on a review of 60 publications of very diverse nature, dated back to 2009 and revised later in 2011. Although such qualitative work is important and should be an integral part of any data driven strategy, on its own it cannot be considered comprehensive or objective, and it is not easy to update. Progress in the adoption of green technologies during the last decade has been phenomenal, and this progress is being left out of the GEP.

The use of O*NET GEP outside the U.S. and in developing countries is also subject to important assumptions that cast doubts about its validity in these contexts. When O*NET data is used to study countries outside the U.S. the following assumptions are inevitably made: first, the production function (technology, capital, and labor—tasks and skills) is the same as that of the U.S. Internationally comparable firm data, like that from the World Bank Enterprise Surveys, show that the input mix and the returns of firms vary across countries. Country efforts to replicate O*NET in Indonesia and Uruguay show that there is variation in the task content and skills requirement of occupations across each of these countries and the U.S.<sup>38</sup> A second major problem is that most countries when collecting occupational data use the International Standard Classification of Occupations (ISCO) at the 4-digit level of aggregation or higher. This entails relying on a crosswalk to transform the 8-digit O*NET SOC to the 4- digit ISCO and making assumptions about the structure of employment within each 4-digit occupation. Although crosswalks are available<sup>39</sup> and commonly used by the research community, they have been found particularly problematic for capturing green jobs given the low levels of employment and the high measurement errors produced by the crosswalks.<sup>40</sup> For example, Elliot et al. (2021) applies the O*NET classification and Vona et al. (2019) methodology to study how eco-innovations impact employment in the Netherlands, finding that there is no impact on overall employment but that they increase green jobs. However, the share of green jobs is significantly higher than that in the U.S., bringing into question the quality of the crosswalk to measure green employment.<sup>41</sup> Section four of this paper reports the same issue when applying O*NET classification to estimate green employment in Indonesia.

A second modality of the task content approach consists of applying text analysis to occupational manuals with task descriptions. Janser (2019) applied text mining to the German occupational database to calculate the level of "greenness" of occupations. The dictionary included 153 green terms and was applied to a database with occupational requirements at the 8-digit level, containing 14,546 words, to identify green tasks for each occupation. Following Consoli et al. (2016), Janser created a greenness index, which is the share of green requirements (tasks) in the occupation. Janser shows that there was a greening of jobs between 2011 and 2016 in Germany, with slight wage increases, and that the greenness of occupations and employment growth are positively correlated.

This second modality also has some methodological limitations. First, the country of interest needs to have an occupational manual that includes task statements as part of the occupational description. The more narrowly defined the occupations are and the more precisely defined the task statements are, the

> 38 Alatas, Granata and Posadas (2020), Apella & Rovner (2021) for Indonesia and Uruguay respectively.  Granata and Moroz (2022) describe Viet Nam’s efforts to replicate and adjust select modules of O*NET to Viet Nam, but do not attempt to compare it with the U.S.

> 39 https://www.bls.gov/soc/isco_soc_crosswalk.

> 40 Vona, F. (2021).

> 41 Vona, F. (2021).

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