1. business and industrial
2. manufacturing

Why are …rms that export cleaner?
International trade and CO2 emissions
Rikard Forslidy, Toshihiro Okuboz, and Karen Helene Ulltveit-Moex
November 2012, Work in progress
Revised version of CEPR DP 8583
Abstract
This paper develops a model of trade and CO2 emissions with heterogenous …rms, where …rms
make abatement investments and thereby have an impact on their level of emissions. The model
shows that investments in abatements are positively related to …rm productivity and …rm exports.
Emission intensity is, however, negatively related to …rms’ productivity and exports. The basic
reason for these results is that a larger production scale supports more investments in abatement
and, in turn, lower emissions per output. We derive closed form solutions for …rm-level emissions
intensity and abatement investments, and test the empirical implications of the model using detailed
Swedish …rm-level data. The empirical results support our model.
JEL Classi…cation: D21, F12, F15
Keywords:heterogeneous …rms, CO2-emissions, international trade
1
Introduction
There is no consensus on the e¤ect of international trade on the environment, in particular on the e¤ect
of trade on global emissions. Neither the theoretical nor the empirical literature provides a clean cut
answer to the link between trade and CO2 emissions. We do not know if international trade increases
or decreases the emissions of greenhouse gases and contributes to global warming. However, this paper
sets out explain why we may expect exporter to emit less CO2, and why trade liberalization may thus
lead to cleaner industrial production. We do so by focusing on inter-…rm productivity di¤erentials and
interdependence among productivity, abatement and emissions.
In theoretical neoclassical models, international trade has opposing e¤ects. On the one hand, trade
increases income, which will tend to increase the demand for a clean environment and therefore increase
We are grateful for comments from Andrew Bernard, Peter Egger, Beata Javorcik, Gordon Hanson, Peter Neary,
Adrian Wood, and Tony Venables,and thank participants at the GIST conference in Stockholm 2011, the ETSG conference
2011, the PEGGED Research Workshop on International Trade and seminar participants in Oxford for valuable discussion.
Financial support from GIST, Handelsbanken reseach foundation, Grant-in-Aid for Scienti…c Research (JSPS) and Research
Institute of Economy, Trade and Industry (RIETI) is gratefully acknowledged.
y Stockholm
z Keio
University and CEPR; email: [email protected]
university, e-mail: [email protected]
x University
of Oslo and CEPR, e-mail: [email protected]
1
investments in clean technology and abatement. On the other hand, trade liberalisation may also imply
an overall expansion of dirty production, because trade allows countries with low emission standards to
become "pollution havens". Copeland and Taylor (1995) show how trade liberalisation increases global
emissions if the income di¤erences between the liberalising countries are large, as dirty industries expand
strongly in the poor country with low environmental standards.
The empirical literature on the link between trade in goods and emissions is also inconclusive. Using
macro data, Antweiler et al. (2001) and Frankel and Rose (2005) …nd that trade decreases emissions.
Using sector-level data for the U.S., Ederington et al. (2004) do not …nd any evidence that pollution
intensive industries have been disproportionately a¤ected by tari¤ changes. On the other hand, also using
sector-level trade data, Levinson and Taylor (2008) …nd evidence that higher environmental standards in
the US have increased the imports from Mexico in dirty industries. Holladay (2009) analyses …rm-level
data for the US, and …nd that exporters pollute less per output, while on the other hand, also using
…rm level data, Batrakova and Davies (2010) …nd that, for low fuel intensity …rms, fuel expenditures are
positively correlated with exporting. Finally, Rodrigue and Soumonni (2011) employ Indonesian …rmlevel data to investigate the impact of environmental investment on productivity dynamics and exports.
They …nd that while productivity dynamics do not appear to be a¤ected, growth in exports is positively
a¤ect by environmental investments.
We start by providing evidence that …rms’ emissions di¤er sign…cantly across …rms, even within
rather narrowly de…ned industries. Moreover, comparing non-exporters and exporters in Swedish manufacturing, we …nd that on average exporters have lower emission intensity. Motivated by these fact
we build of model of with international trade and CO2 emissions where …rms are heterogeneous with
respect to productivity, abatement and emission intensity. It has been pointed out (see e.g. Levinson,
2009) that trade may contribute to reduced pollution as trade liberalization may encourage technological
upgrading.1 We use the model to propose and develop a mechanism linking trade and emissions that
is closely related to this idea. Assuming that …rms’emissions depend on their abatement investments,
we show that …rms’abatement investments in turn depend on their production volumes, simply because
bigger volumes allow them to spread the …xed costs of abatement investment across more units. Production volumes are moroever determined by …rms’productivity and export status. More productive …rms
have higher volumes and make higher abatement investments. Exporting boosts the scale of production,
and trade therefore enhances investment in abatement. As a consequence, …rms’ emission intensity is
positively related to …rms’productivity and negatively related to …rms’exports.
We derive closed form solutions for …rm-level abatement investments and …rm-level emission intensities. These equations are tested on detailed Swedish …rm-level data. Our dataset contains …rm-level
emissions and …rm-level abatement investments as well as …rm exports.2 We focus on CO2 emissions,
which constitute about 80 percent of the greenhouse gases emitted by Swedish …rms (Ministry of the
Environment 2009). The empirical results are overall strongly supportive of the results derived in the
theoretical model; more productive …rms abate more and emit less, and exports are associated with more
abatement and lower emission intensity.
The outline of the paper is as follows. In the next section we present the Swedish …rm and emission
data to which we have access. Based on these we develop a set of empirical facts which we use to
guide our development of a model of international trade, CO2 emissions and heterogeneous …rms. Our
1 From a methodology point of view, the paper is also related to the literature on heterogeneous …rms and trade induced
technological upgrading, see e.g. Bas (2008) and Bustos (2011).
2 There
are very few studies using …rm-level data on CO2 emissions. One exception is Cole et al. (2011).
2
theoretical model is presented in section 3, and allows us to derive a set of propositions and empirical
implications regarding CO2 emissions, abatement and trade. In section 4 we take the theory to the data,
and examine the relationship between CO2 emissions, export and productivity, and between abatement,
export and productivity. Finally, section 5 concludes.
2
Data and background
2.1
Data
In order to analyse the relationship between trade, emissions and abatement, we use manufacturing
census data for Sweden. The census data contains information on a large number of variables such as
export (tSEK), employment (in thousands), capital stock (tSEK), use of raw materials (tSEK), value
added and production value (tSEK) at the …rm level from 2000-2007.
We match the census data with energy statistics collected for all manufacturing plants with more
than 10 employees from 2004-2007. The energy statistics include all types of fuel use, from which CO2
emissions are obtained by using fuel speci…c CO2 emissions coe¢ cients provided by Statistics Sweden.
CO2 emissions are accurately calculated from fuel inputs since a technology for capturing CO2 at the
pipe is not yet operational.3 The calculated plant level emissions are aggregated to the …rm level. This
provides us with CO2 emission data for around 5 000 manufacturing …rms.
Our analysis requires a measure for CO2 emission intensity. Acorrding to our theoretical model,
emission intensity should be calculated as emissions relative to production value. But such a measure
becomes incorrect if …rms purchase intermediates. As we do not know the emissions related to …rms’
outsourcing operations, we need to concentrate on its in-house activties. Hence, the most correct measure
for emission intensity turns out to be emissions relative to value added. A few …rms reports negative
value added (0.6 percent), these dropped from the sample together with those who report zero production
(0.5 percent).
We also have access to …rm level data on abatement over the period 2000-2007. The abatement data
is collected based on an annual survey where …rms are asked about abatement investments (tSEK) as well
as variable abatement costs. As for abatement investment the …rms are asked to report any investment
in machines and equipment speci…cally aimed at reducing emissions, but also to report costs that arise
due to investment in cleaner machines and technology. In the latter case they are speci…cally asked to
report the extra costs related to the choice of investing in cleaner relative to less clean machines and
technology.The abatement data is based on a semi-random sample of manufacturing …rms, and include
all manufacturing …rms with more than 250 employees, 50 percent of the …rms with 100-249 employees,
and 20 percent of the …rms with 50-99 employees. In total, around 700 manufacturing …rms are surveyed
each year.
In accordance with our theoretical model, Swedish manufacturing …rms face a CO2 tax. Sweden
enacted a tax on carbon emissions in 1991 which has applied through our period of observation. The
tax is a general one, and applies to all sector, but manufacturing industries have from the introduction
of the tax been granted a tax credit. The tax credit is as such uni…ed and identical across industries.
In addition, the most energy intensive, and thus the most emission intensive, industries, which are those
with a CO2 tax bills exceeding 0.8 percent of their production value, get a further tax credit.4 Sweden
3A
few large powerplants are experimenting with capturing CO2 under ground.
4 The
following NACE 2 industries are classi…ed as emission intensive industries: 10 Coke etc, 11 Petroleum and natural
3
is also part of European Union Emissions Trading System which was set up in 2005 to reduce CO2
emissions. However, during our period of observation, of which the latter years coincide partly with the
so called …rst trading period of EU ETS (2005-2007), quotas were in general distributed for free. From
the second trading period, starting in 2008, the cap on total emissions was reduced, and is expected to be
further reduced every years onwards. The most emission intensive manufacturing industries in Sweden
have either been part of the ETS from the beginning or will become so in 2013.
2.2
Some facts on CO2 emissions and trade
The majority of analyses of CO2 emissions and international trade has focused on di¤erences in emissions
across sectors [REFERENCES?]. However, a simple decomposition of the variation in CO2 emission
intensity into (i) variation across …rms within sectors and (ii) variation between sectors, illustrates that
the majority of the variation in emission intensity can be ascribed to …rm heterogeneity. According to
Table 1, as much as 70 percent of the variation in CO2 emissions is due to di¤erences between …rms
rather than sectoral di¤erences.
Table 2 morover shows that exporters are on average 50 percent less dirty than non-exporters. It is a
well known stylized fact, con…rmed by analyses of various countries (see e.g. Bernard et al, 2007), that
exporters are bigger and more capital intensive. Our data for Swedish manufacturing con…rms these
facts, but add to these that exporters also have a lower CO2 emission intensity. Motivated by these
empirical facts on CO2 emissions and their variation across …rms, we proceed by developing a simple
theory of heterogenoeous …rms where …rms within an industry di¤er in their emissions. In particular, we
propose and develop a mechanism for why emissions may di¤er across …rms, and why export performance
may have an impact on …rms’emissions. [LEGG INN TFP TALL OG KOMMENTER DEM]
3
The Model
We develop a model with international trade and heterogeneous …rms (a la Melitz, 2003) whose production entails emitting CO2. After having set up production, …rms make two distinct decisions, whether
to enter the export market and whether to invest in abatement to reduce emissions. Firms make these
descision subject to trade costs and emission taxes.
We consider the case of two countries, where each economy is active in the production in two industries: a monopolitistic competitive industry (M ) where …rms produce di¤erentiated goods under
increasing returns and subject to CO2 emissions, and a background industry (A) characterized by perfect competition and which produces homogenous goods subject to constant returns to scale. To make
things simple, we shall assume that there is just one factor of production, which we refer to as labor.
We solve the model in order to get analytical expressions for equilibrium emission intensity and
equilibrium abatement investments, which allow us to make propositions on the relationship between
emission intensity, abatement investment and trade, which we test empirically.
gas, 12 Uranium, 13 Metal Ore, 14 Other mining products, 21 Paper, paper products and pulp, 23 Coke and re…ned
petroleum products, 24 Chemicals and chemical products, 26 Non-metallic mineral products. 27 Basic metals.
4
3.1
Demand
Consumers preferences are given by a two-tier utility function with the upper tier (Cobb-Douglas) determining the representative consumer’s division of expenditure between goods produced in sectors A and
M , and the second tier (CES), giving the consumer’s preferences over the continuum of di¤erentiated
varieties produced within the manufacturing sector. Hence, all individuals in country j have the utility
function
1
Uj = CM j CAj
;
where
(1)
2 (0; 1) and CAj is consumption of the homogenous good. Goods produced in the A sector
can be costlessly traded internationally and are produced under constant returns to scale and perfect
competition. The A-good is chosen as the numeraire, so that the world market price of the agricultural
good, pA , is equal to unity. By choice of scale, the labor requirement in the A-sector is one, which gives
pA = wj = w = 1
(2)
and thus, wages are normalized to one across both countries and sectors. This holds provided that
< 0:5; which implies that the demand for A goods is su¢ ciently large to guarantee that the A sector
is active in both countries irrespective of the location of sector M . The consumption of goods from the
M sector is de…ned as an aggregate CM j ;
2
CM j = 4
Z
c (i)
(
1)=
i2I
3
=(
1)
di5
;
(3)
where c(i) represents consumption of each variety with elasticity of substitution between any pair of
di¤erentiated goods being
> 1: The measure of set I represents the mass of varieties consumed in
country j. Each consumer spends a share
of his income on goods from industry M , and demand for
each single variety produced in country k and consumed in country j is therefore given by
xj =
pjk
Pj1
where pjk is the consumer price, Yj is income; and Pj
Yj ;
R
i2I
in country j.
3.2
(4)
1
p (i)
!11
di
the price index of M goods
Entry, exit and production costs in the M sector
To enter the M sector in country j, a …rm bears the …xed costs of entry fE measured in labour units.
After having sunk fE ; an entrant then draws a labour-per-unit-output coe¢ cient a from a cumulative
distribution G(a): We follow Helpman et al. (2004) in assuming the probability distribution to be a
Pareto distribution,5 i.e. G(a) =
ak
;
ak
0
where we normalise the scale parameter to unity, a0
1: Upon
observing this draw, a …rm may decide to exit and not produce. If it chooses to stay, it bears the
additional …xed overhead labour costs, fD . If the …rm does not only want to serve the domestic market
but also wants to export, as in Melitz (2003) and Helpman et al (2004), it has to bear the additional
…xed labour costs, fX : Hence, …rm technology is represented by a cost function that exhibits a variable
5 This
assumption is consistent with the empirical …ndings by e.g. Axtell (2001).
5
cost and a …xed overhead cost. In the absence of emissions and abatement investment, labour is used as
a linear function of output according to
l = f + xa
(5)
with f = fD for …rms only serving the domestic market and f = fD + fX for exporters. We make the
simplifying assumption that not just variable costs but also all types of …xed costs are incurred in labor.
However, since we do not focus on issues related to factor markets or comparative advantage, this only
serves as means to simplify the analysis, without having any impact on the results.
Industrial activity in sector M entails pollution in terms of emission of CO2. We follow Copeland and
Taylor (2003) and assume that each …rm produces two outputs: an industrial good (x) and emissions
(e). In order to reduce emissions, a …rm can divert a fraction
the production of x. We may think of
of the primary factor, labour, away from
as a variable abatement cost. Firms pay the …xed overhead
costs, and thereafter the joint production of industrial goods and emisson is given by
x = (1
)
l
a
(6)
l
a
< 1. Emissions are determined by the abatement function
e = '( )
with 0
'( ) =
(7)
1=
(1
)
(8)
(fA )
which is characterised by '(0) = 1; '(1) = 0; '0 (:) < 0 and 0 <
< 1: The abatement function re‡ects
that …rms may reduce their emission intensity through two types of abatement activities that incur
variable and …xed costs respectively. As already noted,
depicts the variable cost related to abatement,
while fA represent investments, e.g. machines and equipment that allow for reduced emissions.6 A
given level of emission intensity may be reached either through increased
we assume
0
or through increased fA ; as
(fA ) > 0: In other words, given a …xed level of emissions, …rms are able to reduce their
variable abatement costs through abatement investments …rms:
We proceed by using (7) and (8) to substitute for
in (6), which gives us an integrated expression
for the joint production technology and exploit the fact that although pollution is an output, it can
equivalently also be treated as an input:7
x = ( (fA ) e)
l
a
1
:
(9)
Hence, with such an interpretation, production implies the use of labor as well as emission. Note that
while …rms are heterogeneous with respect to labour productivity and abatement, they are identical with
respects to their basic production technology and face the same tax rate on emissions. Firms minimize
costs taking wages (w = 1) and emission taxes (t > 0) as given and subject to (9). This allows us to
derive their variable costs functions. Disregarding the sunk entry cost (fE ) while adding …xed overhead
costs related to domestic and possibly export activity as well as abatement costs, we get the total cost
function
t
(fA )
C = f + fA +
a(1
)
x
6 We
(10)
depart from the standard formulation of the abatement function in the literature on trade and emissions by assuming
that …rms can have an impact on emission intensity through …xed abatement investments (fA ).
7 See
Copeland and Taylor (2003) for a discussion of this feature of the model.
6
with
(1
)
1
; where f = fD for …rms only serving the domestic market, and f = fD +fX for
exporters, i.e. …rms serving both the domestic and the foreign market. The cost function communicates
that emissions are not for free, rather they incur a tax t > 0. But through increasing their investments
in abatement, …rms can reduce their emissions as well as their tax bill. Note, that solving the model, we
shall use the speci…c functional form
(fA ) = fA ; with
> 0:
Our analysis exclusively focuses on steady-state equilibria and intertemporal discounting is ignored.
The present value of …rms is kept …nite by assuming that …rms face a constant Poisson hazard rate
of
‘death’independently of productivity. An entering …rm with productivity a will immediately exit if its
pro…t level
(a) is negative, or will produce and earn
(a)
0 in every period until it is hit by a bad
shock and forced to exit.
3.3
Pro…t maximization and equilibrium conditions
Having drawn their productivity, …rms follow a two-step decision process. First, they make their decision
on abatement investment, and second, taking abatement investments as given they maximize pro…ts with
prices. Implicitly they then also decide on emission intensity and on share of input factor to divert away
from production and towards abatement, i.e. the variable costs of abatment.
As usual we proceed by solving the problem backwards. Each producer operates under increasing
returns to scale at the plant level and in line with Dixit and Stiglitz (1977), we assume there to be large
group monopolistic competition between the producers in the M sector. Thus, the perceived elasticity of
demand equals the elasticity of substitution between any pair of di¤erentiated goods and is equal to .
Regardless of its productivity, each …rm then chooses the same pro…t maximizing markup over marginal
costs (M C) equal to =(
1): This yields a pricing rule
pjk =
1
jk M C
(11)
for each producer in country j, and re‡ects that shipping M goods involves a frictional trade cost of the
“iceberg” form. For each unit of a good from country j to arrive in country k,
shipped. It is assumed that trade costs are equal in both directions and that
jj
jk
> 1 units must be
= 1:
Using (4) and (10) we can fomulate expressions for …rms’pro…ts. We let super- and subscript D and
X denote non-exporters and exporters respectively. Firms only serving the domestic market earn pro…ts
D
=
1
t
(fAD )
a1
B
fAD ;
fD
(12)
while the exporting …rms, serving both the local and the foreign market, earn pro…ts
X
1
where B
(
1)1
Pj1
=
Lj
t
(fAX )
a1
1
(B + B )
fD
fX
fAX ;
and re‡ects the market size of the home country, B
8 k 6= j re‡ects the market size of the foreign country, and
=
1
a
)(1
)
1
(
1)1
Pk1
Lk
2 h0; 1] depicts the freeness of trade.
Since a is unit labour requirement, 1=a depicts labour productivity. With
(1
(13)
> 1 it follows that
increases along with productivity and can thus be used as a productivity index. From (12)
and (13), we see that pro…ts are increasing in …rms’ productivity. The least productive …rms expect
negative pro…ts and therefore exit the industry. This applies to all …rms with a productivity below aD
which is the cut-o¤ point at which pro…ts from domestic sales equal zero, and is determined by
a1D
1
t
(fAD )
B = fD + fAD :
7
(14)
Exporters’abatement investments a¤ect the production for both the home market and the foreign market.
The cut-o¤ point for exporters (aX ) is therfore the productivity level where the export pro…ts plus the
net extra pro…t in the home market from the higher abatement investments equals the extra …xed costs
for exporters:
1
t
(fAX )
a1X
1
t
(fAX )
B + a1X
1
t
(fAD )
a1X
B
B = fX + fAX
fAD ;
(15)
We refer to the appendix for details on the the derivation of the equilibrium prdocutivity cuto¤s.
The model is closed by the free-entry condition
fE =
ZaX
X dG(a) +
Z
aD
D dG(a):
(16)
0
0
Together, the zero cuto¤ pro…t conditions and the free entry condition ensure the existence and uniqueness of the equilibrium productivity and pro…t levels. We note that since abatement investments have an
impact on …rms’marginal costs, it also a¤ects the pro…tability of being a domestic versus an exporting
…rm.8
3.4
Fixed cost investments in abatement
Having solved the second stage of …rms’ decision problem, we proceed to the …rst stage. Since …rms’
pro…ts depend on whether they are exporters or non-exporters, abatement investments will di¤er between
the two groups of …rms. Maximizing non-exporting …rms’pro…ts w.r.t. abatement investments fA using
(12) gives
fAD
with
D
n
1
B
(t )
=
1
(
1 1
a
B
o
1
t
1
1
1
(
(
1)
1
(1
=
1)
Da
(
)(
1)
1) 1
;
(17)
1
(
1)
1
1)
, while the optimal …xed cost investment in abatement for
exporters is found using (13):
1
a1
(B + B )
o
1
1
(t
)
)
(
1)
fAX =
with
X
n
1
(B+ B
1
1
1
t
(
(
1)
1)
1
(1
=
Xa
(
)(
1)
1) 1
;
(18)
1
(
1)
1
: From (17) and (18) follow that …rms’abatement invest-
ments depend on their exogenously given marginal productivity, taxes, and market potential. We also
reckognize that trade liberalization will increase exporters market potential and enhance their abatement investments. An internal solution to the pro…tmaximising choice of fAD and fAX ; requires that
(
1) < 1. However, as this condition is a necessary condition for pro…t maxmization, it will always
hold, see the appendix details.
In order to derive analytical expressions for equilibrium abatement investments, we need to make
assumptions about country size and taxation. Hence, we proceed by assuming the two economies to be
identical regarding tax regime and market size, i.e. we solve the model for t = t and B = B : Due
to symmetry it therefore su¢ ces to solve for the equilibrium of the home country.9 This gives us the
8 The
paper is in this sense related to the literature on trade induced technological upgrading. See e.g. Bas (2008) and
Bustos (2011).
9B
is a measure of the average …rm market size. Since technology, factor costs, and transportation costs are identical
for …rms in both countries it must be that B = B in equilibrium. See Helpman et. al. (2005).
8
D
following two expressions for equilibrium abatement investments for non-exporters (f A ) and exporters
X
fA ,
see the appendix for details on their derivation:
(1
D
fA
=
(
1
1)
(
1)
a
aD
fD
(
)(
1)
1) 1
;
(19)
(1
X
fA
1
where
=
(
1
1)
(
1)
(1 + )
1
(
1)
a
aD
fD
(
)(
1)
1) 1
;
(20)
2 (0; 1] depicts freeness of trade, and aD denotes equilibrium cuto¤ productivity for
non-exporters and was de…ned above as the productivity level for which pro…t from domestic sales equal
zero.
Having examined (19) and (20) we can formulate the following propositions on the relationship
between abatement investments, …rms characteristics and trade.
Proposition 1 More productive …rms invest more in abatement.
Proof : The statement follows directly from (19) and (20) and holds as long as
(
1) < 1; which
will hold as long as there is an internal solution to the pro…tmaxmising choice of fAD and fAx :
The logic behind this result is that more productive …rms have higher sales. Hence, the exploiting of
scale economies makes it pro…table for them to make a higher …xed cost investment in order to reduce
the marginal costs.
Proposition 2 For any given level of productivity, exporters invest more in abatement than non-exporters.
Proof: Since (1 + )1
3.5
(
1)
> 1 it follows that fAX > fAD for any given productivity level (1=a).
CO2 emissions
Taking abatement investment as given, …rms decide on their use of labour as well as on emissions. We
are primarily interested in emissions, and use Shepard’s lemma on the cost function (10) to derive the
general expression for emission intensity (emissions per output):
e
= t 1 fA a1
(21)
x
We see that there is a simple relationship between abatement investment and emission intensity. The
more a …rm invest in abatement, the lower its emissions. In order to derive analytical expressions for
euilibrium emission intensity non-exporters and exporters, again we need to make assumptions about
market size and tax regimes. We maintain the assumption of symmertric market size and taxregimes,
and use the expressions for euilibrium abatement investment from above, (19) and (20), to substitute in
(21). We may then state that the equilibrium emission intensity of non-exporters is given by
eD
= a1
x
where
B=
1
fD
(
(
(
1
1)
(
1)) 1
(
1)
(1
t ( 1) aD
1)
)
(
(
1
1+
1
1)
t
1
(
(
1)
1)
B
1
(
1)
;
(22)
1)2 and B depicts equilibrium market size, and is given by
: We shall assume that
is small enough so that
> 0. This limits
the e¤ect of the abatement investments, which prevents …rms from completely substituting away from
the use of emissions. Exporters equilibrium emission intensity is given by
9
1+
(
1)
1
eX
(23)
= a 1 ( 1) t 1 ( 1) ((1 + )B) 1 ( 1) :
x
A set of results on the relationship between emissions, …rm characteristics, taxes and trade emerge
directly from equations (22) and (23):
Proposition 3 More productive …rms have a lower emission intensity.
Proof: The statement follows directly from equations (??) and (??) as an internal solution to the
pro…tmaxmising choice of fAD and fAx requires
(
1) < 1 to hold.
Proposition 4 For any given level of productivity, exporters have a lower emission intensity than nonexporters.
Proof: The statement follows from the fact that (1 + )
1
(
1)
< 1.
Proposition 5 With symmetric market size and tax regimes, higher emission taxes lead to a lower
emission intensity.
Proof: The statement follows directly from equations (22) and (23) since
(
1) < 1 is assumed
to hold.
Proposition 6 Higher abatement investments lead to reduced emission intensity.
Proof: This statement follows directly from equation (21).
3.6
Testable implications
We focus our empirical work on the model’s predictions of the relationship among (i) productivity, exporting and emission intensity and (ii) productivity, exporting and abatement investments. The reduced
form solutions for emission intensity, see equations (22) and (23), suggest a log linear speci…cation of the
following type:
log emission intensityi =
where the model predicts that
1;
2
0
+
1
log productivityi +
2 exportdummyi
+ "i ;
(24)
< 0: In the theoretical model the tax rate on emissions does not vary
across …rms and may therefore be controlled for by time …xed e¤ects. Since all Swedish manufacturing
…rms face the same tax rate on emissions, the theory …ts the data well.
As for the reduced form solutions for abatement investments, see equations (19) and (20), they suggest
log linear speci…cations as:
log abatement investmentsi =
where theory predicts
1;
2
0
+
1
log productivityi +
2 exportdummyi
+ "i ;
(25)
> 0. We proceed by investigating these two relationships empirically,
confronting our theory with the Swedish manufacturing data.
10
4
Empirics
4.1
CO2 emission intensity, exports and productivity
We start by estimating equation (24). Our model predicts that the …rm-level emission intensity should
be negatively a¤ected by productivity and export status. Emission intensity is measured as …rm-level
CO2 emissions per value added. Firms’productivity is measured as total factor productivity (TFP), and
we estimate …rm and year speci…c TFP measures using the Levinsohn-Petrin method (se the appendix
for details). As for export status, we use an export dummy to identify exporters.10 To account for
sectoral variations in emissions we include industry dummies ased on 5-digit industries. As further
controls we include capitalintensity, measured as capital stock relative to number of employees, and …rm
size, measured in terms of number of employees, while year dummies pick up trends as well as the slight
changes in the emission tax over time.
Table 3 shows the OLS regression results.using the entire sample. We report regression results where
errors are clustered at the …rm level, while noting that clustering at the sector level gives very similar
results. As for year dummies, we have also run the regressions without them, but the exclusion of year
dummies does not a¤ect the estimates in any signi…cant way. There are obviously huge variations in
emission intensity across industries. This is illustrated by the substantial increase in R-square that comes
from including industry dummies. We have also explored the impact of using idnustry dummies based
on a more aggregate industry classi…cation (2 digit NACE). Not surprisingly, the results are roughly the
same as with the …ner classi…cation, but the …t of the model as suggested by the R square is reduced.
In line with what our theory would predict, we …nd that the coe¢ cients for the export dummy and
TFP are negative and strongly signi…cant across all speci…cations. Looking at economic e¤ects, and
accounting for sectoral variation, exporting has a relatively small e¤ect on emissions, while productivity
appears to be more important. Still, as we know from the theory (see e.g. Melitz, 2003) and the
descriptive evidence presented in section 2, exporters are typically larger and more productive than nonexporters. This implies that there is a collinearity between productivity and exporting making it di¢ cult
to split the e¤ect of the two from each other. This feature of exporters also explains why the …rms size
control is always signi…cant, and once it is included, the economic impact of export performance on
emission intensity is reduced. Ideally we would also have run a set of regressions with …rm …xed e¤ects.
However, our panel is too short to allow for such a speci…cation to be meaningful.
Emission tax rates do in general not vary between …rms, and as noted above, we control for slight
changes in the emission tax over time, by including time …xed e¤ects. However, the most energyintensive
industries enjoy a tax credit for the part of their CO2 tax bill which exceeds 0.8 percent of their production
value. The same group of …rms have since 2005 gradually become included in the EU Emission Trading
System. Both these features may have an impact on …rms’behaviour and thus on their CO2 emissions.
Hence, we split the sample into energy intensive industries and non energy intensive industries. The
results are reported for both groups of industries in Table 4. Looking at the results for the dirty
intensive industry, the only variable that is has a robust signi…cant impact on CO2 emission intensity, is
…rms’productivity. The coe¢ cient for export performance is insigni…cant. However, since as many as 84
percent of the energy intensive …rms are exporters, this implies that identifying the impact of exporting
is hard. However, as for the results for the non-energy intensive industries these are very similar to those
1 0 Exporters
are de…ned as …rms exporting more than 10 000 SEK (about 1 000 euros) per year.
11
for the complete sample.11 Exporting, productivity and …rm size, all have a signi…cant negative impact
on CO2 emission intensity. Comparing the results in Tables 3 and 4, we also see that the economic
e¤ect of these three variables also appears to be stronger when we limit the analysis to the non-energy
intensive industries.
4.2
Abatement, exports and productivity
Next we move to estimate the impact of trade and productivity on …rms’investment in abatement. Based
on out theoretical model, we expect more productive …rms to abate more. We also expect exporting
…rms to have higher abatement investments. In Table 5 we report the results from estimating equation
(25) using OLS. Independent variables are export status and …rms’productivity. We include the same
controls –capital intensity and …rm size –as in the regressions on emission intensity as well as time and
industry dummies (5 digit level).
We …nd that exporters have higher abatement investment and the higher …rms’ productivity, the
higher their investment abatement. However, as we control for capital intensity and …rm size, the
coe¢ cient of export status becomes insigni…cant. The vanishing impact of the export variable as we
include these controls, is most likely due to the data at hand, which makes taking the theory to the data
hard. As noted in section 2, abatement data are based on a survey rather than the entire population
of manufacturing …rms. The survey is biased towards larger …rms. This leaves us with a much smaller
sample than what was the case when we analysed emission intensity, and almost 95 percent of the
surveyed …rms are exporters. Hence, identi…cation of the impact of exporting as such is challenging.
Adding to this comes the fact that the …rm characteristics capital intenisty and size are according to the
theory highly correlated with export status, and summary statistics for our sample con…rms that there
is a strong and positive relationship between these variables and exporting.
As noted above in the section investigating emission intensity, we also need to take into consideration
the fact that energy intensive industries faces a threshold for their tax bill and are – or will soon be
included – in the EU ETS. Hence, we proceed as we did when analysing emissions, by splitting the
sample into energy and non-energy intensive industries, and estimate equation (25) for the two groups
of industries. Resulst are reported in Table 6. When it comes to the energy intenisve industries, we
note that 98 percent of these …rms are exporters. This enhances the identi…cation problem further. Not
suprisingly, the coe¢ cient for the export variable is insigni…cant irrespective of whether we control for
other …rm characterisitics or not.
But as we turn to the non-energy intensive industries, we …nd that, in line with what our theory would
predict, exporting …rms make higher investments in abatement. As we observed when estimating the
model on the full sample, more productive, more capital intensive and larger …rms have larger abatement
investments. The coe¢ cient of the export status variable does, however, also when we examine this
subsample, turn insigni…cant as we include the …rm size control.
1 1 Note the the identi…cation of e¤ect of exporting is easier for non-energy intensive industries, as the percentage of
exporters in the sample is 73 percent, and thus considerably lower than for the energy-intensive industries.
12
We also confront some further challenges. First, around 23 percent of the observation in our panel
(2000-2007) have zero abatement. Using the log speci…cation derived above therefore implies losing a
substantial number of observation. Second, eyeballing our data on abatement, we realise that abatement
investments are quite volatile for individual …rms over the years. In order to check the robustness of
our results with respect missing and volatile observations, we proceed by constructing an abatement
indicator which takes the value one for …rms with positive abatement investment in a given period,
and the value zero for those without abatement investment. We then estimate a speci…cation using the
indicator variable as left hand side variable. We estimate this speci…cation for the full sample as well
as with the sample into energy and non-energy intensive industries. Results are reported in Tables 7
and 8, and are very similar to those obtained with our base line model with the value of abatement
investments as left hand side variable. The link between productivity and abatement investment comes
out somewhat more signi…cant, but the relationship between export status and abatement investment
remains the same.
5
Conclusion
This paper analyses emissions and endogenous abatement investments within a framework with heterogeneous …rms and trade. The develop a theoretical model that shows how …rms’productivity and …rms’
export status are both positively related to investments in abatement. Emissions per output, in turn,
are negatively related to …rm-level productivity and …rm exports. The basic reason for these results is
that a larger production scale supports more …xed investments in abatement.
We derive closed form solutions for …rm-level abatement …rm-level emission intensity and abatement
investment, and test these using Swedish …rm-level data that includes information on …rm-level abatement
investments and …rm-level emissions of CO2. The empirical results support the notion that the …rm-level
CO2 emission intensity is negatively related to …rm productivity and to being an exporter, and provide
clear evidence in favour of our theoretical model. Investigating the relationship between abatement
investments, export status and productivity is challenging due to identi…cation and collinearity problems.
However, we are still able to trace positive relationship between exporting, productivity and abatement.
The paper provides evidence of one mechanism whereby international trade can be bene…cial for the
environment, since trade promotes investments making production cleaner. This e¤ect stands in stark
contrast to e.g. the pollution haven hypothesis, which suggests that international trade will decrease
the e¤ects of environmental regulations by making it easier for …rms to expand polluting activities in
countries with less stringent environmental standards. Our results therefore suggest one explanation for
the mixed empirical evidence on the e¤ects of globalisation on emissions.
13
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15
A
Appendix
A.1
Optimal abatement and productivity level
The optimal level of abatement investments is given by
fAD
1 1
a
B
=
t
1
1
1
(
(
1)
1
(1
=
1)
Da
(
)(
1)
1) 1
(26)
We di¤erentiate optimal abatement level with respect to productivity level:
@fAD
(1
=
@a
(
)(
1)
1) 1
(1
Da
(
)(
1)
1) 1
1
<0
(27)
i.e. abatement investments are increasing in …rms’productivity level, provided that
(
1)
1 < 0:
(28)
However, this condition will always hold, as it is a necessary condition for pro…t maxmization:
@2
D
2
fAD
@
=(
(
1)
1)
1) a1
(
1
t
(1
fAD
) 2
B<0 r
(
1)
1 < 0; (29)
and implies that abatement has a decreasing marginal e¤ect on …rms’pro…t.
A.2
Equilibrium cuto¤s and abatement investments
We will solve the model for t = t and B = B ; and because of this symmetry it su¢ ces to solve for the
equilibrium of the home country. Equations (17), (18), (14), (15), and (16) determine the endogenous
variables fAD ; fAX ; aD ; aX ; and B:
The equilibrium cut-o¤ productitvities are
0
B
aX = @
0
where #
1
k (1
(
)(
1)
1)
B
aD = @
fE
fX
+1
# 1
fX
fD
+
fE
fX #
+1
#
1
> 0; and
( + 1) 1
#
fX
fD
#
fX
fD
+
1
#f
D# 1
1
(
fX
#
#f
1)
fD
1
fD
D# 1
fX
(30)
1 k1
(31)
C
A
C
A
1 > 0: Exporters are more productive than
#
fX
fD
non-exporters as long as
#
#
1 k1
> 1, and we assume this to hold. We note that the cut-o¤s do not
depend on the emission tax rate, since the tax rate is identical across all …rms.
The equilibrium average market size per …rm B is given by
B=
where
1
(
1)((
1
1
(
1)
) 1)
(
1)
1
fD
(
1)
t
(
1) (1
aD
)
1
=
(
1
(32)
> 0: Plugging this into (17) and (18) gives
(1
D
fA
;
1)
(
1)
16
fD
a
aD
(
)(
1)
1) 1
;
(33)
(1
x
fA
=
(
1
1)
(
1)
(1 + )
1
(
1)
fD
a
aD
(
)(
1)
1) 1
;
(34)
We note that exporters investements in abatement compared to non-exporters is positively a¤ected by
trade liberalization. The …xed investments in abatement are not a¤ected by the symmetric emission tax
rate.
A.3
Measuring TFP
SETT INN NOE HER OM FREMGANGSMÅTE
A.4
Sector classi…cation
TEXT BITER:
DATASETT: ABATEMENT DATA SETT: LEVPETOUTPUTABAT; EMISSION DATA SETT:
LEVPETOUTPUT’
We do not control for size, since the left-hand side variable is given by the absolute level of investment.
17
Table 1: Decomposistion of CO2 emissions
Within sector (5 digit)
Between sectors
70%
30%
CO2 emission intensity
Note: CO2 emission intensity is measured as ln (CO2 emissions/value added).
Table 2: CO2 emissions and …rm characteristics: Exporters versus Non-Exporters
Exporters
Non-Exporters
obs.
mean
std.dev.
obs.
mean
std.dev.
CO2 emission intensity
10959
.014
.196
3297
.021
.434
Productivity (TFP)
10817
81.5
587.8
3221
60.6
381.9
Capital/labour
10913
248
746
3282
153
297
Employees
10959
159
771
3317
28
135
Note: CO2 emission intensity is CO2 emissions per value added
Table 3: CO2 emissions, productivity and exporting (OLS)
Dependent variable: ln CO2 emission intensity
(1)
Export dummy
-.406
(2)
a
(.050)
ln TFP
-.409
(3)
a
(4)
-.553
a
-.254
(5)
a
(.051)
(.050)
(.052)
a
b
a
-.060
(.024)
ln Capitalintensity (K/L)
-.045
-.055
(.022)
(.023)
b
a
.179
(.023)
.247
-.479
(6)
a
(.047)
(7)
(8)
b
-.131a
(.046)
(.047)
(.048)
a
a
-.652a
(.068)
(.066)
-.325
-.886
a
(.068)
-.301
-.873
-.046
(.023)
b
(.020)
-.327a
ln Employment
.001
(.019)
-.255a
(.029)
(.027)
Industry dummies
No
No
No
No
Yes
Yes
Yes
Yes
Year dummies
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
R-squared
.01
.01
.03
.07
.30
.34
.34
.35
14256
13969
13958
13958
14256
13969
13958
13958
Number of obs.
Note: Estimates are based on the panel 2004-2007. Errors are clustered at the …rm level.
CO2 emission intensity gives the ratio emissions to value added. Industry dummies are based on 5 digit industries.
a
signi…cant at 1% level, b signi…cant at 5% level, c signi…cant at 10% level.
18
Table 4: CO2 emissions, productivity and exporting (OLS): Energy and non-energy intensive industries
Dependent variable: ln CO2 emission intensity
Energy intensive industries
(1)
Export dummy
(2)
(3)
Non-energy intensive industries
(4)
(5)
(6)
a
-.133
-.028
-.048
-.055
-.581
(.101)
(.103)
(.104)
(.110)
(.052)
a
a
a
ln TFP
-.545
-.547
(.111)
-.488
(.111)
ln Capitalintensity (K/L)
-.398
-1.089
(8)
a
-.163a
(.051)
(.051)
-.362
(.050)
(.110)
a
-1.060
(.080)
-.073
.042
.079
(.043)
a
-.702a
(.079)
c
(.043)
ln Employment
(7)
a
(.076)
a
-.029
(.022)
(.021)
-.128a
-.321a
(.051)
(.031)
Industry dummies
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Year dummies
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
R-squared
.35
.38
.38
.38
.21
.25
.25
.28
4202
4112
4110
4110
10054
9857
9848
9848
Number of obs.
Note: Estimates are based on the panel 2004-2007. Errors are clustered at the …rm level.
CO2 emission intensity gives the ratio emissions to value added. Industry dummies are based on 5 digit industries.
a
signi…cant at 1% level, b signi…cant at 5% level, c signi…cant at 10% level.
Table 5: Abatement investments, productivity and exporting (OLS)
Dependent variable: ln abatement investments
Export dummy 2000
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
1.486a
1.354a
.519c
-.273
.799a
.872a
.422
-.353
(.335)
(.335)
(.282)
(.218)
(.307)
(.307)
(.283)
(.224)
a
a
a
a
a
.538a
ln TFP
.247
(.061)
ln Capitalintensity (K/L)
.151
.181
.639
(.050)
(.039)
(.151)
(.125)
1.183a
.909a
.801a
.493a
(.085)
(.071)
(.080)
(.068)
ln Employment
1.104
(.164)
.744
a
1.109a
(.053)
(.054)
Industry dummies
No
No
No
No
Yes
Yes
Yes
Yes
Year dummies
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
R-squared
.01
.03
.17
.29
.28
.28
.32
.39
5450
5338
5336
5336
5450
5338
5336
5336
Number of obs.
Note: Estimates are based on the panel 2000-2007.
Industry dummies are based on 5 digit industries. Errors are clustered at the …rm level.
a
signi…cant at 1% level, b signi…cant at 5% level, c signi…cant at 10% level.
19
Table 6: Abatement investments, productivity and exporting (OLS): Energy and non-energy intensive
industries
Dependent variable: ln abatement investments
Energy intensive industries
(1)
Export dummy 2000
(2)
.072
(3)
-.034
(.896)
ln TFP
Non-energy intensive industries
(4)
(5)
(6)
a
-.062
-.088
1.029
(.261)
1.116
(7)
a
.657
(8)
a
-.285
(.801)
(.740)
(.503)
(.261)
(.256)
(.245)
1.386a
1.087a
.286
.049
.365b
.540a
(.345)
(.294)
(.229)
(.167)
(.160)
(.139)
a
a
a
.448a
ln Capitalintensity (K/L)
.961
(.165)
ln Employment
.554
.652
(.136)
(.085)
(.077)
1.365a
1.007a
(.113)
(.062)
Industry dummies
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Year dummies
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
R-squared
.30
.33
.37
.44
.17
.17
.20
.29
2027
1997
1996
1996
3423
3341
3340
3340
Number of obs.
Note: Estimates are based on the panel 2000-2007.
Industry dummies are based on 5 digit industries. Errors are clustered at the …rm level.
a
signi…cant at 1% level, b signi…cant at 5% level, c signi…cant at 10% level.
Table 7: Abatement investments, productivity and exporting (OLS) II
Dependent variable: Abatement indicator
(1)
Export dummy
.210
a
(.040)
ln TFP
(2)
.201
a
(3)
.115
(4)
a
.122
(.042)
(.035)
(.033)
a
a
a
(.006)
ln Capitalintensity (K/L)
.015
.018
(.005)
(.004)
a
a
.121
(.008)
ln Employment
a
.033
(.040)
.024
(5)
.093
(6)
(7)
(8)
b
-.005
(.042)
(.041)
(.037)
a
a
.053a
(.019)
(.017)
a
.060a
(.010)
(.010)
.133
.064
a
(.020)
.081
.076
.093
(.008)
a
.122a
(.007)
(.008)
.115
Industry dummies
No
No
No
No
Yes
Yes
Yes
Yes
Year dummies
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
R-squared
Number of obs.
.01
.02
.10
.17
.17
.18
.20
.25
5450
5338
5336
5336
5450
5338
5336
5336
Note: Estimates are based on the panel 2000-2007. Errors are clustered at the …rm level.
Industry dummies are based on 5 digit industries.
a
signi…cant at 1% level, b signi…cant at 5% level, c signi…cant at 10% level.
20
Table 8: Abatement investments, productivity and exporting (OLS): Energy and non-energy intensive
industries, II
Dependent variable:Abatement indicator
Energy intensive industries
(1)
Export dummy
(2)
(3)
Non-energy intensive industries
(4)
(5)
a
(6)
(7)
-.033
.078
.075
.073
.132
(.103)
(.095)
(.090)
(.072)
(.044)
(.044)
(.045)
(.043)
.147a
.119a
.046c
-.003
.041c
.062a
(.036)
(.032)
(.028)
(.025)
(.024)
(.022)
a
a
a
.065a
(.013)
(.013)
ln Capitalintensity (K/L)
.090
(.017)
ln Employment
.053
.081
(8)
c
.089
ln TFP
.145
a
.090
(.016)
.125a
.123a
(.015)
(.009)
Industry dummies
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Year dummies
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
R-squared
.19
.20
.23
.27
.13
.13
.16
.20
2027
1997
1996
1996
3423
3341
3340
5336
Number of obs.
Note: Estimates are based on the panel 2000-2007. Errors are clustered at the …rm level.
Note: Industry dummies are based on 5 digit industries. Errors are clustered at the …rm level.
a
signi…cant at 1% level, b signi…cant at 5% level, c signi…cant at 10% level.
21