A detailed look into the task statements of green occupations detected through our dictionary and text analysis but not in the O*NET GEP confirms that these occupations have green task content, even those with the lowest GTI. For example, Home appliance repairers with a GTI of 3.2 have the task ‘ _conserve, recover, and recycle refrigerants used in cooling systems_ ’, or Real estate brokers with a GTI of 5.2 have tasks such as ‘ _review property details to ensure that environmental regulations are met’_ ’, or Molecular and cellular biologists with a GTI of 4.7 have a task ‘ _conduct applied research aimed at improvements in areas such as disease testing, crop quality, pharmaceuticals, and the harnessing of microbes to recycle waste_ ’.

# Reliability test #2: Correlation between the GTI and green skills intensity

Since relying on occupational classifications can be rather static because they are not constantly updated, the second reliability test compares the GTI against a similarly constructed green skills intensity index (GSI) after applying the text analysis methodology to an occupational skills database in the same country. The assumption is that green skills are needed to perform green tasks, and therefore the two indexes should be positively correlated. Recall that the green dictionary is not a list of tasks but of green terms, so the dictionary should be able to pick up skills containing green words. For example, the term ‘environmental’ can pick up the task ‘reporting on the environmental impact of existing and proposed construction’ in the task database and also the skill ‘environmental testing’ in the skills database. It is important to note that merely applying the dictionary to the skills database will not capture all the skills needed to perform a green job since these will be more comprehensive than just skills with green words.

This reliability test is carried for two countries: the U.S.—a high-income country with rich data—and Indonesia—a middle-income country with some data. For the U.S., we use the BG skills database for 2017; for Indonesia we also use a database retrieved by BG for 2020. While both databases follow the same protocols, there are important differences related to the size of the online job intermediation market and the localization of the algorithm, mainly the exclusion of postings in Bahasa Indonesian.<sup>56</sup> One problem common to both databases is that they do not cover all occupations. For the U.S., 773 out of 840 SOC titles (version 2010) are observed in the BG database, and for Indonesia, 272 out of 465 occupations appear in the BG database. Given the number of vacancies per occupation in each database, the reliability test includes all occupations for the U.S. and only high-skilled occupations (159) for Indonesia.

Applying the (narrow) green dictionary to skills databases resulted in 343 green skills out of 10,798 unique skills for the U.S. and 193 green skills out of 6,263 unique skills for Indonesia. As we did for tasks, a green skills intensity (GSI) index can be defined as the percentage of green skills in an occupation o , as described in the following formula G =<sup># geee ttkkiilttoo</sup> oo Two things can be concluded from the comparison between the GTI and GSI indexes. First, the applied GG oo # ttkkiil tt Two things can be concluded from the comparison between the GTI and GSI indexes. First, the applied text analysis methodology is appropriate to capture green tasks, and therefore green jobs, since both

> 56 For more details on the data collection as well as potential biases see Granata, Posadas, and Testaverde (2021).

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