Labour market trajectories, social protection and the green transition in France and Viet Nam
Abstract
This study investigates labour market transitions to green occupations in France (2017–2021) and Viet Nam (2021–2022) using the longitudinal dimension of labour force surveys in the two countries. According to a task-based definition of green jobs derived from the ISCO-08 classification, our findings show that 21.5 per cent and 15 per cent of workers in France and Viet Nam, respectively, are employed in green jobs. In particular, education appears to have a substantial impact on the transition to green occupations, suggesting that investment in skills may facilitate green transitions in the labour market. Moreover, women are less likely to transition to green jobs than men. Finally, our findings highlight the role of social protection systems, in particular unemployment insurance, in supporting green transitions in the labour market.
Introduction
Many international organizations stress the importance of a green transition in fostering environmentally friendly economic growth. They highlight the creation of green jobs as a means of reducing carbon footprints and enhancing resource efficiency. Green occupations, characterized by their focus on sustainability and low environmental impact, play a pivotal role in mitigating the human footprint on the environment and adapting to its adverse effects. As the world grapples with the need for a rapid and comprehensive response to climate change, transitioning to green occupations emerges as a key strategy for promoting ecological resilience and ensuring a sustainable future. However, the move towards green jobs will have major impacts on labour markets worldwide, as shifting to a green economy entails both the creation and the destruction of jobs. This may ultimately have a positive net impact on employment (ILO 2018).
This dynamic process reshapes the trajectories of individuals in the labour market, with some transitioning from declining sectors into areas of the green economy experiencing growth. Throughout the transition, the move towards achieving green growth will yield extensive but uneven impacts. As non-green jobs vanish, green occupations will emerge as a result of decarbonization in existing high-carbon economic activities and increased labour demand in low-carbon sectors. New entrants to the labour market may secure initial employment in expanding sectors, while others may face job losses, leading to prolonged unemployment or even their exit from the labour force. Nonetheless, the ability to move to green jobs is not distributed equally, and workers with the right qualifications are expected to be better prepared for the green transition. Demographic features such as age and sex affect overall transition rates in labour markets and can also influence moves from non-green to green jobs. Other factors, such as skills, may also have an impact. For instance, high-skilled employees and employees residing in urban areas tend to work in occupations that have higher green intensity, suggesting that the shift to the green economy could be smoother for them than for less-skilled or rural workers (Bluedorn et al. 2022).
On the other hand, as new green industries often experiment and test business options, we can also expect to see higher volatility in some of the new green industries, which will pose additional challenges for labour market transitions. For example, beyond the risk of failure that new products may face, previous analyses focusing on innovation have highlighted a potential association with substantial displacement effects, such as those induced by productivity gains, but noted that other mechanisms may compensate for such displacements
Within this framework, social protection systems, including unemployment insurance, provide essential tools to support and promote green transition policies, mitigating potential adverse effects on the labour market. Through unemployment benefits and active labour market policies, social protection can provide individuals with the resources and time they need for skills development, job searches and accessing placement services. This includes assistance for those encountering difficulties in securing new employment (ILO 2023). Social protection schemes also serve as a crucial framework at the macroeconomic level, accommodating structural changes and facilitating the transition of workers to sectors that are more environmentally sustainable. The potential role of social protection in supporting adjustments in the labour market following massive shocks in the economy has been highlighted by studies analysing crisis contexts, including the 2008 financial crisis and the coronavirus disease (COVID-19) pandemic
Given the above background, the main purpose of this working paper is to provide empirical evidence on the labour market transitions currently observable in two countries at different levels of development, while also gathering initial evidence on the possible role of social protection in these specific transitions. However, such an analysis requires other key research questions to be addressed first, including those relating to the identification of green occupations through the data currently available to most countries for longitudinal labour market studies, in particular labour force surveys.
The working paper first discusses the various metrics for green jobs used in the literature and explains the reasons for adopting a task-based definition. It then examines the transition of workers in and out of green jobs in France and Viet Nam across time. The choice of these two countries was guided by the availability of survey data in the ILOSTAT microdata repository and by the efforts both countries are both making to “green” their economies.1 Comparing countries from different regions and at different levels of development also allows us to illustrate possible variations in terms of outcome. In addition to overall transition rates, we consider age, sex and education levels in both countries with a view to understanding the ability of various categories of workers to move to a green job. The last part of the working paper highlights the relationship between social protection and the transition to green occupations by focusing on unemployment benefits in France and social security in Viet Nam. Through regression analysis, it compares the mobility of workers benefiting from unemployment assistance and social security with that of individuals without such coverage.
Defining green jobs
Broad classifications
One of the central challenges in identifying green jobs is the lack of a universally accepted definition (ILO 2018). In general, efforts to formulate such definitions can be grouped into two primary approaches to the sectors and occupations concerned: exclusive and inclusive. Narrower classifications often centre on specific industrial sectors, such as the environmental sector, or on particular roles within these sectors. In contrast, inclusive definitions encompass a broader spectrum of employment, recognizing the substantial influence that the transition to net zero emissions will exert on a wide array of occupations (Sofroniou and Anderson 2021). Besides their scope, existing classifications also vary with regard to their focus, each underlining different aspects such as environmental impact, sustainability, decent work, and innovation and transition. For example, the International Labour Organization (ILO) emphasizes decent work that connects developmental goals with environmental protection, while the European Commission bases its description of a green job on information, technologies or materials that preserve or restore environmental quality (ILO 2018; Eurostat n.d.).
The European Commission defines the environmental goods and services sector (EGSS), also referred to as the environmental economy or eco-industries, as “consist[ing] of a heterogeneous set of producers of goods and services aiming at the protection of the environment and the management of natural resources” (Eurostat n.d.). Within this framework, a “green job” refers to any occupation in these specific sectors. However, gathering data using this definition can be challenging, as only a limited number of countries, primarily within Europe, currently publish EGSS estimates.
The ILO provides another internationally recognized definition of green jobs, which differs from the EGSS definition in two key aspects. In addition to activities related to environmental protection, the ILO definition encompasses broader environmental efforts, including community adaptation to climate change. Moreover, to qualify as a green job, an occupation must also meet certain standards for decent work, including considerations relating to fair pay and safe working conditions (ILO 2018). Even though the ILO’s approach is more comprehensive and emphasizes a just transition for the workforce and the creation of high-quality jobs, introducing decent work into the definition raises other challenges, such as the difficulty of measuring quality.
There have also been a number of studies focusing on specific countries. For example, in France, national definitions of greening and green jobs are used to identify green occupations (ONEMEV 2022). In the United States of America, green goods and services are identified by applying the North American Industry Classification System (Bureau of Labor Statistics n.d.).
Task-based classifications
In discussions of green jobs, skills emerge as a recurring theme, particularly when transitions in the labour market are under scrutiny. There is a growing trend in the literature for tasks to be used as the basis for estimating the “greenness” of occupations. Beginning with the taxonomy of green occupations in the United States using the database maintained by the O*NET Resource Center, tasks have been differentiated by their contribution to environmental sustainability and reducing greenhouse gas emissions. In Dierdorff et al. (2009), a whole range of economic activities “related to reducing the use of fossil fuels, decreasing pollution and greenhouse gas emissions, increasing the efficiency of energy usage, recycling materials, and developing and adopting renewable sources of energy” are included in the classification of green jobs into three categories: “new and emerging”, “enhanced skills” and “increased demand”. By taking these groupings into account, Vona et al. (2018) have established a continuous metric to assess the environmental sustainability of a range of Standard Occupational Classification (SOC) eight-digit occupations. This innovative measure of an occupation’s environmental impact is derived from an analysis of both green and non-green tasks specific to each occupation. Green task intensity represents the proportion of green tasks relative to the total tasks for each occupation (Vona et al. 2018). Occupations with no green tasks are assigned a green task intensity of zero. This eight-digit SOC encoding is then aggregated to the six-digit level, for which employment data are available, using a simple averaging approach.
The same process is followed to determine which occupations are “brown”, or polluting. These are jobs that are mainly present in the most polluting or ecologically destructive industries in the United States. Polluting occupations are a subset of those identified as having zero green task intensity within the United States occupational classification system. A two-step process is undertaken to estimate the “brownness” task index for these occupations. In the first step, sectors are labelled as polluting if per worker emissions for at least three polluting substances are above specified thresholds. In the second step, brown occupations that are most prevalent in these sectors are identified by selecting those with a probability of working in polluting sectors seven times higher than in any other job (Vona et al. 2018).
This methodology has been adopted by several other researchers, who have mapped the occupations in question to the International Standard Classification of Occupations 2008 (ISCO-08) scheme (Scholl et al. 2023; Bluedorn et al. 2022; Elliott et al. 2021; Valero et al. 2021). The green and brown task intensities for occupations at the eight-digit SOC level are translated into ISCO-08 terms with different weighting strategies and at different levels of aggregation. For some occupations, owing to the crossover with employment weights, both green and brown intensity scores can be positive under the ISCO-08 classification scheme. There are also occupations that contain neither green nor brown tasks. Within this framework, each worker is assigned a green and a brown intensity score based on their occupation in order to analyse various outcomes across the whole labour market or economy, such as the share of green jobs over time, innovation, the role of green and brown intensity in transitions between different labour market statuses, and the relationship between greenness of employment and productivity.
Green and non-green occupations
The definition of green and brown occupations in this working paper applies the metric obtained by mapping the SOC indices to ISCO-08, as outlined in Scholl et al. (2023). As mentioned above, several other studies have taken the same approach. However, Scholl et al. provide the most detailed green and brown intensity task scores at the four-digit ISCO-08 level. Moreover, the authors present employment-weighted measures for the ISCO mapping to address potential double-counting of tasks. This methodology has the advantage of standardization across countries, as the ISCO classification is widely used. Additionally, a task-oriented approach might be a better fit for the purposes of the present report, as we are primarily interested in labour market transitions.
Nonetheless, there are some disadvantages to identifying green and non-green occupations using a measure of task intensity. First, tasks evolve, but O*NET measures are typically static and do not reflect changes occurring within occupations. Second, the crossover from SOC to ISCO requires many occupations to be aggregated because SOC offers more detailed occupational groupings.
Based on the greenness and brownness scores in Scholl et al. (2023), we have classified all 433 occupations at the ISCO-08 four-digit level as green, brown or neutral. All occupations that have a positive green task intensity score (for each weighting) and a zero brown task intensity score are classed as green, while occupations with a positive brown task intensity and zero green intensity are categorized as brown. The remaining occupations are identified as neutral; a significant majority of these do not involve tasks that are either green or brown. A small percentage, on the other hand, score very similarly in terms of task intensity, making them difficult to categorize as either green or brown. For the purposes of our analysis, these occupations have been categorized as neutral.
This classification leaves us with 83 green, 50 brown and 300 neutral occupations at the four-digit level. The table in the appendix gives the complete list of green, brown and neutral jobs. Since the aim of this report is to examine the trajectories of workers into and out of green occupations and to consider whether unemployment insurance may have an impact on the transition to green occupations, a binary categorization is more suitable. Therefore, in the rest of the paper, we have merged the brown and neutral occupations into a single group to give a clearer picture of transitions into and out of green jobs. As can be seen from table 1 below, the environmental properties of occupations are not necessarily related to the industries in which these jobs can be found or the education levels required. For example, environmental engineers, a profession for which tertiary education is needed, and refuse sorters, who usually have few formal qualifications, both fall into the green category because the tasks they fulfil in these occupations contribute to environmental sustainability. It can also be observed that brown and neutral jobs are found right across the industry and education spectrum.
Finally, given the data challenges involved, this operational classification does not allow us to take into account the decent work dimension in defining green jobs, as recommended by the ILO. In particular, it is likely that significant variations in working conditions may be observed in green jobs as defined here, with substantial differences across the two countries under study.
Table 1. Examples of occupations according to their green and brown intensities
|
Positive green intensity |
Zero green intensity |
|
|
Positive brown intensity |
2131 – Biologists, Botanists, Zoologists and Related 7513 – Dairy-products Makers 8113 – Well Drillers and Borers and Related Workers 9329 – Manufacturing Labourers Not Elsewhere Classified |
2113 – Chemists 3122 – Manufacturing Supervisors 7521– Wood Treaters 8141 – Rubber Products Machine Operators |
|
Zero brown intensity |
1213 – Policy and Planning Managers 2133 – Environmental Engineers 7121 – Roofers 9612 – Refuse Sorters |
2310 – University and Higher Education Teachers 4227 – Survey and Market Research Interviewers 6223 – Deep-sea Fishery Workers
|
Source: Authors’ categorization based on list of occupations developed by Scholl et al. (2023).
Panel construction and descriptive statistics
Panel construction and weighting
France and Viet Nam were selected for our empirical analysis of labour market trajectories towards green occupations because their longitudinal labour force survey data, including the four-digit ISCO-08 code for the occupation of each individual surveyed, are available in the ILOSTAT microdata repository.2 Unfortunately, the repository does not currently include administrative data, such as data from social security registers, which usually form another important source of information for the analysis of labour market trajectories and allow individuals to be followed over longer periods of time than labour force surveys. However, labour force surveys have the advantage of allowing broader coverage of countries’ populations, including people in informal employment, whereas individuals registered in administrative databases are usually likely to be in formal employment only. This is particularly useful for Viet Nam, where the informality rate is high.
The labour force survey in France is structured as a rotating panel, which allows respondents to be traced over six consecutive quarters. For example, individuals who had their first interview in the first quarter of 2017 were followed until the second quarter of 2018 and then exited the survey. In order to ensure we have enough observations to examine transitions between green and non-green occupations, we have pooled eight waves of respondents together. Hence, our sample consists of labour force survey respondents who were included over six quarters between 2017 and 2020. Table 2 displays the number of respondents and attrition rates for each wave. As can be seen, attrition rates were broadly similar except for the last wave, consisting of individuals interviewed in the last quarter of 2018 and traced until the first quarter of 2020. This could be due to changes in the data collection process at the beginning of 2020 as a result of social distancing during the COVID-19 pandemic.
Table 2. Description of panel data construction for France
Source: Authors’ calculations based on data from ILOSTAT microdata repository.
In Viet Nam, a rotating structure for the labour force survey was introduced in 2019. Individuals were traced over two consecutive quarters, left the rotation for the next two quarters, then rejoined for the following two quarters. Although this allows for the possibility of having the same interviewees over six non-consecutive periods, not all the variables required to identify individuals are always available in the Vietnamese data. We were nevertheless able to construct a panel of two waves observed over five quarters, starting from the first and third quarters of 2021 respectively. Table 3 gives respondent numbers and attrition rates for the two waves.3 The numbers of individuals who participated in all periods and the attrition rates are roughly the same for both. It is worth noting that the duration of the observation period from start to finish is shorter for Viet Nam than for France. For the former, the period was 15 months spread over five quarters, while for the latter it lasted 18 months over six quarters. Additionally, there may be seasonality in the data for Viet Nam, given that initial labour market status is only observed for specific quarters of the year (Q1 and Q3), as opposed to France, where results can be averaged across all quarters of the year. Hence, direct comparisons of the transition rates for the two countries cannot be easily made.
Table 3. Description of panel data construction for Viet Nam
Source: Authors’ calculations based on data from ILOSTAT microdata repository.
One of the issues with estimating transition probabilities from rotating panels is non-random attrition bias. This report uses two weighting strategies to account for the possibility of such a bias. First, we weighted the transitions and regressions using sampling weights provided by the Statistical Offices of France and Viet Nam. The purpose of these weightings is to represent the frequency of each observation in the respective countries’ population as a whole. Second, we estimated the attrition probability for various groups of the sample and reweighted every observation using inverse probability weightings. A similar approach has been adopted by other researchers in the literature (Samaniego and Viegelahn 2021; Fallick and Fleischman 2001). To do this, we began by computing the probability of matching the same individual across two periods for each wave using age, sex, level of education, marital status and location as explanatory variables. These probabilities were then used to reweight the observations through an inverse probability weighting procedure.4 All transition probabilities in the next section are presented using attrition-revised weights.
Green jobs in France and Viet Nam
Table 4 shows the number and share of green jobs for those workers in the samples for France and Viet Nam who were followed for the entire period under consideration. It can be observed that the majority of workers in both countries are in non-green jobs. In France, 95,942 of the individuals surveyed (approximately 78.5 per cent of the total sample) were employed in occupations that are either brown or neutral. Nevertheless, a significant portion (21.5 per cent) of workers in France work in green jobs, which indicates a noticeable presence of environmentally sustainable occupations within the country’s workforce. Our results are in line with other estimates for the proportion of occupations in France that involve green tasks. For example, a recent study has argued that, on average, 23.5 per cent of workers in France are employed in a green occupation, with significant regional variation (OECD 2023). While some of the existing studies report a lower proportion of green employment, such as almost 15 per cent in 2013 (CEDEFOP 2018), it should be noted that their definitions are based on a narrower conceptualization of green occupations.
Table 4. Number and share of green jobs among workers in France and Viet Nam
Note: Shares have been estimated using frequency weights.
Source: Authors’ calculations based on data from ILOSTAT microdata repository.
In Viet Nam, 370,083 of the individuals surveyed (almost 85 per cent of the total sample) held non-green jobs, while 62,676 were in occupations considered green. The lower rate of green employment in comparison to France is not surprising, given the disparate levels of economic development. While there are no comparable statistics in the literature for Viet Nam, it has been argued that the demand for green skills is growing in the country. The share of job postings requiring a worker to have at least one green skill increased to 22 per cent in 2023 (Do 2023). Moreover, it has been noted that the presence of green jobs in Viet Nam is expanding in all sectors, not just in traditionally green sectors such as renewable energy and environmental protection (Do 2023). This echoes the commitment that Viet Nam made at the twenty-sixth session of the Conference of the Parties to the United Nations Framework Convention on Climate Change, held in 2021, to reduce its net emissions to zero by 2050. Steps towards achieving this objective include, in particular, the Just Energy Transition Partnership signed by Viet Nam in 2022, the aim of which is to support low-emission and climate-resilient development in Viet Nam, underpin the just energy transition and the decarbonization of the country’s electricity system and develop new economic opportunities to support the transition towards a net zero future.5 Furthermore, in November 2023 Viet Nam undertook to become a pathfinder country for the UN Global Accelerator on Jobs and Social Protection for Just Transitions, which aims to accelerate progress towards achieving the Sustainable Development Goals and support the creation of decent jobs, including within the green economy, as well as extending social protection to people who are excluded.6
Transitions between green and non-green jobs
Transition probabilities in France
In France, movement between green and non-green employment is characterized by a relatively high degree of inertia: 88 per cent and 89 per cent of people in green and non-green occupations, respectively, were still in the same category six quarters later (see table 5 below). In addition, a substantial proportion of workers in a green job had moved to a non-green job after six quarters (5.5 per cent), which is higher than the proportion of those in a non-green job that transitioned to a green one (1.6 per cent). This might be partly due to the instability associated with green occupations, and it is argued that the innovative nature of many green industries can lead to relatively unstable employment. However, being outside the labour force or unemployed was an outcome more frequently observed after a person has held a non-green occupation than a green one (9.1 and 6.8 per cent, respectively).
On the other hand, the challenges of finding a green job appear to be especially striking for unemployed people in France, a significant proportion of whom – 37.5 per cent – remained unemployed after six quarters. Only a small minority (4.7 per cent) ended up in a green job, while almost one third (31.4 per cent) moved from unemployment to non-green employment. Additionally, 84.1 per cent of individuals who were outside the labour force remained so. Only 1.2 per cent of them became employed in a green occupation, while the rate of transition to a non-green job was 8.7 per cent. Unemployed people and those outside the labour force who were in employment after six quarters rarely found a green occupation. Indeed, within these two populations, the share of green occupations among all individuals in employment after six quarters was 13 and 12.1 per cent, respectively, while green jobs represented 21.5 per cent of all jobs (see table 4 above).7 This may point to a possible mismatch in terms of skill requirements for green jobs among unemployed people and those outside the labour force, at least for the period under review.8
Table 5. Transition rates (%) between different occupations and labour market statuses in France
|
|
Situation on the labour market at the end of each wave |
||||
|
Situation on the labour market at the beginning of each wave |
Green |
Non-green |
Unemployed |
Outside the labour force |
Total |
|
All |
|||||
|
Green |
87.8 |
5.5 |
2.1 |
4.7 |
100 |
|
Non-green |
1.6 |
89.4 |
2.9 |
6.2 |
100 |
|
Unemployed |
4.7 |
31.4 |
37.5 |
26.5 |
100 |
|
Outside the labour force |
1.2 |
8.7 |
6.0 |
84.1 |
100 |
|
Young (15–24) |
|||||
|
Green |
73.4 |
15.6 |
5.2 |
5.8 |
100 |
|
Non-green |
3.4 |
72.9 |
11.0 |
13.2 |
100 |
|
Unemployed |
5.0 |
34.0 |
36.6 |
25.4 |
100 |
|
Outside the labour force |
1.5 |
11.8 |
6.3 |
80.5 |
100 |
|
Prime-age (25–54) |
|||||
|
Green |
90.1 |
5.9 |
2.0 |
2.0 |
100 |
|
Non-green |
1.7 |
92.3 |
2.7 |
3.2 |
100 |
|
Unemployed |
4.8 |
34.7 |
37.2 |
23.3 |
100 |
|
Outside the labour force |
2.0 |
14.4 |
11.5 |
72.2 |
100 |
|
Older (55–64) |
|||||
|
Green |
82.7 |
2.2 |
1.8 |
13.3 |
100 |
|
Non-green |
0.5 |
83.9 |
1.6 |
14.0 |
100 |
|
Unemployed |
4.0 |
15.5 |
40.3 |
40.2 |
100 |
|
Outside the labour force |
0.04 |
1.8 |
2.0 |
95.8 |
100 |
|
Women |
|||||
|
Green |
86.4 |
6.5 |
2.6 |
4.5 |
100 |
|
Non-green |
0.95 |
89.4 |
2.7 |
7.0 |
100 |
|
Unemployed |
2.4 |
33.9 |
34.2 |
29.6 |
100 |
|
Outside the labour force |
0.63 |
9.0 |
5.8 |
84.6 |
100 |
|
Men |
|||||
|
Green |
88.3 |
5.0 |
1.9 |
4.8 |
100 |
|
Non-green |
2.3 |
89.3 |
3.2 |
5.2 |
100 |
|
Unemployed |
6.7 |
28.9 |
40.8 |
23.4 |
100 |
|
Outside the labour force |
2.0 |
8.2 |
6.4 |
83.4 |
100 |
|
Education below secondary level |
|||||
|
Green |
84.9 |
6.0 |
2.4 |
6.9 |
100 |
|
Non-green |
0.1 |
84.3 |
4.1 |
10.7 |
100 |
|
Unemployed |
2.3 |
22.4 |
40.7 |
34.6 |
100 |
|
Outside the labour force |
0.1 |
5.0 |
5.3 |
89.1 |
100 |
|
Secondary education |
|||||
|
Green |
86.1 |
5.8 |
2.0 |
6.2 |
100 |
|
Non-green |
1.4 |
89.0 |
3.3 |
6.3 |
100 |
|
Unemployed |
4.9 |
34.3 |
36.4 |
24.5 |
100 |
|
Outside the labour force |
1.2 |
11.9 |
6.7 |
80.3 |
100 |
|
Education beyond secondary level |
|||||
|
Green |
89.9 |
5.1 |
2.1 |
2.9 |
100 |
|
Non-green |
2.1 |
92.4 |
1.7 |
3.8 |
100 |
|
Unemployed |
8.6 |
39.5 |
34.3 |
17.6 |
100 |
|
Outside the labour force |
3.7 |
14.2 |
7.2 |
75.0 |
100 |
Note: Transition probabilities have been estimated using attrition-revised weights.
Source: Authors’ calculations based on data from ILOSTAT microdata repository.
For young workers, the likelihood of moving from non-green to green jobs was higher than for other cohorts (3.4 per cent, compared with 1.7 per cent for prime-age workers and 0.5 per cent for older workers), and the differences are statistically significant.9 However, it should also be noted that younger workers have a probability of 15.6 per cent of switching to a non-green job, suggesting that the observed movements of this population across green and non-green jobs may partly reflect the greater mobility of young workers on the French labour market.10 Older workers, on average, have lower mobility between labour market statuses, which is not surprising given that they are closer to retirement. Regardless of the initial category considered, the proportion of people in this group who were outside the labour force after six quarters was substantial.
With regard to sex, our findings reveal that, on average, women are less likely than men to stay in a green job or to move into one from another category. This result is consistent with existing projections for the effects of the green transition on the labour market, highlighting the fact that, overall, employment gains associated with the green economy tend to be located in industries that are currently male-dominated
The last rows of table 5 show transition rates disaggregated by level of education. As can be seen, the likelihood of a worker with education beyond secondary level moving to a green job is higher across all categories than for a worker with less education, while the rates for moving in the opposite direction are quite similar for all groups. This suggests that formal education assists people in transitioning more smoothly towards the green economy and increases their chances of being employed in a green job. Moreover, a higher share of workers with education beyond secondary level – almost 90 per cent – were still in a green job in the sixth quarter. For individuals with high school diplomas, the figure is around 86 per cent, while for those having not completed secondary education it stands at 85 per cent. In line with our expectations, the proportion of workers in France who become unemployed or exit the labour force is greater, on average, for those with no education beyond secondary level, with the exception of green job holders with high school diplomas. Workers in France with lower levels of education tend to be more likely to remain unemployed or outside the labour force than groups with higher levels of education.
Transition probabilities in Viet Nam
As can be seen from table 6, the transition probabilities are much higher in Viet Nam for green job holders. While 30.6 per cent moved to non-green jobs and another 4.3 per cent exited the labour force over the course of five non-consecutive quarters, only around 65 per cent of workers with green occupations retained the same status. The high rate of transition to non-green jobs could be a reflection of the large share of non-green occupations in Viet Nam (85.1 per cent), as shown in table 4. For non-green job holders in Viet Nam, mobility was lower: more than 88 per cent remained in a non-green occupation after five quarters, while 6 per cent moved into a green job and another 5.2 per cent left the labour force. The probability of becoming unemployed was low for workers in both green and non-green occupations in Viet Nam, at less than 0.05 per cent for each group. However, consistent with what can be observed in many developing economies, unemployment is a relatively rare situation in Viet Nam, as reflected in the country’s fairly low unemployment rate (averaging 2.8 per cent for 2021–2022, according to data from the General Statistics Office of Viet Nam).
It should also be noted that a significant proportion of those who were unemployed – nearly 63 per cent – obtained employment in non-green jobs; 13.6 per cent moved into green economic activities. However, as unemployed workers represent a relatively small share of the population in Viet Nam, movements from unemployment to green occupations were limited in absolute terms. Lastly, a small share (3 per cent) of individuals outside the labour market moved into green occupations, whereas the shift to non-green occupations among this group was higher (22.7 per cent). The majority of individuals in Viet Nam who were outside the labour force tended to remain so for the period under consideration, with only a small percentage of the economically inactive moving into unemployment. Overall, these observations confirm that the absorption capacity of non-green jobs in Viet Nam is relatively high.
Table 6. Transition rates (%) between different occupations and labour market statuses in Viet Nam
|
|
Situation on the labour market at the end of each wave |
||||
|
Situation on the labour market at the beginning of each wave |
Green |
Non-green |
Unemployed |
Outside the labour force |
Total |
|
All |
|||||
|
Green |
64.8 |
30.6 |
0.0 |
4.3 |
100 |
|
Non-green |
6.0 |
88.6 |
0.0 |
5.2 |
100 |
|
Unemployed |
13.6 |
63.1 |
9.2 |
14.2 |
100 |
|
Outside the labour force |
3.0 |
22.7 |
1.0 |
73.3 |
100 |
|
Young (15–24) |
|||||
|
Green |
55.5 |
37.5 |
1.0 |
5.9 |
100 |
|
Non-green |
4.0 |
85.6 |
0.1 |
9.8 |
100 |
|
Unemployed |
9.6 |
54.8 |
16.7 |
18.9 |
100 |
|
Outside the labour force |
2.0 |
14.7 |
1.5 |
82.3 |
100 |
|
Prime-age (25–54) |
|||||
|
Green |
65.7 |
30.4 |
0.0 |
3.7 |
100 |
|
Non-green |
6.6 |
89.3 |
0.0 |
3.9 |
100 |
|
Unemployed |
14.8 |
66.1 |
7.9 |
11.2 |
100 |
|
Outside the labour force |
5.8 |
37.2 |
1.3 |
55.7 |
100 |
|
Older (55–64) |
|||||
|
Green |
61.5 |
29.2 |
0.0 |
9.1 |
100 |
|
Non-green |
4.2 |
87.1 |
0.0 |
8.7 |
100 |
|
Unemployed |
13.8 |
61.0 |
2.5 |
22.6 |
100 |
|
Outside the labour force |
1.6 |
17.6 |
0.1 |
80.6 |
100 |
|
Women |
|||||
|
Green |
53.3 |
41.0 |
0.0 |
5.4 |
100 |
|
Non-green |
3.8 |
89.7 |
0.0 |
6.3 |
100 |
|
Unemployed |
5.3 |
65.4 |
9.1 |
20.2 |
100 |
|
Outside the labour force |
1.7 |
24.3 |
0.1 |
73.1 |
100 |
|
Men |
|||||
|
Green |
68.0 |
27.7 |
0.0 |
4.0 |
100 |
|
Non-green |
8.4 |
87.5 |
0.0 |
3.9 |
100 |
|
Unemployed |
20.3 |
61.1 |
9.3 |
9.3 |
100 |
|
Outside the labour force |
4.7 |
20.3 |
1.3 |
73.7 |
100 |
|
Education below secondary level |
|||||
|
Green |
66.8 |
27.9 |
0.04 |
5.0 |
100 |
|
Non-green |
4.4 |
90.1 |
0.02 |
5.3 |
100 |
|
Unemployed |
15.5 |
64.1 |
4.6 |
15.6 |
100 |
|
Non-employed |
2.8 |
22.6 |
0.1 |
73.9 |
100 |
|
Secondary education |
|||||
|
Green |
58.5 |
36.7 |
0.0 |
4.7 |
100 |
|
Non-green |
7.4 |
87.3 |
0.0 |
4.9 |
100 |
|
Unemployed |
8.8 |
66.4 |
11.7 |
13.1 |
100 |
|
Outside the labour force |
2.9 |
22.7 |
1.7 |
72.7 |
100 |
|
Education beyond secondary level |
|||||
|
Green |
67.1 |
29.9 |
0.0 |
2.9 |
100 |
|
Non-green |
10.2 |
84.5 |
0.0 |
5.0 |
100 |
|
Unemployed |
15.7 |
54.5 |
18.8 |
11.0 |
100 |
|
Outside the labour force |
4.3 |
23.1 |
1.8 |
70.8 |
100 |
Note: Transition probabilities have been estimated using attrition-revised weights.
Source: Authors’ calculations based on data from ILOSTAT microdata repository.
An analysis by age group shows that, in Viet Nam, the probability of moving from a non-green to a green occupation was higher for prime-age workers (6.6 per cent) than for young workers (4 per cent). As in the case of France, green occupations also seem to be less stable for young workers, with a relatively high share of this age group moving from green to non-green employment (37 per cent). With regard to transitioning out of unemployment, in Viet Nam the prime-age group experienced the highest mobility towards both green and non-green jobs, which might be explained by the advantages this group enjoys in terms of human capital (i.e. having more experience). Young workers in Viet Nam were more likely to remain outside the labour force by comparison with older workers.
With regard to sex, in Viet Nam, men had a higher likelihood than women of remaining in green occupations (68 per cent compared with almost 53 per cent). Female workers also showed lower rates of mobility from non-green to green jobs for the period in question, while the proportion of women leaving the labour force was greater among both green and non-green job holders, as well as among the unemployed. A fairly high proportion of unemployed men in Viet Nam – 20.3 per cent – succeeded in obtaining green jobs, while the same ratio for unemployed women was slightly over 5 per cent. A sizable share – 20.2 per cent – of female workers who were unemployed at the beginning of the period had ended up leaving the labour force after five quarters. This may indicate that women face greater challenges in finding a green job, potentially signalling a need to design green transition policies in the labour market that are more inclusive of women. In terms of the economically inactive population in Viet Nam, differences between the sexes are most visible when it comes to moving into employment, with men having a greater probability of being employed in a green occupation and women more likely to take up a non-green occupation.
In the final part of this section, we examine the transition probabilities in Viet Nam for various education levels. Workers with education beyond secondary level had higher rates of transition to green jobs from every other labour market status in comparison to people with fewer years of schooling. For example, 10.2 per cent of individuals in Viet Nam with education beyond secondary level who were employed in non-green occupations at the beginning of the observation period were working in green occupations after five quarters, while the ratio was 7.4 per cent for workers with high school diplomas and 4.4 per cent for workers who had not completed secondary education; these differences are statistically significant. In addition, among unemployed workers, although a higher share of those with education beyond secondary level remained unemployed or outside the labour force after five quarters (29.8 per cent, compared with 24.8 per cent and 20.2 per cent, respectively, for those with high school diplomas and those who had not completed secondary education), workers who managed to find a job were more often employed in green occupations. Among unemployed workers who had found a job by the end of five quarters, the proportion of green jobs was 22.4 per cent for those with education beyond secondary level, compared with 11.7 per cent and 19.4 per cent, respectively, for those with high school diplomas and those who had not completed secondary education. On the other hand, workers who had not completed secondary education displayed greater stability in non-green occupations.
These findings suggest that education level plays a significant role in occupational transitions and labour market dynamics linked to the greening of the economy in Viet Nam. Higher education seems to contribute to more fluid transitions toward green occupations, while lower levels of education are associated with higher rates of stability in non-green occupations.
Unemployment benefits, social protection and green transitions in France and Viet Nam
Unemployment benefits and green transitions in France
As shown in section 3 above, the transition to green jobs from all other statuses (non-green employment, unemployment and being outside the labour force) is substantial, particularly for certain groups. In this section, we examine the relationship between unemployment benefits and the probability of switching to a green job in France. This is based on a question included in the labour force survey asking those registered as jobseekers if they have received unemployment benefit or solidarity unemployment benefit.11
Before we move on to our regression analysis, table 7 provides a description of the key characteristics of unemployment benefit receivers in France compared with the rest of the population. It should be noted that the labour force survey question on unemployment benefit does not distinguish between workers who are currently employed and those who are unemployed, and hence includes individuals who have supplementary incomes in the form of unemployment assistance while employed. As can be observed, in terms of sex, marital status, location of residence and sector of employment, there were no major differences between workers who received unemployment assistance and the rest of the population. Regarding age, the share of older workers is higher among those receiving unemployment benefit (19 per cent) than in the sample overall. This can be explained by the fact that a certain amount of contributions and duration of employment are necessary to become eligible for such benefits. Younger workers might find themselves ineligible for assistance when unemployed. Another distinction may be drawn between the groups based on level of education. While almost 26 per cent of unemployment benefit recipients had been educated beyond secondary level, the ratio is around 20 per cent for those not in receipt of the benefit. Once again, this could be due to the prevalence of stable work histories and payment of unemployment insurance contributions among workers with higher levels of education.
Table 7. Key characteristics of unemployment benefit receivers in France (%)
Note: Shares have been estimated using sampling weights.
Source: Authors’ calculations based on data from ILOSTAT microdata repository.
Table 8 gives the logistic regression results for the transition from being unemployed or outside the labour force to being in green employment. Since our analysis seeks to focus on the impact of unemployment benefits on the probability of finding a green job, individuals in employment who reported receiving unemployment benefit are not considered here. These cases include people who had recently found a job and might therefore be less likely to move within the labour market again in the near future. In France, a positive and significant association between receiving unemployment benefit and transitioning into green employment can be observed for individuals who were jobless at the start of the study period. Receipt of unemployment benefit increases the log odds of transitioning to green employment by 0.48 units, and the coefficient is statistically significant at the 1 per cent level. In line with our expectations, this suggests that unemployment assistance may enable unemployed workers to find jobs in the green economy in France, which may be due to better matching. For the category of people initially outside the labour force, receiving unemployment assistance is linked with a rise of 0.18 units in the log odds of transitioning to green employment, but the relationship is not statistically significant. However, it is likely that labour force survey respondents who reported being outside the labour force were either not actively seeking employment or were not available for work due to, for example, training, thereby affecting their likelihood of finding a job.12 It should be noted that receipt of unemployment benefits also increases the probability of moving from unemployment to a non-green job by 0.66 log units. In France, therefore, unemployment benefits are found to be positively associated with getting a job, for both green and non-green occupations.
Table 8. Unemployment benefits and the transition to green employment in France – logistic regression results
-
Notes: Logistic regressions have been estimated using probability weightings.
*** indicates p < 0.01, ** indicates p < 0.05 and * indicates p < 0.1.
Second rows give standard errors.
Source: Authors’ calculations based on data from ILOSTAT microdata repository.
There may be various reasons why unemployment benefit has a positive effect on the likelihood of being employed after a spell of unemployment. Unemployment benefits can, for instance, increase subsequent employment stability because they give job seekers the financial flexibility to reject poor job offers and keep looking for better jobs
However, it should be noted that the results presented here are merely descriptive, with no causal link drawn between receipt of unemployment benefit and the probability of being employed six quarters later. In particular, the estimates may partly reflect biases linked to the “selection” of the population covered by unemployment insurance, who could have characteristics that are not fully taken into account through the control variables introduced into the model. For example, evidence has highlighted the fact that, in France, people who do not claim unemployment benefit are more likely to have short contracts and to have worked for less time than people who do claim this benefit
On the other hand, although not the focus of the analysis in this section, our estimates for regression control variables also show interesting patterns that confirm some of the results presented above. For example, being female is associated with a substantial decrease in the log odds of transitioning to green employment across all three employment categories, and these effects are statistically significant at the 1 per cent level. Similarly, higher education raises the probability of transitioning into green jobs in France, particularly for the unemployed and economically inactive. Moreover, being an older worker decreases mobility from unemployment and being outside the labour force into green occupations, which could be due to the reduced opportunities this group has after losing a job or leaving the labour force.
Social protection and green transitions in Viet Nam
The labour force survey in Viet Nam does not feature a specific question that can be used as a proxy for receipt of unemployment benefit. However, it does include a variable that aims to measure insurance coverage. In table 9 we give figures for the key characteristics of workers in Viet Nam who have social insurance and those who do not. There are clear differences between the two groups in terms of age, education, residence, working arrangements, informality and earnings. Marital status is quite similar between the groups. Although there is some variation with regard to sex, the groups still have comparable distributions. In terms of age, it can be seen that a significant proportion of workers in Viet Nam who have social insurance are prime-aged, while 18 per cent of those with no social insurance are older individuals. This hints at the challenges that older workers in Viet Nam face in terms of securing social security coverage. Viet Nam presents an uncommon case in that outflow rates from social insurance are quite high. This may be explained in part by the fact that workers in Viet Nam can, at any point in their career, suspend their social insurance contributions for 12 months and subsequently withdraw the entirety of their (and their employers’) social insurance contributions up to that date as a lump-sum payment. This could contribute to creating conditions that push workers to leave social insurance schemes during their careers, leading to higher coverage rates for young workers than for older workers.13 Similarly, the majority of people without social security have not completed secondary education and reside in rural areas, which suggests that a substantial proportion of them might be agricultural workers.
There is no single indicator for understanding the quality of jobs. We utilize informality, part-time work and earnings as proxies. In Viet Nam, a very significant number of workers without social security are in the informal sector, measured by unit of production. While more than 83 per cent of respondents had no social protection and worked in an informal enterprise or were engaged in household production, the ratio was 52.5 per cent for those with social security. There is also a considerable difference with regard to part-time employment among these groups: 28 per cent of those without social protection were part-time workers. Finally, average monthly earnings for the two groups are quite different, at 6,272,596 Vietnamese dong (approximately US$ 246) for individuals without social security and 8,106,764 dong (nearly US$ 318) for the rest. All of these factors suggest that the quality of jobs in Viet Nam tends to be lower among people who are not covered by social benefits.
Table 9. Key characteristics of workers in Viet Nam with social protection (%)
Note: Shares have been estimated using sampling weights.
Source: Authors’ calculations based on data from ILOSTAT microdata repository.
While the labour force survey in Viet Nam does not directly identify individuals who are specifically covered by unemployment insurance, the use of administrative data from Viet Nam Social Security (VSS) for the year 2019 makes it possible to compare the demographic profiles of those individuals recorded as social insurance beneficiaries in the survey with unemployment insurance contributors registered by VSS. Among the latter group, the proportion of young people was slightly higher (34 per cent were aged 15 to 24, according to VSS data) than among the social insurance beneficiaries, while the proportion of prime-age workers was lower (65 per cent). This could be partly because the young people interviewed for the labour market survey might be less aware of their social insurance coverage than those with more labour market experience, which would not be reflected in the administrative data. On the other hand, the distribution of unemployment insurance contributors by sex is exactly the same as that of social insurance contributors in the labour force survey (47 per cent female in both cases).
Since the social insurance question is asked only of people who are currently employed, it is not possible to analyse the relationship between social insurance and the transition into green occupations for people who were unemployed or outside the labour force. As previously shown, education plays an important role in transitioning to green jobs, and there are fewer individuals with education above secondary level who are not covered by social security. Hence, we have disaggregated the relationship between receiving social benefits and moving into green jobs by level of education. Table 10 presents the results of logistic regressions across three educational categories. The link between social insurance and the shift to a green occupation varies substantially in Viet Nam by level of schooling. Our results indicate that, for less educated workers, social insurance is negatively and significantly correlated with moving into a green job from a non-green job. In contrast, social protection increases the likelihood of transitioning into green employment for respondents with education above secondary level. In part, these results suggest that less educated people, once in a job with social security, tend to keep it.
Among the control variables, being female significantly reduces the likelihood of moving to a green occupation in Viet Nam, regardless of educational level. The magnitude of the coefficient ranges from -0.58 in the secondary education category to -1.1 in tertiary education, but it is significant in all cases at a 1 per cent confidence interval. Part-time employment is positively and significantly associated with green employment transitions for people who have not completed secondary education. Informality reduces the likelihood of moving into a green job, but the coefficient is not significant for the least educated group. Earnings, on the other hand, are positively and significantly associated with green employment, and higher wages allow workers to transition from a non-green job to a green job more easily.
Table 10. Social protection and the transition to green employment from non-green employment in Viet Nam by educational level – logistic regression results
-
Notes: Logistic regressions have been estimated using probability weights.
*** indicates p < 0.01, ** indicates p < 0.05 and * indicates p < 0.1.
Second rows represent standard errors.
Source: Authors’ calculations based on data from ILOSTAT microdata repository.
Conclusion
The transition to green occupations is considered a key strategy in responding to climate change and promoting ecological resilience. However, the shift towards a green economy entails both the creation and the destruction of jobs, impacting labour markets worldwide. In this paper, we first defined green jobs, highlighting the use of tasks as a basis for estimating the “greenness” of occupations. We then examined rates of transition to green occupations in France and Viet Nam overall and for different groups of workers. Our findings reveal that in France, 21.5 per cent of workers are engaged in green occupations, while the majority, 78.5 per cent, continue to work in non-green jobs. This aligns with existing studies indicating the prevalence of green employment in the country. Viet Nam has a lower proportion of green jobs, with only 15 per cent of workers holding positions in environmentally sustainable occupations.
With regard to green transitions, we estimate that in France one in ten workers in green or non-green occupations are still in the same job category six quarters later and that the transition to green jobs from non-green ones is limited, with only 1.6 per cent of workers making such a shift, while the share of workers moving from a green job to a non-green one is 5.5 per cent. Furthermore, it seems that a relatively small proportion of people who are unemployed or outside the labour force will have moved into a green occupation after six quarters. Younger workers exhibit higher mobility, especially from non-green to green jobs. Sex and education influence these transitions, with women and workers with lower levels of education facing unique challenges in retaining or shifting to green jobs. The impact of education is especially pronounced, which suggests that investment in skills and education may foster green labour market transitions.
On the other hand, we have found that only 65 per cent of workers with green jobs in Viet Nam remain in the same category five quarters later, but that the shift from non-green to green jobs appears slightly higher than in France, with 6 per cent making such a transition. In comparison to prime-age workers, the share of young workers transitioning from non-green to green occupations is smaller, and women in Viet Nam generally face more significant challenges in transitioning to green jobs than men. Even though fewer than 4 per cent of female workers switch to green occupations from non-green ones, the ratio is more than double that of their male counterparts. Education also plays a substantial role, with more educated individuals more likely to move to a green occupation than the rest of the population.
In this paper we have also analysed the role of social protection systems, including unemployment insurance, in supporting and promoting green transition policies. Through the use of regression techniques, we conclude that unemployed individuals receiving unemployment benefits in France show a positive and statistically significant association with getting a green job. This points towards the relevance of social protection and especially unemployment assistance, in the French context, in facilitating green transitions. Workers with social insurance registration in Viet Nam display diverse patterns with regard to moving from non-green to green jobs, depending on their level of education. While having a job with social protection reduces the likelihood that less educated workers will move into a green job, it seems to have the opposite effect for individuals with education beyond secondary level. For this latter group, social protection is associated with a higher likelihood of transitioning to a green occupation.
It is worth underlining that several avenues for future research could be explored. For example, as the notion of decent work should be encompassed in any definition of green jobs, further research could focus on analysing working conditions in green occupations, including whether the transition from a non-green to a green job is associated with improvements in terms of wages, working hours and job stability. In addition, the use of complementary microdata could certainly help to fine-tune the findings of the present paper. For instance, the availability of longitudinal social security data, including detailed information on each individual’s social protection scheme, employment status and occupation, would make it possible to examine labour market trajectories beyond the horizon usually provided by labour force surveys (five or six quarters) and to link each of these trajectories to the precise nature of the social protection received. Finally, extending the scope of the research to countries with different employment insurance and social protection systems would allow further investigation into the role of social protection in accelerating and facilitating just transitions.
Annex
Classification of ISCO-08 occupations by “greenness”
|
Code |
Name |
Green |
Brown |
Neutral |
|
1111 |
Legislators |
× |
||
|
1112 |
Senior Government Officials |
× |
||
|
1113 |
Traditional Chiefs and Heads of Villages |
× |
||
|
1114 |
Senior Officials of Special-interest Organizations |
× |
||
|
1120 |
Managing Directors and Chief Executives |
× |
||
|
1211 |
Finance Managers |
× |
||
|
1212 |
Human Resource Managers |
× |
||
|
1213 |
Policy and Planning Managers |
× |
||
|
1219 |
Business Services and Administration Managers Not Elsewhere Classified |
× |
||
|
1221 |
Sales and Marketing Managers |
× |
||
|
1222 |
Advertising and Public Relations Managers |
× |
||
|
1223 |
Research and Development Managers |
× |
||
|
1311 |
Agricultural and Forestry Production Managers |
× |
||
|
1312 |
Aquaculture and Fisheries Production Managers |
× |
||
|
1321 |
Manufacturing Managers |
× |
||
|
1322 |
Mining Managers |
× |
||
|
1323 |
Construction Managers |
× |
||
|
1324 |
Supply, Distribution and Related Managers |
× |
||
|
1330 |
Information and Communications Technology Service Managers |
× |
||
|
1341 |
Child Care Service Managers |
× |
||
|
1342 |
Health Service Managers |
× |
||
|
1343 |
Aged Care Service Managers |
× |
||
|
1344 |
Social Welfare Managers |
× |
||
|
1345 |
Education Managers |
× |
||
|
1346 |
Financial and Insurance Services Branch Managers |
× |
||
|
1349 |
Professional Services Managers Not Elsewhere Classified |
× |
||
|
1411 |
Hotel Managers |
× |
||
|
1412 |
Restaurant Managers |
× |
||
|
1420 |
Retail and Wholesale Trade Managers |
× |
||
|
1431 |
Sports, Recreation and Cultural Centre Managers |
× |
||
|
1439 |
Services Managers Not Elsewhere Classified |
× |
||
|
2111 |
Physicists and Astronomers |
× |
||
|
2112 |
Meteorologists |
× |
||
|
2113 |
Chemists |
× |
||
|
2114 |
Geologists and Geophysicists |
× |
||
|
2120 |
Mathematicians, Actuaries and Statisticians |
× |
||
|
2131 |
Biologists, Botanists, Zoologists and Related Professionals |
× |
||
|
2132 |
Farming, Forestry and Fisheries Advisers |
× |
||
|
2133 |
Environmental Protection Professionals |
× |
||
|
2141 |
Industrial and Production Engineers |
× |
||
|
2142 |
Civil Engineers |
× |
||
|
2143 |
Environmental Engineers |
× |
||
|
2144 |
Mechanical Engineers |
× |
||
|
2145 |
Chemical Engineers |
× |
||
|
2146 |
Mining Engineers, Metallurgists and Related Professionals |
× |
||
|
2149 |
Engineering Professionals Not Elsewhere Classified |
× |
||
|
2151 |
Electrical Engineers |
× |
||
|
2152 |
Electronics Engineers |
× |
||
|
2153 |
Telecommunications Engineers |
× |
||
|
2161 |
Building Architects |
× |
||
|
2162 |
Landscape Architects |
× |
||
|
2163 |
Product and Garment Designers |
× |
||
|
2164 |
Town and Traffic Planners |
× |
||
|
2165 |
Cartographers and Surveyors |
× |
||
|
2166 |
Graphic and Multimedia Designers |
× |
||
|
2211 |
Generalist Medical Practitioners |
× |
||
|
2212 |
Specialist Medical Practitioners |
× |
||
|
2221 |
Nursing Professionals |
× |
||
|
2222 |
Midwifery Professionals |
× |
||
|
2230 |
Traditional and Complementary Medicine Professionals |
× |
||
|
2240 |
Paramedical Practitioners |
× |
||
|
2250 |
Veterinarians |
× |
||
|
2261 |
Dentists |
× |
||
|
2262 |
Pharmacists |
× |
||
|
2263 |
Environmental and Occupational Health and Hygiene Professionals |
× |
||
|
2264 |
Physiotherapists |
× |
||
|
2265 |
Dieticians and Nutritionists |
× |
||
|
2266 |
Audiologists and Speech Therapists |
× |
||
|
2267 |
Optometrists and Ophthalmic Opticians |
× |
||
|
2269 |
Health Professionals Not Elsewhere Classified |
× |
||
|
2310 |
University and Higher Education Teachers |
× |
||
|
2320 |
Vocational Education Teachers |
× |
||
|
2330 |
Secondary Education Teachers |
× |
||
|
2341 |
Primary School Teachers |
× |
||
|
2342 |
Early Childhood Educators |
× |
||
|
2351 |
Education Methods Specialists |
× |
||
|
2352 |
Special Needs Teachers |
× |
||
|
2353 |
Other Language Teachers |
× |
||
|
2354 |
Other Music Teachers |
× |
||
|
2355 |
Other Arts Teachers |
× |
||
|
2356 |
Information Technology Trainers |
× |
||
|
2359 |
Teaching Professionals Not Elsewhere Classified |
× |
||
|
2411 |
Accountants |
× |
||
|
2412 |
Financial and Investment Advisers |
× |
||
|
2413 |
Financial Analysts |
× |
||
|
2421 |
Management and Organization Analysts |
× |
||
|
2422 |
Policy Administration Professionals |
× |
||
|
2423 |
Personnel and Careers Professionals |
× |
||
|
2424 |
Training and Staff Development Professionals |
× |
||
|
2431 |
Advertising and Marketing Professionals |
× |
||
|
2432 |
Public Relations Professionals |
× |
||
|
2433 |
Technical and Medical Sales Professionals (excluding ICT) |
× |
||
|
2434 |
Information and Communications Technology Sales Professionals |
× |
||
|
2511 |
Systems Analysts |
× |
||
|
2512 |
Software Developers |
× |
||
|
2513 |
Web and Multimedia Developers |
× |
||
|
2514 |
Applications Programmers |
× |
||
|
2519 |
Software and Applications Developers and Analysts Not Elsewhere Classified |
× |
||
|
2521 |
Database Designers and Administrators |
× |
||
|
2522 |
Systems Administrators |
× |
||
|
2523 |
Computer Network Professionals |
× |
||
|
2529 |
Database and Network Professionals Not Elsewhere Classified |
× |
||
|
2611 |
Lawyers |
× |
||
|
2612 |
Judges |
× |
||
|
2619 |
Legal Professionals Not Elsewhere Classified |
× |
||
|
2621 |
Archivists and Curators |
× |
||
|
2622 |
Librarians and Related Information Professionals |
× |
||
|
2631 |
Economists |
× |
||
|
2632 |
Sociologists, Anthropologists and Related Professionals |
× |
||
|
2633 |
Philosophers, Historians and Political Scientists |
× |
||
|
2634 |
Psychologists |
× |
||
|
2635 |
Social Work and Counselling Professionals |
× |
||
|
2636 |
Religious Professionals |
× |
||
|
2641 |
Authors and Related Writers |
× |
||
|
2642 |
Journalists |
× |
||
|
2643 |
Translators, Interpreters and Other Linguists |
× |
||
|
2651 |
Visual Artists |
× |
||
|
2652 |
Musicians, Singers and Composers |
× |
||
|
2653 |
Dancers and Choreographers |
× |
||
|
2654 |
Film, Stage and Related Directors and Producers |
× |
||
|
2655 |
Actors |
× |
||
|
2656 |
Announcers on Radio, Television and Other Media |
× |
||
|
2659 |
Creative and Performing Artists Not Elsewhere Classified |
× |
||
|
3111 |
Chemical and Physical Science Technicians |
× |
||
|
3112 |
Civil Engineering Technicians |
× |
||
|
3113 |
Electrical Engineering Technicians |
× |
||
|
3114 |
Electronics Engineering Technicians |
× |
||
|
3115 |
Mechanical Engineering Technicians |
× |
||
|
3116 |
Chemical Engineering Technicians |
× |
||
|
3117 |
Mining and Metallurgical Technicians |
× |
||
|
3118 |
Draughtspersons |
× |
||
|
3119 |
Physical and Engineering Science Technicians Not Elsewhere Classified |
× |
||
|
3121 |
Mining Supervisors |
× |
||
|
3122 |
Manufacturing Supervisors |
× |
||
|
3123 |
Construction Supervisors |
× |
||
|
3131 |
Power Production Plant Operators |
× |
||
|
3132 |
Incinerator and Water Treatment Plant Operators |
× |
||
|
3133 |
Chemical Processing Plant Controllers |
× |
||
|
3134 |
Petroleum and Natural Gas Refining Plant Operators |
× |
||
|
3135 |
Metal Production Process Controllers |
× |
||
|
3139 |
Process Control Technicians Not Elsewhere Classified |
× |
||
|
3141 |
Life Science Technicians (excluding Medical) |
× |
||
|
3142 |
Agricultural Technicians |
× |
||
|
3143 |
Forestry Technicians |
× |
||
|
3151 |
Ships’ Engineers |
× |
||
|
3152 |
Ships’ Deck Officers and Pilots |
× |
||
|
3153 |
Aircraft Pilots and Related Associate Professionals |
× |
||
|
3154 |
Air Traffic Controllers |
× |
||
|
3155 |
Air Traffic Safety Electronics Technicians |
× |
||
|
3211 |
Medical Imaging and Therapeutic Equipment Technicians |
× |
||
|
3212 |
Medical and Pathology Laboratory Technicians |
× |
||
|
3213 |
Pharmaceutical Technicians and Assistants |
× |
||
|
3214 |
Medical and Dental Prosthetic Technicians |
× |
||
|
3221 |
Nursing Associate Professionals |
× |
||
|
3222 |
Midwifery Associate Professionals |
× |
||
|
3230 |
Traditional and Complementary Medicine Associate Professionals |
× |
||
|
3240 |
Veterinary Technicians and Assistants |
× |
||
|
3251 |
Dental Assistants and Therapists |
× |
||
|
3252 |
Medical Records and Health Information Technicians |
× |
||
|
3253 |
Community Health Workers |
× |
||
|
3254 |
Dispensing Opticians |
× |
||
|
3255 |
Physiotherapy Technicians and Assistants |
× |
||
|
3256 |
Medical Assistants |
× |
||
|
3257 |
Environmental and Occupational Health Inspectors and Associates |
× |
||
|
3258 |
Ambulance Workers |
× |
||
|
3259 |
Health Associate Professionals Not Elsewhere Classified |
× |
||
|
3311 |
Securities and Finance Dealers and Brokers |
× |
||
|
3312 |
Credit and Loans Officers |
× |
||
|
3313 |
Accounting Associate Professionals |
× |
||
|
3314 |
Statistical, Mathematical and Related Associate Professionals |
× |
||
|
3315 |
Valuers and Loss Assessors |
× |
||
|
3321 |
Insurance Representatives |
× |
||
|
3322 |
Commercial Sales Representatives |
× |
||
|
3323 |
Buyers |
× |
||
|
3324 |
Trade Brokers |
× |
||
|
3331 |
Clearing and Forwarding Agents |
× |
||
|
3332 |
Conference and Event Planners |
× |
||
|
3333 |
Employment Agents and Contractors |
× |
||
|
3334 |
Real Estate Agents and Property Managers |
× |
||
|
3339 |
Business Services Agents Not Elsewhere Classified |
× |
||
|
3341 |
Office Supervisors |
× |
||
|
3342 |
Legal Secretaries |
× |
||
|
3343 |
Administrative and Executive Secretaries |
× |
||
|
3344 |
Medical Secretaries |
× |
||
|
3351 |
Customs and Border Inspectors |
× |
||
|
3352 |
Government Tax and Excise Officials |
× |
||
|
3353 |
Government Social Benefits Officials |
× |
||
|
3354 |
Government Licensing Officials |
× |
||
|
3355 |
Police Inspectors and Detectives |
× |
||
|
3359 |
Government Regulatory Associate Professionals Not Elsewhere Classified |
× |
||
|
3411 |
Legal and Related Associate Professionals |
× |
||
|
3412 |
Social Work Associate Professionals |
× |
||
|
3413 |
Religious Associate Professionals |
× |
||
|
3421 |
Athletes and Sports Players |
× |
||
|
3422 |
Sports Coaches, Instructors and Officials |
× |
||
|
3423 |
Fitness and Recreation Instructors and Programme Leaders |
× |
||
|
3431 |
Photographers |
× |
||
|
3432 |
Interior Designers and Decorators |
× |
||
|
3433 |
Gallery, Museum and Library Technicians |
× |
||
|
3434 |
Chefs |
× |
||
|
3435 |
Other Artistic and Cultural Associate Professionals |
× |
||
|
3511 |
Information and Communications Technology Operations Technicians |
× |
||
|
3512 |
Information and Communications Technology User Support Technicians |
× |
||
|
3513 |
Computer Network and Systems Technicians |
× |
||
|
3514 |
Web Technicians |
× |
||
|
3521 |
Broadcasting and Audio-visual Technicians |
× |
||
|
3522 |
Telecommunications Engineering Technicians |
× |
||
|
4110 |
General Office Clerks |
× |
||
|
4120 |
Secretaries (general) |
× |
||
|
4131 |
Typists and Word Processing Operators |
× |
||
|
4132 |
Data Entry Clerks |
× |
||
|
4211 |
Bank Tellers and Related Clerks |
× |
||
|
4212 |
Bookmakers, Croupiers and Related Gaming Workers |
× |
||
|
4213 |
Pawnbrokers and Money-lenders |
× |
||
|
4214 |
Debt Collectors and Related Workers |
× |
||
|
4221 |
Travel Consultants and Clerks |
× |
||
|
4222 |
Contact Centre Information Clerks |
× |
||
|
4223 |
Telephone Switchboard Operators |
× |
||
|
4224 |
Hotel Receptionists |
× |
||
|
4225 |
Inquiry Clerks |
× |
||
|
4226 |
Receptionists (general) |
× |
||
|
4227 |
Survey and Market Research Interviewers |
× |
||
|
4229 |
Client Information Workers Not Elsewhere Classified |
× |
||
|
4311 |
Accounting and Bookkeeping Clerks |
× |
||
|
4312 |
Statistical, Finance and Insurance Clerks |
× |
||
|
4313 |
Payroll Clerks |
× |
||
|
4321 |
Stock Clerks |
× |
||
|
4322 |
Production Clerks |
× |
||
|
4323 |
Transport Clerks |
× |
||
|
4411 |
Library Clerks |
× |
||
|
4412 |
Mail Carriers and Sorting Clerks |
× |
||
|
4413 |
Coding, Proofreading and Related Clerks |
× |
||
|
4414 |
Scribes and Related Workers |
× |
||
|
4415 |
Filing and Copying Clerks |
× |
||
|
4416 |
Personnel Clerks |
× |
||
|
4419 |
Clerical Support Workers Not Elsewhere Classified |
× |
||
|
5111 |
Travel Attendants and Travel Stewards |
× |
||
|
5112 |
Transport Conductors |
× |
||
|
5113 |
Travel Guides |
× |
||
|
5120 |
Cooks |
× |
||
|
5131 |
Waiters |
× |
||
|
5132 |
Bartenders |
× |
||
|
5141 |
Hairdressers |
× |
||
|
5142 |
Beauticians and Related Workers |
× |
||
|
5151 |
Cleaning and Housekeeping Supervisors in Offices, Hotels and Other Establishments |
× |
||
|
5152 |
Domestic Housekeepers |
× |
||
|
5153 |
Building Caretakers |
× |
||
|
5161 |
Astrologers, Fortune-tellers and Related Workers |
× |
||
|
5162 |
Companions and Valets |
× |
||
|
5163 |
Undertakers and Embalmers |
× |
||
|
5164 |
Pet Groomers and Animal Care Workers |
× |
||
|
5165 |
Driving Instructors |
× |
||
|
5169 |
Personal Services Workers Not Elsewhere Classified |
× |
||
|
5211 |
Stall and Market Salespersons |
× |
||
|
5212 |
Street Food Salespersons |
× |
||
|
5221 |
Shopkeepers |
× |
||
|
5222 |
Shop Supervisors |
× |
||
|
5223 |
Shop Sales Assistants |
× |
||
|
5230 |
Cashiers and Ticket Clerks |
× |
||
|
5241 |
Fashion and Other Models |
× |
||
|
5242 |
Sales Demonstrators |
× |
||
|
5243 |
Door-to-door Salespersons |
× |
||
|
5244 |
Contact Centre Salespersons |
× |
||
|
5245 |
Service Station Attendants |
× |
||
|
5246 |
Food Service Counter Attendants |
× |
||
|
5249 |
Sales Workers Not Elsewhere Classified |
× |
||
|
5311 |
Child Care Workers |
× |
||
|
5312 |
Teachers’ Aides |
× |
||
|
5321 |
Health Care Assistants |
× |
||
|
5322 |
Home-based Personal Care Workers |
× |
||
|
5329 |
Personal Care Workers in Health Services Not Elsewhere Classified |
× |
||
|
5411 |
Fire Fighters |
× |
||
|
5412 |
Police Officers |
× |
||
|
5413 |
Prison Guards |
× |
||
|
5414 |
Security Guards |
× |
||
|
5419 |
Protective Services Workers Not Elsewhere Classified |
× |
||
|
6111 |
Field Crop and Vegetable Growers |
× |
||
|
6112 |
Tree and Shrub Crop Growers |
× |
||
|
6113 |
Gardeners, Horticultural and Nursery Growers |
× |
||
|
6114 |
Mixed Crop Growers |
× |
||
|
6121 |
Livestock and Dairy Producers |
× |
||
|
6122 |
Poultry Producers |
× |
||
|
6123 |
Apiarists and Sericulturists |
× |
||
|
6129 |
Animal Producers Not Elsewhere Classified |
× |
||
|
6130 |
Mixed Crop and Animal Producers |
× |
||
|
6210 |
Forestry and Related Workers |
× |
||
|
6221 |
Aquaculture Workers |
× |
||
|
6222 |
Inland and Coastal Waters Fishery Workers |
× |
||
|
6223 |
Deep-sea Fishery Workers |
× |
||
|
6224 |
Hunters and Trappers |
× |
||
|
6310 |
Subsistence Crop Farmers |
× |
||
|
6320 |
Subsistence Livestock Farmers |
× |
||
|
6330 |
Subsistence Mixed Crop and Livestock Farmers |
× |
||
|
6340 |
Subsistence Fishers, Hunters, Trappers and Gatherers |
× |
||
|
7111 |
House Builders |
× |
||
|
7112 |
Bricklayers and Related Workers |
× |
||
|
7113 |
Stonemasons, Stone Cutters, Splitters and Carvers |
× |
||
|
7114 |
Concrete Placers, Concrete Finishers and Related Workers |
× |
||
|
7115 |
Carpenters and Joiners |
× |
||
|
7119 |
Building Frame and Related Trades Workers Not Elsewhere Classified |
× |
||
|
7121 |
Roofers |
× |
||
|
7122 |
Floor Layers and Tile Setters |
× |
||
|
7123 |
Plasterers |
× |
||
|
7124 |
Insulation Workers |
× |
||
|
7125 |
Glaziers |
× |
||
|
7126 |
Plumbers and Pipe Fitters |
× |
||
|
7127 |
Air Conditioning and Refrigeration Mechanics |
× |
||
|
7131 |
Painters and Related Workers |
× |
||
|
7132 |
Spray Painters and Varnishers |
× |
||
|
7133 |
Building Structure Cleaners |
× |
||
|
7211 |
Metal Moulders and Coremakers |
× |
||
|
7212 |
Welders and Flame Cutters |
× |
||
|
7213 |
Sheet Metal Workers |
× |
||
|
7214 |
Structural Metal Preparers and Erectors |
× |
||
|
7215 |
Riggers and Cable Splicers |
× |
||
|
7221 |
Blacksmiths, Hammersmiths and Forging Press Workers |
× |
||
|
7222 |
Toolmakers and Related Workers |
× |
||
|
7223 |
Metal Working Machine Tool Setters and Operators |
× |
||
|
7224 |
Metal Polishers, Wheel Grinders and Tool Sharpeners |
× |
||
|
7231 |
Motor Vehicle Mechanics and Repairers |
× |
||
|
7232 |
Aircraft Engine Mechanics and Repairers |
× |
||
|
7233 |
Agricultural and Industrial Machinery Mechanics and Repairers |
× |
||
|
7234 |
Bicycle and Related Repairers |
× |
||
|
7311 |
Precision-instrument Makers and Repairers |
× |
||
|
7312 |
Musical Instrument Makers and Tuners |
× |
||
|
7313 |
Jewellery and Precious Metal Workers |
× |
||
|
7314 |
Potters and Related Workers |
× |
||
|
7315 |
Glass Makers, Cutters, Grinders and Finishers |
× |
||
|
7316 |
Sign Writers, Decorative Painters, Engravers and Etchers |
× |
||
|
7317 |
Handicraft Workers in Wood, Basketry and Related Materials |
× |
||
|
7318 |
Handicraft Workers in Textile, Leather and Related Materials |
× |
||
|
7319 |
Handicraft Workers Not Elsewhere Classified |
× |
||
|
7321 |
Pre-press Technicians |
× |
||
|
7322 |
Printers |
× |
||
|
7323 |
Print Finishing and Binding Workers |
× |
||
|
7411 |
Building and Related Electricians |
× |
||
|
7412 |
Electrical Mechanics and Fitters |
× |
||
|
7413 |
Electrical Line Installers and Repairers |
× |
||
|
7421 |
Electronics Mechanics and Servicers |
× |
||
|
7422 |
Information and Communications Technology Installers and Servicers |
× |
||
|
7511 |
Butchers, Fishmongers and Related Food Preparers |
× |
||
|
7512 |
Bakers, Pastry-cooks and Confectionery Makers |
× |
||
|
7513 |
Dairy Products Makers |
× |
||
|
7514 |
Fruit, Vegetable and Related Preservers |
× |
||
|
7515 |
Food and Beverage Tasters and Graders |
× |
||
|
7516 |
Tobacco Preparers and Tobacco Products Makers |
× |
||
|
7521 |
Wood Treaters |
× |
||
|
7522 |
Cabinet-makers and Related Workers |
× |
||
|
7523 |
Woodworking Machine Tool Setters and Operators |
× |
||
|
7531 |
Tailors, Dressmakers, Furriers and Hatters |
× |
||
|
7532 |
Garment and Related Patternmakers and Cutters |
× |
||
|
7533 |
Sewing, Embroidery and Related Workers |
× |
||
|
7534 |
Upholsterers and Related Workers |
× |
||
|
7535 |
Pelt Dressers, Tanners and Fellmongers |
× |
||
|
7536 |
Shoemakers and Related Workers |
× |
||
|
7541 |
Underwater Divers |
× |
||
|
7542 |
Shotfirers and Blasters |
× |
||
|
7543 |
Product Graders and Testers (except Foods and Beverages) |
× |
||
|
7544 |
Fumigators and Other Pest and Weed Controllers |
× |
||
|
7549 |
Craft and Related Workers not Elsewhere Classified |
× |
||
|
8111 |
Miners and Quarriers |
× |
||
|
8112 |
Mineral and Stone Processing Plant Operators |
× |
||
|
8113 |
Well Drillers and Borers and Related Workers |
× |
||
|
8114 |
Cement, Stone and Other Mineral Products Machine Operators |
× |
||
|
8121 |
Metal Processing Plant Operators |
× |
||
|
8122 |
Metal Finishing, Plating and Coating Machine Operators |
× |
||
|
8131 |
Chemical Products Plant and Machine Operators |
× |
||
|
8132 |
Photographic Products Machine Operators |
× |
||
|
8141 |
Rubber Products Machine Operators |
× |
||
|
8142 |
Plastic Products Machine Operators |
× |
||
|
8143 |
Paper Products Machine Operators |
× |
||
|
8151 |
Fibre Preparing, Spinning and Winding Machine Operators |
× |
||
|
8152 |
Weaving and Knitting Machine Operators |
× |
||
|
8153 |
Sewing Machine Operators |
× |
||
|
8154 |
Bleaching, Dyeing and Fabric Cleaning Machine Operators |
× |
||
|
8155 |
Fur and Leather Preparing Machine Operators |
× |
||
|
8156 |
Shoemaking and Related Machine Operators |
× |
||
|
8157 |
Laundry Machine Operators |
× |
||
|
8159 |
Textile, Fur and Leather Products Machine Operators Not Elsewhere Classified |
× |
||
|
8160 |
Food and Related Products Machine Operators |
× |
||
|
8171 |
Pulp and Papermaking Plant Operators |
× |
||
|
8172 |
Wood Processing Plant Operators |
× |
||
|
8181 |
Glass and Ceramics Plant Operators |
× |
||
|
8182 |
Steam Engine and Boiler Operators |
× |
||
|
8183 |
Packing, Bottling and Labelling Machine Operators |
× |
||
|
8189 |
Stationary Plant and Machine Operators Not Elsewhere Classified |
× |
||
|
8211 |
Mechanical Machinery Assemblers |
× |
||
|
8212 |
Electrical and Electronic Equipment Assemblers |
× |
||
|
8219 |
Assemblers Not Elsewhere Classified |
× |
||
|
8311 |
Locomotive Engine Drivers |
× |
||
|
8312 |
Railway Brake, Signal and Switch Operators |
× |
||
|
8321 |
Motorcycle Drivers |
× |
||
|
8322 |
Car, Taxi and Van Drivers |
× |
||
|
8331 |
Bus and Tram Drivers |
× |
||
|
8332 |
Heavy Truck and Lorry Drivers |
× |
||
|
8341 |
Mobile Farm and Forestry Plant Operators |
× |
||
|
8342 |
Earthmoving and Related Plant Operators |
× |
||
|
8343 |
Crane, hoist and related plant operators |
× |
||
|
8344 |
Lifting Truck Operators |
× |
||
|
8350 |
Ships’ Deck Crews and Related Workers |
× |
||
|
9111 |
Domestic Cleaners and Helpers |
× |
||
|
9112 |
Cleaners and Helpers in Offices, Hotels and Other Establishments |
× |
||
|
9121 |
Hand Launderers and Pressers |
× |
||
|
9122 |
Vehicle Cleaners |
× |
||
|
9123 |
Window Cleaners |
× |
||
|
9129 |
Other Cleaning Workers |
× |
||
|
9211 |
Crop Farm Labourers |
× |
||
|
9212 |
Livestock Farm Labourers |
× |
||
|
9213 |
Mixed Crop and Livestock Farm Labourers |
× |
||
|
9214 |
Garden and Horticultural Labourers |
× |
||
|
9215 |
Forestry Labourers |
× |
||
|
9216 |
Fishery and Aquaculture Labourers |
× |
||
|
9311 |
Mining and Quarrying Labourers |
× |
||
|
9312 |
Civil Engineering Labourers |
× |
||
|
9313 |
Building Construction Labourers |
× |
||
|
9321 |
Hand Packers |
× |
||
|
9329 |
Manufacturing Labourers Not Elsewhere Classified |
× |
||
|
9331 |
Hand and Pedal Vehicle Drivers |
× |
||
|
9332 |
Drivers of Animal-drawn Vehicles and Machinery |
× |
||
|
9333 |
Freight Handlers |
× |
||
|
9334 |
Shelf Fillers |
× |
||
|
9411 |
Fast Food Preparers |
× |
||
|
9412 |
Kitchen Helpers |
× |
||
|
9510 |
Street and Related Service Workers |
× |
||
|
9520 |
Street Vendors (excluding Food) |
× |
||
|
9611 |
Garbage and Recycling Collectors |
× |
||
|
9612 |
Refuse Sorters |
× |
||
|
9613 |
Sweepers and Related Labourers |
× |
||
|
9621 |
Messengers, Package Deliverers and Luggage Porters |
× |
||
|
9622 |
Odd Job Persons |
× |
||
|
9623 |
Meter Readers and Vending-machine Collectors |
× |
||
|
9624 |
Water and Firewood Collectors |
× |
||
|
9629 |
Elementary Workers Not Elsewhere Classified |
× |
||
Source: Based on Scholl et al. 2023.
References
Bureau of Labor Statistics. n.d. Green Goods and Services Industries by NAICS Code.
Bluedorn, John, Niels-Jakob Hansen, Diaa Noureldin, Ippei Shibata and Marina M. Tavares. 2022. “Transitioning to a Greener Labor Market: Cross-Country Evidence from Microdata”, IMF (International Monetary Fund) Working Paper No. 2022-146.
CEDEFOP (European Centre for the Development of Vocational Training). 2018. Skills for green jobs in France: an update [unedited proof copy]. Available only at https://www.cedefop.europa.eu/files/france_green_jobs_2018.pdf
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Acknowledgements
We would like to express our appreciation to several colleagues, in particular Catherine Saget, Trang Luu, Valentina Barcucci, André Gama, Nguyen Hai Dat, Marek Harsdorff, and Stefan Kühn. Their insightful comments have significantly improved this paper.
