Download The Direct Trade-Induced Composition Effect and Its Environmental

The Direct Trade-Induced Composition Effect and
Its Environmental Outcomes in Different Continents
Isabelle Cadoret
Xuan Tran
∗
CREM CNRS 6211, University of Rennes 1, France
March 8, 2015
Abstract
In this empirical work, we study the effect of openness to trade on environment outcomes. More precisely, we focus on the effect of liberalized trade on
country’s composition of ”dirty” and ”clean” industries. This study estimates
the direct trade-induced composition effect on CO2 emission in different continents. We treat trade share as endogenous and estimates the model using the
instrumental variables technique. The contribution of this paper is therefore:
first, the trade-induced composition effect is significantly different among continent; second, it appears that with liberalized trade, country’s comparative
advantage is led by its environmental policy rather than its resource abundance ; finally, we observe that African countries become dirtier with openness
to trade.
1
Introduction
The recent debate over the environmental outcomes of trade openness has become
a center of interest for researchers and policy makers. In this study, we analyze the
effect of trade on environmental outcomes in different regions, address the problem
of endogeneity of trade share and check whether country’s comparative advantage is
led by its environmental regulation or/and its resource abundance with trade liberalization. The most standard approach is proposed by Antweiler et al. (1998)1 . This
∗
Corresponding author, [email protected], 7 place Hoche 35000 Rennes,
France.
1
see also Grossman & Krueger (1994) and Grossman & Krueger (1991)
2
I. Cadoret X. Tran
model has made a very lasting and significant contribution to economic literature on
trade, growth and environment relationships. They provide a theoretical framework
that link principal essays, which are abundant and sometime contradictory in this
new land research.
Using this approach, these authors are able to distinguish empirically between
the negative environment consequences of scalar increases in economic activity (scale
effect), the positive environment consequences of a greater real income on the cleaner
production technique (technique effect) and the composition of output (the mix of
dirty/clean industries). The question is how to interpret the role of openness to trade
on environmental outcomes using the scale-technique-composition decomposition of
emission?. First, international trade leads to a greater scale of economic activity while
increasing the demand for productivity, transportation and consumption of goods and
services (trade-induced scale technique, expected to be positive). Second, economic
literature usually argues that liberalized trade creates the opportunity for a country to grow, that is, trade leads to higher real income per capita. Moreover, higher
real income generates cleaner production technique. Consequently, trade openness
can lower the dirtiness of production through the impact of income gain on environmental regulation (trade-induced technique effect, expected to be negative). Finally,
liberalized trade can affect on the composition of country’s mix of industries by the
degree of trade openness and by the comparative advantage of the country (tradeinduced composition effect). The trade-induced composition effect is expected to be
positive or negative, depending on two factors: first, country’s resource abundance
(trade patterns are determined by relative factor abundance, the so-called ”Factor Endowment Hypothesis”)and second, its environmental policy (Rich countries will have
comparative advantage in cleaner industry and Poor countries will have comparative
advantage in dirtier industry, the so-called ”Pollution Haven Hypothesis”). 2 . Hereafter, we denote ERE (environmental regulation effect) the effect of environmental
policy and KLE (capital/labor effect) the impact of country’s resource abundance on
environmental outcomes.
This literature of scale-composition-technique effect has since produced a fast
growing empirical studies 3 , . However, in addition to differences between concentrations and emissions, the ACT model is expected to generate different results by
2
Dirty industries will shift from country with stringent environmental regulation (rich countries)
to those with weaker environmental regulation (poor countries)
3
see, for example, Stokey (1998), John & Pecchenino (1994), Selden & Song (1994)
Trade, Growth and the Environment: Direct Trade-Induced Composition Effect
3
pollutant 4 , also by country 5 . We summarize below some important papers working
on the concern of environmental outcomes of liberalized trade.
Using these theoretical framework, ACT estimate how trade openness and GDP
affect pollution by using data on sulfur dioxide (SO2) concentrations. They find that
liberalized trade creates relatively small changes in pollution concentration when it
alters the composition of national output. The trade-induced technique effect tends
to lower SO2 concentrations while the trade-induced scale effect tends to raise the
pollution. The net effect of trade-induced scale and technique effect yields a reduction
in pollution. Cole & Elliott (2003) estimate the impact of trade on the emission of 4
different pollutants (SO2, CO2, BOD and NOx) by examining whether the changes
of composition output arising from liberalized trade originate due to differences in
capital/labor endowment or differences in environmental policy
Managi et al. (2009) have analyzed the causal effects of trade openness on pollution
emissions (CO2, SO2 and BOD) by using data for OECD and non-OECD countries.
They find that freer trade reduces emissions in OECD countries and increases emissions in non-OECD countries In this study, we focus on the effect of liberalized trade
on the composition of national output and therefore on environmental outcomes.
Our main findings are as follow: 1. The relative strength of trade-induced composition effect changes among continents. More precisely, it appears that both ERE
and KLE lead the composition of output for African countries, and only the ERE has
significant effect on the composition of industries for Asian, American and European
countries. 2. Using Frankel and Romer’ instrument for trade share, we find a positive
and significant scale-technique effect of real gdp per capita on CO2 emissions. 3.
African countries become dirtier with openness to trade. That is, we observe a strong
evidence of the Pollution Haven Hypothesis.
2
Empirical implementation
2.1
Theoretical background: the ACT model
The key purpose of the ACT model is a simple general equilibrium model when
government policy and private sector behavior interact to determine the equilibrium
level of pollution. According to these authors, the government determines the level
4
5
see, for example, Cole and Elliott (2003)
see, for example, Managi et al. (2009)
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I. Cadoret X. Tran
of taxes on emissions τ that maximizes the sum of utility of N agents in economy.
Through the level of τ , the government play the role of pollution supply. In another
hand, private sector behavior considers pollution emissions as a sacrifice to economic
growth, that is, they decide the pollution demand. Combining the two functions of
pollution demand and supply yields the reduced form of pollution emissions equation.
We present below a brief outline of the model. Consider a small economy open
economy that faces fixed world prices and produces two goods X and Y. Good X is
capital intensive and therefore generates pollution during its production and good
Y does not (Y is labor intensive) 6 . Consequently, the X industry jointly produces
two outputs-good X and emissions Z. Let B denote the wedge between domestic and
world prices induced by trade frictions (the direct and indirect costs associated with
good transaction), if p is the relative price of X (good Y is the numeraire, py = 1)
then domestic prices will differ from world prices (pw ), the function of domestic price
can be written as:
p = βpw
(1)
More precisely, β > 1 if country import X and β < 1 if country export X. The
important question is what is trade liberalization and the role it plays in equation 1.
ACT define trade liberalization as the gradual reduction in trade frictions that moves
domestic price closer to world prices. Consequently, for an exporter of polluting good
X, β rises with freer trade and this also raises the relative price p of the dirty good
X. The composition of national output shift toward X. By contrast, β falls with freer
trade for an importer of the polluting good and this also lowers the relative price p
of the dirty good X. Consequently, the composition of national output shift toward
clean industry Y.
Let focusing on the supply and demand side of emission Z. On the supply side,
suppose that firms face a price τ ( imposed by the government) for each unit of
emission they generate. The level of τ reflects the supply side of the emission Z. The
government preferred a level of pollution tax that maximizes the weighted sum of
each group’s utilities. The composition of pollution supply is:
τb = Tb + δ1 β + δ2 pb + δ3 Ib
Where T represents ’country type’
6
7
(2)
and I is real per capita income.
That means, for any r and w (market returns for K and L), the capital / labor ratio in X is
Ky
x
higher than Y: K
Lx > Ly
7
Variable ”country type” refers to the proportion of two citizen groups whom differ in their
Trade, Growth and the Environment: Direct Trade-Induced Composition Effect
5
On the demand side, emission z is defined as:
z = ex = eϕS
(3)
Where e is emission intensity (the proportion of emission z for each unit of output
x), ϕ is the share of X in total output. Finally, S is defined as an economy’s scale 8 .
In differential form it becomes
zb = eb + ϕb + Sb
(4)
Where the composition of output ϕ = ϕ(k, p(β, pw , τ )), pollution intensity e =
e(τ , p(β, pw , τ )) The last stage is replacing τ in the pollution demand equation on
the pollution supply. This fact yields the equilibrium reduced form equation:
b
zb = π1 Sb + π2 kb − π3 Ib − π4 Tb + π5 pcw + π6 β,
(5)
Where all πi are positive, and none of the right hand-side variables are determined
simultaneously with emissions. Emission z depends on: the scale of economic S, the
composition of country’s factor abundance (k/l ratio), the cleanliness of production
technique (GDP per capita I), the type of country (the proportion of ”Greens” and
”Browns” T), world’s price of dirty good X (pw ) and finally trade friction (β).
3
Strategy estimation
We begin our estimation with the standard approach of ACT model at the national
emission level 9
lnEkt =α0 + α1 Iit + α2 (Iit )2 + α3 KLit + α4 (KLit )2 + α5 KLIit
+ α6 Oit + α7 Oit RKLit + α8 Oit (RKLit )2 + α9 Oit RIit + α10 Oit (RIit )2
(6)
+ α11 Oit RKLit RIit + α12 Kyotoit + α13 P recipitationi + it + νi ,
Where Eit denotes emissions (CO2) per capita of country i at time t (for example,
kilograms of carbon dioxide per capita), Iit is one period lagged per capita income.
preferences over pollution
8
S = p0x x + p0y y
9
see, for example, Cole & Elliott (2003)
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I. Cadoret X. Tran
KLit denotes the capital-labor ratio. KLIit is the cross product of KL and I. Lagged
income per capita I and its quadratic I 2 capture the scale-technique effect of a change
of real income per capita on emission. Capital-labor ratio K/L and its quadratic
(K/L)2 represent the composition effect of country’s mix of industries on emission.
The interaction term KLI represents the fact that the effect of income on emission
can be dependent on the existing level of capital to labor ratio, and vice versa. 10 . νi
is a random disturbance.
As mentioned in introduction, this study focus on the direct effect of trade liberalization on the composition of output. More precisely, we focus on variables that
include trade intensity Oit in equation 6. These are: trade intensity Oit (liberalized
trade will lead country to specialize in the industries in which it has comparative
advantage), the interaction between trade intensity and country’s relative capitallabor ratio and its quadratic Oit RKLit and Oit (RKLit )2 (these variables capture the
KLE), the interaction between trade openness and country’s relative income and its
quadratic Oit RIit and Oit (RIit )2 (these variables capture the ERE). Finally the interaction of openness to trade, country’s relative income and its relative capital/labor
ratio Oit RKLit RIit captures the fact that the interaction between openness and relative income on pollution is likely to depend on the existing level of relative capital
to labor ratio, and vice versa.
We also introduce P recipitationi as the average precipitation of country i and
Kyotoit
We introduce the country’s geography location in equation 6 by conditioning all
variables that present direct-trade induced composition effect by the continent on
which country i located. In the limit of our study, we focus on 4 continents: Asia,
Europe, Africa and America.
10
In these empirical work, Managi et al.(2009) argue that K/L, (K/L)2 and KLI represent the
indirect trade-induced composition effect on emission
Trade, Growth and the Environment: Direct Trade-Induced Composition Effect
7
The direct trade-induced composition effect can be written as:
Cit =β1 Oit Asit + β2 Oit Eui + β3 Oit Afi + β4 Oit Ami
+ β5 Oit RKLit Asi + β6 Oit RKLit Eui + β7 Oit RKLit Afi + β8 Oit RKLit Ami
+ β9 Oit (RKLit )2 Asi + β10 Oit (RKLit )2 Eui + β11 Oit (RKLit )2 Afi + β11 Oit (RKLit )2 Ami
+ β12 Oit RIit Asit + β13 Oit RIit Euit + β14 Oit RIit Afi + β15 Oit RIit Ami
+ α16 Oit (RIit )2 Asi + β17 Oit (RIit )2 Eui + β18 Oit (RIit )2 Afi + β19 Oit (RIit )2 Ami
+ β20 Oit RKLit RIit Asi + β21 Oit RKLit RIit Eui + β22 Oit RKLit RIit Afi + β23 Oit RKLit RIit Ami ,
(7)
With Asi , Eui , Afi , Ami are dummies variables that indicate that country i comes
from Asia, Europe, Africa or America. Using this approach, we observe how openness
to trade lead to the change of country’s mix of industries in different region, also the
relative strength of ERE and KLE on emission.
Equation 6 becomes:
lnEkt =α0 + α1 Iit + α2 (Iit )2 + α3 KLit + α4 (KLit )2 + α5 KLIit
+ Cit + α12 Kyotoit + α13 P recipitationi + it + νi ,
(8)
We also pay attention for the problem of endogeneity of trade share. In fact, an
increase in per capita income (that generated by trade) will lead to an increase in the
demand for environmental quality, and endogenous policy differences across countries
can themselves be a cause of international trade 11 . That is, liberalized trade can cause
environmental quality and vice versa. We account for the problem of endogeneity by
construct an instrument for trade intensity that based on Frankel and Romer’ work.
12
. Descriptive statistics of variables in the gravity equation, gravity equation estimate
and first stage of instrument are reported in Appendix.
3.1
Carbon dioxide emission
The ACT model is expected to generate different results by pollutant. ACT argue
that a pollutant should possess as many of the following characteristics as possible: (i)
it should by a by product from goods production; (ii) it should be emitted in greater
11
12
see, Jones & Manuelli (2001)
see, Frankel & Romer (1999)
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I. Cadoret X. Tran
quantities per unit of output in some industries than others; (iii) it should have strong
local effects; (iv) it should be a subject to regulations because of its adverse effects on
the population; (v) it should have well-known abatement technologies; (vi) it should
have data available from a wide mix of countries. Previous work of Cole & Elliott
(2003) provides information on 4 pollutants (SO2, CO2, BOD, NOx), they argue that
only 3 pollutants SO2, BOD and NOx possess all of these characteristics. CO2 does
not have a local impact and has not historically been subject to regulations 13 In our
preliminary work, we focus only on CO2 emission for two reasons: first, CO2 does not
have trans-boundary effect, that is the dispersion of CO2 would never be restricted to
certain region or group of country 14 ; second, CO2 has recently subject to regulations
in the Kyoto Protocol, which extends the 1992 United Nations Framework Convention
on climate change (UNFCC).
4
4.1
Data and Results
Data
The study covers 99 countries from Asia (24), Europe (23), Africa (29), America
(23) from 1971 to 2010. Table 1 displays summary statistics for our sample. The
data on CO2 emission per capita and average precipitation are obtained from World
Development Indicators - World Bank Database. The data on trade intensity (at
2005 constant price), capital, population and real GDP per capita (2005 US$) come
from Penn World Table 8.1.
Table 2 reports the correlation between our variables of interest. Real GDP per
capita and the capital-labor ratio are very correlated, that is, rich countries have
comparative advantage in capital-intensive production. 15 The result also shows the
strong correlation between emission and real income. 16
13
Cole and Elliot study the CO2 emission during the period 1975-1990, before the treaty of Kyoto
Protocol (1995)
14
Both SO2, BOD and NOx have a strong trans-boundary effect. Because this air pollution is
transported far from their sources, there is no clear relationship between how much pollution a
country emits and how much is deposited there. A more cited example of trans-boundary pollution
is the case of Europe. For more information, see, for example, the Convention on Long-Range
Transboundary Pollution (1979).
15
see, for example, ”Trade, Growth and the Environment”, Copeland and Taylor (2004)
16
Economic literature expect that the relationship between real gdp per capita and emission
has the form inverted U, the so-called ”Environmental Kuznets Curve”. The EKC is the primary
literature on the relationship between an economic factor (per capita income) and environmental
Trade, Growth and the Environment: Direct Trade-Induced Composition Effect
9
Table 1: Descriptive statistics
Variable
Dimension
Obs.
Mean
SD
Min
Max
GDP per capita
CO2 emission per capita
Capital-labor ratio
Trade intensity
Estimated Trade intensity
Population
Average precipitation
Kyoto
$ 10k
tons
$ 10k/worker
%
%
person
inch per year
(dimensionless)
3960
3960
3960
3960
3960
3960
3960
3960
1.017
4.317
2.812
0.681
0.099
4.43e+07
1249.515
0.033
1.226
0.016
9.796
5.901
0.016
67.416
3.485
0.028
21.935
0.486
0.031
4.330
0.078
0.004
0.524
1.45e+08 118880 1.33e+09
761.5549 51
3240
0.179
0
1
Table 2: Pairwise correlation
Variable
emission per GDP per Capital-labor
capita
capita
ratio
Trade
intensity
Estimated trade
intensity
Emission per capita
GDP per capita
Capital-labor ratio
Trade intensity
Estimated Trade
1.000
0.709
0.701
0.257
0.292
1.000
0.651
1.000
4.2
1.000
0.929
0.287
0.420
1.000
0.309
0.4256
Results
Table 3 and 4 present the main results from our estimations. We present estimates
using OLS, random effect GLS and IV-reandom effect estimations. Table 3 reports
our estimation of the standard ACT model. scale, technique and composition effect We find a positive and significant relationship between lagged per capita income I and
CO2 emission. Note that ACT have used two variables and estimated separately the
positive effect of a scalar increase of production (GDP/km2 ) and the negative effect of
higher real income (lagged income per capita) on emission. In our study, the income
terms capture both scale and technique effects. The positive coefficient of I reflects
that in the case of CO2 emission, scale effect dominates technique effect. The sign of
I 2 is negative with statistical signifiance in all three estimates. These results suggest
quality. Following this hypothesis, environment degradation first rises and then fall with income
increases.
10
I. Cadoret X. Tran
that increases in the real income per capita lead to increases in per capita emissions
with a diminishing marginal effect. Next consider the impact of nation’s capital
to labor ratio. The results in columns (1), (2) indicate a positive and significant
effect between emissions and capital/labor ratio using OLS and Random effect (GLS)
estimates. However, we find no statistically significant relationship between emissions
and the capital-labor ratio when using IV estimation (column 3) while the sign of
(K/L)2 is negative with statistical significance in all estimates. The cross-product of
K/L and I is statistically significant with OLS estimate.
Direct trade-induced composition effect - The sign of trade intensity is positive with
statistical significance in all estimates. The results also show a positive sign for KLE
and a negative sign for (KLE)2 . That is, an increase in the relative capital-labor
ratio lead to an increase in CO2 emissions per capita with a diminishing marginal
effect. By contrast, the sign of ERE is negative and statistically significant, that
means emssions per capita are also lead by country’s environmental regulation.
The dummy for ratification of the Kyoto protocol is statistically significant with
a negative sign. That is, the countries ratifying and having a binding target are
associated with lower CO2 emissions relative to non ratifying countries and/or .
Trade, Growth and the Environment: Direct Trade-Induced Composition Effect 11
Table 3: The determinants of CO2 emission per capita
OLS
(1)
I
1.955***
(15.42)
2
I
-0.131***
(3.86)
K/L
0.340***
(8.00)
2
(K/L)
-0.006
(1.06)
I ∗ K/L
-0.115***
(4.53)
Openness
0.630***
(11.68)
Openness*Relative I
-0.662***
(5.07)
2
Openness*(RelativeI)
-0.007
(0.21)
Openness*Relative K/L
0.237**
(2.03)
Openness*(RelativeK/L)2
-0.175***
(4.10)
Openness*Relative K/L* Relative I 0.306***
(4.06)
Precipitation
-0.003***
(6.83)
Kyoto
-0.200**
(2.08)
Constant
-1.450***
(32.56)
2
R
0.71
GLS
random effect
(2)
G2SLS
IV-random effect
(3)
0.788***
(8.81)
-0.067***
(4.52)
0.165***
(6.15)
-0.010***
(3.46)
-0.009
(0.79)
0.281***
(6.87)
-0.524***
(7.54)
0.014
(0.98)
0.213***
(3.46)
-0.050***
(2.63)
0.091***
(2.82)
-0.005**
(2.03)
-0.220***
(5.26)
-0.204***
(1.30)
1.029***
(6.93)
-0.087***
(4.70)
-0.038
(0.90)
-0.007**
(2.05)
0.002
(0.16)
1.592***
(9.09)
-0.930***
(6.67)
0.021
(1.16)
0.494***
(4.22)
-0.079***
(2.70)
0.119**
(2.47)
-0.009***
(3.40)
-0.401***
(7.39)
-0.567***
(3.57)
Notes: Values in parentheses are t-value. *, **, *** significant at the 10 %, 5 % and 1
% level, respectively.
Table 4 provides a summary of the trade-induced composition effect for all country (equation 6) and for countries in each continent (equation 8). To conserve space,
we report only the direct trade induced composition effect Cit from equation 8. First,
12
I. Cadoret X. Tran
the trade intensity is positive and significant will all estimates in the standard approach, but the trade intensity conditioned by continent indicates that the effect is
significantly positive for Asian and American countries, negative for African countries
and insignificant in the case of Europe. Second, the compositional changes with trade
liberalization are lead by both the ERE and KLE for African countries. However,
for Asian, American and European countries, the composition of national output
seems to be affected by the ERE rather than the KLE. The coefficients of KLE and
(KLE)2 are insignificant in the case of Asia, Europe and America.
Finally, the sign of ERE is positive and statistically significant for African countries show the existence of the pollution haven hypothesis.
Trade, Growth and the Environment: Direct Trade-Induced Composition Effect 13
5
Conclusion
The standard approach of the trade-growth and environment relationship is to decompose emission by scale, technique and composition effect. In this study we treat
trade share as endogenous and testing whether the trade-induced composition effect
differs among continents. More precisely, we examine whether the changes of national
composition output are led by country’s factor abundance (KLE) or/and its environmental regulation and whether the results change by country’s geography location.
CO2 emissions per capita is our dependent variable. The study covers 99 countries
over the period 1971-2010. The results suggest that:
Scale-technique-composition effects We find a positive and significant relationship between real gdp per capita and emissions. That is, for CO2 emissions, the scale effect
dominates the technique effect. However, using IV estimate, we find no evident relationship between capital/labor ratio and CO2 emissions per capita.
Direct trade-induced composition effect The direct trade induced composition effect
conditioned by geography’s location seems to be fruitful while generating different
effect of trade on country’s comparative advantage among continents. The compositional changes with trade liberalization are lead by both the ERE and KLE for
African countries. However, for Asian, American and European countries, the composition of national output seems to be affected by the ERE rather than the KLE.
We find that African countries become dirtier with trade liberalization, and this fact
proves the existence of the ”Pollution Havens”.
Appendix: Constructed instrument for trade share
Bilateral trade data are from the IFS Direction of Trade statistics (current price),
data of Gross Domestic Product are from release 7.1 of the Penn World Table (Alan
Heston, Robert Summers and Bettina Aten, current price) . Distance is measured as
the great-circle distance between countries’ principal cities. Population is also from
Penn World Table 7.1, constructed from real GDP per capita (RGDPCH), real GDP
per worker (RGDPW) and total Population (POP). Finally, we observe and add other
geography dummies as Landlocked, Common Border.
We construct our data set of instrument for country’s trade share. The instrument
is based on countries’ geographic characteristics, which are presented in the working
14
I. Cadoret X. Tran
Table 4: Direct trade induced composition effect among continent
OLS
Random effect
IV-random effect
Direct Trade-Induced Composition effect, equation 6
Openness
Openness*Relative I
Openness*(RelI)2
Openness*Relative K/L
Openness*(RelK/L)2
Openness*Relative I*Relative K/L
0.630***
-0.662***
-0.007
0.237**
-0.175***
0.306***
0.281***
-0.524***
0.014
0.213***
-0.050***
0.091***
1.592***
-0.930***
0.021
0.494***
-0.079***
0.119**
Direct Trade-Induced Composition effect, equation 8, Asia
Openness
Openness*Relative I
Openness*(RelativeI)2
Openness*Relative K/L
Openness*(RelK/L)2
Openness*Relative I*Relative K/L
0.856***
-0.579***
0.081***
0.155
0.001
-0.006**
0.850***
-0.526***
0.055***
0.103
0.011*
-0.006***
1.404***
-2.400***
0.124***
0.198
-0.021
0.099
Direct Trade-Induced Composition effect, equation 8, Europe
Openness
Openness*Relative I
Openness*(RelativeI)2
Openness*Relative K/L
Openness*(RelativeK/L)2
Openness*Relative I*Relative K/L
2.668***
-0.962***
0.056*
-1.511***
0.313***
0.035***
0.341**
-0.192*
0.004
-0.751***
0.201***
0.009*
-1.560
-1.250***
-0.012
1.116
-0.062
0.086**
Direct Trade-Induced Composition effect, equation 8, Africa
Openness
Openness*Relative I
Openness*(RelativeI)2
Openness*Relative K/L
Openness*(RelativeK/L)2
Openness*Relative I*Relative K/L
-1.235***
11.841***
-2.277***
-4.586***
2.697***
-4.909***
-0.489***
4.705***
-0.330*
-0.425
0.932***
-2.039***
-1.297***
5.884***
-0.705*
-2.909**
1.983***
-3.114**
Direct Trade-Induced Composition effect, equation 8, America
Openness
Openness*Relative I
Openness*(RelativeI)2
Openness*Relative K/L
Openness*(RelativeK/L)2
Openness*Relative I*Relative K/L
-0.260**
1.319***
-0.714***
1.851***
-0.066
-0.701***
1.014***
-0.220
-0.107**
-1.209***
0.410**
0.019
1.177***
-2.173***
0.040
-1.737
0.058
1.503*
Notes: To conserve space, no standard errors or t-statistics are shown. *, **, ***
significant at the 10 %, 5 % and 1 % level, respectively.
Trade, Growth and the Environment: Direct Trade-Induced Composition Effect 15
Table 5: Descriptive Statistics of Gravity Equation, 130 countries
Variables
Bilateral Trade ( % GDP)
Population (Millions)
Distance (Miles)
Area ( Thousand of square miles)
Common Border (Dimensionless)
Landlocked (Dimensionless)
Number of
observations
81679
81679
81679
81706
81679
81679
Mean
SD
Min
Max
0.004
0.044
E-6
6.979
2,25E+01 82.249 0.029 7,95E+02
4598.00 2776.643 13.13 12340.00
392.600 7406.586 0.11 3,60E+03
0.028
0.167
0
1
0.233
0.452
0
2
paper of Frankel and Romer (1999). In these original work, Frankel and Romer
estimate a variant of the gravity equation where bilateral trade between two countries
i and j (percentage share of country i’s GDP) is determined by the geographical
distance between them, their respective areas and populations, whether they share a
common border, the number of landlocked countries in the pair, the interaction terms
with the common border dummy. Frankel and Romer’ version of bilateral equation
can be written as:
ln(τij /GDPi ) = α0 + α1 lnDij + α2 lnNi + α3 lnAi + α4 lnNj + α5 lnAj + α6 (Li + Lj )
+α7 Bij +α8 Bij lnDij +α9 Bij lnNi +α10 Bij lnAi +α11 Bij lnNj +α12 Bij lnAj +α13 Bij (Li +Lj )ij ,
(9)
They use this set of estimated coefficients and obtain the fitted bilateral trade
values τˆij . Finally, for each country i, they aggregate all fitted bilateral trade and
obtain the instrument T̂i for trade share T. Managi et al. (2009) use this method
to construct the instrument for trade share. However, they only keep 6 variables in
the right-hand side of equation 9. By using equation 9 to construct the instrument,
we are able to improve the quality of the instrument in one hand and observe the
sensibility of the results regard to different instruments for trade share in another
hand.
16
I. Cadoret X. Tran
Table 6: Gravity Model
1970
−10.79∗∗∗
(0.46)
Ln Distance
−0.51∗∗∗
(0.04)
Ln Popi
−0.34∗∗∗
(0.02)
Ln Areai
−0.03
(0.02)
Ln Popj
0.92∗∗∗
(0.02)
Ln Areaj
−0.22∗∗∗
(0.02)
Landlocked
−0.37∗∗∗
(0.07)
Borders
1.14
(1.95)
B*Ln Distance
0.18
(0.25)
B*Ln Popi
0.08
(0.15)
B*Ln Areai
−0.12
(0.16)
B*Ln Popj
−0.17
(0.15)
B*Ln Areaj
0.09
(0.15)
B*Landlockedij
0.67∗∗
(0.22)
2
R
0.44
Adj. R2
0.44
Num. obs.
3811
(Intercept)
∗∗∗ p
< 0.001,
∗∗ p
< 0.01, ∗ p < 0.05
1980
−7.51∗∗∗
(0.43)
−0.79∗∗∗
(0.04)
−0.29∗∗∗
(0.02)
−0.04∗
(0.02)
0.83∗∗∗
(0.02)
−0.16∗∗∗
(0.02)
−0.35∗∗∗
(0.07)
0.49
(2.05)
0.34
(0.26)
0.01
(0.16)
−0.08
(0.16)
−0.10
(0.16)
0.01
(0.17)
0.07
(0.24)
0.40
0.40
4668
1990
−7.31∗∗∗
(0.38)
−0.84∗∗∗
(0.03)
−0.27∗∗∗
(0.02)
−0.09∗∗∗
(0.02)
0.89∗∗∗
(0.02)
−0.22∗∗∗
(0.01)
−0.61∗∗∗
(0.06)
2.80
(1.73)
0.23
(0.23)
−0.12
(0.12)
0.02
(0.12)
−0.30∗∗
(0.12)
0.21
(0.13)
0.29
(0.20)
0.41
0.41
7244
2000
−9.38∗∗∗
(0.35)
−1.04∗∗∗
(0.03)
−0.13∗∗∗
(0.02)
−0.10∗∗∗
(0.01)
1.02∗∗∗
(0.02)
−0.26∗∗∗
(0.01)
−0.91∗∗∗
(0.05)
2.31
(1.70)
0.35
(0.22)
−0.04
(0.12)
−0.11
(0.12)
−0.12
(0.12)
−0.01
(0.13)
0.46∗
(0.20)
0.42
0.42
9725
2010
−9.87∗∗∗
(0.34)
−1.16∗∗∗
(0.03)
−0.10∗∗∗
(0.02)
−0.05∗∗∗
(0.01)
1.03∗∗∗
(0.02)
−0.21∗∗∗
(0.01)
−1.12∗∗∗
(0.05)
2.44
(1.76)
0.59∗∗
(0.22)
−0.09
(0.12)
−0.13
(0.12)
−0.16
(0.12)
0.00
(0.13)
0.78∗∗∗
(0.20)
0.42
0.42
11120
Trade, Growth and the Environment: Direct Trade-Induced Composition Effect 17
Table 7: First stage: Regress Trade intensity on its instrument
I
-0.016
(0.69)
0.008
(1.06)
KL
-0.034
(3.73)**
KL2
0.007
(5.27)**
-0.012
INC KL
(2.03)*
Constructed Openness 3.862
(46.66)**
cons
0.329
(29.75)**
R2
0.44
I
18
I. Cadoret X. Tran
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