Green Stimulus in a Post-pandemic Recovery: the Role of Skills for a Resilient Recovery (2020)

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Article by Ziqiao Chen, Giovanni Marin, David Popp and Francesco Vona, Green Stimulus in a Post-pandemic Recovery: the Role of Skills for a Resilient Recovery, Environmental and Resource Economics (2020) 76:901-911, accepted 9 July 2020 and published online 4 August 2020. The abstract says green investments in the 2009 American Recovery and Reinvestment Act were most effective in communities whose workers have the appropriate green skills. It reports a poor match between green jobs and occupations at risk due to COVID-19, and some similarities between energy sector workers and green jobs. It also provides suggestive evidence on the potential for job training programs to ease the transition to a green economy.

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Environmental and Resource Economics (2020) 76:901–911
https://doi.org/10.1007/s10640-020-00464-7

Green Stimulus in a Post‑pandemic Recovery: the Role
of Skills for a Resilient Recovery

Ziqiao Chen1 · Giovanni Marin2,3 · David Popp1,4 · Francesco Vona5,6,7

Accepted: 9 July 2020 / Published online: 4 August 2020
© Springer Nature B.V. 2020

Abstract
As nations struggle to restart their economy after COVID-19 lockdowns, calls to include
green investments in a pandemic-related stimulus are growing. Yet little research provides
evidence of the effectiveness of a green stimulus. We begin by summarizing recent research
on the effectiveness of the green portion of the 2009 American Recovery and Reinvestment
Act on employment growth. Green investments are most effective in communities whose
workers have the appropriate “green” skills. We then provide new evidence on the skills
requirements of both green and brown occupations, as well as from occupations at risk of
job losses due to COVID-19, to illustrate which workers are most likely to benefit from a
pandemic-related green stimulus. We find similarities between some energy sector work-
ers and green jobs, but a poor match between green jobs and occupations at risk due to
COVID-19. Finally, we provide suggestive evidence on the potential for job training pro-
grams to help ease the transition to a green economy.

Keywords Green subsides · Green stimulus · American Recovery and Reinvestment Act ·
Heterogeneous effect · Distributional impacts

JEL Classification E24 · E62 · H54 · H72 · Q58

* David Popp
dcpopp@maxwell.syr.edu
1
Syracuse University, New York, USA
2
University of Urbino Carlo Bo, Urbino, Italy
3
SEEDS, Urbino, Italy
4
NBER, Cambridge, USA
5
OFCE Sciences-Po, Paris, France
6
SKEMA Business School, Université Côte d’Azur, Valbonne, France
7
CMCC, Ca’ Foscari, Venice, Italy

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902 Z. Chen et al.

1 Introduction

Nations around the world have shut down large portions of their economy in response to
the COVID-19 pandemic. Government spending will be important to both maintain the
economy during these shutdowns and to help restart the economy as restrictions are lifted.
Among the many proposals for pandemic-related stimuli include calls for a “green” stimu-
lus that both restarts the economy and helps it transition to a cleaner, more sustainable
path (e.g. Helm 2020, Agrawala et al. 2020).1 Notably, the new European Commission puts
the green fiscal stimulus at the center of its growth strategy to achieve social, economic,
and environmental goals. The European Green Deal (EGD henceforth) is “a new growth
strategy that aims to transform the EU into a fair and prosperous society, with a modern,
resource-efficient and competitive economy where there are no net emissions of green-
house gases in 2050 and where economic growth is decoupled from resource use” (Euro-
pean Commission 2019, p. 2). Funding for the EGD will be expanded in the context of
the COVID-19 plans within the Recovery Plan for Europe (Next Generation EU, €750 bil-
lion for 2021–2014) and the reinforced long-term EU budget (€1.1 trillion for 2021–2027),
approved on May 27th, 2020, with 25% of the funding allocated towards climate-friendly
expenditures. Similar proposals have been made by the head of the International Monetary
Fund and the International Energy Agency.2
The only effort of a similar scale for the green economy has been mobilized by the 2009
United States stimulus in response to the Great Recession, formally known as the Ameri-
can Recovery and Reinvestment Act, or ARRA, which set aside funds for “… environmen-
tal protection, and infrastructure that will provide long-term economic benefits” (American
Recovery and Reinvestment Act of 2009). In a recent working paper (Popp et al. 2020),
we use the US experience to estimate the effectiveness of such spending on job creation in
the aftermath of the 2009 crisis. There, the issue of skill availability is crucial for the suc-
cess of green stimuli. We find that areas whose labor force was already endowed with the
relevant green skills benefit the most in terms of job creation. In this note, we discuss the
policy implications of this research for any future green investments as part of a pandemic-
related stimulus while providing new evidence on (1) the skills required for in-demand
green jobs, brown jobs likely to be at risk in a green economy, and other jobs at risk due to
COVID-19; and (2) the potential of investments in training for the skills required to operate
green technologies.

2 Background

The US American Recovery and Reinvestment Act (ARRA) of 2009, commonly known
as the stimulus package, invested over $800 billion stimulate the US economy. ARRA
included several programs designed to promote clean energy and green jobs (Aldy 2013).

1
Here, and throughout the paper, we use “pandemic-related stimuli” to refer to both stimulus packages
designed to see the economy through a lockdown, and later investments designed to pull the economy out
of recessions resulting from the pandemic.
2
https://www.climatechangenews.com/2020/04/29/imf-chief-1-trillion-post-coronavirus-stimulus-must-
tackle-climate-crisis/, last accessed June 24, 2020, and https://www.iea.org/news/iea-and-denmark-host-
ministerial-roundtable-discussion-on-making-clean-energy-a-key-part-of-the-global-economic-recovery,
last accessed June 24, 2020.

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Green Stimulus in a Post-pandemic Recovery: the Role of Skills… 903

These include both direct spending intended for immediate job creation, such as Depart-
ment of Energy (DOE) spending for renewable energy and energy efficiency retrofits and
Environmental Protection Agency (EPA) grants for brownfield redevelopment, as well as
tax breaks and loan guarantees for renewable energy.
In Popp et al. (2020), we estimate the impact of direct spending programs managed by
the DOE and EPA, all of which are plausibly green. Combining project data from Federal-
Reporting.gov with employment and economic data from local communities, we estimate
the employment effects of green ARRA investments. As in most recent evaluations of other
types of ARRA spending (e.g. Dupor and Mehkari 2016; Dupor and McCrory 2018), our
unit of analysis is a commuting zone, which represents the local labor market. Overall, the
stimulus included over $61 billion of direct spending on green investments and $265.5 bil-
lion on non-green investments. The mean value of green ARRA and non-green ARRA per
commuting zone in our sample are $103 million and $442 million dollars, respectively.
Thus, just under 20% of ARRA funded green investments, slightly less than the 25% cur-
rently proposed by the European Union. However, while the proposed European Green
Deal is larger than the green ARRA stimulus, the definition of the plan and its composition
between guaranteed loans and direct spending is unclear so far.3

3 Discussion of the Key Results

Our first key finding is that green stimulus investments increased total employment, but
that they worked more slowly than other stimulus investments. In our preferred speci-
fication in Popp et al. (2020), we estimate that each $1 million of green ARRA invest-
ments created just under 15 jobs, or roughly $67,750 per job, in the post-ARRA period
(2013–2017). While the persistency of the job creation effect is clearly a positive aspect of
the green fiscal stimulus, we find little evidence of short-run employment gains. Although
the magnitude of jobs created is similar to what studies of other types of ARRA invest-
ments find, studies of other stimulus investments, such as construction and highway infra-
structure, typically find evidence of short-run job creation (e.g. Wilson 2012; Garin 2019;
Chodorow-Reich 2019).
Second, the impacts of a green stimulus on employment are heterogeneous. Not sur-
prisingly, we find a large effect on green job creation, measured using the task approach
proposed by Vona et al. (2018). Because nearly half of the jobs created by green ARRA
investments were in construction or waste management, nearly all of the employment gains
are in manual labor occupations. The employment gains are greatest for those manual
laborers with more than a high school degree. Interestingly, however, these new jobs are
not necessarily well-paying jobs, as the largest gains in employment for manual workers
are in occupations with an hourly wage below the US median for manual workers. Workers
specialized in manual tasks are gaining new jobs, but not necessarily well-paying jobs.
Third, and most important for this note, our research also illuminates where green stim-
ulus investments work. In Vona et al. (2018), we use data from the U.S. Department of
Labor’s O*NET database to identify the skills demanded in occupations expected to be

3
At the time we are writing this article, it is not yet clear which share of the EU stimulus will be consti-
tuted by subsidies and which by guaranteed loans and other financial instruments. See, for instance, https
://www.europarl.europa.eu/RegData/etudes/BRIE/2020/649371/EPRS_BRI(2020)649371_EN.pdf, last
accessed June 27, 2020.

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904 Z. Chen et al.

prominent in a green economy. For each occupation, the O*NET database provides the
tasks expected to be performed by workers in their occupation and the skills needed to
complete these tasks. Tasks are further divided into ‘general’ tasks, which are common
to all occupations, and ‘specific’ tasks that are unique to individual occupations. Using
O*NET data on the importance of general skills to each occupation, Vona et al. (2018)
identify Green General Skills. Such skills are potentially used in all occupations, but are
in higher demand in jobs relevant for a green economy. These skills include engineering,
operations management, monitoring, and science skills. While such skills are mostly asso-
ciated with college graduates, the engineering skills required by a green economy also
include building and construction skills.4
We find that the effectiveness of green stimulus spending varies depending on the preva-
lence of jobs using green skills in a community prior to the Great Recession. The long-run
benefits of the green stimulus were highest in those communities with more green skills.
Moreover, the green stimulus was ineffective in areas with less than the median penetration
of occupations emphasizing green skills: statistically significant positive employment gains
occur in communities in the 46th percentile and higher of green skills penetration.
This result is consistent with findings in labor economics (e.g., Kambourov and Manovs-
kii 2009; Gathmann and Schönberg 2010) where the cost of job reallocation (in this case
induced by environmental policy) is proportional to the skill distance between jobs. Thus,
the larger the distance between skills required by green jobs created and brown jobs lost
in a green economy, the more costly the transition to green economy will be. The issue is
relevant both theoretically, to calibrate general equilibrium model that explicitly incorpo-
rate costly job reallocation (Hafstead and Williams 2018), and politically, for the political
acceptability of green fiscal policy in front of the emerging populistic platforms that deny
climate change or over-emphasize the “job killing” argument (Vona 2019). As the Yellow
Vest movement has shown, the green stimulus will remain an empty box without the politi-
cal support of those left behind. Workers displaced from brown jobs will find green poli-
cies less acceptable if their skill endowments, i.e. their main asset, is completely destroyed
by such policy. Not by chance, an important fraction of the proposed European Green Deal
is devoted to upskilling the work force as well to a Just Transition fund to ensure that no
country and worker is left behind.5 We provide evidence on the workers most likely to ben-
efit from green investments in our discussion of policy implications below.

4 Policy Implications

Our findings in Popp et al. (2020) provide guidance as to how green investments can be
used effectively as part of a pandemic-related stimulus package. While a green stimulus
can help reshape the economy, it is less likely to help restart the economy. To put limited
government funds to the best use, the majority of a pandemic-related stimulus should ini-
tially focus on investments that get people back to work quickly. Green investments appear
less likely to help with this task.

4
See Vona et al. (2018) for details and descriptive statistics on green skill constructs.
5
“The Just Transition Mechanism will focus on the regions and sectors that are most affected by the transi-
tion because they depend on fossil fuels or carbon-intensive processes. […] It will also strive to protect the
citizens and workers most vulnerable to the transition, providing access to re-skilling programmes, jobs in
new economic sectors, or energy-efficient housing” (European Commission 2019, p. 1).

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Green Stimulus in a Post-pandemic Recovery: the Role of Skills… 905

That does not mean there is no role for green investments in a pandemic-related stimu-
lus. The long run benefits of a green stimulus are real. The investments put forward in
response to the pandemic must be designed for the world to come, which includes the need
to respond to the climate crisis. Investments in infrastructure, for example, could consider
future demand for public transportation and electric vehicle charging stations. But it is
important to understand who benefits from these investments. Green stimulus investments
will be most effective in communities where workers have the skills needed for a green
economy. To highlight which types of workers may benefit, Table 1 provides information
on several dimensions of skills and training requirements for specific green and brown
manual occupations: education, experience, training requirements, and the importance of
general green skills. For green skills we include the average of the four green skill groups
in Vona et al. (2018), as well as the importance of green engineering and technical skills,
which are most relevant for these manual workers. To coincide with the finding that a green
stimulus is most effective in communities with a high presence of jobs in the upper quartile
of green skills importance, we report the quartile for each green skill, as well as the aver-
age value for each group of occupations. High-demand and low-demand green and brown
occupations are those green and brown occupations with the highest and lowest projected
percentage growth rate in employment for 2018–2028 in the BLS Employment Projections
database. We note, however, that high-demand brown jobs include several positions in the
oil and gas industry that have since become vulnerable due to falling oil prices, and that
would be in lower demand should a true transition to a green economy begin.
We also include information on 10 manual occupations at risk due to COVID-19 and
social distancing requirements, based on Mongey et al. (2020). Mongey et al. (2020)
develop occupation-specific measures for the ease of working at home and physical prox-
imity to other people in the workplace. Montenovo et al. (2020) use these measures to show
that job losses from COVID-19 are greatest in jobs with low work from home potential and
high proximity in the workplace. Our at-risk occupations all have a low capacity of work-
ing at home and a high physical proximity measure (> 0.75). From all such occupations, we
identified the 10 occupations with the highest employment levels in the 2-digit occupation
classes 35 (food prep and serving), 39 (personal care and service) or 53 (transportation),
excluding those personal care workers involved in health care. Helping workers in jobs
such as these will likely be a high priority for a pandemic-related stimulus.
Most notably, the data suggest a green stimulus can be effective for workers in previ-
ously high-demand brown occupations such as the oil and gas industry, whose demand
increased substantially thanks to the fracking boom. Workers in this industry have been
hit hard by falling oil and gas prices. Most of these occupations are in the third or fourth
quartile of overall green skills importance, and 9 of the 10 are in the top quartile for green
engineering and technical skills. The average importance measure for these jobs is quite
similar to the average measure for the top green jobs. Both high-demand green and brown
jobs require similar levels of related work experience. Because green jobs require over a
year of on-the-job training, some training will likely be required for workers transitioning
from high-demand brown to green jobs. However, the similar skill set for these workers
provides hope that training will be successful. We elaborate on the potential of job training
to complement green subsidies below.
Green stimulus investments are less likely to help brown workers in low-demand
jobs, which are primarily manufacturing positions. These jobs make less use of the
general skills used in green jobs and also have significantly lower training and experi-
ence requirements. Also, note that the positions at risk from COVID-19 make little use
of green skills and require little training or experience. This further emphasizes that a

13
Table 1 Skill requirements for select occupations 906
SOC Occupation Required level of educa- Related work expe- On-the-job General green skills Engineering & Tech. GGS Employment
tion (average years of rience (months) training Importance Quartile importance Quartile in 2016
formal education) (months) (1: low; 4: high) (1: low; 4: high)
13
High demand green jobs
47-2061: construction laborers 12.0 6.1 5.6 3 4 39,530
47-2073: construction equipment operators 11.9 32.5 14.2 3 4 335,160
47-2111: electricians 12.9 52.0 43.4 4 4 519,850
47-2181: roofers 11.1 22.9 21.4 2 4 97,650
47-2211: sheet metal workers 12.4 25.4 24.0 4 4 133,420
47-2231: solar photovoltaic installers 12.1 11.2 9.8 4 4 4710
47-4041: hazardous materials removal 12.3 10.1 4.2 4 4 37,440
49-9044: millwrights 12.3 30.5 38.1 4 4 38,050
49-9081: wind turbine service Tech. 12.9 21.5 11.0 4 4 3200
53-7081: refuse & recyclable collectors 11.1 8.9 3.1 2 3 117,670
Average 12.1 years 22.1 months 17.5 months 0.42 (GGS imp) 0.56 (imp) 132,668
Brown high demand
47-1011: Construction & extraction supervisor 12.6 59.3 37.8 4 4 456,640
47-2151: pipelayers 9.9 17.1 14.1 1 3 43,590
47-2221: structural iron & steel workers 12.3 50.8 32.6 4 4 57,070
47-5011: derrick operators, oil & gas 10.1 18.2 6.3 2 4 21,950
47-5012: rotary drill operators, oil & gas 11.8 43.3 33.7 3 4 25,090
47-5013: service unit oper.; oil, gas, & mining 11.2 19.0 8.0 3 4 57,180
47-5021: earth drillers, except oil & gas 11.5 13.9 7.4 2 4 17,680
47-5071: roustabouts, oil & gas 10.7 7.7 4.8 2 4 59,320
47-5081: helpers-extraction workers 12.1 19.4 15.1 3 4 25,840
53-7072: pump operators, except wellhead 12.2 11.0 9.5 3 4 11,870
Average 11.4 years 26.0 months 16.9 months 0.38 (GGS imp) 0.45 (imp) 77,623

Z. Chen et al.
Table 1 (continued)
SOC Occupation Required level of educa- Related work expe- On-the-job General green skills Engineering & Tech. GGS Employment
tion (average years of rience (months) training Importance Quartile importance Quartile in 2016
formal education) (months) (1: low; 4: high) (1: low; 4: high)

Brown low demand
43-5041: meter readers, utilities 12.0 6.1 5.6 2 3 39,530
51-4022: forging machine SOT, metal & plastic 11.9 17.4 7.2 2 3 22,270
51-4023: rolling machine SOT, metal & plastic 11.6 9.7 8.5 2 3 36,040
51-4052: pourers & casters, metal 11.6 2.9 2.4 1 2 10,620
51-4062: patternmakers, metal & plastic 12.6 43.8 34.9 3 4 4130
51-4071: foundry mold & coremakers 10.6 1.1 9.4 1 3 12,510
51-4191: heat treating equipment SOT, metal & plastic 12.0 6.5 6.3 3 3 21,760
51-4193: plating & coating machine SOT, metal & plastic 11.2 4.0 9.3 1 3 34,420
51-6061: textile bleaching & dyeing MOT 11.8 5.5 3.5 3 3 11,350
53-7111: mine shuttle car operators 11.6 9.3 2.0 2 3 2990
Average 11.7 years 10.6 months 8.9 months 0.29 (GGS imp) 0.33 (imp) 19,562
At risk from COVID-19
35-1012: first-line Superv. of food Prep. & servers 11.7 8.1 14.4 2 3 908,550
53-3022: bus drivers, school or special client 11.9 2.5 3.5 1 2 515,020
Green Stimulus in a Post-pandemic Recovery: the Role of Skills…

35-9011: dining room & Café. attendants & bar helpers 10.1 3.0 5.0 1 1 423,080
35-9031: Host(ess)es, restaurant, lounge, & coffee shop 10.6 1.3 4.3 1 2 404,360
39-5012: hairdressers, hairstylists, and cosmetologists 13.0 4.0 13.8 2 3 352,380
39-3091: amusement and recreation attendants 10.1 4.1 6.8 1 2 286,740
53-3021: bus drivers, transit and intercity 12.1 5.5 13.8 2 3 169,680
35-1011: chefs and head cooks 12.8 9.6 41.9 2 3 134,190
39-3031: ushers, lobby attendants, and ticket takers 11.3 0.8 2.3 1 1 117,920
53-2031: flight attendants 13.2 2.3 20.1 1 2 113,390
Average 11.7 years 4.1 months 12.6 months 0.24 (GGS imp) 0.16 (imp) 342,531

Acronyms in occupation titles: SOT setters, operators, and tenders, MOT machine operators & tenders. Data sources and definitions: High-demand and low-demand occupations were identified by considering the projected per-
centage growth rate in employment for 2018–2028 as estimated by the BLS Employment Projections database (https://www.bls.gov/emp/). At Risk from COVID-19 occupations selected as low work-from-home occupations
with highest physical proximity at work using data from Mongey et al. (2020), as described in the text. Brown occupations are defined as occupations highly represented in pollution-intensive sectors (see Vona et al. 2018).
Green occupations are defined as occupations performing at least one green task. Required level of education, related work experience and on-the-job training are retrieved from O*NET 18.0. Green General Skills quartiles
refer to the occupational employment distribution for year 2000 (see Popp et al. 2020). GGS average importance score and average Engineering & Technical importance score is based on data from O*NET 18.0 after renormal-
907
13
izing such scores to vary between 0 and 1 (see Vona et al. 2018 for further information on the construction of GGS indicators)
908 Z. Chen et al.

Fig. 1 Green ARRA and Employment Growth: The Role of Training. Notes: 579 Commuting Zones with
population > 25 k. DOE + EPA ARRA spending refers to cumulative ARRA spending awarded by the
Department of Energy and the Environment Protection Agency over 2009–2012 (Source: FedSpending.org).
Employment growth: logarithmic difference in total employment (source: Quarterly Census on Employment
and Wages, BLS) between 2012 and 2008 (panel A) and 2017–2008 (panel B). Size of circles is propor-
tional to CZ’s population in 2008. Correlation coefficients and linear interpolation are weighted with popu-
lation in 2008. The median value of ARRA green training per capita for areas with positive funding is $2.6

green stimulus will not, by itself, provide immediate assistance to those workers hurt
most by COVID-19 lockdowns.
What about workers lacking the pre-requisite skills? We provide a preliminary
answer to these questions based on the experience of the ARRA green package. The
green package of ARRA included investments to retrain workers for green jobs. While
small relative to the overall stimulus, DOE ARRA investments included $228 million in
job training, and the Department of Labor provided an additional $496 million for four
green job training programs. Together, these programs helped develop skills for energy
efficiency retrofit and the renewable energy industry.
Figure 1 provides suggestive evidence that these training programs improved the
effectiveness of green ARRA subsidies. The figure provides unconditional correlations
between the log of green ARRA investments per capita and employment growth (panel
A for short run (2008–2012), panel B for long run (2008–2018). We compute such cor-
relations for areas which did not receive any ARRA funding for green training (charts
A1 and B1, 335 commuting zones), areas that received below-median per-capita ARRA
funding for green training (charts A2 and B2, 112 commuting zones) and areas that

13
Green Stimulus in a Post-pandemic Recovery: the Role of Skills… 909

received above-median per-capita ARRA funding for green training (charts A3 and B3,
112 commuting zones).6
There are two main takeaways from this figure. First, the positive association between
green ARRA spending and employment growth is present only in the 224 commuting
zones (out of 559) receiving training investments. Such associations are much larger for
areas above the median of green training spending. Second, these findings may explain
why green stimulus funds took longer to create jobs, as the correlations between green
ARRA and employment growth are greatest in the long-run. The correlation between green
ARRA spending and employment growth is 0.261 for commuting zones receiving the larg-
est amount of green training, 0.086 for commuting zones with some green training and
negative for commuting zones with no green training.
These suggestive data, coupled with the findings of Popp et al. (2020) about the impor-
tance of green skills for the success of a green stimulus, imply a role for job training in
the transition to a green economy. Recent studies suggest that climate policies may reduce
jobs in specific sectors, particularly for lower skilled manual labor (Marin and Vona 2019;
Yip 2018). The green jobs featured in Table 1 require over a year of on-the-job training,
and workers from the energy sector who may be displaced in a greener economy have
the prerequisite general skills for these new green jobs. That combination bodes well for
the potential of job training to help energy sector workers displaced in the transition to a
green economy. The larger gap in training requirement and green skills importance for low-
demand brown occupations and occupations at risk from COVID-19 lends further support
on the role of training investments for the design of the future green stimulus packages.
While there are examples of high-demand green jobs that require less training, such as con-
struction laborers, it is still the case that those green occupations require more green skills
than most low-demand brown jobs. Understanding which types of workers benefit from job
training, and in particular whether job training can help those without green skills develop
the skills needed for a green economy, requires data on training program participants and is
left for future research.
The extent to which these preliminary results on the importance of on-the-job train-
ing applies to other countries depends on the institutional setup supporting investments in
training by companies and workers (Hall and Soskice 2001). Labour economists suggest
that stronger employment protection, the presence of unions and the quality of vocational
schools all favour training investments. For instance, Acemoglu (2003) and Acemoglu and
Pischke (1999) develop models where higher minimum wages or firing costs induce invest-
ments in training to enhance the productivity of unskilled workers. European countries,
especially German speaking ones with their well-established systems of vocational and
technical schools, appears well positioned to retrain workers for green jobs.
While we expect the skill set of European unskilled workers to better prepare them for
the green transition than American unskilled workers, the lack of data for European coun-
tries does not allow us to compute the skill and training requirements similar to those of
Table 1. However, indirect evidence using standardized test scores for workers in several
countries show that not only is the distribution of key general skills (i.e., literacy and math)

6
78.1% of the population lives in areas that received at least some funding for green training from ARRA.
The average funding for green training is $2.4 per capita, with substantial heterogeneity across areas: the
average green training funding per capita for areas with positive but below-median funding (48.9% of the
population) was $1.2, while people in areas above the median (22.9% of the population) received $11.7 per
capita on average.

13
910 Z. Chen et al.

more dispersed in the US than in Europe, but also that the share of low skilled workers is
higher in the US than in most European countries (Blau and Kahn 2005; Broecke et al.
2016). Moreover, since the technologies in use in Europe are similar to those in the US, we
expect the skill gaps described in Table 1 to be relevant also for EU countries.

5 Conclusions

While green investments can play a role in a pandemic-related recovery, they should be
seen as measures designed to assist in the inevitable transition to a greener economy, so
that investments made now do not contribute to stranded energy assets likely to be obso-
lete in 10 years. Even without new climate policy initiatives, the COVID-19 crisis may
trigger long-term structural transformations in the economy that are largely unpredictable
now. On-going technological trends in the green and digital economy will not come to a
halt, and may even be reinforced. For instance, might demand for fossil fuels fall as busi-
ness travelers realize the potential of replacing face-to-face gatherings with video confer-
encing? Are communities dependent on shale oil production prepared for a world of low
oil prices? With appropriate investments in job training, many of these workers have the
skills needed to transition to greener jobs. Training support appears even more important
for brown workers displaced in traditional manufacturing jobs in heavy industries (low-
demand brown occupations) for which the gap in green skills is larger. However, given
the importance of green skill endowments for the success of green ARRA investments,
whether job training will help workers overcome large skill gaps is a question that remains
to be addressed in the empirical literature.
A more pressing need for pandemic-related assistance is helping workers in occupa-
tions most at risk from COVID-19. Here, green investments are less likely to help. It would
be irresponsible for politicians to confuse the short-term and long-term needs and tell the
voters that a green stimulus is going to magically bring back all the jobs, just as it would
be irresponsible to argue that because we are in a crisis we can ignore problems of pollu-
tion and climate change. In addition to the skill mismatch between these displaced work-
ers and green jobs, the longer lag time observed for green stimulus funds to take effect
argues against using such funds to get workers back on their feet quickly. Instead, stimulus
investments could help reduce the risks that workers in these jobs face, such as by provid-
ing technology and security equipment (e.g. masks, plexiglass barriers). Such investments
should aim to both make it easier and safer to return to work, and to also give potential
customers confidence in the safety of using these services.
For future research, richer data would enable more detailed study of green job training.
For example, can job training help those without green skills develop the skills needed
for a green economy, or is its effectiveness limited to those whose skills are close to what
green jobs demand? Moreover, in Popp et al. (2020) we find that wage gains did not fol-
low the increase in the demand of manual tasks in areas receiving higher green subsidies.
Exploring whether this is due to the fact that the green jobs created are of low quality
compared to similar jobs, or to the widespread deterioration of employment opportunities
of the unskilled requires the use of longitudinal worker-level data and is left for future
research.

Acknowledgements This project has been supported in part through the Smart Prosperity Institute Research
Network and its Greening Growth Partnership, which is supported by a Social Sciences and Humani-
ties Research Council of Canada Partnership Grant (No. 895-2017-1018), as well as by Environment and

13
Green Stimulus in a Post-pandemic Recovery: the Role of Skills… 911

Climate Change Canada’s Economics and Environmental Policy Research Network (EEPRN). This work
was also supported by Horizon 2020 Framework Programme, project INNOPATHS [Grant number 730403].

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