Labour market trajectories, social protection and the green transition in France and Viet Nam
Author: Anil Duman, Sevane Ananian
Date: 2024-12-11

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.

About the authors

Anil Duman is a Professor at Central European University in Vienna, Austria. Her broad research interests include labor economics, political economy, industrial relations, welfare state policies, and redistribution. In her recent research, she has been specializing on informal employment and migration.

Sevane Ananian joined the Research Department of the International Labour Organization (ILO) as an economist in September 2021. His areas of expertise are inequality, wages and working conditions, and the transition to a green economy.

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 (Harrison et al. 2008; Kancs and Siliverstovs 2017).

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 (Spadafora 2023; Kekre 2023). For example, unemployment insurance can enable individuals to secure employment that matches their skills, rather than hastily accepting any available job, and in this regard may have a positive effect on workers’ willingness and capacity to find employment in green occupations. Similarly, social protection can give workers enough resources to rely on during spells of unemployment or other negative labour market experiences. Such safety nets can allow individuals to seek and obtain jobs in green sectors.

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.

[1]  In both countries, these efforts are also reflected in recent developments. At the twenty-sixth session of the Conference of the Parties to the United Nations Framework Convention on Climate Change, held in 2021, Viet Nam undertook to reduce its net carbon emissions to zero by the middle of the twenty-first century. France, meanwhile, set up an institution in July 2022 to ensure the effectiveness of environmental planning.

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
Professionals

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

9412. – Kitchen Helpers

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]

[2]  For more information on the ILOSTAT microdata repository, see https://ilostat.ilo.org/about/data-collection-and-production/.

[3]  Owing to data availability, the period covered for Viet Nam includes the COVID-19 pandemic. When more recent longitudinal data become available, an analytical perspective on the impact of the pandemic on transitions in the labour market in Viet Nam would probably allow some light to be shed on the possible effects of the pandemic on these transitions.

[4]  The inverse probability weighting procedure employs the propensity score to balance the attributes of respondents who remain in the sample against those of respondents who leave it.

[5]  For more information, see https://ec.europa.eu/commission/presscorner/detail/es/statement_22_7724. Other countries, such as Indonesia, South Africa and Senegal, have also entered into Just Energy Transition Partnerships.

[6]  See https://www.unglobalaccelerator.org/.

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 (ILO 2018; ILO 2019; ILO and IDB 2020). The shift from green to non-green occupations is higher for female workers in France than for males, at 6.5 per cent compared with 5 per cent, which is statistically significant. It can be also observed that the chances of women becoming unemployed are quite similar for green and non-green job holders, but that the probability of exiting the labour market is higher for female workers in non-green occupations. For male workers in France, the probability of becoming unemployed is slightly higher for those in non-green jobs, while the ratios for men leaving the labour force at the end of six quarters are almost identical for those employed in green and non-green occupations at the start of the observation period. Finally, the proportion of women in non-green jobs who move to inactivity is slightly higher, at 7 per cent, than for men in the same category, which might imply that it is somewhat more difficult for female non-green job holders to remain in the labour force.

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.

[7]  The proportion of workers in green occupations who were previously unemployed or outside the labour force has been estimated based on the figures in table 5, corresponding to the following ratios for the respective groups: 4.7/(4.7+31.4) and 1.2/(8.7+1.2).

[8]  A recent study, for instance, has pointed to recruitment challenges in the green economy in France in 2020–2021, owing mainly to a mismatch between the skills required by employers and those available among jobseekers (Margontier 2022).

[9]  The statistical differences among transition probabilities throughout this report were tested using a logistic regression with group dummy variables.

[10]  For example, previous studies using data for France have found that, regardless of the greenness of the occupations involved, occupational changes are more frequent at the beginning of a worker’s career (Lhommeau and Michel 2018).

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 (Tatsiramos 2014). Moreover, in France, receipt of unemployment benefit is conditional on actively seeking work by means of a personalized employment access project that includes activities carried out by the public employment service. Overall, this unemployment benefit scheme could be particularly useful for finding specific jobs, such as those in the green economy.

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 (Hentzgen et al. 2022).

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.

[11]  In the case of France, the provision examined here and identified in the labour force survey consists of “unemployment assistance”, which includes both unemployment benefit (dependent on the unemployment insurance contributions paid) and an unemployment solidarity scheme for unemployed people who have exceeded their period of entitlement to unemployment benefit.

[12]  Those in receipt of French unemployment benefit must perform activities with a view to finding a job or setting up or taking over a business.

[13]  In 2019, the coverage rate for the cohort aged 25–29 was as high as 58.5 per cent for women and 38.6 per cent for men, while the coverage rate for both sexes for any cohort above the age of 40 was below 30 per cent (and below 15 per cent for all cohorts above 50 years old). For more information, see https://www.ilo.org/hanoi/Whatwedo/Publications/WCMS_819801/lang--en/index.htm.

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

Dierdorff, Erich C., Jennifer J. Norton, Donald W. Drewes, Christina M. Kroustalis, David Rivkin and Phil Lewis. 2009. “Greening of the World of Work: Implications for O*NET®-SOC and New and Emerging Occupations”. Raleigh, North Carolina: National Center for O*NET Development.

Do, Oanh. 2023. “Green Transition Placing Pressure on Labour Market”. Viet Nam Investment Review, 31 August 2023. https://netzero.vn/en/green-transition-placing-pressure-on-labour-market/.

Elliott, Robert J.R., Wenjing Kuai, David Maddison and Ceren Ozgen. 2021. “Eco-Innovation and Employment: A Task-Based Analysis”. IZA (Institute of Labor Economics) Discussion Paper No. 14028.

Eurostat. n.d. “Glossary: Environmental goods and services sector (EGSS)”. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Environmental_goods_and_services_sector_(EGSS).

Fallick, Bruce C., and Charles A. Fleischman. 2001. “The Importance of Employer-to-Employer Flows in the U.S. Labor Market”, Federal Reserve Finance and Economics Discussion Series No. 2001-18.

Harrison, Rupert, Jordi Jaumandreu, Jacques Mairesse and Bettina Peters. 2008. “Does Innovation Stimulate Employment? A Firm-Level Analysis Using Comparable Micro-Data from Four European Countries”, NBER (National Bureau of Economic Research) Working Paper No. 14216.

Hentzgen, Carole, Chloé Pariset, Kévin Savary and Emeline Limon. 2022. “Quantifier le non-recours à l’assurance chômage”. France, Ministry of Labour, Dares Working Paper No. 263.

ILO. 2018. World Employment and Social Outlook 2018: Greening with jobs.

———. 2019. Skills for a Greener Future: A Global View – Based on 32 country studies.

———. 2023. “Social Protection for a Just Transition”. ILO Policy Brief. Available at https://www.ilo.org/global/topics/green-jobs/publications/just-transition-pb/WCMS_867426/lang--en/index.htm.

——— and IDB (Inter-American Development Bank). 2020. Jobs in a Net-Zero Emissions Future in Latin America and the Caribbean.

Kancs, d’Artis, and Boriss Siliverstovs. 2017. “Employment Effect of Innovation”. KOF (Swiss Economic Institute) Working Papers No. 428. Available at https://doi.org/10.3929/ETHZ-A-010852605.

Kekre, Rohan. 2023. “Unemployment Insurance in Macroeconomic Stabilization”. The Review of Economic Studies 90 (5): 2439–2480.

Lhommeau, Bertrand, and Christophe Michel. 2018. “Changer de métier : quelles personnes et quels emplois sont concernés ?” France, Ministry of Labour, Dares Analysis No. 49. Available at https://dares.travail-emploi.gouv.fr/sites/default/files/pdf/2018-049v2.pdf.

Margontier, Sophie. 2022. “Les métiers de l’économie verte rencontrent des difficultés de recrutement en 2020 et 2021”. France, Ministry for the Ecological Transition and Territorial Cohesion, Statistical Data and Studies Department (SDES). 10 March 2022. https://www.statistiques.developpement-durable.gouv.fr/les-metiers-de-leconomie-verte-rencontrent-des-difficultes-de-recrutement-en-2020-et-2021.

OECD (Organisation for Economic Cooperation and Development). 2023. Job Creation and Local Development 2023: Bridging the Great Green Divide.

ONEMEV (France, Ministry for the Ecological Transition and Territorial Cohesion, National Observatory for Jobs and Occupations in the Green Economy). 2022. “Liste des professions vertes et verdissantes identifiées dans la nomenclature des professions et catégories socioprofessionnelles (PCS 2003)”. Available at https://www.ecologie.gouv.fr/sites/default/files/Onemev_emploi_metiers_PCS.pdf.

Samaniego, Brenda, and Christian Viegelahn. 2021. “Estimating Labour Market Transitions from Labour Force Surveys: The Case of Viet Nam”, ILO Working Paper No. 35.

Scholl, Nathalie, Sebastien Turban and Peter Gal. 2023. “The Green Side of Productivity: An International Classification of Green and Brown Occupations”, OECD Productivity Working Papers No. 33.

Sofroniou, Nick, and Pauline Anderson. 2021. “The Green Factor: Unpacking Green Job Growth”. International Labour Review 160 (1): 21–41.

Spadafora, Francesco. 2023. “U.S. Unemployment Insurance through the Covid-19 Crisis”. Journal of Government and Economics 9: 100069.

Tatsiramos, Konstantinos. 2014. “Unemployment Benefits and Job Match Quality”, IZA World of Labour Article No. 44. Available at https://doi.org/10.15185/izawol.44.

Valero, Anna, Jiaqi Li, Sabrina Muller, Capucine Riom, Viet Nguyen-Tien and Mirko Draca. 2021. “Are ‘Green’ Jobs Good Jobs? How Lessons from the Experience to-Date Can Inform Labour Market Transitions of the Future”. London: Grantham Research Institute on Climate Change and the Environment and Centre for Economic Performance, London School of Economics and Political Science.

Vona, Francesco, Giovanni Marin, Davide Consoli and David Popp. 2018. “Environmental Regulation and Green Skills: An Empirical Exploration”. Journal of the Association of Environmental and Resource Economists 5(4): 713–753.

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.

1. Labour market trajectories, social protection and the green transition in France and Viet Nam

1. Abstract*

1. Abstract

2. About the Authors*

1. About the authors

3. Introduction*

1. Introduction

4. Chapter 01*

1. Defining green jobs

1. Broad classifications
2. Task-based classifications
3. Green and non-green occupations

5. Chapter 02*

1. Panel construction and descriptive statistics

1. Panel construction and weighting

2. Green jobs in France and Viet Nam

6. Chapter 03*

1. Transitions between green and non-green jobs

1. Transition probabilities in France

2. Transition probabilities in Viet Nam

7. Chapter 04

1. Unemployment benefits, social protection and green transitions in France and Viet Nam

1. Unemployment benefits and green transitions in France

2. Social protection and green transitions in Viet Nam

8. Conclusion

1. Conclusion

9. Annex

1. Annex

10. References*

1. References

11. Acknowledgments

1. Acknowledgements