# green economy.<sup>11</sup>

We find that a substantial number of South African jobs and a broad range of workers can be classified as green. We estimate that 5.5 percent of South Africa’s jobs can be strictly defined as green while 32 percent can be defined as broadly green.<sup>12</sup> This maps to 46 strictly green occupations and 137 broadly green occupations, from a list of 428 occupations. The green jobs estimates are within the range of other countries. While 65 percent of strictly green occupations in the South African labor market require highly skilled workers, resulting in a wage premium for those employed in these occupations, they employ only 55 percent of workers in strictly green jobs. Instead, a smaller share of mid-level and elementary occupations are responsible for 45 percent of total (strictly) green employment. While the share of strictly green jobs has barely changed over the past eight years, there was an increase in the share of broadly green jobs between 2013 and 2014. Consistent with international literature, relative to the general labor force, the average workers in strictly green occupations tend to be male, of prime working age (25-44) and have some type of tertiary qualification.

This exercise contributes to the literature in several ways. First, it provides a benchmark against which to measure South Africa’s evolution of green jobs on average and among traditionally excluded groups. Second, it offers guidance on how to prepare today’s South Africans for today’s green jobs. Third, it joins a small literature using the occupational task approach (Granata and Posadas 2024; Vona et al. 2019; Consoli et al. 2016; Vona et al. 2018) and illustrates how to utilize the methodology for a country-specific analysis.

The rest of this paper is organized as follows. Section 2 describes the data and methodology while Section 3 shares a descriptive analysis. In Section 4 we present and discuss regression results. Section 5 concludes and offers five policy messages for a more inclusive and successful green labor force and economy.

# 2 Data

We use the Post-Apartheid Labour Market Series (PALMS) dataset (Kerr et al. 2019). PALMS is constructed from several South African Labour Force Surveys for the years 1993-2019,<sup>13</sup> curated and harmonized by DataFirst at the University of Cape Town. We extract the Quarterly Labour Force Survey (QLFS) 2012-2019 data from the PALMS dataset for our analysis. The QLFS is a nationally representative household survey collected by the national statistical agency Statistics South Africa (Stats SA) since 2008. It includes data on the labor market activities of individuals aged 15 years and older who live in South Africa. Each survey samples approximately 33,000 dwelling units based on about 3,324 Primary Sampling Units (PSUs). For each quarter of the QLFS, a quarter of the sampled dwellings are rotated out of

> 11 Examples of studies that make projections about employment due to the transition to a greener South African economy include Rutovitz (2010), Maia et al. (2011) and Merven et al. (2019). 12 As of 2019, there were approximately 16.4 million jobs in South Africa.

> 13 The PALMS dataset includes the 1993 Project for Statistics on Living Standards and Development (PSLSD), the annual October Household Surveys (1994-1999), the biannual Labour Force Surveys (20002007) and the Quarterly Labour Force Surveys (2008-2019). We only use the QLFS 2012-2019 in this analysis.

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