DISCUSSION PAPER SERIES
No. 8583
INTERNATIONAL TRADE, CO2 EMISSIONS AND HETEROGENEOUS FIRMS Rikard Forslid, Toshihiro Okubo and Karen-Helene Ulltveit-Moe
INTERNATIONAL TRADE AND REGIONAL ECONOMICS
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INTERNATIONAL TRADE, CO2 EMISSIONS AND HETEROGENEOUS FIRMS Rikard Forslid, Stockholm University and CEPR Toshihiro Okubo, Keio University Karen-Helene Ulltveit-Moe, University of Oslo and CEPR Discussion Paper No. 8583 September 2011 Centre for Economic Policy Research 77 Bastwick Street, London EC1V 3PZ, UK Tel: (44 20) 7183 8801, Fax: (44 20) 7183 8820 Email:
[email protected], Website: www.cepr.org This Discussion Paper is issued under the auspices of the Centre’s research programme in INTERNATIONAL TRADE AND REGIONAL ECONOMICS. This paper is produced as part of a CEPR project "Globalization, Investment and Services Trade (GIST) Marie Curie Initial Training Network (ITN)" funded by the European Commission under its Seventh Framework Programme Contract No. FP7-PEOPLE-ITN-2008-21. Any opinions expressed here are those of the author(s) and not those of the Centre for Economic Policy Research. Research disseminated by CEPR may include views on policy, but the Centre itself takes no institutional policy positions. The Centre for Economic Policy Research was established in 1983 as an educational charity, to promote independent analysis and public discussion of open economies and the relations among them. It is pluralist and nonpartisan, bringing economic research to bear on the analysis of medium- and long-run policy questions. These Discussion Papers often represent preliminary or incomplete work, circulated to encourage discussion and comment. Citation and use of such a paper should take account of its provisional character. Copyright: Rikard Forslid, Toshihiro Okubo and Karen-Helene Ulltveit-Moe
CEPR Discussion Paper No. 8583 September 2011
ABSTRACT International trade, CO2 emissions and heterogeneous firms* This paper develops a model of trade with heterogenous firms, where firms invest in abatement technology and thereby have an impact on their level of emissions. The model shows how firm productivity and firm exports are both positively related to investments in abatement technology. Emission intensity is, however, negatively related to firms' productivity and exports. The basic reason for these results is that a larger production scale supports more fixed investments in abatement technology and, in turn, lower emissions per output. In contrast to the standard models of heterogeneous firms, firms' productivity, and thus export performance, is not exogenous, but endogeneously determined by firms' investment in abatement technology. We derive closed form solutions for firm-level abatement investments and emissions per output, and test the empirical implications of the model using detailed Swedish firmlevel data. The empirical results strongly support the model. JEL Classification: D21, F12 and F15 Keywords: co2-emissions, heterogeneous firms and international trade Rikard Forslid Department of Economics Stockholm University 106 91 Stockholm SWEDEN
Toshihiro Okubo Research Institute for Economics & Business Administration (RIEB) Kobe University 2-1, Rokkodai cho, Nada-ku, Kobe 657-8501 JAPAN
Email:
[email protected]
Email:
[email protected]
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Karen-Helene Ulltveit-Moe Department of Economics University of Oslo P.O. Box 1095 Blindern 0317 Oslo NORWAY Email:
[email protected] For further Discussion Papers by this author see: www.cepr.org/pubs/new-dps/dplist.asp?authorid=117878
* We are grateful for comments from Andrew Bernard, and participants at the GIST conference in Stockholm May 2011. Financial support from Grant-in-Aid for Scientific Research (JSPS) and Research Institute of Economy, Trade and Industry (RIETI) is gratefully acknowledged by Okubo. Submitted 19 September 2011
International trade, CO2 emissions and heterogeneous …rms Rikard Forslidy, Toshihiro Okuboz, and Karen Helene Ulltveit-Moex
September 2011
Abstract This paper develops a model of trade with heterogenous …rms, where …rms invest in abatement technology and thereby have an impact on their level of emissions. The model shows how …rm productivity and …rm exports are both positively related to investments in abatement technology. 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 …xed investments in abatement technology and, in turn, lower emissions per output. In contrast to the standard models of heterogeneous …rms, …rms’productivity, and thus export performance, is not exogenous, but endogeneously determined by …rms’investment in abatement technology: We derive closed form solutions for …rm-level abatement investments and emissions per output, and test the empirical implications of the model using detailed Swedish …rm-level data. The empirical results strongly support the 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, thereby contributing to global warming. But this paper sets out to shed some further light on the forces at work by focusing on inter-…rm productivity di¤erentials and interdependence among productivity, abatement and emissions. We are grateful for comments from Andrew Bernard, and participants at the GIST conference in Stockholm May 2011. Financial support from Grant-in-Aid for Scienti…c Research (JSPS) and Research Institute of Economy, Trade and Industry (RIETI) is gratefully acknowledged by Okubo. y
Stockholm University and CEPR; email:
[email protected]
z
Keio university, e-mail:
[email protected]
x
University of Oslo and CEPR, e-mail:
[email protected]
1
In theoretical neoclassical models, there are opposing e¤ects. On the one hand, trade increases income, which will tend to increase the demand for a clean environment and therefore increase investments in clean technology and abatement. However, 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 industry expands 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 …rm-level data to investigate the impact of environmental investment on productivity dynamics and exports. While productivity dynamics do not appear to be a¤ected, growth in exports does. It has been pointed out (see e.g. Levinson, 2009) that trade may have reduced pollution as trade liberalization may encourage technological upgrading. Our paper is closely related to this idea. We present a model of trade with di¤erentiated goods and heterogenous …rms (see Melitz, 2003) where …rms invest in abatement technology and thereby a¤ect their level of emissions. We show that more productive …rms …nd it pro…table to invest in abatement technology as their bigger volumes allow them to spread the …xed costs of abatement investment across more units. Exporting increases investment in abatement technology, since exports sales boost the scale of production. Hence, …rm productivity and …rm exports are both positively related to investments in abatement technology, while emission intensity is negatively related to …rms’productivity and exports. In contrast to the standard models of heterogeneous …rms, …rms’productivity and thus, also export performance, is not exogenous, but endogenously determined by …rms’investment in abatement We derive closed form solutions for …rm-level abatement investments and …rm-level emission intensities (emissions per unit produced). These equations are tested on detailed Swedish …rmlevel data. Our dataset contains …rm-level emissions and …rm-level abatement expenditures as well as …rm exports.1 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 1
There are very few studies using …rm-level data on CO2 emissions. One exception is Cole et al. (2011).
2
productive …rms abate more and emit less, and exports are associated with more abatement and lower emission intensity.
2
The Model
To analyse the impact of trade on global CO2 emission, we use an augmented version of Melitz’ seminal model on monopolistic competition, heterogeneous …rms and trade (see Melitz, 2003). More speci…cally, we introduce emissions, CO2 taxes and abatement technology in the standard model. We assume there to be two countries with production in two sectors, agriculture (A) and manufacturing (M ). Each country j has a single primary factor of production, labour, Lj , used in both the A and the M sector. The A sector produces homogenous goods subject to constant returns to scale. The M sector is characterized by increasing returns, monopolistic competition and heterogeneous …rms.
2.1
Demand
Consumers preferences are given by a two-tier utility function with the upper tier (CobbDouglas) determining the representative consumer’s division of expenditure between agricultural and manufactured goods (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. Agricultural (A) goods
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 unit labour requirement in the A-sector is one, which gives pA = wh = wf = 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 agricultural goods is su¢ ciently large to
guarantee that the agricultural sector is active in both countries irrespective of the location of manufacturing. The consumption of …nal goods from the manufacturing sector is de…ned as an aggregate CM j ;
2
CM j = 4
Z
c (i)(
i2I
1)=
3
di5
=(
1)
;
(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
3
consumed in country j. Each consumer spends a share
of his income on manufactures, and
demand for each single variety produced in country k and consumed in country j is therefore given by xj =
pjk Pj1
Yj ;
where pjk is the consumer price, Yj is income; and Pj
(4) N Rj
1
p (i)
!
di
0
manufacturing goods in country j.
2.2
1 1
the price index of
Entry, exit and production in the manufacturing sector
To enter the manufacturing 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,2 i.e. G(a) = scale parameter to unity, a0
ak ; ak0
where we normalise the
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, it has to bear the additional …xed labour costs, fX : Hence, …rm technology is represented by a cost function that exhibits a variable 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. 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 manufacturers. 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
(6)
for each producer in country j, and re‡ects that shipping manufactured goods involves a frictional trade cost of the “iceberg” form. For each unit of a good from country j to arrive in country k,
jk
> 1 units must be shipped. It is assumed that trade costs are equal in both
directions and that
jj
= 1:
Let us now also account for the fact that manufacturing activity entails pollution in terms 2
This assumption is consistent with the empirical …ndings by e.g. Axtell (2001).
4
of emission of CO2. We follow Copeland and Taylor (2003) and assume that each …rm produces two outputs: a manufactured good (x) and emissions (e). Abatement is possible, but this requires diverting a fraction
of the primary factor, labour, away from the production of x.
Firms pay the …xed overhead costs, and thereafter the joint production is given by x = (1
)
l a
(7)
e = '( )x with 0
(8)
1. Emission intensity (e=x) is determined by the abatement function '( ) =
(1
)1= (fA )
(9)
which is characterised by '(0) = 1; '(1) = 0; '0 (:) < 0 and 0
0: Below,
(fA ) = fA :
We use (8) and (9) to substitute for
in (7), which gives us l a
x = ( (fA ) e)
1
(10)
from which we can derive the variable costs function. 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 + with f = fD + fX
(1
)
1
a(1
)
x
(11)
; where f = fD for …rms only serving the domestic market, and
for exporters, i.e. …rms serving both the domestic and the foreign market.
Emissions are not for free; rather they incur a tax t. By investing in more e¢ cient abatement technology, emissions can be reduced as can the tax bill. Note that while …rms are heterogeneous with respect to labour productivity, a; they are identical in all other respects, i.e. they share the same cost function and face the same tax rate. 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
every period until it is hit by a bad shock and forced to exit.
5
(a)
0 in
2.3
Investing in abatement
Firms maximize pro…ts with respect to volume and investment in abatement. They choose investment in abatement e¢ ciency in order to maximize pro…ts px
=
f
fA ;
(12)
which may be written as =t
(1
) (1
a
1
using (4), (11), and where B
)(1
( 1)1 P1
) L
fA
(
1)
B
f
fA ;
(13)
: Equation (13) reveals that an internal solution
to the pro…tmaximising choice of fA requires that
(
1) < 1: We will assume this condition
to hold in the remainder of the paper. The condition implies a decreasing marginal e¤ect of abatement on …rm pro…t. The optimal …xed cost investment in abatement technology will depend on …rm type: non-exporter or exporter. As argued below, abatement investments have an impact on …rms’ marginal costs, and on the pro…tability of being a domestic versus an exporting …rm. Exporting status is thus not only determined by …rms’labour productivity, but also by their abatement investments. Maximizing domestic …rms’pro…ts w.r.t. fA using (6) and (11) gives fAD 1
where B
=
( 1)1 Pj1
1 1 a B Lj
t
1
1
1
(
(
(1
1) 1
1)
8 j is exogenous to the …rm and
=
Da
D
n
(
1 B
)( 1) 1) 1
;
(14) 1
1
(t )
(
1)
while the optimal …xed cost investment in abatement for exporters is fAx 1
with B
=
1 a1 (B + B )
( 1)1 Pk1
Lk
t
8 k 6= j and
( =
1
1 (
1) 1
(
(1
1) 1
=
1) and where
X
Xa
n
(
)( 1) 1) 1
1 (B+ B )
Proposition 1 More productive …rms invest more in abatement given that
(t )
(
;
(15) 1
1 (
1)
o
1) < 1.
Proof: Investment in abatement rises when …rm productivity (1=a) rises since @fAD =@a < 0 and @fAX =@a < 0: These inequalities will always hold as long as the second-order condition for pro…t maximization is satis…ed, as this requires that @ 2 that (
1)
2 D =@fA
< 0 which, in turn, requires
< 1 (see the Appendix).
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.
6
,
1
1
1
o
1 (
1) 1
:
Proposition 2 For any given level of productivity, exporters invest more in abatement than non-exporters, and trade liberalization increases exporting …rms’investments in abatement given that
(
1) < 1.
Proof: Since B < (B + B ), then
X
>
D;
from which it follows that fAx > fAD for any
given productivity level (1=a). If trade is liberalized (a higher ), then (B + B ) rises as does X;
and thus, fAx :
The intuition for this result is that the production volume increases with exports, and the exploiting of scale economies warrants a higher …xed cost investment in abatement. Proposition 3 A higher tax rate on emission leads to lower investments in abatement technology given that
x @fA @t
(
1) < 1.
Proof : It follows from (14) and (15) that n o = (( 1)1) 1 (B+1 B ) a(1 )( 1) ( 1 1)
1 (
1) 1
(1
t
) ( 1)+1 ( 1) 1
< 0.
This result may seem counterintuitive. But the logic behind it is well known from the theory of production: say that a …rm is considering investing in a new machine. Using the machine requires labour. If the price of labour increases, this decreases the incentive to invest in the machine. In our case, a higher tax rates implies that the price on emission, i.e. the cost of emission, rises. As a consequence, …rms want to emit less. As …rms substitute away from emissions, it becomes less pro…table to invest in abatement.
2.4
Equilibrium conditions
Equilibrium in the model is determined by the zero pro…t conditions for …rms only serving the domestic market and exporters, respectively = a1
D
X
= a1
t
1
t
1
B
fD
(B + B )
fA = 0;
fD
fX
fA = 0:
Since a is unit labour requirement, 1=a depicts labour productivity and, with that
a(1
)(1
)
(16) (17) > 1; it follows
increases along with productivity and can thus be used as a productivity index.
From (16) and (17), we see that pro…ts are increasing in …rms’productivity. The least productive …rms expect negative operating pro…ts and therefore exit the industry. This applies to all …rms (1
with a productivity below aD
)(1
)
which is the cuto¤ at which pro…ts from domestic sales
equal zero, and is determined by fD =
a1D
1
t (fA )
B
fAD (aD ):
(18)
In order to break even as an exporter, …rms have to cover a higher …xed cost, and the cuto¤ productivity level at which producers serving the domestic as well as the foreign market just 7
break even is determined by fX + fD = where
1
1
t (fA )
a1X
fAX (aX );
( B + B)
(19)
2 [0; 1] depicts freeness of trade.
The model is closed by the free-entry condition
fE = n
ZaX 0
+n
f
Z
X (a)
fX
fD
fA (a)g dG(a)
(20)
aD
aX
f
D (a)
fD
fA (a)g dG(a):
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. Based on these conditions, we are also able to derive some results on the relative productivity and the relative abatement investment of domestic versus exporting …rms. First, the relative cut-o¤ productivities of exporters and non-exporters can be calculated using the cuto¤ conditions (18) and (19), and the expressions for optimal abatement investment (14) and (15): (1
aX aD
(
)( 1) 1) 1
=
D X
fX 1+ fD
=
B 1+ B
1 (
1) 1
1+
fX fD
:
(21)
Endogenous exporter status We see that exporters are more productive than non-exporters when trade costs are high ( close to zero). This follows from the assumption that the entry cost in foreign markets fX is larger than the entry cost in the domestic market fD : From (21), it can also be seen that higher foreign market entry costs fX increase the relative productivity of exporters. A well known feature of the models with heterogeneous …rms is that cut-o¤ productivities of exporters and non-exporters converge as trade is liberalised ( increased). While our model shares this feature, it di¤ers from the standard models as we may get aD = aX before trade is completely liberalized (
= 1).3 The reason for this is that in our model, …rms’ marginal
costs are endogenous and depend on investment in abatement technology. As a consequence, and unlike what we …nd in the standard models of heterogeneous …rms and trade, …rms’export status is not purely given by their randomly drawn productivity, but also by their deliberate choice of abatement investment. If the foreign market is su¢ ciently large, all …rms will …nd it optimal to invest in abatement to lower their marginal cost and thereby become exporters. Finally, we note that the tax on emissions a¤ects exporters and non-exporters in the same 3
The critical trade cost is determined by
=
B B
fD fX +fD
8
(
1) 1
1 .
fashion and it will therefore not a¤ect the relative cut-o¤ productivities. However, since the tax on emissions will have an impact on abatement investment, everything else equal, the tax rate will have an impact on the pro…tability of domestic versus exporting …rms and thus, on the choice of becoming an exporter, and then, in turn, on the number of exporters in the economy.
2.5
Emissions
Taking abatement investment as given, …rms decide on the use of labour as well as on emissions. We focus on emissions and use Shepard’s lemma on the cost function (11) to derive optimal emissions and emission intensity (emissions per output). After having substituted for optimal fA using (14) and (15), respectively, we state that the emission intensity of non-exporters is given by 1 eD i =a xi
where
1
(
small enough so that
t
(
1) > 0 and
B
(
1))
;
(22) (
1)2 : We assume that
is
> 0. This limits the e¢ ciency of the abatement technology, which
prevents …rms from completely substituting away from the use of emissions. For exporters, the expression is given by: 1 eX i =a xi
t
(B + B )
:
(23)
Several results emerge directly from equations (22) and (23). Proposition 4 More productive …rms have a lower emission intensity. Proof: Since pro…t maximization with respect to abatement investment requires that ( 1 (see the Appendix), then @
eD i xi
1)
=@a > 0:
Proposition 5 For any given level of productivity, exporters have a lower emission intensity than non-exporters, and trade liberalisation (higher
) reduces the emission intensity of
exporters. Proof: Since B < (B + B ), then
eD i xi
>
eX i xi
must be true for any given level of a, exporters
will have a lower emission intensity than non-exporters. If trade is liberalized (a higher ), then (B + B ) rises, while
eX i xi
declines, i.e. @
eD i xi
=@ < 0:
These two propositions are closely related to those on abatement investments. Firms with higher sales and thus a higher production volume will invest more in abatement technology, and they will have lower emission intensities. Proposition 6 Higher emission taxes lead to lower emission intensity when
9
> .
therefore implies a restriction on ; which must be su¢ ciently small. 5
The sector classi…cation is shown in the appendix.
10
CO2 emissions using data on all types of fuel use at the plant level together with the relevant fuel coe¢ cients provided by Statistics Sweden. Data on fuel use is collected for all manufacturing plants with more than 10 employees from 2004-2007. Plant emissions are aggregated to the …rm level. This gives CO2 emission data (kg/mioSEK) for about 5 000 …rms. We also use …rm level data on abatement investments per …rm (tSEK) over the whole period 2000-2007. The abatement data is a semi-random sample of manufacturing …rms (all …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). Thus, the abatement data is collected for fewer and larger …rms and the data set covers about 1 500 …rms. This implies that almost 95 percent of the …rms in the latter sample are exporters, which makes identi…cation of the export dummy harder. Table 1 and table 2 illustrate that there are signi…cant di¤erences across …rms in our datasets. It can be seen that exporters, on average, are larger and more capital intensive than nonexporters. Exporters also, on average, invest more in abatement and emit less CO2 per output. Table 1: Descriptives for abatement data
Abatement inv. Capital/labour Employment
obs. 5450 6971 6997
All mean std.dev. 2801 15310 337.4 1100 446.0 1157
obs. 5226 6598 6619
Exporters mean std.dev. 2838 15501 344.6 1122 459.3 1179
Non-Exporters obs. mean std.dev. 224 1919 9820 373 209.8 559.0 378 213.2 620.5
Table 2: Descriptives for CO2 data
CO2 per output Capital/labour Employment
4.2
obs. 14325 14262 14384
All mean 13.70 226.1 128.3
std.dev. 249.5 671.3 681.4
obs. 11026 10980 11067
Exporters mean std.dev. 11.65 160.2 248.1 746.2 158.3 770.9
Non-Exporters obs. mean std.dev. 3299 20.56 429.6 3282 152.5 296.8 3317 28.28 134.5
Abatement and export
We start by estimating equation (25) with OLS. 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 3 we report the results with the log of total abatement investments as the dependent variable. Firms’productivity is measured as TFP (estimated using the LevinsohnPetrin method), while we use an export dummy to identify exporters.6 We also employ the capital labour ratio as a control variable. We do not control for size, since the left-hand side variable is given by the absolute level of investment. To account for changes in the CO2 tax rate over time, we use year …xed e¤ects, but it turns out that our results are actually little a¤ected by this. All variables are in logs, and errors are clustered at the …rm level. 6
Exporters are de…ned as …rms exporting more than 10 000 SEK (about 1 000 euros) per year.
11
Table 3: Abatement investments, productivity and exporting (OLS)
(1) ln TFP Export dummy
1.486a (.335)
ln capitalintensity (K/L) Industry dummies Year dummies R-squared Number of obs.
No Yes .01 5450
Dependent variable: ln abatement investments (2) (3) (4) (5) (6) (7) .258a .247a .151a .970a .970a (.061) (.061) (.050) (.173) (.173) 1.354a .519c .998a 1.041a (.335) (.282) (.300) (.296) a 1.183 (.085) No No No Yes Yes Yes Yes Yes Yes Yes Yes Yes .02 .03 .17 .13 .14 .14 5338 5338 5336 5450 5338 5338
(8) .987a (.149) .241 (-.267) 1.019a (.085) Yes Yes .22 5336
Note: Estimates are based on the panel 2000-2007. 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.
The coe¢ cient for productivity is positive and signi…cant at the one percent level in all regressions. The export dummy is positive and signi…cant regardless of whether we control for productivity di¤erentials, but becomes insigni…cant in the within sector estimation when controlling for capital intensity. The results are overall supportive of the theoretical model. But as pointed out above, estimating the impact of exporting on abatement investments we encounter an endogeneity problem regarding the export variable. Our model predicts that exporters and high-productivity …rms have higher investments in abatement. However, according to theory, …rms’ exporting status depends on their investments in abatement technology. We aim at correcting for this by instrumenting for the export dummy using …rm-speci…c exchange rates. We construct the instrument variables using the USD/SEK exchange rate times …rms’ export share in the year 2000 (the …rst year of our panel). Firms that do not export that year are assigned the industry (nace 2) average export share,7 the idea being that …rms with a relatively high export share are relatively more a¤ected by the exchange rate. First stage estimation results are reported in Table 4. The instrument is strongly correlated with the endogenous regressor with a t-value of 9.0 and 3.9 respectively in regression 1 and 2 (thus passing the F-stat.>10 test for weak instruments). 7
Industry 10 (Coke) consists of 10 …rms that do not export. This means that there is no industry average export share and these observations are therefore dropped.
12
Table 4: First stage estimation
expshare*exchrate ln TFP ln capitalintensity (K/L) Industry dummies Year dummies R-squared Number of obs.
(1) 0.017a (0.0019) 0.0075a (0.0024) 0.025a (0.0049) No Yes 0.072 6817
Dependent variable: export dummy (2) 0.0068a (0.0017) -0.0065 (0.0074) a 0.033 (0.0053) Yes Yes 0.15 6817
Note: Estimates are based on the panel 2000-2007. 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.
The results from the second stage estimation are reported in Table 5. Table 5: Abatement investment, productivity and exporting (IV)
ln TFP Export dummy ln capitalintensity (K/L) Industry dummies Year dummies Number of obs.
Dependent variable: ln abatement investments (1) (2) (3) (4) (5) (6) .186a .130b .995a .999a (.067) (.056) (.207) (.167) a a b a a 1.604 1.334 .563 1.802 1.676 .860 (.258) (.267) (.228) (.591) (.564) (.633) 1.079a .786a (.102) (.213) No No No Yes Yes Yes Yes Yes Yes Yes Yes Yes 5434 5322 5320 5434 5322 5320
Note: Estimates are based on the panel 2000-2007. 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.
Comparing the results with and without instruments (see Tables 3 and 5), we observe that export status has a positive signi…cant impact on abatement also after instrumenting. Eyeballing our data on abatement, we realise that investments in abatement technology are, nevertheless, quite volatile for individual …rms over the years. Therefore, we calculate average abatement investments for our period of observation 2000-2007, and reestimate (25) based on the resulting cross-section data set. The OLS results are reported in Table 6. We use explanatory variables from the sample mid-point, 2004, but robustness checks prove that the choice of year is of less importance. As before, abatement investments turn out to be positively and signi…cantly
13
a¤ected by …rms’productivity and export status. This also holds when controlling for capital intensity. Table 6: Abatement investments, productivity and export (OLS), III
ln TFP Export dummy ln capitalintensity (K/L) Industry dummies R-squared Number of obs.
Dependent variable: ln average abatement 2000-2007 (1) (2) (3) .311b .335b (.131) (.125) a .823 .828b .639b (.343) (.338) (.322) .514a (.061) Yes Yes Yes .23 .23 .31 655 642 641
Note: 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.
As a …nal robustness check, we look at how abatement investments are a¤ected by …rms switching from being non-exporters to exporters. We would expect non-exporters to carry out particularly high investments in abatement as they switch from being non-exporters to exporters. Table 7 shows the average abatement investments for non-exporters, exporters, and switchers for the period 2000-2007. . Table 7: Export status, productivity, size and capital intensity
Switchers Exporters Non-exporters
Abatement investment 27.78 27.38 3.34
TFP
Size (employees)
1.18 2.80 0.96
172.26 206.15 61.33
Capital intensity 93.99 176.71 58.51
Note: All number are averages for 200-2007 All variables are in logs.
According to what we would expect, Table 4 shows that exporters, which are the largest, most productive, and most capital intensive …rms, have much higher abatement investments than non-exporters, while the highest abatement investments are found among the switching …rms, i.e. the …rms that switch from being non-exporters to exporters during our period of observation. We further investigate the relationship between abatement and switching export status by running a probit panel regression with the switch from non-exporter to exporter as the
14
dependent variable. We only consider pure switches while dropping cases where …rms alternate back and forth between being non-exporters and exporters. Table 8 shows the results. Table 8: Switching to exporting: the role of abatement investments (probit) Dependent variable: Probablity of switching to exporting ln abatement investment, t ln abatement investment, t-1
(1)
(2)
(3)
(4)
(5)
(6)
.035b (.016) .047a (.016)
.037 (.030) .048c (.027) .044c (.025)
.036b (.017) .048a (.017)
.025 (.020) .037b (.0.17)
.136a (.035)
.035 (.030) .052c (.030) .045c (.027) .212a (.055)
No Yes 3391
Yes Yes 2360
.025 (.030) .038 (.030) .031 (.030) .208a (.058) .175b (.084) Yes Yes 2359
ln abatement investment, t-2 ln TFP ln capitalintensity (K/L) Industry dummies Year dummies Number of obs.
No Yes 3452
No Yes 2395
.129a (.036) .152a (.057) Yes Yes 3389
Note: Estimates are based on the panel 2000-2007. a signi…cant at 1% level, b signi…cant at 5% level, c signi…cant at 10% level.
We consider the impact of abatement investment in the period where the switch takes place, and the impact of abatement investment taking place one and two periods before the switch on …rms’ probability to become exporters.8 In particular, abatement investments lagged one time period turn out to be a signi…cant predictor of …rms’ switching to exporting. This is consistent with …rms investing in abatement to become exporters. A weakness with our data is that almost all …rms are exporters since abatement data is primarily sampled from larger …rms.9 This means that there are very few switchers; just somewhat more than ten per year on average. This leaves us with little variation, and reduces the potential for identi…cation. Adding lags further diminishes the sample and in the last regression with all variables included, we do not get any signi…cant coe¢ cients for abatement, even if the estimated coe¢ cients are of the expected sign and very similar to the signi…cant ones in terms of magnitude.
4.3
CO2-emission intensity
Next we move on to estimate equation (24). Our model predicts that the …rm-level emission intensity should be negatively a¤ected by productivity and export status. Emission intensity is 8
We have also run the regressions with productivity and capital labour ratio lagged one period with almost identical results. 9
The abatement data is a random sample of 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.
15
measured as …rm-level CO2 emissions per output. We have emission data for the years 20042007. Emission tax rates do not vary between …rms, and are therefore not controlled for.10 We control for emission tax changes by including time …xed e¤ects. However, the exclusion of year dummies does not a¤ect the estimates in any signi…cant way. We report regression results where errors are clustered at the …rm level. Clustering at the sector level gives very similar results. Table 9 shows the OLS regression results. Table 9: CO2 emissions, productivity and exporting (OLS) Dependent variable: ln CO2 emission intensity
(1) ln TFP Export dummy
(2)
(3)
(4)
(5)
-.060a (.023)
-.060b (.024) -.460a (.050)
-.055b (.022) -.550a (.050) .180a (.023)
No Yes .02 14038
No Yes .03 14027
-.060b (.023) -.250a (.052) .250a (.023) -.330a (.029) No Yes .07 14027
-.450a (.050)
ln capitalintensity (K/L) ln employment Industry dummies Year dummies R-squared Number of obs.
No Yes .01 14325
No Yes .01 14038
(6)
(7)
(8)
-1.020a (.072)
-.970a (.073) -.250a (.047)
Yes Yes .21 14038
Yes Yes .21 14038
-.420a (.047)
Yes Yes .17 14325
(9) -.980a (.073) -.270a (.048) .040c (.021)
Yes Yes .21 14027
Note: Estimates are based on the panel 2000-2007. 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.
The coe¢ cients for productivity and the export dummy are negative and strongly signi…cant in all regressions. The capital-labour ratio is positively signi…cant at the one percent level in all regressions as is …rm size (employment). In terms of magnitudes, export has a relatively small e¤ect on emissions, maybe some 0.3-0.5 percent decrease in emission intensity, after controlling for productivity. Productivity appears to be more important. In the within sector estimates, we …nd a negative emission intensity elasticity w.r.t. productivity of around one percent. However, given the interconnection between productivity and export status, these results should be interpreted with caution.
5
Conclusion
This paper analyses emissions and endogenous investments in abatement technology within a framework with heterogeneous …rms and trade. The model shows how …rms’productivity and 10 Manufacturing …rms enjoy a rebate on CO2-taxes in Sweden. The same rebate (and tax rate) applies to all …rms in our sample.
16
(10) -.760a (.071) -.100b (.049) .090a (.021) -.250a (.028) Yes Yes .23 14027
…rms’export status are both positively related to investments in abatement technology. 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 technology. As a consequence, and unlike what we …nd in the standard models of heterogeneous …rms and trade, …rms’ export status is not purely given by their randomly drawn productivity, but also by their deliberate choice of abatement investment. If the foreign market is su¢ ciently large, all …rms will …nd it optimal to invest in abatement to lower their marginal cost and thereby become exporters. We derive closed form solutions for …rm-level abatement expenditures and …rm-level emissions per output, and test these using Swedish …rm-level data that contains …rm-level investments in abatement technology and …rm-level emissions of CO2. The empirical results show that abatement investment is positively related to export status and productivity. A probit regression for switchers from non-export to export status shows that lagged abatement investments are also a signi…cant predictor of a switch to becoming an exporter. The empirical results also strongly support the notion that the …rm-level CO2 emission intensity (CO2-emissions per output) is negatively related to …rm productivity and to being an exporter. Overall, the estimations provide strong evidence in favour of our theoretical model. The paper provides evidence of one mechanism whereby international trade can be bene…cial for the environment, since trade promotes investments in cleaner technology. 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.
17
References [1] Antweiler, W., Copeland, B.R. and Taylor, M.S. (2001), "Is Free Trade Good for the Environment?" American Economic Review, Vol. 91, no. 4, pp. 877-908 (Sept). [2] Axtell, R. L. (2001), "Zipf distribution of u.s. …rm sizes", Science 293. [3] Batrakova, S. and R. B. Davies, (2010). "Is there an environmental bene…t to being an exporter? Evidence from …rm level data," Working Papers 201007, School Of Economics, University College Dublin. [4] Copeland, B.R. and M.S. Taylor (1995), "Trade and Transboundary Pollution," American Economic Review, 85 : 716-737. [5] Cole, M.A. and Elliott, R.J.R. (2003), "Determining the Trade-Environment Composition E¤ect: The Role of Capital, Labour and Environmental Regulations", Journal of Environmental Economics and Management, Vol. 46, 3, pp. 363-83. [6] Cole, M.A., Elliott, R.J.R. and Fredriksson, P. (2006), "Endogenous Pollution Havens: Does FDI In‡uence Environmental Regulations?" Scandinavian Journal of Economics, Vol. 108, 1, pp. 157-178. [7] Cole, M.A., Elliott, R.J.R. and Okubo, T (2010), "Environmental Outsourcing", RIETI Discussion Paper Series No.10-E-055 [8] Cole, M.A., Elliott, R.J.R. and Okubo, T (2010), "Trade, Environmental Regulations and Industrial Mobility: An Industry-Level Study of Japan" Ecological Economics vol.69 (10), pp.1995-2002. [9] Cole, M.A., Elliott, R.J.R., Okubo, T. and Y. Zhou (2011),"Examining the Carbon Dioxide Emissions of Firms:A Spatial Approach", mimeo, University of Birmingham. [10] Cole, M.A., Elliott, R.J.R. and Shimamoto, K. (2005), "Industrial Characteristics, Environmental Regulations and Air Pollution: An Analysis of the UK Manufacturing Sector", Journal of Environmental Economics and Management, Vol. 50, 1, pp.121-43. [11] Dean, J. M., M.E. Lovely, and H. Wang. "Are Foreign Investors Attracted to Weak Environmental Regulations?: Evaluating the Evidence from China." Journal of Development Economics 90. (2009): 1-13. [12] Ederington, J., A. Levinson and J. Minier, (2005). "Footloose and Pollution-Free," The Review of Economics and Statistics, MIT Press, vol. 87(1), pages 92-99. [13] Ederington, J., A. Levinson and J. Minier, (2004). "Trade Liberalization and Pollution Havens," The B.E. Journal of Economic Analysis & Policy, Berkeley Electronic Press, vol. 0(2). 18
[14] Eskeland, G.S. and Harrison, A.E. (2003), "Moving to Greener Pastures? Multinationals and the Pollution Haven Hypothesis", Journal of Development Economics, Vol. 70, pp. 1-23. [15] Frankel, J. A. and Rose, A. K. (2005), "Is Trade Good or Bad for the Environment? Sorting Out the Causality", The Review of Economics and Statistics, Vol. 87, 1, 85-91. [16] Greenstone, M., List, J.A. and Syverson, C. (2011), "The E¤ects of Environmental Regulation on the Competitiveness of US Manufacturing", Center for Economic Studies Working Paper CES 11-03. [17] Helpman, E., M. J. Melitz, and S. R. Yeaple, (2004). "Export versus FDI with Heterogeneous Firms",American Economic Review, 94(1): 300–316. [18] Holladay, S. "Are Exporters Mother Nature’s Best Friends?", mimeo, NYU, 2009. [19] Levinson, A. (2009). "Technology, International Trade, and Pollution from US Manufacturing," American Economic Review, vol. 99(5), pages 2177-92, December. [20] Levinson, A and M. S. Taylor, (2008). "Unmasking The Pollution Haven E¤ect," International Economic Review, vol. 49(1), pages 223-254. [21] Levinson, A. (2010), "O¤shoring Pollution: Is the US Increasingly Importing Pollution Intensive Production?" Review of Environmental Economics and Policy, Vol. 4(1), Winter 2010, pp. 63-83. [22] Lovely, M. and Popp, D. (2011), "Trade, Technology and the Environment: Does Access to Technology Promote Environmental Regulation?" Journal of Environmental Economics and Management, 61, pp. 16-35. [23] M. Mani and D. Wheeler (1998), "In Search of Pollution Havens? Dirty Industry in the World Economy, 1960-1995", Journal of Environment and Development, Fall. [24] Melitz, M.J. (2003), "The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity", Econometrica, Vol. 71, 6, pp 1695-1725. [25] Moulton, B (1990), "An illustration of a pitfall in estimating the e¤ects of aggregate variables on micro units", Review of Economics and Statistics, Vol. 72, 2, pp. 334–338.
19
A
Appendix
A.1
Optimal abatement
To maximise pro…t, …rms choose the optimal level of abatement investments. We …rst derive the optimal level of abatement for …rms only supplying the domestic market: @ D = @fAD
1) a1
(
1
t
(1
fAD
) 1
B
1 = 0:
(26)
The optimal level of abatement investments is given by fAD
with
n
D
1 B
=
1 1 a B
1
(t )
1 (
1)
t
o
1
1
1
(
(
(1
1) 1
=
1)
Da
(
)( 1) 1) 1
(27)
1 (
1) 1
@fAD (1 = @a (
. )( 1) 1) 1
(1
Da
)( 1) 1) 1
(
1
X
D,
) 2
(B + B ) < 0:
aD > aX and
(
1)
(35) 1 < 0,
exporters will invest more in abatement.
A.2
Relative cut-o¤s
From the cut-o¤ conditions, we have 1
fAD
aD 1
=
1
fAX
aX 1
B B
fAD + fD fAX + fX
(36)
while from the conditions for optimal abatement investment @ D = @fAD @ X = @fAX
(
(
1 t 1) aX
1
a1D a1X
1
1 t 1) aD
1
1
(fAD ) (fAX )
(1 )(1 ) D aD (fA ) (1 )(1 ) X aX (fA )
(1
fAD
(1
fAX
(1
) 1B
(1
) 1 (1 (1
) 1
=
B
B
1
1=0
1=0
(37)
(38)
(39)
B )B
)
) 1
B
, =
fAD : fAX
We can derive one relationship: fAD + fD fAD = : fAX + fX fAX Thus,
21
(40)
fAX fD fAX fAD
(1
X aX (1
D aD (1
aX aD
(
,
d(aX =aD ) d
=
=
)( 1) ( 1) 1
fX fD
(42)
,
)( 1) ( 1) 1
D
=
X (1
< 0 and
(41)
fX >1 fD
=
)( 1) 1) 1
aX aD d(aX =aD ) d X d X d
The cut-o¤ level ratio is given by d(aX =aD ) dfX
fAD fX
=
(
)( 1) 1) 1
=
fX : fD
D X
fX fD .
This is the standard property.
> 0. Trade liberalisation and TBT liberalisation
lead to more selection mechanisms. Related to environmental policy, tax has no impact on the cut-o¤ ratio. The cuto¤ ratio is (1
not a function of tax, i.e.
A.3
aX aD
(
)( 1) 1) 1
1
=
B B+B
Sector classi…cation
22
(
1) 1
1+
fX fD
.
