Pollution havens, environmental trade barriers and
international regulatory spillover:
A firm level study of China
Tuotuo Yu
∗
Paris School of Economics
Sandra Poncet
†
Université de Paris 1 Panthéon-Sorbonne and CEPII
January 17, 2013
Abstract
In this paper, we use a conditional logit model to study how domestic environmental regulations and foreign environmental trade barriers (ETB) jointly affect
firms’ location choice in China. Our results show that while in general dirty sectors tend to locate in provinces with lax environmental regulations, firms in these
sectors which are constrained by ETB in export markets are much less likely to
do so than their counterparts. In other words, ETB can attenuate the pollution
haven effect. Our findings have two major implications: 1) international regulations can make up for the deficiencies of national regulation in an integrated
world economy; 2) green protectionism can have beneficial impacts for developing
countries in the long term, providing that they are not excluded from the world
market.
Keywords: Environmental regulation; Export; Pollution haven; Environmental
trade barriers; Green protectionism
JEL Classification Numbers: F18, O19, Q56.
∗
†
Address: B107, 48 Boulevard Jourdan, 75014 Paris, email: yu@pse.ens.fr.
Address: 113 rue de Grenelle, 75007 Paris, email: sandra.poncet@cepii.fr.
1
1
Introduction
The impact of environmental regulations on FDI is a long debated issue. Most em-
pirical studies center on the pollution haven theory, which posits that pollution intensive
industries from developed countries are attracted to weak environmental regulations in developing countries. Some also extend this theory to trade: environmental stringency affects
comparative advantage and industrial location, which in turn determines trade flows.
However, environmental regulations can affect trade and FDI in a very different way.
In reality, the augmentation of domestic environmental standards is often accompanied by
a simultaneous rising of environmental trade barriers on imports. This harmonization of
internal and external standards can largely reduce the incentive for seeking pollution havens,
because all producers, regardless of where they locate, should meet the same environmental
requirements if they want to access the markets behind them at all.
1
Moreover, the high
level of pollution and the low rate of green technology adoption in pollution havens can
generate negative externalities for potential exporters by augmenting their compliance costs.
In other words, environmental trade barriers can create a level playing field for all producers.
During the last few decades, the world has witnessed a rising trend of “green protectionism”, a large part of the job being done by developed countries. This may have turned the
tide against developing country pollution havens, as firms in these places are increasingly
pressed to improve their internal environmental practices beyond what local regulations require, otherwise they would be forced to drop out of developed country markets. In other
words, environmental trade barriers may have acted as a sort of international governing
mechanism which overrides the weak environmental regulations of many developing countries. In some cases, environmental trade barriers can even push developing countries to
align their national regulations with international ones. This is the case for Japan in the
1
It should be noted that environmental trade barriers put requirements on product quality, not on their
production process. Nevertheless, it is difficult to separate product quality from their production process in
many cases (e.g., the replacement of toxic materials by safe ones). In order to produce green goods, firms
usually need to apply a thorough change to their mode of production, such as adopting green technologies,
using green inputs, controlling waste emissions to avoid product contamination, etc.
2
1970s when it decided to adopt US car emissions standards (Vogel, 2000), and later for Korea
when it upgraded its car pollution abatement regulations to enter US, EU and Japan (Lee,
1992).
In this paper, we use firm level data to test whether environmental trade barriers can
reduce pollution haven behavior. We focus on China for two reasons. First, it is a developing
country with relatively weak environmental regulations (Esty and Porter, 2001); moreover,
environmental stringency varies across its different provinces, which invites both foreign
and domestic pollution haven seekers. Second, the importance of export to Chinese economy suggests that a large proportion of firms may be subject to foreign market pressures.
Therefore, it is very likely that domestic and international regulations jointly determine the
environmental practices of industrial firms in China.
Our results show this is indeed the case. In general, dirty industries are attracted to
Chinese provinces with low environmental stringency, showing a standard pollution haven
effect. However, when comparing exporters to non-exporters, we find that the former group
shows much less pollution haven behavior than the latter. After careful analysis, we find that
this difference can be explained by the fact that some exporters face stringent environmental
trade barriers imposed by developed countries. This result is robust to the introduction
of various firm controls, such as size, productivity and foreign/domestic ownership. We
conclude that it is the environmental trade barriers per se that press firms to adopt green
technology and reduce their reliance on pollution havens.
The political implications are rich. First, environmental trade barriers can make up for
the deficiencies of national environmental regulations and foster a global “race up to the top”
in the long term. Second, developing countries need not be afraid of green trade barriers if
they carry genuine environmental concerns; instead, they should regard it as an impetus for
domestic reform and capacity building.
The rest of the paper is organized as follows: Section 2 reviews some recent literature,
Section 3 presents the empirical model, Section Section 4 describes the data and shows the
3
results, and Section 5 concludes.
2
Review of Literature
Taken at face value, the pollution haven theory is apparently in contradiction with real
world facts: capital do not fly to pollution havens in mass as the theory suggests; instead, the
most important FDI flows are between developed countries, whose environmental regulations
are almost equally stringent. This is because FDI flows are mainly determined by market
size, resource abundance, labor costs, infrastructure, fiscal incentives, etc. Compared to
these factors, environmental regulations can hardly play a significant role in determining
where to locate investment. Moreover, lax environmental regulations can be a signal of poor
institutions, which deter foreign investors away rather than attracting them in.
In order to test the pollution haven theory in a meaningful way, most empirical studies
weaken it to the following hypothesis: lax environmental regulations add a competitive edge
to developing countries, other things being equal. They also focus on pollution intensive
industries, which face higher pollution abatement costs and should be more sensitive to
environmental regulations than the rest of the economy. After doing these manoeuvres, the
pollution haven effect finally begins to emerge (Cole and Elliott, 2005; Cole et al., 2010;
Dean et al., 2009).
As for environmental trade barriers, previous empirical studies mostly focus on their trade
deterrence effect. The results differ across sectors: for agriculture, environmental barriers
significantly reduce trade; for manufacturing, the impact is insignificant or even positive
(Disdier et al., 2008; Fontagne et al., 2005; Moenius, 2004). A possible explanation is that in
manufacturing the adjustment costs are lower and environmental trade barriers can provide
valuable information to foreign producers.
Our study, which sits at the intersection of these two branches of research, can shed
some new insight into the problems each tries to answer. On the one hand, we show that
4
environmental trade barriers can counterbalance the undesirable pollution haven effect. On
the other, we add evidences that environmental trade barriers can improve the social and
environmental well-being of developing countries, on top of the finding that they do not
necessarily cause economic losses. In one word, we depict an (even) brighter picture of
globalization and green trade barriers.
Our study also contributes to the trade-environment debate by providing evidence for
the “race up to the top” theory, whose main idea is that trade and high environmental
standards can be mutually enhancing. The reasons are multiple: strict environmental policies
can foster innovation and improve efficiency (Porter and Linde, 1995); multinationals apply
equal environmental standards to all their operations, which are stronger than many of
their host countries’ (Eskeland and Harrison, 2003); trade spread ISO 14001, a voluntary
environment management system along the supply chain (Prakash and Potoski, 2006), etc.
Above all these mechanisms, environmental trade barriers have long been believed to be a
strong motivator for standards upgrading (Gray, 2002), but empirical evidence is absent till
present. We try to fill this gap.
3
Empirical Specification
3.1
Measures
The aim is to evaluate the joint impact of local regulations and foreign environmental
barriers on firms’ location choice in China. To do this, we first need to define the pollution
and ETB measures.
Similar to Dean et al. (2009), we measure local environmental stringency by the average
pollution levy that province k actually collects on very unit of waste water:
Envregk =
5
Levyk
W aterk
This effective levy rate varies across provinces for three reasons: first, the concentration of
pollutants (mainly Chemical Oxygen Demand) differs; second, provincial governments are
free to set their own levy schemes, notably the concentration threshold above which firms
begin to pay pollution fees; third, the level of enforcement also differs from province to
province (Wang and Wheeler, 2005). Our measure of environmental stringency should only
reflect the last two sources of variation, so we exclude the first one by controlling for the
effluent intensity:
Ef fk =
CODk
W aterk
which is the average concentration of COD in waste water discharged in province k.
2
It
also reflects the industrial composition of province k: the higher the percentage of pollution
intensive industries, the higher the average effluent intensity. To avoid endogeneity problem,
all pollution statistics should be lagged for one year.
For each sector i, its pollution intensity is measured by its COD emission per unit of
value added:
P Ii =
CODi
V Ai
The firm specific ETB measure is defined as:
P P
ET BjV
=
c
p
ET B c (p) · ExVjc (p)
T otalSalesj
ET B c (p) is a dummy indicating whether country c imposes any environmental trade barrier
on product p, and ExVjc (p) is the value of product p exported by firm j to country c. The
sum of ET B c (p) · Exportcj (p) over all countries c and all products p, divided by total sales
revenue, gives the value share of firm j’s products covered by environmental trade barriers
abroad. ET BjV is bounded between 0 and 1 by definition.
Alternatively, we can measure the line share of firm j’s products covered by environmental
2
In fact, China collects pollution levy over a variety of pollutants, such as heavy metal, SO2, soot, dust,
solid waste, etc. We focus on COD because it accounts for the majority of pollution tax revenue in China .
Moreover, data limitation impedes us from breaking down pollution levy on each pollutant.
6
trade barriers:
ET BjL
P P
c
c
c
p ET B (p) · ExDj (p) T otalExportj
P
·
=
c
P
T otalSalesj
c p ExDj (p)
where ExDjc (p) is a dummy indicating whether firm j exports product p to country c. If we
replace it by export value ExVjc (p), we regain the value share ET BjV .
Before presenting the model, a last problem needs to be addressed: foreign environmental
trade barriers are binding for Chinese firms only if they are stricter than local standards.
In other words, to evaluate the impact of stringent environmental trade barriers, we should
drop those not-stringent-enough ones. As it is difficult to analyze environmental stringency
case by case, we make a simplifying assumption: those imposed by developed countries,
notably OECD members, are stricter than the Chinese standards, and those imposed by
other developing countries are not. This is highly probable, considering the relative standing
of China’s industrial development in the world. Moreover, some developing countries may
not be able to enforce their environmental trade barriers as strictly as developed countries
do.
According to this assumption, we split the ETB measures into two parts, one reflecting
the stringent ETBs imposed by OECD countries and the other the non-stringent ETBs by
developing countries. Taking ET BjV for example, these two parts are respectively defined
as:
ET B c (p) · ExVjc (p)
T otalSalesj
P
P
c
c
c∈OECD
/
p ET B (p) · ExVj (p)
V
nsET Bj =
T otalSalesj
P
sET BjV =
c∈OECD
P
p
The sum of sET BjV and nsET BjV is equal to ET BjV .
7
3.2
Basic model
We assume that firms make their location choices according to the following conditional
logit model:
i
exp(ln(πjk
))
i
k0 ∈K exp(ln(πjk0 ))
pijk = P
i
pijk is the probability that firm j of sector i chooses to locate in province k. πjk
0 is the “latent
0
”profit it would make in each province k , which can be expressed in the log-linear form:
i
ln(πjk
) = αk + αj + γ1 P I i × Envregk + γ2 P I i × Ef fk + ijk
(1)
αk is the province fixed effect; it incorporates all province level controls such as GDP per
capita, labor cost, infrastructure, institutions quality, industrial policies and agglomeration
effect. αj is the firm fixed effect; it absorbs all sector controls such as capital/pollution
intensity, as long as other firm characteristics such as size, productivity, ownership structure,
green technology adoption, the ability of firm leaders, etc.
The first interaction term P I i × Envregk is our variable of interest; it captures the
pollution haven effect at industrial level. Its coefficient γ1 is expected to be negative and
significant, i.e., the more polluting the industry is (the higher P I i ), the more likely it goes to
a province with low environmental stringency (the lower Envregk ). The second interaction
P I i × Ef fk controls for industrial composition effect.
3.3
Export and environmental trade barriers
To test the assumption that environmental trade barriers can offset pollution haven effect,
we take a step-by-step approach: first we test if exporters show less pollution haven behavior
than non-exporters, then we see if this difference can be explained by environmental trade
barriers.
The first step can be done by interacting the exporter dummy ExDj with the pollution
8
haven effect P I i × Envregk :
i
ln(πjk
) = αk + αj + γ1 P i × τk + γ2 Pi × Ef fk
(2)
+γ3 P I i × Envregk × ExDj + γ4 P Ii × Ef fk × ExDj + ijk
If γ3 > 0, i.e., opposite to γ1 , we can say that exporters are less likely than non-exporters to
locate in provinces with low environmental stringency.
Then we see if the ETB measures defined above can help to explain the difference between
exporters and non-exporters:
i
ln(πjk
) = αk + αj + γ1 P I i × Envregk + γ2 P Ii × Ef fk
(3)
+γ3 P I i × Envregk × ExDj + γ4 P I i × Ef fk × ExDj
V (L)
+γ5 P I i × Envregk × ET Bj
V (L)
+ γ6 P I i × Ef fk × ET Bj
+ ijk
We expect γ6 to be positive and significant; meanwhile, the “residual” explicative power of the
export dummy ExDj or the magnitude of γ3 should diminish or even become insignificant.
As mentioned earlier, not all environmental trade barriers are equally stringent. To test
this assumption, we decompose ET Bj into its OECD and non-OECD components and add
them to the regression separately.
3.4
Controls and robustness tests
Three problems should be addressed. First, P I i × Envregk could capture a spurious
correlation rather than the presumed pollution haven effect. Wang and Wheeler (2000)
found that richer and cleaner Chinese provinces have higher environmental stringency, and
Cole and Elliott (2005) found that pollution intensive industries tend to be capital intensive
too. Therefore, if capital intensive industries prefer to locate in rich provinces, the coefficient
of the above interaction would turn out significant in the absence of any direct link between
environmental stringency and pollution intensity. However, we can show this is not the case
here, and P I i × Envregk can still be interpreted as the pollution haven effect in our study.
9
The explanations will be given in detail later.
Second, export and environmental stewardship could be correlated with other firm characteristics, such as firm size, productivity and ownership type. More precisely, larger/more
productive/foreign firms may be more likely to export and more capable of adopting green
technology at the same time. Therefore, the simple correlation between export and environmental stewardship cannot be interpreted as a causality; they may be both caused by a third
effect. To avoid this problem, we add firm size (measured by total sales revenue), productivity (measured by sales per employee) and foreign dummy as controls. Like the exporter
dummy and ETB measures, they also enter the regression in triple interaction forms.
The last problem is that the “intercepts” or province fixed effects may be different for
exporters/non-exporter and foreign/domestic firms. For example, some provinces may be
especially attractive to exporters, because they possess numerous ports or provide extra tax
rebates; some provinces may be more open to foreign investors, either for historical or political reasons. Forcing the same intercepts on all types of firms could bias our estimation.
Therefore, we allow different types of firms to have different intercepts by adding two additional groups of fixed effects, αke and αkf . The former is called province-exporter fixed effects
and the latter province-foreign fixed effects.
4
Data and results
4.1
Data sources and descriptive analysis
Basic firm information such as year of creation, location, sector, ownership, annual sales,
number of employees, etc. are obtained from Chinese Industrial Enterprises Database,
3
from which we only select firms created in 2003. For each firm in this subsample, we look for
3
This database includes more than 200,000 enterprises whose annual sales revenue is above 5 million
RMB, the total output of which accounts for over 95% of total Chinese industrial production in 2004.
10
its export records in Chinese Imports and Exports of Customhouse Database 2004.
4
The
reason for using 2004 export records is that they are the best proxies of firms’ expectations
about their future export activities in 2003. Intuitively, a firm starting to export just one
year after its creation should have anticipated the difficulties it would encounter in foreign
markets (like environmental trade barriers) and have incorporated such considerations in its
initial investment decision (like green technology adoption and location choice).
The merged dataset contains 25,828 manufacturing firms, among which 5,360 are foreign
owned and 3,275 are exporters.
5
Using a country-product matrix of environmental trade
barriers compiled by Fontagne et al. (2005), we calculate ET BjV and ET BjL for each exporter.
The provincial distribution of exporters and foreign firms are very different from that
of non-exporters and domestic firms (see Table A.1 in Appendix), which justifies our use of
province-exporter and province-foreign fixed effects. A simple OLS regression shows that the
exporter dummy ExDj is indeed correlated with firm size, productivity and foreign ownership
(see Table B.1 in Appendix).
6
Therefore, our use of firm controls is also justified.
Pollution statistics of previous years, such as waste water discharge, COD emissions and
pollution intensity are taken from China Statistical Yearbook.
7
After a simple analysis, we
find that on the one hand, provincial environmental stringency is uncorrelated with GDP per
capita and effluent intensity (see Figure 1 and Table B.2); on the other, sectoral pollution
intensity is uncorrelated with capital intensity (see Table B.3). Therefore, we can say that
the interaction P I i × Envregk indeed captures the pollution haven effect, not the attraction
4
In the Customhouse Database, each record is composed by a 10-digit product code, the 8 first digits of
which correspond to the HS 2002 classification, the name and location of the exporting/importing firm, the
value of the transaction and the destination/source country.
5
In fact, according to the Industrial Database, 6,130 firms have a positive export value. However, about
half of them export via intermediaries and do not have their own records in the Customhouse Database.
The same problem has been found by previous studies. Here we choose to ignore these unrecorded exports,
because they are likely to be occasional and should have no direct link to firm’s long term strategy.
6
The negative correlation between export dummy and productivity may be due to the fact that Chinese
firms specializes in labor intensive export.
7
We use the pollution data of 2003. Ideally, we should use 2002 data to avoid the endogeneity problems.
However, 2002 data are not available because China statistic bureau only report pollution levies in their
yearbooks from 2003 on. Nevertheless, the sample we use - firms created in 2003, accounts for a very small
fraction of the full sample, and their impact on the overall pollution level and levy colletion should be very
limited.
11
Figure 1: GDP per capita and waste water levy rate
between economically developed provinces and capital intensive industries.
4.2
Regression results
Column 1 of Table 1 contains the results of the benchmark regression (Equation 1). The
coefficient of the interaction P I i × Envregk is negative and significant, implying polluting
industries go to provinces with lower environmental stringency. This “standard” pollution
haven effect is robust to the introduction of province-exporter and province-foreign fixed
effects (column 2 and 3).
Column 4 include the triple interaction P I i ×Envregk ×ExDj , whose coefficient is largely
positive and significant. The interpretation is that exporters are much less likely to show
pollution haven behavior than non-exporters, other things being equal. In fact, if we add
this coefficient over that of Envregk × P I i , we can obtain the net pollution haven effect on
exporters, which is positive but not significant. This is to say, exporters are indifferent to
local environmental regulations, and the pollution haven effect found in the basic model is
completely driven by non-exporters.
Adding firm controls such as size, productivity and foreign ownership does not change
the results (column 5 to 7). In fact, none of these variables turns out significant. The interpretation is that size, productivity and foreign ownership have no direct impact on firms’
12
environmental performance, although they may affect their ability to export and to adopt
green technology. This seemingly contradictory fact has an intuitive explanation: better
access to green technology does not guarantee a greater willingness to adopt them in practice, unless firms are constrained to do so. Therefore, to understand why exporters behave
differently, we should better look at external factors (like foreign market requirements).
If we further divide exporters into two groups, those trading with OCED countries and
those trading exclusively with non-OECD countries, we can see more clearly how market orientation affects firms’ environmental performance. Table 2 shows that non-OECD exporters
actually do worse than their domestically oriented counterparts. However, this drawback is
more than compensated by OECD exporters, and the overall performance of all exporters is
still better than that of non-exporters. Replacing the export dummy by the export rate (total
exports over total sales) does not change the results. Again here we have controlled for firm
size, productivity and foreign ownership, so the difference between OECD and non-OECD
exporters is not a result of self-selection into different markets.
Then we test if the environmental stewardship of OCED exporters can be explained
by the fact that they are constrained by environmental trade barriers. In the first place,
we run the ETB model specified in Equation (3), without differentiating OECD and nonOECD exporters. It turns out that both ETB measures, value share or line share, have
additional explicative power over the exporter dummy ExDj (Table 3). Then we separate
the ETB measures into their OECD and non-OECD components and rerun the regression.
The results confirm our intuition: OECD environmental trade barriers have a large and
significant impact on firms’ location choice, while non-OECD ones have no impact at all.
8
It is worth noting that the exporter dummy ExDj remains significant after adding ETB
measures. This is to say, environmental trade barriers do not tell the whole story; export
market conditions can motivate firms to improve their environmental practices through a
8
A total of 1,454 exporters face environmental trade barriers, 779 in OECD and 1,022 in non-OECD
countries (some firms face ETB both in OECD and non-OECD countries). It is thus unlikely that the lack
of significance of non-OECD ETB measure is caused by the small number of non-zero observations.
13
variety of channels, and environmental trade barriers are just one of them. For example,
consumers in developed countries may have a special taste for green goods; trade partners
in developed countries may urge their Chinese suppliers to adopt ISO 14001; NGOs may
launch a boycott against foreign polluters’ products; socially responsible investment funds
may block their access to international financial market, etc.
Table 1: Conditional logit
Pollution Haven Effect
(P I i × Envregk )
(1)
-0.058a
(0.011)
Dependent variable: firm location choice
(2)
(3)
(4)
(5)
(6)
-0.063a -0.062a -0.071a -0.073a -0.073a
(0.011) (0.011) (0.011) (0.012) (0.012)
(7)
-0.071a
(0.013)
0.141a
(0.050)
PHE*export dummy
PHE*foreign
0.126b
(0.057)
0.126b
(0.057)
0.126b
(0.056)
0.020
(0.036)
0.020
(0.036)
0.024
(0.036)
0.000
(0.000)
0.000
(0.000)
PHE*productivity
PHE*firm size
Industrial Composition Effect
(P I i × Ef fk )
-0.000
(0.000)
0.016a
(0.001)
0.014a
(0.001)
0.014a
(0.001)
ICE*export dummy
ICE*foreign
ICE*productivity
0.014a
(0.001)
0.014a
(0.001)
0.014a
(0.001)
0.012a
(0.001)
0.010c
(0.006)
0.011c
(0.007)
0.011c
(0.007)
0.011c
(0.007)
-0.001
(0.004)
-0.001
(0.004)
-0.005
(0.005)
-0.000
(0.000)
-0.000c
(0.000)
0.000a
(0.000)
Province-exporter Fixed effects
no
yes
yes
yes
yes
yes
yes
Province-foreign Fixed effects
no
no
yes
yes
yes
yes
yes
Observations
800668 800668 800668 800668 800668 800172
800172
Note: Standard errors corrected for clustering at firm level are shown in parentheses; a , b and c respectively
denote significance at the 1%, 5% and 10% levels.
ICE*firm size
14
Table 2: Decomposition of export: OECD vs. non-OECD
Pollution Haven Effect
PHE*Export
Dependent variable: firm location decision
Export dummy
Export rate
All
Separated
All
Separated
-0.071a
-0.071a
-0.070a
-0.071a
(0.013)
(0.013)
(0.013)
(0.013)
0.126b
(0.056)
0.115
(0.099)
PHE*Export OECD
0.554a
(0.184)
0.357a
(0.136)
PHE*Export non-OECD
-0.345c
(0.181)
-0.857a
(0.238)
PHE*foreign
0.024
(0.036)
0.010
(0.037)
0.045
(0.030)
0.045
(0.031)
PHE*productivity
0.000
(0.000)
0.000
(0.000)
0.000
(0.000)
0.000
(0.000)
PHE*firm size
-0.000
(0.000)
-0.000
(0.000)
-0.000
(0.000)
-0.000
(0.000)
Industrial Composition Effect
0.012a
(0.001)
0.012a
(0.001)
0.012a
(0.001)
0.012a
(0.001)
ICE*Export
0.011c
(0.007)
0.024b
(0.009)
ICE*Export OECD
-0.018
(0.011)
0.023b
(0.011)
ICE*Export non-OECD
0.026b
(0.012)
0.047a
(0.012)
ICE*foreign
-0.005
(0.005)
-0.004
(0.005)
-0.006
(0.005)
-0.006
(0.005)
ICE*productivity
-0.000c
(0.000)
-0.000c
(0.000)
-0.000c
(0.000)
-0.000c
(0.000)
0.000a
0.000a
0.000a
0.000a
(0.000)
(0.000)
(0.000)
(0.000)
Observations
800172
800172
800172
800172
Notes: Standard errors corrected for clustering at firm level are shown in
parentheses; a , b and c respectively denote significance at the 1%,
5% and 10% levels.
ICE*firm size
15
Table 3: Decomposing ETB measures: OECD vs. non-OECD
Pollution Haven Effect
PHE*exporter dummy
Dependent variable: firm location decision
Benchmark
Value share
Line share
All
Separated
All
Separated
-0.071a
-0.071a
-0.071a
-0.071a
-0.071a
(0.013)
(0.013)
(0.011)
(0.013)
(0.013)
0.126b
(0.056)
0.107c
(0.057)
0.121b
(0.056)
0.561b
(0.245)
PHE*ETB all
0.107c
(0.057)
0.122b
(0.056)
0.651b
(0.317)
PHE*ETB OECD
0.555b
(0.242)
0.656b
(0.311)
PHE*ETB non-OECD
-0.295
(0.218)
-0.371
(0.246)
PHE*foreign
0.024
(0.036)
0.026
(0.035)
0.026
(0.035)
0.026
(0.035)
0.026
(0.035)
PHE*productivity
0.000
(0.000)
0.000
(0.000)
0.000
(0.000)
0.000
(0.000)
0.000
(0.000)
PHE*firm size
-0.000
(0.000)
-0.000
(0.000)
-0.000
(0.000)
-0.000
(0.000)
-0.000
(0.000)
Industrial Composition Effect
0.012a
(0.001)
0.012a
(0.001)
0.012a
(0.001)
0.012a
(0.001)
0.012a
(0.001)
ICE*exporter dummy
0.011c
(0.007)
0.011
(0.007)
0.004
(0.007)
0.010
(0.007)
0.002
(0.007)
0.056c
(0.033)
ICE*ETB all
0.096b
(0.039)
ICE*ETB OECD
0.058c
(0.034)
0.093b
(0.040)
ICE*ETB non-OECD
0.131a
(0.037)
0.149a
(0.035)
ICE*foreign
-0.005
(0.005)
-0.005
(0.005)
-0.005
(0.005)
-0.005
(0.005)
-0.005
(0.005)
ICE*productivity
-0.000c
(0.000)
-0.000c
(0.000)
-0.000c
(0.000)
-0.000c
(0.000)
-0.000c
(0.000)
0.000a
0.000a
0.000a
0.000a
0.000a
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
Observations
800172
800172
800172
800172
800172
Notes: Standard errors corrected for clustering at province level are shown in parentheses;
a b
, and c respectively denote significance at the 1%, 5% and 10% levels.
ICE*firm size
16
5
Conclusion
In this paper, we use a conditional logit model to evaluate the impact of environmental
trade barriers on exporting firms’ location choices. We find that exporters are much less likely
to show pollution haven behavior than non-exporters, and this difference can be partially
explained by stringent environmental trade barriers imposed by developed countries. This
result is robust to different controls and measures. The interpretation is that environmental
trade barriers oblige exporters to adopt green technology, which in turn reduces their reliance
on pollution havens to cut emission costs.
The political implications are rich. First, we do not live in a world of isolated jurisdictions;
national laws and regulations are interlinked by economic connections and have important
repercussions beyond their borders. Economic agents are not only constrained by national
regulations but also by international ones, and the two can be equally effective sometimes. In
our case, some Chinese firms are virtually operating under foreign environmental standards,
which override their less stringent local counterparts. In some sense, international regulations
can make up for the deficiency of national regulations.
This raises some hope for a virtuous institutional competition and an upward convergence
of environmental standards all around the world. It is true that the pollution haven effect
can limit many developing countries’ willingness to “move up the ladder”, but the benefits of
“staying in the bottom” would diminish as economic integration deepens, and the incentives
for aligning with developed countries standards would become stronger and stronger over
time. This has already happened to a number of former developing countries, as mentioned
at the beginning of this paper.
It is worth noting that the presumed environmental convergence is not only driven by economic convergence but also by the direct interaction between different national regulations.
The environmental Kuznet curve posits that as income rises, pollution would first aggravates
and then alleviates. However, we think this “turning point” could come well ahead of what
the domestic economic schedule dictates, if external conditions make it possible to reconcile
17
environmental protection with future economic development.
The second question is what attitude developing countries should adopt face to environmental trade barriers imposed by developed countries. Such measures have long been
regarded as disguised protectionism, excluding developing countries from the world market
and hampering their economic development. Although this argument may have some truth
in it, the actual impact of environmental trade barriers on export, especially on that of
manufactured goods, is still debatable (see Fontagne et al. (2005)). Moreover, these measures have clear social and environmental benefits for developing countries, by encouraging
their firms to adopt green technology and reduce pollution. Taken together, the relationship
between environmental trade barriers and development may be worth a new examination.
However, we are not encouraging countries to multiply their trade barriers. Remember
that environmental trade barriers do not translate automatically into higher standards and
better practices; the enforcement is crucial. Trade barriers, if necessary, should be enforced
carefully in order to amplify their positive and limit their negative impact. For example,
detailed technical information should be provided to facilitate product adaptation, as Moenius (2004) suggests. If possible, standardized certification procedure and green labeling
should be made available to reduce producers’ compliance costs and augment their market
recognition. In one word, environmental trade barriers should carry genuine environmental
concerns, not disguised protectionism. This is just what WTO proposes.
A final remark is on the Least Developed Countries, which are the most likely to be hurt
by excessive trade barriers. In fact, a country’s ability to benefit from foreign environmental
trade barriers depends on its own capacity; without enough strength to withstand the initial
“chilling” effect, trade flows would be drastically cut back and the long term benefits would
fail to materialize. This is another reason for us to call for moderation in the use of environmental trade barriers. More precisely, special assistances and even exonerations should
be given to LDC producers to help them overcome the difficulties of complying with export
requirements.
18
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20
Appendix A
Distribution
Table A.1: Firm distribution by province
Province
Total
Exporters
Foreign
11 Beijing
487
1.9%
47
1.4%
99
1.8%
12 Tianjin
570
2.2%
69
2.1%
111
2.1%
13 Hebei
744
2.9%
44
1.3%
59
1.1%
14 Shanxi
248
1.0%
6
0.2%
9
0.2%
15 Inner Mongolia
290
1.1%
5
0.2%
12
0.2%
21 Liaoning
855
3.3%
140
4.3%
190
3.5%
22 Jilin
273
1.1%
21
0.6%
21
0.4%
23 Heilongjiang
266
1.0%
9
0.3%
18
0.3%
31 Shanghai
1030
4.0%
223
6.8%
329
6.1%
32 Jiangsu
4635
17.9%
639
19.5% 1193
22.3%
33 Zhejiang
3888
15.1%
598
18.3%
943
17.6%
34 Anhui
589
2.3%
42
1.3%
62
1.2%
35 Fujian
1080
4.2%
146
4.5%
410
7.6%
36 Jiangxi
494
1.9%
34
1.0%
82
1.5%
37 Shandong
2806
10.9%
389
11.9%
442
8.2%
41 Henan
1046
4.0%
35
1.1%
31
0.6%
42 Hubei
666
2.6%
30
0.9%
55
1.0%
43 Hunan
880
3.4%
29
0.9%
38
0.7%
44 Guangdong
2865
11.1%
664
20.3% 1107
20.7%
45 Guangxi
345
1.3%
30
0.9%
42
0.8%
46 Hainan
25
0.1%
7
0.2%
8
0.1%
50 Chongqing
209
0.8%
8
0.2%
15
0.3%
51 Sichuan
675
2.6%
32
1.0%
42
0.8%
52 Guizhou
191
0.7%
4
0.1%
10
0.2%
53 Yunnan
169
0.7%
6
0.2%
8
0.1%
54 Tibet
4
0.0%
0
0.0%
0
0.0%
61 Shaanxi
143
0.6%
4
0.1%
11
0.2%
62 Gansu
116
0.4%
7
0.2%
3
0.1%
63 Qinghai
32
0.1%
2
0.1%
3
0.1%
64 Ningxia
81
0.3%
3
0.1%
3
0.1%
65 Xinjiang
126
0.5%
2
0.1%
4
0.1%
Total
25828 100.0% 3275 100.0% 5360
100.0%
Note: Pearson’s chi-square tests strongly reject the hypothesis that the distribution of firms are identical across different groups. For exporter and nonexporters, Pearson Chi2(30)=880.6372, p-value=0.000; for domestic and foreign firms, Pearson Chi2(30)=1.9e+3, p-value=0.000.
21
22
Table A.2: Firm distribution by sector
Sector
Total
Exporter
Foreign
13 Processing of Food from Agricultural Products
1,538
6.0%
149
4.5%
167
3.1%
14 Manufacture of Foods
487
1.9%
49
1.5%
87
1.6%
15 Manufacture of Beverages
315
1.2%
7
0.2%
38
0.7%
16 Manufacture of Tobacco
13
0.1%
0
0.0%
1
0.0%
17 Manufacture of Textile
2,965
11.5%
383
11.7%
597
11.1%
18 Manufacture of Textile Wearing Apparel,Footware, and Caps
1,242
4.8%
287
8.8%
527
9.8%
19 Manufacture of Leather, Fur, Feather and Related Products
693
2.7%
177
5.4%
244
4.6%
20 Processing of Timber,Manufacture of Wood,Bamboo,Rattan,Palm,and Straw Products
795
3.1%
81
2.5%
97
1.8%
21 Manufacture of Furniture
327
1.3%
104
3.2%
119
2.2%
22 Manufacture of Paper and Paper Products
708
2.7%
29
0.9%
101
1.9%
23 Printing,Reproduction of Recording Media
301
1.2%
18
0.5%
37
0.7%
24 Manufacture of Articles For Culture, Education and Sport Activity
294
1.1%
105
3.2%
118
2.2%
25 Processing of Petroleum, Coking, Processing of Nuclear Fuel
275
1.1%
1
0.0%
11
0.2%
26 Manufacture of Raw Chemical Materials and Chemical Products
1,732
6.7%
131
4.0%
236
4.4%
27 Manufacture of Medicines
356
1.4%
34
1.0%
47
0.9%
28 Manufacture of Chemical Fibers
239
0.9%
8
0.2%
36
0.7%
29 Manufacture of Rubber
244
0.9%
41
1.3%
61
1.1%
30 Manufacture of Plastics
1,134
4.4%
160
4.9%
292
5.4%
31 Manufacture of Non-metallic Mineral Products
2,008
7.8%
123
3.8%
234
4.4%
32 Smelting and Pressing of Ferrous Metals
1,331
5.2%
31
0.9%
91
1.7%
33 Smelting and Pressing of Non-ferrous Metals
631
2.4%
45
1.4%
70
1.3%
34 Manufacture of Metal Products
1,345
5.2%
133
4.1%
235
4.4%
35 Manufacture of General Purpose Machinery
1,768
6.8%
155
4.7%
295
5.5%
36 Manufacture of Special Purpose Machinery
949
3.7%
120
3.7%
216
4.0%
37 Manufacture of Transport Equipment
1,024
4.0%
112
3.4%
224
4.2%
39 Manufacture of Electrical Machinery and Equipment
1,499
5.8%
251
7.7%
398
7.4%
40 Manufacture of Communication Equipment,Computers and Other Electronic Equipment
886
3.4%
340
10.4%
507
9.5%
41 Manufacture of Measuring Instruments and Machinery for Cultural Activity and Office Work
283
1.1%
70
2.1%
116
2.2%
42 Manufacture of Artwork and Other Manufacturing
446
1.7%
131
4.0%
158
2.9%
Total
25,828
100.0%
3,275 100.0%
5360 100.0%
Note: Pearson’s chi-square tests strongly reject the hypothesis that the distribution of firms are identical across different groups. For exporter and
non-exporters, Pearson Chi2(28)=1.7e+3, p-value=0.000; for domestic and foreign firms, Pearson Chi2(28)=2.1e+3, p-value=0.000.
Appendix B
Correlation
Figure B.1: Correlation between export dummy, firm size, productivity and foreign ownership
Figure B.2: No correlation between province environmental stringency, GDP per capita and
effluent intensity
Figure B.3: No correlation between sector pollution intensity and capital intensity
23