Revisiting South African Employment Trends in the 1990s

Altman, M.

doi:10.1111/j.1813-6982.2008.00185.x

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…8 As expected these results are on average further away from unity than the standard FGNLS estimates. Many papers have investigated the problems in comparing the StatsSA household surveys (Altman 2008;Burger and Yu 2006;Casale et al 2004;Kingdon and Knight 2005) and particularly the effect that modifications in questionnaire design and sampling methodology may have had on the comparability of the household surveys over time. The most serious comparability problems occur for informal sector or self-employed workers, so that the effect of these inconsistencies can be limited by omitting these workers from the sample and restricting our dataset to formal sector employees only.…”

Section: Labourmentioning

confidence: 99%

The elasticity of substitution and labour-displacing technical change in post-apartheid South Africa

Kreuser¹,

Burger²,

Rankin³

2015

WIDER Working Paper

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Section: Labourmentioning

confidence: 99%

The elasticity of substitution and labour-displacing technical change in post-apartheid South Africa

Kreuser¹,

Burger²,

Rankin³

2015

WIDER Working Paper

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“…For the mid-term assessment, first we analyzed the mortality, sick-time, and turnover data collected in the Personnel Salaries (PERSAL) database, the human resource database used for payroll and related administrative functions within the public sector in South Africa ( 18 , 19 ). Turnover was calculated as the number of employees who left during a particular year, divided by the average number of employees that worked during that year.…”

Section: Methodsmentioning

confidence: 99%

Learning from a cluster randomized controlled trial to improve healthcare workers’ access to prevention and care for tuberculosis and HIV in Free State, South Africa: the pivotal role of information systems

Yassi

Adu

Nophale

et al. 2016

Global Health Action

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BackgroundOccupational tuberculosis (TB) continues to plague the healthcare workforce in South Africa. A 2-year cluster randomized controlled trial was therefore launched in 27 public hospitals in Free State province, to better understand how a combined workforce and workplace program can improve health of the healthcare workforce.ObjectiveThis mid-term evaluation aimed to analyze how well the intervention was being implemented, seek evidence of impact or harm, and draw lessons.MethodsBoth intervention and comparison sites had been instructed to conduct bi-annual and issue-based infection control assessments (when healthcare workers [HCW] are diagnosed with TB) and offer HCWs confidential TB and HIV counseling and testing, TB treatment and prophylaxis for HIV-positive HCWs. Intervention sites were additionally instructed to conduct quarterly workplace assessments, and also offer HCWs HIV treatment at their occupational health units (OHUs). Trends in HCW mortality, sick-time, and turnover rates (2005–2014) were analyzed from the personnel salary database (‘PERSAL’). Data submitted by the OHUs were also analyzed. Open-ended questionnaires were then distributed to OHU HCWs and in-depth interviews conducted at 17 of the sites to investigate challenges encountered.ResultsOHUs reported identifying and treating 23 new HCW cases of TB amongst the 1,372 workers who used the OHU for HIV and/or TB services; 39 new cases of HIV were also identified and 108 known-HIV-positive HCWs serviced. Although intervention-site workforces used these services significantly more than comparison-site healthcare staff (p<0.001), the data recorded were incomplete for both the intervention and comparison OHUs. An overall significant decline in mortality and turnover rates was documented over this period, but no significant differences between intervention and comparison sites; sick-time data proved unreliable. Severe OHU workload as well as residual confidentiality concerns prevented the proper implementation of protocols, especially workplace assessments and data recording. Particularly, the failure to implement computerized data collection required OHU staff to duplicate their operational data collection duties by also entering research paper forms. The study was therefore halted pending the implementation of a computerized system.ConclusionsThe significant differences in OHU use documented cannot be attributable to the intervention due to incomplete data reporting; unreliable sick-time data further precluded ascertaining the benefit potentially attributable to the intervention. Computerized data collection is essential to facilitate operational monitoring while conducting real-world intervention research. The digital divide still requires the attention of researchers along with overall infrastructural constraints.

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“…Given the important role assigned to measurement error in explaining estimates in the cross-country human capital-growth literature, it is worth briefly discussing the nature of the measurement issues that affect our data. Many papers have investigated the problems in comparing the Stats SA household surveys (Altman, 2008;Burger & Yu, 2006;Casale, Muller, & Posel, 2004;Kingdon & Knight, 2005) and particularly the effect that modifications in questionnaire design and sampling methodology may have had on the comparability of the household surveys over time. The most serious comparability problems occur for informal sector or self-employed workers, so that the effect of these inconsistencies can be limited by omitting these workers from the sample and restricting our dataset to formal sector employees only.…”

Section: Datamentioning

confidence: 99%

The Effect of Schooling on Worker Productivity: Evidence from a South African Industry Panel

Burger

Teal

2015

J Afr Econ

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Schooling is typically found to be highly correlated with individual earnings in African countries. However, African firm or sector level studies have failed to identify a similarly strong effect for average worker schooling levels on productivity. This has been interpreted as evidence that schooling does not increase productivity levels, but may also indicate that the schooling effect cannot be identified when using a schooling measure with limited variation. Using a novel South African industry-level dataset that spans a longer period than typical firm-level panels, this paper identifies a large and significant schooling effect. This result is highly robust across different estimators that allow for correlated industry effects, measurement error, heterogeneous production technologies and cross-sectional dependence.

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Revisiting South African Employment Trends in the 1990s

Abstract: This article revisits South

Cited by 9 publications

References 9 publications

The elasticity of substitution and labour-displacing technical change in post-apartheid South Africa

The elasticity of substitution and labour-displacing technical change in post-apartheid South Africa

Learning from a cluster randomized controlled trial to improve healthcare workers’ access to prevention and care for tuberculosis and HIV in Free State, South Africa: the pivotal role of information systems

The Effect of Schooling on Worker Productivity: Evidence from a South African Industry Panel

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