South African coal worker data in publicly available Social and Labour Plans
Description
The global concern over climate change impacts has catalyzed widespread commitment to achieving a “just transition” toward cleaner energy sources and reduced coal dependence. This has significant implications for coal sector workers who face potential job losses, geographical relocation, and downward wage pressure. South Africa is particularly vulnerable due to its heavy reliance on coal for electricity generation and persistently high rates of unemployment. Understanding the socio-economic characteristics and specific vulnerabilities of the coal workers is critical for designing effective and equitable just transition policies. The national Department of Mineral Resources requires every mining company with a mining right to submit a comprehensive Social and Labour Plan (SLP) that describes how the mine will contribute to the development of its employees and its local communities. A SLP must also include statistics on workers gender, race, geographic origin, education level and skill level. This data were extracted from 33 publicly available SLPs for 45,850 coal mine workers, over half the national total. The data were used to generate socio-economic profiles of South Africa's coal workers and to support the identification of the coal workers most at risk of losing their jobs. The SLP data shows a male-dominated (82%) industry with the majority of workers originating from the host province, It highlights the vulnerability of contractors who make up 43% of the workforce. The findings reveal critical skills gaps: only 30% of permanent employees and 10% of contractors have higher education and/or specialized training, while 53% of all workers are unskilled or semi-skilled, limiting their re-employment prospects following mine closures. Geographically, the most at-risk workers are concentrated in Emalahleni, Steve Tshwete, Victor Khanye and Msukaligwa local municipalities in Mpumalanga province.
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Steps to reproduce
I downloaded the publicly available Social and Labour Plans from company websites. I extracted data on gender, race, geographic origin, education levels and skill levels for permanent employees and contractors, as well as on financial provisions earmarked for human resource development, local economic development, housing, enterprise and supplier development, and mine downscaling and retrenchments. I analyzed most recent SLPs; there is a time delay from when an SLP is submitted to the Department of Mineral Resources for approval and when it can be published hence the latest SLP was not always available. I compiled the data in Excel spreadsheets. I aggregated the data by gender (male/female), contract type (permanent or contractor), age, and company.
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Funding
World Bank