On the Employment Effects of Climate Policy:
The Evidence from Carbon Tax in British Columbia∗
Akio Yamazaki†
University of Calgary
Version: September 2014
Working Paper
Please Do Not Cite Without Author’s Permission.
Abstract
This paper examines how the unilateral imposition of the carbon tax in British
Columbia (BC) affected the regional labor market. The unique structure of the tax allows the exploitation of variation in tax exposure across time, industries, and provinces
to isolate its causal effect on employment. The estimates suggest that the employment
effect is heterogeneous across industries due to the differences in emission intensity.
The overall employment effect is significantly positive as more industries experienced
an increase in employment. As these findings are robust across many specifications, this
paper provides an evidence that a carbon tax does not provide an adverse employment
effect in a regulated region.
Key Words: Environmental regulation; carbon tax; employment; unilateral climate policy
JEL Codes: E24, H23, J2, Q5
∗
I am very grateful to my advisors, Jared C. Carbone and M. Scott Taylor, for their great supervision
and invaluable advice. The paper also benefited from extensive discussions with Pamela Campa, Nicolas
Rivers, and Trevor Tombe. I also thank Jevan Cherniwchan, Eugene Choo, Arvind Magesan, Kenneth J.
McKenzie , Matthew Webb, and seminar participants at University of Calgary for their helpful comments.
All remaining errors are my own.
†
Email: ayamazak@ucalgary.ca
1. Introduction
With growing concern about global warming and climate change, many countries around
the world have either implemented or are planning to implement various environmental regulations, one of which is a carbon tax. According to a report by the Climate Commission
(SBS, 2013), 33 countries and 18 sub-national jurisdictions would have a carbon tax in place
by 2013. The benefits of these regulations, such as an improvement of health outcomes
and reductions of various pollutants and emissions, have been investigated extensively by
many researchers.1 These estimated benefits seem to be quite large. Yet many policy analysts worry that the environmental benefits may come with unintended consequences. One
prominent concern is job loss. The public fears that a regulatory burden imposed on firms
would lead to an increase in product price, which lowers the quantity demanded and decreases sales and profits of firms. As a result, layoffs might occur due to a shutdown or
relocation of firms to an area where there is less, or even no, environmental regulation in
place. This issue is especially sensitive when unemployment rates are high such as in the
recent recession, and the environmental regulations are easily blamed. Despite the considerable amount of attention in the media about these concerns, some analysts argue that
the overall employment effect of environmental regulation is small, as the job loss in the
regulated regions will be offset by the job creation in other parts of the economy. To provide
additional evidence towards this active debate, this paper examines the employment effect
of the unilateral implementation of the carbon tax in British Columbia (BC).
On July 1st, 2008, BC implemented a carbon tax on the consumption of all fossil fuels.
Anecdotal evidence suggests the policy is a success — achieving large reductions in pollution
at relatively modest cost to its economy (CBC, 2013; Elgie and McClay, 2013). The policy
intervention has a number of characteristics that make it an ideal natural experiment with
which to study the employment effects of environmental regulation. First, the BC carbon tax
1
There are many studies that look at the effect of environmental regulation on health outcomes and
pollution level. To name a few, see Chay and Greenstone (2003) and Currie and Neidell (2005).
1
is a textbook pollution tax, subjecting almost all sources of carbon pollution in the region
to a uniform price per tonne of carbon emitted, making it easy to connect the predictions of
theory to an empirical test. Second, the speed with which the policy was implemented made
it a surprise to most stakeholders, ruling out the possibility that polluters would adjust their
behavior in anticipation of the regulation. Third, the relatively high tax rate adopted meant
that it provided a strong signal to polluters to change their behavior when the policy was
introduced.
To take advantages of these unique characteristics of the BC carbon tax, my empirical
strategy exploits three sources of variation. As BC unilaterally implemented the carbon
tax, the policy placed additional costs on residences of the BC2 . This creates temporal (prevs. post-carbon tax) and regional (BC vs. non-BC province) variation. A third source
of variation arises from the heterogeneity in emission intensity across industries. Although
the carbon tax is levied at a uniform rate, the actual burden differs across industries as
it depends on how much emission each industry emits. The heterogeneity in tax exposure
across industries creates a cross-industry variation. Having these three types of variation
allow me to employ an empirical approach similar in spirit to triple differences that control
for any unobserved time-invariant industry and province heterogeneity, time-varying industry
shocks, and aggregate trends that would otherwise confound identification of the causal effect.
Using this approach and after controlling for various shocks at province and industry
level, I find that the implementation of a carbon tax in BC led to decreases in employment
for industries whose emission intensity is greater than 0.57 tonnes per thousand dollars of
production, and employment increases for industries whose emission intensity falls below this
threshold. For example, at $10 per tonne of carbon dioxide equivalent (CO2 e), the cement
and concrete product manufacturing sector, one of the most emission intensive sectors, ex2
Quebec introduced a carbon policy in 2007 as well (CBC, 2007); however, given that Quebec’s carbon
tax rate is set at much lower rate, and its treatment of tax revenue differs from the BC carbon tax, I exclude
it from the analysis. Alberta also implemented a carbon levy in 2007 (Alberta Environment and Sustainable
Resource Development, 2014). But only facilities that emit more than 100,000 tonnes of GHG emission a
year is subject to pay $15 a tonne. As the levy is only targeted to the small fraction of industries, I included
Alberta in the analysis as non-carbon tax province.
2
perienced a 22% decline in employment relative to the pre-carbon tax periods. In contrast,
the insurance carrier sector, one of the least emission intensive sectors, experienced a 7%
increase in employment. As there are many more industries whose emission intensity falls
below the threshold, the overall employment effect of the carbon tax is significantly positive.
The results suggest that, on average, the implementation of the carbon tax generated 33,000
net job gains annually during the first 5 years following the implementation of the carbon
tax, which is approximately a 2.5% increase in aggregate employment in BC during the period. This means that job gains in relatively clean industries exceed job losses in relatively
dirty industries.
In the context of the literature, these results are in line with studies that found a positive employment effect of the environmental regulations in the United States (EPA, 1981;
Berman and Bui, 2001; Bezdek et al., 1989; Bezdek, 1993; Morgenstern et al., 2002; Wendling
and Bezdek, 1989). Among these studies, Berman and Bui (2001) and Morgenstern et al.
(2002) suggested an important channel through which the positive employment effects arise.
They both point out that as regulated firms or industries engage in production, their labor
demand could increase if either abatement activity requires additional labors or the production process becomes more labor intensive. This is particularly interesting as it relates to
the degrees of substitutability between fossil fuels and labor in the case with a carbon tax.
Other studies suggested that environmental regulations will create a new environmental protection (EP) industry. EP industry would provide services to abate pollution and
maintain these services. As such, it may create more jobs in EP industry than jobs lost in
regulated industries. Alternatively, stringent environmental standards lead firms to invest in
technology to pollute less but also lower costs and improve quality. This would lead to more
production, sales, and profits, which encourage increases in labor demand. These arguments
are also quite relevant to a carbon tax. To avoid facing higher fuel prices, firms may invest
in technology to obtain higher fuel efficient operation and labor demand may increase in EP
industries that provide the technology.
3
Many earlier studies in this literature, including the studies just mentioned, estimated
the effect based on simulation of macroeconomic and general equilibrium models. As the
results of these studies are quite sensitive to the model assumptions, also other studies found
a negative effect (Hollenbeck, 1979; Hazilla and Kopp, 1990; Greenstone, 2002). Hollenbeck
(1979) and Hazilla and Kopp (1990) both showed that employment declined due to a negative
effect of the Clean Air Act (CAA) of the United States on output. Hollenbeck’s results are
based on the assumption that employment is proportional to physical output, and the results
of Hazilla and Kopp are driven from the reduction of quantity supplied due to an increase in
output prices. These assumptions about the output effect on employment are also recognized
by the studies that found positive employment effects. However, these studies point out that
the negative output effect on employment is rather small and that other positive effects on
employment outweighs the negative effects.
From the perspective of a methodology, this paper is closely related to Berman and
Bui (2001) and more recent study by Greenstone (2002) as they empirically examined the
employment effect using a difference approach. Berman and Bui analyzed the effect of the
local regulation of air pollution in the manufacturing sector in Los Angeles3 by comparing
it with Texas and Louisiana. They exploit the variation in stringency of regulation as
LA imposed a stricter local regulation in addition to federal and state regulations where
Texas and Louisiana did not. They found a positive employment effect, resulting in the
creation of a couple of thousand jobs. On the other hand, Greenstone showed that an
implementation of the Clean Air Act Amendments (CAAA) led to roughly 590,000 job losses
in the manufacturing sector in nonattainment counties from 1972 and 1987, which he claimed
was a relatively small negative effect when compared to the size of the entire manufacturing
sector in the United States. Even though both studies focus on the employment effect in the
manufacturing sector, the estimated results support the opposite effects.
In this literature, mostly some types of pollution regulations in the United States are
3
To be precise, their region of interest is South Coast Air Basin, which includes Los Angeles County,
Orange County, Riverside County, and the non-desert portion of San Bernardino County.
4
investigated such as CAA and CAAA. The CAA is a “command-and-control” regulation,
whereas a carbon tax is a “market-based” regulation. An important prediction of economic
theory is that these two types of regulation will produce different behavioral responses with
consequences for policy cost (Goulder and Schein, 2013). The only empirical study that
examined the effect of a carbon tax on economic activity is by Rivers and Schaufele (2013).
However, their focus was on the trade implication of the BC carbon tax in the agricultural
sector. Having said that, this study would be the first to empirically examine the employment
effect of a market-based regulation.
The rest of the paper proceeds as follows. Section 2 describes the BC carbon tax. Section
3 presents a simple theory to explain the mechanism through which an imposition of a carbon
tax affects a labor market. This framework predicts that an employment effect of the BC
carbon tax varies across industries based on four factors: an elasticity of substitution between
inputs, a own price elasticity of demand, a cross price elasticity of demand, and an income
elasticity of demand. Then section 4 contains my empirical analysis where I explain data
and methodology, and provide results and robustness checks. Lastly, I discuss an implication
on wages in section 5, and then conclude the paper in section 6.
2. Overview of the BC Carbon Tax
A carbon tax is believed to be the most cost-effective way to reduce greenhouse gas
(GHG) emissions as it is a type of Pigovian tax (Elgie and McClay, 2013). Its purpose is to
give incentives to households and businesses to consume less of the fuels that release GHG
emissions into the atmosphere. The BC carbon tax was introduced with the objective of
reducing emissions by a minimum of 33% below the 2007 levels by 2020 (Ministry of Finance,
2013).
On February 2007, BC’s new climate policy agenda to prioritize the environmental concerns was unveiled by Premier Gordon Campbell in the throne speech (Harrison, 2012). As
5
the Liberal government, led by Premier Campbell, had undergone deep cuts to the environmental budget and supported offshore oil and gas exploration, the speech was a surprise to
many, including members of his own party (Harrison, 2012). In October 2007, the Ministry
of Finance publicly acknowledged that a carbon tax was under consideration, and officially
announced in their budget plan in February 2008 that a carbon tax would be implemented.
The enforcement of the carbon tax was rather quick since it was implemented in just five
months following the official announcement. Although the announcement took the public by
surprise, polls indicated that 72% felt that the introduction of a carbon tax was a positive
step (Duff, 2008). Others, including businesses and industry associations, worried about the
adverse effect on their operational costs and requested tax relief or exemption (Ministry of
Finance, 2013). Overall, the implementation of the carbon tax has been supported by the
residences of BC as the Liberal party has won the majority seats in post-carbon tax elections,
2009 and 2013.
Carbon taxes often include some form of exemptions, mostly for energy intensive and
trade exposed industries, to protect the domestic economy, which makes the tax relatively
narrow-based. However, the BC carbon tax is intended to be broad-based, i.e. exemptions
were applied to none of the industries initially.4 In addition, the tax-base is broad in terms
of the types of fuels that are subject to the tax. It taxes the carbon content of all fossil
fuels including gasoline, diesel, natural gas, coal, propane, and home heating fuel (Ministry
of Finance, 2008; Ministry of Small Business and Revenue, 2008).
In the budget and fiscal plan for 2014 (Ministry of Finance, 2014), the carbon tax revenue
raised in the 2012-2013 fiscal year was $1.1 billion and is estimated to be $1.2 billion for
2013-2014. There are three different ways to deal with these tax revenues from the carbon
tax: revenue-raising, revenue-neutral, and deficit-reduction. BC took the revenue-neutral
approach. In these cases, all of the carbon tax revenue will be returned to individuals and
businesses through reductions of other taxes such as income and corporate taxes (Ministry of
4
This has changed as of January 1st, 2014. To protect agricultural industries, a carbon tax relief is
granted to commercial greenhouse growers. For further information, see http:www.gov.bc.ca/agri/.
6
Finance, 2008). The corporate income tax rate and the two lowest personal income tax rates
were reduced by 5 percent, which resulted in the BC’s corporate and personal income tax
rates the lowest in Canada5 (Elgie and McClay, 2013). In addition, to protect low-income
households, the government provides these households a lump-sum credit. In 2012-2013,
$1.4 billion was credited back to individuals and businesses. In fact, tax credits exceeded
the tax revenue by $260 million. This excess is estimated to decline to only $20 million in
2013-2014.
In the initial year of 2008, the tax was set at $10/t CO2 e emissions from burning fossil
fuels and set to increase by $5/t CO2 e annually until 2012 (Ministry of Small Business and
Revenue, 2008). This means that the carbon tax would increased 2.41 cents per litre for
gasoline, rising gradually to 7.24 cents a litre by 2012 (Ministry of Finance, 2008). The
gradual increase of the tax rate intends to allow consumers to adjust their fuel usage rather
slowly to minimize the financial burden from the tax. As of 2014, the tax rate is expected
to stay at $30/t CO2 e.
Aside from the economic impact of the BC carbon tax, a recent report (Elgie and McClay,
2013) showed that the BC carbon tax has reduced a per capita use of fossil fuel by 19% more
than that of the rest of Canada during the first four years following the implementation.
Similarly, per capita GHG emissions have declined by 10% in BC from 2008 to 2011. It has
only been several years since the implementation, but we have already seen a substantial
reduction of use of fossil fuel and GHG emissions. The BC carbon tax has been fulfilling its
purpose so far.
3. How Could a Carbon Tax Raise Employment?
In this section, I establish a simple theory that explains the mechanisms through which
an employment effect of a carbon tax come about and shows that both positive and negative
5
In fact, BC has tied with Alberta and New Brunswick for the lowest corporate tax rate in Canada, and
BC has the lowest personal income tax rate in Canada, but for only those earnings up to $119,000.
7
employment effects are possible. I do this by taking several steps towards a theory that
incorporates most of the characteristics of the BC carbon tax.
I. Partial Equilibrium: One Industry Case
I first start with the simplest case, a partial equilibrium analysis with only one industry.
To simplify the analysis, I assume that a firm only use labor and fossil fuel as inputs and
takes the price of fossil fuel as given. Then a perfectly competitive firm would minimize its
cost by solving:
min c = w1 l + w2 e subject to q = f (l, e)
e,l
where l denotes labor, e denotes fossil fuel, and w1 and w2 are corresponding factor prices.
By solving this, I can drive conditional input demands for l and e as l∗ = l(w1 , w2 , q) and
e∗ = e(w1 , w2 , q). Then using these equilibrium input demands, I can define a cost function
c = c(w1 , w2 , q). By assuming a constant return to scale production function, I can show
that c(w1 , w2 , q) = qc(w1 , w2 ). With this cost function, I can derive a labor demand function
by Shepard’s lemma as follows:
ld = qc1
(3.1)
where ci indicate the partial derivative with respect to ith argument. c1 is the amount of
labor required to produce one unit of q, which I redefined it as aLQ . Then taking logs and
total differentiating (3.1) yields labor demand decomposition:
lbd =
+
qb
|{z}
Output effect
b
aLQ
|{z}
(3.2)
Factor effect
where lbd = dld /ld , and so on (i.e. “ b ” denotes percent change). This labor demand
decomposition implies that the percentage change in labor demand is determined by two
effects, the output and factor effect. Now let me investigate one effect at a time.
From a demand side, the demand function is given by q = D(p), holding income fixed.
8
Taking logs and total differentiating yields:
dq
1 ∂D(p)
=
dp
q
D(p) ∂p
(3.3)
Then by algebraic manipulation, I can show (3.3) as follows:
qb = εDP pb
(3.4)
where εDP ≤ 0 is a price elasticity of demand.
Now to figure out an expression for pb, a firm maximize its profit by solving:
max π = pq − qc(w1 , w2 )
q
Then the first order condition of firm’s maximization problem yields p = c(w1 , w2 ). Now let
me introduce a carbon tax on fossil fuel as follows:
w20 = w2 (1 + t)
Then p = c(w1 , w20 ). Totally differentiate and dividing by p yields:
dp
c1 dw1 c2 dw20
=
+
p
p
p
(3.5)
With a simple manipulation, I can show (3.5) as follows:
\
pb = θl w
b1 + θe (w
b2 + (1
+ t))
where θi refers to the cost share of input i (i.e. θi = (ci wi )/c). However, in this paper we are
only interested in an effect of a carbon tax on employment, which leads to an assumption
9
that w
b1 = w
b2 = 0. Then I am left with,
\
pb = θe (1
+ t)
(3.6)
Finally, by plugging (3.6) back into (3.4), I have an expression for the output effect.
\
qb = εDP θe (1
+ t)
(3.7)
(3.7) shows that the change in output from a carbon tax is determined by two factors: the
price elasticity of demand and the cost share of fossil fuel.
Similarly, let me drive an expression for the factor effect. As aLQ = c1 (w1 , w2 ), total
differentiate and divide both side by aLQ yields,
daLQ
c11
c12 0
=
dw1 +
dw
aLQ
aLQ
aLQ 2
(3.8)
where c1i refers to the partial derivative of c1 with respect to ith argument (i.e. second
derivative of the cost function).
By simple algebraic manipulation and w
b1 = w
b2 = 0, I can show (3.8) as follows:
c c12 \
θe (1 + t)
c1 c2
\
= σθe (1
+ t)
b
aLQ =
(3.9)
(3.10)
where σ ≥ 06 is the elasticity of substitution between labor and fossil fuel. The steps from
(3.9) to (3.10) are shown in appendix A. (3.10) shows that a percentage change in labor
requirement by a carbon tax is determined by two factors: the elasticity of substitution
between labor and fossil fuel and cost share of fossil fuel. Finally, putting (3.7) and (3.10)
6
When we allow for more than two inputs, σ can be negative if labor and fossil fuels are complement.
10
back into (3.2) yields,
\
\
lbd = εDP θe (1
+ t) + σθe (1
+ t)
|
{z
} | {z }
Output effect
(3.11)
Factor effect
\
= (εDP + σ) θe (1
+ t)
(3.12)
From (3.11), the sign of the employment effect of a carbon tax is determined by the
degree of the negative output and positive factor effect. The size of the negative output
effect depends on the price elasticity of demand. The size of the positive factor effect, on
other hand, depends on the elasticity of substitution between labor and fossil fuel. (3.12)
makes it clear that the sign of the employment effect comes down to a fight between the
price elasticity of demand and the elasticity of substitution between factors.
Even in the one industry case, both positive and negative employment effects are possible.
For example, for a tradable goods industry facing highly elastic demand and limited factor
substitutability, the negative effect from the price elasticity of demand outweighs the positive
effect from the elasticity of substitution. On the contrary, a non-tradable industry like the
service sector face relatively inelastic demand and their factors are highly substitutable, and
the positive effect from the elasticity of substitution outweighs the negative effect from the
demand elasticity. In addition, if the size of the output and factor effects are the same,
then an employment effect of a carbon tax could even be nil because these two effects would
exactly offset each other.
II. Partial Equilibrium: Many Industry Case
One caveat with regard to equation (3.12) is that it does not allow for heterogeneity in
employment effect across industries. To investigate heterogeneous industry-specific employment effects, I need multiple industries to see how interactions between industries affect the
labor market. To simplify the analysis, I work with two industries. As employment effects of
a carbon tax would differ by energy intensity of each industry, I denote these two industries
11
as clean and dirty. With these two industries, I define a demand function more generally as
follows:
qi = Di (pi , pj )
where pi denotes the price for industry i, say the dirty industry and pj denotes the price for
industry j, say the clean industry. This demand function assumes a quasi-linear preferences
as it is independent of income. By making the same algebraic manipulation that derive (3.4),
I have,
qbi = εii pbi + εij pbj
where εii ≤ 0 is a own price elasticity of demand for industry i and εij ∈ [−∞, ∞] is a cross
\
price elasticity of demand. Then similar to (3.6), pbi = θei (1
+ t), which yields,
\
qbi = (εii θei + εij θej )(1
+ t)
(3.13)
where θei denotes an cost share of fossil fuel for the industry i.
As there is no interaction of two industries on the supply side, the factor effect would be
the same as the case with one industry, σi denoting the elasticity of substitution between
labor and fossil fuel for industry i. This gives an expression for the heterogeneous industryspecific employment effect of a carbon tax as follows:
\
\
+ t)
lbid = σi θei (1
+ t) + (ε θ + εij θej )(1
| {z } | ii ei
{z
}
Factor effect
Output effect
\
=
(εii + σi )θei +
εij θej
(1
+ t)
|
{z
}
| {z }
Own industry effect
(3.14)
Cross industry effect
By the symmetry, the percentage change in labor demand for industry j can be shown as:
\
lbjd = (εjj + σj )θej + εji θei (1
+ t)
12
(3.15)
Figure 1: Shifts in Labor Demand
Based on (3.14) and (3.15), an industry-specific employment effect of a carbon tax with
two industries depends on two factors: own industry and cross industry effect. The labor
demand decomposition under two industry case is almost same as (3.12) except that now
it has an output effect from the other industry, which means the cross price elasticity of
demand and cost share of fossil fuel from the other industry also affect own labor demand.
In addition to the own industry effect, cross industry effect adds additional complications to
the determination of the sigh of the employment effect.
As now a percentage change in labor demand depends on three factors, it is impossible
to figure out the sign of the changes in labor demand in a response to the BC carbon tax
a priori. However, there are two cases where the signs can be determined analytically. The
first case is when the own industry effect is negative, and clean and dirty industries produce
complementary goods. In this case, the changes in labor demand would be negative because
both own and cross industry effects are negative.
The second case is when the own industry effect is positive and goods are substitutes. In
13
this case, the employment effect would be positive as both own and cross industry effects are
positive. Other than these two cases, an employment effect could be positive, negative, or
even nil. Then the aggregate employment effect of the BC carbon tax could also be positive,
negative, or zero, which is demonstrated in Figure 1.
In addition to the sign of each effect, the size of each effect also determines the employment effect. The size of each effect depends on how much an industry spends on fossil fuels
relative to labor. This will be an important variation to identify the employment effect of
the BC carbon tax in the estimation as it relates to industry’s emission intensity.
Now to think of aggregate employment, simply adding (3.14) and (3.15) yields:
\
b = lbd + lbd = (σi + εii + εji )θe + (σj + εjj + εij )θe (1
+ t)
L
j
i
i
j
(3.16)
By using the Slutsky equation, I can show that εij = εji . Then I can simplify and rearrange
(3.16) as follows:
\
b = (σi + εii )θe + (σj + εjj )θe + (θe + θe )εij (1
+ t)
L
j
i
j
i
(3.17)
(3.17) makes it clear that the sign of the aggregate employment effect of the BC carbon tax
is ambiguous.
III. Income Redistribution
As a part of the BC carbon tax policy, tax revenues were redistributed back to the
residence of BC via reductions of income and corporate taxes as well as lump-sum transfers.
To investigate the effect of the income redistribution on labor market, I go back to the
simplest case, the partial equilibrium with one industry, and now incorporate a lump-sum
transfer.
To see how an inclusion of the lump-sum transfer could change the employment effect, I
14
need to relax the assumption on the demand function as follows:
q = D(p, I)
where I denotes income. Once again, taking the same algebraic manipulation that derive
(3.4) yields,
\
qb = (εDP θe )(1
+ t) + εDi I Ib
(3.18)
where εDi I ≥ 0 is an income elasticity of demand. Now to accommodate the lump-sum
transfer from the government, I define income as follows:
I0 = I + T
where T is the lump-sum transfer. Then by simple algebraic manipulation, I can show that
Ib0 = φTb, where φ denotes a share of the lump-sum transfer in after-transfer income. By
plugging this back into (3.18), I have,
\
qb = (εDP θe )(1
+ t) + εDI φTb
Then, putting this together with the factor effect yields:
\
\
+ t) + εDI φTb
lbd = σθe (1
+ t) + (ε θ )(1
| {z }
| {z } | DP e{z
}
Factor effect
Output effect
(3.19)
Income effect
Now from this carbon tax, government receives tax revenues and gives them all back to
the residence of BC, which yields the following budget constraint:
T = tw2 e(w1 , w20 )q
15
Taking logs and totally differentiating above yields:
\
Tb = b
t + εew20 (1
+ t) + qb
(3.20)
where εew20 < 0 is a price elasticity of input demand for fossil fuel, i.e., the changes in demand
\
for fossil fuel as inputs in a response to the changes in its price. As qb = εDP θe (1
+ t) from
(3.7), I can rearrange (3.20) as follows:
\
Tb = b
t + (εew20 + εDP θe )(1
+ t)
(3.21)
(3.21) shows that the size of the tax revenue, equivalently the lump-sum transfer, depends
on three factors, the size of the carbon tax, the price elasticity of factor demand, and the
price elasticity of demand. The lump-sum transfer could increase as the carbon tax rate goes
up. At the same time, it could decrease because the firm uses less fossil fuel now that a price
of fossil fuel is higher, which leads to an erosion of a tax-base, or the demand for fossil fuel
is less as the firm produces less, which requires less of inputs. One point to be careful about
(3.21) is that it is never negative. Although these two elasticities in (3.21) are negative,
Tb being negative means either money is taken away from consumers or the amount of the
lump-sum transfer is smaller than the previous year. However, the latter case is irrelevant
here as there is no dynamics in this simply theory. Therefore, it does not make sense for Tb
to be negative.
Finally, plugging (3.21) into (3.19) yields:
\
\
\
+ t) + εDI φ(b
t + (εew20 + εDP θe )(1
+ t))
lbd = σθe (1
+ t) + (ε θ )(1
} |
| {z } | DP e{z
{z
}
Factor effect
Output effect
(3.22)
Income effect
\
= ((σ + εDP (1 + εDI φ))θe + εDI εew20 φ)(1
+ t) + εDI φb
t
As income elasticity of demand is always positive7 , the income effect would also be
7
Here I assume goods are normal.
16
positive. Then, the chances of the employment effect being positive are higher because now
in addition to positive factor effect, there is the positive income effect that could outweigh
the negative output effect. From (3.22), I show that revenue-neutrality of a carbon tax could
increase the likelihood of positive employment effects of a carbon tax as it pushes the labor
demand curve further out to the right.
One possible criticism that this subsection may receive is that the income redistribution from the BC carbon tax does not take the form of only lump-sum transfers. Even so
the results from this subsection serves as a lower-bound for an employment effect of the
revenue-neutral carbon tax policy because lump-sum transfers are roughly 20% of the total
redistribution (Ministry of Finance, 2008).
IV. Employment Dividend
So far, my simply theory has focused on the labor demand. The employment effect of
a carbon tax could also arise from the changes in labor supply, especially when the tax
revenues are refunded back to the residence of BC. The positive employment effect from
a revenue-neutral carbon tax is known as “employment dividend” in the double dividend
literature (see Goulder (1995), Bovenberg and Goulder (2002), and Bovenberg (1999)). It
basically means that by recycling the revenue from the carbon tax in a form of reductions
of distortionary taxes, not only does the carbon tax bring environmental benefits, but also
it lessens the sizes of distortion in the other tax system.
As there are many theoretical studies in the current double dividend literature, I would
not provide theories here. Rather I would simply point out that the employment effect of the
carbon tax could also come from the supply side. In particular, if an employment dividend
exits, reducing the personal income tax and providing a lump-sum transfer would increase
labor supply. Then the overall employment effect would likely to be positive unless the
employment effect from the labor demand is negative and large.
After considering changes in both labor demand and supply in a response to a revenue17
Figure 2: Shifts in Labor Demand and Supply
neutral carbon tax, it is not clear which direction the equilibrium level of labor would go a
priori. Both directions are possible, which is illustrated in Figure 2. Even with my simple
theory on labor demand and the double dividend literature on labor supply, the sign of the
employment effect is ambiguous.
4. Empirical Analysis
I. Data Sources
To examine the employment effects of a carbon tax, I use industry-level data for employment and GHG emission intensity. Employment is measured as a number of all employees in
an industry whereas emission intensity is measured in tonnes per thousand current dollars of
production. Both data are from the Canadian Socio Economic Information Management System (CANSIM) at Statistics Canada and categorized based on the North American Industry
Classification System (NAICS). NAICS is used by federal statistical agencies in classifying
18
Table 1: Descriptive Statistics: 2001-2012
Mean
Overall (N = 5,100 , J = 83)
Employment
20323
Emission Intensity 0.4979
Employment
BC (N = 816 , J = 71)
Overall
21689.3
High
6074
Low
18668
Non-BC (N = 4284 , J = 83)
Overall
20063
High
7083.4
Low
13021.4
Std. Dev.
Median
Min
Max
53689
0.50118
4601.5
0.35
24
0.12
675227
3.26
40885.8
6730.7
9005
6776.5
2958
18329.5
333
333
3832
262568
21338
34685
55796.1
10857.8
23235.9
3923.5
3313
3478
24
97
110
675227
57855
133551
Note: N refers to the number of observations.
J refers to the number of industries.
High refers to industries whose emission intensity is in 90th percentile and Low
refers to industries whose emission intensity is in 10th percentile.
business establishments based on the types of economic activity in Canada, Mexico, and
United States (United State Census Bureau). Its numbering system consists of a six-digit
code at the most detailed industry level. The employment data has 19 two-digit industries,
65 three-digit industries, and 170 four-digit industries, whereas GHG emission intensity data
has 19, 53, and 54 industries respectively. After merging these two data by matching the
digits, in total, 83 industries are in the sample. List of industries and corresponding NAICS
codes are reported in Appendix B. Descriptive statistics are reported in Table 1. A characteristic of British Columbia (BC) that is worth noting from Table 1 is that BC is relatively
less emission intensive compared to the rest of Canada as the employment ratio between high
and low emission intensive industries is smaller in BC than the rest of Canada. More concisely, industry composition in BC is dominated by low emission intensive industries relative
to the rest of Canada.
CANSIM provides employment data for all provinces from 2001 to 2012. GHG emission
intensity data is only available at the national level and covers from 1999 to 2008. Ideally,
19
data for all provinces and more post-carbon tax periods would be used to exploit emission
intensity variations across time and regions. Therefore, I impose an assumption that national
GHG emission intensity level for each industry can serve as a proxy for each industry in all
provinces. While the emission intensity level might be different across provinces for each
industry, the relative emission intensity level across industries within provinces is likely
to be the same. For instance, a relatively dirty industry in Alberta, such as oil and gas
extraction, would also be a relatively dirty industry in other provinces as well. Therefore,
GHG emission intensity data at national level is sufficient.
To examine the employment effect of the carbon tax, only emission intensity variation
across industries in the pre-carbon tax period is required. With data for more post-carbon
tax periods, one can examine an effect of the carbon tax on a level of GHG emissions.
However, that is not the purpose of this study. GHG emission intensity is likely to decline
in BC after the implementation of the carbon tax; yet, the relative emission intensity across
industries is not likely to change (i.e. a dirty industry will not suddenly become a clean
industry solely due to the implementation of the carbon tax). Therefore, not having data
for post-carbon tax periods is not an issue. I can simply use an emission intensity data at
national level from pre-carbon tax period like 2007.
Before quantitatively evaluating the employment effect of the carbon tax, I plot the
percentage change in employment for high and low emission intensive industries to visualize
the data. Unfortunately, as the carbon tax in BC was implemented in the same year as
the global financial crisis, employment might fall regardless of the financial burden from the
carbon tax. To isolate the adverse employment effect of the financial crisis, there needs to
be employment fluctuations around 2008 in BC that are different from the rest of Canada.
To more closely examine this concern, changes in employment for BC and the rest of
Canada are assessed for two industries: the high emission cement industry and the low
emission insurance industry (Figure 3). Both sectors experienced a decline in employment
right before the implementation of the carbon tax. After the implementation of the carbon
20
Figure 3: Comparison of % changes in employment over time between high emission intensive
and low emission intensive industry
Note: Cement and concrete product manufacturing is one of the high emission intensive industry and
insurance carrier is one of the low emission intensive industry.
Source: Author’s calculation
tax, however, cement and concrete product manufacturing sector in BC experienced a smaller
increase in employment than the rest of Canada. Conversely, insurance carrier sector in BC
experienced a larger increase in employment than the rest of Canada. These findings suggest
that the changes in employment in BC are correlated with the implementation of the carbon
tax through the heterogeneity in industry’s emission intensity level. The financial crisis has
caused a decline in employment in all provinces as it is more a nation-wide incidence. The
fact that changes in employment in BC compared to the rest of Canada are different between
these sectors is an evidence for the existence of the employment effect of the carbon tax.
II. Methodology
In this section, the econometric design to estimate the employment effect of the carbon
tax is discussed. The simple theories presented in the last section are used to motivate the
estimation equation as follows:
ln Lipt = λi + ηp + φit + γt + β1 (EIi × BCp × τt ) + β2 (BCp × τt ) + ipt
21
(4.1)
where ln Lipt is the natural log of employment for industry i in province p at time t.8 I
isolate the employment effect of the BC carbon tax into two part, an industry-specific and
industry-common effects. I do this so that I can identify which industries gain or lose from
the implementation of the BC carbon tax. The first interaction term captures the industryspecific employment effect and the second interaction term capture the common employment
effect of the carbon tax across industries. Let EIi be a GHG emission intensity level for an
industry i in 2007, BCp be an indicator for British Columbia, and τt be carbon tax variable,
defined as follows:
τt =



0







10





 15
if t ≤ 2007
if t = 2008
if t = 2009


20







25





 30
if t = 2010
if t = 2011
if t = 2012
λi is an industry fixed effect that captures time-invariant industry heterogeneity, such as
industry factor intensity. ηp are province fixed effects that control for time-invariant province
heterogeneity such as geographic characteristics9 that are common across industries. φit are
a industry-specific time trend that controls for industry specific shocks across provinces at
given year. Controlling for industry-specific shock is particularly important as this will
control for incidences like financial crisis, exogenous changes in prices of natural resources
and commodities, and any shocks that are specific to industries but common across provinces.
γt are time fixed effects that capture aggregate shocks that are common across industries
and provinces, such as an economic growth at the national level. ipt is an error term that
captures idiosyncratic changes employments.
8
I employ the natural log of employment to remove the skewness in the distribution of employment. After
the transformation, employment is approximately distributed normal.
9
For example, as BC is closest to a coast, employment might fluctuate because of the level of export. If
exports go up in BC, productions need to increase. This increases the demand for labor. Province fixed-effect
will control for any fluctuation in employment due to the effect like this.
22
β1 and β2 are the coefficients of interest. Then let αit ≡ (β̂1 EIi + β̂2 )∆τt 10 , and it is
interpreted as an approximation of percentage change in employment for an industry i at
time t in a respond to the carbon tax. The estimated coefficient β̂1 is identified from across
industry × province comparisons over time and β̂2 is identified from across province comparisons over time. Equation (4.1) will be my fundamental equation to identify the employment
effect of the carbon tax. To properly identify the employment effect, the underlying identification assumption requires that there be no other factors other than the carbon tax creating
a difference in changes in employment for industries in BC that were affected differently by
the carbon tax. In other words, there are no unobserved industry × province shocks that
are correlated with the carbon tax. This assumption will be violated if the government of
BC concurrently implement other policy induced by the carbon tax that affect all industries
in BC differently while no other province implement similar policy.
III. Results
The results are presented in the following subsections. By estimating the regression equation (4.1), the heterogeneous employment effect across industries are discussed in subsection
A. Then in subsection B, I constructed a counterfactual based on the estimation in the
subsection A to calculate the changes in employment for each industry and the aggregate
employment effect.
A. The Industry-Specific Employment Effect
The results of five specifications based on equation (4.1) are reported in Table 2. The
first specification, reported in column (1) in Table 2, is a simple OLS with no controls.
This specification is the most vulnerable to an omitted variable bias (OVB) as there is
no control variables. To lessen the concern of OVB, I include fixed-effects in the rest of
specifications, reported in the column (2) through (5). These estimates are identified with
10
∆τt ≡ τt − τt−1
23
clustered standard errors (clustered on industries11 ), and interpreted as the average treatment
effect of the carbon tax on employment.
The second specification, reported in the column (2), adds time fixed effects to control
for aggregate time-varying factors, while the third specification, reported in the column (3),
includes industry fixed effects that control for time-invariant industry characteristics. In
addition to the time and industry-fixed effects, the specification in the column (4) controls
for any provincial characteristics. The last specification, reported in the column (5), controls
for any unobserved industry-specific shocks over time.
As β̂1 measures the heterogeneous employment effect of the carbon tax across industries,
the significant negative results verify that the employment effect of the carbon tax is, indeed,
heterogeneous across industries. The negative sign of β̂1 means that high emission intensive
industries experience a decline in employment due to the carbon tax because their emission
intensity is so large that βˆ1 EIi exceeds βˆ2 , which makes αit negative. Depending on the size
of βˆ2 , low emission intensive industries experience an increase in employment because of the
similar argument. This confirms the results from some of earlier studies that emphasized the
heterogeneous employment effect of environmental regulation across industries (Wendling
and Bezdek, 1989; Hollenbeck, 1979; Hazilla and Kopp, 1990; Greenstone, 2002).
All results show statistically significant negative heterogeneous effects and positive common effects of the carbon tax. As the last specification in the column (5) includes the full
set of fixed effects based on the equation (4.1), my preferred specification is the result in the
column (5).
To quantify the magnitude of the employment effect from the carbon tax in BC, the
value of αit is calculated for each industry based on the values from the column (5) of both
coefficients. The threshold level of emission intensity that changes the sign of αit between
positive and negative is about 0.57 tonnes per thousand dollar of production. If emission
intensity is bigger than 0.57, the employment effect of the carbon tax is estimated to be
11
I also clustered on provinces. The results did not change in terms of significances. The corresponding
tables for the results with clustered on provinces are available upon a request.
24
Table 2: Estimated Regression Models for the Percentage Changes in Employment
lnL
EIi × BCp × τt (β1 )
BCp × τt (β2 )
Time FE
Industry FE
Province FE
Industry × time FE
N
R2
(1)
-0.0504a
(0.0167)
0.0495a
(0.01000)
5,100
0.011
(2)
(3)
(4)
(5)
-0.0504a -0.0332a
-0.014a
-0.0151b
(0.0167) (0.00935) (0.00482) (0.00596)
0.0506a
0.0464a
0.00698b 0.00860a
(0.0102) (0.00739) (0.00278) (0.00320)
Y
Y
Y
Y
Y
Y
5,100
0.012
5,100
0.314
5,100
0.878
Y
Y
Y
Y
5,100
0.887
c p < 0.1, b p < 0.05, a p < 0.01
Clustered Robust standard errors in parentheses
negative. For example, the emission intensity for cement and concrete product manufacturing
sector is 1.99 tonnes per thousand dollar of production. Thus, at a rate $10/t CO2 e, they
experience a 22% decline in employment on average from the carbon tax. On the other
hand, as the emission intensity for insurance carrier sector is only 0.12, they experience,
on average, a 7% increase in employment. The percentage changes in employment from a
$10/t CO2 e carbon tax for all the industries are reported in Table 7 in the appendix C,
and corresponding scatter plot with 95% confidence intervals is presented in Figure 6 in the
appendix C.
Based on the estimated standard errors for each employment effect, many industries
experienced statistically significant employment effects. The magnitude of the employment
effect is relatively small if the effect is positive, as the highest positive employment effect
is about 7%. On the contrary, if the employment effect is negative, the magnitude varies
largely as it ranges from -0.2% to -40%. This is clearly shown in Figure 7 in the appendix
C. In the sample, about a 30% of industries exceeds the threshold (21 out of 71 industries in
BC that exceeds the threshold). This means that 70% of industries experienced an increase
25
in employment due to the carbon tax.
B. The Aggregate Employment Effect
This subsection discusses the aggregate employment effect of the BC carbon tax. To do
this, I first need to calculate the changes in employment for each industry in a response to
the BC carbon tax so that the aggregate employment effect can be calculated by the sum of
these changes in employment across industries. I do this by constructing a counterfactual,
calculating the level of employment in an absence of the BC carbon tax, and then compare
them with the level of employment from the fitted values in the estimation of my preferred
specification. The difference would be the employment effect of the BC carbon tax.
To visualize the changes in employment for all industries in response to the BC carbon
tax, Figure 8 in the appendix C plots the changes in employment for all industries at $10/t
CO2 e. The air transportation sector experienced the largest reduction in employment (about
4,500 job losses) whereas retail trade experienced the largest increase in employment (about
11,000 job creations). To link back with the example in the previous subsection, the cement
and concrete product manufacturing sector experienced a decline in employment about 650
per year, and the insurance carrier sector experienced an increase in employment per year
by about 860. Although the positive employment effect for the insurance carrier sector is
only a 7%, the 7% increase leads to an increase in employment by 860. On the contrary, the
22% negative employment effect for the cement and concrete product manufacturing sector
leads to a decrease in employment only by 650. This could reflect the fact that the emission
intensive industries are less labor intensive. In fact, the data indeed shows the negative correlation between emission intensity and employment, demonstrated in Figure 4. Therefore,
the relatively large negative employment effects lead to rather a small reduction of employment. This explanation is consistent with one of the factors that determine the employment
effect in the study of Berman and Bui (Berman and Bui, 2001) — the regulation burden
falls on capital-intensive industries disproportionally with relatively little employment.
26
Figure 4: The Correlation between Emission Intensity and Employment
Note: NAICS codes are displayed in the figure along with the scatter plot.
Source: Author’s calculation
Finally, by summing these changes in employment across industries for each case, Figure
5 plots percentage changes in aggregate employment, one with carbon tax and the other
with no carbon tax. Based on the estimation and counterfactual, aggregate employment in
BC increased by about 165,000 over the post-carbon tax periods (i.e. on average 33,000 per
year from 2008 to 2012). This means that the overall employment effect of the carbon tax in
BC is significantly positive. During the same period, aggregate employment in BC is about
6.5 million in an absence of the carbon tax. Then 165,000 job creation corresponds to a 2.5%
of aggregate employment in BC over the post-carbon tax periods.
The positive aggregate employment effect means that increases in employment in cleaner
industries exceeded declines in employment in dirty industries. There are two ways the aggregate employment effect resulted in positive gains in employment. One is that the industry
composition in data is dominated by relatively less emission intensive sectors. The estimation
suggested that less emission intensive industries experienced increase in employment. Then
as the fraction of the emission intensive industries in data is small, number of industries that
experienced the negative employment effect is outnumbered by number of industries that
27
Figure 5: Percentage Change in Aggregate Employment for British Columbia
Source: Author’s calculation
experienced the positive employment effect.
Second is that while the reduction of employment from each negative employment effect
is small, the increases in employment from the positive employment effect are large. The
aggregate employment effect may not be positive even if there are many more clean industries
than dirty industries in BC because the decline in employment from dirty industries could be
so large that the negative effects could still dominate the positive effects from clean industries.
Therefore, these two conditions together ensure the positive aggregate employment effect
from the BC carbon tax.
IV. Robustness Checks
This paper has attempted to estimate the causal effect of a carbon tax on provincial
employment. Even with a perfect econometric design, a nonexperimental research might be
vulnerable to unobserved variations that could confound the causal interpretation. To ensure
the reliability of the estimations, I probed the robustness of the estimates in many different
ways, but found little evidence that undermines the results reported in the last section.
28
Table 3: Probing the Robustness of the Employment Effect of Carbon Tax
lnL
EIi × BCp × τt
BCp × τt
(1)
(2)
(3)
-0.0151b
(0.00596)
0.0086a
(0.00320)
-0.0127b
(0.00559)
0.0078b
(0.00305)
-0.0146b
(0.00582)
0.0085a
(0.00316)
-0.0146b
(0.0059)
0.0085a
(0.00316)
-0.0746
(0.0756)
0.03
(0.0382)
EIi × BCp × τt
BCp × τt
EIi × QCp × τt
QCp × τt
N
R2
Threshold
Carbon Tax
Sample
(4)
5,100
0.887
0.56
5,960
0.879
0.61
5,960
0.879
0.58
5,960
0.879
BC
Exclude Quebec
BC
All
BC & Quebec
All
BC & Quebec
All
c p < 0.1, b p < 0.05, a p < 0.01
Clustered Robust standard errors in parentheses
Note: All specifications include time FE, industry FE, province FE, industry × time FE.
First I re-estimated the regression equation by including Quebec (QC)12 in the sample to
see how the results are sensitive to the different samples. The results are reported in Table 3.
All the specifications include time fixed effects, industry fixed effects, province fixed effects,
and industry × time fixed effects. The result reported in the column (1) is taken from the
column (5) in Table 2, which is the full specification model based on the equation (4.1), and
serve as a base specification. The rest of the results, reported in the column (2) through (4),
should be compared to the base specification.
In this analysis, I first estimate the employment effect of the BC carbon tax including
12
Ideally, I would like to also include Alberta as one of the carbon tax provinces for another robustness
check. However, Alberta carbon tax is only for firms that emit more than 100,000 tonnes. With industry-level
data, I cannot appropriately estimate the employment effect.
29
QC in the sample, but not as the other province that implemented a carbon tax, reported in
the column (2). Then QC is included as the province with a carbon tax. QC implemented
its carbon tax in 2007 at the rate of $3.50/t CO2 e. The result is reported in the column
(3). Regardless of how QC is included in the sample, the estimated results preserved the
statistically significances and the sign of the coefficients of interests. The magnitude also
stayed around the reasonable range of values, i.e., the threshold level which determines
the sign of the employment effect for each industry is about the same as that of the base
specification (0.56 tonnes). Based on the coefficients from the column (2) and (3), the
threshold levels are 0.61 and 0.58 tonnes per thousand dollar of production, respectively.
In the last column, the employment effect of the BC and QC carbon taxes are separately
estimated. The result shows that there is no employment effect of the QC carbon tax while
the employment effect of the BC carbon tax still exits. This is an evidence that inclusion
of QC in the sample does not affect the analysis of the employment effect of the BC carbon
tax.
One of the most potential sources of bias arises from an employment effect that is not
from the carbon tax, such as the adverse effect from the financial crisis and commodity price
shocks. For example, as the effect of the financial crisis on employment is at the national
level, other provinces had also experienced the decline in employment around 2008. To
determine if my econometric design successfully controls for these effects, a placebo test is
conducted. In other words, I have estimated the employment effect of a placebo carbon tax
in other provinces. If the effect of the financial crisis is fully controlled, then implementing
a placebo carbon tax at other provinces in the same year as the BC carbon tax should not
have any employment effects because these carbon taxes are fake. The results are reported
in Table 4. Each column reports the estimation result for each province.
Aside from the results for Saskatchewan, both coefficients for other provinces seem to
be statistically insignificant. For Alberta and Ontario, their βˆ1 are significant at 5% and
10%, respectively, but with opposite signs. However, the fact that their βˆ2 are insignificant
30
Table 4: Placebo Tests by Implementing Fake Carbon Tax in Other Provinces
lnL
EIi × P rovincep × τt
P rovincep × τt
N
R2
(1)
AB
(2)
MB
(3)
NB
(4)
NS
(5)
ON
(6)
PE
(7)
SK
0.0122b
(0.00591)
-0.00166
(0.00401)
-0.00398
(0.00733)
0.00185
(0.00411)
0.00712
(0.0128)
-0.00549
(0.00509)
-0.00586
(0.00996)
-0.000106
(0.00540)
-0.0144c
(0.00838)
0.00288
(0.00474)
-0.000117
(0.0203)
-0.00334
(0.00853)
0.0158a
(0.00555)
-0.00698b
(0.00319)
5100
0.887
5100
0.887
5100
0.887
5100
0.887
5100
0.887
5100
0.887
5100
0.887
c p < 0.1, b p < 0.05, a p < 0.01
Clustered standard errors in parentheses
Note: Provincial abbreviation is as follows: Alberta(AB), Manitoba(MB), New Brunswick(NB), Nova
Scotia(NS), Ontario(ON), Prince Edward Island(PE), and Saskatchewan(SK).
All specifications include time FE, industry FE, province FE, industry × time FE.
in both provinces means that there is no province-level employment effect of their carbon
taxes. In other words, even though the employment effect of a placebo carbon tax is different
across industries, the employment effect might be negligible as a whole. The only province
that shows the different result is Saskatchewan. However, as this is the only province whose
coefficients are both statistically significant, this result might not be compelling enough to
conclude that my econometric design is not robust.
In addition to checking the reliability of my econometric design by the placebo tests, I
would argue that no statistically significant employment effects from the placebo carbon tax
in other provinces could serve as an evidence for no leakage effect from the BC carbon tax.
Another concern that may arise from estimating the employment effect of the BC carbon
tax is that the analysis does not take into account any general equilibrium effects across
provinces. More concisely, job reallocations from BC to other provinces are assumed to be
zero in the analysis. Due to the stricter environmental regulation in BC, some firms in an
emission intensive industry like cement and concrete product manufacturing might relocate
to other provinces. Then job losses from this industry in BC would mean job gains for the
same industry in other provinces. If the leakage effects are large, then the placebo carbon
tax in other provinces should capture these effects. However, this is not the case here unless
31
all the possible reallocations happened in Saskatchewan.
5. Discussion
The positive aggregate employment effect could arise by a rightward shift of either labor
demand or labor supply. As my empirical strategy exploited the variation on industry
emission intensities, the positive employment effect is likely to be from the rightward shift of
labor demand. If that is the case, the equilibrium wage should be increased. To verify this
conjecture, the effect of the BC carbon tax on wage is investigated by a simple regression13 .
Table 5 shows that the imposition of the BC carbon tax has a statistically significant
negative effect on provincial wage. It reduces average hourly and weekly wages by 3% at
the rate of $10/t CO2 e. Although the results might be biased due to the omitted variables
that cannot be controlled by time and province fixed effects, the significant negative wage
effect negates the conjecture that the positive aggregate employment effect is from the shift
of labor demand alone.
In fact, the negative wage effects confirm the existence of an employment dividend as
wages would only go down by increases in labor supply when labor demand also increased.
This implies that the positive aggregate employment effect is the result of the rightward
shifts of both labor demand and supply together. The wages declined because the shift of
labor supply was larger than that of labor demand.
6. Conclusion
This paper provides new evidence that a market-based environmental regulation can generate positive effects in labor markets. As the existing literature fails to provide clear answers
to whether an implementation of an unilateral environmental regulation brings positive or
13
After I collected wage data (hourly and weekly) from CANSIM, I regressed the natural log of average
hourly wages on carbon tax (i.e. BCp × τt from (4.1)), reported in the column (1) in Table 5 and the natural
log of average weekly wages on carbon tax, reported in the column (2).
32
Table 5: Estimated Regression Models for the Percentage Changes in Wages
(1)
hourly
(2)
weekly
Carbon Tax
-0.00303a
(0.000695)
-0.00290a
(0.000796)
Observations
R-squared
108
0.981
108
0.977
ln(Wage)
Clustered Robust standard errors in parentheses
a p<0.01, b p<0.05, c p<0.1
Note: Both specifications include time FE and province FE.
negative effect to its economy, this paper finds that the implementation of the carbon tax in
BC leads to both a decline and an increase in employment across industries depending on
the level of emission intensity. Employment decreases for industries whose emission intensity is greater than 0.57 tonnes per thousand dollar of production and employment increases
for industries whose emission intensity falls below the threshold. As there are many more
industries whose emission intensity falls below the threshold, overall employment effect of
the carbon tax is significantly positive. The preferred result suggests that, on average, the
implementation of the carbon tax generated 33,000 net job gains annually in BC from 2008
to 2012, which is about 2.5% increase in aggregate employment in BC during the period.
Although these estimates are not outcomes from a randomized natural experiments, and
therefore cannot strictly be interpreted as a causal effect, they provide fairly robust evidence
that a carbon tax does not provide an adverse employment effect in a regulated region.
As the latest report (Elgie and McClay, 2013) claimed that the implementation of the
carbon tax did not bring any apparent adverse impact on BC’s economy, this paper adds
another perspective into the evaluation of the BC carbon tax because the report only focused
on the impact on BC’s GDP. To my knowledge, this paper is the first to estimate the ex post
effect of the BC carbon tax on its labor market. As the structure of a carbon tax can take
many different forms in terms of the rate, the coverage of tax-based, and the treatment of
33
the tax revenue, it would bring fruitful contribution to the literature if the implementations
of the carbon tax in other regions are also investigated empirically.
34
References
“Factbox: Carbon taxes around the world,” 2013.
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36
Appendices
Appendix A
Proof for Theory
Elasticity of Substitution in terms of Cost Function
Show
c c12
=σ
c1 c2
From cost function, c(w1 , w2 ), conditional input demand functions can be found by Shepard’s
lemma, c1 = l and c2 = e. Then I can write a following equation as follows:
l
= ln l − ln e
e
= ln c1 (w1 , w2 ) − ln c2 (w1 , w2 )
ln
(A.1)
As a cost function is homogeneous degree of 1, input demand function is homogeneous degree
of 0. Then I can rewrite (A.1) as follows:
ln
l
w2 w2 = ln c1 1,
− ln c2 1,
e
w1
w1
(A.2)
Then take derivative with respect to w2 /w1 ,
w2
2
) c22 (1, w
)
c12 (1, w
d ln el
w1
1
=
−
w2
d w1
c1
c2
(A.3)
w2
As ci (w1 , w2 ) = ci 1, w
∀ i = 1, 2, differentiate both sides with respect to w2 yields
1
w2
1
ci2 (w1 , w2 ) = w1 ci2 1, w1 ∀ i = 1, 2. Then by using these in (A.3),
d ln el
w1 c12 (w1 , w2 ) w1 c22 (w1 , w2 )
−
w2 =
d w1
c1
c2
Multiplying both sides of (A.4) by w2 /w1 yields,
d ln el
w2 w1 c12 (w1 , w2 ) w1 c22 (w1 , w2 )
=
−
2
w1
c1
c2
d ln w
w1
(A.4)
(A.5)
As an input demand function is homogeneous degree of 0, the following holds.
w1 c21 + w2 c22 = 0
(A.6)
Then by using (A.6) in (A.5),
d ln el
c21 −w2 c22 w1 c22
= −
−
2
c
c
c2
d ln w
22
1
w1
−(w2 c2 + w1 c1 )
= −c21
c1 c2
37
(A.7)
Lastly, by one of the properties of cost function, homogeneous degree of 1, and the symmetry
of second derivative by Young’s theorem,
σ=
Appendix B
c12 c
c1 c2
(A.8)
Data
Table 6: Industry list with NAICS code
NAICS
41
44
61
71
72
113
115
213
313
315
316
321
323
324
331
332
333
337
339
481
482
483
484
485
487
491
493
511
512
532
561
562
621
622
811
812
813
2111
2121
2122
Industry
Wholesale trade
Retail trade
Educational services (except universities)
Arts, entertainment and recreation
Accommodation and food services
Forestry and logging
Support activities for agriculture and forestry
Support activities for mining and oil and gas extraction
Textile and textile product mills
Clothing manufacturing
Leather and allied product manufacturing
Wood product manufacturing
Printing and related support activities
Petroleum and coal product manufacturing
Primary metal manufacturing
Fabricated metal product manufacturing
Machinery manufacturing
Furniture and related product manufacturing
Miscellaneous manufacturing
Air transportation
Rail transportation
Water transportation
Truck transportation
Transit and ground passenger transportation
Scenic and sightseeing transportation and support activities for transport
Postal service and couriers and messengers
Warehousing and storage
Publishing industries, information services and data processing service
Motion picture and sound recording industries
Rental and leasing services and lessors of non-financial intangible associations
Administrative and support services
Waste management and remediation services
Health care services (except hospitals) and social assistance
Hospitals
Repair and maintenance
Personal and laundry services and private households
Grant-making, civic, and professional and similar organizations
Oil and gas extraction
Coal mining
Metal ore mining
Continued on next page
38
NAICS
2123
2211
2212
2361
2362
3111
3112
3113
3114
3115
3116
3117
3221
3222
3251
3252
3253
3254
3255
3261
3262
3271
3273
3341
3342
3351
3352
3361
3362
3363
3364
3365
3366
5151
5152
5221
5222
5241
5311
5413
5418
5419
6113
Industry
Non-metallic mineral mining and quarrying
Electric power generation, transmission and distribution
Natural gas distribution, water and other systems
Residential building construction
Non-residential building construction
Animal food manufacturing
Miscellaneous food manufacturing
Sugar and confectionery product manufacturing
Fruit and vegetable preserving and specialty food manufacturing
Dairy product manufacturing
Meat product manufacturing
Seafood product preparation and packaging
Pulp, paper and paperboard mills
Converted paper products manufacturing
Basic chemical manufacturing
Resin, synthetic rubber, and artificial and synthetic fibres and filaments manufacturing
Pesticides, fertilizer and other agricultural chemical manufacturing
Pharmaceutical and medicine manufacturing
Miscellaneous chemical product manufacturing
Plastics product manufacturing
Rubber product manufacturing
Miscellaneous non-metallic mineral product manufacturing
Cement and concrete product manufacturing
Computer and peripheral equipment manufacturing
Electronic product manufacturing
Electrical equipment and component manufacturing
Household appliance manufacturing
Motor vehicle manufacturing
Motor vehicle body and trailer manufacturing
Motor vehicle parts manufacturing
Aerospace product and parts manufacturing
Railroad rolling stock manufacturing
Ship and boat building
Radio and television broadcasting
Pay television, specialty television and program distribution and telecommunications
Depository credit intermediation
Non-depository credit intermediation
Insurance carriers
Lessors of real estate
Architectural, engineering and related services
Advertising, public relations, and related services
Other professional, scientific and technical services
Universities
39
Appendix C
Additional Results
Table 7: Industry specific employment effects at $10/t CO2 e with 95% Confidence Interval
NAICS
41
44
61
71
72
113
115
213
313
315
321
323
324
331
332
333
337
339
481
482
484
485
487
491
493
511
512
532
561
562
621
622
811
812
813
2111
2211
2361
2362
3111
3112
3113
3114
3115
3116
Industry
α
Std Error
Wholesale trade
Retail trade
Educational services (except universities)
Arts, entertainment and recreation
Accommodation and food services
Forestry and logging
Support activities for agriculture and forestry
Support activities for mining and oil and gas extraction
Textile and textile product mills
Clothing manufacturing
Wood product manufacturing
Printing and related support activities
Petroleum and coal product manufacturing
Primary metal manufacturing
Fabricated metal product manufacturing
Machinery manufacturing
Furniture and related product manufacturing
Miscellaneous manufacturing
Air transportation
Rail transportation
Water transportation
Transit and ground passenger transportation
Scenic and sightseeing transportation
Postal service and couriers and messengers
Warehousing and storage
Publishing industries and information services
Motion picture and sound recording industries
Rental and leasing services
Administrative and support services
Waste management and remediation services
Health care services (except hospitals) and social assistance
Hospitals
Repair and maintenance
Personal and laundry services and private households
Grant-making, civic, and professional organizations
Oil and gas extraction
Electric power generation, transmission and distribution
Residential building construction
Non-residential building construction
Animal food manufacturing
Miscellaneous food manufacturing
Sugar and confectionery product manufacturing
Fruit and vegetable preserving and food manufacturing
Dairy product manufacturing
Meat product manufacturing
0.0436b
0.0512b
0.0512b
0.0527b
0.0270c
-0.0064
-0.0276
0.0133
0.0012
0.0421b
0.0088
0.0315b
-0.1215b
-0.0578c
0.0164
0.0345b
0.0360b
0.0300b
-0.1336b
-0.0518c
-0.0821b
-0.0260
0.0315b
0.0285b
0.0542a
0.0603a
0.0330b
0.0482b
0.0618a
0.0164
0.0572a
0.0588a
0.0421b
0.0572a
0.0648a
-0.0790b
-0.3910b
0.0254c
0.0330b
-0.0684b
-0.0321
0.0209
-0.0230
-0.1366b
-0.1230b
0.0179
0.0201
0.0201
0.0206
0.0140
0.0144
0.0198
0.0126
0.0132
0.0175
0.0126
0.0149
0.0537
0.0300
0.0127
0.0155
0.0159
0.0146
0.0583
0.0278
0.0388
0.0193
0.0149
0.0143
0.0210
0.0230
0.0152
0.0192
0.0235
0.0127
0.0220
0.0225
0.0175
0.0220
0.0245
0.0377
0.1587
0.0137
0.0152
0.0338
0.0212
0.0131
0.0184
0.0595
0.0543
c p < 0.1, b p < 0.05, a p < 0.01
95% C.I.
0.0086
0.0118
0.0118
0.0124
-0.0005
-0.0345
-0.0663
-0.0113
-0.0246
0.0079
-0.0159
0.0024
-0.2267
-0.1166
-0.0086
0.0041
0.0049
0.0015
-0.2479
-0.1063
-0.1582
-0.0639
0.0024
0.0005
0.0130
0.0152
0.0032
0.0106
0.0157
-0.0086
0.0141
0.0147
0.0079
0.0141
0.0168
-0.1529
-0.7021
-0.0015
0.0032
-0.1347
-0.0736
-0.0048
-0.0592
-0.2532
-0.2294
0.0786
0.0906
0.0906
0.0930
0.0544
0.0218
0.0112
0.0380
0.0270
0.0763
0.0335
0.0606
-0.0162
0.0009
0.0413
0.0650
0.0672
0.0585
-0.0192
0.0027
-0.0060
0.0118
0.0606
0.0564
0.0955
0.1054
0.0628
0.0858
0.1079
0.0413
0.1004
0.1029
0.0763
0.1004
0.1129
-0.0052
-0.0799
0.0524
0.0628
-0.0022
0.0094
0.0466
0.0131
-0.0200
-0.0166
Continued on next page
40
NAICS
3117
3221
3222
3251
3254
3255
3271
3273
3341
3342
3351
3362
3364
3366
5152
5221
5222
5241
5311
5413
5418
5419
6113
Industry
α
Std Error
Seafood product preparation and packaging
Pulp, paper and paperboard mills
Converted paper products manufacturing
Basic chemical manufacturing
Pharmaceutical and medicine manufacturing
Miscellaneous chemical product manufacturing
Miscellaneous non-metallic mineral product manufacturing
Cement and concrete product manufacturing
Computer and peripheral equipment manufacturing
Electronic product manufacturing
Electrical equipment and component manufacturing
Motor vehicle body and trailer manufacturing
Aerospace product and parts manufacturing
Ship and boat building
Pay television distribution and telecommunications
Depository credit intermediation
Non-depository credit intermediation
Insurance carriers
Lessors of real estate
Architectural, engineering and related services
Advertising, public relations, and related services
Other professional, scientific and technical services
Universities
-0.0018
-0.0624b
0.0042
-0.1684b
0.0466b
-0.0124
-0.1139b
-0.2153b
0.0557a
0.0527b
0.0224c
0.0224c
0.0557a
0.0270b
0.0679a
0.0663a
0.0542a
0.0679a
0.0285b
0.0648a
0.0618a
0.0633a
0.0482b
0.0136
0.0316
0.0129
0.0718
0.0187
0.0156
0.0508
0.0900
0.0215
0.0206
0.0133
0.0133
0.0215
0.0140
0.0255
0.0250
0.0210
0.0255
0.0143
0.0245
0.0235
0.0240
0.0192
c p < 0.1, b p < 0.05, a p < 0.01
41
95% C.I.
-0.0284
-0.1243
-0.0210
-0.3091
0.0099
-0.0431
-0.2135
-0.3918
0.0136
0.0124
-0.0037
-0.0037
0.0136
-0.0005
0.0178
0.0173
0.0130
0.0178
0.0005
0.0168
0.0157
0.0163
0.0106
0.0248
-0.0005
0.0295
-0.0277
0.0834
0.0183
-0.0143
-0.0389
0.0979
0.0930
0.0485
0.0485
0.0979
0.0544
0.1179
0.1154
0.0955
0.1179
0.0564
0.1129
0.1079
0.1104
0.0858
Figure 6: Industry Specific Employment Effects with 95% C.I.
42
Source: Author’s calculation
Figure 7: Industry Specific Employment Effects vs. Emission Intensity
Source: Author’s calculation
43
Figure 8: Changes in Employment at $10/t CO2 e
44
Source: Author’s calculation