Environmental Regulation and Labor Market Reallocation:

A General Equilibrium Analysis

Kunio Tsuyuhara∗

This Version: July 2015

Abstract

This paper studies the impact of an environmental regulation on worker allocation

and investigates its macroeconomic consequences. I analyze the issue within a quan-

titative island-economy model with heterogeneous industries in terms of energy-input

factor intensity and industry-specific human capital accumulation. An environmen-

tal regulation for the policy experiment takes the form of a tax on energy input.

The calibrated model shows that the tax induces labor reallocation not only from

energy intensive (dirtier) industries to less energy intensive (cleaner) industries, but

also toward higher productivity workers in cleaner industries. The overall effects on

employment is nonetheless very small in both the modest energy tax (10%) and high

energy tax (30%) cases. The energy taxes significantly reduce aggregate energy use

in both cases. These results suggest that an energy tax is an effective form of envi-

ronmental regulation without significant adverse effects on the overall employment in

the economy.

Keywords: Environmental regulation, Labor market reallocation, Human capital

JEL Codes: E24, J64, Q52

∗Department of Economics, University of Calgary, 2500 University Dr. N.W. Calgary, Alberta, Canada

(e-mail: kunio.tsuyuhara@ucalgary.ca). I thank Jared Carbone and Trevor Tombe for helpful discussion

and comments.

1

1 Introduction

In political and economic debates, environmental regulation is often suspected to be re-

sponsible for large layoffs in regulated industries. However, it is also said that environ-

mental regulation may allow displaced workers to reallocate toward less regulated, cleaner

industries. Aside from industry-protectionist arguments, at the heart of this policy issue

are the impacts of regulation on the aggregate employment and its welfare implication.

However, economics research has not successfully addressed these macroeconomic aspects

of the issue. The major goal of this study is to fill in this gap and investigate the impact

of an environmental regulation on labor market outcomes.

I analyze this issue within a quantitative island-economy model, along the lines of

Kambourov (2009). The baseline model is a general equilibrium framework with rich

microeconomic structure that admits involuntary search unemployment. The economy

consists of a continuum of islands, where each island itself is a competitive industry.1

The goods produced in each industry constitute a composite consumption good of the

aggregate economy. Workers are initially allocated to an industry, and they accumulate

human capital (low, medium, and high types) by working in a given industry. A worker

may choose to leave the industry and relocate to another industry after one period of

unemployment. However, human capital is industry-specific and is lost after the relocation.

Each industry faces idiosyncratic productivity shocks each period. Those industries which

are more productive attract workers from the other less productive industries, and as a

result, labor reallocation takes place in every period. I augment the baseline model by

introducing heterogeneous energy-input factor intensity in production. This framework

is useful for this study for two main reasons. First, it enables me to capture varying

policy effects with respect to the energy intensity. Second, it allows me to investigate how

the policy and resulting labor market reallocation affect human capital accumulation of

workers in different industries.

Using the calibrated model, I first examine the effects of a 10% energy tax on the

benchmark economy. As expected, the tax significantly reduces energy use in all indus-

tries, and the aggregate energy use decreases by approximately 9%. The energy intensive

industries slightly reduce the overall employment. However, less energy intensive indus-

1I stick to the term industry to represent an island for the rest of the paper.

2

tries increase the employment more than enough to compensate the lost employment in

energy intensive industries. As a result, the aggregate unemployment reduces by 0.002%

points. In addition, those expanding, less energy intensive industries increase the num-

ber of high productivity workers more than they increase the number of low productivity

workers. The employment of high productivity workers increases in those industries be-

cause, once workers land on those industries, they choose to stay longer and accumulate

their human capital unless significantly large productivity shocks hit the industry. That

is, the the energy tax induces labor reallocation not only from dirtier industries toward

cleaner industries but also toward higher productivity workers in those industries in the

steady state. The aggregate real output decreases by 0.462%, which is mostly due to the

reduction of energy use in production.

The effects of the 10% energy tax on labor market outcomes are very small. To see

whether this is the case because the 10% energy tax is too small to generate appreciable

labor reallocation, I examine the case with a significantly higher 30% energy tax. The

results, however, are more or less consistent with the earlier case. Though more industries

with lower energy intensity reduce their employment, the size of displacement is still very

small. In addition, the industries with the lowest energy intensity still increase their

employment. Therefore, the unemployment rate increases only by 0.003% points, which

corresponds to 0.04% reduction of the aggregate employment. The aggregate real output

decreases only by 0.945% even with the significantly higher tax rate.

In summary, this study suggests that both of the arguments at the beginning of this

introduction are plausible, i.e., there will be job losses in the energy intensive industries,

and displaced workers will be reallocated toward less energy intensive industries. More

importantly, the overall effect on unemployment is very small, and employment may even

increase if the tax is small enough. Energy use is reduced significantly in both cases.

These results suggest that an energy tax is an effective form of environmental regulation

without significant adverse effects on the overall employment in the economy.

There is a substantial body of empirical studies that examine the effects of environ-

mental regulations. However, those studies find conflicting results, and evidences are far

from conclusive. Some studies find large and negative effects of regulations on industry

employment (e.g., Bovenberg and Mooij, 1994; Bovenberg, et.al., 1994; and Greenstone,

2002), while other studies find little to no evidence that regulation reduces employment

3

(e.g., Berman and Bui, 2001; Martin, et.al., 2014; and Morgenstern, et.al., 2002). Further

development of this line of industry-level research is called for, but we cannot extrapolate

those results to measure the overall effect. Furthermore, by the very nature of labor mo-

bility, we cannot design an appropriate controlled (natural) experiment to identify the net

effect of environmental regulation on labor reallocation.

In an effort to measure the overall effect, there are several theoretical studies incorpo-

rating general equilibrium perspectives, often in a context of public finance. However, most

of these studies either assume a competitive economy with full employment (e.g. Hazilla

and Kopp 1990) or introduce involuntary unemployment into the model by assuming a

particular market imperfection, such as nominal wage rigidity caused by unions (Boven-

berg, et.al., 1996), hiring costs (Bovenberg, et.al., 1998), and efficiency wage (Schneider,

1997). Even though these studies may capture some aspects of the effects of environmen-

tal regulation on unemployment, one critical issue is that the source of unemployment is

exogenous and is not affected by the regulation.

More recently, Hafstead and Williams III (2014) incorporate search unemployment,

along the lines of Mortensen and Pissarides (1994), into a two-sector general equilibrium

model. Their study departs from the existing general equilibrium studies of environmental

policy in an important way. That is, the sources of unemployment, i.e., job creation and

job destruction in a frictional market, are endogenous, and environmental regulation has

a direct impact on them. Though the proposed model is arguably very simple, it captures

important aspects of environmental regulation and labor reallocation between sectors.

My paper makes two important contributions to the existing theoretical research on

environmental regulation. First, I augment the production technology to include energy

as an input, and expand the model beyond a simple two-sector model to admit multiple-

sectors, each of which uses energy input at different intensity. This additional structure

enriches the inter-sectoral connection through the labor and energy markets. Second,

the model includes industry specific human capital. This is an important feature of the

labor market that endogenously restricts and slows down labor reallocation in the real

world economy. Both of these components introduce additional dimensions to the model

that are previously unexplored in the literature. These features also add a distributional

aspect of the economy, and enable me to derive richer implications from the environmental

regulations.

4

2 A Model of Sectoral Allocation of Labor

The model presented here is a version of Lucas and Prescott’s (1974) island economy. Kam-

bourov (2009) builds a small open-economy version of the island economy with industry-

specific human capital to investigate the effect of trade liberalization on the labor market

outcomes. I follow the similar structure of the preferences and labor market but incor-

porate the industry heterogeneity in terms of energy input intensity in production to

investigate the effect of environmental regulations on sectoral reallocation of workers.

2.1 Preferences and technologies

The economy consists of a continuum of workers with measure one. Each worker die with

probability δ in each period and is replaced by a newly-born worker. Individuals maximize

the expected discounted value of consumption

E∞∑t=0

βt(1− δ)tc̃t, (1)

where β ∈ (0, 1) is a discount factor.

There are a large number of industries each producing a good that is part of the

consumption good c̃. A composite consumption good c̃t is derived from a CES aggregation

c̃t =

(∫ 1

0κic

ρi di

) 1ρ

, (2)

where ci is the good produced in industry i. I assume ρ ∈ (0, 1) so that these goods are

substitute, and elasticity of substitution between any two goods is given by σ = 1/(1−ρ).

I denote the price of good i by pi, and the appropriate price index corresponding to c̃t

is given by

P =

∫ 1

0κi

(κipi

) ρ1−ρ

di. (3)

Given this preferences structure, the demand of for good i is given by

ydi =

(Y

P

)(κipi

) 11−ρ

, (4)

where Y is the aggregate income of the economy.

Let Yi = piysd denote the income received by industry i where ysi is an output in industry

i. Then, total income in the economy is

Y =

∫ 1

0Yidi. (5)

5

Industries are heterogeneous with respect to their idiosyncratic productivity shock and

the factor input intensity. Output in industry i is produced according to the production

function

ysi = zieαii l

1−αii , αi ∈ (0, 1), (6)

where zi is an idiosyncratic productivity shock, ei is the amount of energy input, li is the

efficiency unit of labor employed in the industry, which will be described in detail below,

and αi is the energy-input factor intensity.

The factor intensity of each industry is constant over time. However, the idiosyncratic

productivity shocks z are assumed to evolve according to the following AR(1) process:

ln(z′) = φ ln(z) + ε′, (7)

where ε′ ∼ N(0, σ2ε ) and 0 < φ < 1. I denote the transition function for z by Q(z, dz′).

The energy input is perfectly elastically supplied in the world market. Given the

constant returns to scale technology, each industry is characterized by a representative

firm that operates in a competitive environment and pays each factor of production its

marginal value product. I further assume that all individuals in the economy hold the

equal share of energy inputs, and the returns to the energy input in the economy are

aggregated and redistributed to individuals in a lump-sum manner.2

2.2 Worker allocation across industries

At the beginning of a period, workers are allocated in some way over the industries. There

are three experience level in each industry: type 0, 1, and 2. Type 0 workers are least

productive, and type 2 workers are most productive. In each period, a type 0 worker

becomes a type 1 worker with probability λ1 and a type 1 worker becomes a type 2 worker

with probability λ2. The efficiency units of labor employed in an industry are measured

by

li = a0g0i + a1g1i + a2g2i, a0 < a1 < a2,2∑j=0

aj = 1, (8)

where g0i, g1i, and g2i are the number of type 0, type 1, and type 2 workers employed in

the sector, respectively. The labor market in each industry is competitive and the wage of

2Since the lump-sum transfer does not affect the individual behavior, I abstract it from the rest of the

analysis.

6

each experience level is determined by the marginal value products of the respective labor

input.

Given the current state of the aggregate economy and the labor market condition,

employed workers choose to stay in the industry or leave for another industry. If a worker

chooses to leave, she loses all of her industry-specific experience and stay unemployed for

one period before arriving in a new industry. The worker search is random so that the

probability that an unemployed worker arrives in a specific industries is the same across

all industries.

Let ψ0, ψ1, and ψ2 denote respectively the size of type 0, type 1, and type 2 workers in

an industry at the beginning of a period, and define ψ = (ψ0, ψ1, ψ2). Then, the state of

an industry at the beginning of a period is characterized by a triple (ψ, z, α). From now

on, I use this triple to describe an industry instead of the index i.

The aggregate state of the economy at the beginning of a period is characterized by

the measure of unemployed individuals U , the value of leaving an industry and becoming

unemployed vs, the income in the economy Y , and the price index in the economy P .

To study worker decision problem in industry (ψ, z, α) given U , vs, Y and P , let

vj(ψ, z, α) be the expected present value of the employment for type j worker who has

just arrived in the industry. If the worker stays in the industry, vj(ψ, z, α) will equal the

current wage plus the discounted expected present value of the wage stream from next

period on, taking into account the transition to type j + 1 if j 6= 2. Given the possibility

that the worker chooses to leave an industry immediately upon arrival, vj(ψ, z, α) is defined

as:

v0(ψ, z, α) = max

{vs, w0(ψ, z, α) + β(1− δ)

∫ ((1− λ1)v0(ψ′, z′, α) + λ1v1(ψ′, z′, α)

)Q(z, dz′)

},

(9)

v1(ψ, z, α) = max

{vs, w1(ψ, z, α) + β(1− δ)

∫ ((1− λ2)v1(ψ′, z′, α) + λ2v2(ψ′, z′, α)

)Q(z, dz′)

},

(10)

and

v2(ψ, z, α) = max

{vs, w2(ψ, z, α) + β(1− δ)

∫v2(ψ′, z′, α)Q(z, dz′)

}. (11)

Define g(ψ, z, α) = (g0, g1, g2) be the number of workers of each type that will stay and

work in the current period in the industry. Then, given g(ψ, z, α), the number of workers

of respective type ψ′ = (ψ′0, ψ′1, ψ

′2) in that industry at the beginning of the subsequent

7

period is calculated as

ψ′0 = U + (1− λ1)(1− δ)g0,

ψ′1 = λ1(1− δ)g0 + (1− λ2)(1− δ)g1, and

ψ′2 = λ2(1− δ)g1 + (1− δ)g2.

I denote this law of motion for the starting distribution of workers on an industry as

ψ′ = Γ(g(ψ, z, α)). (12)

In steady state, there is an invariant distribution for each α, µ(ψ, z, α) such that

µ(Ψ′, Z ′, α) =

∫{(ψ,z,α):Γ(g(ψ,z,α)∈Ψ′}

Q(z, Z ′)dµ(ψ, z, α). (13)

Workers who left an industry spend one period as an unemployed, and will land in a

randomly assigned industry in the subsequent period. There is no unemployment compen-

sation or home production. Then, given v0 and the invariant distribution for the respective

sector µc, µd, vs is generated by

vs = (1− δ)β∫v0(ψ, z, α)dµ(ψ, z, α). (14)

2.3 Steady state equilibrium

A steady state satisfies the following market clearing and feasibility conditions. First, as

there is a continuum of firms in each industry, wages are competitively determined, and

workers receive wages that are equal to their marginal value product. Therefore, wages in

industry i are given by

wji = pizi(1− αi)ajeαi(a0g0i + a1g1i + a2g2i)−αi , for j = 0, 1, 2. (15)

Second, in each industry, the number of workers of type j at the beginning of the period

is ψj . Thus, an industry feasibility condition is given by

0 ≤ gj(ψ, z, α) ≤ ψj , for j = 0, 1, 2. (16)

On the other hand, the aggregate feasibility of labor force is given by

U = 1−∫gi(ψ, z, α)dµ(ψ, z, α). (17)

8

The integral on the right-hand side measures the total number of workers employed at any

types and in any industries.

Finally, the aggregate income Y is generated by p(ψ, z, α), g(ψ, z, α), and µ(ψ, z, α):

Y =

∫p(ψ, z, α)ys(g(ψ, z, α), z, α)dµ(ψ, dz, α). (18)

Definition 1. A steady-state equilibrium consists of value functions vj(ψ, z, α), for j =

0, 1, 2, industry employment composition of types g(ψ, z, α), an invariant measure µ(ψ, z, α),

the value of search vs, the measure U of workers switching industries, a price index P , a

price function p(ψ, z, α), total income of the economy Y , such that

1. The equilibrium prices p(ψ, z, α) is consistent with market demand and supply and

with the price index P defined in (3).

2. Taking vs, g(ψ, z, α), U , and p(ψ, z, α) as given, vj(ψ, z, α), for j = 0, 1, 2, maxi-

mized individual’s utility: (9)-(11).

3. The law of motion (12) is consistent with worker decisions to leave the industry.

4. Individual decisions are consistent with the invariant distributions (13).

5. vs is generated by (14).

6. Wages in an industry are determined by (15).

7. The feasibility conditions for the labor force are satisfied: (16) and (17).

8. The aggregate income is given by (18).

3 Quantitative experiments

I use the above model framework to quantitatively assess the impact of environmental

regulations on the labor market outcome. The model is solved numerically and calibrated

following Alvarez and Veracierto (1999) and Kambourov (2009). The goal here is not to

calculate precise predictions for effects of energy tax as this model is still too abstract for

that purpose. Rather, the goal is to examine numerically the likely general equilibrium

implications of the policy and the direction of changes in key labor market outcomes.

9

3.1 Calibration

3.1.1 General parameters

The model period is a quarter. The discount factor β = 0.99 is set to match an annual

interest rate of 4%. The probability of death δ = 0.00625 is chosen to generate an expected

working lifetime of 40 years. I choose κ = 1 so that the goods produced in different

industries are equally valued by individual. The substitutability parameter ρ is set equal

to 0, which implies that the elasticity of substitution is 1.

3.1.2 Human capital parameters

I assume that it takes one year on average for a type 0 worker to become a type 1 worker

and an additional 6 years to become a type 2 worker. That implies that λ1 = 0.25 and

λ2 = 0.0417. Furthermore, I assume that a type 1 worker is 10% more productive than a

type 0 worker within a sector, and that a type 2 worker is 16% more productive than a

type 1 worker within a sector (see Kambourov and Manovskii, 2009; Aguirregabiria and

Alonso-Borego, 2014). That implies that a0 = 0.296, a1 = 0.325, and a2 = 0.378.

3.1.3 Distribution of industries

The quantitative results of the policy effects would crucially depend on how industries are

distributed in the economy. To appropriately evaluate the effects, I divide the industries

into six clusters as follows. First, I set the total number of industries to be 10,000. Second,

I set the highest value of energy factor intensity to be 0.3, based on BLS, manufacturing

and nonmanufacturing productivity data set (Table 17). Then, I choose six discrete points

(0.01, 0.02, 0.05, 0.1, 0.2, 0.3), which represent the upper limit of each cluster. Third, us-

ing the same data set, I calculate the empirical cumulated value output of industries in

each cluster. Then, I set the number of industries in each cluster so that the simulated

distribution of value output matches the empirical distribution, 28.46%, 42.73%, 16.58%,

7.08%, 3.78%, and 1.39%, respectively. From this, distribution of size of industries for

each cluster is 2800, 4300, 1650, 700, 400, 150, respectively.

10

3.1.4 Productivity shocks

Another set of key structural parameters is the persistence in the sectoral log shocks, φ;

and the standard deviation of the innovations to the sectoral log shocks, σε. I choose these

parameters so that the model generates the average standard deviation of real outputs

of the industries equal to 10 and an unemployment rate of 6.8%.3 Note that there is

no analytical relation between these parameters and the corresponding statistics in my

model; therefore, I experimented until a good enough fit was obtained. The calibration

determines that φ = 0.95 and σε = 0.023. Table 1 lists the values of these calibrated

parameters.

3.2 Characterization of the benchmark economy

Before I analyze the effects of energy tax, I describe the benchmark economy. For this

benchmark economy as well as the policy experiment in the following section, I simulate

the model economy for 400 periods (100 years) to compute the average statistics over those

years. Even though the model does not have an aggregate uncertainty, worker reallocation

causes distributional variation and hence endogenous fluctuation of aggregate variables.

This procedure allows me to obtain more reliable prediction of the policy effect.

Table 2 presents the distribution of workers of each type in each cluster and aggregate

unemployment. I normalize the size of the workforce in the economy to 100. Therefore,

these figures represent the percentage of workers who are employed in each category. In

each cluster, type 2, highest productivity workers constitute the majority, approximately

85%, of workforce. On the other hand, type 0, lowest productivity workers constitute only

approximately 2.5% of workforce. Interestingly, even though there is a significant variation

in factor intensity in production among clusters, the distribution of workers among clusters

is more or less aligned with the distribution of output. It is important to note that only

approximately 10% of workers are employed in industries whose energy intensity is more

than 0.1. Table 3 presents the distribution of effective units of labor in each cluster.

3BLS reports sectoral output of manufacturing and nonmanufacturing sectors from 1987 to 2012, using

2009 as a base year. Using this data set, the average standard deviation of annual real output in U.S.

from 2003 to 2012 is 9.98. The labor force statistics from the current population survey reports monthly

unemployment in the U.S. labor market. Based on this data set, the average monthly unemployment from

2003 to 2012 is 6.8.

11

Table 4 presents the wage distribution of each type in each cluster. Unlike above two

tables, which shows more or less expected patterns, the wage distribution shows somewhat

an unexpected feature. Everything else being equal, wages are higher in industries whose

labor intensity is higher, and the similar pattern should appear for the average wage.

However, the benchmark economy shows an inverse V shape where cluster 2 has the

highest average wage. The nominal variables like wages reflect many other endogenous

objects, most importantly the worker distribution. Therefore, specifically identifying the

cause of this pattern is rather difficult. Nonetheless, this feature must be taken into

account when it comes to analyzing the policy effects.

Table 5 presents the distribution of average value outputs, average real outputs, and

average prices in each cluster. The numbers in the parenthesis are the fraction of each

cluster’s output out of total output. The distribution of the average value outputs is the

calibration target for determining the size of each cluster. The distribution of the real

outputs closely follows the distribution of the value outputs. It is important to note that

the average prices are higher for industries with higher energy input intensity.

Table 6 presents the distribution of the average energy inputs in each cluster. Again,

the numbers in the parenthesis are the fraction of each cluster’s energy input out of total

energy use. While its energy intensity is very small, the total energy use of cluster 2

is fairly large because of its large real outputs. More importantly, even though clusters

4 through 6 produce only approximately 10% of total real output, these industries use

almost a half of total energy input in the economy.

3.3 Effects of energy tax

The remaining section describes how the model economy behaves under an environmental

regulation. Specifically, I impose a tax on energy input. Energy input is perfectly elasti-

cally supplied (in the world market) and the price is normalized to be one. Therefore, the

after tax price is (1 + τ). I choose this simple policy experiment, instead of other policies,

such as emissions tax, for two main reasons. First, this model does not consider pollution

per se, and it does not negatively affect consumer welfare. Rather, I focus on the effects

on labor reallocation, and a tax on energy input is the simplest form of regulation for this

purpose. Second, firms in the real economy would respond to emissions tax in the long run

not only by reallocating labor/energy inputs, but also developing abatement technology

12

to reduce their tax burden. This is arguably a very important feature of the policy when

it comes to the trade-off between environmental quality and economic activity. However,

this model does not incorporate such a firm behavior, and analyzing the long run effect of

emission tax in this model would bias the results. Of course, if the optimal environmental

regulation is considered, both of these factors need to be taken into account. I leave these

issues for future research.

I start this policy experiment by examining the effects of modest 10% energy tax on

the benchmark economy. Table 7 presents the effect on employment distribution in terms

of number of workers. The left column of each type represents the actual change in num-

ber of workers and the right column is the percentage change relative to the benchmark

economy. The minus indicates that the policy reduces the employment in that category.

As somehow expected, more energy intensive clusters 4 through 6 reduce their overall em-

ployment, but less energy intensive clusters 1 through 3 increase their overall employment.

However, it shows that the magnitude of effect is small in either direction.4 Moreover, the

increase in employment by cluster 1-3 more than compensates the loss of employment in

cluster 4-6, and the unemployment rate decreases with the energy tax by 0.002% points,

which corresponds approximately to 3,000 workers in the U.S. labor market. These figures

suggest that, as some of the previous empirical studies have found, the net effects of the

policy is small for this modest level of energy tax. Furthermore, it also shows that the

gross effects can be an increase in employment, instead of an increase in unemployment.

The changes in employment of each type in each cluster provide further insights into

the policy effects. Most notably, those expanding clusters increase the employment of type

2 workers more than they increase the employment of type 0 and 1, and the difference is

some orders of magnitude. Similarly, those contracting clusters reduce the employment of

type 2 more than they reduce the employment of type 0 and 1. These results suggest that

the energy tax induces labor reallocation not only from dirtier industries toward cleaner

industries but also toward higher productivity workers in those industries. Moreover, for

more energy intensive industries, energy tax induces substitution of input composition

from energy and higher productive, expensive workers to lower productive, cheap workers.

4As in Table 2, total size of workforce is normalized to 100. The size of the U.S. labor force is approx-

imately 155 million in 2015. So, if I translate these numbers into the current U.S. labor market to get a

better sense of the size of policy effect, 1.0e-04 represents 155 workers.

13

Table 8 presents the effects on effective unit of labor. The middle column shows the

actual change in effective unit in each cluster, and the right column shows its percentage

change relative to the benchmark economy. Reallocation toward less energy intensive in-

dustries is obvious. Though small, the aggregate effective unit of workers slightly increase.

This is partly due to an increase in the aggregate employment and also to the shift toward

more productive type 2 workers in expanding industries.

Table 9 presents the effects on wages. Since the wages for each type are proportional to

each other, the effects of the energy tax are identical for all types. First of all, the average

wage across all clusters decrease with the tax. However, as in the wage distribution in the

benchmark economy, the effects of the tax for each cluster show mixed results. Everything

else being equal, the tax reduces the wages, and the reduction is supposed to be more in

the industries with higher energy intensity. In this model, however, the effects of the tax

on wages are not monotone in terms of energy intensity. Since the effects of the policy

on wages also reflect its effects on prices through the labor reallocation, the decrease in

the average prices of cluster 1 reduces its average wage more substantially than clusters

2 through 5. Nonetheless, the most energy intensive cluster 6 lowers its average wage the

most. However, even the most sever wage reduction is approximately 0.1%. Hence, the

key implication here is that energy tax does not cause a large reduction of wages.

Table 10 presents the effects on the average value outputs, average real outputs, and

the average prices for each cluster. The average value outputs decrease with tax in all

clusters, and the average real outputs decrease in all clusters except cluster 1, which

increases the average real output by approximately 0.06%. Because of this increase in the

real output, the average equilibrium price in cluster 1 slightly decrease. Apparently, the

reduction in the real output is substantial in more energy intensive industries. However,

its contribution to the overall reduction in the real output is small since their share in

the aggregate economy is very small. Moreover, the reduction in those energy intensive

industries is largely offset by a slight increase in the real output by cluster 1, whose share

in the aggregate economy is significantly larger than those energy intensive industries. As

a result, the aggregate real output decreases with tax only by 0.46%.

Finally, Table 11 presents the effects on energy use. The middle column shows the

actual changes in the aggregate energy use in each cluster, and the right column shows

its percentage change relative to the benchmark economy. There are no unusual results

14

here, and all industries reduce their energy input in response to the higher after tax energy

price. The reduction of energy use is almost as much as the tax rate, which is 10%. This

is an implication of the price elasticity of energy demand being close to one and perfectly

elastic world supply of energy input. If I explicitly model the energy market and if it

responds to the tax policy, this result would likely to change in nontrivial way.

These results suggest that effects of energy tax on labor market outcomes are negligibly

small compared to its effects on energy use. Is 10% tax simply too small to generate

appreciable changes in labor market allocation? To examine this question, I consider

the case with significantly higher 30% energy tax.Tables from 12 through 16 present the

respective results. The results, however, are more or less consistent with the earlier case.

With the higher energy tax, more industries with lower energy intensity reduce their

employment. However, the size of displacement is still very small. In addition, cluster

1 with the lowest energy intensity increases its average employment, and as above, the

increase is orders of magnitude larger for type 2 workers than type 0 and type 1 workers.

This also indicates the reallocation toward higher productivity workers in clean industries.

A major difference here is that unemployment rate increases by 0.003% points, which

corresponds to 0.04% (approximately 4500 workers) reduction of aggregate employment.

Another important finding of this experiment is that the aggregate real outputs decrease

only by 0.945%. Overall, these results do not change the key implication of this policy

experiment that the energy tax has very little impacts on labor market allocation despite

its significant contribution to the reduction of energy use in the economy.

4 Conclusion

This study was a theoretical attempt to analyze macroeconomic implication of a environ-

mental regulation, which takes a form of energy tax. This paper complements a rich body

of empirical studies in this literature, and identifies novel and important general equilib-

rium effects of the regulation. Among the key results are: (i) tax induces labor reallocation

not only from energy intensive (dirtier) industries to less energy intensive (cleaner) indus-

tries, but also toward higher productivity workers in those cleaner industries. (ii) The

negative effect on the aggregate employment is very small, if at all. (iii) Energy use is

reduced significantly with the tax. Those results suggest that environmental regulation

15

in the form of energy tax is effective without causing appreciable damage, if at all, to the

overall employment in the economy.

In this paper, I focused on the effect of the regulation on labor allocation, and ignore

the environmental quality aspect of the policy. Therefore, I did not consider the firms’

abatement technology choices and the consumers’ preferences toward environmental qual-

ity and cleaner industries. Augmenting the model with these structures is necessary when

it comes to the analysis of the optimal environmental policy. A richer model would incor-

porate emissions taxes and emissions trading, the choice of price-based or quantity-based

regulations, and endogenous energy market. A fully integrated model would capture the

interaction between the labor and emissions/energy markets and how environmental reg-

ulations influence the market outcomes. This paper proposes a step toward this direction.

16

References

Aguirregabiria, Victor, and Cesar Alonso-Borrego. 2014. “Labor Contracts and Flex-

ibility: Evidence from Labor Market Reform in Spain.” Economic Inquiry, 52(2):

930-957.

Alvarez, Fernando, and Marcelo Veracierto. 1999. “Labor-Market Policies in an Equilib-

rium Search Model.” in Bernanke, B. and Rotemberg, J. (eds) NBER Macroeconomics

Annual, Cambridge, MA: MIT Press.

Berman, Eli, and Linda T.M. Bui. 2001. “Environmental Regulation and Labor Demand:

Evidence from the South Coast Air Basin.” Journal of Public Economics, 79(2):

265-295.

Bovenberg, A. Lans and R. de Mooij (1994). “Environmental levies and distortionary

taxation.” American Economic Review, 84 (4), 1085-1089.

Bovenberg, A. Lans and Frederick van der Ploeg. 1994. “Environmental policy, public

finance and the labour market in a second-best world.” Journal of Public Economics

55(3): 349-390.

Bovenberg, A. Lans and Frederick van der Ploeg. 1996. “Optimal taxation, public goods

and environmental policy with involuntary unemployment.” Journal of Public Eco-

nomics, 62 (1-2), 59-83.

Bovenberg, A. Lans and Frederick van der Ploeg. 1998. “Tax reform, structural unemploy-

ment and the environment.” Scandinavian Journal of Economics, 100 (3): 593-610.

Greenstone, Michael. 2002. “The Impact of Environmental Regulations on Industrial

Activity: Evidence from the 1970 and 1977 Clean Air Act Amendments and the

Census of Manufactures.” Journal of Political Economy, 110(6): 1175-1219.

Hafstead, Marc A.C., and Roberton C. Williams III. 2014. “Unemployment and Environ-

mental Regulation in General Equilibrium.” mimeo.

Hazilla, Michael, and Raymond J. Kopp. 1990. “Social Cost of Environmental Quality

Regulations: A General Equilibrium Analysis.” Journal of Political Economy, 98(4):

853-873.

Kambourov, Gueorgui. 2009. “Labour Market Regulations and the Sectoral Reallocation

of Workers: The Case of Trade Reforms.” Review of Economic Studies, 76(4): 1321-

1358.

17

Kambourov, Gueorgui and Iourii Manovskii. 2009. “Occupational Specificity of Human

Capital.” International Economic Review, 50(1): 63-115.

Lucas, Robert E. Jr. and and Edward Prescott. 1974. “Equilibrium Search and Unem-

ployment.” Journal of Economic Theory, 7(2): 188-209.

Martin, Ralf, Laure B. de Preux, and Ulrich J. Wagner. 2014. “The impact of a carbon tax

on manufacturing: Evidence from microdata.” Journal of Public Economics, 117(1):

1-14.

Morgenstern, Richard D., William A. Pizer, and Jhih-Shyang Shin. 2002. “Jobs Versus the

Environment: An Industry-Level Perspective.” Journal of Environmental Economics

and Management, 43(3): 412-436.

Mortensen, Dale T. and Christopher A. Pissarides. 1994. “Job Creation and Job Destruc-

tion in the Theory of Unemployment.” Review of Economic Studies, 61(3): 397-415.

Schneider, Kerstin. 1997. “Involuntary unemployment and environmental policy: The

double dividend hypothesis.” Scandinavian Journal of Economics, 99(1): 45-59.

18

Table 1: Calibrated Parameters

General parameters Human capital parameters Productivity shocks parameters

β 0.99 λ1 0.25 φ 0.97

δ 0.00625 λ2 0.0417 σε 2.56

κ 1 a0 0.296

ρ 0 a1 0.325

a2 0.378

19

Table 2: Employment distribution in the benchmark model: # of workers

Type 0 Type 1 Type 2 All types

Cluster 1 0.664 3.449 22.870 26.983

Cluster 2 0.992 5.170 34.259 40.421

Cluster 3 0.371 1.935 12.819 15.125

Cluster 4 0.153 0.797 5.280 6.230

Cluster 5 0.078 0.406 2.690 3.174

Cluster 6 0.003 0.134 0.886 1.045

Unemployment 7.021

Table 3: Distribution of effective units of labor in the benchmark model

Aggregate 34.341

Cluster 1 9.966

Cluster 2 14.929

Cluster 3 5.586

Cluster 4 2.301

Cluster 5 1.172

Cluster 6 0.386

Table 4: Wage distribution in the benchmark model

Type 0 Type 1 Type 2

Cluster 1 0.863 0.944 1.098

Cluster 2 0.875 0.957 1.113

Cluster 3 0.869 0.951 1.106

Cluster 4 0.848 0.928 1.079

Cluster 5 0.845 0.925 1.076

Cluster 6 0.842 0.922 1.072

20

Table 5: Distribution of value outputs, real output, and prices

Value outputs Real outputs Average prices

Aggregate 104.338 32.551

Cluster 1 29.239 (0.280) 9.662 (0.297) 3.062

Cluster 2 44.860 (0.430) 14.194 (0.436) 3.197

Cluster 3 17.206 (0.165) 5.120 (0.157) 3.400

Cluster 4 7.302 (0.070) 2.062 (0.063) 3.582

Cluster 5 4.169 (0.040) 1.101 (0.034) 3.829

Cluster 6 1.563 (0.015) 0.412 (0.013) 3.838

Table 6: Distribution of energy inputs

Aggregate 4.083

Cluster 1 0.292 (0.072)

Cluster 2 0.897 (0.220)

Cluster 3 0.860 (0.211)

Cluster 4 0.730 (0.179)

Cluster 5 0.834 (0.204)

Cluster 6 0.469 (0.115)

21

Table 7: Effects on employment distribution: # of workers (10% tax)

Type 0 Type 1 Type 2 All types

Cluster 1 1.4e-04 0.021% 4.9e-04 0.014% 0.002 0.009% 0.003 0.010%

Cluster 2 2.2e-07 2.3e-05% 8.0e-05 0.002% 2.8e-04 8.1e-04% 3.6e-04 8.9e-04%

Cluster 3 -5.3e-06 -0.001% 1.1e-06 5.7e-05% 1.4e-04 0.001% 1.4e-04 9.0e-04%

Cluster 4 -3.7e-06 -0.002% -5.3e-05 -0.007% -1.3e-04 -0.002% -1.9e-04 -0.003%

Cluster 5 -1.1e-05 -0.014% -6.0e-05 -0.015% -5.3e-04 -0.020% -6.0e-04 -0.019%

Cluster 6 -9.5e-08 -3.70e-04% -5.0e-05 -0.037% -3.1e-04 -0.035% -3.6e-04 -0.035%

Unemployment -0.002 -0.03%

Table 8: Effects on employment distribution: effective units of labor (10% tax)

Aggregate 7.2e-04 0.002%

Cluster 1 9.6e-04 0.009%

Cluster 2 1.3e-04 8.8e-04%

Cluster 3 5.2e-05 9.2e-04%

Cluster 4 -6.8e-05 -0.003%

Cluster 5 -2.2e-04 -0.019%

Cluster 6 -1.3e-04 -0.035%

Table 9: Effects on average wages: All types (10% tax)

Cluster 1 -0.084%

Cluster 2 -0.079%

Cluster 3 -0.080%

Cluster 4 -0.079%

Cluster 5 -0.078%

Cluster 6 -0.106%

22

Table 10: Effects on value outputs, real outputs, and prices (10% tax)

Value outputs Real outputs Prices

Aggregate -0.079% -0.462%

Cluster 1 -0.074% 0.061% -0.129%

Cluster 2 -0.078% -0.409% 0.329%

Cluster 3 -0.079% -0.511% 0.403%

Cluster 4 -0.082% -0.818% 0.750%

Cluster 5 -0.097% -2.351% 2.285%

Cluster 6 -0.141% -3.512% 3.451%

Table 11: Effects on energy input (10% tax)

Aggregate -0.375 -9.173%

Cluster 1 -0.027 -9.158%

Cluster 2 -0.082 -9.162%

Cluster 3 -0.079 -9.163%

Cluster 4 -0.067 -9.166%

Cluster 5 -0.077 -9.179%

Cluster 6 -0.043 -9.219%

23

Table 12: Effects on employment distribution: # of workers (30% tax)

Type 0 Type 1 Type 2 All types

Cluster 1 5.7e-05 0.009% 1.8e-04 0.005% 0.001 0.006% 0.002 0.006%

Cluster 2 -2.0e-06 -2.0e-04% -1.1e-05 -2.2e-04% -6.3e-05 -1.8e-04% -7.6e-05 -1.9e-04%

Cluster 3 -9.2e-06 -0.002% -4.7e-05 -0.002% -3.2e-04 -0.002% -3.7e-04 -0.002%

Cluster 4 -3.7e-05 -0.024% -1.9e-04 -0.024% -0.001 -0.024% -0.002 -0.002%

Cluster 5 -3.6e-05 -0.047% -1.9e-04 -0.047% -0.001 -0.047% -0.001 -0.047%

Cluster 6 2.2e-06 0.009% -1.1e-04 -0.086% -7.6e-04 -0.086% -8.7e-04 -0.083%

Unemployment 0.003 0.04%

Table 13: Effects on employment distribution: effective units of labor (30% tax)

Aggregate -0.001 -0.003%

Cluster 1 5.5e-04 0.006%

Cluster 2 -2.8e-05 -1.8e-04%

Cluster 3 -1.4e-04 -0.002%

Cluster 4 -5.6e-04 -0.024%

Cluster 5 -5.5e-04 -0.047%

Cluster 6 -3.2e-04 -0.084%

Table 14: Effects on average wages: All types (30% tax)

Cluster 1 -0.003%

Cluster 2 5.8e-04%

Cluster 3 -0.005%

Cluster 4 0.004%

Cluster 5 7.9e-04%

Cluster 6 -0.060%

24

Table 15: Effects on value outputs, real outputs, and prices (30% tax)

Value outputs Real outputs Prices

Aggregate -0.006% -0.945%

Cluster 1 0.003% -0.257% 0.260%

Cluster 2 3.9e-04% -0.523% 0.527%

Cluster 3 -0.007% -1.306% 1.316%

Cluster 4 -0.020% -2.613% 2.662%

Cluster 5 -0.046% -5.156% 5.388%

Cluster 6 -0.143% -7.659% 8.134%

Table 16: Effects on energy input (30% tax)

Aggregate -0.943 -23.1%

Cluster 1 -0.067 -23.1%

Cluster 2 -0.207 -23.1%

Cluster 3 -0.199 -23.1%

Cluster 4 -0.169 -23.1%

Cluster 5 -0.193 -23.1%

Cluster 6 -0.109 -23.1%

25

A Data

The following table is constructed from the manufacturing multifactor productivity and

nonmanufacturing multifactor productivity tables available at the U.S. Bureau of Labor

Statistics website (http://www.bls.gov/mfp/mprdload.htm). The industries are sorted

with respect to their energy factor share in 2012, which is available in Table 5-3 for the

manufacturing industries and Table 5-4 for the nonmanufacturing industries.

The output column refers to the value of production in 2012 in billion of dollars, which

is available in Table 4-1 for both manufacturing and nonmanufacturing industries.

The lines in the list indicate the threshold for each cluster, and the numbers in the

parenthesis in the cumulative share column are the cumulative output share of each cluster.

26

Table 17: List of industries

NAICS Industry Code Sector or Industry Title Output Output Share Cumulative Share Energy Factor Share

525 Funds, Trusts, and Other Financial Vehicles 113.628 0.00341 0.0034 0.000

512 Motion Picture and Sounds Recording Industries 146.800 0.00441 0.0078 0.001

524 Insurance Carriers and Related Activities 467.973 0.01404 0.0219 0.001

5411 Legal Services 278.164 0.00835 0.0302 0.001

511 Publishing Industries 306.354 0.00919 0.0394 0.002

523 Securities, Commodity Contracts, and Investments 448.278 0.01345 0.0529 0.003

5415 Computer Systems Design and Related Services 320.054 0.00960 0.0625 0.003

334 Computer and Electronic Products 320.482 0.00962 0.0721 0.004

51 Information 1150.247 0.03452 0.1066 0.004

315,316 Apparel and Leather and Applied Products 15.393 0.00046 0.1070 0.005

515,517 Broadcasting and Telecommunications 548.342 0.01645 0.1235 0.005

521,522 Federal Reserve Banks, Credit Intermediation, and Related Activities 558.921 0.01677 0.1403 0.005

336 Transportation Equipment 660.574 0.01982 0.1601 0.006

339 Miscellaneous Manufacturing 156.988 0.00471 0.1648 0.006

333 Machinery 369.526 0.01109 0.1759 0.007

335 Electrical Equipment, Appliances, and Components 117.931 0.00354 0.1794 0.007

518,519 Information and Data Processing Services 185.974 0.00558 0.1850 0.007

5412-5414,5416-5419 Miscellaneous Professional, Scientific, and Technical Services 990.531 0.02972 0.2147 0.007

621 Ambulatory Health Care Services 799.405 0.02399 0.2387 0.007

211 Oil and Gas Extraction 261.497 0.00785 0.2466 0.008

42 Wholesale Trade 1204.778 0.03615 0.2827 0.008

337 Furniture and Related Products 61.016 0.00183 0.2846 (28.46) 0.009

323 Printing and Related Support Activities 79.938 0.00240 0.2870 0.011

42,44-45 Trade 2484.956 0.07457 0.3615 0.011

561 Administrative and Support Services 634.130 0.01903 0.3806 0.011

27

711,712 Performing Arts, Spectator Sports, Museums, and Related Activities 105.311 0.00316 0.3837 0.011

324 Petroleum and Coal Products 793.691 0.02382 0.4075 0.012

532,533 Rental and Leasing Services and Lessors of Intangible Assets 286.777 0.00861 0.4161 0.012

44,45 Retail Trade 1299.642 0.03900 0.4551 0.013

332 Fabricated Metal Products 328.399 0.00985 0.4650 0.014

55 Management of Companies and Enterprises 546.789 0.01641 0.4814 0.014

311,312 Food and Beverage and Tobacco Products 759.997 0.02281 0.5042 0.015

326 Plastics and Rubber Products 199.789 0.00600 0.5102 0.018

213 Support Activities for Mining 97.267 0.00292 0.5131 0.018

54-81 Services 4990.421 0.14975 0.6629 0.018

624 Social Assistance 110.858 0.00333 0.6662 0.018

313,314 Textile Mills and Textile Product Mills 49.853 0.00150 0.6677 0.019

21 Mining 443.529 0.01331 0.6810 0.019

722 Food Services and Drinking Places 490.685 0.01472 0.6957 0.019

562 Waste Management and Remediation Services 75.186 0.00226 0.6980 0.020

81 Other Services, except Government 462.504 0.01388 0.7119 (42.73) 0.020

113-115 Forestry, Fishing, and Related Activities 41.791 0.00125 0.7131 0.021

486 Pipeline Transportation 28.690 0.00086 0.7140 0.021

721 Accommodation 157.911 0.00474 0.7187 0.022

321 Wood Products 63.373 0.00190 0.7206 0.026

23 Construction 1062.925 0.03190 0.7525 0.030

52-53 Finance, Insurance, and Real Estate 2466.262 0.07401 0.8265 0.030

325 Chemical Products 669.455 0.02009 0.8466 0.032

622,623 Hospitals and Nursing and Residential Care Facilities 572.786 0.01719 0.8638 0.033

713 Amusements, Gambling, and Recreation Industries 101.769 0.00305 0.8669 0.036

493 Warehousing and Storage 78.764 0.00236 0.8692 0.038

212 Mining, except Oil and Gas 95.782 0.00287 0.8721 0.048

28

61 Educational Services 184.540 0.00554 0.8776 (16.58) 0.049

11 Agriculture, Forestry, and Fishery 358.538 0.01076 0.8884 0.058

111,112 Crop and Animal Production 344.157 0.01033 0.8987 0.058

531 Real Estate 1065.520 0.03197 0.9307 0.061

331 Primary Metals 203.349 0.00610 0.9368 0.068

327 Nonmetallic Mineral Products 86.698 0.00260 0.9394 0.071

322 Paper Products 134.467 0.00404 0.9434 0.080

487,488,492 Other Transportation and Support Activities 165.024 0.00495 0.9484 (7.08) 0.089

485 Transit and Ground Passenger Transportation 50.597 0.00152 0.9499 0.127

22 Utilities 325.211 0.00976 0.9597 0.175

482 Rail Transportation 80.256 0.00241 0.9621 0.178

48-49 Transportation and Warehousing 802.300 0.02408 0.9861 (3.78) 0.199

484 Truck Transportation 295.042 0.00885 0.9950 0.251

483 Water Transportation 57.087 0.00171 0.9967 0.268

481 Air Transportation 109.498 0.00329 1.0000 (1.39) 0.283

29