richer and more reliable the analysis will be. Second, the occupational manuals need to be regularly revised if the green index is to be updated. While Germany has a rich occupational manual developed at the 6-digit occupational classification, most developing countries simply adhere to ISCO (and at best, at the 4-digit occupational classification), missing the opportunity to profit from the richness of text analysis.

# The skills requirements approach

One of the main limitations of the task approach, for both O*NET and text analysis methodologies, is that updating the green classification over time is not easy and time-consuming. The O*NET methodology would require regularly reviewing the literature to evaluate if there are new green tasks and/or new technologies. The text analysis methodology, however, is also subject to this problem since occupational manuals with task statements are usually updated every 10 years, a period that seems too long for the current pace of technological change. Countries with modern labor market information systems, usually with a more disaggregated occupational classification (6-digit ISCO or equivalent), may overcome this limitation by directly working with online vacancy data to identify green occupations.

The skills approach uses green skills required in job postings as a signal of the greening of the economy. LinkedIn, for example, is relying on this approach. Consistent with this note, LinkedIn defines green skills as those that enable the environmental sustainability of economic activities.<sup>42</sup> However, their definition is broader, as these skills are grouped into _core green skills_ (such as recycling) which are most directly related to these sustainability-promoting activities; _ambivalent green skills_ (such as fleet management) which may or may not be used for sustainability, and _adjacent green skills_ (such as biology) which can support the acquisition of core and ambivalent green skills. To our knowledge, their methodology for coding the 38,000 unique skills terms into green and non-green is not publicly available.<sup>43</sup> For LinkedIn, green jobs are occupations that cannot be performed without extensive knowledge of green skills. Instead, Lightcast (formerly Burning Glass, BG) uses a combination of data sources, including O*NET classification, job titles, and job text searches.<sup>44</sup> Like the others, this approach presents advantages and disadvantages for its implementation in advanced and developing countries. The most salient advantage is the granularity and frequency of the data. However, it is difficult to infer from vacancy data if the job to be filled will require the skills to carry out tasks, or if green skills are simply desired or used as a signal for some other job need. Developing countries face the additional challenge that the use of online matching platforms is not yet widespread, and hence the data is usually biased towards large metropolitan areas and the high-skilled segment of the market.<sup>45</sup> The next section illustrates these challenges applied to the Indonesian and U.S. cases in more detail.

> 42 LinkedIn 2022 Green economy report 2022.

> https://economicgraph.linkedin.com/content/dam/me/economicgraph/en-us/global-green-skills-report/globalgreen-skills-report-pdf/li-green-economy-report-2022-annex.pdf.

> 43 This team has unsuccessfully attempted to connect with LinkedIn to learn about the methodology.

> 44 Based on our communication with Lightcast.

> 45 See Granata, Posadas, and Testaverde (2021) for a discussion of biases applied to the Indonesian labor market.

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