the sample and replaced by new dwellings from the same PSU or the next PSU on the list.

PALMS is well suited for this paper’s analysis. It includes occupational information up to the four-digit level and uses the South Africa Standard Classification of Occupations (SASCO) from 2003 (Statistics South Africa 2003). SASCO 2003 is based on the International Standard Classification of Occupations of 1988 (ISCO-88).<sup>14</sup> To ensure comparability, DataFirst has gone to great lengths to harmonize the PALMS dataset in terms of variable names over time. Additionally, the data set comes with re-calibrated weights using a cross entropy (CE) approach (Branson and Wittenberg 2014) to ensure continuity and comparability between surveys over time.

For this study, we restrict our analysis to the period 2012-2019. Data are available for more recent years but to avoid contaminating the results with the effects of the COVID-19 pandemic, we exclude data collected in 2020 or after. We pool data from the four quarters of each year resulting in 32 quarters (waves) of data.<sup>15</sup> The pooling of the data over the 8 years gives a sample size that will allow analysis at the 4-digit SASCO level. Using these data, we profile the labor force working in green occupations by age, gender, location, skill, and education level.

# 2.1 Methodology to Generate the List of Green Occupations

We adopt the occupational task approach and use Dierdorff et al. (2009) to adapt our definition of green jobs to reference occupational tasks. Specifically, we define strictly green jobs as occupations that require tasks that have environmental impacts. We define broadly green jobs as occupations that are strictly green or occupations with tasks that could have environmental impacts if green technology is used. Some of these jobs benefit from the green revolution, such as miners of lithium for electric car batteries. Others are negatively affected, such as coal miners and those working in coal-fired plants. We include both in our analysis.

While many studies in various countries use tasks information from the US Occupational Information Network (O*NET) inventory and its associated Green Economy Program (GEP) (Consoli et al. 2016; Vona et al. 2019, 2018) to measure green jobs, we find these data are not appropriate for our analysis for several reasons. The O*NET is a US cross-sectional database with detailed information about work context, tasks, activities and skills at the occupational level.<sup>16</sup> The O*NET Green Economy program (GEP) identifies 12 industries that should be impacted by the greening of the economy and an associated list of occupations and 14 International Standard Classification of Occupations 1988 (ISCO-88) is a four-level hierarchically structured system that allows all jobs in the world to be classified into unit groups based on their similarity in terms of the skill level and skill specialization required for the jobs. The most aggregated level is the one-digit code (10 major groups), followed by the two-digit code (28 sub-major groups), the three-digit code (116 minor groups) and the most detailed level of the classification is the four-digit code (390 groups). The SASCO includes an additional 39 occupations, reflecting uniquely South African labor markets.

15 To ensure there is no double counting of individuals, we divide the survey weights by the number of quarters.

16 The O*NET is the primary source of occupational information in the United States. The data are collected through industry expert interviews and surveys of representative samples of workers. https://www.onetonline.org/ 5