# 2.2 Limitations to the Methodology to Generate the List of Green Jobs

There are some shortcomings to this method. First, Granata and Posadas (2024) observe that the GTI may underestimate the level of greenness of an occupation as the ISCO-88 does not present a comprehensive list of tasks for every job in an occupational category, but rather a description of what the occupation involves for the majority of jobs. Second, emerging occupations, some of which are associated to the greening of the economy, may be under the residual occupation code “Not elsewhere classified” and may not be captured since the task description of these residual occupations tend to be quite general (Granata and Posadas 2024). Third, the ISCO descriptions of occupations are static and therefore we might fail to capture new and emerging green tasks overtime. Lastly, it is possible that the text analysis methodology underestimates the share of green tasks in managerial occupations because administrative tasks such as policy makers who form clean energy and environmental policies are not emphasized.<sup>21</sup>

# 2.3 Analytical Methodology

We use standard econometric techniques to understand the factors associated with being in a green job, being in a greener job, and earnings in green jobs.

# _2.3.1_ Factors associated with being in a green job

To understand the factors related to being in a green job, we estimate a logit model of the probability of being in a green job as specified in equation 2. 0 + α + η + δ + θ + ε � (2) ii ii pp ll tt ss ii Y is a binary dependent variable equal to one if the respondent is in a green job (GTI >0) and Pr( YY= 1 ∣XX) = FF�ββ + ββ XX zero otherwise. We use the strictly green job definition for the first round of estimates and the broadly green job definition for a second round of estimates. F is the logit function link while the individual characteristics (age, race, gender, education level) are represented by . α is a location dummy for urban residence, η is a vector of province dummies and δ are ii quarter of year dummies. θ is a vector of industry dummies. is the error term. Our sample XX pp ll tt includes working-age individuals (age 15-65) who are employed. ss ii εε To create the variables, we adapt some variables in the PALMS data. We convert the continuous education variable into a four-category variable:<sup>22</sup> 1=less than grade 12 (those without a high school qualification), 2=matric (complete high school), 3=other tertiary (those with more than a high school qualification but less than university), and 4=degree (those with a university degree). For the age variable, we construct 5 categories from a continuous age variable as follows: age 15-24, age 25-34, age 35-44, age 45-54 and age 55-65. Gender is defined as a binary variable (1=female, 0=male). For ethnicity group or race,<sup>23</sup> we use the

> 21 This final caveat underlies one of the key differences between the text analysis and O*NET tags a much larger share of managerial jobs as green, as compared to the text analysis.

> 22 As we used categorical variables to show green employment by education and age categories in the descriptives sector, for consistency, we maintain the same categories for the regressions. The South African literature (Branson and Leibbrandt 2013) has also found that the relationship between education and labor market outcomes is convex and for this reason it is more informative to use education categories.

> 23 Race is included in the regression analysis because it remains an important determinant of labor

9