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Determinants of attrition in NIDS-CRAM Waves 1 & 2
No full textThis paper investigates the determinants of attrition between Waves 1 and 2 of the National Income Dynamics Study – Coronavirus Rapid Mobile Survey (NIDS-CRAM). The number of successfully interviewed respondents reduced from 7073 in Wave 1 to 5676 in Wave 2, which represents almost 20 percent of the sample. We fit probit regression models to predict the determinants of attrition and estimate marginal effects for four different specifications of the model. A useful finding is that attrition appears to be random across all four regression models based on the observed covariates, when measured by standard goodness of fit statistics. However, one of the most important findings is that respondents who underwent Covid-19 tests are 3 percent more likely to drop out of the survey. While this rate is low, it is a worrying trend that must be closely monitored in future Waves because it will negatively affect the efficacy of the survey to track Covid-19 testing behaviour. More generally, we find that attrition in NIDS-CRAM is not based on the same observable characteristics as its predecessor NIDS, which showed clear evidence that it was correlated with higher income households. Attrition is also not influenced by how often respondents previously participated in NIDS. It is affected by language of the interviewer, the sample batch the respondent was in during Wave 1, and contact effort by the survey organization. The most important factors influencing attrition are therefore related to survey operations rather than respondents. That said, researchers still need to conduct their own investigations about whether attrition on observable characteristics of respondents affect their estimation samples.This paper was funded by the CRAM study and is available on their website. The authors are grateful to Cally Ardington for reviewing this paper. All errors & omissions remain the responsibilities of the authors
Post-school Education and Training in South Africa
No full textPost-school Education and Training in South Africa (PSET): Pathways, qualifications, and organisations making up the system. PSET refers to learning that takes place outside of basic education - where basic education refers to all learning from grade R to 12. PSET in South Africa, therefore, consists of all education and training provision for those who have completed school, those who did not complete their schooling, and those who never attended school.1 The DHET, formed in 2009, is responsible
for bringing together all PSET institutions
The labor market and poverty impacts of covid-19 in South Africa
No full textWe use newly-released South African data to present the first estimates of COVID-19-related employment and poverty impacts in a developing country. We observe a 40% decline in active employment. Half of this comprises job terminations, suggesting persistent labor market effects. Initially vulnerable groups are disproportionately affected. Exploiting the dataset's panel dimension and comparing lockdown incomes of job losers to re-weighted job retainers, we estimate that 20-33% of job losers fall into poverty. Only 20% of those temporarily not working received the intended relief, while a third of job losers had no access to any major form of social protection.The authors contributed equally to this work. Author order is randomized. We are very grateful to Debbie Budlender, Reza Daniels, Arindrajit Dube, Leila Gautham, Edward Glaeser, Claudia Goldin,Alyssa Huberts, Lawrence Katz, Asim Ijaz Khwaja, Gabriel Kriendler, Pramila Krishnan, Murray Leibbrandt, Aarti Malik, Suresh Naidu, Vimal Ranchhod, and participants at the Development Urban Public workshop at Harvard Economics Department and NIDS-CRAM Labor workshop, for helpful comments and discussions. We are also especially grateful to all those on the NIDS-CRAM team - without their hard work producing the data under extreme time pressure this research would not have been possible. The authors gratefully acknowledge funding for the project through NIDS-CRAM. Any errors remain our own
Youth emotional well-being during the COVID-19-related lockdown in South Africa
No full textThe COVID-19 pandemic has not only caused serious threats to people’s physical health but has also triggered a wide range of mental health problems. This study sought to assess the prevalence of, and factors associated with depressive symptoms among young people age 18-35 years during the COVID-19 related lockdown in South Africa. An online survey was conducted leading to a sample of 11 700 participants covering all the provinces of the country; of these 5 693 participants responded to all the emotional well-being questions. The 10-item Centre for Epidemiological Studies on Depression Scale (CES-D 10 Scale) was used to establish the prevalence of, and factors associated with depressive symptoms. Our results show a prevalence of depressive symptoms of 72% among the young participants, which is high and worrisome when compared to mental health results among youth gathered outside of the COVID-19 context. When disaggregated by various characteristics, the prevalence of depressive symptoms was found to be higher among older, female, and white youth and those with higher education. Multivariate regression analysis further shows that depressive symptoms were positively associated with being female, being older, having higher education and residing in urban informal areas, while they were negatively associated with being employed and offering family care. These findings suggest that while combating the COVID-19 pandemic, the government needs to pay closer attention to the mental health issues among young people and the effects of lockdown regulations on mental health, to avoid longer-term negative effects of mental ill-health among youth
Black Tax. Do graduates face higher remittance responsibilities?
No full textA large portion of South Africa’s Black population remains restricted by intergenerational education
and economic disadvantages. Just 10% of Black individuals have a high-value qualification, and
poverty is a daily threat for 76% of South Africans. This risk is greatest among the Black population. In the face of adversity, private transfers between family members are important in the provision of economic and social security. Recently, media discourse on black tax has highlighted the responsibilities that individuals face to financially support their family networks.Support from the Kresge Foundation for this research activity is gratefully acknowledged. Opinions expressed
and conclusions arrived at are those of the authors and cannot necessarily be attributed to the Kresge Foundation
What is a firm census in a developing country? An answer from Ghana
No full textA burgeoning literature in economics uses firm census data to provide explanations for the very large differences in income per capita across countries. Much of this literature takes for granted that the coverage of firm censuses across and within countries is similar. In this paper we use data from four Ghanaian firm censuses conducted between 1962 and 2014 to show that the coverage of each census was very different. Treated as is, the four censuses show dramatic and unbelievable changes in the scale of manufacturing production in Ghana over this period. As a result, we examine and document important changes in what undertaking a “firm census” has meant over 50 years in Ghana, as well as documenting variation in the coverage of firm censuses from several other African countries. We show that it is possible to obtain a believable evolution of the firm size distribution in Ghana over the period for which we have firm microdata, but that this requires substantial work to understand how the coverage of each firm census has varied over time. Our paper shows that the coverage of firm censuses both within and across countries can differ quite dramatically, and that this can impact research that uses firm census data.This paper has been funded by PEDL (Private Enterprise Development in Low Income Countries).
We thank the Ghanaian Statistical Service (GSS) for making the data available and to Anthony
Krakah, Isaac Dadson and Jacqueline Anum from GSS for the extremely helpful assistance in
understanding the data used in this paper. We thank Francis Teal for providing several helpful
suggestions and comments, extra data and census documentation. Participants at SALDRU, IZA/
GLM (Addis Ababa), PEDL (LSE), CAED (Michigan) and CSAE (Oxford) conferences and seminars
provided very useful comments.
Part of the writing of the paper took place while Andrew Kerr was on sabbatical in Oxford from
January to March 2020 as a CSAE fellow. I thank the CSAE for their generous hospitality.
As part of the PEDL project we will be making a sample of the 2014 IBES publicly available.
The population census data from Ghana that we use in this paper was downloaded from IPUMS
International at the University of Minnesota. We acknowledge both IPUMS and the Ghana
Statistical Service in using this data.
This is a joint Saldru and DataFirst working paper
Developing Siyaphambili: A Stronger South African Nation Website. Moving towards a unified goal to combat inequality and unemployment
No full textSouth Africa has one of the highest levels of income inequality in the world (Leibbrandt et al., 2018). Education, both in how it is distributed in the population and in how it is rewarded in the labour market, plays an important role in explaining the distribution of income, particularly income from labour market earnings, and hence inequality (Lam et al., 2015). Since the end of apartheid, South Africa has seen rapid improvements in average years of education, accompanied by declining racial differences and educational inequality. Yet, this has not translated into declining earnings inequality as might have been expected. A closer look at the data reveals that the aggregate picture of decreasing educational inequality hides persistent racial inequalities in post-school education attainment, which together with large and increasing premiums to these high value qualifications, have been inequality inducing. Siyaphambili motivates that increasing overall levels of post-school education attainment, particularly by decreasing between-population group attainment gaps, could contribute towards reducing income inequality
The labour market and poverty impacts of covid-19 in South Africa: An update with NIDS-CRAM Wave 2
No full textWe use Wave 2 of NIDS-CRAM data to provide an update to our original estimates (Jain et al., 2020) of COVID-19-related employment and poverty impacts in South Africa. Compared to the most stringent phase of South Africa's lockdown in April, we find evidence of a limited recovery in the labour market, a decrease in poverty, and an important role for the new Social Relief of Distress grant by June. While temporary unemployment almost returned to February levels, we find that active employment was still 20% lower in June than February, mostly due to job terminations that persisted into June.The authors contributed equally to this work. Author order is randomized. We are very grateful
to Reza Daniels, Vimal Ranchhod, and participants at the NIDS-CRAM Labor workshop, for
helpful comments and discussions. We are also especially grateful to the NIDS-CRAM team
whose work producing the data made this research possible. The authors gratefully
acknowledge funding for this research from the Allan Gray Orbis Foundation Endowment. Any
errors remain our own. Declarations of interest: none
Locked down and locked out: Repurposing social assistance as emergency relief to informal workers (updated)
No full textThe covid-19 pandemic presents a particular challenge to countries with high levels of labour market informality. Informal workers and their households are especially vulnerable to the negative economic consequences of the pandemic and associated lockdown measures, while the very fact of their informality makes it difficult for governments to quickly provide targeted economic relief. Using South Africa as a case study, we examine how an established social assistance system - not originally designed to support informal workers - can be re-purposed to provide emergency relief to these workers and their households. We examine how expansions of this system on the intensive margin (increasing the value of existing social grants) and extensive margin (introducing a new feasibly-implemented grant) can be used to mitigate this covid-19-associated poverty. We compare the efficacy of the different policies by using pre-pandemic nationally representative household survey data to project how a negative shock to informal incomes can be mitigated by the different social grant measures, with a particular emphasis on poverty impacts. We find that an intensive-margin expansion of the existing Child Support Grant is complementary to the extensive-margin introduction of a new Special covid-19 Grant, and that this combined policy intervention performs best out of the options considered. However conclusions as to this "optimal policy" are not simple technical determinations. We show that these conclusions are in fact sensitive to both unavoidable technical assumptions about how resources are consumed and shared within the household, as well as to normative value judgments about which populations to prioritise and how to value poverty reduction spillovers amongst the non-targeted group. While our approach helps identify a range of sensible policy approaches, there is no escaping the limits to our knowledge or the issue of normative goals - a finding likely applicable to a broad range of empirical policy analyses
The effects of credit rationing on re-enrollment rates at a University in South Africa
No full textHow important are credit constraints for educational persistence and performance at the university level in South Africa? We use institutional data to measure the impact of credit rationing on re-enrollment rates at the University of Cape Town (UCT). Identifying variation is obtained from a policy change in the eligibility requirements for continued financial aid that occurred in 2015. Our difference-in-differences point estimate is -0.074 and is statistically significant at the 1% level of significance. We also estimate a difference-in-difference-in-differences model to identify whether the policy had heterogenous effects for relatively lower income students who received funding from the National Student Financial Aid Scheme (NSFAS). We find that the policy resulted in a 5.5 percentage point decrease in re-enrollment rates amongst students who were not previously on NSFAS funding, while the corresponding estimate amongst NSFAS students was approximately 13 percentage points. These findings suggest that credit constraints are binding on the decision to re-enroll, but only for a relatively small proportion of the students who were affected by the change in the policy