metadata: authors: 0: author: Winkler-Seales,Hernan Jorge
1: author: Di Maro,Vincenzo
2: author: Montoya Munoz,Kelly Yelitza
3: author: Olivieri,Sergio Daniel
4: author: Vazquez,Emmanuel Jose
count: Latin America & Caribbean,Latin America
docna: 0: docna: Measuring Green Jobs : A New Database for
            Latin America and Other Regions
docty: Policy Research Working Paper
theme: Inclusive Growth,Mitigation,Data Development and Capacity Building,Economic Policy,Economic Growth and Planning,Environment and Natural Resource Management,Public Sector Management,Climate change,Adaptation,Data production, accessibility and use
abstracts: cdata!: A growing body of literature
            investigates the labor market implications of scaling up
            &#x201c;green&#x201d; policies. Since most of this
            literature is focused on developed economies, little is
            known about the labor market consequences for developing
            countries. This paper contributes to filling this gap by
            providing new stylized facts on the prevalence of green
            occupations and sectors across countries at varying levels
            of economic development. Green occupations are defined using
            the Occupational Information Network, and green sectors are
            those with relatively lower greenhouse gas emissions per
            worker. The paper offers an initial assessment of how the
            implementation of green policies&#x2014;aimed at
            expanding green sectors and strengthening the relative
            demand for green skills&#x2014;may affect workers in
            developing economies. It finds that the share of green jobs
            is strongly correlated with the level of gross domestic
            product per capita across countries. When controlling for
            unobserved heterogeneity, a 1 percent increase in gross
            domestic product per capita is associated with 0.4 and 4.1
            percentage point increases in the shares of new and
            emerging, and enhanced skills green jobs, respectively. The
            paper then focuses on Latin America and finds that only 9
            percent of workers have a green job with respect to both
            occupation and sector.  The findings show that within
            countries, workers with low levels of income and education
            are more likely to be employed in non-green sectors and
            occupations, and to lack the skills for a greener economy.
            This evidence suggests that complementary policies are
            needed to mitigate the potential role of green policies in
            widening income inequality between and within countries.
display_title: Measuring Green Jobs : A New
            Database for Latin America and Other Regions
listing_relative_url: /research/2024/06/34335965/measuring-green-jobs-new-database-latin-america-other-regions
topicv3: Green Growth m1326430 476,Growth, Poverty and Inequality m1327633 1091,Jobs and Development m1503932 2671,Jobs and Climate Change m1503934 2673,Green Jobs m1503935 2674
publishtoextweb_dt: 2024-06-06T00:00:00Z
dois: 10.1596/1813-9450-10794
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 51
author_keys: 0: author_key: 776733
1: author_key: 805510
2: author_key: 1559452
3: author_key: 926510
4: author_key: 1381681
model_extractions: Policy Research Working Paper 10794

# Measuring Green Jobs

A New Database for Latin America and Other Regions _Hernan Winkler Vincenzo Di Maro Kelly Montoya Sergio Olivieri Emmanuel Vazquez_ Poverty and Equity Global Practice June 2024 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 10794

# **Abstract**

A growing body of literature investigates the labor market implications of scaling up “green” policies. Since most of this literature is focused on developed economies, little is known about the labor market consequences for developing countries. This paper contributes to filling this gap by providing new stylized facts on the prevalence of green occupations and sectors across countries at varying levels of economic development. Green occupations are defined using the Occupational Information Network, and green sectors are those with relatively lower greenhouse gas emissions per worker. The paper offers an initial assessment of how the implementation of green policies—aimed at expanding green sectors and strengthening the relative demand for green skills—may affect workers in developing economies. It finds that the share of green jobs is strongly correlated with the level of gross domestic product per capita across countries. When controlling for unobserved heterogeneity, a 1 percent increase in gross domestic product per capita is associated with 0.4 and 4.1 percentage point increases in the shares of new and emerging, and enhanced skills green jobs, respectively. The paper then focuses on Latin America and finds that only 9 percent of workers have a green job with respect to both occupation and sector. The findings show that within countries, workers with low levels of income and education are more likely to be employed in non-green sectors and occupations, and to lack the skills for a greener economy. This evidence suggests that complementary policies are needed to mitigate the potential role of green policies in widening income inequality between and within countries.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at hwinkler@ worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Measuring Green Jobs:**

# **A New Database for Latin America and Other Regions**

Hernan Winkler, Vincenzo Di Maro, Kelly Montoya, Sergio Olivieri, Emmanuel Vazquez<sup>*</sup> _Keywords_ : Green Jobs, Green Sectors, Climate Change, Labor Markets, Structural Transformation. _JEL Codes_ : Q5, Q52, Q56, J01, J21,

> * Di Maro, Montoya, Olivieri, and Winkler are with the World Bank Poverty and Equity Global Practice, Vazquez is with CEDLAS (IIE, FCE) - Universidad Nacional de La Plata, Argentina. We are grateful to Guillermo Beylis, Miki Doan, Alvaro Gonzalez, Ruth Hill, Nancy Lozano, Ghazala Mansuri, Carlos Rodriguez-Castelan, Trang Van Nguyen and Hugo Nopo for useful comments and discussions.
# 1. Introduction

There is a growing body of literature investigating the labor market consequences of scaling up “green” policies.<sup>1</sup> However, most of the evidence is focused on the labor market implications for developed economies, and little is known about the consequences of greener growth on jobs for developing countries.<sup>2</sup> While rich countries account for a disproportionate share of Greenhouse Gas Emissions (GHGE; see IPCC, 2022), developing economies are expected to experience a disproportionate increase in GHGE in the future (Black et al., 2022). In addition, policies to promote a greener growth strategy may induce a skill-biased shift in labor demand, potentially creating bottlenecks in developing countries with lower stocks and adaptability of skills. This shift would raise concerns about the transition path of displaced workers to new jobs in the green economy.

This paper contributes to this literature by developing a new dataset on green occupations for 120 countries. It describes new statistics on green occupations across countries at different levels of economic development. This offers an initial picture of how the availability (or scarcity) of green occupations can affect the implementation of green policies and their impact on the labor market. The paper then focuses on Latin America and Caribbean (LAC) countries to provide more granular insights into the relationship between green occupations, green sectors, and the socio-economic profile of green job holders.<sup>3</sup> Green occupations are measured following the “Green Economy” program developed by the Occupational Information Network (O*NET).<sup>4</sup> It considers three types of occupations: new occupations that are emerging directly because of green policies (“ _new and emerging_ ”), existing occupations that are expected to experience changes in the tasks they entail (“ _enhanced skills_ ”), and existing occupations that will not change substantially but that will be in higher demand (“ _increased demand_ ”). A methodological contribution of this paper is to develop a mapping of the O*NET classification of green occupations into the 4-digit International Standard Classification of Occupations (ISCO)-08 taxonomy.

Using detailed occupational tabulations (2-digit ISCO-08) from labor force surveys for 120 countries from 2011 to 2020,<sup>5</sup> the paper finds that, on average, 21.4 percent of employment is in green occupations, and

> 1 A broad definition of green policies includes all policies to remove barriers to green, clean, and resilient growth (see Awe, 2012).

> 2 Some exceptions include the 2011 UNEP ILO Green Jobs report. More recently, see the 2018 ILO Greening with Jobs report and <u>2021 K4D FDCO Creating Green Jobs in Developing Countries report. See also Timilsina (2022) for a discussion of related issues.</u> 3 See also Alfonso et al. (2022) for a recent analysis of green jobs in LAC.

> 4 O*NET is a database developed by the U.S. Department of Labor on occupational information for the U.S. workforce (see <u>https://www.onetcenter.org/overview.html).</u>

> 5 Data is available for each year in the period 2012-2019 for a set of 47 countries.

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that 1.8, 10.3, and 9.3 percent are the shares of employment in “new and emerging,” “enhanced skills” and “increased demand” categories, respectively. The paper then investigates the links between green jobs and income between countries, to understand if the labor market adjustment to a greener economy is expected to be more demanding in poorer countries. When comparing countries in the latest year of available data, the results show that the share of green jobs is, in fact, lower in poorer countries, but there are substantial differences across types of green jobs. The association with income levels is very strong for “new and emerging” and “enhanced skills” occupations, but weaker for “increased demand” ones. A fixed-effects regression confirms these strong associations.

The second part of the paper asks the following question: if countries were to implement policies that penalize GHGE (such as a carbon tax, a carbon border adjustment, or banning certain forms of energy production), which jobs would be more at risk? To answer this question, the paper estimates the occupational structure of sectors of economic activity according to their GHGE levels per worker.<sup>6</sup> Then it classifies sectors within each country as green and non-green according to whether they are above or below the sector median GHGE level in each country. It is important to emphasize that this definition of “greenness” does not include other important dimensions such as the potential for transformation through the adoption of green technologies. Combining the green job and the sector classification, four groups of workers are defined: (i) Green occupations in green sectors (GOGS); (ii) Green occupations in non-green sectors (GONS); (iii) Non-green occupations in green sectors (NOGS), and (iv) Non-green occupations in non-green sectors (NONS). Green sectors would be less vulnerable to an increase in the cost of carbon. As a result, workers in green occupations in such sectors would face the lowest risk of displacement and highest re-employment likelihood in a greener economy in case of job loss. In contrast, workers with non-green occupations in non-green sectors would face the highest labor market vulnerability in terms of both job-loss risk and re-employment opportunities.

Using data from countries in Latin America and the Caribbean (LAC)—because of better availability of granular occupation data at the ISCO08 2-digit level—we analyze the relationship between green occupations and sectors. The first finding is that the concentration of employment in non-green occupations and non-green sectors – i.e. the most vulnerable group during a green transition – is

> 6 The focus on GHGE implies that the paper focuses on mitigation, rather than adaptation, in defining green sectors. We note here that this is not the only way green sectors can be defined. For instance, an alternative definition of sectors that considers more prominently the adaptation angle would define green sectors not only based on the level of emissions, but also on the potential to create green jobs and/or foster green technologies in the longer run. Examples of sectors that would be affected by this alternative definition include water management and waste management.

3
significant in several LAC countries. Around 90 percent of workers in each country with available data have a job in either a non-green sector or a non-green occupation. Non-green sectors account for a large share of jobs, from 37 percent in Argentina to 59 percent in Guatemala, Bolivia, and Honduras. Most workers with green occupations are in a non-green sector (about 60 percent of them in most countries), which suggests that high-emission sectors could find some of the needed skills for the transition already in the labor force they occupy. It is important to highlight the very high concentration of non-green occupations in a high-emission sector such as agriculture—about 90 percent or more in most LAC countries.

The dynamics of green occupations and sectors also offer important insights. The share of green occupations has been remarkably constant over the last decade (i.e. between 2010 and 2019) in LAC. The share of non-green jobs in non-green sectors and occupations fell slightly from 35 to 34 percent during that period. In addition, we implement a decomposition of changes in GHGE per worker in between- and within-sector components. GHGE per worker increased between 2005 and 2018 in seven large LAC countries, mostly driven by an increase in GHGE per worker within sectors. In contrast, the reallocation of workers from relatively high GHGE sectors—i.e. agriculture and industry—to relatively low GHGE sectors—e.g. utilities, transport, and Information and Communication Technologies (ICT)—contributed to partially mitigate the increase driven by the within-sector changes. This decomposition illustrates some of the implications of the process of structural transformation on the green economy, whereby increasing energy intensity tends to make the economy less green, but the reallocation of workers away from agriculture and manufacturing towards services tends to “green” the economy (assuming everything else remains the same).

The paper also investigates the profile of green job holders. Overall, green occupations are more prevalent for males and residents of urban areas. Total employment (including green and non-green occupations) in green sectors is higher for females and more educated workers. Green occupational shares do not vary much by informality status or size of the employer firm. Still, a large share of informal workers is concentrated in the non-green occupation and sector group, which can be considered the most vulnerable during a transition. Likewise, poorer workers (those in the lowest income quintiles) are concentrated in this most vulnerable group. This is consistent with the fact that green jobs are linked to higher wages.

The rest of this paper is structured as follows. Section 2 discusses the definition of green jobs, the methodology to generate the categories of green jobs and sectors considered in this paper and describes the data. Section 3 presents cross-country patterns of green jobs, patterns of green occupations and 4
sectors, and the profile of green job holders in LAC. Section 4 discusses some caveats of the study and presents some robustness checks. Finally, section 5 draws some conclusions.

# 2. Methodology and Data

## Green occupations

There are different definitions of green jobs. This paper uses the Occupational Information Network (O*NET)<sup>7</sup> classification of occupations developed by Dierdorff et al. (2009, 2011) to define green and nongreen jobs.<sup>8</sup> Dierdorff et al. (2009) argue that the first step to classifying jobs as green or non-green is to define the green economy, which they consider involving economic activities related to reducing the use of fossil fuels, decreasing pollution and GHGE, improving energy efficiency, increasing recycling and adopting renewable forms of energy. Considering this, the “greening” of occupations takes place when the growth of the green economy _increases the demand for existing occupations, shapes the work and worker requirements needed for occupational performance, or generates unique work and worker requirements_ .

The classification is based on the US 2010 Standard Occupational Classification (SOC) System. It provides a list of 1,110 occupations, 204 of which are identified as green and divided into three mutually exclusive categories: Green Increased Demand (64 occupations), Green Enhanced Skills (62 occupations), and Green New and Emerging (78 occupations). Table 1 shows the definition of each group of occupations with examples of jobs falling into each category. The rest of the listed occupations (906) are considered nongreen and represent jobs that are not affected directly by the greening of the economy.

> 7 See also the Appendix for more detailed information on the data sources used in this paper.

> 8 In contrast, ILO (2018) defines green jobs based on whether “they reduce the consumption of energy and raw materials, limit GHGE, minimize waste and pollution, protect, and restore ecosystems and enable enterprises and communities to adapt to climate change. In addition, green jobs have to be decent”. Decent jobs are defined as “opportunities for women and men to obtain decent and productive work in conditions of freedom, equity, security and human dignity” (ILO, 1999). However, this definition combines several dimensions such as the tasks involved in the job, the “greenness” of the economic sector or firm, as well as characteristics of the job not clearly linked to the green economy (for example, social security benefits or wage levels). This paper does not follow ILO’s approach because its goal is to investigate the implications of the greening of the economy (based on the “greenness” of sectors of economic activity) on workers according to their green skills or tasks. Instead of using the “decency” of a job to characterize it as green or non-green, this paper explores the characteristics of jobs related to such dimensions (e.g., wage levels, informal status, etc.) according to their vulnerability to the greening of the economy (i.e., by whether they are in sectors with a high level of emissions and/or have the green skills needed in the new economy).

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**Table 1 – Green occupation categories**

|**Green job category**|**Definition**|**Examples**|
|---|---|---|
|**Increased Demand**<br>**Occupations**|Occupations that already exist. They may<br>face increased demand with an expanding<br>green economy. While their work context<br>may change, the tasks and skills they use will<br>not.|Carpenters and welders that will<br>be required in the construction of<br>new energy-efficient buildings or<br>agricultural workers needed for<br>more organic farming.|
|**Enhanced Skills**<br>**Occupations**|Occupations that already exist. A growing<br>green economy may or may not increase the<br>demand for these occupations, but it would<br>affect the type of tasks or skills required.|Construction<br>inspectors,<br>agricultural<br>technicians,<br>and<br>architects. Workers in these jobs<br>may need to learn how to use new<br>materials or adhere to new<br>energy-efficient building codes.|
|**New and Emerging**<br>**Occupations**|The “purest” forms of green jobs. These<br>occupations emerge because of the green<br>economy creating the need for new and<br>unique work and worker requirements. As<br>such, these occupations emerged more<br>recently.|Wind energy managers, climate<br>change<br>analysts,<br>or<br>water<br>resource specialists.|

Source: Own elaboration based on O*NET database and Dierdorff et al. (2009, 2011).

This paper uses the International Standard Classification of Occupations (ISCO) or its adaptations to classify occupations, instead of O*NET. Previous studies have mapped the O*NET-SOC 2010 classification of green occupations into the latest ISCO-08 taxonomy to allow for more straightforward cross-national comparisons of green occupations (Hogarth, 2011; Sofroniou and Anderson, 2021). However, their mappings are produced at the ISCO three-digit level at best. More importantly, their methodologies are based on a dichotomic definition of green jobs, in which a whole ISCO category is considered either green or non-green. This paper maps the O*NET-SOC classification of green occupations into the most detailed four-digit ISCO-08 taxonomy using a probabilistic instead of a dichotomic approach. Specifically, the paper uses data on total employment at the 2-digit level ISCO for 120 countries from circa 2001 to circa 2020 (676 country-year observations). Since each ISCO code may have several O*NET-SOC occupations associated with it (not all necessarily green), it would be misleading to consider a whole ISCO occupation as green or non-green based on whether the number of O*NET-SOC associated occupations are primarily green or non-green. Therefore, instead of constructing a binary classification, the paper estimates the share of employment within an ISCO-08 category that corresponds to O*NET-SOC occupations considered green and then considers this share as the probability that an occupation with that ISCO code is green. Since the O*NET-SOC classification is based on the US structure of employment, employment shares are 6
based on U.S. estimates, which are obtained from the 2018 Occupational Employment and Wage Statistics (OEWS) published by the U.S. Bureau of Labor Statistics.<sup>9</sup> The O*NET-SOC 2010 codes are linked to their corresponding four-digit ISCO-08 codes using the U.S. Bureau of Labor Statistics correspondence table between SOC 2010 Codes and ISCO-08 categories. This correspondence, as well as the employment estimates in the OEWS, is available at the six-digit level of the SOC. At the same time, the O*NET-SOC 2010 classification of green occupations is presented with an eightdigit level of detail. Since 12.7 percent of the 6-digit SOC occupations are associated with more than one 8-digit SOC, the total number of workers in a 6-digit SOC occupation was equally distributed among the 8-digit SOC codes associated with it.<sup>10</sup> As an example, Table 2 shows how the share of green occupations was constructed for a specific ISCO code, representing the probability that an individual with an occupation in this ISCO code has a green job.

**Table 2 – Example of the methodology used to estimate the probability for an ISCO-08 code to represent a green occupation.**

|ISCO-08<br>Code|O*NET-SOC 2010<br>Code|Green<br>Job?|2010 SOC<br>Code|Number of US<br>workers|“Simulated”<br>Number of US<br>workers|Share of Green<br>occupations|
|---|---|---|---|---|---|---|
|1234|17-2061.01|Yes|17-2061|10|5||
|1234|17-2061.02|No|17-2061||5|5%<br>|
|1234|17-2063.00|No|17-2063|40|40|=5/(5+5+40+50)|
|1234|17-2064.00|No|17-2064|50|50||

Source: Own elaboration based on O*NET database (Green Occupations & O-NET-SOC 2010 Occupation List), U.S. Bureau of Labor Statistics correspondence between SOC 2010 Codes and ISCO-08 categories, and 2018 Occupational Employment and Wage Statistics.

> 9 Estimates for 2018 are used since it is the most recent year in which survey data was collected using the 2010 SOC classification. After this year, estimates use a hybrid of the 2010 and 2018 SOC systems, which makes the link with the O*NET-SOC 2010 classification of green jobs less direct. While employment estimates from this program are not perfect since they exclude selfemployment, they are the only source of publicly available data with information at the 6-digit level of the SOC.

> 10 This assumption had to be used in other cases in which more information was not available: 1) for six SOC occupations at sixdigits for which U.S. employment data was only available at five digits (in these cases employment at 5-digits was equally distributed among the SOC occupations at six-digits corresponding to the 5-digit code); 2) to estimate employment in some of the six cases in which the SOC classification from O*NET and OEWS were different: SOC 211018 in OEWS (Substance Abuse, Behavioral Disorder, and Mental Health Counselors) was equally distributed between SOC 211011 (Substance Abuse and Behavioral Disorder Counselors) and 211014 (Mental Health Counselors) from O*NET; SOC 512028 in OEWS (Electrical, Electronic, and Electromechanical Assemblers, Except Coil Winders, Tapers, and Finishers) was equally distributed between SOC 512022 and 512023 from O*NET (Electrical and Electronic Equipment Assemblers + Electromechanical Equipment Assemblers); SOC 512098 in OEWS (Assemblers and Fabricators, All Other, Including Team Assemblers) was equally distributed between SOC 512092 and 512099 from O*NET (Team Assemblers + Assemblers and Fabricators, All Other).

7
# Green sectors

As mentioned above, this paper uses the level of GHGE per worker to classify sectors into green and nongreen categories. To calculate the level of GHGE by sector, this paper follows the methodology proposed by Alcantara (2007), Alcantara et al. (2010), and Bin Su et al. (2013). This is because to account for the true ‘greenness” of a sector, we should consider both the direct and indirect GHGE. For example, while the financial sector may have low levels of direct GHGE per worker, its total GHGE may be substantially higher once we consider its links with, for example, the utilities or transportation sectors. The methodology applies the Input-Output model proposed by Leontief, W. (1986) to a vector of emissions multiplier. The original Leontief model to calculate gross production has the following structure: Where corresponds to a column vector containing the level of gross production by economic sector, YY= [II−AA]<sup>−1</sup> ff is the identity matrix of order _n_ corresponding to the _n_ -sectors, is an _n x n_ matrix containing the YY II technology coefficients for each included sector, and is a column vector corresponding to the final AA demand for each economic sector. ff An emission multiplier matrix is incorporated into the previous equation to calculate the level of emissions by sector. This matrix is diagonal and contains the number of emissions per production unit for each sector. The new equation for calculating emissions by sector has the following structure: Where corresponds to a column vector containing the level of emissions by economic sector, and CC= cĉ[II−AA]<sup>−1</sup> ff is a diagonal matrix containing the emissions multipliers for all sectors. CC cĉ The level of emissions per worker by sector is estimated as the level of emissions divided by the number of workers aged 15 to 64 years in each sector. In this section, three primary sources of information are implemented to estimate GHGE per worker by sector: the Socioeconomic Database for Latin America and the Caribbean (SEDLAC),<sup>11</sup> the 2018 edition of the Organization for Economic Co-operation and Development (OECD) country-level Input-Output Tables (IOTs),<sup>12</sup> and the Climate Watch (CAIT) Historical

> 11 SEDLAC is a database of harmonized socio-economic statistics constructed from the Latin American and Caribbean (LAC) household surveys. SEDLAC includes information from over 300 household surveys carried out primarily in 18 LAC countries for which a comparable income aggregate (for welfare analysis) can be created: Argentina, Bolivia, Brazil, Colombia, Costa Rica, Chile, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, and Uruguay. The SEDLAC database and project were jointly developed and are jointly maintained by CEDLAS (Universidad Nacional de La Plata) and The World Bank’s LAC Team for Statistical Development (LAC TSD) in the Poverty and Equity Global Practice.

> 12 Available in https://stats.oecd.org/Index.aspx?DataSetCode=IOTSI4_2018.

8
GHGE.<sup>13</sup> The OECD IOTs contain information about how each sector’s gross production is distributed among all economic sectors (intermediate demand) and the final demand. The latter comprises households’ domestic demand, non-profit institutions’ domestic demand, government’s domestic demand, capital formation, change in inventories, exports, and imports. The sector classification corresponds to 2-digits of the 4<sup>th</sup> revision of the International Standard Industrial Classification of Economic Activities (ISIC Rev. 4), with some sectors grouped according to their nature. This information generates the matrix of coefficients and the vector of final demand. The IOTs matrix has the structure described in Table 3: i represents the demand from sector _j_ for production of sector _i_ , represents the added value generated by sector _j_ , and represents the gross production of sector _j_ . Notice that the sum ii yy vv of the intermediate demand plus added value equals the sector’s gross production. Then, the technology ii YY coefficients are calculated by dividing each sector’s intermediate demand from a specific sector by that sector’s gross production. In other words, each one of the technology coefficients represents the ratio i . In this paper, information about imports is not included in the final demand vector since they do not relate directly to the production process of each sector. Then, only domestic demand and exports are ii yy /YY considered for the estimations.

**Table 3 – Input-Output table structure**

||**_Sector 1_**<br>|**_Sector 2_**<br>⋯<br>|**_Sector n_**<br>|**_Final demand_**<br>|
|---|---|---|---|---|
|**_Sector 1_**|y11<br>|y12<br>⋯<br>|y1n<br>|f1<br>|
|**_Sector 2_**|y21|y22<br>⋯|y2n|f2|
|⋮|⋮<br>|⋮<br>⋮<br>|⋮<br>|⋮<br>|
|**_Sector n_**|yn1<br>|yn2<br>⋯<br>|yn<br>|fn|
|**_Added Value (V)_**|v1<br>|v2<br>⋯<br>|vn<br>||
|**_Gross product (Y)_**|Y1|Y2<br>⋯|Yn||

Source: Own elaboration The third source of information, the Climate Watch/CAIT series, contains information about the level of GHGE by industry and end-uses from 1990 to 2018, following the Intergovernmental Panel on Climate Change (IPCC) reporting framework. Table 4 presents the sectors included in CAIT and the activities they

> 13 Available in https://www.climatewatchdata.org/ghg-emissions?end_year=2018&start_year=1990.

9
include according to the IPCC classification. This information builds the vector of emissions multipliers. Emissions from Land-use changes and International Bunkers are not considered since they do not clearly align with economic activities in the other classifications.

**Table 4 – CAIT Sector Data**

|**Sector/ Subsector**|**Content**|**IPCC Category**|**Gas**|
|---|---|---|---|
|Energy||1|C<br>2|
|-<br>Electricity & Heat|Electricity & Heat plants (fossil fuels)<br>-<br>Public plants (electricity, heat, CHP)<br>-<br>Autoproducers (electricity, heat, CHP)<br>Other Energy industries (fossil fuels)|1 A 1 a<br>1 A<br>1 A 1 b,c|C<br>2<br>C<br>2<br>C<br>2|
|-<br>Manufacturing<br> <br>Construction|&<br>Manufacturing & Construction (fossil fuels)|1 A 2|C<br>2|
|-<br>Transportation|Transportation(fossil fuels)|1 A 3|C<br>2<br>|
|-<br>Other Fuel Combustion|Other sectors (fossil fuels)<br>Biomass combustion<br>Stationaryand Mobile Sources|1 A 4<br>1 A 5<br>1 A 5|C<br>2<br>C<br>4<sup>,N</sup>2<sup>C</sup><br>C<br>4<sup>,N</sup>2<sup>C</sup>|
|-<br>Fugitive Emissions|Gas Venting/Flaring<br>Oil & Natural Gas Systems<br>Coal Mining<br>Other EnergySources|1 B 2c<br>1 B 2<br>1 B 1<br>1 B 1,2|C<br>2<br>C<br>4<br>C<br>4<br>C<br>4<sup>,N</sup>2<sup>C</sup><br>|
|Industrial Processes|Cement<br>Adipic and Nitric Acid Production<br>Other Industrial non-Agriculture<br>All Fluorinated Gases|2 A 1<br>2 B 2,3<br>2<br>2|C<br>2<br>N2C<br>C<br>4<sup>,N</sup>2<sup>C</sup><br>CHC5, PHC5, SH6<br>|
|Agriculture|Enteric Fermentation<br>Manure Management<br>Rice Cultivation<br>Agricultural Soils<br>Other Agricultural Sources|4 A<br>4 B<br>4 C<br>4 D<br>4|<br>C<br>4<br>C<br>4<sup>,N</sup>2<sup>C</sup><br>C<br>4<br>N2C<br>C<br>4<sup>,N</sup>2<sup>C</sup><br>|
|Land-Use Change & Forestry|Land Use Total (Forestry land, cropland,<br>grassland,and biomass burning)|5|C<br>2<sup>, C</sup><br>4<sup>,N</sup>2<sup>C</sup>|
|Waste|Landfills (Solid Waste)<br>Wastewater Treatment<br>Human Sewage<br>Other Non-Agricultural Sources (Waste and<br>Other)|6 A<br>6 B<br>6 B<br>6 D|C<br>4<br>C<br>4<br>N2C<br>C<br>4<sup>,N</sup>2<sup>C</sup>|
|International Bunkers|Aviation Bunkers<br>Marine Bunkers|1 A 3ai<br>1 A 3di|C<br>2<br>C<br>2|

Source: CAIT-UNFCCC.

These three sources of information provide different sector classifications that cannot always be mapped to each other immediately. To properly identify sectors that can be used for the analysis, a new sector classification is created by first assigning each CAIT sector to a corresponding OECD sector and then grouping the data to match the SEDLAC’s sector classification. This process produces a 14-sector classification. Table 5 presents the new cross-sector classification and the CAIT, OECD, and SEDLAC sectors included in each case.

10
Since the sum of the sector-level GHGE is lower than the national-level GHGE (both reported by CAIT), this paper assigns the difference to sectors without GHGE data in CAIT (e.g. financial intermediation) following these steps: (i) calculate the participation of these sectors in the national production; (ii) calculate a residue as the total country-level emissions (excluding land-use change, as explained above) minus the emissions for which the source is known (emissions from CAIT sectors); and (iii) assign each sector a level of emissions corresponding to their participation in national production times the country’s residue of total emissions. We then use the sector-level GHGE from CAIT and the imputed GHGE for non-CAIT sector, as well as the IOT, to calculate the total emissions by sector (CAIT and non-CAIT).

**Table 5 – Sector classification based on CAIT, OECD, and SEDLAC classifications**

|**Sector**|**Included sectors**<br>**CAIT**|**Included sectors OECD**|**Included sectors SEDLAC**|
|---|---|---|---|
|Agriculture,<br>hunting,<br>forestry, and fishing|Agriculture, forestry,<br>and fishing|Agriculture, forestry, and fishing|Agriculture, hunting and<br>forestry<br>Fishing|
|Mining and quarrying|Energy<br>–<br>Fugitive<br>Emissions|Mining and extraction of energy producing<br>products<br>Mining and quarrying of non-energy<br>producing products<br>Miningsupport service activities|Mining and quarrying|
|Manufacturing,<br>Construction<br>&<br>Industrial processes|Energy<br>–<br>Manufacturing<br>&<br>Construction<br>Industrial Processes|Food products, beverages, and tobacco<br>Textiles, wearing apparel, leather, and<br>related products<br>Wood and of products of wood and cork<br>(except furniture)<br>Paper products and printing<br>Coke and refined petroleum products<br>Chemicals and pharmaceutical products<br>Rubber and plastics products<br>Other non-metallic mineral products<br>Manufacture of basic metals<br>Fabricated<br>metal<br>products,<br>except<br>machinery and equipment<br>Computer, electronic and optical products<br>Electrical equipment<br>Machinery and equipment n.e.c.<br>Motor vehicles, trailers, and semi-trailers<br>Other transport equipment<br>Other<br>manufacturing;<br>repair<br>and<br>installation of machinery and equipment<br>Construction|Manufacturing<br>Construction|
|Electricity,<br>gas,<br>and<br>water supply|Energy – Electricity &<br>Heat<br>Waste|Electricity, gas, water supply, sewerage,<br>waste, and remediation services|Electricity, gas, and water<br>supply|
|Wholesale and retail<br>trade|-|Wholesale and retail trade; repair of motor<br>vehicles|Wholesale and retail trade|
|Transport, storage, and<br>communications|Energy – Transport|Transportation and storage|Transport, storage, and<br>communications|

11
|||Publishing, audiovisual and broadcasting<br>activities<br>Telecommunications<br>IT and other information services||
|---|---|---|---|
|Hotels and restaurants|-|Accommodation and food services|Hotels and restaurants|
|Financial<br>intermediation|-|Financial and insurance activities|Financial intermediation|
|Real estate, renting and<br>business activities|-|Real estate activities<br>Other business sector services|Real estate, renting and<br>business activities|
|Public<br>administration<br>and defense|-|Public<br>administration<br>and<br>defense;<br>compulsorysocial security|Public administration and<br>defense|
|Education|-|Education|Education|
|Health and social work|-|Human health and social work|Health and social work|
|Other<br>community,<br>social<br>and<br>personal<br>service activities|Energy – Other Fuel<br>Combustion|Arts, entertainment, recreation, and other<br>service activities|Other community, social<br>and<br>personal<br>service<br>activities|
|Activities<br>of<br>private<br>households<br>as<br>employers|-|Private households with employed persons|Activities<br>of<br>private<br>households as employers|

Source: CAIT, OECD, SEDLAC As a quality check for our own estimations of emissions, we compare them to the corresponding emissions calculated using other sources of information. This check is necessary because there is a concern that our process of assigning emissions using IOT all sectors may result in country-level measures of emissions (when adding up the assigned GHGE of all sectors) that are very different from official measures of country-level emissions. In general, the country-level measures are very similar across sources (Table 6). Estimations are slightly higher when using our method, which is expected due to the choice of excluding imports in the calculations.

**Table 6 – Comparison of estimated GHG emissions (Megatons) with other sources (2015 data)**

|**Country**|**Own**<br>**Estimation**|**WDI**|**CAIT**|**OECD**|
|---|---|---|---|---|
|ARG<sup>a</sup>|385.3|372.4|362.6|328.9|
|BRA|1228.7|1105.5|1082.7|1041.7|
|CHL|136.4|102.2|104.0|103.0|
|COL|214.9|175.0|177.0|170.6|
|CRI|19.2|14.3|14.6|13.6|
|MEX|869.9|674.3|670.1|745.8|
|PER|116.1|96.6|94.9|102.0|

_Notes:_ a OECD data for Argentina compiles its GHGs emission data according to the IPCCC 2006 guidelines, using the Agriculture, Forestry and Other Land Use (AFOLU) category instead of Agriculture and Land Use, Land Use Change and Forestry (LULUCF) categories.

12
To classify the 14 sectors of economic activity into two groups (green and non-green), the paper considers whether their emissions per worker are below or above the median within each country (Table 7 reports the results for countries in LAC for which all data sources are available to calculate emissions per worker). In other words, each sector is considered green or non-green relative to the median sector emissions in each country. Since this classification is quite similar across countries (i.e., the same sector ends up as either green or non-green in most cases), the same green/non-green sectoral definition is used in every economy, including those for which it was not possible to estimate GHGE by sector. The classification to either non-green or green sector is clear from Table 7 except for Financial intermediation and Real estate (because they change status over time in Argentina, Costa Rica and Peru), which are classified as nongreen and green in this paper, respectively. The paper also assumes that this sectoral classification stays the same over time.

**Table 7 – Green (green color) and non-green (brown color) sectors**

||||||||**Year( **|**2000 +**|**)**||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Sector**|**Country**|**05**|**06**|**07**|**08**|**09**|**10**<br>**11**|**12**|**13**|**14**|**15**|**16**|**17**|**18**|
||ARG||||||||||||||
||BRA||||||||||||||
|1. Agriculture,<br>hi|CHL||||||||||||||
|untng,<br>forestry and|COL||||||||||||||
|,<br>fishin|CRI||||||||||||||
|g|MEX||||||||||||||
||PER||||||||||||||
||ARG||||||||||||||
||BRA||||||||||||||
|2 Mining and|CHL||||||||||||||
|.<br>uarrin|COL||||||||||||||
|qyg|CRI||||||||||||||
||MEX||||||||||||||
||PER||||||||||||||
||ARG||||||||||||||
|3.|BRA||||||||||||||
|Manufacturing,|CHL||||||||||||||
|Construction &|COL||||||||||||||
|Industrial|CRI||||||||||||||
|processes|MEX||||||||||||||
||PER||||||||||||||
||ARG||||||||||||||
|4. Electricity,<br>|BRA||||||||||||||
|gas, and water|||||||||||||||
|supply|CHL||||||||||||||
||COL||||||||||||||

13
||||||**Year(2000 +**|**)**|||||
|---|---|---|---|---|---|---|---|---|---|---|
|**Sector**|**Country**|**05**|**06**<br>**07**<br>**08**|**09**|**10**<br>**11**<br>**12**|**13**|**14**|**15**|**16**<br>**17**|**18**|
||CRI||||||||||
||MEX||||||||||
||PER||||||||||
||ARG||||||||||
||BRA||||||||||
|5 Whll|CHL||||||||||
|. oesae<br>d til td|COL||||||||||
|an rea rae|CRI||||||||||
||MEX||||||||||
||PER||||||||||
||ARG||||||||||
||BRA||||||||||
|6. Transport,|CHL||||||||||
|storage, and|COL||||||||||
|communications|CRI||||||||||
||MEX||||||||||
||PER||||||||||
||ARG||||||||||
||BRA||||||||||
|7 Hotels and|CHL||||||||||
|.<br>restaurants|COL||||||||||
||CRI||||||||||
||MEX||||||||||
||PER||||||||||
||ARG||||||||||
||BRA||||||||||
|8. Financial|CHL<br>||||||||||
|intermediation|COL<br>CRI||||||||||
||MEX||||||||||
||PER||||||||||
||ARG||||||||||
||BRA||||||||||
|9. Real estate,|CHL||||||||||
|renting and|<br>||||||||||
|<br>business<br>|COL<br>CRI||||||||||
|activities|||||||||||
||MEX||||||||||
||PER||||||||||
||ARG||||||||||
||<br>BRA||||||||||
|10. Public|CHL||||||||||
|administration|COL||||||||||
|and defense|CRI||||||||||
||MEX||||||||||
||PER||||||||||
|11. Education|ARG||||||||||

14
||||||||**Year(2 **|**000 +**|**)**||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Sector**|**Country**|**05**|**06**|**07**|**08**|**09**|**10**<br>**11**|**12**|**13**|**14**|**15**|**16**|**17**|**18**|
||BRA||||||||||||||
||CHL||||||||||||||
||COL||||||||||||||
||CRI||||||||||||||
||MEX||||||||||||||
||PER||||||||||||||
||ARG||||||||||||||
||BRA||||||||||||||
|12 Hlth d|CHL||||||||||||||
|. ea an<br>|COL||||||||||||||
|social work|CRI||||||||||||||
||MEX||||||||||||||
||PER||||||||||||||
||ARG||||||||||||||
|13. Other|BRA||||||||||||||
|community,|CHL||||||||||||||
|social and|COL||||||||||||||
|personal service|CRI||||||||||||||
|activities|MEX||||||||||||||
||PER||||||||||||||
||ARG||||||||||||||
||BRA||||||||||||||
|14. Activities of<br>|CHL||||||||||||||
|private<br>|COL||||||||||||||
|households as|||||||||||||||
|<br>l|CRI||||||||||||||
|empoyers|MEX||||||||||||||
||PER||||||||||||||

Source: Own elaboration based on CAIT, OECD, and SEDLAC data.

# Green jobs: Occupations vs. sectors

By combining the sector and occupation classifications, four mutually exclusive job types (see Figure 1) are created. Each group faces two primary sources of vulnerabilities to green policies. First, workers in non-green sectors would be more vulnerable to policies that increase the cost of GHGE, such as a Carbon Border Adjustment Mechanism (CBAM).<sup>14</sup> If such a policy reduces the competitiveness of non-green sectors, they may be more likely to shrink and shed labor. Second, workers in non-green occupations would be more vulnerable in terms of lacking the skills to remain employed or find a new job if green policies create a skill-biased change in labor demand. By overlapping these two dimensions, the following four categories are created:

> 14 See <u>https://ec.europa.eu/commission/presscorner/detail/en/qanda_21_3661 the CBAM as proposed by the European</u> Commission.

15
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<!-- Start of picture text --> 100<br>90<br>80<br>70<br>z<br>5 «60<br>a<br>&o 020<br>pa<br>=<br>C7)<br>2 40<br>o<br>a<br>30<br>10 aa<br>oa BBBBEBEEA SEs SB<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br>@ Green Occ. in Green Sectors TM Green Occ. in Non-green Sectors<br>@ Non-green Occ. in Green Sectors @ Non-green Occ. in Non-green Sectors<br><!-- End of picture text -->
**Figure 8 - Decomposition of the change in GHG emissions (tn.) per worker by sector in LAC, 2005-2018**

<!-- Start of picture text --> 2000<br>1500<br>1000<br>500<br>0<br>-500<br>-1000<br>-1500<br>-2000<br>-2500<br>Non-green sectors Green sectors<br>Within Between (structural change) Total<br>Education All sectors<br>defence<br>and fishing communications<br>GHG emissions (tonnes) per worker Financial intermediation Mining and quarrying Transport, storage and as employers Health and social work Hotels and restaurants Real estate, renting and business activities<br>Industrial processes personal service activities Public administration and Wholesale and retail trade<br>Agriculture, hunting, forestry Electricity, gas and water supply Manufacturing, Construction & Other community, social and Activities of private households<br><!-- End of picture text --> Source: Own elaboration based on Climate watch data (GHG emissions in tons) and SEDLAC (Number of workers). Notes: (1) LAC region is composed of the 7 countries with emissions data: Argentina, Brazil, Chile, Colombia, Costa Rica, Mexico, and Peru. (2) In Mexico, data from 2006 were used instead of 2005 due to the lack of estimates of the number of workers. For the same reason, data from years 2006 and 2017 were used in Chile instead of 2005 and 2018. This decomposition follows the traditional shift-share approach from structural transformation studies, where changes in labor productivity are decomposed into productivity growth within the sector (“within” component) and productivity growth driven by workers moving from lower to higher productivity sectors (“between” or “structural change” component).

# Profile of green job holders in LAC

Overall, green occupations are more prevalent among male and urban workers (see Figure 9).<sup>18</sup> Granata and Posadas use a different methodology to define green occupations in Indonesia, and also find that their share is higher among males than among females. However, total employment (including green and non-green occupations) in green sectors is higher for females and more educated people. A more granular picture of the gender patterns of green occupations can be grasped in Figure 10. Men are more likely to have green occupations than women (as measured by the gender gap, that is share of green occupations held by males minus share held by females), across all types of green occupations and in all LAC countries

> 18 Some empirical articles find that such policies would generally benefit workers with higher levels of education and specialized in non-manual tasks (Vona, Marin, Consoli, & Popp, 2016; Marin & Vona, 2019).

24
<!-- Start of picture text --> 100<br>90 19<br>27<br>80 40 48<br>70<br>c<br>oa<br>vu<br>BP 50 62 rr)<br>c<br>8 40 31<br>& 31<br>30<br>=<br>20 | a | a |<br>10 Lo po | An<br>| eo 8 8 8 5 (8<br>Male Female Lessthan Primary Primary Secondary Tertiay Rural Urban<br>Gender Education Area<br>@ Green Occ. in Green Sectors © Green Occ. in Non-green Sectors<br>m@ Non-green Occ. in Green Sectors mw Non-green Occ. in Non-green Sectors<br><!-- End of picture text -->
<!-- Start of picture text --> 25.0<br>20.0<br>Oo<br>a<br>aw<br>@ 15.0<br>Cc<br>is)<br>[a<br>a<br>6 100<br>Cc<br>ow<br>a<br>a<br>£<br>= 5.0<br>bo<br>G<br>G<br>Cc<br>a<br>ia]<br>000 —uEe . ee a ee O v L_a—a 0 5 6 6 a 0)<br>a GTM SLV NIC MEX COL ECU CRI LAC PAN PER BOL BRA CHL URY DOM ARG<br>5.0<br>@New Emerging Enhanced Skills TM Increased Demand<br><!-- End of picture text --> <!-- Start of picture text --> 30<br>Ed<br>£oO 25<br>Cc<br>3 _ 20<br>22<br>zx = 15<br>2a<br>S10<br>Cc<br>2 5S<br>Oo<br>Less than Primary Primary Secondary Tertiary<br>m New Emerging wm Enhanced Skills TM Increased Demand<br><!-- End of picture text -->
<!-- Start of picture text --> 100<br>90<br>on<br>&0 40 38<br>4 70<br>£<br>3 60<br>Sp<br>g 0 51<br>5 40<br>4% 40 38<br>a<br>* 30<br>20 13 14 2B 16<br>10<br>», 8 8 =<br>Formal Informal Small Large Public<br>Informality Firm size<br><!-- End of picture text --> m Green Occ. in Green Sectors TM Green Occ. in Non-green Sectors m@ Non-green Occ. in Green Sectors @ Non-green Occ. in Non-green Sectors
<!-- Start of picture text --> 100<br>90<br>80<br>wn<br>= 70<br>2 60 81 78 77 77 78<br>fal}<br>Bo 50<br>=a 40<br>2<br>a 30<br>a<br>20<br>"ee ae 10 | 10 lh<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br>m New Emerging Enhanced Skills Increased Demand mNon-green Jobs<br><!-- End of picture text -->

<!-- Start of picture text --> 100<br>90<br>30<br>80 38<br>70<br>8<br>c<br>a<br>ov<br>o8 650<br>= 47<br>ra} 40 40<br>o<br>a<br>30<br>20<br>10<br>0<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br>@ Green Occ. in Green Sectors TM Green Occ. in Non-green Sectors<br>@ Non-green Occ. in Green Sectors @ Non-green Occ. in Non-green Sectors<br><!-- End of picture text -->
Lastly, the paper investigates how green job holders fare on the labor market in terms of labor income. Overall, green occupations are linked to significantly higher wages, even when controlling for other individual characteristics (see Table 8): green occupations are linked to hourly wages that are about 20 percent higher (as a benchmark, this magnitude is similar to the gender wage gap).<sup>20</sup> When disaggregated by type, the wage premium is significantly higher for new and emerging green occupations (see second column in Table 8). In line with the results above, the most vulnerable workers (i.e., those in non-green occupations and non-green sectors) display lower wages compared to the other groups (see the third column in Table 8, in which this group is entered as the omitted category). It is important to mention that the wage premium associated with green jobs may be upward-biased because workers with green jobs are significantly different from the rest in terms of observable characteristics, which raises concerns about significant differences in unobservable factors that could introduce omitted variable bias. Within these caveats, the results above suggest the green transition in general and bottlenecks in the supply of green occupations (at least in the short/medium term) could lead to higher wage and income inequality.

> 20 Granata and Posadas (2022) use a different method to classify green jobs (based on text-analysis of the ISCO08 task database) and find that they are linked with higher earnings in Indonesia even when controlling for a host of other individual characteristics. IMF (2022) also finds that green jobs are associated with higher earnings.

29
**Table 8 – Green jobs and labor income in LAC, circa 2019**

||**Log**|**Hourly Labor Inc**|**ome PPP 2011**<br>Omitted category:|
|---|---|---|---|
||Omitted ca<br>green oc|tegory: non-<br>cupation|<br>non-green<br>occupation in non-<br>green sector|
|Green Occupation|0.192***|||
||(0.00638)|||
|New Emerging||2.908***||
|||(0.0509)||
|Enhanced Skills||0.114***||
|||(0.0133)||
|Increased Demand||-0.0305***||
|||(0.00943)||
|Green occ. And Green Sector|||0.227***<br>(0.00876)|
|Green occ. And Green Sector|||0.130***|
||||(0.00447)|
|Green occ. And Green Sector|||0.349***|
||||(0.00887)|
|Constant|-0.162***|-0.114***|-0.239***|
||(0.0160)|(0.0159)|(0.0162)|
|Observations|938,361|938,361|938,361|
|R-squared|0.299|0.310|0.302|
|Robust standard errors in parenthesis||||
|*** p<0.01, ** p<0.05, * p<0.1||||

Note: pooled sample for LAC, circa 2019. Controls include dummy variables for country, educational attainment, gender, rural, as well as age and age squared.

# 4. Robustness Checks

This section discusses some caveats and conducts some robustness checks. First, the classification of occupations used in this study is based on O*NET, which describes the occupations that exist in the US. The main assumption when using O*NET as the source of truth to classify occupations in other countries is that the task content of each occupation in any country is the same as in the United States. Lewandowski et al. (2022) have pointed out that this is almost certain to be problematic for less-developed countries, given significant differences in workers’ skills, technologies, and economic activities, which leads to large 30
<!-- Start of picture text --> 30%<br>rt<br>2<br>25% °<br>a<br>Qa<br>°<br>a<br>% 20%<br>oO<br><oo ao? o°8<br>s3 15% e@ ee! > () ae<br>10% ° oace oes *<br>82 Psaead@ og°@ && °<br>Ccg 5% wrGS @ © @<br>@<br>Oo fet ee ee<br>0%<br>0% 5% 10% 15% 20% 25% 30% 35%<br>Green Jobs (% of total employment)<br><!-- End of picture text -->
Third, the level of aggregation within occupational categories may introduce a bias. For example, in cases where the ISCO-08 code is only available at the 2-digit level (as it is the case in the analysis throughout this paper), we need to make assumptions about the distribution of workers across all the 4-digit occupations nested within a 2-digit one.<sup>22</sup> This paper makes the assumption that such nested distribution is identical to that of the U.S.<sup>23</sup> Table 9 analyzes—for the group of countries with 4-digit ISCO-08 data— the share of employment classified as green if one were to assume that such countries’ highest level of disaggregation is 1, 2, 3 or 4-digits. It shows that higher levels of aggregation lead to different estimates of the share of green jobs, in total and by type. At the same time, they lead to different country rankings. For example, Peru and Panama have 22 and 18 percent of jobs in green occupations according to the 4- digit classification, respectively, but such shares are 22 and 24 percent according to the 1-digit classification. To overcome this limitation, a potential extension of our methodology is to use the occupational structure of “similar” countries (instead of that of the U.S.) with 4-digit ISCO08 data to impute the green employment shares at the 2-digit level for countries without more granular occupational data.

> 22 Even though some countries in SEDLAC report the ISCO08 occupations at the 4-digit level, we decided to use the more aggregate 2-digit level since this paper is focused on cross-country comparisons. Thereby, using 4-digit classifications for some countries and 2-digit for others would introduce noise. However, the mapping of green occupations at the ISCO08 4-digit level that this paper develop is available for analyses at that level of disaggregation.

> 23 As mentioned in Section 2, employment shares are based on U.S. estimates, which are obtained from the 2018 Occupational Employment and Wage Statistics (OEWS) published by the U.S. Bureau of Labor Statistics.

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<!-- Start of picture text --> Country — — — —<br>| country | 80 classification ____]<br>SLV 1 1 1 1<br>Ecu 1 1 1 1<br>PER 1 1 1 1<br>DOM 1 1 1 1<br>HND 1 1 1 1<br>URY 1 1 1 1<br>PAN 2 2 2 2<br><!-- End of picture text -->

<!-- Start of picture text --> Country — — —_ —_<br>[ country |, —ecostassification<br>Ecu 10 8 8 8<br>PER 10 8 8 15<br>URY 10 10 10 Ezy<br>SLV 11 9 9 11<br>DOM 11 10 10 3<br>HND 11 10 10 14<br>PAN 12 12 12 3<br><!-- End of picture text -->

||country<br>Country||<br><br>—|0clas<br>—|sification <br>—|____]<br>—|[country <br>Country||,<br>—|—ecostass<br>—|ification<br>—_|—_|
|---|---|---|---|---|---|---|---|---|---|
|<br><br>SLV|1|<br><br>1|<br><br>1|<br><br>1|<br><br>Ecu|<br><br>10|<br><br>8|8|8|
|Ecu|1|1|1|1|PER|10|8|8|15|
|PER|1|1|1|1|URY|10|10|10|Ezy|
|DOM|1|1|1|1|SLV|11|9|9|11|
|HND|1|1|1|1|DOM|11|10|10|3|
|URY|1|1|1|1|HND|11|10|10|14|
|PAN<br>|2<br>|2<br>|2<br>|2<br>|PAN|12|12|12|3|
|bac|P|||t||||||

<!-- Start of picture text --> Country — — — —<br>Se<br>URY 10 10 10 ra<br>PAN 10 10 10 F<br>Ecu 11 3 3 3<br>SLV 11 9 9 ri<br>PER 11 9 9 6<br>DOM 11 11 11 F<br>HND 11 EJ 8 8<br><!-- End of picture text --> <!-- Start of picture text --> Country — — __ ___<br>| country | _1SC0-cassification __—_]<br>Ecu 22 18 18 17<br>PER 22 19 19 22<br>URY 22 21 21 17<br>SLV 23 19 19 18<br>DOM 23 22 22 17<br>HND 24 20 20 23<br>PAN 24 24 24 18<br><!-- End of picture text -->

|Country|—|—|—|—||country<br>Country||<br>_1SC<br>—|0-cassif<br>—|ication <br>__|__—_]<br>___|
|---|---|---|---|---|---|---|---|---|---|
|URY|10|10|10|ra|<br>Ecu|<br><br>22|18|<br>18|<br>17|
|PAN|10|10|10|F|PER|22|19|19|22|
|Ecu|11|3|3|3|URY|22|21|21|17|
|SLV|11|9|9|ri|SLV|23|19|19|18|
|PER|11|9|9|6|DOM|23|22|22|17|
|DOM|11|11|11|F|HND|24|20|20|23|
|HND|11|EJ|8|8|PAN|24|24|24|18|
local governments to facilitate the formalization of workers with green occupations—e.g., see AVINA (2013) for the case of informal recyclers in Chile—to name a few options.

This paper also provides insights on the link between the process of structural transformation and the green transition. In particular, it shows that two offsetting forces have shaped the dynamics of GHGE in Latin America for the last decade. On the one hand, the increase in the GHGE-intensity of sectors contributed to raise the overall GHGE per worker. On the other hand, the reallocation of workers from high- to low-GHGE-intensive sectors (e.g., from agriculture to services) contributed to reduce the total GHGE per worker. This is an important area of future research to better understand the extent to which the reduction in GHGE-intensity can be achieved by green policies vs broader policies aimed at facilitating economic development.

Finally, the paper has not focused on some areas that could be worth exploring in future research. This includes defining green sectors in a different way that gives more prominence to the adaptation angle, in addition to the mitigation one. To do this, future analysis could define green sectors not only based on the level of emissions, but also on the potential to create green jobs and/or foster green technologies in the longer run. Examples of sectors that would be affected by this alternative definition include water management and waste management. Another area for future research is to explore the complementarities between creation of green jobs and potential for lower-skilled labor. For example, more recyclable products could create more job opportunities for lower-skilled labor in the collection of recyclable materials, potentially mitigating the job displacement effects of greener technologies.

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