Documenting the evolution of green employment : a task-based approach for the Netherlands.
Date: 2024-07-05
Institutions: Directorate-General for Employment, Social Affairs and Inclusion
Subjects: Netherlands, economic consequence, equal treatment, green economy, new type of employment, professional qualifications, serial publication

Social 

Situation 

Monitor 

Documenting the evolution of green 
employment: A task-based approach for the 
Netherlands 

Robert J R Elliott, Ceren Ozgen and Wenjing  Kuai 

EUROPEAN COMMISSION 

Directorate-General for Employment, Social Affairs Inclusion 

Directorate F— Employment and Social Governance, Analysis 

Unit F4 — Analysis and Statistics 

Contact: Katarina Jaksic 

E-mail: Katarina.jaksic@ec.europa.eu

European Commission 

B-1049 Brussels

1 

Documenting the evolution of green 
employment: A task-based approach 
for the Netherlands 

Robert J R Elliott 

email: r.j.elliott@bham.ac.uk 

Ceren Ozgen 

email: c.ozgen@bham.ac.uk 

Wenjing Kuai 

email: kuaiwj@hnu.edu.cn 

September 2022 

2 

Manuscript completed in 2022 

The European Commission is not liable for any consequence stemming from the reuse of this publica- tion. 

Luxembourg: Publications Office of the European Union, 2024 

© European Union, 2024 

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12 December 2011 on the reuse of Commission documents (OJ L 330, 14.12.2011, p.  39). Except otherwise noted, the 
reuse  of  this  document  is  authorised  under  a  Creative  Commons  Attribution  4.0  International  (CC-BY  4.0)  licence 
(https://creativecommons.org/licenses/by/4.0/  ). This  means  that  reuse  is allowed  provided  appropriate  credit  is  given 
and any changes are indicated. 

PDF    

 ISBN 978-92-68-18086-0        ISSN 2811-6798      

 doi: 10.2767/83152      

   KE-CF-24-002-EN-N 

3 

Contents 

1 

Introduction 

2  Defining a green job 

3  Data construction 

4  Trends in green jobs 

5 

8 

16 

30 

4.1  Trends in green jobs by sector .................................................................................................... 32 

4.2  Trends in green jobs by occupation ............................................................................................ 34 

4.3  Trends in green jobs by region .................................................................................................... 37 

5  Firms and green jobs 

6 

Individuals and green jobs 

7  Wage differentials for green jobs by gender 

42 

47 

56 

7.1  Econometric Model ..................................................................................................................... 56 

7.2  Decomposition results ................................................................................................................. 58 

8  Conclusions 

Bibliography 

A  Green occupation by greenness 

B  Trends in disaggregated sectors 

C  Trends in green jobs using a Binary measure 

D  Blinder-Oaxaca decomposition method 

E  Brown decomposition 

4 

61 

64 

67 

75 

79 

85 

87 

 
 
 
 
1  Introduction 

1 

INTRODUCTION 

The global economy is currently in the middle of a period of heightened uncertainty fol- 

lowing the COVID-19 pandemic, the war in Ukraine, soaring debt levels, inflationary pressures, 

climate change, and an emerging energy crisis. As a result, policymakers and firms are worried 

about sluggish economic growth, spiraling costs, and growing environmental pressures. The reac- 

tion of governments around the world has been to accelerate plans for their economies to make a 

green transition. At the EU level, the Green Deal sets the overarching target of achieving climate 

neutrality by 2050. As a response to the energy crisis following Russia’s invasion of Ukraine, the 

European commission has adopted the REPowerEU plan with the aims to accelerate the green 

transition and reduce dependence on Russian fossil fuels. 

This research note begins by putting the green transition in a broader context. The general 

premise is that any green transition will consist of a mix of green growth policies that in turn will 

have a range of economic consequences. Policies will be implemented to generate new opportu- 

nities for investment, innovation, and to support the development of skills that workers will need for 

a transition to a low carbon, greener economy. More specifically, a green transition should lead to 

a reshaping of the labour market in a way that creates new environmentally friendly jobs, relocates 

and reshapes exiting jobs, and has the potential to reduce inequalities and skill gaps, and ultimately 

supports the desire for a country to have decent work for all (ILO, 2011). Fairness and solidarity 

are also defining principles of the European Green deal. To ensure that no one is left behind, the 

Council adopted a Recommendation on ensuring a fair transition towards climate neutrality (EC, 

2022).  The  underlying  belief  among  many  academics  and  policymakers  is  that  the  creation  of 

greener  jobs  is  a  win-win  solution  that  enables  a  country  to  address  both  economic  and 

environmental challenges (Jones, 2009). 

However, in the short term, a green transition may potentially have a detrimental impact on 

some individuals, regions, firms, and sectors. For example, part of a green transition may involve 

a country moving away from high polluting, fossil fuel dependent, heavy industry which in turn will 

lead to job losses in those traditional industries. Similarly, in other sectors workers might be  under 

5 

 
 
 
 
 
1 

INTRODUCTION 

pressure to improve their current skills and abilities to match the needs of employers that are part 

of the newer, greener, and cleaner economy. How quickly Europe or any individual country can 

make the green transition is likely to depend to a large extent on the degree of political support 

and accompanying policies. 

There is still considerable debate and confusion on how to: (1) Define a green job and (2) 

How to measure the extent to which a job is green. According to the ILO, green jobs are broadly 

defined as “decent jobs in any economic sector (e.g. agriculture, industry, services, administration) 

which contribute to preserving, restoring, and enhancing environmental quality". 

Whether green jobs are better jobs is essentially an empirical question. The existing liter- 

ature tends to show that green jobs are better jobs in the sense that they pay higher wages and 

require a higher level of skills (e.g., Consoli et al., 2016; Peters, 2014; Vona et al., 2019). Vona et 

al. (2019) also shows that one additional green job is associated with over four new jobs in other 

sectors. Green jobs are also less routine-intensive. However, what is little understood is exactly 

who  green  workers  are  and  how  they  are  distributed  across  sectors,  regions,  firms,  and 

occupational groups. This is important in ensuring a fair and inclusive transition towards climate 

neutrality (EC, 2022). 

The purpose of this research note is therefore to describe and analyse the labour mar- ket 

implications  of  a  green  transition  using  the  Netherlands  as  a  case  study  within  the  EU.  The 

Netherlands is an ideal case study primarily due to the exceptional micro-data that allows a range 

of questions to be addressed that have previously not been considered in research into green jobs. 

More specifically, tax records for the entire population of the Netherlands are accessed which al- 

lows to link employees with employers and use other datasets at different levels of aggregation 

(individual, firm, sector, region, or country). In addition, the Netherlands is well-known for hav- ing 

a relatively strong regulatory environment. For example, the Netherlands has the 4th highest tax 

rate on energy on an economy-wide basis across the 34 OECD and 7 partner economies.1 The 

Netherlands  is  also  very  active  in  green  innovation  and  ranks  at  the  top  of  the  EU27  for  eco-

innovation. 

1For details to see OECD’s report “Revenue from environmentally related taxes in the Netherlands”. 

6 

 
 
 
 
 
 
1 

INTRODUCTION 

The contribution of this research note is to present a broad picture of the overall trends 

and distribution of green jobs by sector, firm, region and occupation groups in the Dutch labour 

market  for  the  last  twenty  years.  The  key  methodological  contribution  is  to  use  a  task-based 

approach to identify the degree to which the workforce is becoming greener. To do this the Green 

Economy Program and Green Task  Development Project from the O*NET is utilized and a list  of 

green  occupations  is  created,  each  of  which  is  associated  with  a  task-based  greenness  index 

categorized using the ISCO job classification system.2 This method can be applied to a broader 

set  of  countries  where  occupational  mapping  from  O*NET  to  ISCO  is  possible.  This  approach 

means it is possible to estimate the share of green jobs by sector, region, and occupation and  to 

distinguish  between  skill  groups.  There  has  previously  been  little  work  on  the  geographical 

distribution of green jobs and this research note provides the first snapshot of whether there is a 

rural-urban dimension for green jobs and whether this has changed over time. 

After presenting the general trends in green jobs, the next stage is to examine the green 

job transition at the firm level. The analysis benefits from having a panel of firms over time which 

means it is possible to estimate changes in the green employment of firms with respect to firm 

size, average wages, sector, region, and other time-invariant firm characteristics. The analyses 

provides a map of the green transition that shows which firms are leading the green transition and 

which are being left behind. 

Finally, the characteristics and distribution of green jobs at the individual worker level are 

explored giving special attention to gender dimension in terms of occupational segregation and 

wages in green jobs. Based on the same task-based approach, detailed individual level data and 

green occupations from O*NET are linked which enables to describe in detail the characteristics 

of workers in green transition jobs. 

The research note is organised as follows. Section 2 defines green jobs while Section 3 

explains how green jobs are measured. Section 4 presents the overall trends in green jobs over 

2The O*NET programme contains information on hundreds of standardized and occupation-specific descriptors. 

For more info details https://www.onetonline.org/help/onet/. The International Standard Classification of Occupations 

(ISCO) is a four-level classification of occupation groups managed by the International Labour Organisation (ILO). 

7 

 
 
 
 
 
 
time by sector, occupation, and region. Section 5 presents the analysis of firms and green em- 

ployment. Section 6 characterises green jobs at the individual level while Section 7 looks in detail 

at the gender pay gap in green employment. Section 8 summarises the results and concludes. 

2  DEFINING A GREEN JOB 

2  Defining a green job 

Defining what is meant by a green job is not a straight-forward exercise and is beset by 

several conceptual challenges. First, how is it possible to distinguish between green jobs and non-

green jobs within the same sector. For example, a bus driver can be thought of as a greener job 

than  a  taxi  driver  because  it  has  smaller  adverse  impact  on  the  environment  per  passenger 

although the vehicle itself is likely to be more polluting than a single taxi. Likewise, driving a diesel-

powered  bus  is  still  carbon  intensive  and  polluting  so  could  be  considered  a  dirty  sector  even 

though  it  is  potentially  taking  a  large  number  of  gasoline  powered  cars  off  the  roads.  Fur- 

thermore, does it make sense to make a distinction between the green and non-green jobs if they 

perform the same tasks on a daily basis but in different sectors. For example, an administrator’s 

job might be considered green if that person works in the renewable energy sector as the work 

contributes  to  the  conservation  of  natural  resources  and  benefits  the  environment.  However, it 

could be considered as non-green if the administrative work was done while employed in a dirty 

manufacturing sector. Nevertheless, the tasks that the administrative worker undertakes would not 

be considered polluting by themselves. These, and similar examples, have often complicated ef- 

forts to document changing green job patterns and different definitions have been used by different 

authors as discussed later in the research note. 

To briefly summarise, the approach taken in this research note is to begin at the level of 

the task and to look at the tasks associated with each occupation and then to look at how many 

tasks can be considered green (in some sense). This means it is possible to come up different 

measures of the greenness of any one occupation. The ability to define green jobs based on tasks 

8 

 
 
 
 
 
 
 
 
 
2  DEFINING A GREEN JOB 

is possible because the definitions specified in the O*NET Green Economy Program and Green 

Task Development program are used.3 

Another conceptual concern is whether those individuals that are employed in firms that 

are part of the ‘supply chain’, or so called ‘induced’ and ‘indirect’ jobs, should be included in a 

measure of green jobs (Connolly et al., 2016). For example, employees of a geothermal energy 

plant are normally considered to have green (‘direct’) jobs. However, for the plant to function there 

will be demand for goods from firms that supply computers, stationery, and other consumables. At 

first glance these induced employees will not normally be regarded as having green jobs, however, 

they form part of the supply chain for green activities and will be impacted by any policy support 

for or against a green economic transition (‘green indirect jobs’). 

As the International Labour Organisation (ILO) points out, it is not easy to make a distinc- 

tion between green jobs and non-green jobs in sectors across all occupational categories (ILO, 

2011). The ILO argue that low carbon intensive sectors like education and finance are normally 

considered green. However, jobs in education or finance do not normally have a direct link to a re- 

duction in GHG emissions nor do they have any noticeable beneficial impact on the environment. 

Likewise, some carbon intensive sectors such as the chemical industry may employ chemists that 

have been hired to develop cleaner and less polluting fertilisers that would have a direct environ- 

mental benefit. Unfortunately, as pointed out by ILO, a classification system that would allow to 

make these distinctions is not currently available (ILO, 2011). 

Leaving aside the challenges of how to define a green job, data collection agencies have 

tried to come up with acceptable definitions. Table 1 presents four official definitions. Existing defi- 

nitions of green jobs have common themes such as preserving and restoring the environment. The 

focus of the OECD (1999) definition is on employment in Environmental Goods and Services Sec- 

tors (EGSS) where all employees in that sector are assumed to have green jobs. This approach is 

known as the Green Sector Approach and has been commonly used by national Bureau of Statis- 

3The source of the task information is https://www.onetcenter.org/reports/GreenTask.html. 

9 

 
 
 
 
 
 
 
 
Authorities 

OECD (1999) 

2  DEFINING A GREEN JOB 

Table 1: Definitions of green jobs 

Definition 

Employment in Environmental Goods and Services 

Sectors (EGSS), which includes activities to pro- 

duce  goods  and  services  that  measure,  prevent, 

limit,  minimise  or  correct  environmental  damage 

to water, air and soil as well as problems related to 

waste, noise and ecosystems. This includes cleaner 

technologies, products and services which reduce 

environmental risk and minimise pollution and re- 

source use. 

ILO (2012) 

Any  decent  job  that  contributes  to  preserving  or 

restoring the quality of the environment in any eco- 

nomic sector such as agriculture, industry, services, 

and administration. 

US Bureau 

of  Labor  Statistics  Jobs in establishments that produce goods and ser- 

(2010) 

vices that benefit the environment, or preserve the 

natural resources; and jobs in establishments that 

involves environmentally friendly production  pro- 

cess. 

US O*NET (2009) 

Occupations that already exist but will increase in 

demand  or  change  in  work  and  worker  require- 

ments, or that are newly generated because of  the 

impact of green economic activities. 

10 

 
 
 
 
 
 
 
2  DEFINING A GREEN JOB 

tics  in  European  countries.  According  to  OECD  (2011),  employment  in  EGSS  is  an  important 

green growth indicator for monitoring progress towards a greener economy. In the Netherlands, 

employment in Environmental Goods and Services Sectors has increased from 1.62% to 1.84% 

from 2001 to 2019, and contributed around 147,800 full-time equivalent jobs to the economy in 

2019.4 

The EGSS definition or Green Sector Approach defines a green job by the sector that the 

person works in. One obvious limitation is that it tends to over-estimate the number of green jobs 

in the so-called green industries given that firms will often produce multiple products and  services 

that could include both green and non-green goods and services but due to the classi- fication 

system have been defined as EGSS. At the same time, This approach also ignores jobs in firms 

in  non-green  sectors  that  involves  some  green  production  processes.  Hence,  such  a  bi-  nary 

approach  may  be  systematically  over  or  underestimating  the  true  number  of  green  jobs  in  the 

economy. As the OECD (1999) states, only when it is possible to identify every green activity in a 

potentially green firm is it possible to accurately talk about environmental industries. 

Some of the earlier studies in this area developed their own terminology based closely on 

the green industry definition. For instance, Yi (2013); Yi and Liu (2015) use a Green Sector defi- 

nition developed by the Pew Charitable Trust to estimate the number of green jobs in the US and 

China, respectively. Connolly et al. (2016) further refined the Green Sector definition by applying 

a hybrid approach that combines ‘top down’ (that uses a list of industry classification from the top) 

and ‘bottom up’ (that uses a variety of survey data on employment and firms at the industry level 

from the bottom) to examine the evolution of green jobs in Scotland. 

In 2007, the ILO/UNEP/IOE/ITUC collaboratively established the ‘Green Job Initiative’ to 

encourage the creation of more green jobs and decent work for all in a greening economic transi- 

4Own calculation based on CBS open data source ”Environmental Goods and Service Sector by industry; national 

accounts”. This number is higher than the EU average, where the EGSS’s contribution to employment grew from 1.3% 

in 2003 to 1.7% in 2015. See ”Environmental Goods and Services Sector: employment and value added” for details. 

11 

 
 
 
 
 
 
 
2  DEFINING A GREEN JOB 

tion. ILO (2012)’s definition states that green jobs can be created in any sectors and all types of 

firms that contributes to conserving and restoring the environment. What is more, they emphasize 

that green jobs should be decent jobs, and any jobs that are not decent, i.e. jobs with unfair social 

justice, bad working conditions, and fail to pay living wage can hardly be considered as green jobs. 

Whilst admirable, if a job has obvious environmental benefits but relatively poor working conditions 

should they be excluded? This is further complicated as working conditions can differ greatly be- 

tween firms in the same sector. 

The other two green job measures shown in Table 1 have been developed with the support 

of the US Department of Labour. This first green job initiative was conducted by the US Bureau of 

Labour Statistics (BLS). BLS combines two approaches to measure green jobs: (1) Green Prod- 

uct Approach that identifies green products and services, and hence the associated jobs in firms 

that produce green goods and services are considered green; (2) Green Process Approach that 

identifies environmentally friendly production processes, and hence those jobs in firms that involve 

green production processes are considered green. Papers that have used BLS definition include 

Deschenes (2013) and Elliott and Lindley (2017). Based on the BLS Green Goods and Services 

Survey, green employment accounted for 2.4% of total employment in the US in 2010 and 2.6% of 

total US employment in 2011 (Elliott and Lindley, 2017). The BLS definition is more comprehen- 

sive than the Green Industry definition. However, as these surveys are only conducted in the US, 

it is difficult to make comparisons across countries. A further practical downside is that this kinds 

of survey is resource intensive which means it is unlikely that enough surveys will be completed so 

that the number of green jobs can be measured consistently over time. Indeed, the BLS stopped 

the survey in 2013. 

The final type of measure, and the one used in this research note, is to define jobs as green 

based on the O*NET classification. O*NET defines three types of green occupation: 

1.  Green Increased Demand (Green ID) occupations, which are occupations that will be in- 

creasingly demanded because of a green economy, but will not require changes in tasks or 

12 

 
 
 
 
 
 
 
 
2  DEFINING A GREEN JOB 

work content. 

2.  Green  Enhanced  Skills  (Green  ES)  occupations,  which  are  occupations  where  the  work 

content is expected to change such as tasks, skills and knowledge etc., and may or may not 

be a result of a change in demand. 

3.  Green New and Emerging (Green NE) occupations, which are occupations that will be newly 

created because of a greening economy. 

Early studies that make use of O*NET green occupations database measure the green em- 

ployment share taking a Binary Approach. What this means is that an occupation is considered 

as green if it belongs to one of the three types of green occupations listed above. Studies of green 

employment in the US labour market usually match O*NET occupations with BLS Occupational 

Employment Statistics (OES). While O*NET classifies the occupations at an 8-digit level, the OES 

occupational classification is only available at the 6-digit SOC level.5  This requires using a cross- 

walk between O*NET-SOC and SOC and assuming that workers are uniformly distributed among 

broader SOC occupations.6  Using this approach, Bowen et al. (2018) estimates that 19.4% of US 

total employment are green (both direct and indirect) in 2014.7 Only measuring the direct green 

employment, Consoli et al. (2016) estimates around 11% of total employment are direct green 

employment between 2011 to 2012 in the US labour market. 

The Binary Approach has also been used to measure green employment for a number  of 

European countries.  The common dataset that researchers use for analyzing the EU green 

5SOC stands for Standard Occupational Classification in the US.Please refer to Data construction for more details. 
6For example, the broad SOC code 11-1011 (Chief Executive) corresponds to two detailed O*NET-SOC codes 11-

1011.00  (Chief  Executive)  and  11-1011.03  (Chief  Sustainability  Executive),  among  which  11-1011.00  (Chief 

Executive) is non-green occupation and 11-1011.03 (Chief Sustainability Executive) is green occupation. By assuming 

workers are uniformly distributed across the broad 11-1011 (Chief Executive) category, the share of green employment 

is half the number employed in 11-1011 (Chief Executive). 

7It is generally understood in the existing literature that Green ID occupations should be considered as indirectly 

“green" since these jobs are only affected by demand and do not involve any green tasks as part of the content of work, 

while Green ES and Green NE are direct green jobs(Consoli et al., 2016). 

13 

 
 
 
 
 
 
 
2  DEFINING A GREEN JOB 

employment share is the EU harmonised Labour Force Survey (LFS). However, the occupational 

classification system used in the LFS is the International Standard Classification of Occupations 

(ISCO) available at the 4-digit level. Hence, a second crosswalk is needed to move from US- SOC 

to ISCO to identify green jobs in the LFS. In addition to the assumption that workers are uniformly 

distributed  across  broader  occupation  codes,  it  is  also  necessary  to  assume  that  Euro-  pean 

countries have broadly the same occupational structure as the US. Any deviation from these two 

assumptions will induce a degree of measurement error. Under these assumptions, Bowen and 

Hancke  (2019)  estimate  the green employment  share  (direct and  indirect) in EU28  to have in- 

creased from 35% to 40% between 2006 to 2016, and in 2016, 22.5% of occupations were Green 

ID, 20% were Green ES occupations and 17.4% were Green NE occupations.8 

Another limitation with using a Binary Measure of green jobs is that, by definition, it is bi- 

nary meaning that a job is either green or non green. From the O*NET classification it is known 

that there are very few jobs where one hundred percent of an employee’s time is spent on so- 

called green tasks. Hence, an ‘all or nothing’ binary approach is likely to overestimate the green 

employment share if a job is counted as green if it involves any green activity however little time 

that activity takes. One way to think of this is that most green jobs should be thought of as ’light’ 

green and only a small number can be considered ’dark’ green. It is one of the reasons that Vona 

et al. (2019) proposed a task-based approach which allows to generate a measure of green jobs 

that captures the amount of time a worker spends on green tasks and hence can be thought of as 

a Continuous Measure of green jobs. Vona et al. (2019) also uses the ‘Green Task Development 

Project’ from O*NET to identify green and non-green tasks in both the Green ES and Green NE 

occupations.9 

8The  most detailed  ISCO hierarchy  is  at 4-digit  level  which  is  considerably  more  aggregated  than  the  8-digits 

available in the O*NET occupational classification. Early studies such as Econometrics (2011) match O*NET-SOC 

with 3-digit ISCO and show that in 2009, 25% of EU employment is in Green ID occupations, and 20% in Green ES 

occupations. 

9As Green ID is considered as indirect green jobs that will only be affect by increasing its demand. The ‘Green 

Task Development Project’ of the O*NET only distinguish the green tasks from non-green tasks for Green ES and 

Green NE. 

14 

 
 
 
 
 
 
2  DEFINING A GREEN JOB 

It is important to understand how tasks are categorized as either green or non-green. The 

way that O*NET is constructed is that each task is evaluated by professional researchers across 

three dimensions: (1) importance score, (2) level, and (3) frequency of tasks in an occupation. 

From these evaluations, Vona et al. (2019) construct a Greenness index (GI) for each occupation 

which is given by the number of green tasks divided by the total number of tasks weighted by the 

importance score. This Greenness index can be thought of as a proxy for the average time spent 

on green tasks weighted by the importance of the task. The GI can then be used to weight the 

occupation-sector or occupation-region level employment share to obtain the share of green em- 

ployment in the whole labour market (Vona et al., 2019). Using this approach Vona et al. (2019) 

estimate  the  share  of  green  employment  in  the  US  labour  market  to  be  around  3%.10  Further 

details on O*NET classification system can be found in the next section. 

To summarise, this research note uses the O*NET task-based classification. This is due in 

part to the obvious weaknesses with the more coarse classifications proposed by EGSS. The US 

BLS approach is more detailed than the EGSS approach but still suffers from the inability to qualify 

or  characterize  green  jobs  continuously  over  time  for  the  US  and  more  importantly  for  other 

countries. A task-based approach is taken for three main reasons. First, the O*NET green jobs 

classification does not just consider jobs in green sectors, but also includes jobs and tasks in non- 

green sectors that perform green tasks. Second, green occupations in the O*NET database are 

given an O*NET-SOC code which means that it is possible to use a crosswalk between O*NET- 

SOC, and the more commonly used EU classification ISCO codes, to identify green jobs in the 

Dutch labour market. The main descriptives in this research note are based on a binary and the 

continuous approach which captures the time a worker spends on green activities weighted by the 

importance of the tasks. 

10A similar type of continuous measure of greenness of a job can be applied to any country that has a detailed 

occupational  classification.  Vona  (2021)  discusses  the  challenges  and  possible  solutions  to  applying  a  task-based 

continuous approach to countries other than the US and is the approach pursued in this research note. 

15 

 
 
 
 
 
 
 
 
 
3  Data construction 

3  DATA CONSTRUCTION 

The O*NET database contains detailed information on the tasks and skills associated with 

a given occupation. In order to investigate the effect of a greening economy on occupational re- 

quirements, the National Centre of O*NET development conducted an extensive research and 

screening process to identify what it believes to be green jobs. In the first instance, O*NET com- 

piled a list of green occupation titles and identified 12 broad sectors. The 12 broad sectors are: (1) 

Renewable Energy Generation; (2) Transportation; (3) Energy Efficiency; (4) Green Construction; 

(5) Energy Trading; (6) Energy/Carbon Capture and Storage; (7) Research, Design, and Consult- 

ing Services; (8) Environment Protection; (9) Agriculture and Forestry; (10) Manufacturing; (11) 

Recycling and Waste Reduction; and (12) Governmental and Regulatory Administration. Similar 

job titles, with similar work content, are grouped together and matched with O*NET-SOC codes. 

For details of the matching process see Dierdorff et al. (2009). 

Following the process outlined above, three types of green occupations are identified in the 

O*NET-SOC system that were first presented in the previous section: (1) Green increased demand 

(Green ID) occupations; (2) Green enhanced skills (Green ES) occupations; and (3) Green new 

and emerging jobs (Green NE) occupations. Recall that the objective is to identify green jobs in 

the Dutch Labour Force Survey (LFS) which uses 4-digit ISCO codes to define an occupation. As 

a result it is necessary to map the green index scores of occupations obtained in O*NET-SOC to 

the Dutch ISCO system. Stage 1 is to define broad green jobs using a binary measure (described 

in the previous section), which includes the three types of green occupations (Green ID, Green 

ES, and Green NE). The broad definition of green jobs is therefore equal to 1 if an occupation is 

included in any of the three categories. 

However, to be able to map O*NET-SOC to ISCO codes first, map O*NET-SOC with SOC 

within the US system (effectively going from 8-digits to 6-digits). The process is as follows. First, 

match O*NET-SOC with SOC using a crosswalk between O*NET-SOC and SOC (which is readily 

16 

 
 
 
 
 
 
 
3  DATA CONSTRUCTION 

available). However, there is still decisions to be made when matching between different levels of 

aggregation (in this case 8-digits to 6-digits). The approach taken in this research note is to follow 

Vona et al. (2019) and calculate the average greenness of each 6-digit SOC code based on 8-digit 

SOC values. Mapping O*NET-SOC with SOC by taking averages at the higher level of aggregation 

is common practice in other studies of the US that use O*NET classification such as Autor et al. 

(2015). 

Table 2 provides an example of the process that is followed to map O*NET-SOC to ISCO. 

In this case a measure of broad greenness is captured although the general process is the same 

for the measures of task-based greenness. First,  from left to right,  Table  2 shows the process of 

mapping  O*NET-SOC  with  SOC.  The  top  row  shows  that  SOC  “11-1011,  Chief  Executives” 

consists of two corresponding O*NET-SOC codes that are “11-1101.00, Chief Executives” that is 

defined as a non-green job and “11-1101.03, Chief Sustainability officers” which, not surprisingly, 

is defined as a green job with broad greenness equal to 1. Taking averages means that the green- 

ness index for SOC “11-1011” is calculated as the average of the two O*NET-SOC codes, which 

in this case gives an overall greenness index of 0.5. A similar exercise is followed for SOC 111021 

“General and Operations Managers” and SOC 119161 “Emergency Management Directors” but in 

both these cases there are one to one mapping between O*NET SOC and SOC. 

The second and final stage is to calculate a greenness index at the ISCO level using the 

SOC-ISCO concordance. In this case ISCO 1112 “Senior government officials” consists of four 

O*NET categories and three SOC categories. As before, the green index values at the SOC level 

are averaged to give Greenness indices at the 4-digit ISCO level. In this case the average of 0.5, 

0.1134 and 0 is taken to give a greenness index value of 0.2045. 

17 

 
 
 
 
 
 
 
Table 2: An example of O*NET-SOC occupation to ISCO occupation 

ONET-SOC  ONET-SOC title 

Greenness index 

SOC 

SOC title 

Greenness index 

ISCO 

Greenness index 

111011.03 

Chief Sustainability officer 

111011.00 

Chief Executives 

1 

0 

111011  Chief Executives 

0.5 

1112 

111021.00 

General and 

0.1134 

111021 

General and 

0.1134 

Senior 

0.2045 

Operations Managers 

Operations Managers 

government 

officials 

119161.00 

Emergency Management 

0 

119161 

Emergency Management 

0 

Directors 

Directors 

1
8

3

D
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3  DATA CONSTRUCTION 

When it comes to using the task-based approach, the greenness of each Green ES and 

Green NE occupation is calculated. This means the greenness of each O*NET green occupa- 

tion is calculated based on the number of green tasks divided by the total number of tasks and 

weighted by an importance score. This step follows Vona et al. (2019). 

More specifically, two datasets from the O*NET database, that have been updated regularly 

during the study period, are used: (1) Task Ratings; and (2) the Green Task Statement. The Tasks 

Ratings data provides a mapping of O*NET-SOC occupations to the ratings of tasks associated 

with each occupation. One of the most important ratings in this file is the importance score given 

to each task. The importance scores come from the O*NET analysts and the employees that are 

currently employed in these jobs (incumbents).11  The Green Task Statement provides a mapping 

of O*NET-SOC occupations with the green tasks associated with each green occupation.12 

Mapping the Task Rating data with the Green Task Statements means it is possible to iden- 

tify green tasks and non-green tasks with an associated importance score for each occupation 

which then feeds in Equation (1) which gives the final task-based greenness index. Importance 

scores are associated with each task in a given occupation and is a score given by job incumbents 

and/or occupational experts on the basis of how important each task is considered to be for that 

occupation. In other words, it can be thought as intensity of each and every green task, predefined 

in the O*NET database, executed in a work day in a particular profession. The greenness score 

of an occupation is then calculated at the O*NET-SOC level as follows: 

n 
L 

Greennessi = 

wij ∗ greenj 

j=1 

(1) 

where wij  is the importance score attached to each task within occupation i and greenj 

11See https://www.onetcenter.org/dictionary/22.1/excel/task_ratings.htm for details on how the importance scores 

are constructed. 

12See https://www.onetcenter.org/dictionary/20.1/excel/green_task_statements.html for details. 

19 

 
 
 
 
 
 
 
 
 
 
3  DATA CONSTRUCTION 

is  a  dummy  taking  the  value  of  1  if  task  j  is  a  green  task.13  Consider  a  simple  hypothetical 

example: assume an occupation has four tasks, two green and two non-green. If the importance 

score for each task, one to four, are 3.5, 1.5, 3 and 2, respectively, then the weighted importance 

scores, normalised to sum to 1, are 0.3182, 0.1364, 0.2727, 0.1818, respectively. The task-based 

greenness index for  this hypothetical occupation would be 0.4546.  Table  3 provides a series  of 

examples  of  green  occupations  and  green  tasks  with  their  associated  task-based  greenness 

indices. 

13The importance score for each occupation is normalised so that is sums to one in the calculation. 

20 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 3:  Examples of green tasks 

O*NET-SOC Code  Occupation Title 

Green tasks/Total tasks  Example of Green tasks 

Greenness 

17-2081.00 

Environmental Engineers 

28/28 

Design, or supervise the design of, systems, processes, or equipment for con- 

1 

(Important score /Normalised importance score) 

trol,  management,  or  remediation  of  water,  air,  or  soil  quality(  4.24/0.0428  ); 

Write  reports  or  articles  for  Web  sites  or  newsletters  related  to  environmental 

engineering issues (2.15/0.0217). 

19-3011.01 

Environmental Economists 

19/19 

Develop economic models, forecasts, or scenarios to predict future  economic 

1 

and environmental outcomes (3.5/0.0508); Develop programs or policy recom- 

mendations to achieve environmental goals in cost-effective ways (3.96/0.575). 

19-1013.00 

Soil and Plant Scientists 

17/27 

Investigate soil problems or poor water quality to determine sources and effects 

0.6218 

17-2051.00 

Civil Engineers 

8/17 

Prepare or present public reports on topics such as bid proposals, deeds,  en- 

0.4516 

(3.7/0.0407 ); Study ways to improve agricultural sustainability, such as the use 

of new methods of composting (3.52/0.0387). 

2
1

vironmental impact statements, or property and right-of-way descriptions (3.53/ 

0.0575); Design energy efficient or environmentally sound civil structures (3.53/ 

0.0575). 

49-3023.02 

Automotive Specialty Technicians 

9/23 

Tune automobile engines to ensure proper and efficient functioning (3.99/0.0454) 

0.3802 

29-9012.00 

Occupational Health and Safety Technicians 

9/26 

Perform tests to identify any potential hazards related to recycled products used 

at green building sites ( 2.7/0.0307); Collect data related to ecological or human 

health risks at brownfield sites (3/0.0341). 

; Replace hydraulically assisted systems with electric-powered systems, such as 

power steering pumps or air conditioning compressors, to improve fuel economy 

(3.76/0.0428). 

3

0.334 
D
A
T
A

13-2051.00 

Financial Analysts 

6/18 

Conduct financial analyses related to investments in green construction or green 

0.2961 

retrofitting projects(2.46/ 0.0407); Forecast or analyze financial costs associated 

with climate change or other environmental factors, such as clean water supply 

and demand(2.14/0.0354). 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
17-2051.01 

Transportation Engineers 

6/26 

Design or engineer drainage, erosion, or sedimentation control systems for trans- 

0.1794 

11-1021.00 

General and Operations Managers 

2/17 

portation projects (3.95/0.0442) ; Analyze environmental impact statements for 

transportation projects (2.83/0.0317). 

Manage the movement of goods into and out of production facilities to ensure 
efficiency, effectiveness, or sustainability of operations (3.83/0.0613); Implement   

0.1134 

or oversee environmental management or sustainability programs addressing 

issues such as recycling, conservation, or waste management (3.25/0.0520). 

43-5071.00 

Shipping, Receiving, and Traffic Clerks 

1/11 

Compare shipping routes or methods to determine which have the least environ- 

0.0734 

41-3031.03 

Securities and Commodities Traders 

1/21 

Identify or pursue investment strategies related to the green economy, including 

0.0401 

mental impact (3.17/0.0734) 

green hedge funds, renewable energy markets, or clean technology investment 

opportunities (3.35/0.0401). 

23-1022.00 

Arbitrators, Mediators, and Conciliators 

1/20 

Specialize in the negotiation and resolution of environmental conflicts involv- 

0.0281 

2
2

47-2061.00 

Construction Laborers 

5/32 

Perform construction laborer duties at green building sites, such as renewable 

0.0138 

ing issues such as natural resource allocation or regional development planning 

(0.0281/2). 

energy plants or wind turbine installations (2.76/0.0260); Perform site activities 

required of green certified construction practices, such as implementing waste 

management procedures, identifying materials for reuse, or installing erosion or 

sedimentation control mechanisms (3.11/0.0293). 

3

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3  DATA CONSTRUCTION 

In addition, following Vona et al. (2019) a small number of green occupations from the  list 

at the O*NET-SOC level are attributed with zero greenness as they are only considered to  be 

marginally  green  in  terms  of  share  of  green  employment  at  SOC  occupational  level.14  In  a 

sensitivity check, those excluded green occupations are included and a uniform weight applied to 

calculate  a  greenness  index  for  SOC  occupations.1 Table 4  shows  the  changes  in  greenness 

indices for those occupations after applying uniform weights in the SOC job classification system. 

Table 4:  Affected occupations 

ISCO  Occupation title 

1321  Manufacturing managers 

2631 

Economists 

3123 

Construction supervisors 

3121  Mining supervisors 

9333 

Freight handlers 

2434 

Information and communications technology sales professionals 

1112 

Senior government officials 

2433 

Technical and medical sales professionals (excluding ICT) 

1120  Managing directors and chief executives 

2152 

Electronics engineers 

3322 

Commercial sales representatives 

2153 

Telecommunications engineers 

Greenness index 

Greenness index 

(Non-uniform weight) 

(Uniform weight) 

0 

0 

0 

0 

0 

0.0375 

0.0378 

0.0562 

0.0567 

0.0984 

0.1531 

0.1967 

0.5714 

0.5 

0.5 

0.5 

0.125 

0.1854 

0.2045 

0.2781 

0.3067 

0.0492 

0.2641 

0.0984 

It is worth noting at this point that O*NET data has an advantage over the ESCO (Euro- 

pean Skills, Competences, Qualifications and Occupations) because it included importance scores 

which are not available in the ESCO system. 

The methodological approach set out above is one that could be applied to any country that 

uses ISCO occupation codes. This research note focuses on the Netherlands. The next stage of 

this section on how to measure green jobs is to look specifically at how to apply the mapping ap- 

proach outlined above to the Dutch labour market and which administrative datasets are needed 

14When the 6-digit occupational group has zero or few (much less than other 8-digit occupations) green tasks, a zero 

1 Dropping marginally green occupations when we transfer O*NET-SOC to ISCO, so that green occupations are 

treated not equally, then we call it non-uniform weight; Otherwise, we call it uniform weight 

23 

3  DATA CONSTRUCTION 
greenness was attributed to all the 8-digit occupations within that group to avoid over-estimation of the greenness. For 

details of those problematic occupations, please see Vona et al. (2019) table A1 of Appendix A. 

24 

 
 
to be able to look at green jobs across multiple dimensions. 

3  DATA CONSTRUCTION 

In order to measure green jobs in the Dutch labour market, two main sources of data are 

used, the Dutch Labour Force Survey (LFS) and the Tax Register Data (TRD) from the confidential 

Dutch Micro-database. The Dutch LFS is a large sample survey that includes detailed information 

on the Dutch labour workforce. In addition to detailed individual characteristics such as gender, 

age, marital status, and so on, the Dutch LFS also provides important information on an individ- 

ual’s current occupation at the 4-digit ISCO2008 classification level. The TRD includes almost the 

universe of employees which in the case of the Netherlands is more than 10 million jobs every 

year and includes the tax records of every worker that is actively paying tax. Matching the LFS 

with the TRD means it is possible to match workers and firms based on the job which means the 

sample includes all workers who are active in the labour market at the time the data is collected. 

If a worker has more than one job, only the job with the longest tenure is kept. As the Dutch LFS 

is a rotating panel with five quarterly waves, the most recent individual level information is kept. 

When the most recent wave of the LFS misses an individual’s information, characteristics from 

the previous waves within the same year are retrieved. Each worker is only counted once in each 

year.15 

After linking the Dutch LFS and the TRD, the final stage of the data generation process is to 

identify green jobs by matching in the ISCO greenness indices from the process discussed earlier 

in this section (See Table 2 for an example of how to move from 8-digit O*NET SOC to 6-digit SOC 

and then from 6-digit SOC to 4-digit ISCO). Put simply, in the final sample, each individual has a 

greenness index for their current job. At end of the merging process, the sample is around 57,000 

workers per year for period 2000 to 2016. 

15The TRD database is the successor to the Social Statistics Survey (REOS) conducted by Statistics Netherlands. 

The Social Statistics Survey database provides a number of characteristics for each job which is matched to firms and 

individuals using a combination of employer/business entity data and employee data and when the date an employee is 

recorded as starting or finishing a job. 

25 

 
 
 
 
 
 
 
 
 
3  DATA CONSTRUCTION 

Recall that there are different ways to measure green jobs. When using a binary measure, 

an individual is defined as a green worker if their occupational greenness score is greater than a 

pre-defined threshold. It should be noted that to choose the threshold for greenness we have to 

make an informed decision to the extent possible. Given that we needed to use a crosswalk for 

our coding of greenness into ISCO, which is broader at occupational level, we use the average 

greenness index value to identify green occupations for those occupations that do not one-to-one 

match to ISCO. Besides, by using the average greenness index in our dataset as the threshold, it 

means that those occupations with few green tasks in the green occupation list are excluded. In 

the  Dutch  sample,  using  the  mean  value  of  greenness  index,  an  occupation  is  considered  as 

green if the occupation has a greenness index greater than 0.034. 

The data generating process is summarised using a schematic presentation in Figure 1 

which shows the two different strands of data collection that come together to provide the final 

sample of individual level data which can be aggregated in different ways to get an overall picture 

of green jobs in the Netherlands. 

26 

 
 
 
 
 
2
6

Figure 1:  Data construction 

3

D
A
T
A

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3  DATA CONSTRUCTION 

After following the process described in Figure 1 the main structure of the data can be sum- 

marised. Of the 436 ISCO occupations, 161 have a broad greenness index value that is greater 

than zero. Of those, 86 have a greenness index (using uniform weights) that is greater than zero 

(excluding Green ID jobs) while 81 task-based occupations have a greenness index greater than 

zero (using non-uniform weights). The full list of ISCO green occupations with their corresponding 

greenness scores are given in Table A1. 

To clarify further it is useful to illustrate the details of data construction process for one year 

to show the decisions that are made at each stage of the data generating process and some of 

the more technical issues not previously discussed. Taking the Dutch LFS2010 as an example, it 

is found that 109,344 people were surveyed. Of these, 3,907 (3.6%) have no occupational infor- 

mation and are therefore dropped from the sample. Of those remaining, 77% have occupational 

information at the ISCO 4-digit level, and are hence matched easily with the green occupation list. 

A further 19% of individuals only have occupation information at the 2 or 3-digit level. To 

include these individuals in the sample the ISCO 4-digit greenness indices are aggregated to the 

2 and 3-digit level. Based on the ISCO greenness scores associated with each occupation at  the 

4-digit  level, the  simple  average  greenness  score  for each  group  is  calculated.  For exam-  ple, 

ISCO “1110, Legislators and Senior Officials”, includes four occupations, “1111, Legislators” with 

an ISCO broad greenness score of 0, “1112, Senior Government Officials” with an ISCO broad 

greenness  score  of  0.5,  “1113,  Traditional  Chiefs  and  Heads  of  Village”  with  an  ISCO  broad 

greenness score of 0.25, and “1114, Senior Officials of Special-interest Organizations” with an 

ISCO broad greenness score of 0.53. As a result, the overall broad greenness score for “1110” is 

0.32. This process is repeated for each of the three greenness indices (broad green, task-based 

(uniform weights) and task-based (non-uniform weights). 

Specific to the Netherlands, before 2012 the LFS interviews were face-to-face or by tele- 

phone and after 2012 Statistics Netherlands started to include additional internet-interviews. It 

27 

 
 
 
 
 
 
 
 
 
3  DATA CONSTRUCTION 

was decided that the old LFS questionnaire was not appropriate for use as an internet interview 

survey and therefore a number of changes were made to the questionnaire. One result from the 

new sampling method was that there are more observations after 2012. 

When studying trends in green jobs at different levels of aggregation there are other de- 

cisions to make. For example, imagine wanting to look at sectoral trends (which is what is done 

first in this research note). When using a task-based approach, ideally the term “green" would be 

measured as a continuous characteristic of a job within the sector and occupation it is associated 

with rather than a binary feature of the job (e.g. when a job is defined as green or not depending 

on whether a certain percentage of tasks, weighted by importance, are considered green) (Bowen 

et al., 2018; Vona et al., 2019, 2018). In other words, it is important to calculate the within-sector, 

across occupations greenness since some sectors by definition have more green workers, which 

cannot be simply attributable to compositional changes in those sectors. 

Therefore, to obtain a continuous measure of the share of green employment in a sector 

the  employment  share  of  an  occupation  in  total  employment  in  a  sector  is  re-  weighted  by  its 

greenness index using the following formula: 

I 
L 

GEs 

= 

Greennessi 

i=1 

Eis 

· 

Es

(2) 

where GEs stands for the share of green jobs in sector s; Greennessi stands for the green- 

ness index of occupation i in sector s; Eis stands for the number of green jobs i and Es stands for 

the total number of jobs in sector s, respectively. This approach provides continuous measure of 

green jobs, where the share of green jobs is a proxy for the share of workers who spend their time 

on green tasks when the employment in a sector within the total employment is taken into account. 

For this research note the analysis has been done using both binary and continuous def- 

initions  of  green  jobs  as  these  have both  been  used  in  the  previous  academic  literature.   The 

28 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
analysis that appears in the main body of the text uses the continuous measure which is also the 

preferred measure. The results from using the binary measure of green jobs are also reported 

and discussed in the appendix. 

3  DATA CONSTRUCTION 

Table 5: Definition of green occupations 

Green occupation 

Green occupation categories 

Method 

Broad green occupation 

Task-based green occupation 

(Uniform weight) 

Task-based green occupation 

(Non-uniform weight) 

Green ID 

Green ES 

Green NE 

Green ES 

Green NE 

Green ES 

Green NE 

Binary measure 

Continuous measure 

Continuous measure 

To get an overall picture of green occupations by sector, Table 6 presents a list of 1-digit 

ISCO codes alongside the number of occupation categories within each 1-digit occupation and 

also the number of occupations that have a positive greenness index (i.e. a greenness index > 0). 

As seen in Table 6, for the task-based indices, there are no green occupations in occupational 

groups ‘Clerical support workers’, ‘Skilled agricultural, forestry and fishery workers’ and ‘Armed 

forces occupations’, and only a small number in the middle-level service occupations. Green oc- 

cupations are most prevalent in the high skilled occupations which are more likely to involve a 

greater use of analytical and technical skills and includes managers, professionals, and techni- 

cians and associate professionals. Relatively high numbers are also found in non-routine manual 

occupations such as ‘Craft and related trades workers’, and ‘elementary occupations’. 

When three different greenness indices are compared, occupations that are considered as 

Green ID jobs (included in the broad green definition) are mainly in primary sectors and some 

services sectors. For example, there are 12 broad green occupation categories in ISCO category 

29 

 
 
 
 
 
 
 
 
 
 
 
 
4  TRENDS IN GREEN JOBS 

Table 6: Number of green occupations by 1-digit ISCO code 

ISCO 

Occupation title  Total 

Broad 

Task-based 

Task-based 

# 

green# 

green # 

green # 

(Uniform weights) 

(Non-uniform weights) 

0 

1 

2 

3 

4 

5 

Armed forces occupations 

Managers 

Professionals 

Technicians and associate professionals 

Clerical support workers 

Service and sales workers 

6  Skilled agricultural, forestry and fishery workers 

7 

8 

9 

Craft and related trades workers 

Plant and machine operators, and assemblers 

Elementary occupations 

3 

31 

92 

84 

29 

40 

18 

66 

40 

33 

0 

20 

35 

25 

4 

1 

12 

24 

11 

9 

0 

19 

26 

21 

0 

1 

0 

8 

3 

5 

0 

18 

25 

19 

0 

1 

0 

8 

3 

4 

6 “Skilled agricultural, forestry and fishery workers", but no task-based green occupations, which 

indicates these 12 occupations must be Green ID occupations. 

4  Trends in green jobs 

In this section the methodology discussed in the previous section is used to determine the 

share of green jobs in the Dutch labour market and how the share has evolved over time across 

different  sectors  and  occupational  groups.  For  the  main  research  note  results  the  continuous 

measure(non-uniform weight) of green jobs is used which is the preferred index. Note that the 

share of green jobs is generated from a sample and not the whole economy. Therefore, these 

survey-based estimates should be interpreted with caution and the trends shown should be taken 

as indicative only. As the Dutch LFS is a rotating panel with five waves each individual is counted 

only once each year dropping any individual who appears in both the current year and in the final 

30 

 
 
 
 
 
 
 
 
 
 
4  TRENDS IN GREEN JOBS 

Figure 2: Share of green jobs (2003 - 2021) 

wave of the previous year. Trends are for the period 2003 and 2021.16 

The simplest way to present trends is to use simple line graphs. An overview of the whole 

country is presented first. Figure 2 shows that the share of green jobs did not change dramatically 

over this  period  and  tended  to  move  within  a  fairly  tight  range  of  2.4%  to  2.7%.  The  headline 

number is similar to the US estimates from Vona et al. (2019), who, using the same task-based 

continuous measure, found that about 3% total employment can be considered as green employ- 

ment. Looking more carefully, the share of green jobs fluctuated around 2.5% to 2.7% between 

2003 and 2011, then fell slightly from 2012 (2.6%) to 2017 (2.4%) before increasing again for the 

final five years of the sample. Over the whole period the share of green jobs increased but only 

slightly  (from  2.5%  to  2.7%).  In  terms  of  absolute  numbers  this  translates  in  an  increase  from 

168,000 to 210,000 full time equivalent (FTE) jobs. 

16In this section the new Labour Force Survey (EBBnw) from the Statistics of Netherlands is used which is a 

condensed version of the old EBB. 

31 

 
 
 
 
 
 
 
 
 
 
4.1  Trends in green jobs by sector 

4  TRENDS IN GREEN JOBS 

Figure 3 presents the trends in the share of green jobs for three broad economic sectors. 

The primary sector, which corresponds to Dutch SBI division A-B, are sectors involving extracting 

raw materials.17 Examples of activities within this sector includes agriculture, fishing and mining. 

The secondary sector corresponds to Dutch SBI division C-F relating to the production of finished 

goods, e.g. manufacturing, construction and utilities. The tertiary/service sector corresponds to 

Dutch SBI division G-U and it includes providing goods and services to customers in wholesale 

and retail trade, transport, and government, financial, professional, and personal services such as 

education, health care, and real estate services.18 

Figure 3 shows that the secondary sector has the highest share of green jobs,  ranging 

between 4.3% and 5.1%. In the secondary sector the share of green jobs gradually increased be- 

tween 2003 to 2011, fell back in 2012 and then remained fairly stable for the rest of the period with 

a small temporary increase in 2020. Breaking down the secondary sector down into its constituent 

parts it is found that this result is driven by Sector D "Energy supply", and Sector E "Water supply", 

which, although traditionally capital intensive sectors, have a relatively high percentage  of green 

jobs. The tertiary sector was the second largest in terms of the share of green jobs between 2003 

and 2012 after which it was overtaken by green jobs in the primary sector.  In the tertiary sector 

17The Dutch Standaard Bedrijfsindeling (SBI 2008) is based on the activity classification of the European Union 

(Nomenclature statistique des activités économiques dans la Communauté Européenne, NACE) and on the classification 

of the United Nations (International Standard Industrial Classification of All Economic Activities,  ISIC). The first 

four digits  of  the  SBI  are  the  four digits  of  NACE  and the  first  two  digits  of  the  SBI  and  NACE  are  the  same  as 

the first two digits of ISIC. For details see https://www.cbs.nl/en-gb/our-services/methods/classifications/activiteiten/ 

standard-industrial-classifications--dutch-sbi-2008-nace-and-isic--. 

18The classification into three broad sectors is based on the Metadata Glossary of the World  Bank.  According   to 

the World Bank, an industry corresponds to ISIC divisions 10-45 (ISIC V3.1), and this corresponds to first digit  of 

ISIC  division  C-F.  See  https://databank.worldbank.org/metadataglossary/wdi-database-archives-(beta)/series/NV. 

IND.TOTL.CD. Services correspond to ISIC divisions 50-99, which is equivalent to first digit of ISCI G-U. For details 

see https://databank.worldbank.org/metadataglossary/world-development-indicators/series/NV.SRV.TOTL.ZS. 

32 

 
 
 
 
 
 
4  TRENDS IN GREEN JOBS 

Figure 3: Share of green jobs by sectors (2003 - 2021) 

over the whole period some slight growth from 2.1% to 2.3% is found) but otherwise the share was 

remarkably stable. The primary sector saw the largest percentage change in the share of green 

jobs moving from around 1% to over 2.7% by the end of the period. 

Figures B1, B2 and B3 in Appendix B present green job share trends by 1-digit sectors for 

the  primary,  secondary  and  tertiary  sectors,  respectively.  Appendix  C  contains  the  equivalent 

figures using the binary broad green jobs measure (Figures C1 to C5). Although the trends are 

broadly similar, the magnitudes are considerably higher when using a broad measure of green 

jobs. For example, Figure C1 shows that the share of green jobs in total jobs ranges between 

15.5% and 18% and the share of green jobs in the secondary sector shown in Figure C2 ranges 

between 25% and 30%. These values are similar to the US studies that have used broad green 

jobs as their indicator. 

33 

 
 
 
 
 
 
 
 
 
 
4.2  Trends in green jobs by occupation 

4  TRENDS IN GREEN JOBS 

This section of the research note focuses on occupation level analysis of green jobs. As 

previously mentioned, the classification of occupational groups is based on ISCO2008. ISCO2008 

divides occupations into ten major groups based on their similarity in terms of skill levels and what 

specialization a worker  needs to do the job. As shown in Table 6, according to the continuous 

measure, there are no green jobs in the occupation groups "Clerical support workers", "Skilled 

agricultural, forestry and fishery workers", and "Armed forces occupations" so these groups have 

been excluded from the following analysis. 

Figure 4 presents the share of green jobs for the seven major occupation groups that have 

some green occupations between 2003 to 2021 where the total share and the share of males and 

females in green jobs is presented to also get an early snapshot of the breakdown of green oc- 

cupations by gender. Except for "Service and sales workers", the share of male green workers is 

greater than the share of females in green jobs. The male/female gap is especially large for "Craft 

and related trades workers" and "Plant and machine operators, and assemblers", where almost all 

the green jobs are held by males. 

Occupation groups "Manager", "Professional" and "Technicians and associate profession- 

als" are considered as higher skilled occupations. Green jobs account for a relatively large propor- 

tion of jobs in the group "Manager", ranging from 8.3% to 9.4%. The share of green jobs increased 

slightly over the period. When a distinction is made between male and females in the "Manager" 

occupation it can be seen that female green workers steadily increased (from 1.8% to 2.5%) until 

2011 but saw a noticeable fall from 2011 to 2013 and was then relatively stable over the remainder 

of the period. 

Employees who work in the major group "Professional" and the major group "Technicians 

and associate professionals" are normally considered as human resources in science and tech- 

34 

 
 
 
 
 
 
 
 
 
 
4  TRENDS IN GREEN JOBS 

(a) Managers 

(b) Professionals 

(c) Technicians 

(d) Service 

(e) Trade 

(f) Machine operators 

(g) Elementary occupation 

Figure 4:  Share of green jobs: Ma 

35 
jor Occupational 

Groups (2003 - 2021) 

 
 
 
 
 
 
 
 
 
 
 
 
 
4  TRENDS IN GREEN JOBS 

nology (HRST).19 In the major group "Professionals", the share of green jobs (4.5% on average) is 

moderately high. For the years 2003 to 2004 a relatively sharp increase in the proportion of green 

jobs can be seen. The share of green employment showed a downward trend between 2004 to 

2013 before returning to an upward trend between 2013 to 2021.  The share of green jobs in  the 

major group "Technicians and associate professionals" is lower than "Professionals", ranging from 

1.5%  to  2%.  The  share  of  green  jobs  is  relatively  stable  until  2009  after  which  a  significant 

increasing  trend  from  1.5%  to  2.1%  is  observed,  after  which  it  remained  relatively  stable.  The 

overall trend for both groups is similar for male workers, while female green workers experienced 

a slightly increasing trend. 

The share of green jobs in the major group "Service and sales workers" is relatively low, 

and accounts for only 0.1% to 0.4% of total jobs in this group. Before 2009, the share of green 

jobs remained relatively stable at around 0.1%. From 2009 a steady and significant increase in 

the percentage of green jobs is seen ranging from 0.1% to 0.4% until 2020. This is the only sector 

that males and females account for a similar proportion in green jobs. The major group "Craft and 

related trade workers" includes workers mainly in the field of mining and construction. Green jobs 

are dominated by males. The proportion of males in green jobs increased between 2003 to 2011 

ranging from 6% to 8%, fell from 2011 to 2013 and was then fairly stable (around 6%) for the rest 

of the period. 

Of the two remaining occupations, the group "Plant and machine operators and assem- 

blers" involves tasks such as operating and monitoring on-the-spot or remote-controlled industrial 

and agricultural machinery and equipment, driving and operating trains, motor vehicles, and mo- 

bile machinery and equipment, or assembling products. The share of green jobs is relatively low 

and green male jobs dominate. The share of green jobs remained relatively stable (around 1.8%) 

until 2011 before falling quite dramatically from 1.8% to around 1.2% between 2011 to 2016 and 

19See  https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Human_resources_in_science_ 

and_technology_(HRST) Professional knowledge and experience in the fields of physical and life sciences, or social 

sciences and humanities are required in the primary tasks of occupations in these two groups. 

36 

 
 
 
 
 
 
 
4  TRENDS IN GREEN JOBS 

then  remaining  relatively  stable.  Finally, the  major  group  "Elementary  occupations"  consists  of 

workers whose tasks are mainly routine-based that primarily involve the use of hand-held instru- 

ments and some physical effort. The share of green jobs is fairly low and fluctuates around the 

1% level with the jobs tending to be highly male orientated. Figure C7 in Appendix C presents the 

equivalent  results  using  the  binary  measure  of  green  jobs.  As  with  sector  in  Appendix  B,  the 

magnitudes are much higher. For example, the share of green jobs for “Managers” ranges from 

60% to as high as 75% and for “Professional" from 22% to 25%. The proportions for males and 

females are similar but with high magnitudes. 

To summarise, green jobs tend to be male dominated across all occupation groups except 

“Service and sales workers”. The occupation group with the highest share of green jobs is man- 

agerial jobs. The trends in occupational groups that are normally considered as high skilled such 

as “Manager”, "Professional" and "Technicians and associate professionals" tend to show increas- 

ing overall trends while for certain occupation groups that require fewer skills such as "Craft and 

related trade workers" and "Plant and machine operators and assemblers", the share of green jobs 

increased in the first few years of the sample before falling back. These trends explain why green 

jobs are thought to be higher skilled and higher paid. The analysis for the Netherlands certainly 

confirms this. 

4.3  Trends in green jobs by region 

In this section a visual presentation of the geography of green jobs is provided, focusing on 

the share of green jobs in Dutch municipalities between 2003 and 2020. In early 2000s, Figure 5 

shows that the share of green jobs (the number of green workers as a share of total employment), 

tends to be higher in agriculturally intensive areas. Over time the Netherlands has developed a 

highly mechanised and innovative agri-food sector and has become world leading in the adoption 

of automated technologies in agricultural production.  Two implications of this process are that 

37 

 
 
 
 
 
 
 
 
 
 
while the agricultural sector became greener (see Figure 3) as a result of high-tech investment,  it 

also had become less labour intensive, hence number of (green) workers in agriculture has de- 

creased over time. 

4  TRENDS IN GREEN JOBS 

To examine the geography of green jobs in the Netherlands municipality level green jobs 

data are constructed from the TRD tax records and the Dutch LFS. Recall that the data construc- 

tion includes aggregation of the worker level green occupation data based on green workers who 

are active in the labour market at the time of the measurement. Among all workers in the pri- mary 

sector,  there  are  a  number  of  green  occupations  which  constitute  the  significant  portion  of 

employment. These occupations at 4-digit ISCO classification are (7233) Agricultural and indus- 

trial machinery mechanics and repairers; (3142) Agricultural technicians; (1311) Agricultural and 

forestry  production  managers;  (2132)  Farming,  forestry  and  fisheries  advisers.  Over  the  study 

period there is around a 50% decrease in employment in these specific occupations, with a signif- 

icant decrease in the share of green jobs in those agricultural regions with a previously high share 

of green jobs. 

The uptrend and emphasis on non-production, non-agricultural businesses on the greening 

of the economy and sustainability, seems to be another stimuli in mobilising the creation of new 

green jobs in urban core areas, e.g. green financial investors, as well as existing urban sectors 

becoming greener. Figures 6 and 7 show a secular trend across space for the share of green jobs 

to become more prevalent in large urban locations. 

Similarly, production intensive manufacturing facilities seem to have been going through a 

similar trend. Although manufacturing plants have traditionally been located close to large cities, 

with a lower share of green workers, in recent years there has been an increase in the share of 

green workers within the manufacturing sector (shown in the next section of the research note). 

The greening of manufacturing is due in part to an overall decline in manufacturing employment 

while the number of green jobs has remained relatively stable. In other words non-green work- 

ers are leaving the sector and older technologies are replaced with newer more efficient machines. 

38 

 
 
 
 
 
 
 
 
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Figure 5: Geographic distribution of green jobs in the Netherlands (2003) 

39 

 
 
 
 
 
 
 
 
 
 
4  TRENDS IN GREEN JOBS 

Figure 6: Geographic distribution of green jobs in the Netherlands (2011) 

40 

 
 
 
 
 
 
 
 
 
 
4  TRENDS IN GREEN JOBS 

Figure 7: Geographic distribution of green jobs in the Netherlands (2020) 

41 

 
 
 
 
 
 
 
 
 
 
5  FIRMS AND GREEN JOBS 

The reallocation of the workforce within manufacturing is more apparent when looking at 

the occupational distribution within the sector. Indeed a decrease in the share of green workers 

in plant and machine operators and assembler occupations and similar mid-occupations is ob- 

served in Figure 4, coupled with an increase in technicians and associate professionals and a 

slight increase in other higher-skilled occupations. These greener manufacturing areas appear to 

be located in the vicinity of urban cores. These overall trends provides an interesting overview  of 

geographical distribution of the green economy from more rural to more urban areas and their 

peripheries. 

5  Firms and green jobs 

This section analyses how firm characteristics are associated with green job creation. For 

these analyses tax records linked to the LFS over time are used, and as not all firms survive for the 

whole period, an unbalanced panel of firms is constructed to investigate the determinants of the 

number of green workers in each firm over a fifteen-year period (unbalanced in the sense that it is 

not the same firms in each year). The main interest here is to study the relationship between firm 

size and firm level wages and green job creation given the underlying assumption in the literature 

that green jobs are associated with large and high paying firms. To do so, firm characteristics and 

the unobserved  determinants of  firms’ decisions to employ more  green  workers are taken  into 

account as well as the influence of sector specific effects, year effects and the location of firms. 

Although it is difficult to provide a perfect identification, the extensive number of fixed effects that 

are included should, to a large extent, control for the effect of the omitted variables. 

Before presenting the regression results, the average share of green jobs by firm size is 

shown. Firms are assumed to stay in the same size category over time (based on their size the 

42 

 
 
 
 
 
 
 
 
 
 
 
 
 
5  FIRMS AND GREEN JOBS 

Figure 8: Share of green jobs by firm size (2000 - 2016) 

first time they appear in the data). As Figure 8 shows, the green job share tends to be higher in 

medium and large firms. Similarly, in Figure 9 it is shown that higher paying firms have more than 

a percentage point larger share of green workers. Although the relationship between wages and 

green job share is fairly stable over time, it is observed that the green worker share is decreasing 

in medium sized, medium wage firms over time. As shown in the previous section, green jobs are 

prevalent in the top and the bottom end of the occupational distribution and the firm level trends 

shown here are consistent with this trend. The decrease of green jobs in the medium segment of 

the market can be related to the general trend of disappearing middle level jobs due to technolog- 

ical change in the Netherlands (e.g. Koster and Ozgen (2021)) and in developed economies (e.g. 

Autor (2019)). 

Table 7 presents the association between firm characteristics and green job creation. Two 

types of regression are estimated where in the first regression the dependent variable is share of 

green jobs and in the second, the dependent variable is the log number of green jobs in a firm. 

Both regressions are estimated also separately for manufacturing and non-manufacturing firms. 

43 

 
 
 
 
 
 
 
 
 
 
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Figure 9: Share of green jobs by firm wage (2000 - 2016) 

All the models apply an augmented specification that controls for unobserved time-invariant firm 

characteristics. In Column 1, it is found that there is a strong negative correlation between the 

share  of  green  jobs  and  firm  size.  The  small  coefficient  is  not  surprising  in  the  sense  that  the 

green share is only about 3% of the workforce on average, hence the increase in firm size (as 

shown in Column 2) though positive does not translate into large numbers of green workers in 

relative to total firm size. 

Turning to wages, it is found that high wage firms are positively correlated with green job 

creation relative to total employment and in terms of the total number of green jobs. In Column  2 

it is shown that both size and wage levels are strong determinants of the number of green jobs 

and these effects are statistically significant. For example,  a 10% percent increase in firm size  is 

associated with 3.2 percent increase in green workers within the same firm. It is shown that a 10% 

increase in wages leads to a 1.9% increase in the number of green workers. The estimations are 

robust  to  the  inclusion  of  unobserved  sector,  time,  and  location  specific  controls.  It  is  worth 

mentioning that controlling for the sector effects has the largest influence on the magnitude of the 

44 

 
 
 
 
 
 
 
 
 
 
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Table 7: Firms and green jobs 

Full sample 

Manufacturing firms 

Non-manufacturing firms 

(1) 

(2) 

(3) 

(4) 

(5) 

(6) 

Share of 

Number of 

Share of  Number of 

Share of 

Number of 

green jobs 

green jobs 

green jobs 

green jobs 

green jobs 

green jobs 

lnfirmsize 

-0.00175*** 

0.315*** 

0.000453 

0.469*** 

-0.00175*** 

0.342*** 

(0.000518) 

(0.00124) 

(0.00145) 

(0.0313) 

(0.000523) 

(0.0133) 

lnfirmwage 

0.00623*** 

0.189*** 

0.00860*** 

0.285*** 

0.00608*** 

0.182*** 

(0.000842) 

(0.0173) 

(0.00309) 

(0.0448) 

(0.000925) 

(0.0186) 

Firm FE 

Year FE 

Location FE 

Sector FE 

YES 

YES 

YES 

YES 

YES 

YES 

YES 

YES 

YES 

YES 

YES 

NO 

YES 

YES 

YES 

NO 

YES 

YES 

YES 

YES 

YES 

YES 

YES 

YES 

Observations 

284,210 

284,210 

42,116 

42,116 

234,367 

234,367 

R-squared 

0.465 

0.593 

0.315 

0.532 

0.492 

0.610 

Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 

45 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Figure 10: Share of green jobs: Manufacturing firms vs non-manufacturing firms (2000 - 2016) 

estimated effects, meaning that there is a large variation between sectors when it comes to the 

impact of firm size and average wages on green workers. This is consistent with the descriptive 

evidence shown in the section on sectors and green jobs. 

In Figure 10 the share of green workers in manufacturing and non-manufacturing sectors is 

presented. A longer-term trend of an increasing share of green workers in the manufacturing sec- 

tor over the study period is observed, while this share is rather stable for non-manufacturing firms. 

In order to explore this heterogeneity further in terms of how firm effects influence the creation of 

green jobs across these broader sector classifications the sample is split into manufacturing and 

non-manufacturing firms. 

Columns  3  and  4  present  the  results  for  manufacturing  firms.  In  line  with  Figure  10  a 

positive relationship between firm size and the share of green workers is estimated, although the 

coefficient is not significant. Note that the number of observations decreases substantially once 

the sample is split into the two groups. Compared to all industries, for manufacturing firms the ef- 

46 

 
 
 
 
 
 
 
 
 
 
 
 
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INDIVIDUALS AND GREEN JOBS 

fect of firm-level wages is much stronger for green job share. We further estimate the effect of size 

and wages on the number of green workers. In line with expectations, the effect of both indicators 

are about 30% larger in the manufacturing sector sample (compared to non-manufacturing) and 

significant. A 10% increase in firm size and firm level wages is associated with a 4.7% and 2.9% 

increase in the number of workers, respectively. 

Finally,  columns  5-6  provide  the  findings  for  non-manufacturing  firms.  These  estimates 

confirm the intuition that the negative relationship between firm size and the share of green jobs 

stems from the non-manufacturing sector firms as also shown in Figure 10. It should be noted 

that many labour intensive low-skilled business sectors that do not necessarily invest in a green 

transition would fall into this category. Following the same firms over time, it seems that the non- 

manufacturing firms are growing in size faster in terms of non-green employment rather than green 

employment. However, for non-manufacturing sector firms, wage levels are an important positive 

and significant determinant of green job share. Similarly, firm size and wages are also positively 

associated with the number of green workers in the non-manufacturing sector, although the mag- 

nitude of the coefficients are smaller than those in manufacturing sector. 

Overall, these findings stress that the larger the firm the higher the number of green jobs 

created, although the share of green jobs is lower. In terms of wages, the greener jobs in a firm, 

the higher the overall wage bill with the value being more pronounced in manufacturing firms. 

6  Individuals and green jobs 

This section examines the socio-economic characteristics of those who are considered to 

be employed in a green job, and how they are distributed across occupations, with special atten- 

tion given to an examination of gender differences. Employees in the data are aged between 15 

47 

 
 
 
 
 
 
 
 
 
 
 
 
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INDIVIDUALS AND GREEN JOBS 

and 65 years old. For each employee, if they have multiple jobs, the job which they have had for 

the longest duration is kept. As the Dutch LFS is a rotating panel of five quarterly waves, the most 

recent information on personal characteristics is kept. For the worker level analysis, each worker 

is only counted once within each year. Given that the LFS is not a longitudinal survey over the 

years, the LFS with the TRD and municipal registrations data is matched for the years 2001 to 

2016.20 This step ensures that all workers are active at the time of the measurement and their 

work histories over time can be identified. 

In these pooled datasets, the workers who hold a green occupation based on 4-digit ISCO 

are identified, thus in the final sample, green workers are classified using a dummy variable. Note 

that, different from other sections of this research note, it is not possible to use a continuous mea- 

sure in this context as the characteristics of the workers in a green job are being considered which 

means a job has to be defined as either green or not for this purpose (given the nature of the data 

construction and the analyses a person cannot hold a job that is 50% green for example). Recall 

that using a binary measure of green jobs gives a higher overall percentage of green jobs (from 

approximately 3% up to around 15%). 

The final dataset includes close to a million workers (which is one seventh of the universe 

of employed workers in the Dutch labour market) for the period 2001 to 2016.  Table 8 provides 

summary statistics for the characteristics of the workers performing green jobs and non-green jobs 

for total employees and split by gender. Of those workers, 16.99%, which is 153,392 individuals, 

worked in a green job out of a total sample of 902,671 workers.  The number of males and the 

females are fairly comparable, 443,376 and 469,295, respectively. However, there are nearly four 

times as many males in green jobs as females (123,041 males compared to 30,351 females). 

Therefore, of the 153,392 green employees in the sample, 20% are female, which indicates green 

jobs tend to be male dominated. Table 8 also reveals that green jobs are more likely to be prevalent 

20The reason 2001 is the first date is because that the variable that captures the proportion of the days worked in  a 

year (deeltijdfactorbaanid) is not available before 2001. 

48 

 
 
 
 
 
 
 
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INDIVIDUALS AND GREEN JOBS 

in high skilled occupations compared to non-green jobs (40% against 28% for green and non-green 

jobs, respectively), with more than half of all female green workers employed in high skilled green 

occupations. It is shown that 58% of female green workers are high skilled compared to 28% of 

female non-green workers. 

Table 8 also shows that workers employed in green occupations tend to be of native origin 

(over 90%), married (and predominantly male) with children under 12, are older in age, and have 

longer tenure compared to those individuals in non-green jobs. There is however no substantial 

difference between individuals in green jobs and non-green jobs in terms of household size. 

49 

 
 
 
 
5
0

Table 8: Characteristics of green jobs and non-green jobs in the Dutch labour market (2001 - 2016) 

Green Jobs 

Non-Green Jobs 

All sample 

Characteristics (%) 

Total 

Female  Male 

Total 

Female  Male 

Total 

Female  Male 

Female 

Married 

High Skill 

Middle Skill 

Low Skill 

Native 

Foreign born 

Children (under 12) 

Children (over 12) 

20 

62 

40 

39 

22 

93 

7 

33 

37 

100 

53 

58 

30 

12 

91 

9 

34 

27 

0 

65 

35 

40 

25 

94 

6 

33 

39 

54 

53 

28 

44 

28 

92 

8 

28 

44 

100 

54 

28 

45 

26 

92 

8 

30 

43 

0 

53 

28 

42 

29 

92 

8 

27 

45 

48 

55 

30 

44 

26 

92 

8 

30 

42 

100 

54 

31 

44 

25 

92 

8 

30 

42 

0 

56 

30 

43 

28 

92 

8 

29 

42 

Characteristics 

Total 

Female  Male 

Total 

Female  Male 

Total 

Female  Male 

Age (years) 

Tenure (years) 

41 

9 

39 

8 

42 

9 

38 

8 

38 

7 

39 

8 

39 

8 

39 

7 

40 

9 

Household size 

3.05 

2.81 

3.11 

3.08 

3.07 

3.09 

3.06 

3.03 

3.08 

Firm wage (in Euros)  30,402 

29,325 

31,915 

23,243 

20,164 

26,828 

24,485 

20,952 

27,764 

Firm size 

4,879 

6,386 

4,504 

6,427 

5,951 

6,983 

6,163 

5,982 

6,331 

N 

153,392  30,351  123,041  749,279  403,025  346,254  902,671  433,376  469,295 

Sources: Dutch Labour Force Survey and Tax Register Data. 

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The next stage is to look at the distribution of occupations between green and non-green 

jobs by gender. The summary statistics are shown in Table  9.  As noted previously,  there are  no 

green  jobs  in  ‘Clerical  support  workers’  and  ‘Skilled  agricultural,  forestry  and  fishery  workers’. 

Table 9 also confirms that green jobs are more often found in high skilled occupational groups 

such as ‘Managers’ and ‘Professional’. Indeed almost 50% of all green workers are in these two 

occupation groups. As seen in the occupation trends section of the research note, both males and 

females in green jobs are more likely to be found in the occupation groups described as ‘Managers’ 

and ‘Technicians and associate professionals’, and females are especially more likely to be found 

in ‘Professional’ occupations. Of the green female workers 36% are in ‘Professional’ occupations. 

For  the  female  dominated  occupation  ‘Service and  sales  workers’,  there  are  few  green 

jobs and even fewer held by males. For the male dominated occupational groups ‘Craft and re- 

lated traded workers’ and ‘Plant and machine operators and assemblers’, there are more male 

green workers in ‘Plant and machine operators and assemblers’ and fewer in ‘Craft and related 

traded workers’ compared to males in non-green jobs. Finally, for ‘Elementary occupations’,the 

share of male green workers is higher than that of female green workers although the difference 

between non-green jobs is relatively small. 

51 

 
 
 
 
 
 
 
 
 
 
Table 9: Occupational Distribution: Green jobs VS Non-green Jobs (2001-2016) 

Occupation groups (%) 

Total 

Female  Male 

Total 

Female  Male 

Green Jobs 

Non-Green Jobs 

Managers 

Professional 

Technicians and associate professionals 

Clerical support workers 

Service and sales workers 

Skilled agricultural, forestry and fishery workers 

5
2

Craft and related traded workers 

Plant and machine operators and assemblers 

Elementary occupations 

OBS 

29 

22 

19 

0 

1 

0 

12 

10 

10 

31 

36 

20 

0 

3 

0 

1 

1 

8 

25 

19 

18 

0 

1 

0 

15 

12 

11 

2 

21 

17 

17 

23 

2 

8 

4 

9 

1 

20 

18 

17 

31 

1 

1 

1 

9 

2 

23 

15 

10 

14 

3 

16 

7 

9 

153,392  30,351  123,041  749,279  403,025  346,254 

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The final set of descriptive statistics presented in Table 10 shows the mean hourly wage 

differences between those employed in green jobs and those employed in non-green jobs (also 

broken down by gender). The first panel shows the mean wage differential for the full sample over 

different periods. The main finding is that green jobs pay higher average hourly wages than non- 

green jobs, and this is true for both females and males in the full sample. When looking at the four 

different periods, it is found that the average hourly wage is increasing over time as expected. It is 

worth noting that the gap between green jobs and non-green jobs gets larger over time where the 

wage gap increases from around 1.6 Euros in the early years, to 4.88 Euros by 2013-2016 period 

(although the percentage difference has not increased significantly). 

When the gender pay gap is considered it is found that there is little evidence of a difference 

across genders for green and non-green jobs. Indeed, if anything females are paid slightly more 

on average for both types of job. These trends are entirely in line with the Statistics Netherlands 

recent study which shows that on average the females are paid slightly higher than males in the 

Dutch labour market due to increasing education levels among the females younger than 45 years 

old. When median net hourly wages are considered this pattern becomes even clearer. Further 

reasons for this finding are investigated later in next section.21 

The second panel of Table 10 shows the wage differential between green jobs and non- 

green  jobs  by  occupation  and  gender.  Surprisingly,  for  high  quality  occupations  that  includes 

‘Managers’, ‘Professional’ and ‘Technicians and associate professionals’ who tend to have more 

green jobs, it is found that the green job wage is greater than the non-green job wage only for 

‘Professional’ occupations, and this is mainly because of the wages paid to males in green jobs 

in that occupation group. For the female dominant occupation ‘Service and sales workers’, it is 

shown that green jobs pay less than non-green jobs on average, but this is only found for female 

green workers. For the male dominant occupations ‘Craft and related traded workers’ and ‘Plant 

and machine operators and assemblers’, it is found that green jobs pay higher wages than non- 

21https://www.cbs.nl/en-gb/news/2020/18/gender-pay-gap-still-narrowing. 

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INDIVIDUALS AND GREEN JOBS 

green jobs, especially for females in green jobs where females earn higher wages than female 

non-green workers, and also more than male green workers for those two occupational groups. 

Finally, there is little difference in wage rates for green jobs and non-green jobs in ‘Elementary 

occupations’, while female green workers are paid less than female non-green workers, and male 

green workers are paid more than male non-green workers in this group. 

54 

 
 
 
Table 10: Hourly Wage: Green jobs VS Non-green Jobs (2001-2016) 

Hourly wage(Euros) 

Whole sample 

2001-2004 

2005-2008 

2009-2012 

2013-2016 

Occupation groups 

Managers 

Professional 

5
5

Green Jobs 

Non-Green Jobs 

Total 

Female  Male 

Total 

Female  Male 

27.31 

27.44 

27.23 

23.94 

24.64 

23.12 

22.23 

22.16 

22.25 

20.63 

21.50 

19.69 

25.87 

25.15 

26.04 

22.74 

23.61 

21.74 

30.00 

30.30 

29.92 

25.64 

26.19 

24.97 

31.36 

30.70 

31.55 

26.48 

26.87 

26.03 

36.35 

29.09 

38.61 

36.81 

30.89 

40.30 

31.61 

29.91 

32.41 

31.25 

31.91 

30.55 

Technicians and associate professionals 

24.94 

25.19 

24.87 

25.44 

25.55 

25.30 

Clerical support workers 

N/A 

N/A 

N/A 

22.45 

22.75 

21.85 

Service and sales workers 

19.91 

18.52 

21.19 

21.79 

22.34 

20.35 

Skilled agricultural, forestry and fishery workers  N/A 

N/A 

N/A 

16.31 

17.89 

15.91 

Craft and related traded workers 

18.17 

20.04 

18.15 

17.54 

19.18 

17.41 

Plant and machine operators and assemblers 

20.28 

20.75 

19.68 

19.16 

17.80 

19.42 

Elementary occupations 

18.17 

17.98 

18.21 

18.47 

19.27 

17.47 

OBS 

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7  Wage differentials for green jobs by gender 

7.1  Econometric Model 

Aggregate statistics for the Netherlands show that labour market disparities between men 

and women have been decreasing over time. On the one hand, the labour force participation rate 

has increased dramatically for women in the Netherlands, from only 32% in 1977 to 75.8% in 2007 

(Nientker and Alessie, 2019), although most of the increase can be attributed to increases in part- 

time workers (Wielers and Van der Meer, 2003). On the other hand, the educational attainment for 

women has increased and the difference in over-education has also declined according to Van der 

Meer (2008). However, the wage gap between female and male remains significant (around 20%) 

(Fransen et al., 2012), although this gap has significantly narrowed recently.22 

This research note attempts to understand whether there is less wage discrimination in 

green  jobs.  The  limited  number  of  studies  of  the  Dutch  labour  market  have tended  to  use  the 

human capital model to decompose the gender wage gap. A review of Dutch studies by Fransen 

et al. (2012) finds that 71.5% of the gender wage gap in the Netherlands can be explained by the 

difference in observable characteristics, and 28.5% remained unexplained which is what is usually 

attributed to discrimination. The remaining (unexplained) gender wage gap was largely a result of 

an unequal distribution of men and women across occupational groups, rather than the unequal 

pay within occupations. 

From the descriptive statistics, it is found that women are substantially under-represented 

in green occupations although the descriptive statistics do not point to a significant wage differen- 

tial between male/female green workers. However, to really understand whether this means there 

is less discrimination in green jobs, a traditional wage equation is estimated. What is found is that, 

once a range of individual characteristics are controlled for, females in green jobs are paid less 

22https://www.cbs.nl/en-gb/news/2020/18/gender-pay-gap-still-narrowing. 

56 

 
 
 
 
 
 
 
 
 
7  WAGE DIFFERENTIALS FOR GREEN JOBS BY GENDER 

than male green workers, especially when occupational fixed effects are included.23 Given that 

there is a substantially higher share of high skilled female in green jobs (58%) compared to male 

green jobs (35%) one suggestion is that there is still some discrimination against females. There- 

fore, the remainder of this section focuses on examining the wage differential between females 

and males in green jobs in more detail. 

The Blinder–Oaxaca decomposition (Blinder, 1973; Oaxaca, 1973) is the most commonly 

used method in the labour economics to study the wage differentials across different groups. The 

Blinder–Oaxaca decomposition explains wage differentials based on linear regression models and 

decomposes the wage differential between two groups into ‘explained part’ and ‘unexplained part’, 

where the ‘explained differential’ can be estimated by the productivity characteristics, such as edu- 

cation or work experience, while the ‘unexplained part’ is commonly considered as discrimination, 

though it also captures the effect of differences due to any unobservable characteristics (Jann, 

2008). A detailed description of the Blinder-Oaxaca decomposition can be found in Appendix D. 

The Blinder-Oaxaca decomposition has been criticised because it ignores possible occu- 

pational differences and inappropriately accounts for these differences in occupational distribution 

of  the  men  and  women.  Furthermore,  even  though  some  studies  have  considered  the  effect 

of occupational differences by incorporating occupational dummies in the earnings regressions, 

these studies are likely to underestimate the actual discrimination as they are implicitly treating 

all the difference  in occupation  difference  between  men and  women  as  justified  (Brown et al., 

1980). Therefore,  Brown et al. (1980) proposed a method that extends the traditional Blinder- 

Oaxaca wage decomposition between male and female by incorporating inter-occupational and 

intra-occupational differences which measures the wage differences within and between  the oc- 

cupations more sensitively. A detailed illustration of the principle of the Brown decomposition can 

be found in Appendix E. For this research note, results using both the classic Blinder-Oaxaca de- 

23The control variables include marital status, number of children, educational attainment, age, tenure, immigration 

status, household size, firm wage, firm size, year fixed effects, sectoral fixed effects, occupational fixed effects and 

regional fixed effects. 

57 

 
 
 
 
 
 
 
7  WAGE DIFFERENTIALS FOR GREEN JOBS BY GENDER 

composition and the Brown decomposition are reported. 

7.2  Decomposition results 

Table 11 provides the results of the standard Blinder-Oaxaca (BO) decomposition based 

on equation (5) (from Appendix D) in Column (1) and results of a Blinder-Oaxaca-Neumark (BON) 

decomposition in Column (2). Both models use different non-discriminatory coefficients β∗: while 

the former uses male coefficients, the latter applies the coefficients obtained from a pooled model 

over both gender groups as the reference coefficients β∗.24 The control variables include marital 

status, number of children, educational attainment, age, tenure, immigration status, household 

size, firm wage, firm size, year fixed effect, sectoral fixed effect, occupational fixed effect and re- 

gional fixed effect. 

Table 11: Blinder-Oaxaca decomposition of male-female green wage differential 

(1) 

(2) 

BO decomposition 

BON decomposition 

ln (hourly wage) 

ln (hourly wage) 

Total wage differential 

-0.0351*** 

-0.0351*** 

Explained 

-0.0757*** 

-0.0792*** 

Unexplained 

0.0405*** 

0.0440*** 

Controls 

Occupation 

N 

Yes 

Yes 

Yes 

Yes 

153,392 

153,392 

*** p<0.01, ** p<0.05, * p<0.1 

The results are similar when different reference coefficients β∗ are used. Hence, based 

24See Appendix D for details. 

58 

 
 
 
 
 
 
 
 
 
 
 
 
 
7  WAGE DIFFERENTIALS FOR GREEN JOBS BY GENDER 

on a BON decomposition and controlling for occupation dummies,  is is shown that the mean  log 

hourly  wage  differential  between  male  green  workers  and  female  green  workers  is  -0.0351 

(relatively small), of which, -0.0792 is explained by productivity difference, while 0.0440 remain 

unexplained. The negative value in the explained difference indicates that female green workers 

should have been paid even more than green male workers based on the differences in human 

capital. The positive value in the unexplained difference refers to the unjustified wage premium 

that male green workers receive which can be attributed to discrimination or other unobservable 

factors. 

A Brown decomposition is applied to consider the occupational distribution difference be- 

tween  male/female  green  workers  in  contributing  to  the  overall  wage  differential.  To  do  so,  a 

reduced form multinomial model of occupational choice is first estimated for males and females 

separately.25 Then the occupational distribution of female green workers is predicted under the 

assumption that they are facing the same occupational choice as men in the labour market by 

substituting the female data into the estimated male probability model. Based on equation (8) (in 

Appendix E), the gender wage differential in green jobs is decomposed using the Brown decom- 

position approach. 

Table 12 presents the results of Brown decomposition that are broken down into two dis- 

tinct parts: The intra-occupation difference (explained and unexplained) and inter-occupation dif- 

ferences  (explained  and  unexplained).  Making  this  distinction  is  important  as  it  can  clearly  be 

seen which effect is driving the overall wage differential. The overall log of hourly wage differ- 

ence between male and female is the same as above (-0.0351), of which 0.0995 can be attributed 

to intra-occupation difference, while -0.1347 are due to inter-occupation effect. Clearly, the inter- 

occupational difference decreases the male wage premium and drives the overall wage differential 

between male and female green workers. 

25When the dependent variable is categorical (here refers to the occupational choice) without any particular order, a 

multinomial model is used to predict the probability of category membership on a dependent variable based on multiple 

independent variables. 

59 

 
 
 
 
 
 
 
 
7  WAGE DIFFERENTIALS FOR GREEN JOBS BY GENDER 

Table 12: Brown decomposition of male-female green wage differential 

Brown Decomposition 

ln (hourly wage) 

ln (hourly wage) 

Total wage differential 

-0.0351*** 

-0.0351*** 

Intra-occupation difference 

 0.0996*** 

Explained 

Unexplained 

0.0968*** 

0.0028*** 

Inter-occupation difference 

-0.1347*** 

Explained 

Unexplained 

-0.1253*** 

-0.0094*** 

N 

153,392 

153,392 

*** p<0.01, ** p<0.05, * p<0.1 

Focusing on intra-occupational differences, it is shown that most of the wage difference 

within occupations is captured by differences in human capital. More specifically,approximately 

97% (0.0968 over 0.0996) of male/female wage differential can be attributed to productivity differ- 

ence within occupational groups, and only about 3% difference remain unjustified. This is relatively 

small compared to previous study mentioned on the gender wage gap in the Dutch labour market 

for all jobs (71.5% of gender wage gap is attributable to human capital difference, and 28.5% re- 

main unexplained.) 

Turning to the inter-occupational differences, both explained and unexplained inter-occupational 

difference lead to a decrease in the male/female wage differential in green jobs, while the unjusti- 

fied difference is again, relatively small compared to justified difference, where the former accounts 

for 94% (-0.1253 over -0.1347) of the inter-occupational difference, and the latter only accounts 

for 6%. Previous studies have shown that gender wage gap is likely to be reduced due to the fact 

that comparable male and female workers are distributed differently across occupations (see e.g. 

60 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Kidd (1993)). Here it is shown this is also true in green jobs.26 

8  CONCLUSIONS 

To summarize, in the standard BO and BON decomposition that treat the occupational dis- 

tribution as exogeneously determined, it is shown that female green workers should have been 

paid even more than male green workers after controlling for human capital, which results in an 

unjustified positive male wage premium that could be due to discrimination. However, when occu- 

pational distribution is treated as endogenously determined in a Brown decomposition, it is shown 

that it is the inter-occupational distribution differences which dominate the overall gender wage 

differential in green jobs, and the unjustified wage differences are rather small in both the inter- 

and intra-occupational decomposition results. 

Overall, it is possible to say that whilst some wage discrimination persists in green jobs, the 

gap is significantly smaller than studies that consider all jobs in the Dutch labour market suggest. 

The mechanisms are open to debate, but one possibility is that highly skilled educated females are 

self-selecting into green jobs. The reason why (and if) this is the case is subject to future research. 

8  Conclusions 

This research note uses a task-based approach to analyse the evolution of green jobs in 

the Netherlands over the last twenty years. It examines the distribution of green jobs at country, 

sector, region, firm, occupation, and individual level. 

The main contribution is to show how to construct a task-based measure of green jobs 

using existing data sources that are readily available (or could be made available) for a large num- 

26These results are also consistent with the previous finding that female green workers are more likely to be observed 

in high skill and high paid occupations such as ‘Professionals’. 

61 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
8  CONCLUSIONS 

ber of European countries. Beginning with a detailed definition of what a green task is and how 

green tasks vary within occupations according to the O*NET(Occupational Information Network) 

descriptions in the US, the research note explains how administrative data constructed from micro- 

confidential datasets can be used to map green occupations from O*NET to ISCO (International 

Standard Classification of Occupations) in the Netherlands and provides a detailed description of 

the matching process. A series of sensitivity analyses are also provided to show how the number 

of green jobs changes depending on the parameters chosen. 

Although  the  methodological  approach  outlined  in  this  research  note  has  a  number  of 

weaknesses related to the use of crosswalks between different classification methods and nec- 

essary aggregation, it still provides a good approximation of green jobs in the Netherlands (and 

possibly the EU). While it may be desirable to construct a European equivalent of green occupa- 

tion codes, this would come at a considerable cost both in terms of money and time. 

The results show a number of clear trends. First, for the Netherlands for the period 2003 

to 2021 there was a slight increase in the share of green jobs. However, it was still rather low, 

around 2.7% of the workforce (around 210,000 FTEs). These overall trends mask differences at 

the sector level where it is found that the share of green jobs in the secondary sector is up to  5% 

of the workforce. When green jobs are broken down by occupation it is found that green jobs tend 

to  be  male  dominated  and  concentrated  in  relatively  high  skilled  occupations  such  as  “Man- 

agers”, “Professionals” and “Technical and associate professionals”. The occupational breakdown 

of green jobs explains why they tend to be higher paid, less routine-intensive and higher skilled. 

Regarding the location of green jobs at the municipality level, the trends appear to match 

the changes in structural composition of the economy with greener manufacturing seeming to take 

place close to the urban core and away from rural areas as the agricultural sector becomes in- 

creasingly mechanised.  Results  from  a  firm  level analysis show  that  larger  firms  have a  larger 

number of green jobs but that the share is lower and that green jobs are more prevalent in  man- 

62 

 
 
 
 
 
 
 
 
 
ufacturing firms. The share and number of green jobs is also higher in high wage firms matching 

the results from the occupational analysis. 

8  CONCLUSIONS 

Finally, the research note investigates the characteristics of green workers, with special 

attention given to the gender dimension. From a sample of close to a million workers between 

2001 and 2016, around 17% were found to work in a green job. Although the sample contains 

roughly  the same  number  of  males and  females  it is  found  that  there  are  nearly  four  times  as 

many males in green jobs. The analysis of gender pay gap in green jobs suggests that the wage 

differences between males and females are mostly driven by inter-occupational differences, while 

the unexplained part is rather small. 

In terms of future research, each of the different dimensions that were studied in this re- 

search note could be extended into a stand-alone-research note. From an EU perspective it would 

be interesting to pursue the regional dimension of green job locations and how these may change 

over time as the EU continues to accelerate the green transition and to identify where structural 

funding may be required to help support communities that may be left behind but also to ensure 

that the EU has the skills base to take advantage of the new wave of industries that will be needed 

to support a successful European green transition. 

63 

 
 
 
 
 
 
 
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66 

 
 
 
 
Appendix 

A  Green occupation by greenness 

67 

 
 
 
 
Table A1: Green occupation in ISCO system by greenness 

ISCO  Occupation title 

Greenness 

Greenness 

Broad greenness 

2143 

Environmental engineers 

9612  Refuse sorters 

(Non-uniform weight) 

(Uniform weight) 

1 

1 

1 

1 

1 

1 

7119 

Building frame and related trades workers not elsewhere classified 

0.5333 

0.5333 

0.5333 

7411 

Building and related electricians 

3141 

Life science technicians (excluding medical) 

9611  Garbage and recycling collectors 

2112  Meteorologists 

1223  Research and development managers 

2164 

Town and traffic planners 

1213 

Policy and planning managers 

1322  Mining managers 

6
8

1349 

Professional services managers not elsewhere classified 

1439 

Services managers not elsewhere classified 

2422 

Policy administration professionals 

2161 

Building architects 

2162 

Landscape architects 

1323  Construction managers 

7111  House builders 

1113 

Traditional chiefs and heads of villages 

2132 

Farming, forestry and fisheries advisers 

2153 

Telecommunications engineers 

1324 

Supply, distribution and related managers 

1431 

Sports, recreation and cultural centre managers 

2151 

Electrical engineers 

2412 

Financial and investment advisers 

3322  Commercial sales representatives 

3132 

Incinerator and water treatment plant operators 

0.5 

0.5 

0.5 

0.4624 

0.4612 

0.3604 

0.3268 

0.3268 

0.3268 

0.3268 

0.2857 

0.2683 

0.2601 

0.251 

0.251 

0.25 

0.2073 

0.1967 

0.1662 

0.1634 

0.1607 

0.1593 

0.1531 

0.15 

0.5 

0.5 

0.5 

0.4624 

0.4612 

0.3604 

0.3268 

0.3268 

0.3268 

0.3268 

0.2857 

0.2683 

0.2601 

0.251 

0.251 

0.25 

0.2073 

0.0984 

0.1662 

0.1634 

0.1607 

0.1593 

0.2641 

0.15 

1 

0.5 

0.5 

0.75 

0.8333 

1 

0.6 

0.6 

0.6 

0.6 

0.2857 

1 

1 

1 

1 

0.25 

0.6667 

0.5 

0.75 

0.3 

1 

1 

0.375 

0.45 

 
 
 
 
 
8114  Cement, stone and other mineral products machine operators 

3119 

Physical and engineering science technicians not elsewhere classified 

1114 

Senior officials of special-interest organizations 

2149 

Engineering professionals not elsewhere classified 

9329  Manufacturing labourers not elsewhere classified 

3131 

Power production plant operators 

1420  Retail and wholesale trade managers 

5221 

Shopkeepers 

7233 

Agricultural and industrial machinery mechanics and repairers 

3257 

Environmental and occupational health inspectors and associates 

6
9

2152 

Electronics engineers 

1221 

Sales and marketing managers 

3323 

Buyers 

2421  Management and organization analysts 

7126 

Plumbers and pipe fitters 

3116  Chemical engineering technicians 

7213 

Sheet-metal workers 

3114 

Electronics engineering technicians 

3522 

Telecommunications engineering technicians 

3155 

Air traffic safety electronics technicians 

8211  Mechanical machinery assemblers 

3117  Mining and metallurgical technicians 

3115  Mechanical engineering technicians 

2131 

Biologists, botanists, zoologists and related professionals 

2114  Geologists and geophysicists 

1120  Managing directors and chief executives 

1343 

Aged care services managers 

1346 

Financial and insurance services branch managers 

2433 

Technical and medical sales professionals (excluding ICT) 

1219 

Business services and administration managers not elsewhere classified 

3113 

Electrical engineering technicians 

0.15 

0.1477 

0.1467 

0.1308 

0.125 

0.1195 

0.1134 

0.1134 

0.1111 

0.1107 

0.0984 

0.086 

0.0828 

0.0823 

0.0804 

0.0797 

0.0714 

0.0668 

0.0668 

0.0668 

0.0648 

0.0638 

0.0628 

0.0622 

0.0581 

0.0567 

0.0567 

0.0567 

0.0562 

0.0545 

0.0531 

0.15 

0.1477 

0.1467 

0.1308 

0.125 

0.1195 

0.1134 

0.1134 

0.1111 

0.1107 

0.0492 

0.086 

0.0828 

0.0823 

0.0804 

0.0797 

0.0714 

0.0668 

0.0668 

0.0668 

0.0648 

0.0638 

0.0628 

0.0622 

0.0581 

0.3067 

0.0567 

0.0567 

0.2781 

0.0545 

0.0531 

0.45 

0.3679 

0.5333 

0.4333 

0.375 

1 

1 

1 

0.4444 

0.5625 

0.25 

0.5 

0.6667 

0.3333 

0.3333 

0.8462 

0.6667 

0.6667 

0.6667 

0.3333 

1 

0.7564 

0.5865 

0.2 

0.8333 

0.75 

0.5 

0.5 

0.5 

0.1 

0.8333 

 
 
 
 
 
3112  Civil engineering technicians 

8332  Heavy truck and lorry drivers 

3111  Chemical and physical science technicians 

3339 

Business services agents not elsewhere classified 

1112 

Senior government officials 

2434 

Information and communications technology sales professionals 

3142 

Agricultural technicians 

1311 

Agricultural and forestry production managers 

1312 

Aquaculture and fisheries production managers 

2619 

Legal professionals not elsewhere classified 

7513  Dairy-products makers 

2633 

Philosophers, historians and political scientists 

2642 

Journalists 

8131  Chemical products plant and machine operators 

7
0

9313 

Building construction labourers 

8111  Miners and quarriers 

7231  Motor vehicle mechanics and repairers 

8113  Well drillers and borers and related workers 

2519 

Software and applications developers and analysts not elsewhere classified 

7223  Metal working machine tool setters and operators 

3311 

Securities and finance dealers and brokers 

3324 

Trade brokers 

2529  Database and network professionals not elsewhere classified 

1321  Manufacturing managers 

2631 

Economists 

3123  Construction supervisors 

3121  Mining supervisors 

9333 

Freight handlers 

2142  Civil engineers 

2356 

Information technology trainers 

2424 

Training and staff development professionals 

0.0528 

0.0428 

0.041 

0.039 

0.0378 

0.0375 

0.0367 

0.0361 

0.0361 

0.0281 

0.027 

0.0225 

0.0193 

0.018 

0.0172 

0.0153 

0.0151 

0.0083 

0.0057 

0.0055 

0.005 

0.0033 

0.0028 

0 

0 

0 

0 

0 

0 

0 

0 

0.0528 

0.0428 

0.041 

0.039 

0.2045 

0.1854 

0.0367 

0.0361 

0.0361 

0.0281 

0.027 

0.0225 

0.0193 

0.018 

0.0172 

0.0153 

0.0151 

0.0083 

0.0057 

0.0055 

0.005 

0.0033 

0.0028 

0.5714 

0.5 

0.5 

0.5 

0.2 

0.5 

0.7222 

0.1107 

0.5 

0.3333 

0.3333 

0.25 

0.25 

1 

0.5 

0.1667 

0.5 

0.6667 

0.25 

0.125 

0.2333 

0.1667 

0.1538 

0.3333 

0.125 

0.25 

0.0769 

0.8571 

0.5 

0.5 

0.5 

0.125 

0.375 

0 

0 

0 

1 

1 

1 

 
 
 
 
 
7
1

2432 

Public relations professionals 

7121  Roofers 

7543 

Product graders and testers (excluding foods and beverages) 

2145  Chemical engineers 

3122  Manufacturing supervisors 

3133  Chemical processing plant controllers 

3143 

Forestry technicians 

3359  Regulatory government associate professionals not elsewhere classified 

4322 

Production clerks 

4323 

Transport clerks 

7413 

Electrical line installers and repairers 

8182 

Steam engine and boiler operators 

9624  Water and firewood collectors 

2113  Chemists 

7127 

Air conditioning and refrigeration mechanics 

3331  Clearing and forwarding agents 

7115  Carpenters and joiners 

7214 

Structural-metal preparers and erectors 

2133 

Environmental protection professionals 

9622  Odd job persons 

2144  Mechanical engineers 

2141 

Industrial and production engineers 

2263 

Environmental and occupational health and hygiene professionals 

2512 

Software developers 

7124 

Insulation workers 

7232 

Aircraft engine mechanics and repairers 

7234 

Bicycle and related repairers 

7312  Musical instrument makers and tuners 

7515 

Food and beverage tasters and graders 

8219 

Assemblers not elsewhere classified 

8344 

Lifting truck operators 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

0.8333 

0.8333 

0.75 

0.6667 

0.6667 

0.625 

0.625 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

 
 
 
 
 
7
2

9215 

Forestry labourers 

2413 

Financial analysts 

4321 

Stock clerks 

2163 

Product and garment designers 

7114  Concrete placers, concrete finishers and related workers 

7212  Welders and flamecutters 

9312  Civil engineering labourers 

7421 

Electronics mechanics and servicers 

1222 

Advertising and public relations managers 

2643 

Translators, interpreters and other linguists 

2146  Mining engineers, metallurgists and related professionals 

4222  Contact centre information clerks 

8312  Railway brake, signal and switch operators 

8331 

Bus and tram drivers 

6210 

Forestry and related workers 

6111 

Field crop and vegetable growers 

6112 

Tree and shrub crop growers 

6114  Mixed crop growers 

6121 

Livestock and dairy producers 

6122 

Poultry producers 

6123 

Apiarists and sericulturists 

6129 

Animal producers not elsewhere classified 

6221 

Aquaculture workers 

6222 

Inland and coastal waters fishery workers 

6223  Deep-sea fishery workers 

6224  Hunters and trappers 

7311 

Precision-instrument makers and repairers 

8212 

Electrical and electronic equipment assemblers 

8342 

Earthmoving and related plant operators 

2111 

Physicists and astronomers 

8181  Glass and ceramics plant operators 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0.5 

0.3333 

0.3333 

0.3333 

0.3333 

0.3333 

0.3333 

0.2857 

0.25 

0.25 

0.25 

0.25 

0.25 

0.25 

0.2333 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

0.1667 

0.1667 

 
 
 
 
 
8311 

Locomotive engine drivers 

7412 

Electrical mechanics and fitters 

8142 

Plastic products machine operators 

7422 

Information and communications technology installers and servicers 

6130  Mixed crop and animal producers 

5419 

Protective services workers not elsewhere classified 

3353  Government social benefits officials 

3354  Government licensing officials 

3118  Draughtspersons 

3351  Customs and border inspectors 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0.1667 

0.1538 

0.1538 

0.1429 

0.1333 

0.125 

0.0714 

0.0714 

0.0667 

0.0476 

7
3

 
 
 
 
 
Table A2: Average greenness by different type of green jobs 

Obs  Mean  Std. Dev.  Min  Max 

580 

0.189 

0.288 

0.103 

0.098 

0 

0 

0 

1 

1 

1 

Variable 

Broad Greenness 

Task-based Greenness (Uniform weights) 

580 

0.034 

Task-based Greenness (Non-uniform weights)  580 

0.033 

74 

 
 
 
 
 
B  Trends in disaggregated sectors 

B  TRENDS IN DISAGGREGATED SECTORS 

(a) Agriculture forestry and fishing 

(b) Mineral extraction 

Figure B1: Share of green jobs: Primary sectors (2003 - 2021) 

75 

 
 
 
 
 
 
 
 
 
B  TRENDS IN DISAGGREGATED SECTORS 

(a) Industry 

(b) Energy supply 

(c) Water supply  (d) Construction 

Figure B2: Share of green jobs: Secondary sectors (2003 - 2021) 

76 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
B  TRENDS IN DISAGGREGATED SECTORS 

(a) Whole Sale and Retail Trade 

(b) Transport and storage 

(c) Accommodation and food service 

(d) Information and communication 

(e) Financial services 

(f) Rental and trading of real estate 

(g)  Professional, scientific and technical 

(h) Administrative and support service ac- 

services 

77 

tivities 

Figure B3: Share of green jobs: Tertiary sectors (2003 - 2021) Part one 

 
 
 
 
 
 
 
 
 
 
 
 
 
B  TRENDS IN DISAGGREGATED SECTORS 

(a) Public administration and government 

services 

(b) Education 

(c) Health and social work activities 

(d) Arts, entertainment and recreation 

(e) Other services 

Figure B4: Share of green jobs: Tertiary sectors (2003 - 2021) Part two 

78 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
C  TRENDS IN GREEN JOBS USING A BINARY MEASURE 

C  Trends in green jobs using a Binary measure 

Figure C1: Share of green jobs (2003 - 2021) 

Figure C2: Share of green jobs by sectors (2003 - 2021) 

79 

 
 
 
 
 
 
 
 
 
C  TRENDS IN GREEN JOBS USING A BINARY MEASURE 

(a) Agriculture forestry and fishing 

(b) Mineral extraction 

Figure C3: Share of green jobs: Primary sectors (2003 - 2021) 

80 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
C  TRENDS IN GREEN JOBS USING A BINARY MEASURE 

(a) Industry 

(b) Energy supply 

(c) Water supply  (d) Construction 

Figure C4: Share of green jobs: Secondary sectors (2003 - 2021) 

81 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
C  TRENDS IN GREEN JOBS USING A BINARY MEASURE 

(a) Whole Sale and Retail Trade 

(b) Transport and storage 

(c) Accommodation and food service 

(d) Information and communication 

(e) Financial services 

(f) Rental and trading of real estate 

(g)  Professional, scientific and technical 

(h) Administrative and support service ac- 

services 

82 

tivities 

Figure C5: Share of green jobs: Tertiary sectors (2003 - 2021) Part one 

 
 
 
 
 
 
 
 
 
 
 
 
 
C  TRENDS IN GREEN JOBS USING A BINARY MEASURE 

(a)  Public administration and government 

services 

(b) Education 

(c) Health and social work activities 

(d) Arts, entertainment and recreation 

(e) Other services 

Figure C6: Share of green jobs: Tertiary sectors (2003 - 2021) Part two 

83 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
C  TRENDS IN GREEN JOBS USING A BINARY MEASURE 

(a) Managers 

(b) Professionals 

(c) Technicians 

(d) Service 

(e) Trade 

(f) Machine operators 

(g) Elementary occupation 

Figure C7: Share of green jobs: Ma 

84 
jo

Occupational Groups (2003 - 2021) 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
D  Blinder-Oaxaca decomposition method 

D  BLINDER-OAXACA DECOMPOSITION METHOD 

To further illustrate the principle of Blinder-Oaxaca decomposition method, two groups of 

people are assumed. To provide some intuition, think of a group of males and a group of females. 

WF  is  the  female  wage  and  WM  is  the  male  wage.  The  wage  level  is  determined  by  human 

productivity characteristics, that is: 

ln(Wi) = Xiβi + ϵi 

(3) 

where i = M or F , X is a vector of variables that include determinants of wage level, β a 

vector of coefficients, and ϵ is the error term, which is assumed to have a zero mean.27 Therefore, 

the difference in mean of log of wage between female and male can be given by: 

ln(W¯ M ) − ln(W¯ F ) = X¯M βM − X¯F βF 

(4) 

To examine the existence of potential discrimination, a non-discriminatory coefficient vector 

β∗ is assumed to construct the counterfactual group. Hence, the wage differential can be further 

restructured as follows: 

ln(W¯ M ) − ln(W¯ F ) = (X¯M  − X¯F )β∗ + X¯M (βM − β∗) + X¯F (β∗ − βF ) 

(5) 

where the first part refers to the explained part, which can explain the wage differential by 

human  capital  differences,  while  the  second  part  is  the  unexplained  part,  which  is  normally 

considered as an evidence of discrimination, though it also captures the effect of differences in 

unobservable characteristics. 

27The control variables include marital status, number of children, educational attainment, age, tenure, immigration 

status, household size, firm wage, firm size, year fixed effect, sectoral fixed effect, occupational fixed effect and regional 

fixed effect. 

85 

 
 
 
 
 
 
 
 
 
 
 
 
 
D  BLINDER-OAXACA DECOMPOSITION METHOD 

This decomposition method is also known as twofold decomposition. To determine the two 

components of the twofold decomposition shown in equation (3), the unknown non-discriminatory 

coefficient β∗ needs to be estimated. Several attempts have been made in the existing literature. 

For instance,Reimers (1983) proposed to use an average coefficient over two groups to be an esti- 

mator for β∗, while Cotton (1988) believed using a group size weighted average coefficients would 

be even better. Furthermore, a more advanced proposal was suggested by Neumark (1988), who 

advocates to use the coefficients from a pooled model over both groups(Blinder-Oaxaca-Neumark 

decomposition). The most commonly used is to suppose there is only a positive wage discrimina- 

tion towards women, and no discrimination against men, then βˆM  can be used as an estimate for 

β∗, and the equation (3) can be rewritten as follows: 

ln(W¯ M ) − ln(W¯ F ) = (X¯M  − X¯F )βˆM  + X¯F (βˆM  − βˆF ) 

(6) 

86 

 
 
 
 
 
 
E  Brown decomposition 

E  BROWN DECOMPOSITION 

First, Brown et al. (1980) rewrote the wage equation (1) for different groups as follows: 

ln(¯Wi) = 

L 

PjiX¯jiβji 

j 

(7) 

where Pji refers to sample proportion of group i in occupation j, i = M/F and j = 

1, 2, ..., J . Brown et al. (1980) took βjM as the reference index, which is considered as the non- 

discriminatory coefficient in occupation j.  Then mean log wage differential between male and 

female can be written as follows: 

ln(W¯ M ) − ln(W¯ F ) = 

PjM X¯jM βjM − 

L 

PjF X¯jF βjF 
j 
L 

PjM X¯jM βjM  − 

PjF X¯jF βjM 

PjF X¯jF βjM  − 

PjF X¯jF βjF 

j 

L 

j 

L 

j 
L 

= 

j 

L 

+ 

j 
L

= 

j 

(PjM X¯jM  − PjF X¯jF )βjM  + 

PjF X¯jF (βjM  − βjF ) 

L 

j 

(8) 

The first part 

j (PjM X¯jM  − PjF X¯jF )βjM  can be interpreted as explained part just as the 

first part in  equation (4),  and the second part     j PjF X¯jF (βjM  − βjF ) can be interpreted as the 

unexplained part just as the second part in equation (4). Then Brown et al. (1980) incorporate the 

effect of occupation segregation on the wage differential in different groups by assuming PˆjF  as 

the reference index which is the percentage of women in the sample who would be in occupation 

j if women had equal access to all occupations as men. The wage differential equation then can 

be further decomposed as follows: 

87 

 
 
  
  
 
 
 
 
 
 
 
 
 
E  BROWN DECOMPOSITION 

ln(W¯ M ) − ln(W¯ F ) = 

(PjM X¯jM  − PˆjF X¯jM  + PˆjF X¯jM  − PjF X¯jM 

L 

j 

+ PjF X¯jM  − PjF X¯jF )βjM  + 

L 

PjF X¯jF (βjM  − βjF ) 

PjF (X¯jM  − X¯jF )βjM + 

PjF X¯jF (βjM − βjF ) 

(9) 

L 

= 

j 

L

+ 

j 

j 
L 

j 

L

(PˆjF  − PjF )X¯jM βjM + 

(PjM − PˆjF )X¯jM βjM 
j 

This is full decomposition of the Brown decomposition, where the first part on the right hand 

side     j PjF (X¯jM  − X¯jF )βjM  denotes within occupational wage differential that can be explained 
by productivity characteristics, and the second part     j PjF X¯jF (βjM  − βjF ) refers to unexplained 

within occupational wage differential that can be attributed to discrimination holding the occupa- 

tional distribution for women constant. The third part on the right hand side     j (PˆjF −PjF )X¯jM βjM 

stands for the wage differential explained by occupational segregation difference between two 

groups,  while  the  last  part  sumj(PjM  − PˆjF )X¯jM βjM  represents  the  unexplained  occupational 

segregation, holding observable characteristics constant. 

88 

 
 
  
  
  
 
 
 
 
 
 
 
 
 
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