green jobs. The most reliable and common data source to estimate the number of jobs is specific firm surveys.<sup>13</sup> Despite its simplicity, the output approach comes with several important shortcomings. First, specialized data is needed to classify goods and services as green or environmentally friendly. There are several working definitions. Some researchers use estimates of CO2 emissions;<sup>14</sup> others, like the Bureau of Labor Statistics (BLS), collect specialized data for that purpose.<sup>15</sup> Once this is known, a second challenge arises as firms might produce both green and non-green goods and services. In these cases, if there is a rich firm survey with detailed input and output variables, assumptions can be made about the proportion of labor utilized for green outputs. For example, that proportion could be estimated by the percentage of working time or wages spent on the production of green outputs, or the share of revenues coming from green outputs.<sup>16, 17</sup> When output data is not available, then granular industry information about the main activity of the firm can be used to approximate it. This implies further assumptions to assign a category or a share of green outputs to each industry code.

Applying the output approach in developing countries can pose additional challenges. Usually, developing countries do not have detailed data to classify locally produced goods and services as environmentally friendly or not. Measurement of CO2 emissions is not widespread<sup>18</sup> and conducting specific surveys mapping industry to occupation, such as the BLS GGS, might be unaffordable and not exempt from its own challenges. Consequently, most analysts appeal to relying on classifications of EGS and EGSS from other countries and estimate emissions based on the industry and input-output data. In the first case, the analyst must assume that the technology of production of goods and services is similar in both countries and will need a detailed mapping from EGS to the industrial classifications, both in the country used as reference and in the country of its application. Such mappings are not usually available in all countries, which means that further assumptions are needed regarding the shares of green and non-green outputs for each industry code, and across country-specific industrial classifications.<sup>19</sup> The output approach is useful for understanding the implications of structural transformation on sectoral employment, as well as the linkages between green industrial policy,<sup>20</sup> green stimulus packages, and employment growth. For example, Bontadini and Vona (2021) are interested in understanding the comparative advantage of green production as a way to reconcile economic growth with environmental preservation and the mitigation of climate change-related risks. The authors find that green production is highly concentrated in a set of high-tech industries producing capital goods, while polluting and green 13 Eurostat (2009).

14 For example, the UK uses multiple data sources including satellite images. For more information consult the UK Office for National Statistics at - <u>https://www.ons.gov.uk/economy/environmentalaccounts/bulletins/ukenvironmentalaccounts/2023#measuring the-data.</u>

> 15 BLS (2013a).

> 16 Eurostat data collection handbook (2009) proposes various ways to adjust the estimate.

> 17 Winkler et al. (2022) forthcoming.

18 Countries report their emissions through what is known as a ‘bottom up’ approach, where national emissions are estimated by combining data on types of activity with the emissions typically produced by those activities.

> 19 The implications of these assumptions are further explored for the task approach.

> 20 Including policies related to trade on green outputs and services.

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