OLS estimates, as per equation (4). Dependent variable is ln(earnings). Sample includes employed individuals aged 15-65. Income bracket weights applied. The ‘baseline’ columns control for survey quarter dummies and 9 province dummies. The ‘Full’ columns add demographic characteristics (gender, age, education, and race), 9 main industry dummies, a location dummy and 9 main occupation dummies to the variables in the baseline model. Source: PALMS v3.3 data. Our variable of interest is the variable _green_strict_ which is a binary variable equal to one if an individual is in a green job.

# 5 Conclusion and Policy Directions

This paper aims to profile green jobs in the South African labor market. We utilize the PALMS labor force survey data 2012-2019 and apply an occupational task-based approach to identify green jobs, count them, and profile the workers and the wages for green jobs. Through the process, we adapt a green terms dictionary to be relevant for the South African labor market.

We find that 5.5 percent of South Africa’s jobs can be classified as strictly green, while 32 percent can be classified as broadly green. The share of strictly green jobs is in the range of estimates from other countries, spanning 2.3 percent in Indonesia to 39 percent in the UK. South Africa’s share of strictly green jobs has barely changed over the last 10 years, while broadly green jobs are slowly increasing. This may be a statistical anomaly driven by the limitations in our data that do not allow us to update the task content of jobs as they evolve. Alternatively, it may be a signal that strictly green job growth is stagnant in the South African economy, even as broadly defined green sectors are growing. This brings into question if today’s green policies are sufficiently transformational to shift the South African economy, and its labor force, to greener jobs.

While 65 percent of South Africa’s strictly green occupations can be classified as high-level, only 55 percent of green workers are in those jobs. South Africa has a higher number of strictly green occupations among high-level occupations (e.g., science and engineering professionals and technicians) than among mid-level and elementary occupations. Those working in these jobs enjoy a significant wage premium. However, elementary and mid-level strictly green occupations employ a disproportionately larger share of people in South Africa than highlevel strictly green jobs do. In short, a narrow set of occupations have the potential to sweep up large numbers of typically excluded workers as the economy greens.

The green economy offers jobs across the socio-economic spectrum in South Africa. While studies from the global north find that green jobs are concentrated in professional and managerial jobs (Vona 2021; Consoli et al. 2016; Bowen et al. 2018; Valero et al. 2021), our results, especially under the broad definition of green jobs, show that there is an opportunity for green jobs in mid-level and elementary occupations. Similar results emerge in the middleincome country of Viet Nam (Doan et al 2022). This is a significant finding for an economy such as South Africa that has a large low-skilled workforce and high rates of unemployment among more vulnerable groups.

The profile of green job workers illustrates exclusion of some groups, but inclusion of others. Most strictly green occupations are dominated by older males, a fact that reflects persistent gendered occupational segregation and high levels of youth unemployment. Green occupations are concentrated in engineering and mathematical professional occupations or mid-level and elementary skill blue collar jobs where women are underrepresented. Women in professional occupations are concentrated in teaching, health professions and office clerks, all of which have a very low (or zero) GTI. Younger individuals (15-24) are also less likely to be in green jobs. However, workers in the greenest of the green occupations are those who tend 24