phenothiazine derivatives than to others. In the author's opinion regular examination of the eyes of patients who are being treated with phenothiazine preparations in high dosage and for for a long period of time is indicated

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

A National Minimum Wage in the Context of the South African Labour Market

Understanding the composition and wage structure of the South African labour market is crucial in the progressing national minimum wage debate in the country. This study highlights the centrality of wages in household income, and in determining inequality and poverty levels in the county. It then charts key trends in the labour market, before presenting a snapshot of the composition and earnings of the workforce in the current environment. A definition for a “working-poor” threshold is developed in the paper by linking individual earnings to household poverty. Finally, we consider the differential coverage that a national minimum wage would have on different sectors and demographic groups in the economy.This paper forms part of the National Minimum Wage Research Initiative (NMW-RI) undertaken by CSID in the School of Economics and Business Science at the University of the Witwatersrand. The NMW-RI presents theoretical and case-study evidence, statistical modeling and policy analysis relevant to the potential implementation of a national minimum wage in South Africa. For more information contact Gilad Isaacs, the project coordinator, at [email protected] or visit www.nationalminimumwage.co.za. SALDRU is grateful to the NMW-RI for making this paper available as a SALDRU Working Paper

Estimating the Effects of South Africa's Youth Employment Tax Incentive – An Update

Our previous study of the effects of South Africa’s Employment Tax Incentive (ETI) (Ranchhod and Finn, 2014) found that the ETI did not have a statistically significant impact on youth employment probabilities in the first six months of 2014. In this update we extend the period of analysis from six months to all twelve months of 2014 and find that this does not alter our qualitative findings. These are that the ETI has not resulted in a statistically significant change in the probability of young people finding jobs, despite its cost of R2 billion over the first year of its existence. Furthermore, there is no evidence to suggest that the introduction of the ETI resulted in an increase in the level of churning for youth in the labour market.We acknowledge support from the National Research Foundation’s Human and Social Dynamics in Development Grand Challenge