Gravity Analysis of Environmental Goods Focusing on Bilateral Tariff Rates
Atsuko Matsumura
Department of Economics, Tokyo International University
1. Introduction
This paper investigates the determinants of bilateral trade of environmental goods(EGs) with a focus on bilateral tariff rates. Bilateral tariff rates are a main part of traderelationships developed through regional trade agreements (RTAs), and thus it isimportant when conducting this study to examine the effect of RTAs along with theeffect of bilateral tariff rates such as most-favored nation (MFN) tariffs and tariffs ofgeneralized system of preferences (GSP) under World Trade Organization (WTO).
EGs are useful for protecting the environment, and the importance of tradeliberalization of the EGs has been widely recognized since the 2001 Doha Ministerial
Declaration of WTO. This declaration initiated the reduction or elimination of tariff andnon-tariff barriers to trade in environmental goods and services (EGSs). In 2011, theWTO report TN/TE/20 was released with the Annex II.A, which contains a list of theEGs based on HS (Harmonized System) classification six-digit level. In 2012, theAsia-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 by the end of 2015. This is specified in Annex C of the 2012 Vladivostok Declaration.
Then on July 8, 2014 at the WTO, 14 members, including China, the European Union(EU) and the United States, launched plurilateral negotiations on trade liberalization
for EGs. Presently there are 46 countries engaged in the negotiations.1
The potential impact of trade liberalization and trade increase of EGs is easily
illustrated. This leads to import demand expansion through import price decrease
resulting from tariff reduction, especially in fast-growing developing economies withcomparatively high tariff rates on these goods. Importing EGs at a lower price makes it
possible to access climate-friendly goods with clean energy technologies, which could
have substantial effect for climate change mitigation. Especially for the renewableenergy sector, cost has been the principal obstacle to the deployment of renewableenergy-based electricity generation in developing countries; the reduction or removal ofthese tariffs contributes significantly to improving access to these goods. Access to moreenergy-efficient technologies at a lower cost may be particularly important for This research is funded by Grants-in Aid Scientific Research (C), Japan Society for

Promoting Science, no.17K03703.
1 See Matsumura (2016a), (2016b), (2019) for details about the trade liberalization ofenvironmental goods.
1industries that must comply with environmental policies, which place the burden ofemission reductions on the emitters.2
Effects of trade liberalization on climate change mitigation can be seen not only on
the demand side of the importing countries, but also on the production side of those
goods in the importing countries. Increased imports give producers in the importing
countries the opportunity to learn and benefit from technological advances, and allow
larger markets for environmental goods leading to profits from economies of scale byproduction increase. There are examples that indicate that trade liberalization ofenvironmental goods increases local capabilities for innovation and adaptation ofdomestic technology rather than simply fostering dependence on the transfer of foreigntechnology. 3
Trade increase in EGs affects producers in the exporting countries as well. The
production of EGs has the following characteristics. Firstly, at the initial stage of
production, sunk costs for research and development are enormous, and secondly, thedegree of product differentiation can be large depending on the producers. Consequently,
many EGs are produced in monopolistic competitive industries having monopolistic
power such that each variety is produced with scale economies. Production increase
realized by trade liberalization in the exporting countries having comparativeadvantage leads to average cost decrease and profit increase based on economies of scale,
which induces the entry of new firms with new varieties.
The trade benefit for users on the demand side realized by trade liberalization is the
expansion of choices among the varieties with lower prices as a result of increased
competition among producers of both exporting and importing countries. Accordingly,
these effects on the monopolistic competitive environmental industries will lead to anincrease in the world deployment of EGs, which will possibly induce successfulenvironmental protection.
The present analysis investigating trade determinants is conducted for ten
disaggregated EGs. Seven goods are especially useful for renewable energy production.
However, the photovoltaic cells sector is not included as the effect of bilateral tariff rates
for this sector was previously examined in Matsumura (2016b) and Matsumura (2019).
Also three sectors are instruments for monitoring environmental conditions. These ten
goods are taken from APEC list of environmental goods, shown in Table 1 under HS
2012 classification at the six-digit level.

See WTO (2009), UNEP (2012) and UNEP (2014) for details.
WTO (2009) shows the example of Ghana where the reduction of certain import tariffs
has encouraged the adoption of energy-efficient lighting.
2
32

The estimation model is based on the fixed effect approach of the gravity model with
importer and exporter dummy variables. This model is useful for examining the effect ofbilateral tariff rates and other proxies of trade costs, such as distance and dummy
variable of common language and that for each region. As bilateral tariff rates are
reduced through preferential treatment in RTAs, the effects are examined together by
including the detailed bilateral tariff rates in each RTA. Following Hayakawa (2013),
exporters are assumed to use the lowest tariff rates in this paper although multiple
tariff schemes are available in most country pairs.
The estimation results are carefully investigated to show cases in which thecoefficients of bilateral tariff rates are robustly significant for the ten environmental
goods examined in this study. In addition to the tariff rates, other important
determinants of trade of each EG are derived by examining the details of estimation
results.
The paper is structured as follows. In the next section, the literature on the effects oftariffs and determinants of trade in EGs are briefly reviewed. Section 3 describes
theoretical foundation of the gravity model for the disaggregated goods, specification of
the estimating model and data. Section 4 presents the estimation results for thedeterminants of trade in each EG and the comparison among the results of ten goods.
Section 5 concludes.
2. Related Literature for Trade in Environmental Goods
While the present study examines the determinants of bilateral trade indisaggregated EGs, Disdier et al. (2009) examines the determinants of bilateral trade ineach items included in cultural goods. In Disdier et al. (2009), the effect of common
language, colonial links, proximity of cultural tastes on bilateral trade in cultural goods
are examined carefully based on the gravity equation with importer and exporter fixed
effects interacting with year dummies. Although they emphasize the importance of theliberalization of trade of cultural goods in future multilateral trade negotiations, they
do not introduce bilateral tariffs in the estimation.
Other papers reviewed here introduce bilateral tariff rates implicitly in the gravity
model. Hayakawa (2013) examines the seriousness of omitting bilateral tariff rates fromgravity equations for aggregated manufacturing goods. He concludes that omitting
bilateral tariff rates presents no serious issue related to the omitted variable bias, andhe also clarified that the dummy variable for RTAs and importer and exporter fixed
effects are not a statistical substitute for tariff rates.
3

Disdier et al. (2015) investigates impacts of tariff cuts on three components of thethree types of trade margins for the exports from 18 emerging countries to 25 main
importing countries. Based on the model with the first-differences trade data between
1996 and 2006 with country-pair fixed effects to control for the potential endogeneity oftariffs and long-run bilateral trade growth shocks, Disdier et al. (2015) clarifies apositive effect of tariff cuts at both the extensive and intensive trade margins, though
the effects are relatively modest. Also, based on the basic gravity model, the coefficientsof tariff rates are negative and significant in all cases estimated for extensive marginand intensive margin, although the results are confined to a case for a part of world
bilateral trade confined for exports from emerging countries.
In papers which examine the determinants of the trade of environmental goods, thefollowing papers show the estimation results according to their specific purposes. Jomit(2014) examines the aggregated environmental goods using the gravity model estimatedby OLS. For exports of EGs from India to 58 countries for the period between 1991 and2011, the coefficient of GDP of the importing countries, the common colonizer, andmembership in bilateral trade agreements are positively significant. Cantore et al.
(2018) shows that the trade of EGs based on OECD classification depends on GDP,
transaction costs and uncertainty using trade data from 1999 to 2014 across 71
countries. In an analysis focusing on the renewable energy industries including solar
photovoltaic cells and wind power, Kuik et al. (2018) shows the effectiveness of demandpull policies on trade increase.
Related to research on environmental protection by change in trade, Zhais andMartinez-Zarzoso (2018) estimates the effects of two international agreements(Rotterdam Convention and the Stockholm Convention) on environmental protection
which reduce trade in hazardous chemicals or persistent organic pollutants. The resultsfrom the gravity model show that when the exporters ratify the Rotterdam Convention
we observe a significant reduction of imports of non-OECD countries from OECD
countries in hazardous chemicals. In the case of the Stockholm Convention, a reductionin persistent organic pollutants is observed.
Matsumura (2016a) examines the determinants affecting trade structure in EGs, byinvestigating the trade of environmental goods in the APEC list for each group of HS84,
HS85, and HS90, from 2009 to 2012.4 Based on the gravity model, the trade share ofparts and components is the driving force of trade increase in EGs in the APEC region
for the HS84 and HS90 groups, and in Japan-ASEAN FTA for the HS85 group, because
This analysis includes 43 countries with 17 countries in APEC, 21 countries in EU,
and 5 countries such as Brazil, India, South Africa, Switzerland, and Turkey.
44

of the proliferation of complex supply chain networks in EGs. On the other hand, for EU
countries, trade is higher for the two countries with higher ratio of final goods than thetwo countries with higher ratio of parts and components. This suggests thatinternational production fragmentation is not found to be the major determinant oftrade of EGs in the EU region.
The analyses from these five papers for the determinants of the trade of EGs are
based on the traditional gravity model by including some explanatory variables
according to the objectives of each study, and they do not include importer and exporter
fixed effects.
Matsumura (2016b) investigates the effect of bilateral tariff rates for specificrenewable energy related products, taking example of photovoltaic cells sector, which isclassified in HS nine-digit level under the heading of HS854140. This analysis focuseson the period 2000-2004, a period with tariff reduction and trade increase for thephotovoltaic cells sector, and clarifies the significant effect of bilateral tariff rates on the
trade by the fixed effect approach of the gravity model in addition to the traditionalmodel.
However, later Matsumura (2019) shows that the effect of tariffs on trade depends
on the goods being traded. It was found that there was a clear effect of tariffs on the
trade of photovoltaic cells but not on the trade of wind-powered electric generating sets
and equipment, during the period of trade liberalization of each good being investigated.
With regards to the regional effects, this paper shows that the trade of wind-powered
electric generating sets and equipment is active among EU countries and the trade ofphotovoltaic cells is active among APEC countries. This type of estimation includes
bilateral tariff rates influenced by all the existing trade integration agreements, so thatboth the effects of trade integration agreements and the effect of bilateral tariff rates
are taken into consideration. The present study examining the trade determinants often EGs is the extension of Matsumura (2019).
3. Estimating Model and Data
3-1 Theoretical Foundation of Gravity Model for Disaggregated EGs
The theoretical foundation of the gravity model is based on the trade theory ofmonopolistic competition with CES type expenditure function and iceberg trade costs.5
A detailed theoretical explanation is given in Anderson and van Wincoop (2003),
Anderson (2010), De Benedictis and Taglioni (2011), and Feenstra (2016), and the way
how to derive the estimation equation from the theory is given by Disdier et al. (2009),
Hayakawa (2013), Head and Mayer (2014), and Tanaka (2015).
5

5

Increasing returns to scale is assumed in producing differentiated varieties of the goodin each country and they ship those to all countries with an iceberg trade cost in which
the trade costs are proportional to the volume shipped.
According to Anderson (2010), the frictionless and homogeneous world implies thatthe proportion of spending by destination country j on goods from country i in the sum ofpurchases from all origins is equal to the global proportion of spending on goods fromcountry i(1)

𝑋𝑖𝑗 /𝐸𝑗 = 𝑌𝑖 /𝑌

where 𝑋𝑖𝑗 is the import value of country j from country i, 𝐸𝑗 is the sum of purchases incountry j from all origin countries, 𝑌𝑖 is the total sales by origin country i , and Y isworld spending. On the other hand, as the observed trade value 𝑋𝑖𝑗 is affected by
frictions in the real world along with random influences, the ratio of observed tradevalue 𝑋𝑖𝑗 to predicted frictionless trade, 𝑌𝑖 𝐸𝑗 /𝑌 can be explained by various proxies for
trade costs for frictions by the empirical gravity models
For the analysis of gravity model for each disaggregated EGs, this relationship canbe applied to disaggregated goods, indexed by k.
𝑋𝑖𝑗𝑘 =𝑌𝑖𝑘 𝐸𝑗𝑘 /𝑌 𝑘 =𝑠𝑖𝑘 𝑏𝑗𝑘 𝑌 𝑘(2)

where 𝑠𝑖𝑘 = 𝑌𝑖𝐾 /𝑌 𝐾 is country i’’ s share of the world’s sales of goods k and 𝑏𝑖𝑘 = 𝑌𝐼𝐾 /𝑌 𝐾
is country j’s share of the world spending on good k, and 𝑌 𝑘 is world sales of good k.
According to Anderson and van Wincoop (2003), the theory based gravity model canbe derived in the following way. By specifying the demand structure as Constant
Elasticity of Substitution (CES) exports from country i to country j in product k, 𝑋𝑖𝑗𝑘
expenditure is given by
𝑘
𝑘 1−𝜎
𝑝𝑖𝑗
𝑘
𝑋𝑖𝑗 = ( 𝑘 )
𝐸𝑗𝑘
𝑃

(3)

𝑗

where 𝜎𝑘 is the elasticity of substitution among differentiated varieties of the goods,𝑘𝑝𝑖𝑗
is the price charged by country i for exports to country j, and 𝑃𝑗𝑘 is the CES priceindex, 𝐸𝑗𝑘 is expenditure in country i for good k. The CES price index is given by𝑘

1/(1−𝜎𝑘 )𝑘 1−𝜎𝑃𝑗𝑘 = [∑𝑖(𝑝𝑖𝑗
)
]

(4)

𝑘
𝑘
As iceberg trade costs are assumed, 𝑝𝑖𝑗
can be written as 𝑝𝑖𝑘 𝑡𝑖𝑗, using supply price𝑘𝑘received by producers in country i , 𝑝𝑖𝑘 and trade costs, 𝑡𝑖𝑗, where 𝑡𝑖𝑗− 1 isad-valorem tax equivalent of trade costs.
Together with the market-clearing conditions, the gravity equation system for good k
can be shown as6

𝐸𝑘 𝑌 𝑘

𝑡𝑘

1−𝜎 𝑘

(5)

𝑋𝑖𝑗𝑘 = 𝑗𝑌𝑘𝑖 [ 𝑘𝑖𝑗 𝑘 ]
𝑃𝑗 П𝑖

𝑘

1−𝜎 𝑘

𝑡𝑘

(П𝑘𝑖 )1−𝜎 = ∑𝑗 [ 𝑖𝑗𝑘 ]
𝑃𝑗

𝑘

𝑘
𝑡𝑖𝑗

1−𝜎 𝑘

(𝑃𝑗𝑘 )1−𝜎 = ∑𝑖 [ 𝑘 ]
П𝑖

𝐸𝑗𝑘
𝑌𝑘
𝑌𝑖𝑘
𝑌𝑘

(6)
(7)

where П𝑘𝑖 and 𝑃𝑗𝑘 are outward and inward multilateral resistance, respectively, andthis system shows that the bilateral trade depends on relative trade barriers, with those
key variables. It suggests that the trade flow of good k from country i to country j isincreased by high trade costs from other suppliers to j as captured by inward
multilateral resistance, and high resistance to shipments from country i to its othermarkets than country j, captured in outward multilateral resistance, increases tradefrom country i to country j.
3-2 Econometric Specification and Data
Based on the theoretical foundation in the previous section, the determinants of the
trade of each of the ten disaggregated EGs are examined by fixed effect approach of thegravity model, using importer and exporter dummy variables.
The specification of the fixed effect approach of the gravity model in this study canbe shown as follows:
ln𝑋𝑖𝑗 = 𝜃 + 𝜇𝑖 𝑓𝑒𝑖 + 𝜆𝑗 𝑓𝑒𝑗 + 𝛿1 𝑙𝑛𝐷𝐼𝑆𝑇𝑖𝑗 + 𝛽1 ln(1 + 𝑇𝐴𝑅𝑖𝑗 ) + 𝛽2 𝐶𝐿𝑖𝑗 + 𝛽3 𝐴𝑃𝑖𝑗 + 𝛽6 𝐸𝑈𝑖𝑗 + 𝑢𝑖𝑗
・・・ (8)where 𝑋𝑖𝑗 denotes the value of exports from country i to partner j, 𝑓𝑒𝑖 is country
dummy variable, 𝑓𝑒𝑗 is partner country dummy variable and 𝑢𝑖𝑗 is a stochastic error.
𝐷𝐼𝑆𝑇𝑖𝑗is the distance between capitals of the pair countries, 𝑇𝐴𝑅𝑖𝑗 is bilateral tariff

rates of the country j from country i. 𝐶𝐿𝑖𝑗 is the dummy variable for common language,
𝐴𝑃𝑖𝑗 is the dummy variable for the membership of APEC, and 𝐸𝑈𝑖𝑗 is the dummy
variable for the membership of EU.
The bilateral tariff rates reflected by RTAs are introduced in the estimating equation
to take into account the trade cost from the point of view of trade policy. For the proxies
to examine the effects of other trade costs, some dummy variables are introduced in theestimating equation. The dummy variable for common language takes one for two
countries with common official language and zero in other cases, regional dummy
variable of APEC takes one for two countries belonging to APEC and zero otherwise,
and the regional dummy variable of EU takes one for two countries belonging to EU andzero otherwise.
7

According to Redding and Venables (2004), one is added to all trade flows before
taking logarithms, as zeros are genuine zeros rather than missing values based on theaccuracy of the bilateral trade flows data. Importer and exporter fixed effects areincluded in all the regressions in the form of country dummies, based on the pioneering
work of Redding and Venables (2004). As Disdier et al. (2009) clarifies, those fixed
effects incorporate the size effects, but also the price and number of varieties in theexporting country and the size of demand and the price index of the importing country.
In order to examine the trade network in South-East and East Asia instead of APEC,
the dummy variable of the 15 countries and regions including so-called ASEAN plus 3
countries (Japan, Korea, and China), and the regions of Hong Kong and Taiwan is
introduced. This dummy variable AS3 takes 1 for two countries among those 15
countries and regions, and 0 otherwise. The gravity equation is altered as shown by
equation (9).
ln𝑋𝑖𝑗 = 𝜃 + 𝜇𝑖 𝑓𝑒𝑖 + 𝜆𝑗 𝑓𝑒𝑗 + 𝛿1 𝑙𝑛𝐷𝐼𝑆𝑇𝑖𝑗 + 𝛽1 ln(1 + 𝑇𝐴𝑅𝑖𝑗 ) + 𝛽2 𝐶𝐿𝑖𝑗 + 𝛽3 𝐴𝑆3𝑖𝑗 + 𝛽6 𝐸𝑈𝑖𝑗 + 𝑢𝑖𝑗
・・・ (9)The recent four years 2013-2016 are selected for the estimation period based on thepanel data. In addition to Pooling Ordinary least squared method (OLS) and Random
effect model, Poisson pseudo-maximum-likelihood methods (PPML) proposed by Silva
and Tenreyro (2006) is also applied in the analysis, to avoid the inconsistency occurring
when the gravity equation is estimated using a log-log functional form, in the presenceof heteroscedasticity and zero trade flows, for a robustness check. In this case, the left
hand side term is taken in levels. Random effect model is selected because the timeinvariant distance variable is included in the estimation model.
The ten EGs selected from APEC list of EGs are based on six-digit level ofharmonized system HS2012 for estimation as shown in Table 1. The environmental
benefits for these ten goods are also indicated in this table. The first seven items in the
Table are environmental goods used for renewable energy production. Specifically,
HS841290 is the item which includes the wind turbines and hubs, HS850231 includes
wind-powered electric generating sets and equipment, HS850239 includes electric
generating sets and rotary converter for production of renewable energy, and HS850300
includes the parts and components for those goods. Also, HS850490 includes importantparts and components for electrical transformers, static converters and inductors.
HS901380 includes heliostats orient mirror in concentrated solar power systems,and HS901390 includes parts of HS901380. The last three items belonging to theprecision machinery, HS90, are the principal instruments for measuring or checking theenvironmental conditions, such as the flow, level, pressure or other variables of liquids8

or gases, and their parts and components.

Table 1. List of Environmental Goods Included in this Study
HS2012 number HS Code Description
Environmental Benefit
841290 Engine and motor parts
Integral components to wind turbines (wind turbines and hubs)
850231 Wind-powered electric generating sets and equipments
Electricity generation from renewable resources
850239 Electric generating sets and rotary convertors
Electricity generation from renewable resources
Biogas generating sets; Gas generator
850300 Parts suitable for use with the machines of heading 8502.
Parts for electricity generation from renewable resource
850490 Parts for electrical transformers, static converters and inductors Used to convert DC from renewable energy generatingsets into conventional AC electricity
901380 Optical devices, appliances and instruments
Heliostats orient mirrors in concentrated solar powersystems to reflect sunlight on to a ESP receiver901390 Parts of optical devices, appliances and instruments
Heliostats orient mirrors in concentrated solar powersystems to reflect sunlight on to a ESP receiver902610 Instruments for measuring or checking the flow, level, pressure Used to measure, record, analyse and assess
or other variables of liquids or gases.
environmental samples or environmental influences, such as .
air quality monitors and dust emissions monitors
902620 Instruments for measuring or checking the flow, level, pressure Manometers used in power plants, water delivery systems,
or other variables of liquids or gases.
and other applicatiosn such as monitoring indoor air, whichhave important environmental applications.
902690 Parts for articles of subheading 9026.
Used to measure, record, analyse and assess environmental
samples or environmental influences.
Source: APEC (2012)

The data used for the estimation are described briefly as follows. The bilateral tradevalues of each of the ten sectors (in US dollars) for 70 trading countries are taken fromthe Global Trade Atlas online data, providing customs trade data reported by thegovernment of each country and region. The data of bilateral distance between capitalsof the pair countries and the data for common language are from the CEPII (Centred’Etude Prospectives d’Informations Internationales) database.
The data sources of bilateral tariffs reflected by trade integration agreements are
the WTO Tariff Download Facility and FedEx Trade Network, World Tariff AccountInformation. Together with the information from the WTO website for “Regional Trade
Agreements Database” for each country, the analysis uses the preferential tariff rate ofeach trade integration agreement for each country, based on the assumption thatexporters are assumed to use the lowest tariff rates in this paper although multiple
tariff schemes are available in most country pairs.
70 countries are included in the analysis as shown in the appendix as Table A.1.
Developed and newly industrialized countries belonging to APEC and EU, and anotherseven countries are introduced in the estimation as exporting and importing countries.
18 developing countries are included only as the importing countries, as those countries
have no significant exports.
9

The descriptive statistics of each variable by good are shown from Table A2 to Table
A11 in the appendix.
Ten figures, from Figure 1 to Figure 10, present applied MFN tariff rates of thetwenty-two selected countries, for each HS six-digit number respectively. These tariff
rates are taken from the database for bilateral tariff rates constructed for the present
study, in order to show a trend of tariff rates levied by the representative countries.
Together with the information with respect to bilateral tariff rates in the descriptive
statistics, in Table A2 to Table A11, it is shown that sectors of HS902610, HS902620,
and HS 902690 have relatively low bilateral tariff rates, because of tariff elimination ofmany main countries. On the contrary, the sectors of HS850300, HS850490, andHS901390 have high tariffs because of the relatively high tariffs in the main countries
including the USA and the EU. Japan is not included in those figures as its applied
MFN tariff rates are zero for those EGs.
In some APEC member countries and regions, applied MFN tariff rates werereduced to less than 5% in 2015 or 2016 following the commitment of APEC. For
example, Thailand and Taiwan reduced those of HS841290, HS850231, HS850239, andHS901380 in 2015 or 2016.
Figu re 1 . Applie d MFN Tariff Rate s for Se le c te d Cou n trie s, HS8 4 1 2 9 0 (% )
16.0
14.0

12.0
10.0
8.0

2013

6.0

2014

4.0

2015

2.0

2016

USA

UAE

TurkeyUkraineThailandRussiaTaiwanPhilippines

MyanmarIndonesia

Korea (South)

India

EU

Egypt

China

Chile

BrazilCambodia

Bangladesh

Australia

Argentina

Algeria

0.0

Source: Tariff Download Facility, WTO.

10

Figu re 2 . Applie d MFN Tarif f Rate s f or Se le c te d Cou n trie s, HS8 5 0 2 3 1

(%)

16.0
14.0
12.0

10.0
8.0

2013

6.0

2014

4.0

2015

2.0

2016

USA

UAE

UkraineTurkeyThailandTaiwanRussiaMyanmarPhilippines

Korea (South)

India

Indonesia

EU

Egypt

Chile

China

CambodiaBrazilBangladesh

Australia

Argentina

Algeria

0.0

Source: Tariff Download Facility, WTO.
Figu re 3 . Applie d MFN tarif f rate s f or se le c te d c ou n trie s, H S8 5 0 2 3 9 ( % )
16.0
14.0
12.0
10.0
8.0

850239 2013

6.0

850239 2014

4.0

850239 2015

2.0

850239 2016

USA

UAE

UkraineTurkeyThailandRussiaTaiwanPhilippines

Myanmar

Korea (South)Indonesia

India

EU

Egypt

Chile

China

CambodiaBrazilBangladesh

Australia

Argentina

Algeria

0.0

Source: Tariff Download Facility, WTO.

Figu re 4 . Applie d MFN tarif f rate s f or se le c te d c ou n trie s, H S8 5 0 3 0 0 ( % )
16.0
14.0
12.0
10.0

8.0

850300 2013

6.0

850300 2014

4.0

850300 2015

2.0

850300 2016

USA

UAE

TurkeyUkraineThailandTaiwanRussiaPhilippines

Myanmar

Korea (South)Indonesia

India

EU

Egypt

China

Chile

CambodiaBrazilBangladesh

Australia

Argentina

Algeria

0.0

Source: Tariff Download Facility, WTO.

11

Figu re 5 . Applie d MFN Tarif f Rate s f or Se le c te d Cou n trie s, H S8 5 0 4 9 0 ( % )
25.0
20.0
15.0
2013
10.0

2014
2015

5.0

2016

USA

UAE

UkraineTurkeyThailandTaiwanRussiaMyanmarPhilippines

Korea (South)Indonesia

EU

India

Egypt

China

Chile

CambodiaBrazilBangladesh

Australia

Argentina

Algeria

0.0

Source: Tariff Download Facility, WTO.

Figu re 6 . Applie d MFN Tarif f Rate s f or Se le c te d Cou n trie s, H S9 0 1 3 8 0 ( % )
40.0
35.0
30.0
25.0
20.0

2013

15.0

2014

10.0

2015

5.0

2016

USA

UAE

UkraineTurkeyTaiwanThailandRussiaPhilippines

Myanmar

Korea (South)Indonesia

EU

India

Egypt

China

Chile

CambodiaBrazilBangladesh

Australia

Argentina

Algeria

0.0

Source: Tariff Download Facility, WTO.

Figu re 7 . Applie d MFN Tarif f Rate s f or Se le c te d Cou n trie s, HS9 0 1 3 9 0 ( % )
40.0
35.0
30.0
25.0
20.0
15.0
10.0
5.0
0.0

2013
2014

2015

USA

UAE

UkraineTurkeyThailandTaiwanRussiaPhilippines

Myanmar

Korea (South)Indonesia

India

EU

Egypt

China

Chile

CambodiaBrazilBangladesh

Australia

Argentina

Algeria

2016

Source: Tariff Download Facility, WTO.

12

Figu re 8 . Applie d MFN Tarif f Rate s f o r Se le c te d Co u n trie s, H S9 0 2 6 1 0 ( % )
16.0
14.0
12.0
10.0
8.0

2013

6.0

2014

4.0

2015

2.0

2016

USA

UAE

UkraineTurkeyThailandRussiaTaiwanPhilippines

Myanmar

Korea (South)

India

Indonesia

EU

Egypt

China

Chile

CambodiaBrazilAustralia

Bangladesh

Algeria

Argentina

0.0

Source: Tariff Download Facility, WTO.

Figu re 9 . Applie d MFN Tarif f Rate s f o r Se le c te d Co u n trie s, H S9 0 2 6 2 0 ( % )
20.0
18.0
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0
0.0

2013
2014
2015

USA

UAE

UkraineTurkeyThailandTaiwanRussiaPhilippines

Myanmar

Korea (South)Indonesia

India

EU

Egypt

China

Chile

BrazilCambodia

Bangladesh

Australia

Argentina

Algeria

2016

Source: Tariff Download Facility, WTO.
Figu re 1 0 . Applie d MFN Tarif f Rate s f o r Se le c te d Co u n trie s, H S9 0 2 6 9 0 ( % )
18.0
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0
0.0

2013
2014
2015

Algeria
Argentina
Australia
Bangladesh
BrazilCambodia
Chile
China
Egypt
EU
India
Indonesia
Korea (South)
MyanmarPhilippines
RussiaTaiwanThailandTurkeyUkraine
UAE
USA

2016

Source: Tariff Download Facility, WTO.

13

4. Estimation Results
This section presents the results of the estimation of gravity equation (8). Importerand exporter fixed effects are included in all the regressions for the three estimation
methods, Pooling OLS, Random effect model, and PPML.
4-1 Effects of Bilateral Tariff Rates and Trade Integration Agreements
Table 2 shows the estimation results of Pooling OLS, Random effect model, and
PPML, for each of the environmental goods belonging to HS84 and HS85, and Table 3
shows those for each of the environmental goods belonging to HS90.
Focusing on the effects of the bilateral tariff rates with the effects of RTAs, theresults for each environmental good show differences, which suggest the existence of
good-specific characteristics. For HS850300 (parts and components for electricity
generation from renewable sources), the coefficients of bilateral tariff rates are
significant at the 1% level in all the three estimation methods ranging between -0.308
and -0.413. For the good HS902620 (manometers for monitoring environmental
conditions), the coefficients are significant at the 1 % level in Pooling OLS and PPML
and significant at the 5% level in Random effect model ranging between -0.175 and-0.242. For the good HS902610 (instruments for monitoring the air quality and otherenvironmental conditions), those coefficients are significant at the 1% level in Pooling
OLS and Random Effect Model and significant at the 10% level in PPML ranging
between -0.117 and -0.434.
For HS850490 (parts for electrical transformers, static converters and inductors forrenewable energy production), those coefficients are significant at 1% level in Pooling
OLS at -0.308 and PPML at -0.254, and for HS902690 (parts and components formonitoring environmental conditions) the coefficients are significant at the 1% level in
Pooling OLS at -0.283 and significant at the 5% level in Random effect model at -0.259.
For HS841290 (wind turbines and hubs), the coefficients of bilateral tariff rates are
significant at the 1% level in PPML at -0.378.
For three goods with significant coefficients in all the three estimation models, theelasticity of bilateral tariff rate for imports ranges between 0.117 and 0.434 which is ona similar scale as the results in Disdier et al.(2015) for the exports of emerging countries.
Furthermore, even for the six goods shown above, the elasticities are stable between
0.117 and 0.434. The effects of bilateral tariff rates together with the tariff liberalization
by RTAs are shown to be robustly important in such an item as HS850300 which
includes many kinds of parts and components of the machines used for renewable
14

energy production. This is also true for such items as HS902620 and HS902610 which
include instruments for monitoring air quality or pressure for environment protection
purposes.
Table 2. Estimation Results for the EGs of HS84 and HS85ln(Xij )Pooling OLS RE model
HS841290

XijPPMLln(Xij )Pooling OLS RE model
HS850231

Integral components to wind turbines

30.135 *** 30.131 *** 20.505
Constant(0.665)(1.132)(0.588)-0.0060.038-0.177ln(Tariffij + 1)
(0.064)(0.098)(0.075)-1.975 *** -1.981 *** -0.663
ln(DISTij )(0.053)(0.093)(0.045)1.033 *** 1.035 *** 0.062
DCLij(0.121)(0.206)(0.091)0.490 *** 0.493 ** 0.677
DAPECij(0.151)(0.250)(0.139)0.671 *** 0.684 *** 0.617
DEUij(0.133)(0.220)(0.156)
2
0.683
0.683
Adjusted R
Observations
15,552
15,552
15,552
Exporter Fixed Effects
yes
yes
yes
Importer Fixed Effects
yes
yes
yes
ln(Xij )Xij
Pooling OLS RE model PPML
HS850300

***
**
***

***
***

Parts of machines under heading 8502

33.141 *** 33.106 *** 19.713 ***Constant(0.718)(1.216)(0.547)-0.400 *** -0.308 *** -0.413 ***ln(Tariffij + 1)
(0.068)(0.102)(0.053)-2.380 *** -2.394 *** -0.793 ***ln(DISTij )(0.059)(0.101)(0.040)1.102 *** 1.100 *** 0.392 ***DCLij(0.137)(0.228)(0.086)0.250
0.308
0.546 ***DAPECij(0.171)(0.276)(0.127)-0.184-0.159-0.167
DEUij(0.147)(0.242)(0.133)0.665
0.665
Adjusted R2
Observations
15,552
15,552
15,552
Exporter Fixed Effects
yes
yes
yes
Importer Fixed Effects
yes
yes
yes
Note: Xij is bilateral exports from country I to partner j plus1.
Standard errors are shown in parentheses.
*** shows significant at 1% level, ** at 5% level, and * at 10% level.

Xij
PPML

ln(Xij )Pooling OLS RE model
HS850239

XijPPML

Wind-powered electric generating sets

Electric generating sets, rotary convertors

8.809 *** 8.821 *** 23.126 ***(0.649)(0.991)(2.166)-0.039-0.0930.149(0.068)(0.090)(0.260)-0.644 *** -0.639 *** -1.092 ***(0.053)(0.082)(0.192)
0.498 *** 0.499 *** 0.405(0.122)(0.184)(0.304)-0.020-0.029-0.771(0.126)(0.185)(0.603)0.219 * 0.181
1.340 *
(0.126)(0.192)(0.752)0.387
0.387
15,552
15,552
15,552
yes
yes
yes
yes
yes
yes
ln(Xij )Xij
Pooling OLS RE model PPML
HS850490

20.465 ***(0.781)-0.386 ***(0.081)-1.541 ***(0.063)0.900 ***
(0.156)0.512 ***
(0.179)-0.746 ***(0.148)0.397
15,552yes
yes

20.319 *** 19.608 ***(1.210)(1.167)-0.1480.035(0.112)(0.115)-1.561 *** -0.936 ***(0.100)(0.089)0.914 *** -0.478 **(0.242)(0.233)0.583 ** 0.047(0.274)(0.355)-0.649 *** -0.807 **(0.222)(0.393)0.396
15,552
15,552
yes
yes
yes
yes

Parts for transformers, converters and inductors

28.931 *** 28.813 *** 18.990 ***(0.686)(1.197)(0.540)-0.308 *** -0.050-0.254 ***(0.080)(0.111)(0.069)-2.007 *** -2.030 *** -0.848 ***(0.056)(0.100)(0.038)0.709 *** 0.723 *** 0.494 ***(0.128)(0.218)(0.107)1.261 *** 1.330 *** 0.967 ***(0.152)(0.255)(0.120)-0.439 *** -0.375 * -0.750 ***(0.136)(0.225)(0.117)0.681
0.681
15,552
15,552
15,552
yes
yes
yes
yes
yes
yes

15

Table 3. Estimation Results for the EGs of HS90ln(Xij )Pooling OLS RE modelHS901380

Xij
PPML

Heliostats orient mirrors in concentrated solar powerln(Xij )Pooling OLS RE modelHS901390

Xij
PPML

Parts of HS901380

ln(Xij )Pooling OLS
HS902610

RE modelXij
PPMLInstruments for measuring or checking the flow, level, pressureor other variables of liquids or gases.

19.511 *** 19.564 *** 18.456 ***Constant(0.695)(1.168)(1.037)-0.011
0.025
0.261 ***ln(Tariffij + 1)
(0.073)(0.102)(0.090)-1.467 *** -1.473 *** -0.967 ***ln(DISTij )(0.055)(0.094)(0.076)1.240 *** 1.240 *** 1.255 ***DCLij(0.124)(0.203)(0.170)1.108 *** 1.115 *** -0.399
DAPECij(0.159)(0.255)(0.275)0.390 *** 0.397 *-0.417
DEUij(0.136)(0.220)(0.274)
0.657
0.657
0.965
Adjusted R2
Observations
15,552
15,552
15,552
Exporter Fixed Effects yesyesyes
Importer Fixed Effects yesyesyesln(Xij )Xij
Pooling OLS RE model
PPML
HS902620
Manometers for monitoring indoor air etc. with many

17.977 *** 18.091 *** 15.439 ***(0.670)(1.109)(1.280)-0.287 *** -0.196 ** -0.233(0.079)(0.083)(0.160)-1.183 *** -1.196 *** -0.699 ***(0.053)(0.089)(0.095)1.031 *** 1.028 *** 1.274 ***(0.121)(0.202)(0.246)1.649 *** 1.663 *** 0.352(0.159)(0.266)(0.305)-0.387 *** -0.359 * -0.832 ***(0.132)(0.214)(0.282)0.629
0.629
15,552
15,552
15,552
yes
yes
yes
yes
yes
yes
ln(Xij )Xij
Pooling OLS RE model
PPML
HS902690

30.335 ***(0.662)-0.434 ***(0.090)-1.923 ***(0.053)1.353 ***
(0.125)0.056(0.146)0.430 ***
(0.131)
0.679
15,552yes
yes

30.306 ***(1.122)-0.312 ***(0.120)-1.933 ***(0.092)
1.353 ***
(0.210)0.056(0.235)0.416 *
(0.212)0.679
15,552yes
yes

17.690 ***(0.644)-0.117 *(0.066)-0.554 ***(0.050)0.510 ***
(0.067)0.535 ***
(0.128)0.291 **
(0.113)
15,552yes
yes

Parts for articles of subheading 9026.

environmental applications

26.153 *** 26.137 *** 15.171 ***(0.612)(1.025)(0.450)-0.242 *** -0.175 ** -0.193 ***ln(Tariffij + 1)
(0.075)(0.082)(0.059)-1.739 *** -1.744 *** -0.477 ***ln(DISTij )(0.048)(0.083)(0.038)1.363 *** 1.364 *** 0.491 ***DCLij(0.112)(0.189)(0.072)0.452 *** 0.451 ** 0.607 ***DAPECij(0.138)(0.225)(0.099)0.708 *** 0.700 *** 0.390 ***DEUij(0.120)(0.197)(0.092)
0.719
0.719
0.879
Adjusted R2
Observations
15,552
15,552
15,552
Exporter Fixed Effects yesyesyes
Importer Fixed Effects yesyesyes
Note: Xij is bilateral exports from country I to partner j plus1.
Standard errors are shown in parentheses.
*** shows significant at 1% level, ** at 5% level, and * at 10% level.

Constant

25.919 ***(0.631)-0.283 ***(0.085)-1.719 ***(0.050)1.209 ***
(0.117)0.554 ***
(0.142)0.107(0.123)0.702
15,552yes
yes

25.937 *** 17.633 ***(1.036)(0.397)-0.259 ** -0.020(0.109)(0.077)-1.721 *** -0.664 ***(0.085)(0.031)1.209 *** 0.192 ***(0.192)(0.066)
0.553 ** 0.530 ***(0.225)(0.097)0.104
0.147(0.196)(0.099)0.702
15,552
15,552
yes
yes
yes
yes

4-2 Effects of distance and common language
The effects of other proxies for trade costs, such as distance and dummy variable forcommon language are examined. As shown in Table 2 and Table 3, distance has anegative and significant effect on trade flows of all the ten goods in all the threeestimation methods. The coefficients are significant at the 1% level, ranging between-0.644 for HS850231 (wind-powered electric generating sets and equipment) and -2.380
16

for HS850300 (parts for electricity generation from renewable resources) in the Pooling
OLS, between -0.639 for HS850231 and -2.394 for HS850300 in Random effect model,
and between -0.477 for HS902620 (Manometers for monitoring environmental
conditions ) and -1.092 for HS850231 in PPML. Despite the coefficients varying among
the goods and estimation methods, the clear impact of distance between two countries isrobustly clarified by these results.
The effects of a common official language between two countries on trade flows are
examined by the dummy variable, shown as DCL in Table 2 and Table 3. The
coefficients of DCL are positive and significant at the 1% level, for seven goods out of ten,
HS850300 (parts and components for electricity generation from renewable sources),
HS850490 (parts for electrical transformers, static converters and inductors forrenewable energy production), HS901380 (heliostats orient mirrors), HS901390 Parts
and components of heliostats orient mirrors), HS902610 (instruments for monitoring
environmental conditions), HS902620 (manometers for monitoring environmental
conditions), HS902690 (parts and components for monitoring environmental conditions).
The coefficients range between 0.709 for HS850490 and 1.363 for HS902620 in the
Pooling OLS, between 0.723 for HS850490 and 1.364 for HS902620 for Random effectmodel, and 0.192 for HS902690 and 0.494 for HS850490 in PPML. For HS841290 (windturbines and hubs) and HS850231 (wind-powered electric generating sets andequipment)), the coefficients in the Pooling OLS and Random effect model are positiveand significant at the 1% level, but this effect is not significant in the PPML method. As
the largest effect, common official language makes countries’ bilateral trade in
HS902620 exp(1.364)-1=291% larger in Random effect model, and as the smallest effect,
common official language makes countries’ bilateral trade 21% larger for HS902690 in
PPML, everything else being equal.
If those effects in PPML for distance and common language for EGs are comparedwith those in Disdier et al. (2009) for cultural goods, the effects of distance between-0.23 and -1.04 for cultural goods are slightly smaller than the case of EGs, and theeffects of common language between 0.65 and 1.68 for cultural goods are much larger
than the case of EGs between 0.19 and 0.51. Accordingly, the common language haslarger impact on the trade in the cultural goods in which language is influential andproximity has larger impact on the trade in the EGs in which proximity is influential
with smaller transport cost.
4-3 Regional Effects
As for the EU dummy variable, the coefficients for only two goods, HS841290 (wind17turbines and hubs) and HS902620 (manometer for monitoring the environmental
conditions) are positive and significant at the 1% level in all the three estimation
methods. In the case of HS841290 the coefficients are stable among the estimation
methods, ranging between 0.617 and 0.684, which signifies that EU membership makes
countries’ bilateral trade 85.3% ~ 98.2 % larger. Those coefficients vary between 0.390
and 0.708 for HS902620, which signifies that EU membership makes countries’
bilateral trade 47.7% ~103.0% larger. For the other 8 goods, the EU dummy coefficientsare not significant.
On the other hand, the dummy variable for APEC is positive and significant (always
significant at the 1% level, except for HS902690 in Random effect model: significant at
the 5% level) in all the three methods for four goods out of ten, HS841290 (windturbines and hubs), HS850490 (Parts for electrical transformers, static converters andinductors for renewable energy production), HS902620 (manometers for monitoring
environmental conditions), and HS902690 (parts and components for monitoring
environmental conditions). The coefficients vary between 0.490 for HS841290 and 1.330
for HS850490, which signifies that APEC membership makes countries’ bilateral trade
63.2% ~ 278.1% larger.
For HS850239 (electric generating sets and rotary converters for renewable energy
production), HS901380 (heliostats orient mirrors), and HS901390 (parts andcomponents for heliostats orient mirrors), the coefficients of APEC dummy are positiveand significant in the Pooling OLS and Random effect model, but the effect is denied in
PPML, while for the case of HS850300 (parts for electricity generation from renewable
resources), the APEC dummy is positive and significant only in PPML.
For the case of HS902620, the coefficients of both bilateral tariff rates and regionaldummies of APEC and EU are significant with right sign at the 1% or 5% level, so thatas in Hayakawa (2013), it is clarified that dummy variable of RTA is not a statisticalsubstitute for tariff rates. This suggests that the membership in EU and APEC foster
trade flows not only through the tariff reduction but also through regional elements.
For examining the effect of trade networks among the countries of South-East andEast Asia, equation (9) is estimated in the same methods as equation (8). The results
are shown in Table 4 for the goods belonging to HS84 and HS85 and in Table 5 for the
goods belonging to HS90. In all the regressions, as the coefficients of EU dummy are notstable, it is not included, and the analysis focuses on the effect of the trade network only
in South-East and East Asia.
The estimated coefficients of dummy variable of South-East and East Asia arepositive and significant at the 1% level in all the three estimation methods for three18

goods, HS850490 (parts for electrical transformers, static converters and inductors forrenewable energy production), HS901380 (heliostats orient mirrors), HS901390 (partsand components for heliostats orient mirrors), except the coefficient in PPML of
HS901390 which is significant at the 5% level. The magnitude of the coefficients in
Pooling OLS, Random effect model and PPML are 2.158, 2.182, and 0.517 respectively
for HS850490, 1.822, 1.837, and 1.568 respectively for HS901380, and 3.519, 3.531, and1.066 respectively for HS901390. Accordingly, considerably large effects are clarified forthe trade network among countries in South-East Asia and East Asia in these goods.
For example, this kind of trade network raises bilateral trade by a factor ofexp(1.066)=2.9 to exp(3.531)=34.2 for HS901390, exp(1.568)=4.8 to exp(1.837)=6.3 forHS901380, and exp(0.517)=1.7 to exp(2.182)=8.9, everything else being equal.
For HS841290 (wind turbines and hubs), HS850239 (electric generating sets androtary convertors for renewable energy production), HS850300 (parts for electricity
generation from renewable resources), HS902610 (instruments for monitoring air
quality etc.), HS902620 (manometers for monitoring environmental conditions), andHS902690 (parts and components for monitoring environmental conditions), thesecoefficients are positive and significant at the 1% level in both Pooling OLS and Random
effect model, but it is not positive and significant in PPML.

19

Table 4. Estimation Results including DAS3, for the Environemental Goods of HS84 and HS85ln(Xij )Xijln(Xij )Pooling OLS RE model
PPML
Pooling OLS RE model
HS841290
HS850231
Integral components to wind turbines

30.702 *** 30.728 *** 23.071 ***Constant(0.595)(1.021)(0.501)-0.061-0.008-0.378 ***ln(Tariff + 1)
(0.064)(0.096)(0.082)-2.006 *** -2.016 *** -0.851 ***ln(DIST)(0.050)(0.087)(0.040)1.013 *** 1.015 *** 0.061
DCL(0.121)(0.206)(0.092)
1.344 *** 1.346 *** -0.250
DAS3(0.205)(0.339)(0.155)
0.683
0.683
0.833
Adjusted R2
Observations
15,552
15,552
15,552
Exporter Fixed Effects yesyesyes
Importer Fixed Effects yesyesyesln(Xij )Xij
Pooling OLS RE model
PPML
HS850300
Parts of machines under heading 8502

Pooling OLS RE model
PPML
31.197 *** 31.277 *** 20.694 ***Constant(0.650)(1.097)(0.513)-0.371 *** -0.292 *** -0.460 ***ln(Tariff + 1)
(0.065)(0.097)(0.047)-2.177 *** -2.198 *** -0.835 ***ln(DIST)(0.056)(0.096)(0.042)
1.129 *** 1.125 *** 0.393 ***DCL(0.137)(0.228)(0.082)
1.977 *** 1.999 *** 0.011
DAS3(0.220)(0.373)(0.131)
0.667
0.667
0.831
Adjusted R2
Observations
15,552
15,552
15,552
Exporter Fixed Effects yesyesyes
Importer Fixed Effects yesyesyes
Note: Xij is log bilateral exports from country I to partner j plus1.
Standard errors are shown in parentheses.
*** shows significant at 1% level, ** at 5% level, and * at 10% level.

Xij
PPML
HS850239

Wind-powered electric generating sets

Electric generating sets, rotary convertors

9.121 *** 9.085 *** 24.186 ***(0.617)(0.940)(1.677)-0.084-0.114-0.063(0.065)(0.086)(0.244)-0.673 *** -0.666 *** -1.238 ***(0.053)(0.081)(0.168)0.491 *** 0.492 *** 0.291(0.122)(0.184)(0.324)-0.046-0.048-1.706 **(0.202)(0.300)(0.711)
0.387
0.387
0.851
15,552
15,552
15,552
yes
yes
yes
yes
yes
yes
ln(Xij )Xij
Pooling OLS RE model
PPML
HS850490

17.172 ***(0.729)-0.303 ***(0.078)-1.190 ***(0.060)0.960 ***
(0.155)2.853 ***
(0.288)0.401
15,552yes
yes

17.268 ***(1.133)-0.103(0.108)-1.226 ***(0.093)0.968 ***
(0.241)2.885 ***
(0.454)0.401
15,552yes
yes

18.768 ***(1.124)0.075(0.112)-0.844 ***(0.104)-0.509 **(0.231)0.355(0.437)0.646
15,552yes
yes

Parts for transformers, converters and inductors

Pooling OLS RE model
PPML
28.328 *** 28.435 *** 19.615 ***(0.614)(1.069)(0.511)-0.351 *** -0.080-0.228 ***(0.078)(0.109)(0.067)-1.867 *** -1.910 *** -0.800 ***(0.052)(0.091)(0.043)0.730 *** 0.741 *** 0.514 ***(0.127)(0.217)(0.103)2.158 *** 2.182 *** 0.517 ***(0.201)(0.358)(0.181)
0.682
0.681
0.884
15,552
15,552
15,552
yes
yes
yes
yes
yes
yes

20

Table 5. Estimation Results including DAS3, for the Environemental Goods of HS90ln(Xij )Xijln(Xij )Pooling OLS RE model
PPML
Pooling OLS RE modelHS901380
HS901390

Xij
PPMLln(Xij )Pooling OLS RE modelHS902610

Xij
PPML

Instruments for measuring or checking the flow, level, pressure
Heliostats orient mirrors in concentrated solar power

20.380 *** 20.434 *** 15.830 ***Constant(0.645)(1.089)(0.891)
0.002
0.034
0.275 ***ln(Tariff + 1)
(0.074)(0.104)(0.082)-1.496 *** -1.502 *** -0.685 ***ln(DIST)(0.051)(0.087)(0.084)1.228 *** 1.228 *** 1.182 ***DCL(0.124)(0.202)(0.162)
1.822 *** 1.837 *** 1.568 ***DAS3(0.241)(0.396)(0.337)
2
0.657
0.657
0.965
Adjusted R
Observations
15,552
15,552
15,552
Exporter Fixed Effects yesyesyes
Importer Fixed Effects yesyesyesln(Xij )Xij
Pooling OLS RE model
PPML
HS902620
Manometers for monitoring indoor air etc. with manyenvironmental applications

27.321 *** 27.297 *** 17.830 ***(0.550)(0.924)(0.478)-0.177 ** -0.143 * -0.166 ***ln(Tariff + 1)
(0.075)(0.082)(0.056)-1.844 *** -1.845 *** -0.685 ***ln(DIST)(0.044)(0.076)(0.045)
1.338 *** 1.339 *** 0.455 ***DCL(0.112)(0.191)(0.064)0.783 *** 0.790 ** -0.356 **DAS3(0.201)(0.341)(0.169)
0.718
0.718
0.874
Adjusted R2
Observations
15,552
15,552
15,552
Exporter Fixed Effects yesyesyes
Importer Fixed Effects yesyesyes
Note: Xij is log bilateral exports from country I to partner j plus1.
Standard errors are shown in parentheses.
*** shows significant at 1% level, ** at 5% level, and * at 10% level.
Constant

Parts of HS901380

or other variables of liquids or gases.

17.028 *** 17.068 *** 13.657 ***(0.616)(1.019)(0.997)-0.156 ** -0.130-0.194(0.077)(0.082)(0.153)-0.996 *** -1.001 *** -0.440 ***(0.048)(0.081)(0.093)
1.056 *** 1.055 *** 1.135 ***(0.119)(0.198)(0.260)3.519 *** 3.531 *** 1.066 **(0.248)(0.431)(0.427)
0.633
0.633
0.906
15,552
15,552
15,552
yes
yes
yes
yes
yes
yes
ln(Xij )Xij
Pooling OLS RE model
PPML
HS902690

29.586 ***(0.580)-0.359 ***(0.090)-1.860 ***(0.047)1.353 ***
(0.124)
1.390 ***
(0.203)0.679
15,552yes
yes

29.526 *** 19.727 ***(0.971)(0.384)-0.267 ** -0.121 *(0.118)(0.064)-1.863 *** -0.708 ***(0.081)(0.033)1.354 *** 0.481 ***(0.210)(0.062)
1.409 *** -0.236 *(0.339)(0.133)0.679
0.849
15,552
15,552
yes
yes
yes
yes

Parts for articles of subheading 9026.

25.935 *** 25.935 ***(0.561)(0.932)-0.214 ** -0.215 **(0.084)(0.109)-1.692 *** -1.692 ***(0.045)(0.077)1.210 *** 1.210 ***(0.117)(0.192)
1.179 *** 1.179 ***(0.204)(0.335)0.703
0.703
15,552
15,552
yes
yes
yes
yes

19.577 ***(0.374)0.009(0.075)-0.814 ***(0.033)0.198 ***
(0.065)-0.326 ***(0.107)0.868
15,552yes
yes

5. Conclusion
This paper investigates the major determinants of trade of ten disaggregated EGs
selected from the APEC list with each purpose for environmental protection. The
analysis is based on the fixed effect approach of gravity model including importer and21

exporter dummy variables and the effects of bilateral tariff rates are focused together
with the effects of RTAs. The trade data cover a wide range of exporting and importing
countries, and the estimations are conducted according to the three estimation methods:
Pooing OLS, Random effect model and PPML.
Firstly, through examination of the estimation results for the effects of bilateral
tariff rates, the good-specific characteristics are clarified. For three out of ten EGs, thecoefficients are negative and significant in all three methods, with the relatively modest
elasticities of tariff rates ranging between 0.117 and 0.434. Secondly, while all of ten
EGs examined in this study are impacted by distance with relatively large effect, seven
goods out of ten are impacted by common language with smaller coefficients. Despite
the coefficients varying among the goods and estimation methods, the clear impact of
distance between two countries is clarified by these results. This shows that trade of all
the EGs examined in this paper is quite active among the countries closer to each otherwith cheaper transport costs. Thirdly, although the effect of joining in the trade networkof South-East and East Asia is clarified only for three goods, the coefficients of thesethree EGs are much larger than the cases of EU and APEC. Accordingly, a concrete
trade network must be constructed in some of the EGs among the South-East and East
Asian countries.
The characteristics of determinants of each EG are clarified by this analysis. For thecase of HS902620, manometer for monitoring environmental conditions, as thecoefficients of both bilateral tariff rates and regional dummies of APEC and EU aresignificant with right sign at the 1% or 5% level, it is clarified that dummy variable of
RTA is not a statistical substitute for tariff rates. For HS850300,the important partsand components for the necessary machines for electric generation from renewable
resources, while the coefficients of tariff rates are significant at the 1% level and stable
among the three estimation methods ranging between -0.308 and -0.413, the regional
effects cannot be seen. For HS850490 which includes the important parts andcomponents of electrical transformers, converters and inductors, while the effect of
APEC membership is very large and stable among the three estimation methods, theeffect of tariff rate is not clarified in Random effect model.
According to the detailed investigation for the effects of each EG, trade expansion
through tariff reduction together with the regional effects are clarified for some of theEGs examined in this study. For this reason it is worth the effort to continue reducing
tariff rates on more EGs among APEC and WTO members joined in the plurilateral
trade negotiations to expand trade in EGs for beneficial effects of environmental
protection.
22

Table A1. Countries Included in this Study
Exporting and Importing Countries
Importing Countries
APEC
EU
OthersAustralia
Austria
BrazilAlgeria
CanadaBelgium
India
Argentina
Chile
Bulgaria
IsraelBangladesh
China
Croatia
NorwayBelarusHong Kong
Cypress
South Africa Cambodia
Indonesia
Czech Republic Switzerland Colombia
Japan
Denmark
TurkeyCosta Rica
Korea
Estonia
Egypt
Malaysia
Finland
Ghana
MexicoFranceKazakhstan
New ZealandGermany
Kenia
Papua New Guinea *GreeceMyanmarPeru
Hungary
Nigeria
Philippines
IrelandParaguay
RussiaItaly
Saudi Arabia
Singapore
LatviaUkraineTaiwanLithuania
United Arab Emirates
Thailand
Luxembourg
Uruguay
United States
Malta
VetnamNetherland
Slovakia
Slovenia
Spain
SwedenUnited Kingdom*Papua New Guinea is inculded in the study only as importing country,
though it is listed in the group of APEC not in the group of importing countries.

Tabl eA2. Descr i pt i ve St at i st i cs f or HS841290
EX
TAR
DIS
ln EX
ln TARObs.
15,552
15,552
15,552
15,552
15,552
Mean
1,561,627
1.979
6,987
6.692
0.678
Std. Dev. 12, 400, 000
3.221
5,201
5.983
0.850
Min
0
0
60
0
0
780, 000, 000
Max
15.000 120,560
20.4742.773

ln DIS
15,552
8.471
1.013
4
11.700DCL
15,552
0
0
0
1

DAPEC
15,5520001DEU
15,552
0
0
0
1

DAS3
15,55200
01

Tabl e A3. Descr i pst at i st i cs f or HS850231
EX
TAR
DIS
ln EX
Obs.
15,552
15,552
15,552
15,552
Mean
1,807,471
1.625
6,987
1.389
30, 200, 000
Std. Dev.
2.695
5,201
3.976
Min
0
0
59.61702, 210, 000, 000
Max
14.000 120,560
21.516ln DIS
15,552
8.471
1.013
4.088
11.700DCL
15,552
0
0
0
1

DAPEC
15,5520001DEU
15,552
0
0
0
1

DAS3
15,5520001ln TAR
15,552
0.582
0.810
0
2.708

23

Tabl eA4. Descr i pt i ve St at i st i cs f or HS850239
EX
TAR
DIS
ln EX
ln TARObs.
15,552
15,552
15,552
15,552
15,552
Mean
743,543
1.293
6,987
2.657
0.473
Std. Dev. 8,278,161
2.431
5,201
4.968
0.755
Min
0
0
59.61700432, 000, 000
Max
14.000 120,560
19.8842.708

ln DIS
15,552
8.471
1.013
4.088
11.700DCL
15,552
0
0
0
1

DAPEC
15,5520001DEU
15,552
0
0
0
1

DAS3
15,55200
01

Tabl e A5. Descr i pt i ve St at i st i cs f or HS850300
EX
TAR
DIS
ln EX
ln TARObs.
15,552
15,552
15,552
15,552
15,552Mean
4,069,086
2.171
6,987
7.673
0.692
24, 500, 000
3.574
5,201
6.410
0.897
Min
0
0
59.61700834, 000, 000
Max
15.000 120,560
20.5422.773

ln DIS
15,552
8.471
1.013
4.088
11.700DCL
15,552
0
0
0
1

DAPEC
15,5520001DEU
15,552
0
0
0
1

DAS3
15,55200
01

Tabl e A6. Descr i pt i ve St at i st i cs f or HS850490
EX
TAR
DIS
ln EX
ln TARObs.
15,552
15,552
15,552
15,552
15,552Mean
2,737,374
2.064
6,987
7.899
0.594
Std. Dev. 31, 900, 000
4.231
5,201
6.044
0.889
Min
0
0
59.6170
0
1, 860, 000, 000
Max
20.714 120,560
21.3463.078

ln DIS
15,552
8.471
1.013
4.088
11.700DCL
15,552
0
0
0
1

DAPEC
15,5520001DEU
15,552
0
0
0
1

DAS3
15,55200
01

Tabl e A7. Descr i pt i ve St at i st i cs f or HS901380
EX
TAR
DIS
ln EX
ln TARObs.
15,552
15,552
15,552
15,552
15,55216, 200, 000
Mean
2.007
6,987
5.330
0.596
Std. Dev. 298, 000, 000
4.512
5,201
5.968
0.874
Min
0
0
59.6170
0
16, 000, 000, 000
Max
35.000 120,560
23.4943.584

ln DIS
15,552
8.471
1.013
4.088
11.700DCL
15,552
0
0
0
1

DAPEC
15,5520001DEU
15,552
0
0
0
1

DAS3
15,55200
01

Tabl e A8. Desct i pt i ve St at i st i cs f or HS901390
EX
TAR
DIS
ln EX
ln TARObs.
15,552
15,552
15,552
15,552
15,552Mean
2,793,435
1.829
6,987
4.228
0.532
Std. Dev. 63, 400, 000
4.606
5,201
5.592
0.843
Min
0
0
59.6170
0
5, 690, 000, 000
Max
35.000 120,560
22.461
3.584

ln DIS
15,552
8.471
1.013
4.088
11.700DCL
15,552
0
0
0
1

DAPEC
15,5520001DEU
15,552
0
0
0
1

DAS3
15,55200
01

Tabl e A9. Descr i pt i ve St at i st i cs f or HS902610
EX
TAR
DIS
ln EX
ln TARObs.
15,552
15,552
15,552
15,552
15,552
Mean
1,224,328
1.251
6,987
7.536
0.343
Std. Dev. 6,486,765
3.286
5,201
5.855
0.770
Min
0
0
59.6170
0216, 000, 000
Max
15.000 120,560
19.1932.773

ln DIS
15,552
8.471
1.013
4.088
11.700DCL
15,552
0
0
0
1

DAPEC
15,5520001DEU
15,552
0
0
0
1

DAS3
15,55200
01

Tabl e A10. Descr i pt i ve St at i st i cs f or HS902620
EX
TAR
DIS
ln EX
ln TARObs.
15,552
15,552
15,552
15,552
15,552Mean
2,075,104
1.481
6,987
7.661
0.372
Std. Dev. 13, 600, 000
4.064
5,201
5.789
0.813
Min
0
0
59.6170
0489, 000, 000
Max
18.000 120,560
20.0092.944

ln DIS
15,552
8.471
1.013
4.088
11.700DCL
15,552
0
0
0
1

DAPEC
15,5520001DEU
15,552
0
0
0
1

DAS3
15,552000124

Tabl e A11. Descr i pt i ve St at i st i cs f or HS902690
EX
TAR
DIS
ln EX
ln TARObs.
15,552
15,552
15,552
15,552
15,552
Mean
1,014,818
1.393
6,987
6.902
0.316
Std. Dev.
6,323,640
4.063
5,201
5.781
0.800
Min
0
0
59.6170
0192, 000, 000
Max
16.000 120,560
19.0752.833

ln DIS
15,552
8.471
1.013
4.088
11.700DCL
15,552
0
0
0
1

DAPEC
15,5520001DEU
15,552
0
0
0
1

DAS3
15,5520001References
Anderson, James E. (2010). The Gravity Model. NBER Working Paper No.16576,
National Bureau of Economic Research, Cambridge, MA.
Anderson, James E. and Eric van Wincoop. (2003). “Gravity with Gravitas: A Solution to
the Border Puzzle.” American Economic Review, 93, no.1, 170-192.
APEC (Asia-Pacific Economic Cooperation). (2012). “Vladivostok Declaration-Integrate
to Grow, Innovate to Prosper.” http://www.apec.prg.
Cantore, Nicola, Charles Fang, and Chin Cheng. (2018). “International Trade ofEnvironmental Goods in Gravity Model.” Journal of Environmental Management 223,
1047-1060.
De Benedictis and Taglioni, (2011) “The Gravity Model in International Trade.” In Luca
De Benedictis and Luca Salvatici (ed.) The trade Impact of European Union

Preferencial Policies. Springer.
Disdier, Anne-Celia, Silvio H.T.Tai, Lionel Fontagne, and Thierry Mayer. (2009).
“Bilateral Trade of Cultural Goods.” Review of World Economics 145, no.1, 575-595.
Disdier, Anne-Celia, Lionel

Fontagne, and Mondher

Mimouni..(2015). “Tariff

Liberalization and Trade Integration of Emerging Countries.” Review of International

Economics 23, no.5, 946-971.
Feenstra, Robert C. (2016). Advanced International Trade, Theory and Evidence (second

edition). Princeton and Oxford: Princeton University Press.
Hayakawa, Kazunobu. (2013). “How serious is the omission of bilateral tariff rates ingravity?” Journal of The Japanese and International Economies 27, 81-94.
25

Head , Keith and Thierry Mayer. (2014). “Gravity Equations: Workhorse, Toolkit, andCookbook”. In Gita Gopinath, Elhanan Helpman and Kenneth Rogoff (eds.) Handbookof International Economics, vol.4, Amsterdam: Elsevier B.V.
International Institute for Sustainable Development (IISD) and United Nations
Environmental Programme (UNEP). (2014). Trade and Green Economy: A Handbook

Third Edition. Geneva: IISD.
Jomit, C.P. (2014). “Export Potential of Environmental Goods in India: A Gravity Model
Analysis,” Transnational Corporations Review, 6, no.2, 115-131.
Kuik, Onno, Frederic Branger and Philippe Quiron. (2018). “Competitive Advantage in
the Renewable Energy Industry: Evidence from a Graviry Model.” Renewable Energy
131, 472-481.
Matsumura, Atsuko. (2016a). “Regional Trade Integration by Environmental Goods,”

Journal of Economic Integration, 31no.1, 1-40.
Matsumura, Atsuko. (2016b). “World Trade Flows in Photovoltaic Cells,” The Journal ofTokyo International University, Economic Research, no.1, 19-33.
Matsumura, Atsuko. (2019). “The Effects of Tariffs and Regions on Bilateral Trade forEnvironmental Goods: Cases for Some Renewable Energy Goods,” Journal of Japan

Academy for International Trade and Business, no.56, 3-21.
Redding.Stephen J. and Anthony J. Venables. (2004). “Economic Geography andInternational Inequality.” Journal of International Economics, 62,no1, 53-62.
Santos Silva, J.M.C. and Silvana Tenreyro. (2006). “The Log of Gravity.” Review of

Economics and Statistics, 88,no.4, 641-658.
Tanaka, Ayumu.(2015). New New Trade Theory (written in Japanese). Tokyo: Minerva
Publisher.
United Nations Environment Programme (UNEP) (2012). Trade and Environment
26

Briefings: Trade in Environmental Goods, Policy Brief 6. New York: United Nations.
UnitedNations

Environmental

Programme (UNEP)(2014).

Green Economy,South-South Trade in Renewable Energy. New York: United Nations.
World Trade Organization (2011). Universe of Environmental Goods: Official HS

Descriptions: Committee on Trade and Environment Special Session TN/TE/20. Geneva:
WTO.
World

Trade

Organization.(2009). Tradeand Climate Change, World Trade

Organization-United Nations Environmental Programme Report, Geneva: WTO and
UNEP (United Nations Environment Programme).
Zhais, Numez-Rocha and Immaculada Martinez-Zarzoso. (2018). “Are International
Environmental Policies Effective? The Case of the Rotterdam and the Stockholm
Conventions.” Discussion Papers, Center for European, Governance and Economic
Development Research.

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