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1、Policy Research Working Paper10927Characterizing Green and Brown Employment in IndiaAndrs Ham Emmanuel Vazquez Monica Yanez-Pagans Education Global PracticeSeptember 2024 Public Disclosure AuthorizedPublic Disclosure AuthorizedPublic Disclosure AuthorizedPublic Disclosure AuthorizedProduced by the R

2、esearch Support TeamAbstractThe 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 pape

3、rs 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 affilia

4、ted organizations,or those of the Executive Directors of the World Bank or the governments they represent.Policy Research Working Paper 10927Transitioning toward sustainable development practices is expected to result in broad changes to economic activity,which will subsequently impact labor markets

5、 and change the demand for skills.India established the Skill Council for Green Jobs to identify green jobs and define the skills required for these occupations.This paper applies the Skill Council for Green Jobs definition of green jobs and an inter-national definition of brown jobs to data from th

6、e 201920 Periodic Labour Force Survey to estimate the size of green and brown employment,document patterns between and within occupations,characterize workers by attributes and skills,and study wage differentials.The results highlight the importance of monitoring green and brown jobs with robust lab

7、or market monitoring systems to guide decisions on the sustainability transition and suggest key aspects to consider when investing in green skills and the potential distributive consequences of sustainability policies on the population.This paper is a product of the Education Global Practice.It is

8、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 myanezpagansworldbank.

9、org,a.hamuniandes.edu.co,and evazquezcedlas.org.Characterizing Green and Brown Employment in India*Andrs Ham Emmanuel Vazquez Monica Yanez-Pagans Keywords:climate change,sustainability,labor markets,green jobs,brown jobs,worker skills.JEL Classification:J23,J24,J49,Q01,Q56.*This document is a backgr

10、ound paper for the World Banks India Country Climate and Development Report(P179782).We are grateful to Keiko Inoue,Toby Linden,Josefina Posadas,Stephane Hallegatte,Shobhana Sosale,and Xiaoyan Liang for valuable feedback and comments during peer review that have greatly improved this research.Corres

11、ponding Author.Associate Professor,School of Government,Universidad de los Andes.Email:a.hamuniandes.edu.co.Physical Address:Carrera 1#19-27,Edificio Aulas,Piso 3;Bogot,111711,Colombia.Tel:+57 601 339-4949,Extension 5317.Senior Researcher,Center for Distributive,Labor and Social Studies(CEDLAS),Inst

12、ituto de InvestigacionesEconmicas,Facultad de Ciencias Econmicas,Universidad Nacional de La Plata.Email:evazquezcedlas.org.Senior Economist.Education Global Practice.The World Bank.Email:myanezpagansworldbank.org.2 1.Introduction Climate change is currently at the top of the development agenda in mo

13、st countries,due to its expected consequences on multiple dimensions of well-being,including food security(Wheeler and von Braun,2013);agricultural production,conflict,and economic growth(Carleton and Hsiang,2016);land use(De Chazal and Rounsevell,2009);health(McMichael and Haines,1997);and other ou

14、tcomes.The challenges that arise from climate adaptation and mitigation ambitions have led to the creation of country-specific strategies for sustainable development.However,the distributional consequences of these strategies may vary,with diverse countries requiring different strategies to combat c

15、hanging climate conditions(Mendelsohn et al.,2006).Transitioning towards sustainable development requires immediate and structural changes.While the former require rapid decisions,the latter should not be overlooked.For instance,lowering emissions will likely have a direct effect on economic product

16、ion and a subsequent effect on labor markets(Govindhan and Bhanot,2014).Some jobs will be created,benefitting some workers,while others will be destroyed and thus affect other employees.To connect the transition towards sustainability and adapt to new labor markets,the“Green Jobs Initiative”was prop

17、osed by the United Nations Environment Programme,the International Trade Union Confederation,the International Organization of Employers,and the International Labour Organization,to promote opportunities,equity,and a fair transition towards sustainability(Stanef-Puic et al.,2022).This initiative int

18、ends to promote green employment and reduce the number of brown jobs.Green jobs are occupations that have a positive impact on the planet and contribute to environmental welfare(Bowen,2012),while brown jobs are occupations in industries that are pollution intensive(Vona et al.,2018).Evidence on the

19、prevalence of green and brown occupations and the skills and 3 attributes of workers in both types of jobs is essential to understand the labor market and distributional challenges of transitioning towards sustainable development(Rubio et al.,2022).This paper analyzes patterns in green and brown job

20、s,as well as the characteristics of their workers,using Indias national definition for green occupations and Vona et al.s(2018)classification for brown occupations.India intends on being energy independent by 2040 and have net zero emissions by 2070.This proposed transition to a net-zero carbon econ

21、omy is expected to result in significant changes to the economy and the labor market,increasing the demand for sustainable occupations that require specific skills while reducing the need for brown jobs.India has addressed green skills development through its Skill Council for Green Jobs(SCGJ),estab

22、lished in 2016 with a mission to identify skilling needs of service users,manufacturers,and service providers within the green business sector,and implement nation-wide,industry-led,collaborative skills,and entrepreneur development initiatives(Pavlova,2019).The SCGJ focuses on the renewable energy,t

23、ransportation,waste management,construction,and water management sectors(Bishnoi and Rai,2022).Through collaboration between industry and its 200 training centers,10 assessment agencies,and 400 certified trainers,the SCGJ has rolled out a range of training programs,focused on sectors such as solar a

24、nd wind energy.As part of this work,the SCGJ has established a national definition of green jobs and developed qualification files for each of these occupations linked to its National Skills Qualification Framework.These qualification files clearly define the green skills and competencies required t

25、o support a sustainable transition.We employ data from the 2019-20 Periodic Labour Force Survey(PLFS)to perform our analysis.The PLFS is a nationally representative survey that provides statistics for urban and rural areas,at the state level,and by some individual attributes.We apply Indias national

26、 definition for green jobs and Vona et al.s(2018)classification of brown jobs to occupation codes in the PFLS 4 to identify these jobs in the data.We then calculate descriptive statistics and profile workers in all jobs across sociodemographic attributes.While our results are mostly descriptive,we a

27、re unaware of previous research that studies green and brown occupations using a joint approach.We hope this evidence contributes to identify and diagnose existing challenges to green transitions and helps inform policy decisions that may facilitate the transition towards sustainable development,whi

28、le highlighting the empirical challenges that arise when measuring green and brown jobs using labor force surveys frequently collected in most countries that have measurement limitations.We find that green jobs account for about 5.9 percent of all employment when using the national definition,while

29、4.6 percent of all jobs can be classified as brown when using Vona et al.s(2018)definition.There is a heterogeneous distribution of green and brown occupations across sectors.Some sectors are mixed,since they include workers that are employed in both green and brown occupations.Green jobs seem to be

30、 more prevalent in states with higher per-capita GDP,but we observe no relationship between the percentage of brown jobs and local GDP.We find that green and brown workers tend to be male,younger,and slightly more educated.Green and brown jobs are slightly worse off compared to other jobs in type of

31、 contract,social security benefits,employment categories,and firm size.We find suggestive evidence of a significant wage premium for green jobs and an earnings penalty for brown jobs,which varies by skill level.The remainder of this paper is organized as follows.Section 2 describes the different app

32、roaches to measure green and brown jobs.Section 3 describes our data and how we identify green and brown jobs.Section 4 presents a descriptive analysis of green and brown jobs.Section 5 characterizes workers in green and brown occupations.Section 6 explores differences in compensation between green,

33、brown,and all other occupations.Section 7 concludes with a discussion on our findings,their policy implications,and highlights directions for future research.5 2.Measuring green and brown jobs Let us begin by defining what it means for a job to be either green or brown.“Green jobs”refer to occupatio

34、ns that are environmentally sustainable,which have a direct positive impact on the planet and contribute to overall environmental welfare.They include jobs which seek to use or develop renewable energy,conservation of resources,ensuring energy efficient means,waste management,and sustainable develop

35、ment.“Brown jobs”are occupations in pollution-intensive industries,which result in the contamination of water,land,and air,loss of biodiversity,exhaustion of natural resources like water,fish,land,and fossil fuel extraction,among others.While arriving at a conceptual definition of green and brown jo

36、bs is intuitively simple,there is no unique or straightforward way to identify them in practice.Identifying environmentally sustainable(green)and pollutant(brown)jobs is complex because they can be defined in multiple ways.For instance,we may consider firms output,their technology,workers skills,or

37、occupation task content when trying to identify them in the data(Granata and Posadas,2024).The literature identifies four potential ways to measure green jobs(Granata and Posadas,2024).First is the output approach,which focuses on firms and the goods and services they produce.For example,all workers

38、 employed by a firm that produces electric cars would be considered to have a green job under this approach.Second,is the technology approach,that also looks at firms but focuses on their production technologies.For example,all workers in a firm that prepares food using energy efficient refrigerator

39、s would be considered green.Third,is the skills approach,focused on worker abilities required to produce goods and services.A person who has the skills required to conduct economic analysis related to the environment,independently of the sector in which she works may be classified as having a green

40、job.Fourth is the task content approach,where the focus lies on the workers and tasks required to produce goods and services.6 A person who conducts economic analysis(does not just have the skills to do so)related to the environment can be classified as having a green job,independently of the firm o

41、r sector in which she works.The suggested method to measure green and brown jobs in practice is the latter,the task content approach.Granata and Posadas(2024)developed a methodology that can be applied to emerging economies to identify green jobs using information collected in most labor force surve

42、ys,which tends to classify workers using the skills required for their occupations.They claim that this approach is also the most suitable one to inform skills policy because it allows understanding whether an occupation has specific tasks that promote environmental sustainability.These authors deve

43、lop a dictionary of common green terms relevant to emerging economies,5 apply the green terms dictionary to task statements of all occupations included in the ILOs International Standard Classification of Occupations(ISCO)using text analysis,and then calculate a Green Task Intensity(GTI)index by occ

44、upation.This GTI measures the share of tasks within each occupation that directly contribute to the greening process.Formally,the Green Task Index for occupation o is defined as follows:0=#0#0 The GTI provides a list of occupations with their percentage of green tasks from 0 to 100.The higher the GT

45、I,the larger the number of green tasks performed in that occupation.When 5 These terms are drawn from multiple sources,such as:common terms in the environmental economics literature;terms from the Green Technologies and Practices survey from the US Bureau of Labor Statistics;Occupational Information

46、 Network(O*NET)task statements from the US Department of Labor;and Skills and Competencies from the European Skills,Competencies,Qualifications,and Occupations Taxonomy(see Granata and Posadas,2024 for details).7 applying this procedure to 3,245 task statements in a comprehensive occupations databas

47、e,329 are classified as green using a broad version of the dictionary and 83 are classified as green under a narrow version of the dictionary.The former 329 are considered green tasks,while the latter 83 are considered strictly green tasks.To determine if an occupation is green,the GTI index should

48、be greater than 0 under either the broad or narrow definition.Therefore,green occupations are defined as those in which workers perform at least one green task.This procedure results in a list of occupations using the ILOs ISCO-08 at 4-digit level that are classified as green jobs(see Granata and Po

49、sadas,2024).While there is general agreement on this task content approach when carrying out international comparisons of green jobs using labor force surveys,very few emerging countries have adopted a national classification for green jobs.India stands out in this regard,as the SCGJ has established

50、 a national definition of green jobs and clearly outlined the skills and competencies required to perform those jobs.The SCGJs national definition includes 44 occupations related to the production of renewable energy,environment,forest,climate change,and sustainable development.The list with these 4

51、4 green occupations,which are all identifiable when applying a crosswalk between occupational codes described below,is shown in Appendix Table A.1.6 While the SCGJ has a national approach to identify green jobs,there is no corresponding national definition for brown jobs.We propose using the definit

52、ion employed by Vona et al.(2018),whose taxonomy of brown jobs has been used by most research that aims to measure and 6 The 44 green jobs are described in detail in the SCGJs“Green Jobs Handbook”published in 2022.Renewable energy includes solar PV&solar thermal,wind,hydro,energy storage,biomass pow

53、er/waste to energy,clean cooking stoves,biofuel,and biogas.Environmental,forest,and climate change include solid waste management,water management,e-waste management,and carbon sinks.Sustainable development includes green construction,green transportation,pollution prevention&control,green hydrogen,

54、and energy storage.8 study pollutant occupations and transitions towards sustainability in labor markets across the world(e.g.,Mealy et al.,2018;Marin and Vona,2019;Cerimelo et al.,2022;Rubio et al.,2022).Vona et al.s(2018)definition of brown jobs uses the Standard Occupational Classification(SOC)fr

55、om 2010,which is the standard occupation system used in the United States,at the 6-digit level.They start identifying pollution-intensive industries and then occupations within these industries.First,they select pollution-intensive industries using emissions of the six air pollutants(CO,VOC,NOx,SO2,

56、PM10,PM2.5,and lead)and CO2 emissions.Pollution-intensive industries are defined as those with 4-digit North America Industry Classification System(NAICS)industries that are in the 95th percentile of the pollution intensity(measured in terms of emissions per worker)for at least three pollutants.Base

57、d on this,they identify a total of 62 pollution-intensive industries or brown industries.Second,they identify brown occupations within these industries as those which are most prevalent in these 62 pollution-intensive industries.These are identified as those with a probability of working in pollutin

58、g sectors being seven times higher than in any other job.This procedure results in a total of 87 brown jobs using the SOC-2010 at the 6-digit classification,which we list in Appendix Table A.2.We also require implementing a crosswalk to apply this definition since Vona et al.(2018)use different occu

59、pational codes than those found in our data.How reliable are these methodologies to accurately trace out green and brown jobs?These methods rely on the assumption that certain skills are compatible with either green or brown occupations.However,Granata and Posadas(2024)note that the same occupation

60、may have skills compatible with both types of occupations,so while the task content approach to measurement is the most useful from a policy perspective,it does have its limitations whether it uses international standards,those for the United States or are defined by a specific country.For instance,

61、while the national definition of green jobs in India responds directly to the needs identified by the 9 government,this taxonomy is a work in progress that will likely change over time.One way to ensure that Indias definition of green jobs is consistent is to compare it with other approaches such as

62、 the O*NET definition that uses a task content approach for the United States and the one proposed by Granata and Posadas(2024)that aims to be used widely in emerging economies.With respect to brown jobs,there are fewer available definitions to use and compare.The Indian government only identified t

63、he skills needed to conduct green jobs but did not identify those for pollutant jobs.Therefore,the available method relies on task content definitions for the United States,which may not accurately capture the industry composition and labor market structure in India.While developing a new taxonomy o

64、f brown jobs either in India or other developing countries is beyond the scope of this paper,we do believe that it is difficult to speak about the importance of a sustainable transition towards green employment without analyzing brown jobs.Therefore,measuring and characterizing both types of occupat

65、ions provides a broader view of the policy challenges for environmental sustainability.3.Data and definitions 3.1.Data We employ data from the 2019-20 India Periodic Labour Force Survey(PLFS),which is collected by the National Statistical Office of the Ministry of Statistics and Program Implementati

66、on(MoSPI).This is a nationally representative survey that covers the majority of the Indian Union,except for the villages in the Andaman and Nicobar Islands,which are difficult to access(MoSPI,2016).The PLFS data for this round were collected between July 2019 and June 2020,and include information o

67、n household characteristics,individual-level demographics,educational attributes of respondents,as well as labor market information over the past 12 months and 7 days prior to the interview.Despite the availability of more recent rounds of this survey,we do not use them to 10 avoid potential biases

68、due to effects from the COVID-19 pandemic,7 which impacted how data were collected and may result in atypical estimates and wrongful conclusions.While the survey allows obtaining nationally representative statistics,it is also designed to provide reliable indicators disaggregated by urban and rural

69、areas,at the state level,and gender.Given that the PFLS is a labor force survey,it collects detailed information on job attributes that we require to identify green and brown occupations with the methodologies described in the previous section.We rely on the primary occupation variable in the PFLS,w

70、hich employs the 2004 National Classification of Occupations(NCO)at the 3-digit level to classify workers into occupations.However,several steps and assumptions are required to link observations in the PLFS data to the definitions of green and brown jobs that we described in the previous section.We

71、describe these assumptions and empirical choices in this section,highlighting their limitations.3.2.Identifying green occupations in the PFLS data The national definition of green jobs developed by the SCGJ employs ILOs ISCO-08 at the 4-digit level for the classification of the occupations.However,t

72、he PLFS employs Indias National Classification of Occupations(NCO)from 2004 at the 3-digit level.Therefore,to link the SCGJs national definition of green jobs to the Indian PLFS data,we follow a three-stage approach.First,we use the correspondence developed by Khurana and Mahajan(2020)between NCO-20

73、04 at 3-digits and ISCO-88 at 3-digits to translate the NCO-2004 occupation codes included in the 2019-20 PLFS to ISCO-88 at 3-digit level.The codes are the same in 109 of the 113 occupations.Among the four that are different,two have the same description and are assigned a different number and 7 So

74、me studies have looked at how the pandemic directly affected the green transition in India(see Kedia et al.,2020;Saxena et al.,2021,and Marimuthu et al.,2022),but this is not our objective in this paper.11 the other two are assigned to a more general or specific group,representing a small percentage

75、 of 0.44%of all employment.8 Figure 1.Construction of database to measure green employment Source:Own elaboration.Second,we use the ILOs correspondence table between ISCO-88 at 4-digit level and ISCO-08 at 4-digit level to convert the ISCO-88 at 3-digit level to ISCO-08 at 4-digit level.To carry out

76、 this conversion,we assume an equal distribution of workers within each occupation,which is something done quite frequently in the labor economics literature in the absence of additional information to estimate the distribution of workers within occupations.9 Last,we use 8 The two occupations with t

77、he same description and a different number are“Agricultural,Fishery and Related Labourers”(920 in NCO-2004,but 921 in ISCO-88)and“Subsistence Agricultural and Fishery Workers”(920 in NCO-2004,but 921 in ISCO-88).The reported occupation“Other Teaching Professionals”is coded with the number 233 in the

78、 NCO-2004 and assigned to the more general group“Teaching professionals”(code 230)in the ISCO-88,while the reported occupation“General Managers”in the NCO-2004 is assigned to the specific ISCO-88 code 131(“Managers of small enterprises”)in the Khurana and Mahajan(2020)correspondence.For more informa

79、tion,see https:/ This assumption is only relevant when there are correspondences between green and non-green occupations,which means that it is effectively applied in 16%of all ISCO-88 at 4-digit level occupations in the conversion ISCO-88 at 12 the ISCO-08 classification at the 4-digit level to mer

80、ge the SCGJs national definition of green jobs to the Indian 2019-20 PLFS data.Figure 1 presents a summary of the three-stage approach we use to identify green jobs in our data based on the SCGJs national definition.As Granata and Posadas(2024)mention,any methodology we employ to measure green jobs

81、in household surveys requires making assumptions.In this case,since we cannot apply the SCGJs national definition directly to the PFLS data,we must translate occupational codes to be able to analyze green employment in India.We compare our results from this methodology with other approaches in Secti

82、on 5.This comparison may lend support to the reliability of the SCGJs definition,although we do highlight that this area of study is active and may change quickly.3.3.Identifying brown occupations in the PFLS data As with the SCGJs national definition for green jobs,the correspondence between the in

83、ternational definition of brown jobs and the data in the PFLS is not a one-to-one match.We need to translate brown occupations that use SOC-2010 codes at 6-digits into ISCO-88 at 3-digits.To link Vona et al.s(2018)definition of brown jobs to the Indian PLFS data,we also follow a three-stage approach

84、.First,we map the brown occupations identified by Vona et al.(2018)using the 2010-SOC at 6-digit to the ISCO-08 at 4-digit classification using a crosswalk from the US Bureau of Labor Statistics.When a single ISCO-08 occupation code is mapped to more than one 6-digit SOC,a brown proportion measure i

85、s estimated,which reflects the proportion of corresponding 6-digit SOC occupations that have been classified as brown within the ISCO-08.Second,we convert this measure of brown jobs at the ISCO-08 4-digit level to ISCO-88 at 4-digit using the ILOs crosswalk.Similarly,when an ISCO-88 is associated to

86、 more than one 4-digit-ISCO-08 at 4 digits and in 28%of all ISCO-88 at 3-digit level occupations in the conversion ISCO-88 at 4-digit-ISCO-88 at 3 digits.13 ISCO-08,we assume an equal distribution of workers in ISCO-08 occupations within an ISCO-88 occupation,and therefore use a brown proportion mea

87、sure that reflects the proportion of corresponding ISCO-08 occupations that have been classified as brown within the ISCO-88.Last,brown jobs are estimated at the ISCO-88 3-digit level assuming an equal distribution of workers in 4-digit occupations within each 3-digit occupation.Figure 2 presents a

88、summary of the approach we use to identify brown jobs based on this international definition.Figure 2.Construction of database to measure brown employment Source:Own elaboration.As with the employed definition to measure green jobs,there may be some limitations to identify brown jobs since the occup

89、ation codes proposed by Vona et al.(2018)are not the same as those found in the PFLS survey.Additionally,there is a second limitation in measuring brown jobs,since contrary to green jobs that have at least two different methodologies to identify them that we can compare,we only have one available me

90、thod at the time of writing for brown jobs.The existing definition was created with the US as a reference and using it in other contexts may not accurately capture the industry composition and the labor market particularities in India.While 14 developing a new taxonomy of brown jobs is beyond the sc

91、ope of this paper,we do expect future research to address this issue since studying both green and brown jobs is essential to better understand and discuss the importance of a sustainable transition for labor markets worldwide.We hope that our findings promote greater discussion not only for measuri

92、ng green employment more accurately,but also on how to identify brown jobs in developing countries with greater precision.3.4.Arriving at mutually exclusive categories Given that green jobs are defined using the SCGJ method while brown jobs are identified using Vona et al.s definition,there is the p

93、ossibility of some overlap between brown and green jobs in the PFLS data because of the assumptions that need to be made to merge the more aggregated(with 3-digit code)PLFS data to the definitions that we use(4-digits for green jobs and 6-digits for brown jobs)when using the relevant crosswalks.This

94、 leads to some occupations not being fully green nor fully brown,but a mix between the two.Some degree of overlap is reasonable given the translation required since the survey data are less disaggregated than the definitions that we employ to measure green and brown jobs,as shown by the different di

95、gits of disaggregation.About 7.9%of all occupations in the PFLS had both a percentage of green and brown tasks when applying the definitions(9 in total).10 For the 9 jobs with both green and brown tasks,we rescale the percentages so that the sum of green,brown,and other tasks equals 100.For example,

96、if an occupation has 20%green tasks,20%brown tasks,and 80%neither green nor brown tasks,we divided each of these numbers by 120%to rescale the weights to 16.67,16.67,and 66.67%.This standardization ensures that we do not double count any green or brown tasks.If the 10 These occupations are:Electrica

97、l and Electronic Equipment Mechanics and Fitters,Chemical-Products Machine Operators,Rubber and Plastic-Products Machine Operators,Wood-Products Machine Operators,Printing-,Binding-And Paper-Products Machine Operators,Textile-,Fur-And Leather-Products Machine Operators,Food and Related Products Mach

98、ine Operators,Assemblers,Other Machine Operators and Assemblers.15 correspondence between surveys was a one-to-one match,this would not be necessary,but it is required in this case because of the need to translate definitions across codes.We do note that this assumption may accurately capture that t

99、he job has both green and brown tasks,but it could also reflect that the 3-digit occupation in the surveys includes narrowly defined jobs that are green and others that are brown.While we cannot disentangle which of the 9 jobs with this mix is the former or the latter,this should be an important dis

100、cussion for future work in this area,since applying theoretical definitions to observational data will present similar challenges in other contexts.We return to this point when discussing the policy implications of our findings in the last section.4.Patterns and trends in green and brown employment

101、This section presents descriptive statistics on the size of green and brown employment in India,as well as patterns and trends to characterize these occupations.Figure 3 shows the percentage of green and brown employment after implementing the definitions discussed in the previous sections.About 5.9

102、 percent of jobs in India are green,which amounts to approximately 27.1 million workers.In turn,4.6 percent or 21.2 million workers are employed in brown occupations.However,most jobs in India are neither green nor brown under these definitions(89.5 percent).11 For green jobs,we can calculate altern

103、ative statistics on the prevalence of green jobs using other definitions frequently used in the international literature.These include the O*NET definition for the US and Granata and Posadas(2024)green jobs taxonomy for developing countries.12 These alternative definitions of green jobs support the

104、finding that Indias green 11 We use the 2019 Periodic Labor Force Survey sampling weights as a base to compute all the statistics in this paper,except to estimate the total population(and number of workers),since they are underestimated in the survey.Therefore,we rescaled the sampling weights in the

105、 PFLS using the official projected population at the state-level for 2019 published in the Population Projection Report 2011-2036 of the Indian Ministry of Health and Family Welfare.12 O*NET is an international definition of green jobs based on a taxonomy developed for labor markets in the United St

106、ates(https:/www.onetcenter.org/dictionary/22.0/excel/green_occupations.html).GP is an international taxonomy 16 workforce is small,ranging between 9 and 14 percent on average,suggesting that the SCGJs definition seems to be robust with other forms of measurement,although narrower because green emplo

107、yment is lower than the other definitions.The exception is the European Classification of Occupations,Skills ad Competences(ESCO)definition,which uses a less strict approach to classify a job as green than the other definitions.A simple correlation between green job definitions is positive and range

108、s from 0.24 to 0.29,suggesting that the definitions do have some overlap,but capture slightly different concepts of what each considers to be green skills.Figure 3.Prevalence of green and brown jobs using alternative definitions in India,2019 Source:Own estimates using the India Periodic Labor Force

109、 Survey(2019).Notes:O*NET is an international definition of green jobs based on a taxonomy developed by the USA using occupational information.GP is an international taxonomy of green jobs developed by Granata and Posadas(2024)applying task-content text analysis to the ISCO-08 occupations.ESCO is th

110、e definition used by the European Classification of Occupations,Skills ad Competences in which green jobs are occupations for which at least one of the essential skills used is green.SCGJ refers to the governments definition of green jobs developed by the Skill Council for Green Jobs using 4-digit I

111、SCO-08 occupations.At-risk occupations are those defined by Vona et al.(2018)at the 6-digits level of the 2010-SOC using pollution-intensive industries.We also estimate the prevalence of green and brown jobs with the national definition using the 2007,2009,and 2011 Employment and Unemployment Survey

112、(EUS),as well as the 2018 and of green jobs developed by Granata and Posadas(2024)applying task-content text analysis to the ISCO-08 occupations.14%9%92%6%5%86%91%8%94%95%0%20%40%60%80%100%O*NETdefinition ofgreen jobsGP definition ofgreen jobsESCO definitionof green jobsSCGJ definitionof green jobsB

113、rown jobs 17 2019 Periodic Labour Force Survey(PLFS)for India to see whether there are any trends.We find that the percentage of green jobs has increased from 5 to 6 percent in a decade.The percentage of brown occupations stays relatively constant around 4.5 percent over this period.These results su

114、ggest that green jobs are increasing at a faster rate than brown jobs,suggestively indicating that the green transition seems to be favoring growth in environmentally sustainable employment.Figure 4.Share of green and brown employment in India by sector of activity,2019 Source:Own elaboration based

115、on microdata from the 2019 Periodic Labour Force Survey(PLFS)for India.Notes:(1)Green occupations are those with any of the 44 green qualifications defined by the Skill Council for Green Jobs.(2)Brown occupations are those defined by Vona et al.(2018)at the 6-digits level of the 2010-SOC,mapped to t

116、he 4-digit ISCO-08 classification using a crosswalk from the US Bureau of Labor Statistics(when a single ISCO occupation code is mapped to more than one 6-digit SOC,a brown proportion measure is used that reflect the proportion of corresponding 6-digit SOC occupations that have been classified as br

117、own),converted to ISCO-88 at 4-digits using ILOs crosswalk and then estimated at the ISCO-88 3 digit level assuming an equal distribution of workers in 4-digits occupations within each 3-digit occupation.Next,we explore how green and brown employment is distributed by sector of activity.Figure 4 sho

118、ws the share of green,brown,and rest of workers by broad economic sectors.The sectors with the largest share of green employment are construction(39 percent),manufacturing(21 percent),and agriculture(14 percent).Most brown jobs are in manufacturing(53 percent)and 142481102153931139308751432642321263

119、90102030405060708090100GreenBrownRestOther ServicesPublic AdministrationFinancial and BusinessServicesTransport andCommunicationsCommerceConstructionPublic utilitiesManufacturingMiningAgriculture 18 construction(30 percent).These results show that there is a heterogeneous distribution of green and b

120、rown jobs by economic sector.In fact,looking within each sector,we find that green jobs are a larger fraction of all workers in public utilities and construction,while brown jobs are a larger share of all workers within mining and manufacturing.Moreover,the results suggest that both construction and

121、 manufacturing face considerable opportunities in driving the green transition,but also important challenges as they would need to strive to reduce their overall brown footprint.We proceed to explore the distribution of green and brown employment across geographical locations.Thirty-seven percent of

122、 green workers live in urban areas,while 42 percent of brown workers are also in these areas.Most workers in India therefore reside in rural areas,which also applies to jobs that are neither green nor brown.In unreported results,we find that there is a greater share of green and brown employment in

123、stratums 1 and 2.We also estimate that there are slightly more green jobs in districts with a larger share of urban population(measured in quintiles).Figure 5 presents maps of green and brown employment shares by State/Union Territory(UT)to better capture geographic differences in the prevalence of

124、green and brown employment across India.All States have some fraction of green and brown workers,but states where there is a larger prevalence of green jobs are not necessarily those where there are more brown occupations.Green workers are largely found in the UTs of Damam&Diu and Dadra&Nagar Haveli

125、,where they represent approximately 14 and 15 percent of the workforce,respectively.Brown workers are mainly concentrated in Damam&Diu and Dadra&Nagar Haveli,making up about one-fifth of the workforce in those locations.The States with more green jobs after those are Punjab,Delhi,and Haryana;while t

126、hose with more brown jobs after the top two States are Gujrat,Tamil Nadu,and Pondicheri.19 Figure 5.Green and brown employment shares by State/Union Territory(in percent),India,2019 Source:Own elaboration based on microdata from the 2019 Periodic Labour Force Survey(PLFS)for India.Notes:Green occupa

127、tions are those with any of the 44 green qualifications defined by the Skill Council for Green Jobs.Brown occupations are those defined by Vona et al.(2018)at the 6-digits level of the 2010-SOC,mapped to the 4-digit ISCO-08 classification using a crosswalk from the US Bureau of Labor Statistics(when

128、 a single ISCO occupation code is mapped to more than one 6-digit SOC,a brown proportion measure is used that reflect the proportion of corresponding 6-digit SOC occupations that have been classified as brown),converted to ISCO-88 at 4-digits using ILOs crosswalk and then estimated at the ISCO-88 3

129、digit level assuming an equal distribution of workers in 4-digits occupations within each 3-digit occupation.While green and brown workers are more prevalent in the aforementioned states,their presence in the overall workforce of each state and UT is relatively modest(at most 9 percent),except for D

130、amam&Diu and Dadra&Nagar Haveli where their representation is in the double digits(see Figure 6).This reinforces the general finding that green employment remains a small fraction of total employment in India,but that there is heterogeneity in its distribution over states.Figure 6.Green and brown em

131、ployment shares within State/Union Territory(percent),India,2019 Source:Own elaboration based on microdata from the 2019 Periodic Labour Force Survey(PLFS)for India.Notes:Green occupations are those with any of the 44 green qualifications defined by the Skill Council for Green Jobs.Brown occupations

132、 are those defined by Vona et al.(2018)at the 6-digits level of the 2010-SOC,mapped to the 4-digit ISCO-08 classification using a crosswalk from the US Bureau of Labor Statistics(when a single ISCO occupation code is mapped to more than one 6-digit SOC,a brown proportion measure is used that reflect

133、 the proportion of corresponding 6-digit SOC occupations that have been classified as brown),converted to ISCO-88 at 4-digits using ILOs crosswalk and then estimated at the ISCO-88 3 digit level assuming an equal distribution of workers in 4-digits occupations within each 3-digit occupation.We also

134、analyze whether the percentages of green and brown occupations are correlated with state per capita GDP in Figure 7.There is a positive relationship between the share of green jobs and per capita GDP,but no visible correlation between brown jobs and GDP.This suggests that areas of the country that h

135、ave a higher concentration of green jobs are also the ones with the highest per capita income on average.The same does not apply when we consider brown jobs.66344745578455614155741074687638799764226353465653471618344652477453466349092959093889392898887919292877067928992848894918686909289898585909001

136、02030405060708090100BiharSikkimNagalandManipurMizoramTripuraMeghalayaAssamWest BengalHimachal PradeshJharkhandOdishaChhattisgarhMadhya PradeshGujratDaman&DiuDadra&Nagar HaveliMaharastraAndhra PradeshKarnatakaPunjabGoaLakshadweepKeralaTamil NaduPondicheriAndaman&NicoberTelanganaChandigarhUttaranchalH

137、aryanaDelhiRajasthanUttar PradeshGreen occupationsBrown occupationsRest of occupations 21 Figure 7.Percentage of green and brown occupations and per capita gross domestic product by State/UT,2019 Source:Own elaboration based on microdata from the 2019 Periodic Labour Force Survey(PLFS)for India and

138、Per Capita Net State Domestic Product in 2019-20 from Reserve Bank of India.Notes:Green occupations are those with any of the 44 green qualifications defined by the SCGJ.Brown occupations are those defined by Vona et al.(2018)at the 6-digits level of the 2010-SOC,mapped to the 4-digit ISCO-08 classi

139、fication using a crosswalk from the US Bureau of Labor Statistics(when a single ISCO occupation code is mapped to more than one 6-digit SOC,a brown proportion measure is used that reflect the proportion of corresponding 6-digit SOC occupations that have been classified as brown),converted to ISCO-88

140、 at 4-digits using ILOs crosswalk and then estimated at the ISCO-88 3 digit level assuming an equal distribution of workers in 4-digits occupations within each 3-digit occupation.Andaman&NicoberAndhra PradeshAssamBiharChandigarhChhattisgarhDelhiGoaGujratHaryanaHimachal PradeshJharkhandKarnatakaKeral

141、aMadhya PradeshMaharastraManipurMeghalayaMizoramNagalandOdishaPondicheriPunjabRajasthanSikkimTamil NaduTelanganaTripuraUttar PradeshUttaranchalWest Bengal0%2%4%6%8%10%12%4.64.85.05.25.45.65.8Green occupations(%)Log(Per capita product)Andaman&NicoberAndhra PradeshAssamBiharChandigarhChhattisgarhDelhi

142、GoaGujratHaryanaHimachal PradeshJharkhandKarnatakaKeralaMadhya PradeshMaharastraManipurMeghalayaMizoramNagalandOdishaPondicheriPunjabRajasthanSikkimTamil NaduTelanganaTripuraUttar PradeshUttaranchalWest Bengal0%1%2%3%4%5%6%7%8%4.64.85.05.25.45.65.8Brown occupations(%)Log(Per capita product)22 5.Who

143、works in green and brown jobs?To better understand the individuals working in green and brown employment,we now characterize them by demographics,skills,employment indicators,and wages to profile workers who are employed in green and brown jobs in India,and how they compare between each other and wi

144、th other employees in occupations that are neither green nor brown.We provide a specific focus on the skill distribution within and between occupations since the transition towards sustainable development emphasizes certain required skills for workers.Table A.3 in the Appendix summarizes our main fi

145、ndings and compares attributes within green,brown,and the rest of occupations,conducting mean tests to identify statistically significant differences across groups.5.1.Profile of green and brown workers in demographic attributes In Figure 8,we present the distribution of green and brown workers by g

146、ender.The disparities in labor force participation between men and women in India is well known according to national estimates of labor force participation,in 2020,only 27 percent of females aged 15 and above participate in the labor force vis-vis 75 percent of men in the same age group.The strikin

147、gly low participation of women in Indias labor force reflects deep-rooted societal norms and economic barriers and is widely documented as a key barrier for economic growth(See Banerjee et al.,2013;Klasen and Pieters,2015;Carranza,2014;and Chatterjee et al.,2015).In terms of green jobs,this pattern

148、is largely replicated.Green jobs are male dominated 78 percent are held by men and 22 percent by women.This gender gap is larger when compared to employment rates in non-green occupations.Similar patterns emerge when analyzing brown jobs,where 79 percent of brown workers are men and 21 percent are w

149、omen.The gender gap is also larger in brown occupations compared to other occupations that are neither green nor brown.23 Figure 8.Share of green and brown employment in India by gender,2019 Source:Own elaboration based on microdata from the 2019 Periodic Labour Force Survey(PLFS)for India.Notes:Gre

150、en occupations are those with any of the 44 green qualifications defined by the Skill Council for Green Jobs.Brown occupations are those defined by Vona et al.(2018)at the 6-digits level of the 2010-SOC,mapped to the 4-digit ISCO-08 classification using a crosswalk from the US Bureau of Labor Statis

151、tics(when a single ISCO occupation code is mapped to more than one 6-digit SOC,a brown proportion measure is used that reflect the proportion of corresponding 6-digit SOC occupations that have been classified as brown),converted to ISCO-88 at 4-digits using ILOs crosswalk and then estimated at the I

152、SCO-88 3 digit level assuming an equal distribution of workers in 4-digits occupations within each 3-digit occupation.Figure 9 presents the share of green and brown employment by age groups.Green workers are relatively younger,43 percent are between 15 and 34 years old compared to 36 percent among n

153、on-brown nor green workers.Brown workers are also relatively younger,with 46 percent between the ages of 15 and 34.Few older workers seem to be employed in green or brown jobs,with at most 10-11 percent of workers above the age of 55 years working in these occupations.2221277879730102030405060708090

154、100GreenBrownRestFemaleMale 24 Figure 9.Share of green and brown employment in India by age groups,2019 Source:Own elaboration based on microdata from the 2019 Periodic Labour Force Survey(PLFS)for India.Notes:Green occupations are those with any of the 44 green qualifications defined by the Skill C

155、ouncil for Green Jobs.Brown occupations are those defined by Vona et al.(2018)at the 6-digits level of the 2010-SOC,mapped to the 4-digit ISCO-08 classification using a crosswalk from the US Bureau of Labor Statistics(when a single ISCO occupation code is mapped to more than one 6-digit SOC,a brown

156、proportion measure is used that reflect the proportion of corresponding 6-digit SOC occupations that have been classified as brown),converted to ISCO-88 at 4-digits using ILOs crosswalk and then estimated at the ISCO-88 3 digit level assuming an equal distribution of workers in 4-digits occupations

157、within each 3-digit occupation.5.2.Profile of green and brown workers by educational attainment and skill level Figure 10 shows the distribution of workers by their educational attainment in green,brown,and other jobs.Overall,the differences in educational attainment across the three types of worker

158、s are not large.About two-thirds of workers in all categories have not completed secondary,while the remaining third have some secondary or university studies.In terms of vocational education and training received by green and brown workers,we find that about 1 in 4 green and brown workers have some

159、 formal vocational training.These vocational training programs taken by green and brown workers tend to last between 1 and 2 years and were largely funded by the workers themselves or their families(72 percent of green workers used private funds to get vocational 151711282925272726191821111017010203

160、0405060708090100GreenBrownRest15-2425-3435-4445-5455+)25 training vis-vis 63 percent for brown workers).However,educational attainment and worker skills are two different concepts,so we proceed to study differences by skill level.Figure 10.Distribution of green and brown employment in India by educa

161、tional attainment,2019 Source:Own elaboration based on microdata from the 2019 Periodic Labour Force Survey(PLFS)for India.Notes:Green occupations are those with any of the 44 green qualifications defined by the Skill Council for Green Jobs.Brown occupations are those defined by Vona et al.(2018)at

162、the 6-digits level of the 2010-SOC,mapped to the 4-digit ISCO-08 classification using a crosswalk from the US Bureau of Labor Statistics(when a single ISCO occupation code is mapped to more than one 6-digit SOC,a brown proportion measure is used that reflect the proportion of corresponding 6-digit S

163、OC occupations that have been classified as brown),converted to ISCO-88 at 4-digits using ILOs crosswalk and then estimated at the ISCO-88 3 digit level assuming an equal distribution of workers in 4-digits occupations within each 3-digit occupation.In Figure 11,we classify green and brown workers i

164、nto three groups based on skills.The classification of skills that we use is based on the ISCO-08 skills classification defined using the primary job at 7-day recall.ISCO-08 classifies occupations in four categories based on two dimensions of skills skill level and skill specialization(ILO,2012).The

165、 four ISCO-08 skill level 242025677141512222620212321321107130102030405060708090100GreenBrownRestUniversity incomplete orcompleteHigher than secondary butnot universitySecondary completeSecondary incompletePrimary completePrimary incompleteNo education 26 categories are low(category 1),medium(catego

166、ry 2),and high(categories 3 and 4).We employ this classification of occupations by skills to study differences across workers for the remainder of this section.Low skilled workers include elementary occupations.Medium skilled workers include plant and machine operators,assemblers,skilled agricultura

167、l and trade workers,and clerical,service,and sales workers.High skilled workers include managers,professionals,and technicians.Figure 11.Share of green and brown employment in India by skill levels,2019 A.Green employment B.Brown employment Source:Own elaboration based on microdata from the 2019 Per

168、iodic Labour Force Survey(PLFS)for India.Notes:Green occupations are those with any of the 44 green qualifications defined by the Skill Council for Green Jobs.Brown occupations are those defined by Vona et al.(2018)at the 6-digits level of the 2010-SOC,mapped to the 4-digit ISCO-08 classification us

169、ing a crosswalk from the US Bureau of Labor Statistics(when a single ISCO occupation code is mapped to more than one 6-digit SOC,a brown proportion measure is used that reflect the proportion of corresponding 6-digit SOC occupations that have been classified as brown),converted to ISCO-88 at 4-digit

170、s using ILOs crosswalk and then estimated at the ISCO-88 3 digit level assuming an equal distribution of workers in 4-digits occupations within each 3-digit occupation.Based on this definition,green workers are 44.87 percent low-skilled,45.98 percent medium-skilled,and 9.15 percent high-skilled.Ther

171、efore,we identify two types of green workers based on skills those at the lower end of the skill distribution,performing elementary occupations such as working in recycling waste collection or waste picker and those in the medium to high end such as waste managers or environmental engineers.In contr

172、ast,brown workers are 27.16 percent 44.8745.989.15Low skilledMedium skilledHigh skilled27.1671.161.68Low skilledMedium skilledHigh skilled 27 low skilled,71.16 percent medium skilled,and 1.68 percent high skilled,so there is much less variability in their skill levels compared to individuals employe

173、d in green occupations.Among high skilled green workers,72 percent have tertiary education;for medium skilled green workers,12 percent have tertiary education;and for low skilled green workers,only 3 percent have tertiary education(Figure 12).Conversely,among medium skilled brown workers,65 percent

174、have not completed secondary education,while 80 percent of low skilled brown employees have not completed secondary education.Figure 12.Share of green and brown employment in India by educational attainment and skill level,2019 A.Green employment B.Brown employment Source:Own elaboration based on mi

175、crodata from the 2019 Periodic Labour Force Survey(PLFS)for India.Notes:Green occupations are those with any of the 44 green qualifications defined by the Skill Council for Green Jobs.Brown occupations are those defined by Vona et al.(2018)at the 6-digits level of the 2010-SOC,mapped to the 4-digit

176、ISCO-08 classification using a crosswalk from the US Bureau of Labor Statistics(when a single ISCO occupation code is mapped to more than one 6-digit SOC,a brown proportion measure is used that reflect the proportion of corresponding 6-digit SOC occupations that have been classified as brown),conver

177、ted to ISCO-88 at 4-digits using ILOs crosswalk and then estimated at the ISCO-88 3 digit level assuming an equal distribution of workers in 4-digits occupations within each 3-digit occupation.We examine green and brown employment,focusing on the differences in skill levels and specific occupational

178、 fields of workers in Figure 13.3221186142367162619312720%20%40%60%80%100%Low skilledMedium skilledHigh skilledSome tertiary/post-secondarySecondary completePrimary completebut secondaryincomple3016087042424172512310830%20%40%60%80%100%Low skilledMedium skilledHigh skilledSome tertiary/post-secondar

179、ySecondary completePrimary completebut secondaryincomple 28 Figure 13.Green and brown workers by skill level and field(percent),India,2019 A.Green employment B.Brown employment Source:Own elaboration based on microdata from the 2019 Periodic Labour Force Survey(PLFS)for India.Notes:Green occupations

180、 are those with any of the 44 green qualifications defined by the Skill Council for Green Jobs.Brown occupations are those defined by Vona et al.(2018)at the 6-digits level of the 2010-SOC,mapped to the 4-digit ISCO-08 classification using a crosswalk from the US Bureau of Labor Statistics(when a si

181、ngle ISCO occupation code is mapped to more than one 6-digit SOC,a brown proportion measure is used that reflect the proportion of corresponding 6-digit SOC occupations that have been classified as brown),converted to ISCO-88 at 4-digits using ILOs crosswalk and then estimated at the ISCO-88 3 digit

182、 level assuming an equal distribution of workers in 4-digits occupations within each 3-digit occupation.0010119131217151202110461371144102201810014192315Media-journalism,mass communication,etcTelecomHealthcare and life sciencesHospitality and tourismChemical engineering,hydrocarbons,AutomotiveIron a

183、nd steel,mining,earthmoving and infraOffice and business-related workArtisan/creative arts and cottage-basedAllied manufacturing gems and jeweler,Textiles and handlooms,apparelsCivil engineering-construction,plumbing,Mechanical engineering,strategicOtherIT-ITeSElectrical,power and electronicsHigh sk

184、illedMedium skilledLow skilled31019921115151622233532717121400012311023081821Hospitality and tourismTelecomSecurityAutomotiveOffice and business-related workHealthcare and life sciencesChemical engineering,hydrocarbons,Iron and steel,mining,earthmoving andArtisan/creative arts and cottage-basedAllie

185、d manufacturing gems and jeweler,Mechanical engineering,strategicCivil engineering-construction,plumbing,Textiles and handlooms,apparelsOtherIT-ITeSElectrical,power and electronicsHigh skilledMedium skilledLow skilled 29 For high-skilled positions in the green sector,the primary areas of employment

186、are information technology(IT)and IT infrastructure services.Medium-skilled workers in the green sector are predominantly found in the electrical,power,and electronics industries.Low-skilled workers in the green sector are also primarily engaged in IT and IT infrastructure services,but additionally,

187、they are also found in textiles and handlooms,as well as in the apparel industry.In contrast,the brown sector presents a different distribution.Medium-skilled workers in this sector are largely employed in textiles,handlooms,and apparel.On the other hand,low-skilled workers in the brown sector are t

188、ypically found in electrical,power,and electronics fields.5.3.Profile of green and brown workers in occupational and job characteristics We proceed to explore the distribution of green and brown employment across occupations,highlighting differences in skill levels,and characterize job attributes in

189、 both types of employment.Figure 14 shows that green and brown jobs are a small percentage of workers within occupations.There is a larger prevalence of these individuals as craft workers,machine operators and elementary occupations.Out of all craft operators,10 percent are in green jobs and 18 perc

190、ent are in brown employment.Eleven percent of machine operators are employed in green jobs and 22 percent in brown jobs.Twelve percent of elementary occupations are carried out by green workers and 6 percent by brown employees.In most other occupations,the participation of green and brown employment

191、 is much smaller,less than 5-6 percent across the other classifications we consider in this paper.30 Figure 14.Share of green and brown employment in India by type of occupation,2019 Source:Own elaboration based on microdata from the 2019 Periodic Labour Force Survey(PLFS)for India.Notes:Green occup

192、ations are those with any of the 44 green qualifications defined by the Skill Council for Green Jobs.Brown occupations are those defined by Vona et al.(2018)at the 6-digits level of the 2010-SOC,mapped to the 4-digit ISCO-08 classification using a crosswalk from the US Bureau of Labor Statistics(whe

193、n a single ISCO occupation code is mapped to more than one 6-digit SOC,a brown proportion measure is used that reflect the proportion of corresponding 6-digit SOC occupations that have been classified as brown),converted to ISCO-88 at 4-digits using ILOs crosswalk and then estimated at the ISCO-88 3

194、 digit level assuming an equal distribution of workers in 4-digits occupations within each 3-digit occupation.Figure 15 explores whether green and brown workers in these occupations are low,medium,or high skilled.Our findings indicate a distinction in the types of roles occupied by workers in green

195、jobs based on their skill level.Most high-skilled green workers,about 37 percent,are employed as managers and technicians.In contrast,40 percent of the medium-skilled workers in green jobs are primarily engaged as craft workers.For low-skilled green employment,94 percent,are in elementary occupation

196、s.When it comes to brown jobs,we find similar trends.Medium-skilled workers are predominantly machine operators,accounting for 36 percent,and craft workers,making up 61 percent.About 94 percent of low-skilled workers in the brown sector perform elementary occupations.24502210111202000018226989595100

197、98987267820102030405060708090100ManagersProfessionalsTechniciansClerksService andmarket salesworkersSkilledagriculturalCraft workersMachineoperatorsElementaryoccupationsGreen occupationsBrown occupationsRest of occupations 31 Figure 15.Share of green and brown employment in India by type of occupati

198、on and skill level,2019 A.Green employment B.Brown employment Source:Own elaboration based on microdata from the 2019 Periodic Labour Force Survey(PLFS)for India.Notes:Green occupations are those with any of the 44 green qualifications defined by the Skill Council for Green Jobs.Brown occupations ar

199、e those defined by Vona et al.(2018)at the 6-digits level of the 2010-SOC,mapped to the 4-digit ISCO-08 classification using a crosswalk from the US Bureau of Labor Statistics(when a single ISCO occupation code is mapped to more than one 6-digit SOC,a brown proportion measure is used that reflect th

200、e proportion of corresponding 6-digit SOC occupations that have been classified as brown),converted to ISCO-88 at 4-digits using ILOs crosswalk and then estimated at the ISCO-88 3 digit level assuming an equal distribution of workers in 4-digits occupations within each 3-digit occupation.Next,we exp

201、lore employment attributes that reflect job quality,including employment status(paid employees,non-paid employees,employers,and self-employed),having a labor contract,social security contributions,and firm size(1-5 workers,6-9 workers,10-19 workers,and more than 20 workers).These results are shown i

202、n Figure 16.We find that green workers are more likely to have a written labor contract(16 percent)compared to brown employees(14 percent),but not as much as workers in other occupations(23 percent).Green workers are less likely to contribute to social security than the rest of workers(22 versus 29

203、percent).We find that green workers tend to work as paid employees(72 percent)or self-employed workers(21 percent),while non-green workers are less likely to be hired as paid employees or salaried workers.Green workers seem to work mostly in larger firms,since the distribution of green employees is

204、further to the right when compared to non-green jobs.0000500941100284023137283300000020406080100ManagersTechniciansSkilledagriculturalMachineoperatorsLow skilledMedium skilledHigh skilled00005009400002613610851400100020406080100ManagersTechniciansSkilledagriculturalMachineoperatorsLow skilledMedium

205、skilledHigh skilled 32 Figure 16.Share of green employment in India by job characteristics,2019 A.Employment contract B.Social security insurance C.Employment status D.Firm size Source:Own elaboration based on microdata from the 2019 Periodic Labour Force Survey(PLFS)for India.Notes:Green occupation

206、s are those with any of the 44 green qualifications defined by the Skill Council for Green Jobs.Brown occupations are those defined by Vona et al.(2018)at the 6-digits level of the 2010-SOC,mapped to the 4-digit ISCO-08 classification using a crosswalk from the US Bureau of Labor Statistics(when a s

207、ingle ISCO occupation code is mapped to more than one 6-digit SOC,a brown proportion measure is used that reflect the proportion of corresponding 6-digit SOC occupations that have been classified as brown),converted to ISCO-88 at 4-digits using ILOs crosswalk and then estimated at the ISCO-88 3 digi

208、t level assuming an equal distribution of workers in 4-digits occupations within each 3-digit occupation.Brown workers tend to have similar work conditions as workers in green jobs.They are less likely to have a written contract and contribute to social security but are also more likely to be paid e

209、mployees and work in larger firms compared to workers that are neither green nor brown.8486771614230102030405060708090100GreenBrownRestWithout contractWith contract7880712220290102030405060708090100GreenBrownRestWithout social security insuranceWith social security insurance7271426516113212339010203

210、0405060708090100GreenBrownRestPaid employeeNon-paid employeeEmployerSelf-employed5657651615127762121170102030405060708090100GreenBrownRest1,5 workers6,9 workers10,19 workers20+workers 33 Overall,these results suggest that other workers have better job benefits but are followed by individuals employe

211、d in green jobs and those in brown jobs have the least benefits.When analyzing these indicators by skill level,we find that higher skilled workers tend to have better working conditions across the board.Higher skilled green workers are even more likely to have a written contract,contribute to social

212、 security,work as paid employees and be employed in large firms when compared to green workers with low and medium skills.The same qualitative findings also apply for brown workers with higher skills when compared to those with lower skills.Once again,such results highlight the heterogeneity within

213、green and brown occupations.6.Compensation for green and brown jobs Where are green,brown and other workers located on the income distribution?Figure 17 shows the distribution of employees by quintile of monthly labor income.Green and brown workers are found in the upper part of the distribution,mak

214、ing up 12 percent of workers in quintiles 3 and 15 percent in quintile 4.The remainder of jobs are distributed more evenly across the distribution.34 Figure 17.Green and brown employment by quintile of monthly labor income,2019 Source:Own elaboration based on microdata from the 2019 Periodic Labour

215、Force Survey(PLFS)for India.Notes:Green occupations are those with any of the 44 green qualifications defined by the Skill Council for Green Jobs.Brown occupations are those defined by Vona et al.(2018)at the 6-digits level of the 2010-SOC,mapped to the 4-digit ISCO-08 classification using a crosswa

216、lk from the US Bureau of Labor Statistics(when a single ISCO occupation code is mapped to more than one 6-digit SOC,a brown proportion measure is used that reflect the proportion of corresponding 6-digit SOC occupations that have been classified as brown),converted to ISCO-88 at 4-digits using ILOs

217、crosswalk and then estimated at the ISCO-88 3 digit level assuming an equal distribution of workers in 4-digits occupations within each 3-digit occupation.6.1.Unconditional wage differences for green and brown workers Green workers have higher average hourly wages than brown and the rest of workers

218、in India(Figure 18).Hourly wages are 59 rupees for green workers,29 rupees for brown workers,and 43 rupees for workers in neither green nor brown occupations.This suggests a wage premium of almost 40 percent for green jobs and a penalty of 31 percent for brown jobs when compared to other occupations

219、.When we observe median wages,we find that green jobs have higher wages(47 rupees)but the differences between brown jobs and the rest of jobs disappear(33 rupees per hour for both).Here,the unconditional premium and penalties are lower(9 percent premium for green workers and a 22 percent penalty for

220、 brown workers compared to workers in other jobs).367863457494908885900102030405060708090100Quintile 1Quintile 2Quintile 3Quintile 4Quintile 5Green occupationsBrown occupationsRest of occupations 35 Figure 18.Mean and median hourly wages of workers,2019 Mean hourly wages Median hourly wages Source:O

221、wn elaboration based on microdata from the 2019 Periodic Labour Force Survey(PLFS)for India.Notes:(1)Green occupations are those with any of the 44 green qualifications defined by the Skill Council for Green Jobs.Brown occupations are those defined by Vona et al.(2018)at the 6-digits level of the 20

222、10-SOC,mapped to the 4-digit ISCO-08 classification using a crosswalk from the US Bureau of Labor Statistics(when a single ISCO occupation code is mapped to more than one 6-digit SOC,a brown proportion measure is used that reflect the proportion of corresponding 6-digit SOC occupations that have bee

223、n classified as brown),converted to ISCO-88 at 4-digits using ILOs crosswalk and then estimated at the ISCO-88 3 digit level assuming an equal distribution of workers in 4-digits occupations within each 3-digit occupation.(2)Mean(median)hourly wages of green,brown and rest of occupations are estimat

224、ed from the constant and the coefficients of a linear(quantile)regression of hourly wages on the green job and brown job variables.However,we noted in previous sections that green and brown workers are spread out across different skill levels.In Figure 19,we plot average wages within each occupation

225、 by skill levels.We observe that in green occupations,average hourly wages are 54 rupees for low-skilled workers,50 rupees for medium-skilled workers,and 283 rupees for high-skilled workers.In turn,mean pay for brown workers are 76,44,and 419 rupees per hour for low,medium,and high skilled individua

226、ls,respectively.These results indicate substantial heterogeneity within occupations by skill levels.These numbers are similar when looking at median wages.Green workers earn 57,40,and 255 rupees per hour,while brown workers earn 86,40,and 460 rupees per hour.$59$29$43$-$10$20$30$40$50$60$70GreenBrow

227、nRest$47$33$33$-$5$10$15$20$25$30$35$40$45$50GreenBrownRest 36 Figure 19.Mean hourly wages of green and brown workers by skill levels,2019 A.Green jobs B.Brown jobs Source:Own elaboration based on microdata from the 2019 Periodic Labour Force Survey(PLFS)for India.Notes:(1)Green occupations are thos

228、e with any of the 44 green qualifications defined by the Skill Council for Green Jobs.Brown occupations are those defined by Vona et al.(2018)at the 6-digits level of the 2010-SOC,mapped to the 4-digit ISCO-08 classification using a crosswalk from the US Bureau of Labor Statistics(when a single ISCO

229、 occupation code is mapped to more than one 6-digit SOC,a brown proportion measure is used that reflect the proportion of corresponding 6-digit SOC occupations that have been classified as brown),converted to ISCO-88 at 4-digits using ILOs crosswalk and then estimated at the ISCO-88 3 digit level as

230、suming an equal distribution of workers in 4-digits occupations within each 3-digit occupation.(2)Mean(median)hourly wages of green,brown and rest of occupations are estimated from the constant and the coefficients of a linear(quantile)regression of hourly wages on the green job and brown job variab

231、les.As we noted in the last section,most green workers are in the low or medium-skill categories,with only 9.15 percent in the high category.Most brown workers are also in low or medium-skill groups,with only 1.68 percent in the high skill categories.Therefore,these wage estimates should be interpre

232、ted with caution because they are estimated on fewer observations.6.2.Estimating conditional wage differences for green and brown workers To better approximate the earnings differential of working in green and brown jobs,we estimate Mincer equations with the logarithm of hourly wages as the dependen

233、t variable.Table 1 shows the results from two specifications:i)only controlling whether the worker is employed in a green or brown job;and ii)specification i)but adding demographic,regional,and educational attainment control variables.We also include state/UT fixed effects,to compare workers within

234、each state.$54$50$283$-$50$100$150$200$250$300$350$400$450Low skilledMedium skilledHigh skilledGreen jobs$76$44$419$-$50$100$150$200$250$300$350$400$450Low skilledMedium skilledHigh skilledBrown jobs 37 Regression results confirm our descriptive findings that there seems to be a wage premium for gre

235、en jobs and a penalty for brown jobs when compared to occupations not in these two categories.Without controls,the premium for green jobs is 6.54 percent while the penalty for brown jobs is 35.3 percent.Once we include controls,these estimates are 13.3 percent premium for green workers and a 21.7 pe

236、rcent penalty for brown workers.We also estimate quantile regressions at the median,to avoid any potential influence from outliers that may affect mean estimates in Table 2.The findings paint a similar story to the results for the conditional mean in Table 1.We confirm a pay premium for green jobs a

237、nd a penalty for brown jobs.In the full specification,green workers earn 23.7 percent more at the median and brown employees earn 10.8 percent less than workers in neither green nor brown occupations,when controlling for demographics,education,region,and fixed effects.In unreported results,we also e

238、stimate Mincer regressions with different reference groups for mean and median wages,which are available upon request.The results lead to identical conclusions to the ones in Tables 1 and 2,with green jobs consistently showing a wage premium and brown jobs consistently showing a wage penalty,irrespe

239、ctive of the reference group and included controls.38 Table 1.Mincer regressions of mean hourly wages,2019 log(Hourly wages)(1)(2)(3)(4)Green job 0.0654*0.133*0.254*0.237*(0.0165)(0.0149)(0.0159)(0.0156)Brown job-0.353*-0.217*-0.155*-0.108*(0.0192)(0.0165)(0.0171)(0.0165)Male 0.401*0.435*0.411*(0.00

240、548)(0.00566)(0.00549)Age 0.0384*0.0249*0.0366*(0.00224)(0.00230)(0.00222)Age squared -0.000333*-0.000230*-0.000320*(2.83e-05)(2.92e-05)(2.80e-05)Primary incomplete 0.0208*0.00867 (0.00894)(0.00897)Primary complete 0.0641*0.0425*(0.00707)(0.00711)Secondary incomplete 0.126*0.0924*(0.00627)(0.00634)S

241、econdary complete 0.260*0.200*(0.00645)(0.00659)Higher than secondary but not university 0.525*0.400*(0.0193)(0.0190)University incomplete or complete 0.795*0.645*(0.00797)(0.00854)Urban 0.177*0.221*0.146*(0.00434)(0.00455)(0.00436)Medium skill 0.142*0.0695*(0.00454)(0.00461)High skill 0.582*0.312*(

242、0.00683)(0.00715)Constant 4.046*2.417*2.773*2.409*(0.0124)(0.0449)(0.0459)(0.0445)Observations 94,366 94,353 94,353 94,353 R-squared 0.075 0.304 0.257 0.320 Source:Own elaboration based on microdata from the 2019 Periodic Labour Force Survey(PLFS)for India.Notes:1)Standard errors in parentheses.2)*p

243、0.01,*p0.05,*p0.1.3)Mincer equations estimated by quantile(median)regression,where the dependent variable is the logarithm of hourly wages in the primary occupation for workers aged 2555.The main explanatory variable is the proportion of green jobs(panel(a)or brown jobs(panel(b)or both(panel(c)in th

244、e occupation at 3 digits level of the ISCO-88 39 under the assumptions described in the methodology section.4)Control variables in column(2)include a male dummy,age,age squared,educational dummies(no education omitted)and an urban area dummy.5)All specifications control for State/union territory fix

245、ed effects.40 Table 2.Mincer regressions of median hourly wages,2019 log(Hourly wages)(1)(2)(3)(4)Green job 0.0875*0.146*0.279*0.263*(0.0180)(0.0169)(0.0175)(0.0176)Brown job-0.180*-0.140*-0.0973*-0.0658*(0.0191)(0.0179)(0.0182)(0.0182)Male 0.419*0.440*0.426*(0.00547)(0.00538)(0.00550)Age 0.0395*0.0

246、253*0.0375*(0.00247)(0.00247)(0.00247)Age squared -0.000380*-0.000256*-0.000361*(3.09e-05)(3.09e-05)(3.09e-05)Primary incomplete 0.0245*0.0126 (0.0109)(0.0109)Primary complete 0.0490*0.0340*(0.00871)(0.00874)Secondary incomplete 0.0938*0.0630*(0.00758)(0.00767)Secondary complete 0.199*0.146*(0.00744

247、)(0.00763)Higher than secondary but not university 0.431*0.332*(0.0178)(0.0181)University incomplete or complete 0.768*0.615*(0.00827)(0.00899)Urban 0.150*0.165*0.118*(0.00473)(0.00470)(0.00479)Medium skill 0.126*0.0754*(0.00598)(0.00611)High skill 0.542*0.322*(0.00722)(0.00794)Constant 3.994*2.477*

248、2.807*2.475*(0.0120)(0.0498)(0.0496)(0.0499)Observations 94,366 94,353 94,353 94,353 Source:Own elaboration based on microdata from the 2019 Periodic Labour Force Survey(PLFS)for India.Notes:1)Robust standard errors in parentheses.2)*p0.01,*p0.05,*p0.1.3)Mincer equations estimated by OLS,where the d

249、ependent variable is the logarithm of hourly wages in the primary occupation for workers aged 2555.The main explanatory variable is the proportion of green jobs(panel(a)or brown jobs(panel(b)or both(panel(c)in the occupation at 3 digits level of the ISCO-88 under the assumptions described in the met

250、hodology section.4)Control variables in column(2)include a male 41 dummy,age,age squared,educational dummies(no education omitted)and an urban area dummy.5)All specifications control for State/union territory fixed effects.While this evidence only provides correlation between the type of occupation

251、and wages,it does shed some light on how the labor market values these occupations on average and at the median in India when we control for demographics,education,and regional variables.While further analyses are required to better understand what explains wage differentials between these occupatio

252、ns,there seems to be highly suggestive evidence that the market values green or environmentally friendly jobs more than brown or pollution-intensive occupations in terms of pay.7.Conclusions and policy discussion The green transition is expected to significantly affect labor demand,creating new jobs

253、,eliminating others,and changing the required skills in the labor market.To manage this transition effectively and prepare the current and future labor force,it will be crucial for countries to strengthen their existing Labor Market Information System(LMIS)and to establish skills development strateg

254、ies that allow to align the workforces skills with the new demands.Since its creation,almost a decade ago,the Indias SCGJ has made substantial progress on the green skills agenda,setting a strong precedent for other emerging countries.In close collaboration with industry and academia,the SCGJ has id

255、entified 44 green low and medium-skilled occupations related to renewable energy,environment,forestry,climate change,and sustainable development,which are expected to reduce environmental impact and,therefore,be in high demand as the green transition evolves.The SCGJ has also developed detailed qual

256、ification files for each of these occupations,which outline their competencies standards,curricula,assessment,and certification requirements.Beyond this,the SCGJ has also rolled out a range of training programs,in collaboration between industry and its 200 training centers,10 assessment agencies,and

257、 400 certified trainers,with a majority focused on solar and wind energy.42 This paper analyzes the patterns of green and brown jobs,as well as the characteristics of their workers,using Indias SCGJ definition for green occupations and Vona et al.s(2018)international classification of brown occupati

258、ons.We use data from the 2019-20 India Periodic Labour Force Survey to estimate the size of green and brown employment in India,document patterns between and within each type of occupation,characterize workers across multiple sociodemographic characteristics in both types of employment,and study wag

259、e differentials for green and brown occupations compared to other jobs in the country.To our knowledge,this is among the first research that employs mutually exclusive definitions of green and brown jobs to provide a detailed analysis of these occupations and characterizes individuals that work in e

260、ither environmentally friendly(green)or pollutant(brown)jobs.Our findings indicate that green jobs account for about 5.9 percent of all employment,while 4.6 percent of all jobs can be classified as brown in India.There is substantial heterogeneity within green and brown occupations across multiple d

261、imensions,including economic sector,geographic location,and workers attributes.Many economic sectors are mixed,with workers employed in both green and brown occupations.Green jobs seem to be more prevalent in Indian states with higher per-capita GDP,but we observe no relationship between the percent

262、age of brown jobs and state-level GDP levels.We also find that green and brown workers tend to be male,younger,and slightly more educated.Green and brown jobs are slightly worse-off compared to other jobs in quality measures,including type of contract,social security benefits,employment categories,a

263、nd firm size.However,green workers have slightly better working conditions than employees in brown jobs.Lastly,we also find suggestive evidence of a statistically significant wage premium for green jobs and a wage penalty for brown jobs,which varies by skill levels.43 There are four main policy impl

264、ications from the results presented in this paper.First,to support the green transition,it will be essential to strengthen existing LMIS to monitor green and brown jobs and to support the green skills development agenda.13 While lot of progress has been made in India in recent years to create a nati

265、onal definition of green jobs and to develop qualifications files,moving forward it will be critical to also strengthen the LMIS to make it more adaptable and able to monitor the rapid changes in the labor market.This might involve leveraging the use of online vacancy data,conducting more regular an

266、d comprehensive surveys to monitor labor market changes,and strengthening the collaboration with the industry and the tertiary education system for a more real-time understanding of the labor market trends.Investing in more robust data and better measurement practices would provide a platform to mon

267、itor jobs and skills needed and allow policy makers to react quickly to changes in the labor market,technological changes,and other factors that might affect climate change policy.14 Second,it will also be important that the large number of training programs that have been rolled out by the SCGJ in

268、collaboration with industry and tertiary education centers are evaluated to ensure that individuals are demanding these training programs,that they are able to afford them,to ensure that the curricula responds to the needs of the industry,that those who enroll are able to complete the courses,and mo

269、st importantly,whether the training results in employment.Third,the potential distributive consequences of the green transition must be taken into consideration when designing skills development policies so that the training programs are rolled out and targeted properly across low,medium,and high sk

270、illed groups to prevent an unequal 13 This is consistent with the findings reported by Granata and Posadas(2024).14 A road map for the development of a robust national LMIS system is described in Testaverde et al.,(2021),with an application for Indonesia.44 transition to sustainability(Azad and Chak

271、raborty,2018).Promoting green skills and jobs should be carried out with the knowledge that pollution-intensive or brown jobs may be lost and that not all individuals may benefit from this transition.Experience from past economic transformations suggests that the transition away from fossil fuels ma

272、y have substantial effects on the structure of employment and earnings.To better understand how the green transition may affect workers and help devise a wide range of policies to transform labor markets while protecting vulnerable workers,it is essential to consider all the potential distributive c

273、onsequences of the green transition(World Bank,2024).Lastly,one of our main results is that there is significant heterogeneity in green and brown employment across economic sectors.Future work should explore this stylized fact in greater detail to promote a discussion on whether different sectors re

274、quire different approaches to transition towards sustainable practices(Varghese et al.,2018).While the green transition is a nationwide policy,there are nuanced effects that need to be considered in terms of workers,sectors,firms,and other factors such as firm size(Kumar et al.,2010).For instance,yo

275、ung people seem to be those acquiring green skills,but it is important to address barriers to access and inclusion and promoting a breadth of green skills that are portable for helping them succeed in future labor markets(United Nations Chidrens Fund,2024).Transitioning towards sustainable developme

276、nt requires both immediate and longer-term structural changes to avoid any potentially negative consequences for the existing and future workers.While many research and policy efforts have focused on the short-term implications of a green transition(Green,2018),some have turned their attention towar

277、ds the medium-and long-term implications of sustainable development for well-being(Marin and Vona,2023),especially concentrating on how labor markets will transform in the next few years and decades(Garca-45 Garca et al.,2020;Green and Gambhir,2020;Kumar and Majid,2020;Acemoglu et al.,2023).Ensuring

278、 a just transition towards sustainable development will require evidence on who stands to gain and lose.This evidence should be both quantitative and qualitative to ensure that we have robust estimations but also that firm and workers voices are heard to gather valuable insights into their experienc

279、es and challenges.Such diagnoses can help determine where and whom to invest in,to ensure that most individuals benefit from this new form of development and historical inequalities are not perpetuated.Also,different countries will require different strategies and policies to achieve their sustainab

280、ility practices.India may be a particular case that need not apply to other countries in the South Asia region or in other parts of the world.Future studies could begin comparing the distinct challenges faced by countries within the same geographic region or across different ones.Only with more evid

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301、rical exploration.Journal of the Association of Environmental and Resource Economists,5(4),713-753.Wheeler,T.and Von Braun,J.(2013).Climate change impacts on global food security.Science,341(6145),508-513.World Bank.2023.Toward Faster,Cleaner Growth.South Asia Development Update(October 2023).World

302、Bank,Washington,DC.doi:10.1596/978-1-4648-2026-7.License:Creative Commons Attribution CC BY 3.0 IGO.World Bank.2024.Jobs for Resilience.South Asia Development Update(April 2024).World Bank,Washington,DC.doi:10.1596/978-1-4648-2103-5.License:Creative Commons Attribution CC BY 3.0 IGO 49 Table A.1.Ind

303、ias national definition of green jobs developed by the SCGJ Source:List of green jobs qualifications from Skill Council for Green Jobs(2022).Green Jobs Handbook.National Skills Qualification Framework(NSQF)Qualification files from National Qualifications Register webpage(https:/www.nqr.gov.in/).Note

304、:*https:/www.openriskmanual.org/wiki/ISCO_Occupation_Group_7412.12_Wind_Turbine_Technician 50 Table A.2.Brown job qualifications in India and associated occupation based on ISCO-08 Source:Vona et al.(2018).SOC 2010SOC Title 17-2041Chemical Engineers17-2151Mining and Geological Engineers,Including Mi

305、ning Safety Engineers17-2171Petroleum Engineers19-1012Food Scientists and Technologists19-2031Chemists19-4031Chemical Technicians43-5041Meter Readers,Utilities45-4023Log Graders and Scalers47-4071Septic Tank Servicers and Sewer Pipe Cleaners47-5011Derrick Operators,Oil and Gas47-5012Rotary Drill Ope

306、rators,Oil and Gas47-5013Service Unit Operators,Oil,Gas,and Mining47-5021Earth Drillers,Except Oil and Gas47-5031Explosives Workers,Ordnance Handling Experts,and Blasters47-5042Mine Cutting and Channeling Machine Operators47-5051Rock Splitters,Quarry47-5061Roof Bolters,Mining47-5071Roustabouts,Oil a

307、nd Gas47-5081Helpers-Extraction Workers49-2095Electrical and Electronics Repairers,Powerhouse,Substation,and Relay49-9012Control and Valve Installers and Repairers,Except Mechanical Door49-9041Industrial Machinery Mechanics49-9043Maintenance Workers,Machinery49-9045Refractory Materials Repairers,Exc

308、ept Brickmasons49-9051Electrical Power-Line Installers and Repairers49-9093Fabric Menders,Except Garment51-1011First-Line Supervisors of Production and Operating Workers51-2091Fiberglass Laminators and Fabricators51-3091Food and Tobacco Roasting,Baking,and Drying Machine Operators and Tenders51-3092

309、Food Batchmakers51-3093Food Cooking Machine Operators and Tenders51-4021Extruding and Drawing Machine Setters,Operators,and Tenders,Metal and Plastic51-4022Forging Machine Setters,Operators,and Tenders,Metal and Plastic51-4023Rolling Machine Setters,Operators,and Tenders,Metal and Plastic51-4033Grin

310、ding,Lapping,Polishing,and Buffing Machine Tool Setters,Operators,and Tenders,Metal and Plastic51-4051Metal-Refining Furnace Operators and Tenders51-4052Pourers and Casters,Metal51-4062Patternmakers,Metal and Plastic51-4071Foundry Mold and Coremakers51-4191Heat Treating Equipment Setters,Operators,a

311、nd Tenders,Metal and Plastic51-4192Layout Workers,Metal and Plastic51-4193Plating and Coating Machine Setters,Operators,and Tenders,Metal and Plastic51-4194Tool Grinders,Filers,and Sharpeners51-6061Textile Bleaching and Dyeing Machine Operators and TendersSOC 2010SOC Title51-6063Textile Knitting and

312、 Weaving Machine Setters,Operators,and Tenders51-6064Textile Winding,Twisting,and Drawing Out Machine Setters,Operators,and Tenders51-6091Extruding and Forming Machine Setters,Operators,and Tenders,Synthetic and Glass Fibers51-6093Upholsterers51-7011Cabinetmakers and Bench Carpenters51-7021Furniture

313、 Finishers51-7031Model Makers,Wood51-7032Patternmakers,Wood51-7041Sawing Machine Setters,Operators,and Tenders,Wood51-7042Woodworking Machine Setters,Operators,and Tenders,Except Sawing51-8012Power Distributors and Dispatchers51-8091Chemical Plant and System Operators51-8092Gas Plant Operators51-809

314、3Petroleum Pump System Operators,Refinery Operators,and Gaugers51-9011Chemical Equipment Operators and Tenders51-9012Separating,Filtering,Clarifying,Precipitating,and Still Machine Setters,Operators,and Tenders51-9021Crushing,Grinding,and Polishing Machine Setters,Operators,and Tenders51-9022Grindin

315、g and Polishing Workers,Hand51-9023Mixing and Blending Machine Setters,Operators,and Tenders51-9031Cutters and Trimmers,Hand51-9032Cutting and Slicing Machine Setters,Operators,and Tenders51-9041Extruding,Forming,Pressing,and Compacting Machine Setters,Operators,and Tenders51-9051Furnace,Kiln,Oven,D

316、rier,and Kettle Operators and Tenders51-9111Packaging and Filling Machine Operators and Tenders51-9121Coating,Painting,and Spraying Machine Setters,Operators,and Tenders51-9191Adhesive Bonding Machine Operators and Tenders51-9192Cleaning,Washing,and Metal Pickling Equipment Operators and Tenders51-9

317、193Cooling and Freezing Equipment Operators and Tenders51-9195Molders,Shapers,and Casters,Except Metal and Plastic51-9196Paper Goods Machine Setters,Operators,and Tenders51-9197Tire Builders53-4013Rail Yard Engineers,Dinkey Operators,and Hostlers53-7031Dredge Operators53-7032Excavating and Loading M

318、achine and Dragline Operators53-7033Loading Machine Operators,Underground Mining53-7041Hoist and Winch Operators53-7063Machine Feeders and Offbearers53-7071Gas Compressor and Gas Pumping Station Operators53-7072Pump Operators,Except Wellhead Pumpers53-7073Wellhead Pumpers53-7111Mine Shuttle Car Oper

319、ators17-2041Chemical Engineers17-2151Mining and Geological Engineers,Including Mining Safety Engineers 51 Table A.3.Summary statistics of Green and Brown occupations in India Source:Own elaboration based on microdata from the 2019 Periodic Labour Force Survey(PLFS)for India.Notes:The column shows in

320、 parenthesis the sample standard deviations of the means and the standard error of the difference in sample means estimated as the robust standard error of the coefficient of a regression of the green/brown/rest job variable on the group membership dummy variable.(1)Green occupations are those 4-dig

321、it ISCO-08 occupations with any of the 44 green qualifications defined by the Skill Council for Green Jobs(see Table 0).To estimate the proportion of green jobs for each 3-digit ISCO-88(as specified in the Indian labor force survey),4-digit ISCO-08 occupations are mapped to the 4-digit ISCO-88 class

322、ification using the ILOs crosswalk(when a single ISCO-88 occupation code is mapped to more than one ISCO-08,a green proportion measure is used to reflect the proportion of the corresponding ISCO-08 occupations that have been classified as green)and then estimated at the ISCO-88 3 digit level assumin

323、g an equal distribution of workers in 4-digits occupations within each 3-digit occupation.(2)Brown occupations are those defined by Vona et al.(2018)at the 6-digits level of the 2010-SOC,mapped to the 4-digit ISCO-08 classification using a crosswalk from the US Bureau of Labor Statistics(when a sing

324、le ISCO occupation code is mapped to more than one 6-digit SOC,a brown proportion measure is used that reflect the proportion of corresponding 6-digit SOC occupations that have been classified as brown),converted to ISCO-88 at 4-digits using ILOs crosswalk and then estimated at the ISCO-88 3 digit level assuming an equal distribution of workers in 4-digits occupations within each 3-digit occupation.