World Trade Flows in Photovoltaic Cells: A Gravity
Approach Including Bilateral Tariff Rates

MATSUMURA, Atsuko

THE JOURNAL OF TOKYO INTERNATIONAL UNIVERSITY
Economic Research No. 1 offprint
March 20, 2016

World Trade Flows in Photovoltaic Cells: A Gravity
Approach Including Bilateral Tariff Rates
MATSUMURA, Atsuko

Abstract

This paper investigates the determinants of world trade flows in one of the principal environmental
goods, photovoltaic cells, focusing on the period of growing trade of this industry between 2000 and
2004, with the emphasis on the effects of bilateral tariffs. Cross-section analysis based on the gravity
model including bilateral tariffs as one of the trade costs demonstrates the systematic effects ofbilateral tariff rates as well as distance effect and APEC bloc effect on the bilateral trade flows in thephotovoltaic cells sector among 51 countries in each year between 2000 and 2004. Result of the panel
analysis based on a fixed effect model of the gravity equation by allowing trading-pair heterogeneity
using the data of 2000-2004 consolidates the significant effect of bilateral tariff rates on the tradeflows, and demonstrates the globalization effect on trade growth in this sector. As for the pattern ofbilateral trade of this sector, the cross-section analysis shows that the bilateral trade flows are larger
for the country-pairs with similar GDP per capita by exchanging various varieties of the differentiatedproducts, which can be explained by the monopolistic competition model. Results of the gravity
analysis for photovoltaic cells suggest the effectiveness of the current efforts of APEC and the WTO
for tariff reduction on various kinds of environmental goods for expanding bilateral trade flows of
those goods among the covered countries of either developed or newly industrialized countries, which
would lead to environmental protection through worldwide diffusion of those goods.
Keywords: Trade Liberalization of Environmental Goods, Trade in Photovoltaic Cells, Gravity Model,
Tariff Rates, Monopolistic Competition, Globalization

Ⅰ．Introduction
This paper examines the determinants of the world trade network between 2000 and 2004
focusing on one of the principal environmental goods, photovoltaic cells, with emphasis on the effects
of import tariffs. The importance of trade liberalization in environmental goods has been discussed
since the end of the Uruguay Round in the World Trade Organization (WTO); and Paragraph 31 ( ⅲ )of the Doha Ministerial Declaration in 2001 considers the reduction or elimination of tariffs and non-̶ 20 ̶東京国際大学論叢

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tariff barriers to trade in environmental goods and services to pursue win-win-win results for trade,
economic development and the environment. Members of the WTO have been working to liberalize
trade in goods and services that can benefit by protecting the environment, and the talks for this kindof trade liberalization take place in “Special Sessions” of the Trade and Environment Committee of theWTO. On the other hand, in 2012, Asia-Pacific Economic Cooperation (APEC) put together a list of 54
environmental goods and affirmed their commitment to reduce applied tariff rates to five percent orless on these environmental goods by the end of 2015. Recently, on 8 July 2014 at the WTO, fourteen
WTO members launched plurilateral negotiations of the trade liberalization for Environmental Goods.
What is important is that their talks will build on the APEC list of 54 environmental goods.1)
The purpose of this paper is to investigate how the tariff barriers affected trade growth in
one of the important environmental goods, photovoltaic cells, in order to evaluate the effectivenessof current efforts for tariff reduction in environmental goods in APEC and the WTO. Based on
the gravity model estimation including tariff rates as one of the trade costs, rigorous investigation
is made about the effects of tariffs as well as the other determinants on trade for the sector ofphotovoltaic cells among 51 countries, including APEC countries, European countries, and the newly
industrialized countries.2) It seems that the tariff reduction has been playing an important role in thegrowth of world trade since World War Ⅱ, together with income growth, transport cost declines, andother factors. For example, the empirical results of Baier and Bersgtrand (2001) suggest that income
growth explains about 67%, tariff reductions about 25%, and transport cost declines about 8% of
average world trade growth between the 1950s and 1980s, using the gravity model estimation.
This paper is organized as follows. Section Ⅱ provides the movement of world tariff reductionof photovoltaic cells from the late 1990s, and shows how the trade of photovoltaic cells increased
with the tariff reduction. Section Ⅲ presents the analytical model and equations used in the empirical
study of the photovoltaic cells sector, and the last part of section Ⅲ discusses the characteristics ofdata used in the econometric analysis. Section Ⅳ investigates the estimation results of both crosssection analysis and panel analysis to clarify the main findings for the world trade of photovoltaic
cells in the first half of the 2000s when the trade of photovoltaic cells increased just after the tariff
removal in the developed countries and the tariff reduction of developing countries. In Section Ⅴ, theconclusion of this paper is presented.

Ⅱ．World Trade and Tariffs of Photovoltaic Cells
The world trade and tariffs of photovoltaic cells are classified in HS 854140, and this HS code
is included in the list of environmental goods of both the WTO and APEC as the principal item inthe category of renewable energy. The content of HS code 854140 is photosensitive semiconductor
devices, including both photovoltaic cells whether or not assembled in modules or made up into
panels and light emitting diodes. In order to sort out photovoltaic cells by excluding light emitting
diodes, we have to investigate more detailed trade data than provided by the six digit HS. As the
HS code of those disaggregated data depends on the countries, we collected the detailed datafor photovoltaic cells by examining trade data of each of 51 countries included in the analysis.
According to the description in the APEC list of environmental goods, ANNEX C of APEC (2012),

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the environmental benefit of photovoltaic cells is to generate electricity in an environmentally benign
manner (with no emissions, noise or heat generated).
Table 1 provides the MFN applied tariff rates in photovoltaic cells in the countries with atleast one non-zero tariff rate during 1998 and 2005, included in the gravity analysis. The tariff barriers
were removed in the late 1990s in many countries including countries such as Australia, USA, and EU
members, and also in the newly developed countries such as South Korea, Philippines, Taiwan and
Thailand. The countries which joined EU in 2004 such as Cyprus, Czech Republic, Hungary, Malta,
and Slovakia had removed tariffs a few years earlier than their entry to EU. The countries which still
had high tariff rates in photovoltaic cells in the first half of 2000s are Brazil, Chile, China, India, Peru,
and Russia, although China removed its tariff on photovoltaic cells in 2002.
Figure 1 shows the world trade in photovoltaic cells from 1999 to 2004 by aggregating thetotal export value to the world from 51 countries included in this analysis. We can confirm that thetrade value in this environmental good increased in the early 2000s in various countries in accordancewith the worldwide trade liberalization. The years 2000‑2004 were selected in this analysis for thefollowing reasons. The estimation of the gravity equation for 1999 was not satisfactory because of too
few observations of trade pairs in the photovoltaic cells sector, and after 2005, it is difficult to identify
the influence of bilateral tariffs as India, a significant country, removed tariffs on photovoltaic cells in
2005.
Table 1MFN Applied Tariﬀs for Photovoltaic Cells (Average), 1998‑2005（%）

Source: WTO, Tariﬀ Download Facility
Note: The selected countries are those with at least one non-zero tariﬀ rate between 1998 and 2005.

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Source: Data by Global Trade Atlas.

Figure 1

Photovoltaic cells exports of 51 countries (1,000 US $)Ⅲ．Analytical Framework and Data
This section presents the methodological base of this research on world trade in thephotovoltaic cells sector, and the data used for the set of variables. The gravity model is used for the
analysis, which Bayoumi and Eichengreen (1997) described as the workhorse for empirical studiesof the pattern of trade. Also, Rauch (1999) expressed the gravity equation as the standard empiricalframework used to predict how countries match up in international trade.3)
The traditional form of the gravity model can be expressed as equation (1), according to
Bergstrand and Egger (2010).
(1)
where PXij is the value (in current prices) of the merchandise trade flow from exporter i toimporter j, GDPi (GDPj) is the level of nominal gross domestic product in country i (country j) Zijm,(m = 3, ..., M) is a set of observables to which bilateral trade barriers are related, including thebilateral physical distance between the economic centers of countries i and j, and ε ij is a disturbanceterm, with expected signs for coefficients β 1 ＞ 0, β 2 ＞ 0, and β m＜ 0. ln refers to the natural logarithm.
A statistical overview in Cheng and Wall (2005) clarifies various forms of the gravity model toestimate bilateral trade flows, using both cross-section and panel analysis. By starting with a generalmodel (GM) shown in equation (2) with no restrictions on the parameters, the standard single-yearWorld Trade Flows in Photovoltaic Cells

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cross-section model (CS), a pooled-cross-section model (PCS), and a fixed effects model (FE) areintroduced with some specific restrictions on the parameters. The following overview of the fourtypes of the gravity model is quoted from Cheng and Wall (2005).
General Model (GM): ln Xijt = α 0 + α t + α ij + β′ijt Zijt + ε ijt

t = 1,...,T(2)

where Xijt is exports from country i to country j in year t, Zijt = [z1t, z2t,…] is the 1 × k vector of gravity
explanatory variables. The intercept has three parts: one common to all years and country pairs, α 0,
one specific to year t and common to all pairs, α t, and one specific to the country pairs and common
to all years, α ij. The disturbance term, ε ijt is assumed to be normally distributed with zero mean andconstant variance for all observations, and the disturbances are pairwise uncorrelated.
The standard single-year cross-section model imposes the restrictions that the slopes andintercepts are the same across country pairs, that is α ij = 0, and β ijt = β t as specified in equation (3).
Cross-Section Model (CS): ln Xijt = α 0 + α t + β′t Zijt + ε ijt

t = 1,...,T(3)

where α 0 and α t cannot be separated.
The pooled-cross-section model imposes the restriction on the general model that theparameter vector is the same for all t, although it normally allows the intercepts to differ over time,
and takes the form of equation (4).
Pooled Cross-Section Model (PCS): ln Xijt = α 0 + α t + β′ Zijt + ε ijt

t = 1,...,T(4)

In the following fixed effect model, the restriction that the country-pair intercept terms equal
zero is removed, and this model can be estimated using least squares with dummy variables for eachof the country pairs. In this specification, the explanatory variables such as distance and common
language are eliminated, because they are fixed over time, even though they are not collinear with thecountry-specific effects.
Fixed Effect Model (FE): ln Xijt = α 0 + α t + α ij + β′ Zijt + ε ijt

t = 1,...,T(5)This model is also considered to be the preferred method in Feenstra (2004), in order to take accountof the unobserved bilateral price indexes, multilateral resistance, termed by Anderson and van
Wincoop (2003) in the theoretical explanation of the gravity model under the usual assumptions inhorizontal differentiation models using CES utility function.
In Ⅲ-1, the estimation model of cross-section analysis is presented, in Ⅲ-2, the specifications
for panel analysis are developed, and in Ⅲ-3, an explanation of the data is presented.
Ⅲ-1

Specifications of Cross-Section Analysis
Bergstrand and Egger (2010) point out two aspects of equation (1). Firstly, actual bilateral

trade flows can be explained quite well by this specific and simple log-linear equation, and secondly,̶ 24 ̶東京国際大学論叢

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bilateral trade flows are strongly influenced by economic trade frictions (or trade costs). They showthat trade frictions have two forms, one being natural trade costs and the other unnatural (or policybased) trade costs. Natural trade costs are made of transport costs and other related costs suchas time, contract enforcement costs, costs associated with the use of different currencies, legal
and regulatory costs, and local distribution costs. On the other hand, unnatural trade costs can be
decomposed into tariffs, taxes on goods crossing international borders, and nontariff barriers on
international trade.
In this paper, tariff rates of the photovoltaic cells sector are explicitly included, as well as
distance between two pair countries, and dummy variables for common language and APEC bloc
to take into account the effects relating to trade costs in equation (1). According to the gravity
estimation for manufacturing trade by Ando and Kimura (2013), in order to take into account theeffect of differences in factor endowments on bilateral trade, the difference in GDP per capita
between two countries is introduced. The coefficient will be positive if the difference in factor
endowments is the important determinant for trade increase of the photovoltaic cells sector
as Heckscher-Ohlin Model predicts, and it will be negative if the similarity of pattern of factor
endowment is important for trade increase in this sector as predicted by the new trade theory
based on monopolistic competition with differentiated products.4) The following equation (6) will be
estimated for the cross-section analysis.
ln PXij = β 0 + β 1 ln GDPi + β 2 ln GDPj + β 3 ln GDPPCij + β 4 ln DISTij+ β 5 ln(1 + TARij) + β 6CLij + β 7 APij + ε ij

(6)where ln PXij is log of exports of photovoltaic cells from country i to country j, lnGDPi and lnGDPj
are log of GDP of country i and country j respectively, lnGDPPCij is log of the absolute value of thedifference in GDP per capita between country i and country j, ln DISTij is log of the geographicaldistance between capitals of country i and country j, ln(1 + TARij) is log of bilateral tariff rates forphotovoltaic cells, CLij is a dummy variable taking unity if an official language is common to bothcountries and zero otherwise, and APij is a dummy variable with unity if both of exporting country
and importing country are members of APEC. We estimate the gravity equation for photovoltaic
cells trade among 51 countries, including developed and newly industrialized countries, for each of 5
years from 2000 to 2004, by OLS according to equation (6). The expected signs in equation (6) are as
follows: β 1 ＞ 0, β 2 ＞ 0, β 4 ＜ 0, β 5 ＜ 0, β 6 ＞ 0, β 7 ＞ 0. The sign of the absolute value of the differencein GDP per capita between country i and country j, β 3, depends on the characteristics of the goods as
already mentioned. In order to examine the effects of the combination of the explanatory variables,
four types of specifications are estimated selecting some of the variables, as shown in Table 4.
Ⅲ-2

Specifications of Panel Analysis
As Bergstrand and Egger (2010) state, researchers have turned more to panel data toestimate the effects of each determinant of the gravity equation in order to avoid unobservable
heterogeneity across country pairs.
According to Cheng and Walls (2005), we estimate a specification with a country-pairWorld Trade Flows in Photovoltaic Cells

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fixed effect and a time effect, by introducing dummy variables for each of the country pairs andtime dummy variables. The dependent variable ln RXij is log of real exports of photovoltaic cells
from country i to country j, and independent variables are log of real GDP of exporting country i,(ln RGDPi), log of real GDP of importing country j, (ln RGDPj), and log of tariff rates of photovoltaic
cells in the import of country j from country i, ln(1 + TARij).
ln RXijt = α ij + α t + β 1 ln(RGDPit) + β 2 ln(RGDPjt) + β 3 ln(1 + TARijt) + ε ijt(7)where α ij is the specific country-pair effect between the trading partners. The country-pair intercepts
include the effects of all omitted variables that are cross-sectionally specific but remain constant overtime, such as distance and common language.
We also estimate the following equation of the PCS model by OLS with time dummies, tocompare the estimation results with the Fixed Effects model.
ln RXijt = α 0 + α t + β 1 ln(RGDPit) + β 2 ln(RGDPjt) + β 3 ln(1 + TARijt)+ β 4 ln(DISTij) + β 5 APij + β 6CLij + ε ijt(8)where ln RXijt is log of real exports of photovoltaic cells as dependent variable, α 0 denotes the partof the intercept common to all years and trading pairs, α t is the year-specific effect common to
all trading pairs, ln RGDPi is log of real GDP of importing country j, ln RGDPj is log of real GDP ofimporting country j, TARij is tariff rates of photovoltaic cells, ln DISTij is log of distance between
country i and country j, APij is a dummy variable with 1 if both countries are members of APEC
and 0 otherwise, and CLij is a dummy variable if both countries share a common official language.
Explanatory variables other than ln RGDPi and ln RGDPj take the same form as in equation (6).
Ⅲ-3

Data
The data used for the estimation are described briefly as follows. The nominal bilateral tradevalues of the sector of photovoltaic cells (in 1,000 US dollars) for 51 trading countries are taken fromthe Global Trade Atlas online data, providing customs trade data reported by the government of eachcountry. The data of each country are examined in order to exclude the data for light emitting diodes
from HS 854140 data. In cross-section analysis, zero trade flows are excluded, and in panel analysis,
these are excluded from the data set if all of the five years’ data are zero. The data source of bilateral
tariffs of photovoltaic cells in all five years for each country is the WTO Tariff Download Facility.
Nominal GDP (in 1,000 US dollars), real GDP (in 1,000 US dollars), and nominal GDP
per capita (in 1,000 US dollars) are taken from the World Bank’s World Development Indicators.
Trade data of photovoltaic cells are scaled by GDP deflators to generate real trade flows for thepanel analysis. The data of bilateral distance between capitals of the pair countries (in kilometers)
and common official language are from the CEPII (Centre d’Etude Prospectives d’Informations
Internationales) database. Data of GDP and GDP per capita for Taiwan are from the JETRO databaseoriginally from Directorate-General of Budget, Accounting and Statistics, Executive Yuan, Republic ofChina (Taiwan).

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List of countries in the analysis

The list of 51 countries included in this analysis is shown in Table 2. All APEC countries
except Brunei Darussalam and Papua New Guinea are included, along with 28 European countries,
and Brazil, India, South Africa, and Turkey, which are significant newly industrialized countries.
Table 3(a) and 3(b) presents the descriptive statistics of the variables used in cross-section
analysis and panel analysis respectively. As the zero trade flows are excluded from trade data incross-section analysis, it is clear from the trade enlargement of the sector of photovoltaic cells from2000 to 2004 that the observations of non-zero trade are increasing year by year. In panel analysis, thetrade flows of the country-pairs with at least one non-zero trade in the five years’ data are included,
as indicated earlier.

Ⅳ．Empirical Results
This section presents estimation results of the gravity analysis for the sector of photovoltaic
cells. In Ⅳ-1, the results of cross-section analysis are presented and in Ⅳ-2, those of panel analysis
are reported.

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Table 3

Ⅳ-1

Estimation Results for Cross-Section Analysis
Table 4 shows the results in the cross-section analysis for the estimation of equation (6)using OLS for the sector of photovoltaic cells. In this table, the results of the four cases are shownwith different specifications, for each of five years. Case 1 and case 2 are estimated for the comparisonof the results with and without tariffs as the explanatory variable. Case 3 is the specification including
all the variables, and case 4 excludes only the difference of GDP per capita between country pairs
because of its comparatively low statistical significance. Adjusted R-squared is highest in case 3 inthe results of all years with the values between 0.332 and 0.361. Those are low as compared to theestimation results of the gravity analysis with entire trade or manufacturing trade as independent
variable. In the results of gravity analysis for transport equipment and electric machinery sector in
Ando and Kimura (2013), adjusted R-squared is shown with the values between 0.5 and 0.6 in manycases. Because this analysis focuses only one item of photovoltaic cells sector, adjusted R-squared
is even lower. In case 4, all the coefficients are statistically significant at the 1% level except that ofbilateral tariff rates in 2002, which is significant at the 5% level.

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Estimation Results (Cross-Section Analysis)

Note: t values are in parentheses .
***: signiﬁcant at the 1% level; ** at 5% level; * at 10% level.

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The main findings of cross-section analysis of each of five years can be summarized as follows.
At first, the coefficient estimates for GDPs in both exporting and importing countries are positive andstatistically significant at the 1% level, and the former is bigger than the latter in all the cases for all
the years. The coefficients of GDPs in exporting countries take values in the range of 0.73‑0.82, andthose in importing countries are in the range of 0.64‑0.71, suggesting that an increase in a country’s
GDP will lead to a less-than proportional increase in its imports and exports.
The coefficients of the difference between two countries’ GDP per capita are always negativeand significant at the 1% level in the more recent years of 2003 and 2004, and significant at the 5 %level in 2000 and 2002. Also, the absolute values of these coefficients are bigger in 2003 and 2004
than those of the earlier years, with 0.168 and 0.174 respectively in case 3. This suggests that trade isaccelerated between countries with similar patterns of factor endowment, reflecting that trade in the
sector of photovoltaic cells can be explained by a new trade theory based on monopolistic competition
with differentiated goods, and contradicts the prediction of the traditional Heckscher-Ohlin theory of
trade. Furthermore, this tendency is strengthened in 2003 and 2004.
The coefficients for distance are negative as expected and statistically significant in all cases
for all years. If we focus on the estimates in case 4, the absolute values of these coefficients are
relatively small in 2000 with 0.49, but after 2001 those absolute values tended to increase, with nearly
0.54 in 2001 and 2002, 0.57 in 2003, suggesting that the country pairs with a shorter distance tended
to increase trade more than in 2000. In 2004, however, the effect of the distance on trade decreasedslightly to 0.52 in case 4.
The coefficients for bilateral tariff rates are negative as expected and statistically significant
at the 1% level except those in 2002, which are negative and statistically significant at the 5% level.
Also, the absolute value of the coefficient in 2002 is smallest with 4.245 in case 4, but this increased
to 6.738 and 7.399 in 2003 and 2004 respectively. Hayakawa (2011) shows that the absolute value ofthe estimate of the coefficient of bilateral tariff rate in the analysis of manufacturing trade is nearly 4,
which can be considered a reasonable size considering the theoretical model of the gravity equation
based on the horizontal differentiation using CES utility function.5) Since the adjusted R-squared
is higher in case 2 as compared to case 1 for all years, the importance of bilateral tariff rate in thespecification of the gravity equation for the sector of photovoltaic cells can be clarified.
The coefficient of the dummy variable for common official language in the photovoltaic cells
is statistically significant in all cases of all years. If we focus on case 4, the coefficient value increased
until 2002, with 1.063 in 2000, 1.152 in 2001, and 1.323 in 2002. On the other hand, the coefficient
reduced after 2003, with 0.806 in 2003 and 0.698 in 2004. Contrary to the results for the dummy
variable for common official language, the effects of the dummy variable for the APEC bloc are higher
in 2003 and 2004 with 1.495 and 1.291 respectively, as compared to this effect in 2001 and 2002
with 1.040 and 1.049 respectively, in case 4. The coefficient estimate for 2004, 1.291 suggests thaton average, two APEC members trade photovoltaic cells about 3.3 times as much as other country
pairs. Surprisingly, this estimate of 1.29 is the same value estimated by Frankel (1997) for the APEC
bloc dummy variable in the gravity model for 1992, including the effects of prospective trading blocs
shown in Table 4‒3 of Frankel (1997).

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Estimation Results for Panel Analysis
Table 5 reports the estimation results of the panel analysis of the gravity equation for the

trade of photovoltaic cells among 51 countries. The regression results for the fixed effects modelaccording to equation (7) are shown in the first column and those for the pooled cross-section model
estimated according to equation (8) are shown in the second column. In panel analysis, the variablesof exports and GDP are in real values instead of nominal values used in the cross-section model. The
fixed effect model is preferable to pooled cross-section model as explained in section Ⅲ, but pooled
cross-section model is also estimated to verify the robustness of the results for cross-section analysis
of each year.
Regarding the estimation results for the fixed effects model of equation (7), adjusted
R-squared is highest in this analysis with 0.754, and the signs of the coefficient of the exporting
country’s real GDP and bilateral tariff rates are as expected and are statistically significant. The
negative coefficient on the importing country’s real GDP is not as expected, although it is notstatistically different from zero. In the fixed effects model, by allowing for trading-pair heterogeneity,
Table 5

Estimation Results (Panel Analysis)

Note: t values are in parentheses .
***: signiﬁcant at the 1% level; ** at 5% level; * at 10% level.

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the estimated coefficient for bilateral tariff rates, in absolute value, is smaller than that in the pooled
cross-section model, representing the value of 4.97 consistent with the results in Hayakawa (2011).
On the other hand, the estimated coefficient of real GDP of exporting country and the estimatedrole of globalization in the fixed effects model are bigger than in the pooled cross-section model.
An increase in a country’s real GDP by 1% leads to increasing its exports by 1.5%, and globalization
effect shown by year dummies has increased the real volume of trade by 63% between 2000 and 2004,
according to the definition of Cheng and Wall (2005).
For the results of the pooled cross-section model, the signs of all the coefficients of the
included explanatory variables are as expected and are statistically significant, except the time
dummy of 2001. According to the estimates of the pooled cross-section model, the following major
findings can be clarified. Firstly, an increase in a country’s real GDP leads to an approximately
proportional increase in its real exports, and less than proportional increase in its real imports.
Secondly, the estimated coefficient for bilateral tariff rates is −6.304 showing a strong effect. Thirdly,
the estimated coefficient on the log of distance is −0.693, which means that when the distance
between two countries is increased by 1.0 percent, trade between them falls by about 0.69 percent.
Fourthly, regarding the estimate of the coefficients on the dummy variables, that for a common
language is 1.28, which implies that two countries sharing an official linguistic link tend to trade 3.6
times as much as two otherwise similar countries and the estimated coefficient on the dummy for
the APEC bloc is 1.33, which implies that two of the APEC countries trade 3.8 times as much as twootherwise similar countries.6) Accordingly, the effects of common official language and APEC bloc canbe thought to be quite similar. Lastly, the year dummies are statistically significant except in 2001,
and this implies the common trend toward greater real trading volumes, independent of the sizes ofthe economies. As Cheng and Wall (2005) define these year dummies as an indicator of the extent of
globalization, globalization seems to be an important factor for trade growth of photovoltaic cells.

Ⅴ．Conclusions
This paper investigated the importance of reduction of bilateral tariff rates on trade of oneof the representative environmental industries for renewable energy, the sector of photovoltaic cells,in order to assess efforts for tariff reduction for environmental goods among APEC countries and theWTO. Three potential contributions of the study can be clarified as follows. Firstly, the bilateral trade
flow data in the sector of photovoltaic cells have been sorted using detailed data among 51 countries,
as well as the bilateral tariff rates of photovoltaic cells from WTO data. Secondly, the gravity model
has been shown very useful in estimating trade flows in the photovoltaic cells sector by using crosssection data, including difference between per capita income of exporting and importing countries, aswell as bilateral tariff rates. Bilateral tariff rates systematically affected the trade flows in all the years
between 2000 and 2004, and the trade flows of photovoltaic cells are supposed to be explained by
the new trade theory based on monopolistic competition with differentiated goods, since the country
pairs with less difference of GDP per capita trade more actively, allowing the difference of GDP capita
to be a proxy of difference of factor endowment. Thirdly, using the fixed effect model with panel databy allowing trading-pair heterogeneity, this study showed the strong systematic effect of bilateral̶ 32 ̶東京国際大学論叢

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tariff rates on the trade flows, and also the trade increase in the photovoltaic cells sector resulting
from the globalization effect.
Although many countries abolished tariffs on photovoltaic cells in the end of the 1990s, theresults of this analysis shows that the remaining tariffs in BRICs countries and Chile were clear trade
impediments in the first half of the 2000s in this sector. The analysis suggests that further tariff
reductions are important to activate trade flows of environmental goods among the covered countries,
whether developed or newly industrialized countries. The current efforts of tariff reduction of various
kinds of environmental goods with different environmental purposes in APEC and WTO are important
for trade increase and environmental protection through worldwide diffusion of those goods.
Further research estimating the determinants of trade flows of various environmental goods
is required to consolidate the effects of bilateral tariff rates in those goods. In particular, a gravity
analysis of environmental goods having similar purposes as photovoltaic cells for electricity generationfrom renewable resources, such as electric generating sets for wind-power (HS850231), would benecessary for comparing results with those derived from the analysis for photovoltaic cells.
Acknowledgements
This research is funded by Grants -in-Aid Scientific Research(C), Japan Society for Promoting
Science, as research with no.25380319.
This is a revised version of a paper presented at the 74th Annual Meeting of the Japan Society ofInternational Economics at Senshu University. The author would like to acknowledge the discussantand the other participants of the meeting for their valuable comments.
Notes
1) The author has been published the papers on the empirical analysis using gravity model for trade inenvironmental goods, emphasizing the importance of trade of environmental goods on environmental
protection. See Matsumura (2014a), (2014b), (2015), and (2016).
2) The selected countries are indicated in Table 2.
3) Baier and Bergstrand (2001) review how the researchers have expressed the intrinsic qualities of thegravity model in international trade.
4) Bergstrand (1989) introduces generalized gravity equation based on factor-proportions theory andmonopolistic competition theory.
5) Theoretically, the coefficient of bilateral tariff rate in the gravity equation can be shown as 1- σ , where σ iselasticity of substitution among varieties. Concerning the value of σ , Hayakawa (2011) refers the value inthe range of 5 to 10 concluded by Anderson and van Wincoop (2004).
6) The exponential of 1.28 is equal to 3.60, and the exponential of 1.33 is equal to 3.78.

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