The transition to a greener economy will entail reallocating workers from high-polluting to ‘green’ jobs. To minimise the costs of this transition to individuals and communities, policies will need to support re-employment of dismissed workers while managing the scarring effects of job losses in polluting industries. This column investigates the role of structural and policy factors in shaping labour market transitions, uncovering heterogeneity across workforces. Education is the most important individual-level driver of transitioning from non-employment to green jobs, while differences in industrial specialisation across regions call for place-based policy interventions.
The transition to a greener economy is a necessity (Kanzig 2024). It will require significant shifts in the way goods and energy are produced and consumed (European Parliament 2024) as well as the reallocation of production factors, including workers. Although the overall effects in terms of aggregate economic output and employment could be relatively small, the effects are likely to be concentrated – by economic activity, geographical area, and workers’ characteristics – with a risk of amplifying inequalities in outcomes and opportunities. In particular, the transition is expected to induce a contraction of jobs in high-polluting activities (often labelled ‘brown’ jobs) and an expansion of so-called ‘green’ jobs, or those involving green activities according to the O*NET classification (Valero et al. 2021, Vona et al. 2018, Vandeplas et al. 2022, Causa et al. 2024, Causa and Phillips 2024, O*NET 2010). A just transition in the labour market should minimise costs for individuals and communities. Achieving this will require policies to improve the allocation of workers and support the re-employment of dismissed ones, especially towards greener occupations, while managing and minimising the scarring effects associated with job losses in polluting industries. In addition, given the marked differences in industrial specialisation across regions, policy interventions should be place-based.
Workers in high-polluting occupations tend to have lower educational attainment (Causa et al. 2024). In cases of job dismissal, they often experience larger earnings losses compared with workers in non-energy-intensive and transport sectors (Barreto et al. 2024). Comprehensive labour market policies and effective educational systems and upskilling programmes can help mitigate such losses and the risk of scarring, while also accelerating the green transition. Recent empirical work on a large sample of EU countries (Causa et al. 2024) finds that the estimated risk of long-term unemployment for individuals displaced from high-polluting jobs is lower, after accounting for country-specific effects and individual characteristics, in countries where higher shares of the population have a tertiary education and more adults participate in training (Table 1, Panel A). To facilitate the matching of workers to new jobs, training and active labour market programmes should be complemented with balanced and adequate income support and unemployment benefits, which are associated with a higher probability of transitioning from unemployment to employment, particularly among the long-term unemployed (Table 1, Panel B).
In addition, policies fostering access to quality education and training can help fulfil the increasing demand for workers in green jobs (as defined in Causa et al. 2024) since, net of other observable characteristics, the odds of getting a green job are twice as high for workers with high levels of education, especially in STEM fields (Figure 1). In fact, the transitions from unemployment to green jobs are more likely in countries with higher rates of adult proficiency in literacy and numeracy and a higher share of workers with formal training (Table 1, Panel A).
Figure 1 Transitions from unemployment and inactivity to employment in green jobs
A) Transitions from unemployment to green jobs
B) Transitions from study-related inactivity to green jobs
Note: The term ‘green jobs’ indicates the ISCO three-digit occupations which, based on the crosswalk defined in Causa et al. (2024), correspond to the O*NET list (O*NET 2010) of green-enhanced and green-demand occupations. Charts report the estimated odds-ratios from two-steps logit regression estimations for a large panel of European OECD countries over the period 2011–2019, along with the corresponding 95% confidence intervals. For Panel A, the estimation sample includes individuals who are unemployed in year t-1 and move to employment (in any kind of occupation) by year t, and the binary dependent variable takes value one if the employment in t is in a green job and zero otherwise. For Panel B, the sample includes respondents in the age group 20–29 who are out of the labour force and studying in year t-1 and employed in year t. The dependent variable in Panel B takes value one if the individual is employed in a green job in year t, and zero if in a non-green job. Beyond the independent variables shown in the chart, the regressions include controls for the basic individual characteristics, macro-level cyclical indicators, regional labour market conditions, and economic sector, region, and country fixed effects. Further details in Causa et al. (2024).
Source: Causa et al. (2024).
In terms of promoting transitions from joblessness to employment in green jobs, beyond the key role of education and training, labour market institutions are instrumental. These include active labour market policies, cash support to unemployed workers, and well-designed institutions to promote effective collective wage bargaining and social dialogue. Progress in this area is particularly beneficial for women, less-educated workers, and those living in rural areas (Table 1, Panel A). Policies like training, public employment services that support job searches (PES), and employment incentives are associated with higher chances of transitioning from non-employment to green jobs for higher-educated workers (Table 1, Panel B). While not causal, this association suggests the need to better design and target such policies for more exposed and vulnerable workers, especially those with lower education levels.
On the other hand, the transition from unemployment to green jobs is less likely in countries with relatively high employment protection and product markets and occupational entry regulations that hinder business and labour market dynamism (Table 1, Panel C). Such an association is particularly strong for vulnerable groups, like lower-educated individuals and those just entering the labour force after completing their studies. These results are in line with the general literature on the effects of employment protection legislation on job-finding rates and labour market transitions (Bassanini and Garnero 2013, Causa et al. 2022, Scarpetta 2014).
Table 1 Policies to support labour market transitions into a greener economy
Note: In the table, the expression ‘green jobs’ indicates the ISCO three-digit occupations which, based on the crosswalk defined in Causa et al. (2024), correspond to the O*NET list (O*NET 2010) of green-enhanced and green-demand occupations. The symbol ‘>’ indicates a positive association (for example: an increase in the mean literacy score is associated with an increase in the probability of getting a job in a green-skill occupation); ‘<’ indicates a negative association (for example, stronger employment protection on regular contracts is associated with lower probabilities of getting any job); and ‘-‘ indicates the absence of a statistically significant association between the policy and the outcome. When considering sub-populations, such as younger or older workers or women, the sign ‘>>’ (‘<<’) indicates that the association is positive (negative) and significantly more so than the association found for the general population. All associations are computed ceteris paribus, i.e. holding fixed the observable characteristics of workers, the year of the survey, the country and the corresponding macro-economic conditions, and, when relevant, the industry of occupation. For details on the sample and estimation see Causa et al. (2024).
The labour market literature shows that housing policies favouring residential mobility tend to facilitate the spatial reallocation of workers and promote business dynamism, enhancing the ability of workers to seize job opportunities (Causa et al. 2021, Causa et al. 2020, Andrews et al. 2011). This is also the case in the context of the green transition: for example, hirings from studies are hindered by strong house price dynamics, with higher house prices acting as possible barriers to geographical mobility. Social rental housing and the provision of housing allowances (i.e. housing-related monetary benefits) increase the odds of an individual moving from joblessness to a job, including a green job, with the benefits of housing allowances being more widespread than those of social housing. At the same time, these housing support policies are associated with significantly lower risks of long-term unemployment, especially for lower-educated individuals. Finally, reducing excessively rigid rental market regulations could also lift barriers to geographical mobility and enhance transitions from joblessness to employment (Table 1, Panel D).
Our findings demonstrate that structural policies known to support labour market inclusiveness and efficiency are also likely to support a green transition that is both smooth and fair. Yet, they also highlight that the impact of the transition to a greener economy and climate mitigation policies is uneven across socioeconomic groups characterised by different educational and skill background as well as geographical areas, such as territories and regions characterised by different industrial specialisation structures. Successful policy experiences in the past can help national and sub-national governments build tailored approaches to support an efficient and fair labour market transition process.
Source : VOXeu
